Urban Microfinance in Bangladesh by Bayes Ahmed

Urban Microfinance in Bangladesh January 2010

Professor Salim Rashid, University of Illinois Md. Toriqul Bashar, InM

Institute of Microfinance (InM) E-4/B Agargaon, Dhaka-1207

ACKNOWLEDGEMENTS We are grateful to Professor M. A. Baqui Khalily, Executive Director, Institute of Microfinance(InM) and to the InM for giving us the opportunity to conduct this research. We are also grateful to Dr. M. A. Latif, whose supervision and guidance enabled this report to materialize, and to all the InM staff who helped us different times in conducting the research. Fieldwork and editing are specially indebted to Ms. Rokshana Binta Samad, Ms. Akramunnahar Ashrafi, Md. Musleh Uddin Hasan and Salman Rashid The following individuals need particular thanks for their time and cooperation: Dr. Abul Hossain ,Dr Faridur Rahman Khan, Dr Najmul Hassan, Dr Anju Majid, Dr Rumana Akhtar,Dr Rezaul Huq, Mark Staehle, Edward Abbey, Dr M G Quibria, Zahida Fizza Kabir, Zakir Hossain, Dr. Humaira Islam, Md. Shafiqual Haque Choudhury, Zakir Hossain, Md. Shariful Islam and Shahida Pervin. PKSF, ICDDRB, World Bank, UNDP, United Hospital, Apollo Hospital, Ad-Deen Nursing Home, Bangladesh Nursing Council, PLAN Egypt,Center for Urban Studies (CUS), Sajida Foundation, Shakti Foundation, ASA, BURO, TMSS, Grameen Bank, and other MFIs, gave us both ideas and supporting papers on Urban Microfinance. We are grateful for their comments to all the participants at the Seminar on Urban Microfinance on Jan 12, 2010 and to Dr Sajjad Zohir in particular for his suggestions. The MEASURE program at the University of North Carolina, Chapel Hill is to be thanked for sharing their work on slums and Dr Peter Lance in particular was very helpful. In further work we hope to examine the overlap between our data sets and use it to corroborate our findings. We would also like to particularly thank the many people of the low income communities (â&#x20AC;&#x2DC;slumsâ&#x20AC;&#x2122;) whose cooperation and support have both enriched and made possible this research.

Date: January, 2010

Professor Salim Rashid Md. Toriqul Bashar

ORGANIZATION OF THE REPORT

Part 1: INTRODUCTION AND INPORTANCE OF URBAN MICROFINCE Part 2: HISTORICAL OVERVIEW Part 3: STATE AND IMPACT OF MICROFINANCE Part 4: POTENTIAL & CONCLUSION APPENDICES

iii

ABBREVIATION ADB BBS BD CBN CDF CUS DCI DFID DPS GB GoB HIES HH HHH InM LDC LGED LPUPAP MDG MF MFI NGO Slums SPBB Sqf. STIFPP-II TB Tk. UGIIP UNDP WASA

Asian Development Bank Bangladesh Bureau of Statistics Bangladesh Cost of Basic Need Credit & Development Forum Center for Urban Studies Direct Calorie Intake Department for International Development Deposit Pension Scheme Grameen Bank Government of Bangladesh Household Income and Expenditure Survey Household Household Head Institute of Microfinance Least Developed Country Local Government Engineering Department Local Partnership for Urban Poverty Alleviation Program Millennium Development Goal Microfinance Micro Finance Institute Non Government Organization Shorthand for low income communities or LICâ&#x20AC;&#x2122;s.As it is less familiar it would make for awkward reading. Sun Prairie Band Boosters/ Shortest Path Backbone Bridging/ Strategic Performance Based Budgeting Squarefoot Secondary Towns Integrated Flood Protection Project, Phase-II Tuberculosis Taka Urban Governance Infrastructure Improvement Project United Nation Development Program Water Supply and Sewerage Authority

Part 1: INTRODUCTION AND IMPORTANCE OF URBAN MICROFINANCE

1.1 BACKGROUND Poverty has always been a fundamental concern of development planning in Bangladesh. The Government, practitioners, researchers, donor agencies and all others concerned have tried to stress the need for undertaking poverty alleviation programs. In the past, policies had been prepared to address poverty at the rural end, with the assumption that urban poverty is generally a â&#x20AC;&#x153;spill-overâ&#x20AC;? of rural poverty and that, if rural poverty is contained, urban poverty will also be kept under control. But this approach has not been very effective for a number of reasons. First, rural poverty is of such great magnitude that its alleviation has not been adequately accomplished. Secondly, in the meantime, urban poverty has also assumed massive proportions. Thirdly, the possibility of climate change and the displacement of some 20 millions makes urban poverty an important issue for policymakers, since most of the displaced individuals will seek new lives in urban areas. Microfinance may be called upon to bear new responsibilities if the unfortunate happens. The nature of the poverty in

urban areas is somewhat similar all over the

countryâ&#x20AC;&#x201D;low incomes, lack of food, little education, limited skills and large families; inadequate capital and no land resource base (unlike many rural poor) deprive them of good food or education. So they are usually malnourished, unhealthy and unaware. Again minor illness, stress or accidents push them more deeply into the poverty trap. This general situation prevails all year round but the severity varies seasonally. Therefore, the prevailing state of urban poverty necessitates both macro and micro economic intervention. Access to financial services is considered one of the fundamental rights of a citizen by a number of constitutions such as article 36 of The Charter of Fundamental Rights of the European Union. Such access is essential to enable people participate in the benefits of the modern, market-based economy. Thus it is analogous to access to housing, to safe drinking water, basic healthcare services, and primary education. Since the 1980s, microfinance (MF) has emerged as a significant component of poverty reduction and economic development strategies around the world. By the early 21st

century,

microfinance institutions (MFIs) have become a vast global industry, involving large numbers of governments, banks, aid agencies, NGOs, cooperatives and consultancy firms,

and directly employing hundreds of thousands of branch-level staff. Much of the initial excitement about MF centered on Bangladeshâ&#x20AC;&#x2122;s much lauded Grameen Bank, which propounded a â&#x20AC;&#x2DC;bottom-upâ&#x20AC;&#x2122; approach mobilizing the marginalized communities, particularly women. However, Grameen Bank is working only in rural areas and its charter does not allow its operations in urban areas; most other MFIs are also working primarily in rural areas. It is because of the initial fear that rural-urban migration would be increased by urban MF operation that the administration also encouraged MF only in rural areas. The urban poor are just as vulnerable as the rural poor; they are homeless and asset less, they come to cities and live hand to mouth .In view of the extent of urban MF activity the decision to exclude the cities from MF services is being reconsidered. The fact that the MF industry has been able to provide access to credit to 2,863,903 urban members (the total national number of MF borrower is 23,955,780, so the urban members are about 12%) is remarkable. (Bangladesh Microfinance Statistics, 2007). According to these statistics, out of a total of 535 MFIs, 220 are operating microcredit programs in urban areas and the total disbursement risen to Tk. 106, 284 million which is one-seventh of the total amount. The statistics also show that 50 MFIs are working only in the urban areas. The philosophy of the MFI is to provide access of financial services to the poor. Where conventional banking practice requires collateral, MFI has reached there and disbursed credit on mutual trust. Credit is an effective weapon to fight poverty and it serves as a catalyst in the over all development of socio-economic conditions of the poor. Professor Muhammad Yunus, the founder of microcredit program, reasoned that if financial resources can be made available to the poor people on terms and conditions that are appropriate and reasonable, "these millions of small people with their millions of small pursuits can add up to create the biggest development wonder." As a strategy for achieving the poverty reduction required to reach the Millenium development Goals (MDGs), government and non-government organizations have been facilitating microfinance programs. Urban poverty is being tackled as a component of a number of development programs such as Local Partnership for Urban Poverty Alleviation Program (LPUPAP) implemented jointly by UNDP and LGED, Secondary Towns Integrated Flood Protection Project, Phase-II (STIFPP-II) and Urban Governance Infrastructure Improvement Project (UGIIP) funded by ADB. It is worth asking if these

are the most efficient agents for community empowerment or if they need supplementing with MFIs? There are a number of urban poverty studies currently available. Among the studies, the most prominent are “Slums of Urban Bangladesh, 1995” and “Slums of Urban Bangladesh: Mapping and Census 2005” of the Center for Urban Studies (CUS) ,the “Study of Urban Poverty in Bangladesh, 1996” by the Planning Commission and the Asian Development Bank, and the Urban Sector Review 1998 done for CARE. These studies generally focus either on Dhaka or other divisional cities and are more directed at spatial statistics or on the spatial characteristics of poverty, than on the policy questions that motivate MF. Research on microfinance has so far been focused upon rural microfinance, the interest rate charged, group lending, women’s empowerment, or some other aspects. But these rural efforts are only parts of a more complex economic question. There are a number of MFIs working in urban areas and the number of outstanding borrowers has reached over two millions (Microfinance Statistics, 2007). These MFI’s may be facing some peculiar problems, or their depth of outreach may not get to many urban poor. With the rapid urbanization of many LDC’s and of Bangladesh, in particular, there is now both a need and an opportunity to initiate studies of urban MF. This research sought to get a better understanding of the dimensions of urban poverty, its links with urban enterprise and rural-urban migration as well as the potential of MF to impact upon the urban poor. This study is an exploratory one and aims at testing out the viability of an effective study of MF. It was conducted in thirteen cities, and modeled after an earlier conducted study by the authors of Brahmanbaria. That study was based on the belief that the eastern half of the country had, on the whole, participated in the economic growth of the last two decades somewhat disproportionately and it seemed to be worthwhile to look at both the poverty indicators and potential for MF in major mofussil town. The Brahmanbaria study focused on some issues such as better understanding of urban poverty and of MF borrowers, evaluation of its performance, improved understanding and regulation of urban MFIs, the problems of commercial banks entering into urban MF. It needs emphasizing that urban MF is a blank slate in Bangladesh at the moment and that the study presented here breaks new ground. The best evidence for the

neglect of Urban MF lies in the surprising fact that the recent Bangladesh Microfinance Statistics 2007 does not provide a separate category for urban microfinance. By using the detailed tables in this book, we can construct the following summary.

Total

Urban

Rural

Remarks

No. of working MFIs

535

220

485

No. of active members

23,955,780

2,863,903

21,091,877

50 MFIs are working only in urban area

Total loan disbursement(Tk.)

732,122,907,537

106,284,856,701

625,838,050,836

18,732

21,702

5,081,186,216

25,833,509,584

99.44%

99.21%

Avg. loan size (Tk.) Net savings (Tk.) Recovery rate

30,914,695,800

Source: Bangladesh Microfinance Statistics 2007 (As of December, 2007)

Out of Taka 2182.86 million (loaned by 195 MFI’s), 23.95 and 9.57 million are used respectively for housing and education purposes. That urban MF may be more problematic than its established rural variant is part of the folklore. The main difference that strikes one in looking at the urban poor is their potential mobility and following on from their anonymity. Transients do not make good targets for MF since the ability to check on borrower payments is reduced, and the anonymity of city life may introduce other sociological variables that make it develop the human capital essential for successful microfinance. The optimism that accompanied this study arose from a long-standing interest in rickshaw pullers on the part of one of the authors. Continuing work with the rickshaw pullers has suggested that a study of the urban situation is manageable for two reasons, and a third is suggested by the nature of urban life. First, despite the presence of many transients, a substantial proportion of the slum dwellers are urban dwellers for long periods of time. Secondly, many of the transients keep visiting their ‘bari’ regularly, where their parents, their wives and their children reside; as a result, these individuals do in fact have a ‘thikana’ that can be used for MF purposes. Thirdly, the heterogeneity of urban populations is sometimes considered a risk in forming stable organizations; while the fact

of diversity is true, this may also be seen as an opportunity in that it allows a MFI to pool a variety of risks and become more viable.

Appendix--Introduction It should be noted that BD was not only a pioneer in rural microcredit, it has also been in the forefront of urban microcredit. However, the urban aspects of BD MF are so poorly known that one can hardly expect the rest of the world to be aware of them. India came late to this venture, but, particularly with the poverty that has persisted despite its high growth rates, India is rapidly moving into the position of an innovator, particularly with the Self-Help-Groups or SHG’s that have flourished there. For comparative purposes, the situation of urban MF in India may be summarized as follows:

Reaching the Other 100 Million Poor in India: Case Studies in Urban Micro finance Centre for Micro finance India’s urban market—approximately 28% of India’s total population—consists of 280 million people. This market is expected to grow to over 600 million by year 2030, with the urban poor as a significant portion of this growth.1 The success in rural areas in providing services such as facilities to deposit savings and access to credit for production, consumption, and emergencies is well known, but these financial services have yet to be sufficiently offered in the urban space. According to the2001 census of India, the urban poor comprise 35 to 40% of the population, but only 0.01% of them have banking relationships. This can be difficult to comprehend, given that the urban market contributes to 62% of India’s GDP. Approximately 89% of workers in the informal economy are women whose contributions appear invisible to state planners and policymakers. Occupations for India’s poor vary from self-employed vegetable vendors and small shop owners to salaried employees working as maids, cooks, and factory-workers. In spite of the large network of bank branches and ATMs that exist in cities, many of the poor find their financial needs largely unmet. Reasons such as a lack of documentation, regular incomes, and a low degree of comfort in visiting banks for transactions help explain why the urban poor feel forced to rely on informal credit from moneylenders who demand interest as high as 10% per day from borrowers. In fact, the lack of access to basic services and productive inputs, trading spaces for vendors, and skill and livelihood training further pushes the poor into poverty, in spite of their status as an essential component of the country’s workforce. Providing financial services in the urban context can be a tremendous opportunity given the existing demand and expanding market.

1.2 IMPORTANCE OF URBAN MICROFIANNCE

A majority of the people of Bangladesh still live in rural areas. Therefore, the rate of urbanization rate is one of the highest in the world and it is expected to be continue so in the future. However, more than 23.39% of the total population of the country live in the 522 urban areas (Begum, A. 2009) and a considerable portion of the urban population is poor. When climate change occurs ,it will add more poor to the urban areas. A projection states that by 2015 the countryâ&#x20AC;&#x2122;s urban poor will be 63 million and constitute 25.70% of the urban population (Khan, M.K. 2005).

Table 1.1: Growth of Urban Population in Bangladesh 1901 - 2008 Census Years

Urban Population (as % of Total population) million

Total Urban Population

1901

2.43

0.70

1911

2.54

0.80

14.96

1.39

1921

2.61

0.87

8.85

0.84

1931

3.01

1.07

22.20

1941

3.66

1.54

43.20

3.59

1951

4.34

1.83

18.38

1.58

1961

5.19

2.64

45.11

3.72

1974

8.87

6.0

137.57

6.7

1981

15.17

13.22

115.76

10.97

1991

19.63

20.87

57.87

5.79

2001

23.53

29.26

40.20

4.0

2008

25.4

36.7

25.43

3.6

Decadal increase of Urban Population (%)

Annual Growth rate of Urban Population (%)

Source: The Urban Poor in Bangladesh, May1996,BBS

From Table 1.3 it is evident that the proportion of urban population has been increasing constantly with time. But in 1981 along with the growth, it shows a remarkable change which almost doubles the growth rate in the span of 7 years. The annual growth rate of the urban population has also shown a similar, drastic increase to 10.97% in the year 1981. The spatial distribution of this urban growth also merits attention. Dhaka, the capital, has nearly 40% of the urban population. The three other metropolitan centres 7

Chittagong, Rajshahi, Khulna- has an additional 16%. The 309 pourashavas or municipalities have 31% (PPRC seminar on Urban Poverty, on 12.01.10). If the dangers of climate change are as portrayed in the press, then another 20 million may be moving to the cities in the next decades. Therefore it is important to emphasize the growth of urban areas including the secondary towns. Since, the growth of slums or informal settlements is characterized by dense concentrations of the poor. Urban poverty in general, as well as its spatial and economic and social characteristics, is deserving of a higher level of policy engagement.

Table 1.2: Urban Poverty in Bangladesh Year BBS Estimate Poverty line)

Poverty Line I

Poverty Line II

(“Absolute Poor” with 2122 k.cals/person/day)

(“Hard core Poor” with 1805 k.cals/person/day)

Poverty

total

population

1973 – 74

81.4

28.6

1981 – 82

30.7

1983 – 84

1985 – 86

1988-89

47.6

1991-92

42.6

23.6

1995-96

27.8

13.7

2000

35.1

19.9

2005

28.4

14.6

below

Source: The Urban Poor in Bangladesh, May1996, BBS, WB

The urban poverty table 1.4 indicates that urban poverty, as a whole, has declined since the independence of Bangladesh.1 Keeping in mind the very low initial levels of urbanization we find urban poverty declining from 1973 to 1986. Then, we can see the percentage under both Poverty Line I and Poverty Line II showing an increase during the period 1988-1992. However, this starts to fall again as the latest figure in 2005 shows in 1 It may be noted that there is some confusion about the proper criteria to measure poverty and its exact amount.According to PPRC seminar on Urban Poverty on 12.01.12-As a result, the BBS poverty head count ratio for 2000 using the ‘cost of basic needs’ method was 36.6% in Urban areas compared to a rural figure of 53%, while estimates for the same year using measures of ‘direct calorie intake’ were 52.5% in urban areas against only 42.3% in rural settings.

the case of both poverty lines. If we recall the fact that the base from which these percentages are being calculated is constantly increasing, we will see that the numbers of urban poor are increasing in importance. Table 1.3: Absolute Number of Urban Poor Area

1991/92

1995/96

2000

2005

Rural

59.0

54.5

52.3

43.8

Urban

42.6

27.8

35.1

28.4

National

56.8

50.1

48.9

40.0

Rural

44.0

39.4

37.9

28.6

Urban

23.6

13.7

19.9

14.6

National

41.3

35.1

34.3

25.1

Upper Poverty Line

Lower Poverty Line

Source: WB estimation using the Upper and Lower Poverty Line of 2005 adjusted for price changes between years

Table 1.4: People living below the poverty line in the study areas District

Estimated for 2009

% living below the poverty level-2009 in the study areas

2005

1.Barisal

2.Chittagong

3.Dhaka

4.Khulna

5.Rajshahi

6.Sylhet

Area

Lower Poverty Line

Upper Poverty Line

Lower Poverty Line

Upper Poverty Line

Lower Poverty Line

Upper Poverty Line

Rural

753.13

926.21

979.07

1204.07

Urban

800.15

950.74

1040.195

1235.96

31.94

41,67

(6033)

(7169)

Rural

752.57

891.28

978.34

1158.66

Urban

749.04

963.33

973.75

1252.33

10.40

19.20

(4616)

(5936)

Rural

728.43

842.08

946.96

1094.70

Urban

749.12

889.75

973.86

1156.68

7.24

10.54

(4597)

(5460)

Rural

653.97

742.63

850.16

965.42

Urban

670.39

825.29

871.51

1072.88

18.50

45.50

(3931)

(4849)

Rural

655.90

766.48

852.67

996.42

Urban

696.16

856.61

905.01

1113.59

16.56

26.49

(3882)

(4777)

Rural

697.43

822.31

906.66

1069.00

Urban

806.41

1020.31

1048.33

1326.40

59.00

73.00

(4413)

(5584)

Source: WB estimation for 2005

[Note: Poverty lines have been estimated for 2009 using CPI 2005-06 and 2009 (July). Product of CP 2009/CP 2004-05 and poverty line of 2005 have been uses to estimate poverty line of 2009. In parentheses, Poverty Lines are calculated based on HH members in the study areas.]

To assess the trend of the urban poor, the move, seemingly unquestioned, towards a market economy with mobile labor, has to be noted. In a more planned economy we can control rural-urban migration through a variety of means such as ID cards or licenses etc.. The contribution of the urban sector to the GDP is about double that of the rural sector2: In a market economy, we can expect economic factors to dominate. and so migration from rural to urban areas will continue in the foreseeable future Bangladesh development strategy aims at alleviating poverty and over the past two decades microfinance has emerged as one of the key strategies. But the program have hitherto had a marked rural bias. To address urban poverty alleviation, the MF programs may play a vital role in the future.

According to Prof Sarwar Jahan, Head, Urban and Regional Planning, BUET, Dhaka

Part 2: HISTORCAL OVERVIEW

LIST OF TABLES Table 1: Distribution of Percentage of Slums by Year of Establishment Table 2: Percentage Distribution of Slums by Area and City Table 3: Land Ownership Patterns in Slums by City Table 4: Pattern of house Ownership of Slums by City Table 5: Percentage Distribution of Living Space in Slums by City Table 6: Percentage Distribution of Structures by the Type of Housing Structure Table 7: Access to Municipal Services by the Slum People Table 8: Source of Drinking Water Table 9: Distribution of Slum Population by Major Districts of Origin Table 10: Education of the Head of the Family Table 11: Duration of Stay of Head of the Household in the Slum Table 12: Major Occupation of the Household Head Table 13: Preference of Jobs Table 14: Total Monthly Family Income Table 15: Monthly Expenditure of the Family Table 17: Earning in Rent Table 18: Willing To Pay for Purchase of a House Table 19: Monthly Paying Capacity of Tenants against a House purchase Table 20: Source of Finance for House Building Table 21: Mobility Pattern of the Squatter Table 22: Average size of Household Table 23: Summary Information on Slums in Dhaka City Table 24: Summary Information of 40 Selected Slums in Dhaka City Table 25: Age of the Slum Table 26: Population Density of Slums by Type of Ownership Table 27: Slum on land Owned by Public and Other Authorities Table 28: Rent Structure in Slums Table 29: Access to Community Facilities by the Slum People in Dhaka City Table 30: Other Facilities for the Slum People in Dhaka City Table 31: General Information about Slums in Dhaka City by Thana Table 32: Monthly Expenditure of the Family in Different Items Table 33: Reasons for Migration in Dhaka City REFERENCES

HISTORY It will have been clear from chapter 3 that MFIâ&#x20AC;&#x2122;s have been quietly effective in the urban areas of BD for over two decades. Yet a continuous history is difficult to piece together. We went through the Bengal District Gazetteers for all the districts that we could find, but references to urban poverty could not be found. That such poverty did not exist is implausible, but since urbanisation itself was minor, ignoring such urban poverty is understandable. Unfortunately, this lacuna means that we have no historical base to lean upon. Many studies of slums and of urban poverty exist, but there is no database that permits a systematic view of the evolution of urban poverty and the attempts at its amelioration. Not only does every book engage in a new independent survey, there is almost no attempt to stitch extant research into a composite whole. A fertile source of continuous new data lies in the theses of students , but even these ,despite being from the same institution, show no signs of cumulative progress. To provide some glimpse of the dynamics of urban poverty we have pieced together a series of Tables on each item of interest and inserted the facts from each earlier study that bore upon a given area. A detailed list of references has been added, where we are quite conscious that the list is incomplete, and we encourage those in other cities and universities to communicate their findings . Our hope if that every future researcher---even if they are student theses--- will provide both a copy of their work, and indicate how their facts bear upon the Tables shown below.

Table 1: Distribution of Percentage of Slums by Year of Establishment Cities

Before 1971

19711975

19761980

19811985

19861990

19911995

19962000

20012005

Dhaka

12.5%

9.1%

14.9%

12.8%

17.9%

13.1%

12.3%

7.3%

Chittagong

16.3%

7.4%

17.9%

11.7%

13.4%

13.3%

14.3%

5.1%

Khulna

27.3%

15.2%

15.8%

12.9%

10.2%

8.3%

6.3%

3.4%

Rajshahi

47.3%

16.5%

17.2%

7.6%

6.2%

2.6%

1.7%

0.8%

Sylhet

1.8%

2.8%

7.7%

13.9%

15.1%

34.3%

16.8%

Barisal

47.6%

10.8%

13.4%

6.0%

5.4%

2.0%

1.1%

Source: Slums of Urban Bangladesh, 2005, 2005 From the table 1 it is observed that, in Dhaka during the period of 1986-90 the highest percentage of slums were established and in case of Chittagong the period was 1976 – 80. In case of Khulna, Rajshahi and Barisal a significant percentages of slums were established before 1971. The most remarkable establishment of slums took place in Sylhet during 1996-2000 (34 percent). Table 2: Percentage Distribution of Slums by Area and City Size of Slum (acre) Up to 0.33

1983

Dhaka (%) 1988

2005

Chittagong 2005 (%)

Khulna 2005 (%)

Rajshahi 2005 (%)

Sylhet 2005 (%)

Barisal 2005(%)

81.06

61.4

67.5

61.1

31.5

89.4

62.4

0.34–0.66

8.56

13.2

11.5

12.2

16.1

33.2

7.5

14.8

0.67 – 1

2.98

6.9

6.8

8.3

8.5

17.3

1.8

9.4

14.2

16.5

14.2

17.9

1.2

13.4

Above 1

Source

7.4

Slums in Dhaka City, June1983

18.5

Slums and Squatters in Dhaka City, June 1988

Slums of Urban Bangladesh, 2005

From the table 2 it is shown that, the majority of the slums were very small in size. The highest percentage of slums occupied a land area of up to 0.33 acre or 1 bigha in case of all cities except Rajshahi. In Rajshahi slum were bigger in size bur in Sylhet the majority (89.4%) of slums were 0.33 acres or less. Only a small percentage of the slums were more than one acre in all cities.

Table 3: Land Ownership Patterns in Slums by City Land Ownership Pattern

Dhaka

Chittagong

Khulna

Rajshahi

Sylhet

Barisal

1983 (%)

1988 (%)

1996 (%)

2005(%)

11.68

29.2

21.42

10.8

12.5

13.4

0.8

11.1

83.40

67.1

77.05

89.8

85.9

79.2

82.7

98.9

87.2

Non Government Organization

1.16

Mixed (Public and Private)

Unidentified

2.7

Municipality

2.46

Disputed

2.46

0.36

1.2

3.3

8.3

3.9

0.3

1.7

Government / Semiâ&#x20AC;&#x201C;government Private (Individual / organization)

Others

Source

Slums in Dhaka City, June1983

Slums and Squatters in Dhaka City, June 1988

Survey of Slum and Squatter Settlements in Dhaka City, December1996

Slums of Urban Bangladesh, 2005

From the table 3 it is observed that the common and suitable place for establishing slums in all cities is the land owned by the private individual or organization. The data shows the same trend of slum land ownership patterns in all cities. From the data it can be said that the highest percentages of the slums were established on the land owned by the private individual. A significant proportion of the slums were set up on the Government land. Only a few proportions of the slums were owned by the others like NGO, Municipality etc.

Table 4: Pattern of house Ownership of Slums by City Dhaka

Home Ownership

1974

1983 21

1988 41.1

Chittagong 2005 11.7

1974 29

Khulna

2005 16.5

1974 76

Rajshahi 2005

17.5

Sylhet

2005 58.9

2005 0.5

Barisal 2005

Owner

24.9

Tenant

62.5

48.9

77.2

73.6

59.4

17.7

96.3

49.2

Free

16.5

11.1

9.9

23.1

23.4

3.1

25.9

Source

Squatters in Bangladesh Cities, 1974

Slums in Dhaka City, June 1983

Slums and Squatters in Dhaka City, June1988

Slums of Urban Bangladesh, 2005

Squatters in Bangladesh Cities, 1974

Slums of Urban Bangladesh, 2005

Squatters in Bangladesh Cities, 1974

Slums of Urban Bangladesh, 2005

From table 4 it is shown that in most of the cities majority of the households were tenant occupants. But in 1974 reverse findings were found in case of Dhaka and Khulna and in Rajshahi in 2005. In 2005, a high proportion of slum households (58.9%) in Rajshahi. In 1974, 87% of the households in Dhaka and 76% of the households in Khulna were owner occupied. The remaining households of all cities were free occupant. But a significant proportion of households (around 25%) in 2005 in Barisal, Khulna and Rajshahi did not pay any rent.

Table 5: Percentage Distribution of Living Space in Slums by City Year 1974

1983

Living Space (Square feet)

Dhaka (%)

Chittagong (%)

Khulna (%)

Less than 100

101 – 150

151 – 200

201 – 250

251 above

Less than 66

46.1

66 – 115

32.53

116 – 155

13.73

156 – 200

5.30

Above 200

2.33

Rajshahi (%)

Sylhet (%)

Barisal (%)

Source

Squatters in Bangladesh Cities, 1974 Slums in Dhaka City, June 1983

From the table 5 it is evident that the amount of living space available for a single household is very small. In 1974, 41% of houses in Dhaka and 44% of houses in Chittagong have areas less than 100 sq. ft. While in Khulna 24% of the houses have area less than 100 sq. ft. On the other hand only 7% of houses in Dhaka have area over 250 sq. ft. but the corresponding figure for Chittagong and Khulna are 13% and 24% respectively. It can be evident from the above table that the amount of living space for a household is decreasing day by day. In 1983, 46% of houses in Dhaka have area less than 66 sq. ft. and only 2.3% of houses have area more than 200 sq. ft. which was 12% in 1974.

Table 6: Percentage Distribution of Structures by the Type of Housing Structure City Dhaka

Year

Katcha

Semi pucca

Pucca

Others

1974

1983

1988

92.3

7.4

3.3

1996

95.7

3.56

0.44

2005

52.3

1.2

0.5

1974

2005

66.6

32.6

0.3

0.5

1974

2005

85.4

12.3

1.7

0.5

Rajshahi

2005

51.1

45.2

3.5

0.1

Sylhet

1985

Chittagong Khulna

2005

33.1

0.5

0.4

Barisal

2005

11.6

0.4

0.9

Rangpur

1985

Kushtia

1985

Source: 1Squatters in Bangladesh Cities 1974 2

Slums in Dhaka City, June1983

Slums and Squatters in Dhaka City, June 1988

Surveys of Slum and Squatter Settlements in Dhaka City, December1996

The Urban Poor in Bangladesh, May1996

Slums of Urban Bangladesh, 2005

Table 6 shows the proportion of slum households by the type of housing structure. From the data it can be said that slum dwellers live mainly in Katcha house (which are structurally very poor), but the situation has improved greatly over time, especially in Dhaka. Because the slum dwellers have built their structures with all kind of conceivable materials include bricks, tin, bamboo, grass, wood etc. The remaining proportions of households were found to be living in semi Pucca and Pucca house. Four cities Dhaka, Chittagong, Khulna, and Barisal are mainly reflecting the above result. But in case of Rajshahi, Sylhet, Kushtia and Rangpur a significant proportion of households were found to be living in semi Pucca house. In case of all cities a few percentage of households were living in Pucca houses. It should be noted here that in case of Kushtia the highest proportion of households were found to be living in semi Pucca house.

Table 7: Access to Municipal Services by the Slum People City Dhaka

1979

Water Supply

6.5

1980 -

1983 Tap / Tubewell 60%

1988

1996

2005

WASA 50% Tubewell 14.6%

85.83

Electricity

55.7

73.36

95.4

Gas

30.3

57.6

Sanitary Latrine

Private latrine 10% Share Pucca Latrine 25%

Community latrine 86.6%

57.88

Slums in Dhaka City, June 1983

Slums and Squatters in Dhaka City, June 1988

Tap 37% Tubewell 57%

87.4 27.9

Source

Chittagong

% of HH having Municipal Services

Facilities

Urban Poor in Bangladesh,1 979

Slums of Urban Bangladesh, 2005

Water Supply

Gas

Sanitary Latrine

Source The Urban Poor in Bangladesh, May1996 Khulna

Water Supply

Electricity

Tap 30% Tubewell 66% -

Source

Electricity

Source

Sylhet

Source

72.5

72.7

Slums of Urban Bangladesh, 2005 Electricity

93.4

Gas

16.1

Source

Barisal

Slums of Urban Bangladesh, 2005

The Urban Poor in Bangladesh, May1996 Rajshahi

Slums of Urban Bangladesh, 2005

Slums of Urban Bangladesh, 2005 Electricity

95.8

Slums of Urban Bangladesh, 2005

Table 7 shows the percentage distribution of households by services such as electricity, gas, safe drinking water and access to sanitary latrine. From the data it is clear that access to municipal services is being improved gradually. In 1979 only 6.5% of the slum dwellers in Dhaka get sufficient quantity of safe drinking water whereas in 1996 majority proportion of the slum dwellers (85.83%) have access to safe drinking water. In 1996 a significant proportion of the slum dwellers in Chittagong (94%) and Khulna (96%) get sufficient quantity of safe drinking water. In 1983 a small percentage of households have little access to electricity and gas connection. In 1996 more than 70% of the slum dwellers have access to electricity. In 2005 more than 90% of the households in Dhaka, Sylhet, Barisal and more than 70% of the households in Rajshahi, Khulna have electricity connection. In 1996 about 30% of the dwellers in Dhaka have gas connection but in 2005 the percentage is increased into 57%. However in the same year only 16% households in Sylhet and 27% households in Chittagong have gas connection. In case of sanitary latrine most of the slum dwellers use community latrines. Only a few proportions of the dwellers have their own latrine.

Table 8: SourceS of Drinking Water Percentage Town Total Population

Percentage Town Poor Population

Private Well

2.27 – 18.24

7 – 28

Private Tubewell

1.43 – 42.46

0 – 31

Public Tap

1.45 – 23.02

10 – 30

Public Tubewell

5.1 – 24.46

4 – 67

Municipal pipe line

6.1 – 51.40

3 – 36

Supplied by traders

0 – 13.6

Tank/ Pond

0 – 28.8

0 – 36

River

0 – 1.43

0 – 5

Source

Source: The Urban Poor in Bangladesh, May1996 From table 8 it can be observed that highest proportion (6.1% – 51.4%) of town population use Municipal pipe line as their source of drinking water. Whereas highest proportion (4% – 67%) of town poor population use Public Tube well as their source of drinking water.

Table 9: Distribution of Slum Population by Major Districts of Origin District of Origin

Urban Poor in Bangladesh

Urban Poor in Dhaka City 1974 (%)

1979 (%)

Dhaka

19.7

Faridpur

20.8

Comilla

15.3

Barisal

Noakhali

Mymensingh

1996 (%)

1979 (%)

15.9

7.5

18.5

20.6

18.26

14.5

9.63

16.1

18.5

23.4

23.18

18.6

3.3

6.9

3.28

3.6

7.1

4.8

5.75

6.7

Rangpur

0.2

2.1

Tangail

0.1

1.4

1.3

Khulna

0.1

1.2

1.7

Rajshahi

0.2

0.4

0.5

Chittagong

0.2

0.6

1.1

0.7

Sylhet Patuakhali

0.1

1988 (%)

3.1

1.9

2.1

Jamalpur

2.9

2.7

Ctg.Hill Tracts

0.1

Bogra

0.2

Pabna

Dinajpur

0.5

Kushtia

0.3

Jessore

0.5

0.6

1.2

1.5

Outside Bangladesh Others

Source

13.9

Squatters in Bangladesh Cities, 1974

Urban Poor in Bangladesh,1979

Slums and Squatters in Dhaka City, June 1988

29.31

Survey of Slum and Squatter Settlements in Dhaka City, December1996

Squatters in Bangladesh Cities, 1974

Table 9 shows the distribution of slum population by their origin. It can be observed from the table that larger number of slum population originated from few district mainly Dhaka, Barisal, Faridpur, and Comilla. These four districts comprise of 86%, 74%, 74.4%, and 59% of slum population were migrated in Dhaka city in 1974, 1979, 1988 and 1996 respectively. In case of whole Bangladesh, about 73% of slum populations are originated from Dhaka, Barisal, Faridpur, and Comilla.

Table 10: Education of the Head of the Family Dhaka (%)

Education Level

Chittagong (%)

Khulna (%)

1974

1983

1974

No Education

52.36

70.64

72.63

65.57

Can Sign Only

0.52

0.74

4.21

3.28

Class I-V

27.24

20.12

15.79

18.03

Class VI-X

17.80

6.49

5.26

9.84

1.04

1.44

2.11

0.82

1.04

0.57

2.46

S.S.C H.S.C above

and

Source

Squatters in Bangladesh Cities, 1974

Slums in Dhaka City, June 1983

Squatters in Bangladesh Cities, 1974

From the table 10 it can be observed that the literacy rate in the slum population is not high. Majority of the household heads have no education in case of three cities. Among those a significant percentage of households have primary level education. There are only a few households who have secondary and higher secondary education.

Table 11: Duration of Stay of Head of the Household in the Slum Duration of Stay in Years

Percentage in 1974

Duration of Stay in Years

Percentage in 1983

Below 1

5.4

Below 1

1 – 5

39.3

1 – 2

6 – 10

21.7

2 – 3

8.6

11 – 15

18.1

3 – 4

7.4

16 – 20

9.0

4 – 9

20.2

Above 20

6.5

9 – 14

13.6

14 – 19

5.7

19 – 24

5.5

24 – 29

1.9

29 – 34

2.7

34 +

0.8

Since Birth

Source

The Urban Poor in Bangladesh, May1996

Source

8.6

Slums in Dhaka City, June 1983

From table 11 it shows that 14% household head stay in the slum 1 - 2 years, 20% household head stay in the slum 4 – 9 years, 13.6% household head stay in the slum 9-14 years and 8.6% household head stay in the slum since birth. Optimistically, this says that some 14% are upwardly mobile. 11

Table 12: Major Occupation of the Household Head Year

Occupation of the Household Head

1974

Transport Worker

Labour

Khulna (%)

Hotel and Restaurant Personnel

and

other

Personnel

Service Professional Service Shopkeeper and businessmen Office service Beggar

0.5

Rickshawpuller

Cart Puller

2.5

Motor and Auto Rickshaw Driver

2.7

Labour

Factory Worker

Office Clerks

Security Guard Small Shop owner Others (Fish seller, Hawker, Shoe maker, Beggar etc.) 2005

Chittagong (%)

Skilled and semi skilled Labour Household

1983

Dhaka (%)

Transport Labour

Rajshahi (%)

Sylhet (%)

Barisal (%)

Squatters in Bangladesh Cities, 1974

The Urban Poor in Bangladesh, May1996

2 3.5 47.3 24

31.3

30.4

41.3

5.6

18.6

25.3

32.8

29.1

54.3

Factory Worker

22.4

21.3

13.8

0.3

0.6

7.4

Service

10.8

10.2

8.6

8.9

9.2

2.6

3.1

1.5

3.2

1.7

Domestic Worker

Source

Business

10.1

9.4

9.5

20.3

6.1

Hawker

3.7

1.5

1.9

0.6

2.3

Slums of Urban Bangladesh, 2005

The above table 12 shows the occupational pattern of the household head. In all cities head of the family are also invariably males. The three cities also show the similarities result in the different occupational types. Among the head of the family the most important occupational group seems to be Transport workers such as Rickshaw puller, Cart puller, motor and auto rickshaw driver, Labour, Shopkeeper and business, Office or Factory workers. These four types of occupational group comprise majority proportion of the household head in case of all three cities. The remaining proportion of the household heads held in others occupation like Domestic worker, Fish seller, Hawker, Shoe maker, Beggar etc. Table 13: Preference of Jobs Occupational Group

Dhaka (%)

Chittagong (%)

Khulna (%)

Transport Worker

Labour

Hotel and Restaurant Personnel

Household and Other Personal Services

Skilled and Semi skilled Labour

Professional Service

Shop keeper and Business

Official Services

Beggar

Source: Squatters in Bangladesh Cities, 1974 The above table shows that in Dhaka 27%, in Chittagong 47%, in Khulna 37% of slum dwellers wanted to retain their business. The next preferred jobs for the slum dwellers are transport worker (Rickshawpuller), Labour in case of all three cities and official service (26%) in case of Dhaka city.

Table 14: Total Monthly Family Income Source

Year Monthly Total Income (in Taka) 1974

1983

0 – 100

Khulna

Rajshah

Sylhet

Barisal

(%)

i (%)

(%)

2.62

2.10

1.64

6.28

5.26

4.92

201 – 300

35.60

13.68

15.57

301 – 400

19.37

16.84

9.84

401 – 500

14.14

23.57

18.03

501 – 749

13.61

25.26

15.57

750 and above

8.38

13.68

34.43

Less than 100

8.45

101 – 200

6.94

201 – 300

9.09

301 – 400

10.54

401 – 500

11.29

501 – 750

25.36

751 – 1000

17.41 1.7

2000+

2.59

Less than 500

18.8

1001 – 2001

Squatters in Bangladesh Cities, 1974

Slums in Dhaka City, June 1983

6.62

1501 – 2000

501 – 1000

2005

Chittagong

(%)

101 – 200

1001 – 1500

1989

Dhaka

The Urban Poor in Bangladesh , May1996

38.75 34

2000+

<2000

3.8

34.4

8.8

0.8

44.6

2001 – 3000

19.6

36.8

54.3

52.1

1.9

44.8

3001 – 4000

34.5

27.6

9.4

33.9

9.9

4001 - 5000

27.6

11.6

1.5

4.7

69.3

0.6

5000 +

14.6

2.9

0.3

0.5

5.9

0.1

Slums of Urban Bangladesh , 2005

Table 14 describes total monthly income. In 1974, 78% families in Dhaka 61% families in Chittagong and 50% families in Khulna have monthly income less than 500 taka. In Dhaka 46% families in 1983 and only 19% families in 1989 have monthly income less than 500 taka. From the table it is found that in case of Dhaka only 2.59% families and 8% families in 1989 have earned more than 2000 taka per month. But in 2005, in case of all cities majority percentage of the families have earned more than 2000 taka monthly. It is evident from the analysis is that, monthly income of the slum people have been increased gradually. But on the other hand monthly expenditure has also increased at the same rate.

Table 15: Monthly Expenditure of the Family Item

Monthly Expenditure (% of income) 0 – 25

In Food

In rent

In Cloth

Khulna

4.18

1.05

4.1

51 – 75

27.76

30.35

20.49

76 and above

68.06

68.42

75.41

Pays no rent

90.59

30.53

0 – 10

6.8

65.26

24.59

11 – 20

2.09

4.21

0.81

21 and above

0.52

No Expenditure

15.19

4.21

0 – 10

73.82

64.21

82.79

11 – 20

9.42

28.42

13.11

21 and above

1.57

3.16

4.10

62.84

68.47

58.20

0 – 5

21.46

15.78

25.41

8 – 10

12.56

10.50

11.47

3.14

5.25

4.92

transport

11 and above Savings

Chittagong

26 – 50

No Expenditure for In Transport

Percentage of Families Dhaka

No Savings

85.84

90.53

83.61

0 – 20

12.04

7.35

13.11

21 – 40

2.09

2.12

3.28

Source: Squatters in Bangladesh Cities, 1974 In table 15 it shows the percentage of slums in each city in different level of expenditure in different items such as Food, Rent, Cloths, Transport, Recreation and Savings. The expenditure pattern on food is similar in the three cities. In Dhaka and Chittagong 68%, in Khulna 75% households spent more than 75% of their monthly income on food. However there are deviations on monthly expenditure patterns in others items. In Dhaka about 90% and in Khulna 74% of the dwellers do not pay any house rent whereas in Chittagong the percentage is only 31%. In case of all three cities majority proportion of the households spent up to 10% of their monthly income in clothing. But a significant percentage spent up to 5% of their income in transport. Savings pattern is very much neglected within the slum dwellers. More than 80% in all three cities have no savings at all. Table 16: Squatters with Tenants Dhaka (%) Have tenant Do not have tenant No data

Chittagong (%)

Khulna (%)

Source: Squatters in Bangladesh Cities, 1974

Table 17: Earning in Rent Rent in Taka

Dhaka (%)

Chittagong (%)

Khulna (%)

Less than 50

50 – 100

82 9

101 – 150

151 – 200

201 – 250

251 above

Source: Squatters in Bangladesh Cities, 1974

From the table 17 it is said that in Dhaka and Khulna a very few had tenants while in Chittagong none of the respondents had any tenants. From the table 16 it is shown that in two cities those who have tenants earn less than 50 taka per month as rent. Table 18: Willing To Pay for Purchase of a House Taka

Dhaka (%)

Chittagong (%)

Khulna (%)

500 and less

501 – 1000

1001 – 1500

1.5

1501 – 2000

1.5

2001 and above

1.5

Unable to pay

Source: Squatters in Bangladesh Cities, 1974

Table 18 shows that in Dhaka 56% and in Chittagong 57% of people are willing to pay for the purchase of a house that costs them less than Taka 500. Khulna on the other hand has the highest percentage (35%) of being willing to pay above Taka 2001 for a house. Other than that it is shown that in all cities a significant percentage of households were unable to pay for a house. Table 19: Monthly Paying Capacity of Tenants against a House purchase Dhaka (%)

Chittagong (%)

Khulna (%)

Less than 50

50 – 100

101 – 150

151 – 200

Taka

Unable to pay

Source: Squatters in Bangladesh Cities, 1974

Table 19 shows that the head of the household who wanted to buy a house has a very low ability to pay for a house. It is clear that the households expressed ability for purchasing a house is usually less than 50 taka per month. Table 20: Source of Finance for House Building Source of finance

Dhaka (%)

Chittagong (%)

Khulna (%)

Saved money and brought materials

62.83

18.95

55.74

Borrowed money and brought materials

45.03

12.63

23.77

2.09

3.16

3.28

Sold land of the village bought materials Other

3.14

No answer

9.95

70.53

19.67

Source: Squatters in Bangladesh Cities, 1974

Table 20 enquired about the source of finance for house building. In three cities majority of the households saved money and brought materials for house building while a considerable number of households borrowed money and brought materials. In three cities some people sold their land for build a house in the city. Table 21: Mobility Pattern of the Squatter Farthest Distance Moved

Distance

Nearest Distance Moved

in mile

Dhaka (%)

Chittagong (%)

0 – 1/4

1/4 – 1/2

1/2 - 3/4

3/4 – 1

1 – 2

2 – 3

3 – 4

4 – 5

5 and above

Khulna (%)

Dhaka (%)

Chittagong (%)

Khulna (%)

Source: Squatters in Bangladesh Cities, 1974

Table 21 indicates the mobility pattern of the slum dwellers for farthest distance and nearest distance. A significant percentage of the slum dwellers in Dhaka, Chittagong, Khulna move more than 5 miles to reach the farthest location of their working place. A majority percentage of the slum dwellers in Dhaka, Chittagong, Khulna have nearest work place within one mile from home to work place.

Table 22: Average size of Household Locality

Slum Population

Urban Poor Population

Total Population

Urban

4.71

5.73

5.9

Dhaka SMA

4.76

6.13

6.3

Chittagong SMA

4.48

5.44

Khulna SMA

4.79

5.28

5.3

6.05

7.8

Rajshahi SMA

Source: The Urban Poor in Bangladesh, May1996

In table 23 average family size of the slum population is lower than that of either the urban poor or the total population. However average family size of the urban poor population is lower in Khulna SMA and higher in Dhaka SMA. Table 23: Summary Information on Slums in Dhaka City Type of Information Total area (acres)

1988

1996

1340 acres

1038 acres

Estimated Household

146400

220920

Estimated population

878400

1104600

655/acre

1063/acre

80000

1.20 acres

130

Density of population (per acre) Estimated Number of Structures Average size of cluster (acres) Average cluster

Source

number

household

per

Slums Squatters Dhaka City, 1988

and in June

Survey of Slum and Squatter Settlements in Dhaka City, December1996

A comparative scenario about some general information such as total area, population, household, density etc. is discussed in table 26. From the table it is observed that the total number of slum population has been increased in 1996 from 878,400 to 1,104,600. But the total slum area has been decreased in 1996 from 1340 acres to 1038 acres.

Table 24: Summary Information of 40 Selected Slums in Dhaka City Name of Bastee 15/1,J.N.Shaha Road Goni Miar Ghat Rahmatganj Mohalla 230, Bangshal Road

Major

Place of

Land

Tenancy

Occupation

Origin

Ownership

System

Rickshawpuller Service Business Labour Rickshawpuller Service Labour Rickshawpuller

Decorator

Labour Rickshawpuller Others

15/16 Ruplal Das Lane

Rickshawpuller

Bhola Miar Bari

Day Labourer Others.

Behind Rupali

Dhaka Faridpur Dhaka Faridpur Barisal Dhaka Faridpu r Dhaka Faridpur Faridpur Dhaka Dhaka Faridpur Dhaka Faridpur Barisal

Shona Miar Bari

Business Rickshawpuller

Rabidaspara

Labour Mason.

India

Ramijuddin Bastee

Rickshawpuller Labour

Faridpur Dhaka

Private

Owner 5% Tenant 95%

Private

Tenant100%

Private

Tenant 100 %

Private

Owner 10% Tenant 90%

Islambagh Bastee

Shahidnagar Bastee

Rickshawpuller Labour

Faridpur Barisal Noakhali Dhaka Faridpur

Azimpur and Khali

Industries

Graveyard

worker Rickshawpuller

Azimpur Bastee Julo Doctors

Faridpur Comilla

Faridpur

Business

Comilla

Mason

Bastee

Faridpur

Service Business

T & T Staff Quarter Bastee

Service

Faridpur Barisal

Rickshawpuller

Mymensing h

Katabon Bastee

Jheelpar Bastee Hashem Ali Bastee

Shibbar Bastee

Mali Hawker Rickshawpuller

Comilla

Tenant 100 %

Tin, Bamboo

Private

Tenant 100%

Tin, Bamboo

Municipality

Free 100%

Private

Tenant 90% Owner 10%

Private

Faridpur

Rickshawpuller

Barisal

Service Hawker

Tenant 50% Owner 50%

Tenant 60% Owner 40%

Faridpur Barisal Noakhali

Tin, Bamboo Tin and Bamboo, Thatch and Bamboo Tin and Bamboo, Thatch and Bamboo Tin and Bamboo, Thatch and Bamboo Tin and

Private

Tenant 100%

Bamboo, Thatch and Bamboo Semi building

Tenant 75%

Thatch, tin

Owner 25%

and Bamboo

Private

Tenant 100%

Thatch

Private

Tenant 90% Owner 10%

Thatch, Tin and Bamboo

Government

Tenant 50% Owner 50%

Thatch

Tenant 40%

Thatch, Tin

Free 60%

and Bamboo

Private

Government

Faridpur

Labour

Tin, Bamboo

Private

Dhaka

Comilla

bamboo

Tin, Bamboo

Faridpur

9/1, Fulbaria Railway Colony

Thatch, Bamboo

Owner 40% Tenant 60%

Noakhali

Worker

bamboo

Private

Barisal Cottage

Quality Thatch, Tin,

Thatch, Tin,

Dhaka Rickshawpuller Labour

Housing

Government Private

Tenant 100%

Thatch

Tenant 96%

Thatch, Tin

Owner 4%

and Bamboo

Autonomous

Tenant 50%

body

Free 50%

Thatch and Tin

Name of Bastee

37/2, Puranapalton

Major Occupation

Place of Origin

Land Ownership

Rickshawpuller Labour

Faridpur Comilla

Private

Mason Darogartek Bastee Tajmohal Road

Rickshawpuller Labour

Noakhali Comilla Barisal Faridpur

Hossain Shaheb

Rickshawpuller

Bastee

Labour

Barisal Faridpur

Hashem D.S.P

Rickshawpuller Labour

Azgar Goals Bastee

O.C Miar Bastee

Labour Business Worker

Free 70%

Thatch

Government

Free 100%

Thatch

Private

Free 100%

Thatch

Private

Tenant 100%

Tin, Bamboo

Private

Tenant 100%

Tin, Bamboo

Private

Tenant 100%

Private

Tenant 100%

Noakhali

Day Labour Rickshawpuller

Rickshawpuller

Tenant 30%

Housing Quality

Barisal

Bastee

Tenancy System

Tangail Barisal Faridpur Dhaka Barisal Faridpur Dhaka Barisal Faridpur

Bastee

Labour

Faridpur Dhaka

Hajirbagh Bastee

Rickshawpuller Labour

Faridpur Dhaka

Bamboo, Brick and Tin

Barisal

Rickshawpuller

Tin and Bamboo Tin and

Comilla

Garibulla Sardarâ&#x20AC;&#x2122;s

Brick and Tin

Private

Tenant 90%

Thatch, Tin

Owner 10%

and Bamboo

Tenant 100%

Thatch, Tin and Bamboo

Mymensing Lake Side Bastee

Tenar Bari Bastee

Rickshawpuller

Business

Faridpur Dhaka

Rickshawpuller Hawker

Comilla Faridpur

Private

Tenant 60% Free 40%

Tenant 100%

Thatch

Thatch, Tin and Bamboo

Dhaka Tullag Bastee

Business Service

Dhaka Barisal

Thatch, Tin Private

Tenant 100%

Private

Tenant 100%

and Bamboo

Comilla Comilla

Noyatola Bastee

Rickshawpuller

Sylhet

Labour

Faridpu

Labour

Faridpur Barisal

Business

Noakhali

Thatch, Tin and Bamboo

West Nakhalpara

Private

Tenant 80% Owner 20%

i Service

Noakhali

Bastee

Rickshawpuller Driver

Barisal Faridpur

Salimullars Bastee

Worker (Factory) Rickshawpuller

Comilla Barisal

North Begunbari

Private

Tenant 85% Owner 15% Tenant 100%

Tin , Thatch and Bamboo

Tin, Thatch Thatch, Tin and Bamboo

Name of Bastee Dengu Haji’s

Major Occupation Rickshawpuller

Place of Origin Comilla

Bastee

Service Hawker

Majid

Rickshawpuller

Faridpur

Commissioner’s

Service

Barisal

Bastee

Day Labour

Comilla

Labour West Nakhalpara

Bastee

Worker (Factory) Business

Agargaon Bastee

Faridpur

Land Ownership

Tenancy System

Housing Quality

Private

Tenant 100%

Private

Tenant 100%

Tin and Bamboo

Private

Owner 100%

Tin and Bamboo.

Government

Free 100%

Thatch, Tin and Bamboo.

Noakhali Comilla Barisal

Labour

Barisal

Rickshawpuller

Faridpur

Thatch, Tin and Bamboo

Source: Slums in Dhaka City, June1983

Table 25: Age of the Slum Age of Slum (Years)

Age of Slum

1983 (%)

(Years)

Age of Slum

1988 (%)

(Years)

1996 (%)

Less than 5

12.97

5 and Below

25.4

Less than 5

5 – 9

20.62

6 – 10

5 – 9

24.01

10 – 14

14.27

11 -15

22.8

10 – 14

19.16

15 – 19

11.54

16 – 20

15.2

15 – 19

15.43

20+

37.48

21 – 25

4.9

20 -24

7.72

No response

3.11

26 – 30

2.9

25 -29

3.66

30+

5.6

30+

5.22

No response

1.2

Source: Slums in Dhaka City, June1983

Source: Slums and Squatters in Dhaka City, June 1988

24.81

Source: Survey of Slum and Squatter Settlements in Dhaka City, December1996

Table 26: Population Density of Slums by Type of Ownership Land Ownership Pattern

Total area in (acre)

Total Population

Density Person (per acre)

Govt./ Semi-govt.

465.47

546940

1175

Private

540.61

538852

997

29.05

16669

574

2.03

2139

1054

Non-govt. Others

and

Unknown

Source: Survey of Slum and Squatter Settlements in Dhaka City, December1996

Table 29 shows the density in slums by the land Ownership pattern. It is evident from the above table that, slum owned by private or non government authority shows a lower density compare with the slum owned by government (1175 person per acre). Table 27: Slum on land Owned by Public and Other Authorities Owners Private Owner

Percent 80.2

Cantonment

4.2

RAJUK

3.3

Ministry of Housing and Works

2.6

WAPDA

1.7

DCC

1.4

Bangladesh Railway

1.3

WASA

PWD

0.4

Civil Aviation

0.4

Dhaka University

0.4

Other educational institution

0.2

Khasland

0.1

Others

0.4

Unknown

2.3

Source: Survey of Slum and Squatter Settlements in Dhaka City, December1996

Table 30 shows the distribution of slum according to the ownership by private and various public agencies and institutions. The above data shows that about 80% of the slum land is owned by the private owner. The rest of the slums (20%) are on the land owned by number of public and semi public bodies including RAJUK, WASA, WAPDA, Ministry of Housing and Works. Table 28: Rent Structure in Slums Year

Monthly Rent(in Taka)

1983

Below 25

0.8

25 – 50

9.3

51 -75

14.4

76 -100

101 – 150

10.4

151 – 200

4.2

201 -300

1.9

300+

0.8

Without Payment 1988

Dhaka (% of HH)

50 and below

3.6 6.2

101 – 150

12.3

151 – 200

10.2

201 – 300

23.6

301 – 600

18.8

Without Payment

Slums in Dhaka City, June 1983

34.2

51- 100

600+

Source

Slums and Squatters in Dhaka City, June1988

3.4 21.9

Table 31 describes the house rent paid by the slum dwellers. From the table it is shown that house rent paid by the slum dwellers is increasing day by day. In 1983 about 48% of the occupants pay up to 100 taka and only 0.8% of the occupants pay more than 300 taka per month for house rent. But in 1988 only 9.8% of the occupants pay up to 100 taka and 22% of the occupants pay more than 300 taka per month for house rent.

Table 29: Access to Community Facilities by the Slum People in Dhaka City Type of Community Facilities

Availability within the area 1983 (%)

1988 (%)

13.88

10.3

5.84

5.7

Open Space for Children

13.62

10.2

Health/ Medical Center

0.78

3.4

Charitable dispensary

0.52

Various Type of Shops

24.90

26.3

Socio Culture Organization

2.46

Govt. Social Welfare Organization

0.65

Skill Development Center

1.04

Local area Samity

5.32

8.6

Slums in Dhaka City, June 1983

Slums and Squatters in Dhaka City, June 1988

Mosque Primary School

Garbage disposal

Source

Table 32 discusses about various community facilities within the slum area. Community facilities within the slum area like mosque, primary school, health center, shops etc. are also very inadequate in number. School, medical and health facilities, various social organizations for the slum dwellers are very miserable. Although more than 25% of the slums have shops within the slum area. In 1983 about 13% have a mosque or playground for children but in 1988 the percentage was about 10% for these facilities. Table 30: Other Facilities for the Slum People in Dhaka City Facilities

Percentage

Condition of road No Roads at all

51.3

Kutcha Narrow Lanes

31.3

Herring bone road less than 4 ft. wide

7.5

Herring bone road more than 4 ft. wide

1.8

Paved road

8.1 Drainage Condition

Drainage is Good

34.8

Drainage is Poor

43.1

Drainage is Very Poor

22.1

Flood Experience of 1987 Totally inundated

50.7

Partly inundated

15.1

Not flooded at all

34.2

Source: Slums and Squatters in Dhaka City, June 1988

From the table 33 it is said that road condition inside the slums are not in a good situation. In most of the slum areas (51.3%) there is no road, while in few (31.3%) there are some very narrow walkways. Only 17.4% of the slums have better road conditions. From the table 16 it is also clear that the drainage condition within the slum is unsatisfactory. About 34% of the slum areas have good drainage system. In rest of the areas drainage condition is poor. For this reason in 1987 flood majority of the slums (65.8%) get fully or partly inundated.

Table 31: General Information about Slums in Dhaka City by Thana Land Terrain (% of Slum) Above Below On Water Flood flood Bodies Level Level

Area in acer

Slum Popn.

Slum Popn as % Total Popn

Density Person Per Acre

Cantonment

71.84

61000

23.9

849

81.43

18.57

Demra

74.82

92100

13.3

1231

25.31

37400

13.9

1626

16.4

Name of Thana

Dhanmondi Gulshan

Type of House (%)

Facilities (% of Slum)

Pucca

SemiPucca

Katcha

Electricity

Gas

Water Supply

Sanitary Latrine

0.27

99.73

66.92

7.59

88.46

17.14

74.68

0.26

2.59

97.15

84.57

57.23

98.39

25.32

0.5

8.01

91.49

70.73

38.21

86.18

1332

7.14

92.86

2.76

97.24

61.49

4.05

73.65

41.07

46.32

61700

Kotwali

15.06

14600

5.2

969

33.33

66.67

5.33

17.05

77.62

84.48

44.83

58.62

66.67

Lalbagh

144.16

98600

18.3

684

15.14

35.51

42.82

1.14

3.49

95.37

94.25

19.29

96.29

65.27

Mirpur

205.44

345200

28.6

1194

42.24

41.84

0.54

0.02

2.29

97.69

58.89

11.78

78.75

10.75

Mohammadpur

45.9

951

23.53

74.51

1.96

0.14

3.6

96.26

59.42

26.47

88.41

70.59

204.52

194400

Motijheel

13.33

11100

3.7

833

100

0.65

9.53

89.82

67.27

27.27

69.09

100

Ramna

29.94

50800

19.4

1697

47.06

52.94

0.14

7.91

91.95

78.41

42.05

84.09

64.71

Sabujbagh

60.42

66500

1101

8.33

87.28

4.39

0.02

2.45

97.53

82.91

52.04

93.37

95.18

Sutrapur

79.66

70200

17.1

881

59.21

39.47

1.32

2.1

9.69

88.21

80.4

85.6

88.16

Tejgaon

43.27

73900

25.1

1708

68.63

13.73

17.65

0.65

1.21

98.14

51.91

18.58

65.57

86.27

Uttara

25.43

27100

18.7

1066

67.5

1.87

98.13

17.78

62.22

Source: Survey of Slum and Squatter Settlements in Dhaka City, December1996

Table 31 shows area, population, density, land terrain, type of house and facilities in slums by 14 Thanas in Dhaka city. Among these 14 thanas Motijheel Thana has the lowest percentage of slum population (3.7%) and Mohammadpur Thana has the highest percentage of slum population (46%). From the table high population density (more than 1500 person per acre) is found in Ramna, Dhanmondi and Tejgaon Thanas. The Thanas with lowest population density are Lalbagh, Sutrapur and Motijheel. From the table it can be said that most of the slums in Dhaka were developed below flood level. In Motijheel Thana all slums were developed above flood level but in Gulshan 92.86% of slums are below flood level. In Lalbagh 42.82% of alums were developed on water bodies by using bamboo poles. Housing quality of the poor varies significantly by 14 Thanas. Majority proportion of the slum dwellers were found to be living in Kutcha house. Only a few 26

proportions of slum dwellers are living in semi pucca or pucca structure. In case of the availability of services a significant proportion of the slum dwellers have access to electricity, water supply, sanitary latrine. But the percentage of the slum dwellers having access to gas connection is quite lower than the others facilities like electricity, water supply and sanitary latrine. Table 32: Monthly Expenditure of the Family in Different Items % of total expenditure Up to 5

Percent of Families Food

Rent

Cloth

Transport

Education

Entertainment

Medical

Saving

Others 20.15

0.20

18.44

71.82

65.82

79.56

89.63

90.84

34.21

6 – 10

43.24

23.05

21.43

19.34

8.69

7.78

22.37

23.55

11 – 20

34.82

4.61

10.20

1.10

1.34

1.38

17.63

45.69

21 – 30

0.20

3.02

0.51

2.55

9.21

7.66

31 – 40

1.70

0.32

0.34

1.32

1.84

41 – 50

4.00

0.16

5.26

0.57

51 – 60

61 – 80

60.90

0.28

81 – 100

20.30

0.28

Source: Slums in Dhaka City, June 1983

From table 35 it is shown that 80% family spent more than 60% of their monthly income on food. In rent 43% family have to pay up to 10% of their monthly income. Majority proportion of the total family spent up to 5% of their income on Cloth, Transport, Education, Entertainment, Medical and Savings. It is clear from the data that, among slum people the highest priority is always put in food, than shelter, social development gets the minimum priority.

Table 33: Reasons for Migration in Dhaka City 1983 (% of Migrants)

Causes Poverty

39.7

Unemployment

31.5

Famine

23.3

Migrated as Dependents

12.9

River Flood

9.3

Erosion

On Transfer of Job

and

Business

2.9

Partition

2.2

Study

1.8

Family Friends Others

1 2.3

Source: Slums in Dhaka City, June 1983

According to table 36 slum dwellers in most cases were poor in the rural areas. They are willingly or unwillingly migrated from the village to the city. There are many causes for migration. Some causes are forced them for migration such as poverty, unemployment, natural disaster (river erosion, famine) etc. from the above table it is shown that about 40% of the heads had migrated because of poverty and 32% of the heads describe the reasons of their migration is unemployment. Above 30% of the heads migrated because of natural disaster (river erosion, famine). Many of the heads of slums household have migrated with their parents as dependent and the percentage is around 13%. Some have come to the city because of study (1.85%), family friends (1%), and transfer of job (4%) etc.

LIST OF REFERENCES Akhtar H., 1988, “Prospects of Improving Housing Condition of Squatters: Case Study of Selected Areas of Dhaka City” (Supervisor: A.S.M. Mahbub-Un-Nabi), MURP thesis, Bangladesh University of Engineering and Technology. Akther S, T Onishi and T Kidokoro (2006), “ICT for Poverty Alleviation: A Study on Bangladesh” presented in International Conference on Information and Communication Technology for Development held at University of Berkley, USA in May 2006. Alam A., 1994, “An Analysis of Informal Settlement in a Medium Sized Town of Bangladesh” (Supervisor: Mir Shahidul Islam), MURP thesis, Bangladesh University of Engineering and Technology. Amin S. M. A., 2007, “Impact of micro-finance on reducing vulnerability of river erosion and flood affected people in Bangladesh” (Supervisor: Gulsan Ara Parvin), BURP thesis, Bangladesh University of Engineering and Technology. Anwara B., 2009, “Destination Dhaka Urban Migration: Expectation and Reality” University Press Ltd., Dhaka. Begum, A., 1999, “Destination Dhaka-Urban Migration: Expectations and Reality” University Press Limited, Dhaka. Center for Urban Studies (CUS), 1974, “Squatters in Bangladesh Cities-A survey of urban squatters in DACCA, Chittagong and Khulna” prepared for Urban Development Directorate (UDD), Dhaka. Center for Urban Studies (CUS), 1983, “Slums in Dhaka City” prepared for DMC, Dhaka. Center for Urban Studies (CUS), 1998, “Slums and Squatters in Dhaka City” prepared for Dhaka Municipal Corporation, Dhaka. Center for Urban Studies, 1996, “Survey of Slum and Squatter Settlements in Dhaka City” Dhaka. Hafiz R., 1987, “Sites and Services Schemes: A Strategy for Housing Low - Income People in the Dhaka Metropolis” (Supervisor: A.S.M. Mahbub-Un-Nabi), MURP thesis, Bangladesh University of Engineering and Technology. Haque S. O., 1984, “Residential Circumstances of Low Income Earning Single Working Women in Dhaka City” (Supervisor: Prof. Nazrul Islam), Department of Geography, University of Dhaka. Hasan, M.M.U. and Parvin, G. A., (2007) “Actors in Poverty Alleviation: Role of Government, NGOs and Urban Development Partners”, Paper Presented at the DURPUNDP Seminar on Alleviation of Urban Poverty through Good Governances, Dhaka, Bangladesh, 5 June, 2007

Hossain M. F., 2005, “Manufacturing Industries in Kamrangirchar Thana: An Exploratory Locational Analysis.” (Supervisor: Prof. Dr. Nazrul Islam Nazem, Master’s thesis, Department of Geography, University of Dhaka. Hossain. A., 1998, “Role of Community Participation in Developing Community Facilities at Kamrangir Char of Dhaka City” (Supervisor: Sarwar Jahan), MURP thesis, Bangladesh University of Engineering and Technology. Hussain A.Z.M.W., 1969, “Squatting & Squatters in Dhaka City, 1972”. (Supervisor: Nazrul Islam), Department of Geography, University of Dhaka. Islam M., 2001, “Geomorphological Controls over Urbanization: A Case Study of Sylhet City.” (Supervisor: Prof. Md. Abdur Rob), Master’s thesis, Department of Geography, University of Dhaka. Islam N. (ed. 1996 “The Urban Poor in Bangladesh” Center for Urban Studies (CUS), Dhaka. Islam N.(Ed), 1979, “Urban Poor in Bangladesh” Center for Urban Studies (CUS), Dhaka. Islam N., 1999, “A Geographical Analysis of Slum Eviction in Dhaka City and its Impact on Evictees (1975-2001)” (Supervisor: Prof. Rosie M. Ahsan), ), Master’s thesis, Department of Geography, University of Dhaka. Islam N., 2002, “Status of Urban Poverty in Bangladesh” Center for Urban Studies (CUS), Dhaka. Islam S and S Akther (2006), “City Structure Models and Location of Lower Income Settlement: The Context of Khulna in the Proceedings of International Symposium of Urban Planning”, Taipei. Jahan, S. (2005), “Experience of Bangladesh in Reducing Poverty Through Transport Intervention “, paper presented in International Conference on Transport and Millennium Development Goals, Organized by ANTLER (Asia-Pacific Network for Transport and Logistics Education and Research), 14-15 April, Delhi, India. Jahan, S. (2007), “Policies for Alleviating Urban Poverty: A Holistic Approach”, Paper Presented at the DURP-UNDP Seminar on Alleviation of Urban Poverty through Good Governances, Dhaka, Bangladesh, 5 June, 2007. Kabir S. M. I., (2001), “Household Water Demand Analysis for Dhaka City Corporation Area: A Case Study of Zone-4” (Supervisor: Prof. Amanat Ullah Khan), Master’s thesis, Department of Geography, University of Dhaka. Kaiyum K.M., 2005, “Daridra Bimochon o Nagar Doridrader Bosotir Odhikar—Prekkhit o Shombhabona”, World Habitat Day 2005 Souvenir, Center fron Urban Stadies(CUS) and Bangladesh Institute of Planners (BIP), Dhaka. Khan J., 1988, XvKv Dcjwä Ges ÁvbMZ cÖK…wZt ew¯Íevmx wi·v PvjK‡`i Dci mgx¶v (Supervisor: Prof. Nazrul Islam), Department of Geography, University of Dhaka. ii

Khan, A., and Phibbs, P. (2005), “Housing and Education: An Example of a Non-Shelter Outcome” in the refereed 46th annual conference of American Collegiate Schools of Planning (ACSP), Kansas City, USA, October 27-30, 2005 Latif M. A., 2005,”Impact of Asrayan Projects on Poverty Alleviation: A Case Study (Supervisor: Gulsan Ara Parvin), MURP thesis, Bangladesh University of Engineering and Technology. Lvb gvmy`yi ingvb (2006), XvKv kn‡ii ew¯Íi K©gRxex gwnjv‡`i ¯^vm&n¨ SzwKt GKwU mgx¶v (Supervisor: gnv¤§` mwd Dj¨vn), Master’s thesis, Department of Geography, University of Dhaka. Mahjabin Z., 1999, “The Role of Civil Society Organization in Urban Governance in Dhaka City.” (Supervisor: Prof. Nazrul Islam), Master’s thesis, Department of Geography, University of Dhaka. Nabi,A.S.M M. (2002) "The Problem of Big Cities" paper presented in the Seminar on World Town Planning Day , held at the Council Bhaban, BUET in November 8, 2002 ,organized by the Dept. of URP, BUET and BIP. Noor A., 1997, “Impact of Urban Credit Programme on the Socio-Economic Mobility of the Women.” (Supervisor: Prof. Nazrul Islam), Master’s thesis, Department of Geography, University of Dhaka. Parveen S., 1988, “Role of NGO’s in the Development of the Urban Poor: A Case Study in Dhaka City.” (Supervisor: Prof. Nazrul Islam), Department of Geography, University of Dhaka. Parvin, G. A (2004) “Microcredit: An Anti Poverty Tool for Bangladesh- A comparative Evaluation of Three Micro-credit Programs in Improving Quality of Life of the Poor,” Proceeding of the International Symposium on City Planning 2004, Organized by the City Planning Institute of Japan, Sapporo, Japan. Parvin, G. A. (2005) “NGOs in Bangladesh: Examination of People’s Perceptions,” Published in The Jahangirnagar Review, Part II: Social Science. Vol. XXVIII, A journal of Jahangirnagar University, Dhaka, Bangladesh. Parvin. M., 2003, “Quality of life in Dhaka City: A GIS and Environmental Approach” (Supervisor: Prof. Amanat Ullah Khan), Master’s thesis, Department of Geography, University of Dhaka. Rahman S., 2005, “Tenants House Process in Dhaka City: A Case Study of Ward No. 07 and 62.” (Supervisor: Prof. Dr. Nazrul Islam Nazem), Master’s thesis, Department of Geography, University of Dhaka. Rashid T., 1992, “Alleviating Poverty of Rickshaw-pullers of Dhaka City by rehabilitating them in the Restructured Transport Economy: An Advocacy Planning Approach.” (Supervisor: Dr. Ziaush Shams M.M. Haq), Department of Geography, University of Dhaka.

iii

Renesa M. A., 2004, “Women and Small Entrepreneurs: A Case Study of Dhaka” (Supervisor: Prof. Dr. Shahnaz Huq Hussain), Master’s thesis, Department of Geography, University of Dhaka. RMbœv_ ivq (1990), “XvKv kn‡ii ¶z`ª ew¯Í”, (Supervisor: Dr. A.Q.M. Mahbub), Master’s thesis, Department of Geography, University of Dhaka. Rowshan D., 1984, “The process of slum and Squatters Location: An Analysis of two selected Areas in Dhaka City.” (Supervisor: Prof. Rosie M. Ahsan), Department of Geography, University of Dhaka. Shakur, T., 2008, “Squatters of no hope?-An Analysis of Spontaneous Settlements in Dhaka, Bangladesh (1971-1987)” Dhaka Sharmeen F., 2004, “Mohammad Shahidul Islam, Mohammad Aktaruzzaman, “Income generating training program for poverty alleviation” (Supervisor: Gulsan Ara Parvin), BURP thesis, Bangladesh University of Engineering and Technology.

Part 3: STATE AND IMPACT OF URBAN MICROFINANCE

TABLE OF CONTENTS

CHAPTER ONE: THE MFI’S AND THEIR INVOLVEMENT CHAPTER TWO: URBAN MICROFINANCE: WHO are the CLIENTS? 2.1METHOD USED FOR PRIMARY DATA COLLECTION 2.2 DEMOGRAPHY 2.3 EDUCATION AND SKILLS 2.4 EMPLOYMENT 2.5. DEPENDENTS 2.6 LAND AND ASSETS

2.7.1 Land Tenure 2.7.2 Assets 2.7 MIGRATION

2.8.1 Length of urban residence 2.8.2 Reasons for Migration 7.8 CASE STUDIES OF INDIVIDUALS CHAPTER THREE: HOUSING AND UTILITY SERVICES 3.1 HOUSING

3.1.1 House Types and Environment 3.1.2 House Ownership 3.1.3 Number and Size of Rooms 3.1.3 House Rent 3.2 UTILITY SERVICES

3.2.1 Sources of Drinking Water 3.2.2 Water Supply 3.2.3 Sanitation Condition 3.2.4 Electricity 3.2.5 Gas 3.2.6 Sewerage CHAPTER FOUR: OCCUPATION AND FINANCIAL CONDITION OF THE MF BORROWERS 4.1 OCCUPATION OF THE HH

4.1.1 Primary Occupation 4.1.2 Important Occupation, the Small Businesses 4.1.3 Secondary Occupation of HHH 4.2 WORKING HOURS 4.3 WORKING DISTANCE 4.4 INCOME EARNER 4.5 INCOME OF THE BORROWERS

4.5.1 Nature of Income 4.5.2 Monthly Income of HH 4.5.3 Factors Influence the Income of the Households 4.5.4 Monthly Regular Income of the HHH 4.5.5 Additional Income from Secondary Occupation 4.5.6 Occupation and Income

4.6 OTHER SOURCES OF MONEY OTHER THAN THE MF? 4.7 ASSETS 4.8 LOTTERY 4.9 CASE STUDIES OF SLUMS CHAPTER FIVE: MFI IN GENERAL & MICROCREDIT 5.1 MFI’s in GENERAL

5.1.1 Working MFIs 5.1.2 MF Involvement 5.1.3 Overlapping of MFIs 5.2 CREDIT, BORROWERS AND MFIs

5.2.1 Frequency of Loans Borrowed 5.2.2 Loan Sizes 5.2.3 Size and Nature of the Installment 5.2.4 Savings with the MFI 5.2.5 Payback Period and Rules 5.3 CHALLENGES 5.4 PURPOSES OF TAKING LOANS 5.6 INFORMAL GUARANTEE REQUIRED BY THE MFIS 5.7 REASONS FOR LEAVING CHAPTER SIX: INVESTMENT AND ENTERPRISES 6.1 ENTERPRISES AND INVESTMENT 6.2 SAVINGS 6.3 INSURANCE 6.4 SUCCESS 6.5 WHETHER THE BORROWERS ARE CAPABLE TO HANDLE BIG AMOUNT 6.6 POTENTIAL OF SMALL BUSINESS ENTERPRISES CHAPTER SEVEN: URBAN MICROFINANCE: IMPACT 7.1 INTRODUCTION

7.1.1 Occupation and income of the HH 7.1.2 Land and Assets 7.1.3 Housing 7.2 MONETARY IMPACT

7.2.1 Income 7.2.2Nature of Income 7.2.3 Assets 7.2.4 Impact on Children 7.2.4.1 Education 7.2.4.2 Communication 7.2.4.3 Healthcare 7.2.4.4 Food Intake 7.3 NON-MONETARY IMPACT

7.3.1 Housing Materials 7.3.2 Healthcare facility 7.3.3 Sanitation condition 7.3.4 Sources of Drinking water 7.3.6 Daily Food Intake and Clothing

vii

7.3.7 Women empowerment 7.3.8 Awareness of Education 7.3.9 State of Happiness 7.3.10 Hopefulness 7.4 CONCLUSION

viii

LIST OF TABLES Table 1.1: Some basic information of MFIs in the study areas Table 1.2: The major Urban MFI’S in the study areas Table 1.3: Salient features of some local MFIs and projects Table 2.1: Demography of the study areas Table 2.2: Level of education of HHH Table 2.3: Average HH income Table 2.4: Dependent members in the HHs Table 2.5: Average land (decimal) owned by the borrowers Table 2.6: Average assets value (Tk) Table 2.7: Average year of living Table 2.8: Average year of living Table 2.9: Reasons for leaving from previous place Table 3.1: District wise density Table 3.2: Number and size of rooms used Table 3.3: Average rental value of houses Table 3.4: Sources of drinking water Table 3.5: Water supply connection Table 3.6: The state of electricity connection Table 3.7: The state of gas connection Table 3.8: Sewerage connection in the study areas Table 4.1: Primary Occupation of HHH Table 4.2: Common Businesses in the Communities Table 4.3: Secondary Occupation of HHH Table 4.4: The number of Income Earner Table 4.5: HHH’s monthly regular income Table 4.6: HHH’s monthly regular income Table: 5.1 Lists of MFIs Table 5.2: Reasons for which people are not involved with MFIs Table 5.3: Installment sizes against loans Table 5.4: Purpose of the present loan Table 5.5: Guarantee presented by the borrowers Table 5.6: Reasons for which people leave MFIs Table 6.1: Average amount of investment of the popular businesses Table 6.2: Average loan size and required loan Table 6.3: Potential small business enterprises Table 7.1: Occupation of the non-members HHH Table 7.2: GDP Growth Rate & Inflation Rate (2006-2009) Table 7.3: Nature of income Table 7.4: Seasonality of income Table 7.5: Changes of children expenditure Table 7.6: Improvement of housing materials Table 7.7: Improvement of healthcare facility received Table.7.8: Improvement in utility services Table 7.9: Improvement of clothing

LIST OF FIGURES Figure 2.1: Education of the borrowers Figure 2.2: Scatter plot of income earners against family members Figure 2.3: Cumulative percentage of the assets value Figure 2.4: Living period of the borrowers. Figure 2.5: The histogram of year of living in Dhaka city. Figure 3.1: Housing types Figure 3.2: House ownership Figure 3.3: Comparative statistics of room size Figure 3.4: Cumulative percentage of house rents Figure 3.5: Rent of different types houses Figure 3.6: Sanitation condition of the borrowers Figure 3.7: Percentage of hygienic and unhygienic latrine Figure 4.1: Percentage of different occupation Figure 4.2: Distance traveled (travedl) by the HHH Figure 4.3: Number of income earners. Figure 4.4: Average monthly income (Tk) from major occupations Figure 4.5: The average monthly of the HH Figure 4.6: Income groups in Dhaka city Figure 4.7: Cumulative percentage of HH’s income Figure 4.8: Cumulative percentage of HH’s income Figure 4.9: Other sources of money Figure 4.10: Cumulative percentage of assets values Figure 4.11: Cumulative assets values Figure 5.1: Trend of microfinance involvement Figure 5.2: Loan sizes Figure 5.3: Savings per month against loan size Figure 5.4: Payback period (in month) Figure 5.5: challenges to payback Figure 5.6: Guarantees presented by the borrowers Figure 6.1: Cumulative percentage of investment (Tk.) Figure 6.2: Relation between loan sizes and amount of investment Figure 6.3: Loan users Figure 6.4: Savings on households’ income Figure 6.5: Knowledge on insurance Figure 7.1: Scatter plot of income variation (between 2005/earlier & 2009) Figure 7.2: Changes of assets value Figure 7.3: Changes of sanitation condition. Figure 7.4: Changes of drinking water sources Figure 7.5: Comparative expenses (Tk.) on child Figure 7.6: Percentage of HHs whose expenditure increased Figure 7.7: Change in women empowerment Figure 7.8: Change in hope

CHAPTER ONE

THE MFIâ&#x20AC;&#x2122;S AND THEIR INVOLVEMENT There are around twelve hundred microfinance institutions (MFIs) in Bangladesh (CDF, 2002) but the industry is dominated by four large MFIs namely BRAC, Grameen, ASA and Proshika; they cover ninety percent of all clients. Four institutions have over $800 million outstanding loans and around $380 million savings. Some of the important MFIs working in the study areas are mentioned with some basic information. By using the detailed tables of Bangladesh Microfinance Statistics 2007, the following summary can be constructed. Table 1.1: Some basic information of MFIs in the study areas Name of MFI

Service Charge (%)

Disbursement (Tk in million)

Net Savings (Tk in million)

Loans Outstanding (Tk in million)

8568.02

686.73

% of Total 19.81

Urban

12.5

% of Total 3.16

Urban

BRAC

883.12

% of Total 2.45

No. of Outstanding Borrowers Urban % of Total 189,249 2.96

Urban

ASA

12.5

44,243.36

19.78

999.97

21.72

4,520.58

18.29

761,500

14.04

Proshika

12.5

8,830.21

23.14

597.66

28.53

945.19

20.12

256,332

14.73

TMSS

12.5

5459.04

25.29

274.63

27.89

767.82

26.99

104,426

19.41

BURO

12.5

859.99

6.07

149.36

18.17

233.17

11.99

35,203

11.41

Shakti Foundation SAJIDA Foundation SETU ASOD

12.5

9,118.93

100

454.03

100

2,006.08

100

208,073

100

12.5

1,244.06

44.06

65.49

53.65

232.48

56.11

24,689

47.86

12.5 12.5

89.93 7.78

7.92 0.53

7.19 .97

11.57 1.79

19.20 3.76

8.96 1.85

4,185 -

7.46 -

Source: Bangladesh Microfinance Statistics, 2007

According to the field survey, the working policies of the important MFIs are almost same in the study areas, some changes are observed in some cities. details have been shown in Appendix 2 VIII. However, important features might be written under the following headings.

Working Policy of MFIs a. Issuing of membership and loan policy Approval of membership and disbursement of loan vary from one MFI to another. For instance, ASA, compared to other MFIs is flexible in issuing membership and

loans. It does not require any collateral to provide loans but some exception that sometimes borrowers have to show business. However, the amount of loan increases with the higher number of borrowing. Again, BURO has a different policy in issuing membership and loans. It renews all old members after a certain time period, clears all their savings and installment, and provides microcredit as new member. It sometimes causes problem for the borrowers, because they get less amount as a new member. For example, a member first gets 5000 loan, then 10000, then 15000, and so on, but, when membership is renewed, she will get only 5000.

b. Loan Size Size of loan depends on payback capacity of the member and how long a member is attached with the MFI. ASA provides small loan compared to BRAC which provides big loan for macro enterprise business purposes, ranging from Tk. 50,000 to Tk.500, 000.

c. Payback Rules Weekly payment system is followed by most of the MFIs. The payment system is very strict, for example ASAâ&#x20AC;&#x201D;all the member have to pay the installmenttmely. If anyone fails, he/she get 2 to 3 hours extra time to pay; otherwise, it is adjusted from the savings. Sometimes other group-members help pay the installment. Again, BRAC gives no opportunity to adjust from the savingsâ&#x20AC;&#x201D;the borrowers are bound to pay it in due date. However, it provides SME loan with monthly installment system.

d. Insurance Policy Some MFIs provide insurance against credit or accident. For instance, ASA provides insurance facility to its members. Each member pays 20 taka for insurance per week. In case of death of a member, husband would get three times and wife get

would 6 times of the total insurance amount saved so far. BURO also provides insurance facility to its borrowers. They keep a percentage of savings as insurance and pay back at the time of any accident. But, if the borrower has no accident, they don’t payback the amount. It also provides the member deposit (DPS) facility. On the other hand, BRAC provides no insurance facility. e. Other Facilities Three are very few MFIs that provide other facilities like BRAC which serves education facility, healthcare, education loan, etc—it provides healthcare facility to the pregnant women and TB patients, and primary education to the children. Table 1.2: The major Urban MFI’S in the study areas

Dhaka

Working Policies Target Group

Issuing of membership

Low/mid dle income group

-Flexible in issuing membership. -Requires local introducer Investigate s assets.

-Lowermiddle /middle -income group.

-Very strict in issuing membership. -Requires local introducer

BRAC

Dhaka

Other Districts

ASA

Name of MFI’ s

Other i

Investigate s legal papers of assets.

Admissi on Fees

Loan detail

Admissio -1st n Fee: loan 20 Tk. 5000 10000 Tk. -Group based approac h

Admissio -First n Fee: loan 120 Tk. 8000 Tk -Group based approac h

Interest Payback rate Rules /Service Charges -Weekly Interest installment rate: 12.5%. -Installment # 37 -Takes -It was 46 200 Tk. for -Installment receipt size: Tk 28 book. per 1000. -It was 25 per 1000.

-Interest rate is 15%.

-Weekly installment -Takes 28 Tk per 1000 -It was 25 Tk. per 1000 -Installment # 41 -It was 46 -Monthly two installments in Chittagong

Insurance Facility -Provides insurance called ‘husband-wife’ insurance In return: -If dies—gets exemption to pay back loan -Wife gets 6 times more and Husband gets 2 times more of savings. -No insurance facility

Other Facilities -Provides DPS scheme -Education loan for children -Loan to buy computer -Loan for husband.

-Option to pay three months’ installment together. -Provide health facility -Provides primary education.

-Installment # 23

Name of MFIâ&#x20AC;&#x2122; s

Working Policies Target Group Low/Mid dle income group.

Issuing of membership -Renews membership after certain time

Admissi on Fees

Admissio -First n Fee: loan: 50 Tk. 100008000 Tk.

-provides credit under new scheme.

-It was 4000 -Group based policy.

BURO

Dhaka

Loan detail

Low/Mid dle income group.

-Flexible.

-Requires local introducer Investigates assets.

Initial ly provide 5000 to 10000.

Insurance Facility

Low/Mid dle income group.

AHAKTI FOUNDATION

Other Districts

PROSIKA

Dha k

GRAMEEN BANK

Low/Mid dle income group.

-No guarantee is required

Admissio n Fee: 120 Tk.

-Requires local introducer

-Each group consists of five members

1st loan: Tk 500010000 -Group based lendin g policy

Investigate s legal paper of assets. Low/Mid dle income group.

Interest Installment# rate is : 45. 15%.

-No insurance

-Group based lending policy.

-Sometimes have to show the business. -Requires introducer -Very strict in issuing membership.

1st loan:50 00 Tk.

Admissi on Fee: 20 Tk.

1st loan:50 00 10000 - Group based members hip.

Other Facilities

-50 Tk. for -Provides insurance DPS bellow 10,000 Tk. loan -Medical facilities. -100 Tk. from 10001 to -Regular 55,000 Voluntary Savings In return: (RVS) that -Installment -Gets Tk. 2500 offers 10%, # 41 and 5000 8% and 7% -It was 46 respectively interest rate for 10, 5 and 3 years respectively . Service -Installment -It provides -Free charge size 25/1000 no insurance education of the Tk. facility. for MFIs is meritorious 0.5%. -Total no. children of installment -Medical 45 to 46. treatment for pregnant women,

SAJIDA

-Group based lending policy.

Interest Payback rate Rules /Service Charges -Weekly Interest installment rate: -Installment 12.5% size 25 Tk per 1000 -20 Tk. for loan -It was 28 form Tk.

Interest rate 12.5%. Provides Tk. 5000 by taking Tk. 1000 as deposit Interest rate is 12.5%.

-Weekly installment at present.

-No insurance facility

-It was monthly in 1998-99 -installment size Tk. 25/1000 Installment# : 48. -Weekly installment

-provides legal aids to oppressed women -One can take 2nd loan without completing the first.

-It takes 5% of the general loan and 8% of -Installment the SME loan size 28 Tk. as Insurance /1000. -In return: Clients gets -It was 25 5000 to the Tk./1000 children after Installment# the death of any member : 50.

-Provides one month more to payback installment in Chittagong.

-Provides 5000 to the clients and exempts their loan if husband dies.

Table 1.3: Salient features of some local MFIs and projects Local MFI’s PAGE

Salient Features Three types of microcredit programs: macro enterprise, micro enterprise and credit for the ultra poor. The interest rate of micro credit is 10% which is lower than other NGOs. And they do not take any service charge in case of admission fees, fees for photo, book etc. It is difficult to draw out the savings. The coverage is almost every where in the Pouroshava areas in Comilla.

Saptorshi

• It’s totally a local organization and playing effective role in reduce poverty through their policy and action. • At first provides training the female members on different income generating activities:

tailoring,

handicraft,

poultry

firm,

etc.

and

after

that

provides loan for that specific purposes i.e. if any member has a training on tailoring, then she gets a loan for buying a sewing machine. PADABIK

Facilated by Bangladesh Rural Development Board (BRDB). Interest rate is 10% and paid at the time of last installment. Tk. 50 per 1000 of the total loan is kept to the group fund. it gives more time for loan repayment.

LPUPAP/ UGIIP/ STIFPP-II (Microcredit Program)

• Funded by UNDP, ADB & GoB, facilitated by LGED and operated through City Corporations /municipalities. • Under this program a group, ranging from 20-40 people is formed. The members deposit Tk. 20 to Tk. 30 per week at first, to generate an amount. Each group has to open one savings account with a Bank and all transactions are made through it. • The group has a president, a secretary and an accountant—everybody is the signatory to withdraw the money from the account. • A loan is given to a member on the basis of emergency or lottery. • And the amount of loan depends on the availability of fund in account. For the

first

time,

the

borrowers

get

Tk.

5,000

and

then

Tk.

10,000

successful return of the first one. • The borrower pays back the installment on the weekly basis, but one or two weeks delay is allowed, if problems arise. • Members of the group are the owner of profit accrued from borrowing loan. • It

also

provides

utility

facilities

through

the

participation

the

community.

CHAPTER TWO

URBAN MICROFINANCE: WHO are the CLIENTS?

2.1 METHOD USED FOR PRIMARY DATA COLLECTION A comprehensive field survey in thirteen districts was undertaken for this study. Methods like participant observation, in-depth interviewing of individual households, focused group discussion, and case studies were applied for collecting data and information for conducting the research. Two different interview schedules were made to administer to the communities and individual households. The schedule was designed to document both structured and open-ended responses. Population size was considered while selecting the number of sample households in a district. Because of varied population size, nature of MFIs and usage of microcredit in the communities, the number of sample HHs fluctuated across communities. However, at least five low income communities from each secondary town, ten from each metropolitan city, and fifteen from Dhaka Metropolitan city were selected on the basis of advice from experienced individuals, using size, location and age as criteria, in order to provide a representative sample of households.. Study areas and sample size Sl.No.

1 2 3 4 4 6 7 8 9 10 11 12 13

City

No. of sample HH

Control HH

Dhaka Chittagong Khulna Rajshahi Barishal Sylhet Rangpur Dinajpur Bogra Kushtia Comilla Brahmanbaria Mymensingh

1000 250 200 150 100 100 100 100 100 100 100 100 100

400 100 80 60 40 40 40 40 40 40 40 40 40

The sample was drawn from the lists of MFI members and on the spot selection. One innovative feature of the sampling design, intended to account for the mobility and heterogeneity of urban samples, is that the control group was chosen only after the initial sample household was surveyed, as this procedure allowed the selection of a more 6

comparable reference group. Here, it should be noted that similarity in HHâ&#x20AC;&#x2122;s income and occupation at the same community has been taken for selecting the group. Many people may ask about patron-client relationship, or about mastani/chadabagi at slum areas. We did not ask any direct questions on these issues, nor did any of our respondents indicate that these were problems that were occupying them on a daily basis.

Map 1: Location Map of Study Areas

Map 2: Study Areas in Dhaka City

2.2 DEMOGRAPHY The total number of MF borrowers surveyed in thirteen districts is 2500; the average family size of the borrowers is 4.64 which is less than national household (HH) size in urban, 4.9 (BBS, 2001) and it varies in cities. In some cities like Brahmnabaria, Barishal, the HH size is more than the national level and in Kushtia, Bogra, Dinajpur, Rajshahi, sylhet, it is far below the level. The following table 2.1 shows the detail statistics average family members of the study areas. Table 2.1: Demography of the study areas City

Average number Of household (HH) member

Average number of male per HH

Average number of female per HH

Barishal

5.08

2.59

2.49

Bogra

4.31

2.11

2.20

Brahmanbaria Chittagong

5.12 4.74

2.54 2.36

2.58 2.66

Comilla

4.90

2.44

2.45

Dhaka Dinajpur

4.72 4.53

2.41 2.22

2.31 2.37

Khulna

4.51

2.38

2.13

Kushtia Mymensingh

4.03 4.51

1.88 2.10

2.17 2.41

Rajshahi

4.29

2.11

2.20

Rangpur

4.64

2.31

2.36

Sylhet

4.21

2.02

2.18

Source: Field Survey, 2009

The ration of male to female is 99.5 which is different from the national ratio in urban areas, 105 (Statistical Pocket Book of Bangladesh (SPBB), 2008). The data also reveal that, more than 90% household heads (HHHs) of the borrowers is male and their mean age is 40.10 years. On the other hand, the average age of female HHH is a bit more than its counterpart, around 41 years. It should be noted that the average life expectancy at birth are 65.6 and 70.0 years for male and female respectively (SPBB, 2008). However, majority of the borrowers are female, only 2.47% of all borrowers are male. Both male and female of 1.08% HHs are found to take loan from the MFIs.

2.3 EDUCATION AND SKILLS Education is one of the fundamental rights assured by the constitution of the country. But, the country still can not ensure it, particularly for the poor. The survey tried to find the state of education among MFI borrowers in thirteen districts across the country. It reveal that the average year of schooling of HHH is 4.91; it is higher in Comilla followed by Bogra, Chittagong, and Rangpur. Lowest year of schooling is found in Mymensingh and Kushtia which is 3.22 and 3.26 respectively. The condition of literacy rate is better compared to the national rate of 2004, which is 51.6 (Statistics Bangladesh, 2006). The table 2.2 below shows the percentage of different level of education in the study areas. Table 2.2: Level of education of HHH City

Missing data

Zero

Up to 2

3-5

6-10

SSC or HSC

Graduate

Total

Barishal Bogra Brahmanbaria Chittagong Comilla Dhaka Dinajpur Khulna Kushtia Mymensingh Rajshahi Rangpur Sylhet

0.00 5.00 0.00 0.40 0.00 2.02 1.00 0.00 1.00 3.00 13.25 1.00 0.00

37.00 19.00 23.00 12.80 8.00 24.01 22.00 41.50 40.00 44.00 27.15 26.00 49.00

14.00 7.00 36.00 38.40 29.00 12.47 29.00 6.00 11.00 6.00 11.92 12.00 0.00

20.00 17.00 24.00 18.80 23.00 18.33 21.00 23.00 28.00 23.00 19.21 14.00 20.00

23.00 43.00 13.00 22.80 35.00 34.28 22.00 26.00 18.00 24.00 23.84 33.00 30.00

3.00 6.00 2.00 3.20 4.00 5.68 4.00 2.50 1.00 0.00 1.99 11.00 1.00

3.00 3.00 2.00 3.60 1.00 3.21 1.00 1.00 1.00 0.00 2.65 3.00 0.00

100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00

Total

2.08

26.47

15.66

19.75

29.32

4.24

2.47

100.00

Source: Field Survey, 2009

The education of the HHH is categorized into six levels and the percentage of each level has been shown in the figure 2.1. The figure provides a clear picture of poor condition of education among the borrowers. Only 6.71% HHHs are found to have SSC, HSC or graduate level education, others remain below the level. However, more than oneforth of the total HHH are found surprisingly to be illiterate and the others receive some sorts of education, but do not have any certificate of education. On the other hand, the average number of school going children of the borrowers is 1.014/HH and not attending the school is 0.73/HH.

ss pa

miss in

C HS

graduate

C/ SS

g da ta

Figure 2.1: Education of the borrowers

tion duca no e

six to class ten

up to s as cl 2

three to class five

About, 23% HHs do not have children; however, those having children aged 6 years or more, 26% of them never send their children to school. About 50% HHs send one child, 20% HHs send two children and the remaining HHs send more than two children. Again, it varies in cities. More children in Mymenshingh and Sylhet do not attend the schools, whereas, percentage of school going children is greater in Khulna, Kushria and Dinajpur (Appendix: 2A II). Such variation is also found among different communities in Dhaka (Appendix: 2A III). About 22% HHs on average of fifteen communities in the city do not send their children to school. The percentage is less than the average in Kamlapur, Rampura, Mirpur, and Gulshan. However, in Jatrbari, Goran and Gabtoli, more HHs are found not to send their children to school. The income of an MFI member depends on a number of factors. Among all the factors, education and skills are most important. This study found that no MFI member of 92.73% HHs receives training on income generating activities, e.g. traning on sewing, farming, pipe fitting, plumbing, welding, etc.. Only about 6.30% HHs are found to have single member received training on such activities. Only three HHs have two member got trainingâ&#x20AC;&#x201D;they are in Dhaka, Chittagong and Comilla. Moreover, only one HH of each of Khulna and

Rajshahi is found to

have training. In Dhaka, comparatively more people are found to get training at Gabtali/Aminbazar, Goran/Bashabo, Hazaribagh/ Kamrangirchar, Mirpur and Uttara/Khilkhet.

2.4 EMPLOYMENT The average number of income earners of the HH is 1.82 in the study areas, whereas the average family size is 4.64. So, each HH has an average of 2.82 unemployed members. However, there are some HHs of which all the members are income earners. In the study areas, 77 HHs out of 2593, are found to be in such group; half of them are twomember HHs and others vary up to five-member HHs.[are these families poor?] The average per capita income of the HHs having all the members are income earners has been shown in table 2.3. Table 2.3: Average HH income (all the members are income earners) No. of Family member 1 2 3 4 5

Obs. 11 39 14 10 3

Avg. HH income (Tk.) 2,945 8,608 17,864 21,590 10,467

Avg. income/person 2,945 4,304 5,955 5,399 2,094

Source: Field Survey, 2009

The table illustrates that the income of the HHs having two to four income earners, is higher, compared to those having one or five. But, monthly income of the HHs having five members is the lowest which falls below the poverty line1. However, The income earners in the study areas have been shown by scatter plot and relation with number of family members is shown by the fitted line in the figure 2.2 below.

1 Measurement of poverty varies in a country from other countries. However, the state of poverty of Bangladesh is estimated broadly by two methods namely, the Direct Calorie Intake (DCI) Method and the Cost of Basic Need (CBN) Method. According to Bangladesh Bureau of Statistics(BBS), in the DCI method the calorie threshold of 2122 k. cal. is used for determining the poverty lineâ&#x20AC;&#x201D;those who are living below the line, fall in poverty. On the other hand, according to the World Bank, CBN is estimated as US$ 1.25. If a personâ&#x20AC;&#x2122;s earning is below the level, he/she falls in poverty.

25000 5000

10000

15000

20000

Figure 2.2: Scatter plot of income earners against family member

5 10 number of family members Fitted values

household income(present)

2.5 DEPENDENTS The average dependent members of the HH are 2.83â&#x20AC;&#x201D;it is almost the same as national ratio 63.8 (BBS Population Census Report, 2001). A half of the total dependents are not attending the school and aged greater than 12 years, who may be termed as adult1. The other dependents are the children under twelve years of age, some are going to school and are not. Dependents of different cities are shown in the table 2.4 below. Table 2.4: Dependent members in the HHs City

Mean adult dependent

Mean total dependent

Barishal Bogra Brahmanbaria Chittagong Comilla Dhaka Dinajpur Khulna Kushtia Mymensingh Rajshahi Rangpur Sylhet

1.61 1.66 1.41 1.43 1.16 1.37 1.56 1.53 1.21 1.41 1.66 1.86 1.30

3.06 2.77 3.26 2.93 2.85 2.76 2.78 2.91 2.58 2.93 2.76 3.00 2.82

Source: Field Survey, 2009

Dependents per HH are higher in some cities such as Barishal, Brahmanbaria and Rangpur. However, the average adult dependent is comparatively less in Kushtia, Comilla and Sylhet. The data also reveal no significant difference in ratio between metropolitan 1

The adolescents at the poor communities, aged more than 12 years and not having education, are income earner, and their families treat them as adult.

cities and other secondary towns. In Dhaka city, the ratio differs in different communitiesâ&#x20AC;&#x201D;at Mohakhali, Rampura and Gabtali in Dhaka city, the number of dependent member is greater compared to others.

2.6 LAND AND ASSETS 2.6.1 Land Tenure Land is an important asset. But a number of borrowers does not own any land. In the study areas, more than 38% HHs do not possess any land either in city or in rural areas. Among them 68% HHs have no land in urban area and more than 65% HHs have no land in their bari. The survey also reveals that about 85% HHs do not have agricultural land at their bari1 and more than 68% HHs have no homestead land either in urban area or in rural area. Remaining percentage of HHs have some small amount of land, the district wise average land under four categories is shown in the following table 2.5. Table 2.5: Average land (decimal) owned by the borrowers

City

Avg. Agri. land (Urban)

Avg. homestead land (Urban)

Avg. Agri. land (rural)

Avg. homestead land (rural)

Barishal Bogra Brahmanbaria Chittagong Comilla Dhaka Dinajpur Khulna Kushtia Mymensingh Rajshahi Rangpur Sylhet Total

0.00 0.00 30.67 8.33 5.55 20.07 12.0 0.00 2.00 0.00 0.00 99.00 10.10 16.54

2.13 2.34 2.03 2.33 2.01 2.06 2.86 1.57 2.22 2.50 2.12 2.85 2.75 2.24

0.00 2.81 3.67 4.56 4.63 5.07 4.00 0.00 0.00 3.00 0.00 2.75 0.00 4.64

6.80 4.65 2.95 4.26 4.95 5.19 8.00 5.61 5.76 5.00 4.38 7.00 3.00 5.17

Source: Field Survey, 2009

More than 98% people have no agricultural land in urban area. There are 32 HHs found to have such type of land in urban, most of them are in Dhaka and Comilla districts. And their average land is 16.5 dec., if largest two (165 dec. and 200 dec., respectively) are eliminated. [expand info about this group] However, about a half of them possess 5 dec. or less of this type of land, and further 10% owns up to 10 dec. The remaining amount varies 1

Bari is locally known term, synonym to native home at village.

between more than 10 dec. and 99 dec. In Barishal, Khulna, Mymensingh, Rajshahi, no HH is found to have agriculture land in urban area. On the other hand, the average homestead land in urban area is 2.24 dec/HH. The average agriculture land in Bari is 4.64 dec. The HHs in Barishal, Khulna, Kushtia, Rajshahi and Sylhet are found to have no land. The average homestead land in Bari is 5.17/HH Those who have homestead land in urban areas, about 41.34% (two fifth) of them owns less than 2 decimal, additional 36% owns 2 to 5 decimal. Moreover, the borrowers who own homestead land in urban area also have agricultural land in rural areas. On the other hand, borrowers living on government land, about 85% of them neither have any land in urban area nor in their bari. The correlation among urban and rural land and their level of significance of this type of home ownership have been shown below. It shows that negative correlation exists among the home ownership and lands both in urban and rural areas. House on govt. land

Urban land

House on govt. land

1.0000

Urban land

-0.0577

1.0000

Rural land

-0.0764

-0.0067

Rural land

1.0000

2.6.2 Assets [Majority of the MF borrowers possesses small amount of assets, but it spreads on widely range of variation which starts at Tk. 2000 and ends at 6 crore. The data reveal that 10% borrowers hold the lowest property which amounts Tk. 25,000 or less. However, 75% assets values are limited to Tk. 510,000.]. This sounds like 75% own assets of 5 lakhs +. If lower and upper 10% assets values are eliminated, then the average value of assets be Tk. 276,255. The cumulative percentage of the assets value have been shown in the following figure 2.3.

cumulative percentage .4 .6 .8

Figure 2.3: Cumulative percentage of assets value

20000000 40000000 total assets (in Taka)

60000000

The borrowersâ&#x20AC;&#x2122; assets value differs in cities. It means that in different cities, borrowers of different economic condition get the credit. The average asset value in Dhaka is around Tk. 950,000, whereas it is 176,000 in Barishal and 162,000! in Brahmanbaria. The table 4.5 below shows the average assets value of the study areas. Table 2.6: Average assets value (Tk.) City

Average assets value

Barishal Bogra Brahmanbaria Chittagong Comilla Dhaka Dinajpur Khulna Kushtia Mymensingh Rajshahi Rangpur Sylhet

176,250.00 419,257.58 1.62e+06 924,828.09 2.74e+06 949,062.22 266,044.83 70,687.10 86,382.35 41,030.30 395,436.24 496,579.79 43,739.13

Source: Field Survey, 2009

There exists strong correlation between land possessed by the borrowers and the assets valueâ&#x20AC;&#x201D;15% of the assets value arises from the land value of the borrowers.

2.7 MIGRATION 2.7.1 Length of urban residence

The urban poor seem to be transient compared to the rural in Bangladesh. But the data reveal that the average living period is more than 20 years. If earlier migration is considered, the HHs moved from the previous place of living, have an average 13 years living period. The figure 2.2 shows the distribution of the year of living of the borrowers. Figure2.4: Living period of the borrowers

.01

density

.02

.03

(kernel = epanechnikov, bandwidth = 2.5602)

40 60 year of living

100

kernel density estimate normal density

However, the living period varies among cities. The borrowers of Maymensingh, Rajshahi and Sylhet are found to stay longer time at the present place compared to other cities. In all of the three cities, the average period of living is more than 29 years. On the other hand, in some cities, borrowers are staying shorter periods but none of the cities shows less than 15 years of living period. The table below shows districts wise average living years of the borrowers. Table 2.7: Average year of living City

Average years of living (present)

Average years of living (previous)

Barishal Bogra Brahmanbaria Chittagong Comilla Dhaka Dinajpur Khulna Kushtia Mymensingh Rajshahi Rangpur Sylhet

21.27 25.24 17.19 16.66 15.88 17.57 25.11 27.27 21.89 29.29 29.94 26.02 29.90

17.51 15.07 13.87 13.40 14.17 12.48 14.58 16.56 18.44 17.00 14.00 13.62

Source: Field Survey, 2009

About a half of the HHs in the study areas are found to shift from previous place, where they lived an average period of 13.5 years, and none of the cities shows less than 13 years of living period. Those who moved from previous place had spent a good span of time there before coming at the present community [explain]. About a quarter of them shifted within five years, about 15% shifted 6 to 10 years ago, and further about 42% left their previous living place up to 20 years ago. However, the remaining percentage of migrant left their previous communities more than 20 years ago; they are living as a permanent resident there. However, the average living period is not equal for all communities in Dhaka city. At Lalbagh, it is more than 27 years, whereas it is about 7 years in Rampura. The borrowers of Hazaribagh/Kamrangirchar, Uttara/Khilkhet are more transient compared to Gabtali/Aminbazar, Kamlapur/Gopibagh Mirpur, Mohammadpur/ Adabor and Savar. The table 2.8 below shows details statistics of different communities in Dhaka city.

Table 2.8: Average year of living Community

Average years of living (present)

Average years of living (previous)

Badda

17.28

15.58

Gabtali-Aminbazar Goran/ Bashabo

24.06 21.13

7.65 15.32

Gulshan/Banani

17.76

10.59

Hazaribagh/Kamrangirchar Jatrabari

11.58 11.16

12.47 9.57

Kamlapur/Gopibagh

19.24

12.12

Lalbagh Mirpur

27.56 17.80

15.71 15.19

Mohakhali

17.74

11.92

Mohammadpur/ Adabor Rampura

24.17 7.34

16.14 10.05

Razarbagh/Malibagh

16.73

8.90

Savar Uttara/Khilkhet

20.15 10.96

16.74 11.93

Source: Field Survey, 2009

.02

density

.04

.06

Figure 2.5: The histogram of year of living in Dhaka city

40 year of living

In Dhaka, about 60% migrated from different parts of the country. Among them, about 70% migrated from one locality to another within the city; however, about 5% migrated for eviction and others for a number of reasons such as for better job opportunity, encouraged by kith and kin, etc.. For instance, Mrs. Vanu, a MF borrower of Sajida foundation (Group #5, Membership# 2) who was evicted two years ago from Kamlapur rail-side slum, now a resident of east Goran. The rest others migrated from different districts across the country disproportionately, e.g. 2.5% from Faridpur, 2.3% from Barishal and Kishorgonj, 2.0% from Mymensingh 0.15% from Lalmonirhat and Thakurgaon, etc.. About 23% of the borrowers are native dwellers of Dhaka city, whereas, other borrowers or their ancestors migrated about an average of 23 years ago from different districts. But the migration rate from Barishal, Faridpur, Shariatpur, Comilla, Maymenshingh, Kishorganj is greater than other districts such as Lalmonirhat, Thakurgaon, Jhenaidhah of the countries (Annex 2V). At some communities in Dhaka, most of the borrowers are found to be from some specific districts. For instance, at Rampura 24.29%, at Badda 21.05% and at Mirpur about 10% are from Barishal. Again, at Hazaribagh/Kamrangirchar 17.28% came from Shariatpur, and at Uttara/Khilkhet, 25% came from Mymensingh district. Details statistics have beeen shown in Appendix 2 VI.

2.7.2 Reasons for Migration The surge of migration is always towards Dhaka at the current rate of . Though, 60% borrowers in Dhaka city have originated from almost all districts of the country, but the scenario is opposite in other study areas, very small percentage are from other cities. The percentage is about 20% in big cities like Chittagong, Khulna, Barishal, and it is about 5% in others small cities (Appendix: 2. VII).

However, a number of factors are

responsible for borrowers’ migration to the capital city (table 2.9). They are primarily of two types: (i) push factor; and (ii) pull factors. For example, people of a certain area may be pushed off by poverty or natural disaster to move towards a town and/or industrial base for employment. While better scope employment, or higher educational facility might have pulled them to avail the opportunities. borrowers’s decision to migrate from one place to another may also be influenced by many non-economic factors such as, personal maladjustment in the family or community. The data of the study areas reveal that, of the total borrowers, three-forth have migrated for some specific reasons, whereas, the remaining part of the borrowers does not know the reason for which they or their ancestors migrated. Table 2.9: Reasons for leaving from previous place

Reason

Frequency

Percent

Valid Percent

Cumulative Percent

Encouraged by relatives/known people For better job opportunity Better MF facility Eviction by force River erosion Marriage Others Total Not responded

52 425 1 25 120 49 177 849 243

4.8 38.9 .1 2.3 11.0 4.5 16.2 77.7 22.3

6.1 50.1 .1 2.9 14.1 5.8 20.8 100.0 -

6.1 56.2 56.3 59.2 73.4 79.2 100.0 -

Total

1092

100.0 Source: Field Survey, 2009

The most important reason behind migration of the borrowers is to be identified as ‘for better job opportunity’—more than fifty percent of the borrowers who were migrated for specific reasons falls in this group. On the other hand, various opportunities attracted them to city’s life. However, some are pushed by the natural calamities such as river erosion, flood, draught, cyclone, etc.–11% borrowers migrated from their previous living places for this natural incidences. However, an insignificant percentage of borrowers migrated for better MF facility.[this bears on the earlier fears of MF expansion]

2.8 CASE STUDIS OF INDIVIDUALS

Mrs. SHIPAN, 125 power house road, Simrailkandi, B.Baria

As a MFI client, Shipan has been selected for the case study. She was born in B.Baria. Shipan is thirty years old. Her deceased father was the night guard of their community. She has three sisters. She is illiterate and is only able to write her name. The economic condition and the educational status of their family were very bad. Shipan got married with Tara Mia about 13 years ago. During her marriage her father wanted to give dowry of fifteen thousand taka and 12 ana gold. But finally he failed to do this. Her husband has been driving a rented tempo for three years. But three years ago he was a van driver. His monthly income is about five thousand taka only. Now Shipan lives at a rented katcha/Jhupri house for one year and rent is 550 taka per month. But her permanent address is at Jamalpur district. She is a hard working woman. She works in another house as a maid servant and gets 600 taka per month. Shipan has two sons and two daughters. Her elder daughter is twelve years old and now reads in class IV. But the remaining childrenâ&#x20AC;&#x2122;s education has stopped because of the poor economic condition. She cannot provide her children clothing according to their need. Her family doesnâ&#x20AC;&#x2122;t eat completely three times a day. Her monthly family expenditure is -4500 taka for food, 100 taka for medical treatment, and 100 taka for education. She is a member of ASA. She has taken loan five times. In order to repay the loan she borrowed from others. Her son Ali Hossain has passed away five years ago because of illness. For his medical treatment her mother borrowed money (35000 taka) from others, and also sold their van. Now she is free from loan. Her opinion is good about MFI activities. Because she thinks that the MFI loan system is better than any professional money lender. As an overall statement she is unhappy but her mentality is very strong. She has the capability to do any work. She needs some help to do something. They think if they buy a tempo, they will change their fate.

Fatema, Jafor’er bari (Shafi Hazi’r bhara ghar), 1 no. Suparipara, Chittagong

Fatema is a non-MFI member. She lives in rented semi-pucca house. Rental value of her house is 2700 taka per month. She is married but she has no relation with her husband. But sometimes her husband sends money for their expenditure. Fatema was born in Noakhali district. Her permanent address is village-Nalpur, Thana-Kotoali, district-Noakhali. Her father (late) was a mason. She faced financial obstacles during her educational life and stopped her studies at class five. Her father’s economic condition was bad. She has two sisters and one brother. Fatema’s husband, Jahangir married Fatema without the permission of his first wife. Fatema was not informed about his first wife. During her marriage no dowry was fixed, but Jahangir took all of her savings (about eighty thousand taka) by force which she saved from her salary. After six months of their marriage she came to know the real truth. Jahangir tortured her physically and his family members also treated Fatema as a maid servant. After nine months she came back to her mother. She took a job in a garments factory and worked there for thirteen years. Then her monthly salary was 2500 taka. But she quit the job for her younger children. Fatema has no property. She depends on her mother for her family expenditure. Her husband also gives some money. Her mother collects money from others. Her total family expenditures are 1500 taka for food, 2700 taka for house rent, 200 taka for medical treatment and 100 taka for education. They eat only two times in a day. Fatema has three daughters and one son. Because of the poor economic condition, Fatema is not able to send her children to a private tutor. Her views about MFI activities are that- she is afraid because she has no capabilities to pay back the loan. The MFI officials also behave very roughly with the clients. Fatema’s brothers are not cooperative. When she has t deal with any trouble no one helps her except her mother. As an overall statement, she is totally unhappy. She wanted to start a grocery shop. Her wish is to buy a piece of land and construct her own home. She also wants to see her children happy.

Ajmiri Akter, Kanu Mia’r Ghar, West Bagichagaon, Comilla

Ajmiri Akter is twenty three years old. She is married but divorced. She was born in Comilla. Her permanent address is village-Chitusi, thana-Haziganj, and districtChandpur. Her father’s name is Mamtaz who is a small businessman and earns about 2800 taka per month. Her mother’s name is Reshmi who works in other house and receives 1200 taka a month. They live in a katcha/jhupri rented house. The rental value of their house is seven hundred taka only. They have been living here for two years. Ajmiri has two brothers and one sister. Her father’s economic condition is bad. She faced financial difficulties during her education so she could only study till class 5. When Ajmiri was nineteen years old then got married to Ahmed Ali. During her marriage he got a dowry of twenty thousand taka. But Ajmiri’s father gave only two thousand taka. At that time, Ahmed Ali had a small business of towel. The permanent house of Ahmed Ali is Narsingdi District. Ajmiri went to her father-in-laws house after one month. After going there she came to know that her husband has three more wives. Ajmiri wanted to come back from her husband’s house but her husband forced her to stay there. Ahmed Ali also tortured Ajmiri physically. She finally came back to her father’s house. After some days her husband sent her a divorce notice. Their family expenditure is -2000 taka for food, 700 taka for house rent, 400 taka for medical treatment. They eat two times in a day. Her views about MFI activities are that if anyone gets some loan and uses it perfectly then it is ok. But they have no one who can start a business. For this reason, they have not taken any loan from the MFI. If they fall in any trouble, the neighbors of their communities come out to help them. Her brother does not help them because he is not financially solvent. As an overall statement, Ajmiri is unhappy. She is searching for a job. There is a possibility to get a job of 1200 taka per month at Comilla tower hospital. If she gets this job their economic condition will be improved.

Suruj Mia, Khasru Mia colony, Doyel-12, Bastee, Masimpur, Sylhet

Suruj Mia is sixty years old. He is a pushcart driver. His monthly income is about 4500 taka only. He is illiterate but he didn’t face any financial difficulties during his educational life. Five years ago he was a daily labor. He was born in Bajitpur, Kishoreganj. His father is still alive and involved in rice business. He has six brothers and two sisters. His father’s economic condition is bad. His total family size is 8 members. He has four daughters and two sons. Among the four daughters, one daughter has got married whose husband is a teacher. Suruj Mia’s wife, Rokeya is also illiterate. During his marriage, Rokeya’s father didn’t provide any dowry to Suruj Mia. They are conscious about their children’s education and trying to educate them. Suruj Mia has been living here for eight years. He lives in katcha/Jhupri house. He has to pay eight hundred taka per month for the rental value of the house. Now he has no land property. His family expenditure is -3000 taka for food, 800 taka for house rent, 300 taka for medical treatment, 300 for education and 100 for communication. They eat only three times in a day. As an overall statement he is unhappy. Suruj Mia wanted to start a fish business.

Md Raju, Dabtola, Uttarpara, Malgram, Bogra

Md Raju is twenty six years old. He is involved in a saloon business. His wife’s name is Piara Khatun and she is housewife. They have one son and one daughter. Their total family number is seven. Raju’s father is not alive. He has two brothers, one of them is an H.S.C student and another is involved in business. His monthly income is six thousand taka. There are two income earners in their family. His monthly family expenditure is five thousand taka. His brother also gives some money to the family.

They have assets valued at fifty thousand taka only. They also have two cows and a television. His father’s economic condition was not good as he was unemployed. At any critical moment his brother helped him financially. He is a MFI client. He borrowed loan for six times from ASA. The purpose of the first loan was to buy a cow and he successfully did this job. By the present loan he has repaired their house. They think that poverty can eradicated by their business. They wanted to improve the business and save more money for business.

Asad North Chelopara, Bogra Asad is twenty years old. He is involved in net business. His monthly income is 6200 taka only. His wife’s name is Akhi. She is a house wife. They have no children. They have property, which has not yet been distributed. So the asset value is not known. Their family expenditure including food and health is about 3000 taka. Asad’s father is still alive. He is a businessman and his economic condition is moderate. Asad thinks that the reason for his poverty is that he didn’t get any help from his family members. He also thinks that hard work can overcome poverty.

Marjina Begum, 222 Choto Nurpur, Rangpur

Marjina Begum is a MFI member. She has taken loan from BRAC twice and used it in her small business. She is thirty eight years old. Her husband name is Salam. He is a service holder. They have two sons and one daughter. Among the children Malek reads in class ten, Monowara reads in class eight and Ali does not go to school. Their total family income is ten thousand taka only. The total expenditure is about eight thousand taka. There are two income earners in their family. Their income source is

service and shop. They have their own house and some savings. The total asset value is about 2.5 lac. Nobody helps them in their critical moment. His father is not alive. His fatherâ&#x20AC;&#x2122;s economic condition was not good because he was not employed. The educational status of their family was not so good. Marjina thinks that this is the main reason for their poverty. But it seems to them that through hard work this situation can be improved.

Mrs. Nargis, 268 Charpara, Mymensingh

Nargis is about 25 years old. She is an ill-fated woman. Life has given her little but she has lost so much in her life. Her father’s name is Monsur and he was a business man. Her mother’s name is Ambia Begum. She has only one sister. Their economic condition is very bad. She was born in Mymensingh. Her father lost his hand by a train accident when she was studying in class three. After some days her studied had stopped. And she started small business (puri, golgolla). After some years, her wedding was held. One year later her son Sagor was given birth. Her father-in-law wanted 25000 taka as dowry. But her father failed to provide the amount. As a result she got divorced and came back to her father’s house. Nargis’s father is unable to work. She’s the only earning member in their family. Her mother is a housewife. She can not engage in any occupation due to her old age. Nargis earns only 3000 taka per month from her small business which is a very small amount for their family. They eat only one time in a day. Her monthly expenditure on food is 1500 taka and 30 taka for education. Her housing condition is even worse. They have been living in a rented katcha/Jhupri house for the last 25 years. House rent is only 250 taka. Her son (Sagor) reads in class two with free tuition fee. But she is unable to send her son to a private tutor for her economic obstacle. As a whole she is unhappy in her life. She wants to start a grocery shop without help of MFI. She is frightened with MFI for its high interest rate and worst behavior of MFI staff.

Mrs. Monowara, 837 west Medda, B,Baria

Monowara is thirty five years old. She was born at West Medda in B,Baria. Her mother’s name is Maleka Begum. Her father was a sweeper in Krishi Bank. She has three brothers and one sister. She only graduated up to class 5. The economic and educational condition of her family is bad. She cannot get any happiness. Her wedding was held with a rickshaw driver. During her marriage her father gave five thousand taka as dowry. And her father-in-law expanded the money for their family purpose. One year later her son Danis was born. After three years her husband married for a second time and two more sons were born. After four years of her marriage her husband died. In her husband’s absence she backed her younger brother’s house. Her mother also stays with her son. Her brother was a sweeper in Krishi Bank. He got the job after his father. The economic condition of her elder brother is good but he does not help them. They live in their own katcha/Jhupri house. They have only two decimal lands. Their family income is only 1700 taka. Her family doesn’t eat completely three times a day. Their monthly family expenditure is 1500 taka for food, 200 taka for medical treatment, and 200 taka for communication. They cannot buy clothing according to their need. Monowara’s grandmother-in-law adopted her child (Danis). After passing class eight, his Boroma sold her asset and sent him to Malaysia. There is no information about Danis regarding his status there. For her son, Monowara is nearly mad and she is also suffering from asthma. Her brother cannot manage all the expenditure of her medical treatment. For this their neighbors also help them. As a whole she is totally unhappy in her life. If her brother gets a job with a salary of taka 4000-5000, they eat three times in a day. They are frightened with MFI for the worst behavior of MFI staff.

Mrs. Khodeja Khatun, 213 Charpara, Mymensingh.

As a MFI member, Khodeja Khatun has been selected for case study. She is forty years old. She was born in Mymensingh. Her father was a raw materials businessman. She has two brothers and four sisters. The economic condition of her father is moderate. She is illiterate but she is able to write her name. Educational condition of her father’s family is good. But her father is a pious person and did not like girl being educated. She got married with Abdul Khaleque Dilu in 1981. During her marriage her father gave dowry of one thousand taka and a gold ring. And her father-in-law spent the money for the expense of marriage. They live in their own semi-pucca house for the last twenty eight years. They have two rooms, electricity connection and have a tube-well but their toilet condition is unhygienic. She is a hard working woman. They had eight cows but she sold seven cows for shortage of place. Khodeja has two sons and one daughter. Her elder son is nineteen years old and he is ten pass. He is physically disabled but is involved in a rent-a-car business and earns nearly 50000 taka per month. Her younger son is seventeen years old and read in 1st year of honors program. Her daughter is twelve years old and reads in class nine. She is conscious of her children’s education. They have good relationships with their relatives. They help each others during any trouble. Khodeja’s husband is involved in wood business. His monthly income is 7000 taka. She is a member of three MFI-RDS, POPI, SUS. She has borrowed loan for four times from RDS, two times from POPI and seven times from SUS. She used the money in her son’s car business and became successful. Her family eats three times in a day their monthly family expenditure is-10000 taka for food, 600 taka for clothing, 200 taka for medical treatment, 2000 taka for educational purpose and 2000 taka for communication. She is happy in her life. They need three lakhs taka to improve their present business.

Mrs. Nilufa Yasmin, 302 west Bagichagoan, Comilla

Nilufa Yasmin is a MFI member. She has been living in Comilla for the last nine years. She believes that she has improved her economic conditions with the help of MFI. She is twenty seven years old and studied up to class ten. Now she is an assistant nurse. Her father is a farmer and the economic condition of her father is moderate. She has two brothers and four sisters. Her permanent address is village-Dhanti, thana-Debiddar and the district- Comilla. She got married with Abul Bashar. During her marriage they did not have to pay any dowry. Her husband lost one leg in an accident. Now he is involved in a small tea stall business. Because of his physical disability he earns less. She has only one son. He is nine years old and reads in class two. She has six decimal lands which value is 120000 taka. Their monthly family income is 8200 taka. She lives at a rented katcha/jhupri house. She pays 800 taka for house rent. Her monthly family expenditure is -3000 taka for food, 1500 taka for medical treatment and 250 taka for education. She is a member of three MFIs- Grameen Bank, Page and BRAC. She has borrowed loan thrice from BRAC, once from page and thrice from Grameen Bank. She used the money in land banking, existing business and became successful. As an overall statement she is happy in her life. She wants to start a grocery shop for improving their economic condition.

She could not become a nurse due to her educational constraint.

Mrs. Begum, 767/792/ 1 no Suparipara, Chittagong

As a MFI member, Rahima Begum has been selected for the case study. She is thirty-five years old and her educational qualification is up to class 5. She has been living here for the last eighteen years. She was born in Chittagong. Her father was a bus contractor. She has two sisters. The economic condition of her father is moderate. But he married two times. Rahima got married to Abu Taher. During her marriage her father gave furniture as dowry. Her husband is an oil businessman. The economic condition of her husband is good. He has four decimals land and a pucca house. He gets 6000 taka as house rent. His total monthly income is 21000 taka. Rahima has two sons and two daughters. Her elder son is sixteen years old and is a class five pass. Now he learns CNG welding work. Her younger son is ten years old and now he reads in class four. Her elder daughter is six years old and reads in class one. And the younger daughter is only 3 and half years old. Her monthly family expenditure is-6000 taka for food, 2000 taka for clothing, 200 taka for medical treatment, 700 taka for educational purpose and 300 taka for communication. She is a member of two MFIs- BRAC and Gashful. She has borrowed loan for seven times from Gashful, five times from BRAC. She used all the borrowed money in her husbandâ&#x20AC;&#x2122;s oil business and got profit from it. Her father-in-law was a rich man but for the lack of education he could not manage his property properly. So she is conscious about her childrenâ&#x20AC;&#x2122;s education. Rahima wants to send his elder son abroad. As a whole she is happy in her life.

Mrs. Sonarun, Agricultural training institute, Khadim Nagar

Sonarun is a MFI member and she can not change her economic condition with the help of MFI. She was born in Sylhet and thirty five years old. Her father is a farmer. The economic and educational condition of her family is bad. She has four brothers and three sisters. She is an illiterate who is only able to write her name. She has been living here for the last twelve years. Her permanent address is village-Kollogram, thana-Kotoali and the district of Sylhet and she has two decimal land. She got married with Hasan. During her marriage they did not have to pay any dowry. Hasan is a service holder. His monthly income is only 4000 taka which is so small for his family. She has one son and three daughters. Her one daughter is physically disabled. Sonarun is not conscious about her childrenâ&#x20AC;&#x2122;s education. Her younger daughter is twelve years old and now she reads in class five. Her son is only five years old. Her monthly family expenditure is-3500 taka for food, 200 taka for medical treatment, 105 taka for educational purpose and 200 taka for communication. She cannot give her children clothing according to their need. She is a member of Grameen Bank. She has borrowed loan for four times and used the money for agricultural activities. And she gets crops throughout the year. As an overall statement she is moderately happy in her life. She wants to start a business or higher salary job for improving their economic condition.

Mosleuddin, Dashmari, purbopara, Rajshahi

Md. Mosleuddin is a MFI member. He had taken loan from Uddipon. He is a businessman. He is thirty-four years old. He has ten members in his family. He has seven brothers and sisters. He is unmarried. Md. Mosleuddin is the only earning member of his family. His monthly income is only 6000 taka. Now his father is sick and mother is a housewife. All his brothers and sisters are studying. They have eight katha lands. Mosleuddin has a small grocery shop. Their total asset value is worth 8.5 lakh taka. They also earn some money from domestic animals (two cows and six goats). Their total family expenditure is 5500 taka for food, health and educational purpose. Mosleuddin thinks that they can change their economic condition through hard work and financial aid. If he gets a large amount of money, he can improve his existing business and earn much more.

Shafiqul Alam, 647, Raninagar, Talaimari, Rajshahi

Shafiqul Alam is thirty years old. He was born in Rajshahi. His father is a government service holder and his mother is a house wife. He has one brother and one sister. The economic condition of his father is good. Shafiqul Alam is a private job holder. He has a poultry farm. His younger brother and sister are in study. They have four katha land. Their total asset value is 13 lakh taka. Their total family income is 15000 taka. Their total family expenditure is about ten thousand taka. Shafiqul Alam thinks that loss in business and physical sickness is the main reason of their poverty. He believes that hard work can improve this situation.

Rejaul Islam, 169 camp-1, Khulna.

Rejaul Islam is twenty- six years old. He is involved in scrap material business. His father stays in India and his mother is a housewife. The economic condition and the educational condition of his family are bad. He is unmarried. Their total asset value is eighty thousand taka. Rejaul Islam is the only earning member of his family and his monthly income is 6200 taka. They earn some money from domestic animals (three goats). Their monthly family expenditure is five thousand taka. They get help from their relatives at the critical moment. The relationship with their relatives is good. Rejaul thinks that lack of education is the main reason of poverty. He feels that he can overcome this situation by working hard. He also feels that financial help can also change poverty.

Md. Arman, 268 camp-1, Khulna.

Md. Arman is a MFI member. He borrowed money from Unnayon and used the money in his existing business. The economic condition of his father is not good. His father is an unemployed person and his mother is a house wife. He has five brothers and two sisters. Their total asset value is worth sixty thousand taka. Arman is a businessman. He has a small grocery shop. His younger brother is reads in class five. And his younger sister reads in class seven. The rest of the brothers are involved in several jobs. Their total family income is ten thousand taka. Their monthly expenditure is about ten thousand taka. At their critical moment his uncle helps them. He does not like the activities of MFI. He thinks that lack of education is the main reason of poverty. He also thinks that poverty can be overcome through hard working and financial help.

Mrs. Sohagi, House-52, Cahr Mill para, Kushtia

Sohagi is an ill-fated woman. She is forty years old. Her father is not alive. Her mother is a servant. The economic condition of her father was very bad. Her husband has died. Total family member of her family is four. Her daughter (Motijan) is the only earning member of her family. She is a service holder and her monthly income is only 2500 taka. Their total family expenditure is about 2300 taka. Their economic condition has become bad after her husbandâ&#x20AC;&#x2122;s death. They do not know yet how they will overcome this bad situation.

Hasmot, Cahr Mill para, Kushtia

Hasmot is a MFI member. He borrowed money from BURO. He is twenty-three years old. His fatherâ&#x20AC;&#x2122;s economic condition is moderate. But he married two times. His mother is a house wife. Hasmot is married and his wife is a house wife. His younger sister reads in class five. Hasmot is a businessman. He has a small scrap materialâ&#x20AC;&#x2122;s shop. He is the only earning member of his family. His monthly income is about six thousand taka. They have four decimal homestead lands. His total asset value is fifty thousand taka. His monthly expenditure on food, health and education is 3500 taka. In their critical moment his uncle helps them. He is a government service holder. He thinks that lack of education, second marriage of his father and loss of business are the causes of their poverty. He feels that he can improve his economic condition by hard working.

Kamal, Nimongor, Sheikhpura, Dinajpur

Kamal is a businessman. He is forty four years old. The economic condition of his father is not good. His father was a rickshaw puller. His wife is a house wife. He has 3.5 decimal homestead land. His total asset value is worth 4 lakh taka. Kamal has one son and one daughter. They are in study. His younger brother lives with him and he also earns money. They have one cow and it is an earning source of his family. His total family income is 8500 taka. And the monthly total expenditure on food, education and health is 6000 taka. His relatives help them at their critical moment. A rickshaw and a cow have been stolen from his house and he thinks that this is the main reason of their poverty. He feels that he can overcome poverty by hard work and financial help.

Md. Hamidul Islam, East Daptori para, Dinajpur

Md. Hamidul Islam is a MFI Member. He borrowed money from Grameen bank. He used this money in his business and for repairing their house. His father is a retired BDR member. His mother is a house wife. Their total family member number is eleven. They have nine decimal lands. They live in their own house and they also get house rent. Their total asset value is worth 9.5 lakh taka. Hamidul Islam is thirty seven years old and he is a small businessman. His younger brother is a service holder. Their total family income is nine thousand taka. Their monthly expenditure (on food, health and education) is six thousand taka. His brother helps them in their critical moment. He could not utilize the loaned money properly and it is one of the main reasons of his poverty. He thinks that he can improve the situation by hard work and financial help.

Jahanara, KDC, Balur math, Barisal.

Jahanara is a MFI member and she thinks that it is a burden for her. She is sixty years old. She has two sons and both are unemployed. Jahanara is married but divorced. She lives with her sister. Jahanaraâ&#x20AC;&#x2122;s sister earns money by making cigarettes and her son is a day labor. Their total family member size is nine. Their total family income is five thousand taka which is so small for their large family. Jahanaraâ&#x20AC;&#x2122;s father is not alive. The economic condition of her father was not good. During their family crisis their relatives helped them. She thinks that unemployment is the main reason of their poverty. She doesnâ&#x20AC;&#x2122;t know how she can overcome this situation.

Saju, Nurpur, Rangpur

Saju is only eighteen years old. He is unmarried. He makes Jharu and sells it. His mother is a house wife. His father married twice. They have six decimal land. Their total asset value is worth 1 lakh taka. Saju is the only earning member of his family. His monthly income is eight thousand taka. Their monthly expenditure is about six thousand taka. He thinks that the main reason of their poverty is the second marriage of his father. He feels that he can change this situation by hard working.

Jarina Begum, West Kaunia Bisic Shilpo Nagori, Barisal Jarina Begum is an ill-fated woman. She is forty years old. She lost her parents during her childhood. The economical and educational condition of her father was bad. Jarina is a house wife. She has two sons. One son works in a fruit shop and the other son is in jail. Their total family income is four thousand taka. She thinks that lack of education and the death of her husband is the main reason behind their poverty. They do not know yet how will they overcome this bad situation.

CHAPTER THREE

HOUSING AND UTILITY SERVICES OF MFI-MEMBERS

3.1 HOUSING 3.1.1 House Types and Environment The housing and surrounding environment reflect the economic and living condition of a HH. The environmental condition of urban poor habitats is generally bad. Houses are built on flood prone land and water logging is a common phenomenon round the year. Most of the poor communities lack basic infrastructure. Different types of housing and environment are found in the study areas. but they can be categorized into three. Some borrowers are living in kutcha/jhupri1 houses (in picture), usually on the low lying areas or roadsides in a filthy and shabby environment, some live in semi-pucca2 houses surrounded by areas of dense population , and the others are pucca3 houses having a comparatively good surrounding and

environment. The study, found that about three fourths of the MF borrowers live in semipucca., while the remainder live either in Katcha/Jhupri houses or in concrete-made pucca houses.. The details of the housing types used by the borrowers are shown in the following figure 3.1.

Kutcha/Kutcha houses are made of by the polythene, straw and bamboo Semi-pucca houses are two types: I is made of by CI tin on the roof and bamboo or earthen wall and II is made of by CI on the roof and brick wall. 3 Pucca houses are made of by concrete roof and brick wall. 2

31 %

Ku tch a,

Se mi pu cc a-I ,

11 %

Figure 3.1: Housing types

Pucca

, 15%

I, a-I cc pu mi Se % 43

According to the national database of BBS, 2001, 83.20% dwelling structures are kutcha/jhupri, 10.12% structures are semi-pucca, and only 6.68% are pucca. However, in the study areas, the percentage of the kutcha/jhupri houses is the lowest among all the other dwellings, which does not comply with the national database. A significant portion of the borrowers live in pucca-houses. For instance, about 26% of the borrowers are found to live in pucca houses in Dhaka city. The percentage is very small in other small cities in the study areas. It is notable that, about 98% of the people in Dhaka live either in pucca or semi-pucca houses. Comparatively, more borrowers in Brahmanbaria and Khulna live in Katcha houses, whereas in Bogra, Mymensinh and Sylhet, they are fewer in number (Appendix: 3A).

3.1.2 House Ownership There are different types of land ownership for housing found in the study areas. Some houses are built illegally on public or private land, some are on public land endowed by the government, and some are owned by the borrowers themselves. About 52% houses are rented, and 33% are self owned that are 9.7% and 86.37% in national database of 2001. On the other hand, on the whole only 18% of the urban poor have been found to be owning the plot of land they are currently occupying. The situation is extreme in the case of Dhaka city with only 3.2% owning their plots, with the reverse situation in the small

towns where nearly 90% own their plots. (GoB, 1996) [URGENT TO USE THIS TO DEVELOP AND MAKE ATTRACTIVE MOFUSSIL LIVING] Moreover, there are a number of communities in the study areas, for instances, Karail, Vashantek in Dhaka, Kadamtala in Chittagong, Shashanghat in Barishal, etc. which are built on public land. However, about 14% houses of the borrowers in the study areas are built on public land, paying nothing to the authorities. Most of them have been occupied as residences, but some houses are rented to others. Out of 457 HHs, 13 have been found to pay Tk. 50 to 800 per month in Dhaka, Khulna, Rajshahi and Brahmanbaria. Different types of housing have been built on the public land: about 21.85% are kutcha; 77.31% are semi pucca; and less than one percent has been found to be pucca. Pucca houses are mostly built on the land endowed by the government such as Bawniabadh of Mirpur in Dhaka city, where the residents have been living as a permanent resident for more than 25 years. [So permanency of rights has to do with the type of housing] [EARLIER OCCUPIERS TOOK LARGER PLOTS, THEY BUILT MULTIPLE HOMES AND RENTED THEM OUT] There are some other types of house ownership which were found in the study areas, such as living in relativeâ&#x20AC;&#x2122;s houses, or groom living with the bride in her house after marriage. The figure 3.2 shows the comparative statistics of house ownership. It also depicts the sizes of rooms within house ownership.

Figure3.2: House ownership

21 1

Se lf

10 0

743

16 00

14 00

12 00

10 00

80 0

60 0

40 0

20 0

ou se s

pu bl ic

la nd

328

th er

en te d

1,126

100 or more sft

Less than 100 sft

In Dhaka, about 71% borrowers are living in rented houses, 21% are living in their own houses, and only 6% are on public land. In Chittagong, 54% borrowers are living in 41

rented houses, 37% are in their own houses, and 8% are on public land. On the other hand, in Kushtia, the percentages are 10%, 16% and 74% respectively. City wise house ownership has been shown in Appendix: 3B.

3.1.3 Number and Size of Rooms The average number of room used by borrower-HH is 1.60 and the average number of family member is 4.64. Therefore, the population density per room is 2.9. Accoding to GoB, 1996, nearly 81% of the urban poorâ&#x20AC;&#x2122;s houses have only a single room and the situation is the worst with 89% of the urban poor living in single room in Dhaka city. But the density is higher, about 4.27, in those HHs living in single-room houses and room size is less than 100 sft. The density obviously reduces with large house sizes, i.e. for double-room houses, it is 2.5/hh. Again, the density of family members differs in cities. In Dhaka, it is 4.39/hh, in Chittagong, is 4.23/hh for single room houses. Details of occupant density is shown in the following table 3.1. The density of single-room houses is highest in Barishal and lowest in Rajshahi. On the other hand, it is highest in Brahmanbaria, and lowest in Kushtia for double-room houses. Table 3.1: District wise density Density Single-room Double-room house house

City

4.75 3.69 4.62 4.23 4.64 4.39 3.96 4.19 3.70 4.41 3.63 3.79 4.10

Barishal Bogra Brahmanbaria Chittagong Comilla Dhaka Dinajpur Khulna Kushtia Mymensingh Rajshahi Rangpur Sylhet

5.00 4.71 5.32 5.08 5.04 5.10 4.79 5.27 4.08 5.25 4.58 4.76 4.78

Source: Field Survey, 2009

The density varies little with house types. If the houses are kutcha, then the average density is 4.32/HH in a single-room houses and 4.9/HH in double-room houses. It is 4.16 in single-room pucca houses and 5 in double-room houses. And for semi pucca single-room houses density is 4.55, which is higher that other types and for semi pucca double-room houses it is lower, 4.73/HH.

The borrowers live in a wide range of rooms from one to six; about 59% live in single-room houses, about 28% in double-room houses and the remaining others live in more than two room houses. About 86% of the room size is 100 square feet (sft) or more and others are smaller. The percentage of small size & single-room houses is greater compared to the other houses, about 16% of the rooms is smaller than 100 sft. The room sizes are greater in large houses, in five-room and six-room houses; it is 5 and zero percent respectively. The table below shows the number of rooms and sizes in the study areas. Table 3.2: Number and size of rooms used No. of room 1 2 3 4 5 6 Total

Size of room 100 or mo <100 sft

Total

1,283 84.41 636 88.21 225 91.84 71 87.65 18 94.74 6 100.00 2,239 86.38

1,520 100.00 721 100.00 245 100.00 81 100.00 19 100.00 6 100.00 2,592 100.00

237 15.59 85 11.79 20 8.16 10 12.35 1 5.26 0 0.00 353 13.62

Source: Field Survey, 2009

The figure below shows the comparison between room sizes in thirteen districts of study areas: one is small having less than 100 sftt and the other is moderate or bigger having more than or equal to 100 sft area. The percentage of small-room houses is greater in Sylhet, Rajshahi, Dhaka and Bogra; it is about 29% in Rangpur which is the highest. And the percentage is lower in Brahmanbaria, Comilla, Khulna, Mymensingh and Kushtia (Appendix: 3C). The figure below depicts that percentage of small room size is greater in Dhaka, Rajshahi, Comilla, Sylhet and Bogra and the size is bigger in other small towns.

Figure 3.3: Comparative statistics of room size 5

Frequency

10 10

90 ris Ba

l ha

0 24

8 2

a ria ng illa gr ba go om Bo ta C an t i m Ch ah Br

a ak

r jpu na

>=100 sft

2 19

5 12

i r a tia gh ah pu uln sh sin ng jsh a Kh a Ku en R R ym M

t lhe Sy

<100

3.1.3 House Rent The urban population pays a fairly large absolute amount of money as house rent. About 52% borrowers of the study areas live in rented houses. The rents varies for Tk. 50 to Tk. Tk.11,000. However, the average rental value paid by the borrowers is Tk. 1700 in which about 40% borrowers pay up to Tk 1000, and additional 35% pay more than 1,000 to Tk. 2000. The cumulative percentage of house rents has been shown on the following figure3.4. It shows that about 90% of the borrowers’ house rents fall below Tk. 3,000. Moreover, it shows that, about 48% borrowers pay nothing for this purpose—among them, 68% borrowers live in their own houses, and remainder lives either on public land or in others’ houses.

cumulative percentage .4 .6 .8

Figure 3.4: Cumulative percentage of house rents

5000 house rents

10000

The data also reveal that about 4% tenants on public land are paying rent to someone else who occupies the land and gives rent to themâ&#x20AC;&#x201D;the average rental value is Tk. 354 per month. City wise house rent by types of houses has been shown in the Appendix 3C. There are different types of room sizes have been found in the study areas; however, they are classified under two categories: i) rooms having less than 100 sft of area; and ii) rooms having greater than or equal to 100 sft. of area. The data reveal that about 16% of the houses are small-sized single-room houses. The average rental value of single-room houses is Tk. 1,296 per month but it is Tk. 1205 if room size is below 100 sft. If the room is Kutcha/Jhupri, then the rental value is Tk. 684, and for semi-pucca-I, semipucca-II and pucca houses, they are Tk.966, 1469 and 1957 respectively. The house rents of big cities are higher compared to other secondary towns. Particularly in Dhaka, the average rent of a single-room kutcha houses is Tk. 797, whereas, it is Tk. 374 in other cities. In the same way, for single-room semi pacca houses, the average rent is Tk. 1,310 in Dhaka and Tk. 690 outside. The rent for a single-room pucca house in Dhaka is 1,577. The table 3.5 below shows the average rental value of different types of houses.

Figure 3.5: Rent of different types of houses

Rental value (Tk.)

3,5

2,54 5

1,95

7 1,46

1,370

1,104

966

684

Pucca (concrete roof)

Semi-pucca (Tin & Brick)

Semi-pucca (Tin and bamboo/earthen)

Single-room house

Kutcha/Jhupri

Double-room house

In addition, about 28% HHs are living in double-room houses and their average rental value is Tk. 2,422 per month. If the houses are ku tcha, then it is Tk. 1,104 for semi-pucca-I, is Tk. 1370, for semi-pucca-II, is Tk. 2,545 and for pucca, is Tk 3,509. Only about 17% HHs live in the houses haing three to six rooms. The average rental value of the three-room houses is Tk. 4,526 and it is Tk 5,656 for four-room houses. The rental value of houses varies in cities with the number of rooms and type of houses. However, in some cities like Dhaka, Chittagong, Comilla, the rent is high compared to other cities such as Khulna, Rangpur Rajshahi. From the table 3.3 below, a clearer picture of house rent might be depicted. Table 3.3: Average rental value of houses Rental value (Tk.) against number of rooms City

Barishal

532

964

1,317

Bogra Brahmanbaria

422 642

820 1,350

Chittagong

1,139

2,162

3,125

5,000

Comilla Dhaka

1,017 1,760

2,636 3,038

5,579

6,443

Dinajpur

354

Khulna

385

588

625

Kushtia Mymensingh

364 681

1,733

Rajshahi

433

720

1,000

Rangpur

308

719

1,500

Rental value (Tk.) against number of rooms City

Sylhet

773

1,360

2,000

Source: Field Survey, 2009

However, a number of factors affect house price in urban areas; it is a function of physical characteristics, including number of bedrooms, floor condition, housing materials, number of bathrooms, fittings of bathroom, geographic location, proximity to city heart, and plot area. Other constant built in qualities within a community, are age of building, crime rates, openness, communal harmony and local environment. By taking the average price, holding these factors constant, an index can be produced that controls for variation in the types of homes (Bashar T. & Rashid R., 2007). In general, the rent of a house is dependent on the supply and demand for houses. At a particular level of supply and demand, however, a number of factors influence house prices: House Price = F(Land, Structure, Neighborhood, Accessibility, Location) I. Land is the quality and size of the property II. Structure is the quality and size of building III. Neighborhood includes people—such as the income, occupation and education of residents, and place—such as the quality of local schools IV. Accessibility is the proximity to transit, shopping, recreation and employment V. Location is the proximity to enhancing qualities, such as parks, or to detracting qualities, such as crime and, air and noise pollution Neighborhood level price presumes factors III, IV, and V. Only factors related to the house itself (I and II) vary within a neighborhood. But, in reality, all factors vary among neighborhoods; it assumes that newly built houses in a compatible neighborhood with more bedrooms with good floor, bathroom, veranda, and sufficient open space beside it, command higher prices. This relationship is almost linear,

Calculating rental/sq.ft for each type of housing In conclusion, the rental value of per square feet of each type of housing differs. The average rental value of per square feet of kutcha house is Tk 7.00 to Tk. 7.50, and for pucca houses it is Tk.20.50 – Tk. 28.00. Again, per square rent of semi-pucca (Tin and Bamboo/Earthen) house is Tk.10.00 – Tk.13.50 and of semi-pucca (Tin & Brick) is

Tk.15.50 â&#x20AC;&#x201C; Tk. 21.00. However, a number of factors determine the rent of a house; depending on its geographic location and infrastructural condition it varies much.

3.2 UTILITY SERVICES The basic utility services- water supply, electricity, gas, sewerage connection are a must for urban living. This study has tried to find out the state of utilities in the HHs, and found that about 21% HHs have all of these connections and 4% lacks any connection. Others have some but not all utilities. 3.2.1 Sources of Drinking Water Pure drinking water is one of the most basic needs of the people. But poor communities often lack it. About half of the borrowers have pure drinking water sources; most of them own the sources, others collect from neighbors. The source is mainly the shallow or deep tube well. The remaining HHs have municipal water supply connection or some other means. According to GoB 1996, 90% of the poor HHs collect drinking water from tap or tube wells, except in Dhaka, where 55% use WASA supply water but a very few (3.3%) boil water before drinking. Detail of sources has been shown in the following table 3.4. Table 3.4: Sources of drinking water Sources of drinking water

Freq.

Percent

Cum.

Missing value Not pure & other source Other Pure & self-source Pure but other source Supply water

30 9 61 1,178 51 1,263

1.16 0.35 2.35 45.45 1.97 48.73

1.16 1.50 3.86 49.31 51.27 100.00

Total

2,592 100.00 Source: Field Survey, 2009

Only a few HHs are found to collect impure drinking water, mostly from ponds or rivers. These HHs are observed only in Dhaka and Chittagong. The percentage of HHs who collect pure drinking water from their own sources, is higher in secondary towns, compared to larger urbanised cities like Dhaka, Chittagong and Sylhet. HHs of big cities mostly collect water from the supply line. Details of water supply have been shown in Appendix-3.

3.2.2 Water Supply Municipal water supply network is one of the most basic services in the cities. But a half of the total borrowers in the study area deprived of it. In Brahmanbaria, Dinajpur, Khulna and Kushtia, more than 90% HHs are not connected to the water supply line. The connection is found available for the borrowers in big cities - largely, in Dhaka and Chittagong and partly in Rajshahi, Mymensingh and Sylhet. In Dhaka, about 88% and in Chittagong, 53% HHs are found to have water supply connection whereas, they are 39%, 31% and 27% in Rajshahi, Mymenshingh and Sylhet respectively. The condition is worst in some cities like Khulna, Kushtia Dinjapur, etc. (Appendix-3C I). The table below shows the overall condition of water supply. Table 3.5: Water supply connection Water Supply

Freq.

Percent

Cum.

Missing data

1.35

yes

1,275

49.19

50.54

1,282

49.46

100.00

Total

2,592

100.00

Source: Field Survey, 2009

In Dhaka city about 88% percent HHs enjoy having water supply connection. Most of the communities have water supply connection which covers more than 90% of the HHs, except Gabtali/Aminbazar, Rampura. And only in Savar, the condition is very poor, it is only 30%. Details information can be seen in Appendix-3C (II).

3.2.3 Sanitation Condition A latrine is used for defecation and urination which allow people for safer and more hygienic disposal of waste. However, 37.38% of the national population have sanitary latrine, 41.17% have other latrines, but 21.45% have none of these (BBS Census Report, 2001). In the study area, about a half of the borrowersâ&#x20AC;&#x2122; HHs use sanitary1 and pucca latrine. Additional 31% HHs use pucca latrine but they are unhygenic2. The remaining HHâ&#x20AC;&#x2122;s use unhygienic and kutcha or open fields for defecation.

1 Sanitary latrine is one with septic tank or at least two pit pucca latrine: one for decomposition of human waste, and the other is for the simultaneous use, so that. 2 Unhygienic latrine does not assure safer disposal of human waste.

U i yg nh ic en &

cca Pu

yg nh tu bu

ic, ien

% 31

ta ry

pu cc a, 49 %

9% ,1 ha tc ku

Other, 1%

Figure 3.6: Sanitation condition of the borrowers

Surprisingly, the percentage use of unhygienic latrine is higher in small cities such as Brahmanbaria, Rangpur, Dinajpur, Kushtia. More than 60% HHs in these cities fall in this group. However, different types of latrines used by various districts can be seen in the appendix-3B. The percentage of hygienic-latrine user is more in Chittagong, Sylhet, Bogra, Mymensingh, particularly, Dhaka. About 90% HHs of Dhaka use pucca and sanitary latrine. In the figure 3.7 below, the pictures of comparative hygienic latrine user of different cities have been shown. Figure 3.7: Percentage of hygienic and unhygienic latrine

11 25

80%

100%

698

60%

20%

117

40%

Sanitery & Pucca

Sy lh et

R an gp ur

R aj sh ah i

M ym en si ng h

K us ht ia

K hu ln a

D in aj pu r

D ha ka

C om ill a

C hi tta go ng

B ra hm an ba ria

B og ra

B ar is ha l

Unhygienic & Kutcha

The use of sanitary latrine is significantly correlated with HHâ&#x20AC;&#x2122;s income and education. And the influence of education of the HHH on using hygienic latrine is almost double compared to income of the HH. The more people earn the more can spend, so the purchase of materials in order to build latrines may occur. People do also get educated on 50

personal hygiene, so education as a whole may be taken into consideration as it may as well effect on the persons lifestyle.

3.2.4 Electricity About 31.70% of the total population in the country have electricity connection (BBB Census Report, 2001). However, formal access to electricity for lighting or other purposes is available to 44% of the urban poor HHs while another 165 get access through informal or illegal means (GoB, 1996). In the study area about 94% HHs are getting electricity. In some cities: Barishal, Comilla, Mymensingh and Sylhet, all the borrowers have electricity connection and there are also some cities where a significant portion of HHs remain without having it. Among them, Kushtia, Rajshahi, Rangpur and Dinajpur are mentionable, on average, 20% of the HHs is yet to be connected with electricity (Appendix: 3C-V). Table 3.6: The state of electricity connection Electricity

Freq.

Percent

Cum.

Missing data

1.35

no yes

122 2,435

4.71 93.94

6.06 100.00

total

2,592

100.00

Source: Field Survey, 2009

The condition of borrowers in Dhaka, in terms of having electricity conection,is quite good; more than 98% HHs get the electricity connection. Among the 15 communities, situation of Jatrabari is not good --, but even here,only 5% (i.e. 0.75 members) HHs are without electricity (Appendix: 3C-VI).how many pay for electricity

3.2.5 Gas About 44% of the HHs cooks with gas. Others suffer while cooking as they use paper or wood for this purpose. Though there are a few large cities where gas supply is available, all HHs of those cities are not getting connection with gas. Among them, Dhaka, Chittagong, Sylhet, Brahmanbaria and Comilla are notable (Appendix: 3C-III). However, none of the HHs living in Barishal, Dinajpur, Khulna, Kushtia, Rajshai and Rangpur are found to have any gas supply.

Table 3.7: The state of gas connection Gas

Freq.

Percent

Missing data

1.35

Cum. 1.35

1,408

54.32

55.67

Yes

1,149

44.33

100.00

Total

2,592

100.00

Source: Field Survey, 2009

In Dhaka city, 81% HHs are provided with gas supply, the majority of most communities avail this facility, except Mohakhali and Savar having 35% and 27% respectively (Appendix: 3C-IV). Those who are not getting the connection either reside beside the street or can not afford it.

3.2.6 Sewerage Only about 23% HHs in the study areas have connection with the sewerage 1

system . The others let out dirty liquid substances here and there making the communities unhealthy for its inhabitants. However, in most of the cities the poor communities lack the system. But in Dhaka and few parts of Bogra, the condition of sewerage disposal2 is better (Appendix: 3C-VII). The table below shows the sewerage connection of the study areas. Table 3.8: Sewerage connection in the study areas Sewerage

Freq.

Percent

Cum.

Missing data

1.35

1,975

76.20

77.55

Yes

582

22.45

100.00

Total

2,592

100.00 Source: Field Survey, 2009

The condition of sewerage disposal is quiet unsatisfactory in Dhaka city. Only 51% HHs are connected with the system. The condition of Savar, Mohakhali and Jatrabari is extremely bad rather than other communities in the city. In Savar, only about 4% HHs get the sewer line to dispose waste.

Sewerage system: The part of an infrastructure of a city, it allows al the waste from the homes and buildings to flow into a particular area in an organized manner.

Sewerage disposal: The outflow of waste from HHs to the sewerage system itself.

CHAPTER FOUR

OCCUPATION AND FINANCIAL CONDITION OF THE MF BORROWERS

4.1 OCCUPATION OF THE BORROWERS 4.1.1 Primary Occupation A large share of the total income of the households (HHs) comes from the household head (HHH). The HHHs are engaged in a number of occupations; however they have been categorized under eight classes. These are: i) jobless (without any occupation); ii) housewife; iii) agricultural laborer; iv) small businessman1; v) service holder2; both formal and informal vi) day laborer; vii) rickshaw puller; and viii) others3. The table 4.1 shows the distribution of occupation of the HHHs in the study areas. The data reveal that two fifth of the HHH are small businessmen and that very few of the HHHs are agricultural laborers. The others are: service holder, rickshaw puller, day laborer etc. Table 4.1: Primary Occupation of HHH Occupation of the HHH

Jobless Housewife Agricultural laborer Small Businessman Service holder Day laborer Rickshaw puller others Total

Total

Dhaka

Outside Dhaka

Frequency

87 78

3.36 3.01

55 45

4.58 3.76

40 63

2.47 2.47

.46

.47

1,032 468 87 190 638 2593

39.81 18.06 3.36 7.33 24.61 100.0

417 232 45 45 248 1092

38.50 21.45 4.12 4.40 22.73 100.0

597 232 39 141 382 1501

40.77 15.59 2.80 9.46 25.98 100.0

Source: Field Survey, 2009

Agricultural laborers are found at the peripheral communities in cities, such as Savar, Badda, Aminbazaar and Mirpur in Dhaka, Shimrailkandi in Brahmanbaria, 1

Here ‘small business’ is considered to be a business having up to five employees and also not involved in processing of raw materials into finished goods. The ranges of capital invested is usually Tk. 1,000 to 5,00,000. 2

‘Service’ is mostly e.g. garment’s workers, guards, NGO workers but also includes formal jobs.

3 ‘Others’ include blacksmith, goldsmith, potter, carpenter, fisherman, contractor, transport worker, painter etc. .

Keshobpur in Rajshahi, and Malgram in Bogra. They do not posses any agricultural land, (except one in Bogra who has 200 decimal of agricultural land) in the city, but they sell their labor to agricultural land near the city. Among the HHHs, 3.7% are unemployed; others have different types of occupation. However, among the rests, 39.1% are small business men, 17.9% are service holders, 7.2% are rickshaw puller and 24.3% are engaged as housewives, agricultural laborers and daily laborers. Figure 4.1 shows the share of occupation in 13 districts across the country.

3% ho us ew ife

day la b

rs 2 othe

orer 4

Figure 4.1: Percnetage of different occupation

ric

ler pul aw ksh

service 18%

jobless 3%

small Busin ess 40%

In the case of Dhaka city (42% of total sample), 38.50% HHHs are engaged in small businesses, which is the largest share compared to any other occupations. On the other hand, the agricultural laborer occupies a very small percentage, 0.46%, here, in Dhaka which is the smallest. Among other occupations, the percentage of housewives, daily laborers and rickshaw pullers are the same, each takes up around 4%. The situation differs slightly between Dhaka and outside of Dhaka. The small businessmen also comprise of a larger percentage outside Dhaka, occupying 40.77% of the total. The percentage of rickshaw pullers is more here, 9.46%, in the place of 4.40% in Dhaka city. But the percentage of service holders is 5.7% less in outside regions. The data also reveal that the jobless people are less in outside Dhaka. Details are shown in the Appendix: 4. The primary occupations of the HHHs also vary at different communities in Dhaka. Comparatively more HHHs are found to be service holders at Badda, Gulshan54

Banani, Mohammadpur-Adabar and Hazaribagh. At Savar, Mirpur and Uttara, more HHHs are jobless than that of other communities in Dhaka city. On the other hand, the percentage of day laborers is higher at Uttara. Details have been shown in Appendix: 4A.

4.1.2 Important Occupation, the Small Businesses It is evident that small businesses dominate the others occupation. So, letâ&#x20AC;&#x2122;s see the variety of business activities found in the study areas. Among them, some businesses are common in most of the communities. They are: petty shop keeping; hotel restaurant business; rickshaw or garage renting business; shari1 business; tailoring; vegetable, fish and fruit sales, and so on. These vary slightly with the cities located in different parts of the country. In Chittagong, the important small businesses include: fish selling; petty shop keeping; tea stall; and vegetable business. But, in Brahmanbaria, some of the important businesses are rice selling, furniture selling and vegetable business. Details of the important businesses have been shown in the appendix 4A. The variety of small businesses is higher in Dhaka city compared to the rest of the country. More than 50 types of businesses have been found (Appendix: 4A). There are some businesses, like petty shop keeping, tailoring, fish selling, etc. that occur frequently in the communities. Businesses important in each community are shown in the following table. Table 4.2: Common Businesses in the Communities Name of Community

No common business

Gabtali-Aminbazar

1. 2. 3. 1. 2. 1. 2. 1. 2. 3. 4. 5. 1. 2. 1. 2. 1. 2. 3. 4.

Goran/ Bashabo Gulshan/Banani Hazaribagh/Kamrangirchar

Jatrabari Kamlapur/Gopibagh Lalbagh

Common Business

Badda

Petty shop keeping Cosmetics business Rickshaw/garage renting Petty shop keeping Cloth/shari business Vegetable business Petty shop keeping Petty shop keeping Hotel/restaurant Rickshaw/garage renting Tailoring Cloth/shari business Petty shop keeping Fish selling Fish selling Cloth/shari business Cloth/shari business Tea stall Selling plastic material Petty shop keeping

Shari is one kind of long cloth generally used by the women of Indian sub-continent.

Name of Community

Common Business 1. 2. 1. 2. 3. 4. 5. 1. 2. 3. 4. 1. 2. 3. 4. 1. 2. 3. 1. 2. 1. 2.

Mirpur Mohakhali

Mohammadpur/ Adabor

Rampura

Razarbagh/Malibagh Savar Uttara/Khilkhet

Vegetable business Cloth/shari business Hotel/restaurant Rickshaw/garage renting Tailoring Cloth/shari business Meal selling Petty shop keeping Rickshaw/garage renting Tailoring Cloth/shari business Petty shop keeping Vegetable business Fish selling Fruit selling Petty shop keeping Cloth/shari business Tea stall Petty shop keeping Rickshaw/garage renting Vegetable business Fish selling

However, among all the common businesses, petty shop keeping, vegetable business, rickshaw/garage renting, fish selling and cloth and shari business are frequent; these are common in almost in all the communities.

4.1.3 Secondary Occupation of HHH Some HHHs are found to be involved in the secondary occupation alongside their primary one. In the study areas, about 8% HHHs are involved in secondary occupation. This provides them with an extra source of earning. They are mostly in small businesses. Table 4.3: Secondary Occupation of HHH Occupation

No secondary

Total Frequency

Dhaka Frequency

Outside Frequency

2,383

91.94

968

88.73

1,415

94.27

Housewife Agricultural labor

2 4

.08 .15

2 2

.18 .18

0 2

0.00 .13

Small Business

118

4.55

5.87

3.60

Service Day labor

6 5

.23 .19

3 4

.23 .37

3 1

.20 .07

occupation

others

2.84

4.40

1.73

Total

2,592

100.0

1,091

100.0

1,501

100

Source: Field Survey, 2009

In the case of Dhaka, more people are engaged in secondary occupation. More than 11% HHHs have a secondary occupation, which is almost double that outside Dhaka. Of

these, 5.87% are involved in small businesses. The other occupations are: service (0.23%), day labor (0.37%), others (4.4%). And 0.4% covers housewife and agricultural labor and outside Dhaka, they are 0.2%, 0.07%, 1.73% and 0.13%, respectively

4.2 WORKING HOURS The average working hours is almost same in all cities. It is 10.42 hours per day, but varies slightly in certain cities. In some cities like Dinajpur, Khulna and Kushtia, the average working hours is less than that of other cities in the study areas.

4.3 WORKING DISTANCE The distances, traveled by the HHHs for their occupation or income, varies from HH to HH. The average distance is about 3 km., but most of the borrowers travel from zero to 5 km. In the study areas, about 83% of the HHHs are found to travel in this range; the others travel more than 5 km. Figure 4.2 below represents the frequency of the HHH against distance traveled. Figure 4.2: Distance travedl by the HHH 20+ km 15 >15-20 km

>10-15 km

>8-10 km

105

>5-8 km

>2-5 km

253

1-2 km

516

walking distance

500 0

100

200

300

400

500

600

frequency

The borrowers of Khulna, Kushtia and Sylhet travel more than the other districts, the average traveling distances are 5 or more km, while smaller distances are traveled by the borrowers living in Barishal, Chittagong and Brahmanbaria.

4.4 INCOME EARNER The number of income earners obviously increases the monthly income of the HHâ&#x20AC;&#x2122;s, which is shown later. In the study areas, the average number of income earners of the HHs is about 2. About 45.60% of the HHs has one income earner and additional 34.53% HHs have two. The remaining others have three or more income earners. Figure 4.3 shows the comparative statistics of the number of income earners of the borrowers.

Figure 4.3: Number of Income Earners

1, 7% 2, 13%

5, 33%

3, 20% 4, 27%

However, each HH has, at least, one income earner. The average number of income earners in Comilla, Dhaka and Barishal is higher than that in Kushtia and Sylhet. Again, three or more income earners are found to be higher in big cities like, Dhaka, Chitagong and Rajshahi. Details have been shown in Appendix: 4B. The average income earners in Dhaka is 1.96 per HH, whereas, it is 1.73 outside Dhaka. The percentage of HHâ&#x20AC;&#x2122;s with a single income earner is smaller in Dhaka (38.22%) than that in outside (50.92%). Consequently, two or more income earners are higher in Dhaka city rather than other cities in the study areas. The table below shows details of the income earners of the HHs. Table 4.4: The number of Income Earner Total

Dhaka

Outside Frequenc y

Income earner

Frequency

1182

417

38.22

765

50.97

895

415

38.04

480

31.98

170

15.58

175

11.66

134

45.6 34.5 3 13.3 1 5.17

6.23

4.40

1.20

1.56

0.93

.12

.27

345

Total

Dhaka

Outside Frequenc y

Income earner

Frequency

.08 100. 0

.09

.07

100.0

1,501

100.0

Total

2,592

1,091

Source: Field Survey, 2009

4.5 INCOME OF THE BORROWERS 4.5.1 Nature of Income Income is regular for more than 90% of the borrowers, while the others have irregular income because of the nature of their jobs such as karchupi1, selling day labor, etc. Higher percentage of regular income is found in Brahmanbaria, Chittagong, Comilla and Sylhet. On the other hand, lower percentage of regular income is found in Barishal, Bogra and Mymensingh. Details have been shown in Appendix: 4B.

4.5.2 Monthly Income of HH Incidence of poverty varies in a city to others. However, according to BBS, if a personâ&#x20AC;&#x2122;s calorie intake goes below the threshold level of 2122 k. cal, he or she is assumed to fall in poverty. On the other hand, World Bank estimates CBN as US$ 1.25 per day, thus income below this level are poverty incomes. There is a very wide range of income of households (HH) in the study area, starting at Tk. 500 and ending at 400,000 per month. The average monthly income of 98% HHs is Tk.11,076. But, by World Bank estimates, at least Tk 11,925 per month is required to live beyond the poverty line. According to data of the study areas, 36% of one-member HHs, 50% of two-member HHs, 52% of threemember HHs, 67% of the four-member HHs and 70% of the five-member HHs are found to live below the poverty line. However, the average of the highest 10% income is Tk. 43,132 and they are found mostly in big cities, particularly in Dhaka and Chittagong.

Karchupi is one kind of embroidery to make Shari/cloth highly decorative

Figure 4.4: Average monthly income (Tk) from major occupations 9,392

9,273 8,460

8,290 8,000 7,670

7,543

7,600

7,539 7,112

6,700 6,472 6,100

5,833 5,729

5,653 5,500

7,580

7,036

6,833

6,580

6,380 6,000 5,287 5,083

6,440

6,000

5,521

6,187 5,900

5,667 5,773

5,357

5,254 5,130 4,714

4,571

4,911 4,600

4,469

4,314 4,0404,106

4,125

4,078 3,885

3,977 3,375

rickshaw puller

service holder

lh et Sy

R an gp ur

R aj s

ha hi

in gh

sh tia

M ym en s

Kh ul na

in aj pu r

D ha ka

day laborer

om illa C

hi tta go ng

agr. Laborer

ah m

an ba r

Bo gr a

Ba ris

ha l

3,000

businessman

The data reveal that the HH’s income is more in Barishal, Brahmanbaria, Comilla and Dhaka. But the average of Sylhet, Khulna, Mymensingh, Kushtia is about half of the grand average. The figure 4.4 below shows the average income of borrowers in different districts. Figure 4.5: The average income of the HH 13,128 12,247 11,546

11,076 9,455 8,788

8,215 7,042 6,405 5,690 5,044

4,987

3,882

et lh Sy ur gp an R i ah sh aj R h ng si en ym M

tia sh Ku

na ul Kh

r pu aj in

ka ha

illa om C ng go tta hi ria ba an C

m ah Br

a gr Bo

l ha ris Ba

The income of the HHs has been categorized in different ranges and it is found that about 22% HHs’ income is less than or equal to Tk. 5,000, 36.5% HHs’ income falls in the range between Tk. 5,001 to Tk. 10,000, 29.4% HHs’ income falls between Tk. 10,001 to Tk. 20,000, the reaming others are more than Tk. 20,000.

The HH’s monthly income differs between Dhaka and outside. In Dhaka, only about 10% of the HHs’ income is less than Tk. 5,000, but in outside Dhaka the figure is 34%. Again, 42% HHs in Dhaka and 72% in outside Dhaka, fall in the class; their monthly income is Tk. 10,000 or less. The average monthly income of the HHs of the borrowers in Dhaka city is Tk. 12,247 but it varies among communities. The average income, at Jatrabari and GulshanBanani are Tk. 14505.56 and Tk. 15695.74 respectively, is more than at other communities in the city,. Savar, Hazaribagh-Kamrangirchar, Mirpur, and Mohakhali, in particular. Details have been shown in Appendix: 4B. The monthly income of HHs in Dhaka city varies: about 10% of the HH’s monthly income is below or equal to Tk. 5000; about 36% falls between 5001 and 10,000; about 34% is 10,001 to 20,000; about 17% is 20,001 to 50,000; and for the rest the percent is more than Tk. 50,000. Income levels of the HHs have been shown in the following figure 4.5. Figure 4.6: Income groups in Dhaka city

151

156 149 141

frequency

119

92 79

11 3

tha n3 00 00

00 00 1-3

00 50 1-2

00 00 1-2

00 50 1-1

00 20 1-1

0 00 -10

00 -80

00 -60

00 -50

00 -40

00 20

00 -30

re mo

00 25

00 20

00 15

00 12

00 10

01 80

01 60

01 50

01 40

01 30

01 20

4.4.3 Factors Influence the Income of the Households A number of factors influence the earnings of the HH. However, the income of HH varies mostly depending on income of the HHH, level of education and skill of the income earners, number of income earners in the HH and their occupations, etc. Because, if the nature of business differs, profit varies, thus income varies. On the other hand, the income rises proportionately with education and skill. 61

Additionally, there has been found a significant effect of geographic arrangement of the households. The significance of location is high in Dhaka, Chittagong and Barishal. Other cities have insignificant effect on income of the HHs. However, the causality effects of the determinant of household’s income have been shown in the following regression: Source | SS df MS -------------+-----------------------------Model | 1.6008e+11 5 3.2017e+10 Residual | 2.7924e+11 1662 168017174 -------------+-----------------------------Total | 4.3933e+11 1667 263544891

Number of obs F( 5, 1662) Prob > F R-squared Adj R-squared Root MSE

= = = = = =

1668 190.56 0.0000 0.3644 0.3625 12962

-----------------------------------------------------------------------------Income (HH) | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------income (hhh) | .904701 .0405181 22.33 0.000 .8252291 .9841728 Education | 299.8818 77.87749 3.85 0.000 147.1335 452.6301 assets | .0003884 .0001294 3.00 0.003 .0001346 .0006422 incomeearner | 3658.35 389.4299 9.39 0.000 2894.525 4422.174 famiy_member | 476.2102 227.6137 2.09 0.037 29.7704 922.6501 _cons | -5218.839 1147.825 -4.55 0.000 -7470.174 -2967.504 ------------------------------------------------------------------------------

Therefore, HH’s income, y= -5218.839+.904701Y+ 299.8818E+0.0003884A +3658.35N +476.2102M+ ε Here, y denotes the total income of a household Y=income of the HHH N= number of income earners A= total assets E= education (years of schooling) of HHH M=number of members of the household Source | SS df MS -------------+-----------------------------Model | 1.3024e+10 5 2.6048e+09 Residual | 1.0481e+10 1345 7792808.75 -------------+-----------------------------Total | 2.3505e+10 1350 17411388.4

Number of obs F( 5, 1345) Prob > F R-squared Adj R-squared Root MSE

= = = = = =

1351 334.26 0.0000 0.5541 0.5524 2791.6

-----------------------------------------------------------------------------percapitai~e | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------hhh_income| .387666 .0145227 26.69 0.000 .3591764 .4161556 education_hhh| 78.23681 18.82501 4.16 0.000 41.30723 115.1664 total_asset| .0003401 .0000682 4.98 0.000 .0002062 .0004739 income_erner| -1882.875 95.81274 -19.65 0.000 -2070.834 -1694.917 family_member| 253.5324 56.57209 4.48 0.000 142.5533 364.5115 _cons | 5549.902 303.2633 18.30 0.000 4954.981 6144.822 ------------------------------------------------------------------------------

Per capita income of HH depends largely on income of HHH; the other influencing factors are: education, total assets, number of family members, and number of income earners. All the factors, but the number of income earners, have positive impact on it. Nonetheless, the income level is clearly different in Dhaka and outside. The comparative cumulative percentage has been shown in the following figure 4.6. In Dhaka

city, only 11,87% HHs earn Tk. 5000 or less, whereas, it appears to be 36.15% in outside and the percentages at an income level of Tk. 10,000 are 54.68 and 76.4 respectively. Then the line representingof Dhaka has become upward sloping compared to the other line. Therefore, it can be inferred that about a half of the HHs in Dhaka and three fourth of the outside earn less than Tk. 10,000. The comparison might be obvious from the figure 4.6. Figure 4.7: Cumulative percentage of HH's income

80.72

76.4 percentage

100

97.78 95.32

91.97

54.68

36.15

11.87

5000

Dhaka Outside 10000

15000

20000

24000

income

4.5.4 Monthly Regular Income of the HHH Among 2593 sample HHH, 10.06% HHHs have no regular income; they live on irregular income1 sources. The remainder have earned regularly from their occupation. However, of those who have regular income, about 80% of the HHHs’ income is limited by Tk. 10,000 and remaining(20%) others’ income goes up to Tk. 30,000 or more. The following table 4.4 represents the percentage against the income levels in the study areas. Table 4.5: HHH’s monthly regular income Total

Dhaka

Outside Dhaka

Income

Frequency

261

10.1

111

10.2

150

10.0

<2000

138

5.3

3.3

102

6.8

2000-3000

258

9.9

5.6

197

13.1

3001-4000

214

8.3

5.3

156

10.4

4001-5000

350

13.5

118

10.8

232

15.5

5001-6000

284

11.0

114

10.4

170

11.3

6001-8000

318

12.3

145

13.3

173

11.5

8001-10000

254

9.8

133

12.2

121

8.1

There are some HHs that none of their income-earners’ occupation, e.g agri labor, construction labor, etc., does not allow them to earn a fixed amount either daily or monthly.

Total

Dhaka

Income

Frequency

10001-12000

115

12001-15000

174

15001-20000

Outside Dhaka

Frequency

4.4

5.5

3.7

6.7

100

9.2

4.9

101

3.9

6.1

2.3

20001-25000

1.8

2.9

25001-30000

1.2

1.7

30000+

1.9

3.5

Total

2593

100.0

1092

100.0

1501

100.0

Source: Field Survey, 2009

In Dhaka city, HHHsâ&#x20AC;&#x2122; earning is comparatively higher than outside Dhaka; about 20% more of the HHHs living outside have earnings which falls in the lower segment of income level, at Tk 5,000 or less. Again, the cumulative percentage line of Dhaka remains below the line of other cities until the end. It means that the percentage of lower income HHH is higher outside. The figure 4.7 below shows the cumulative percentage of income of the HHH.

Figure 4.8: Cumulative percenatge of HHH's income 120 100 100

100 90 82.5

percentage

56.97

37.57

16.15 12.15

Dhaka Outside

0 2000

5000

10000

15000

income

4.5.5 Additional Income from Secondary Occupation Although most of the HHs, in the study area, do not have a secondary occupation, from which they can earn an additional amount, but about 10% of them are found to earn some amount occasionally or seasonally. Both, in Dhaka city and outside, the scenario is almost the same; about 4.4% HHHâ&#x20AC;&#x2122;s irregular income falls up to Tk. 4,000. The others earn up to Tk. 30,000 more.

4.5.6 Occupation and Income There are no significant effects of occupation of HHH on HH’s income, but the average income varies with the occupations. As mentioned in the previous section the occupations have been categorized under eight headings. The average monthly income of different occupations has been shown in the following table 4.6. Table 4.6: HHH’s monthly regular income Occupation Agriculture labor Non-agriculture day labor Rickshaw Puller

Average monthly income (Tk.) 6318 5667 4300

Service

6593

Small Business

7737 Source: Field Survey, 2009

The table says that the average earning from small businesses is bigger than the other occupations. And it may be noted that the earning from rickshaw pulling is the lowest among all. This last finding may be influenced by the fact that many rickshaw pullers do not work for an entire month but typically take a week off to visit their ‘bari’.

4.6 Other Sources of Money Other Than the MF? The MF borrowers have some other sources of money from which they somehow manage their needs and invest in their business. They are: neighbors, relatives, professional money lenders, commercial bank, and personal savings. The table illustrates that, most of the MF borrowers invest in the enterprises from their personal savings which is about 64.8%. Tthe shares of other sources are: 11.1% from neighbors, 15.7% from relatives, 7.7% from professional money lenders and 1.2% from commercial bBanks.

Figure 4.9: Other sources of money

bank 1%

money lender 8%

relatives 16%

neighbour 11%

saving 64%

4.7 Assets The value of assets of MF borrowers may break down the conventional beliefâ&#x20AC;&#x201D;the average value of the assets of the borrowers is Tk. 7,49,123. However, the lower 5% HHâ&#x20AC;&#x2122;s assets value is limited to Tk. 14,000 or less; most of the HHs having this amount are living in Dhaka or Chittagong. The upper 5% assets values of the HHs living across the study areas go beyond Tk 3,025,000. Figure 4.10 shows the cumulative percentage of assets values.

cumulative percentage .4 .6 .8

Figure 4.10: Cumulative percentage of assets values

20000000 40000000 total assets (at present)

60000000

The figure depicts that 80% assets is limited to Tk. 700,000, then the cumulative percentage line turns sharply right onwards until it reaches at 98% where the assets value is Tk. 6,070,000. However, the percentage amount of each range of assets amount varies

with the location. For instance, the percentage of HHs having assets amounting toof Tk 10,000 or less is 2.4 in Dhaka, and outside, it is 12.4. FTable figure 4.11 represents the percentage of assets amount in Dhaka and outside. Figure 4.11: Comparative assets vaule

1.1

5.6 12.4

8.4

2.8

16.3 16.8

10.4 13.5

4.1

18.8

percentage

12.4

18.4

13.6 15.5

4 9

8.2 6

2.4 4.5

18.8 17.5

17.2

10.7

up to 10,000

10,001-20,000

20,001-50,000

50,001-100,000

100,001200,000

Total

Dhaka

200,001500,000

500,0011,000,000

7.2

1,000,0012,000,000

2.6

5.5

2,000,0015,000,000

5,000,000+

Outside

In the case of Dhaka, more HHs belongs less assets; the higher percentage of assets value is found between the amount of Tk. 10,001 and Tk. 200,000. Beyond the amount, until it reaches to Tk. 5,000,000, the percentage of Dhaka is less, compared to outside. Looking at the distribution of assets in our sample HH provided at once informative and challenging. To begin with, upon compiling the survey data of the members, we were surprised by the extensive value of the assets of the HH. Please see Appendix 1: Resurvey data of assets of both MFI members and non-members. How could so many slum dwellers have such extensive assets? We talked to several experienced researchers but no one could clear the question satisfactorily. So we went back and re-surveyed individuals with the intent of forming a more detailed inventory of their possessions. The slums chosen were A, B, C and the results made are fact stand out- high assets were strongly correlated with land holdings. By and large, anyone who had a plot of land gained hugely from the continuous rise of land prices. Hence, even those who lived in slums had average assets of 12 lakhs. The lessons learned here proved useful in understanding the impact of micro finance by asset growth. The fact that so many non-poor could be given MFI loans is a clear indication that the original focus of mirco-creditâ&#x20AC;&#x201D;lending only to those too poor to qualify for the formal

system- has been compromised and many loans are now given simply because of the high profitability of repayment. One example in particular is striking for its exceptional naturea lady who owns a six-storey building decides to open a boutique. To finance this venture she goes not to a bank, but to a MFI!

4.8 LOTTERY In our study area, 22.45 % of the respondents said that lottery1 is played by the community memberspeople and in response to the question that whether they play or not, 8.87% told that they are directly involved with lottery. The lottery is popular in Comillaâ&#x20AC;&#x201D; where 35% of the respondents regularly participatelay lottery. It is also popular in Rangpur, Dinajpur, and Rajshahi. Appendix: 4 (V) shows the percentage of people involved in lottery in 13 districts in the study areas. People of all occupations and income groups are more involved in lottery but rickshaw pullers and people having shop in bazaar, actively organize the event. In the study areas, about16.67% of the agriculture labors, 8.05% of the day labors, 8.97% of the house wives, 9.47% of the rickshaw pullers, 9.62% of the service men, and 8.91% of the small businessmen are member of the lottery group. Education also doesnâ&#x20AC;&#x2122;t matter in this regards but people having SSC or HSC level education are more involved with lotteryâ&#x20AC;&#x201D; the Appendix:4 (V) depicts the overall picture of situation. The situation differs among communities. In the case of Dhaka, 20.26% respondents said that the lottery is played in their community and it is mostly found to be popular

Gabtoli-Aminbazar

(14.81%),

Kamlapur/Gopibagh

(22.03%)

and

Razarbagh/Malibagh (22.86%) areas but absent in Jatrabari. Appendix:4 (V) shows the detailed statistics of lottery involvement of different communities in Dhaka.

Lottery is usually played among the members of a group consists of ten to fifteen members, have strong cohesion and are living in the same community. All members as a rule take part in all events on a 10/15-day interval, paying 50/100 taka per day. One member of a group gains the total amount in a single event following the other members on a cycle basis; those who win in the previous event are eliminated for getting it. It is found to be popular among uneducated people and rickshaw pullers.

4.9 CASE STUDIES OF SLUMS

Kamlapur Slum The settlement is located near Kamlapur railway station alongside Bashabo Road. Since the main railway station was shifted to Kamlapur about 15 years ago, this settlement started to grow steadilyâ&#x20AC;&#x201D;it offer an excellent place for the new immigrants, particularly those coming by train. 26.3% HHs of the squatters came from Comilla and a similar proportion from a remote district of Rangpur. It acts as a temporary shelter, also provides a good opportunity for getting jobs in the informal sector. For this reasons, a high concentrations of manual transport workers and manual labor (46%) were found in this slum. Interestingly, a highest proportion of 18% of the menial office workers in the formal sectors was also found in Kamalapur because Motijheel commercial area is very close to the site. The site is about 5 feet to 6 feet below the road level and is actually meant for storm water drainage. Even with a little rainfall this area gets very easily flooded, when the occupants are forced to live on the shoulder of the road. In this slum 32.3% houses are in worst condition. The shelters are mostly one-room bamboo shacks with earthen floor and there average size is 52.72 square feet. There are no toilet or drinking water facilities. Dwellers collect drinking water from the neighboring middle-income area or fetch it from a leaking main water supply which runs along the settlement. As the settlement is so visible, there have been repeated attempts from the authority (both Police and Public Works Department) to evict the dwellers, but the settlers always came back.

Bhasantek (Mirpur) Resettlement Camp In the squatter operation of 1975, about 3,000 families were brought to this particular camp. The camp was considered from the beginning to be a temporary settlement site. The permanent site was to be in 'Kalshi' about two miles to the north-west of the present location. Bhasantek resettlement camp is located on the outskirts of Mirpur close to Dhaka Military Cantonment. Although physically it is about 5 miles away from the city, due to restricted access the actual travel distance is about 10 miles. The area is a part of Mirpur Housing Estate belonging to the Housing and Settlement Directorate of the Ministry of Works. The name Bhasantek means â&#x20AC;&#x153;floating islandâ&#x20AC;? and the area is low lying and subjected to flooding during monsoons. Unofficial figures suggest about 10,000 people in around 2000 households are living in 5 wards in an area of about 90 acres. The 69

area is connected with the built-up area of Mirpur Housing estate by one approach road. Houses are densely clustered around this road separated by marsh lands. Due to a lack of internal roads most houses get inundated during monsoons causing distressing conditions. Most houses are in the form of huts with earthen floor, bamboo walls and thatched roof. Soon after the creation of the resettlement camps some voluntary agencies got involved in various kinds of welfare projects. OXFAM installed 3 community latrines (each having 20 latrines) and provided 30 shallow tubewells for the supply of drinking water. However finding later that the community latrines were difficult to maintain, they slatted providing individual pit latrines. Out of 30 tubewells, only 10 were reported to be functioning. During the initial period of the camp the Salvation Army undertook medical care facilities. CONCERN an Irish voluntary Agency also got involved by starting a Women's Work Programme. This was meant for about 200 women and 500 children. They also provided basic home shelters to about 2000 households. These were portable roof pieces which lasted for the first two monsoons. The portable roof pieces were meant to be dismantled in eases of relocation.

Mahammadpur Bheri Bandh Slum Mahammadpur squatter settlement is situated in the periphery of the city near the river Buriganga. There are a few other squatter settlements in Mahammadpur. In this slum there were more than one hundred households. The site acts as reception camp to rural migrants and the average period of residence was found to be less than ten years. In Mahammadpur slum 38% of the squatters were came from Barisal. Being located on the periphery of the city the site provides a secure shelter for the squatters. It was also with the kind permission of the owner of the housing estate that these squatters were living there. The location also provides good opportunities for the laborers as the 'labor market' is close to the area. In this area majority of the slum dwellers (46%) were manual transport worker and manual labor. Like other city sites it does not have any gas, electricity, drainage or toilet facilities. The dwellers steal drinking water from a neighboring middle income residential area. The shelters are made mostly with only one piece of bamboo matting. All shelters were found to have only one room. Being situated on low laying land some of the shelters were built on bamboo platforms. Also there are no access roads. The settlement was found distributed in to 3 small clusters separated by small canals or water-ways. In

one instance one cluster was found connected by a bamboo bridge. When flood water came the affected settlers took shelter on the main road.

CHAPTER FIVE

MFI IN GENERAL & MICROCREDIT

5.1 MFI’S IN GENERAL 5.1.1 Working MFIs More than one MFI’s have been working in every community. They are providing microfinance services to the borrowers. In the study areas, more than 83% borrowers are attached with prominent MFIs of the country. They are mainly: ASA, BRAC, BURO, PROSHIKA, SAJIDA, SHAKTI and TMSS. Others are involved in some small MFI’s. They cover most of the community people, but there are still some people beyond the service. The lists of the MFIs working in the study areas are given below. Table: 5.1 Lists of MFIs -------------------------------------------------------------------------------Name of MFIs | Freq. Percent Cum. ----------------------------------------+---------------------------------------

ASA ARBAN BRAC BURO CAP CARITAS Bangladesh DSK Muslim Aid-UK Others Proshika RIK Sajida Foundation Shakti Foundation Society Development Committee (SDC) Thengamara Mohila Sabuj Sangha (TMSS) Village Education Resource Center (VERC

| | | | | | | | | | | | | | | |

810 3 387 268 1 1 9 1 438 110 11 159 360 1 33 1

31.24 0.12 14.92 10.34 0.04 0.04 0.35 0.04 16.89 4.24 0.42 6.13 13.88 0.04 1.27 0.04

31.24 31.35 46.28 56.61 56.65 56.69 57.04 57.08 73.97 78.21 78.63 84.77 98.65 98.69 99.96 100.00

-------------------------------------------+------------------------------------Total | 2,593 100.00 ---------------------------------------------------------------------------------Source: Field Survey, 2009

There are some people who do not require credit. Again, some people require MF but they never get it. This study is not concerned with those who do not need microfinance services; conversely, it tried to find out the reasons why the poor who need loans are not getting the credit, they require. The potential borrowers are asked that what they think about the reasons for not getting the credit. Their answers have been classified in some categories and have been shown in the following table 5.2 below. The data reveal that about 6.0% of the people did not answer exactly; the others mentioned a number of reasons that lay behind it.

Table 5.2: Reasons for which people are not involved with MFIs ------------------------------------------------------Rsns_N_Borrower | Freq. Percent Cum. -------------------+----------------------------------1 & 2 | 257 9.92 9.92 1 & 4 | 2 0.08 9.99 2 & 3 | 4 0.15 10.15 2 & 4 | 31 1.20 11.34 Don't know | 155 5.98 17.32 Have no asset (1) | 231 8.91 26.23 No need (2) | 1,758 67.82 94.06 Not Permanent (3) | 14 0.54 94.60 To avoid dishonor | 4 0.15 94.75 Vulnerable income (4) | 80 3.09 97.84 Others | 56 2.16 100.00 -------------------+----------------------------------Total | 2,592 100.00 ------------------------------------------------------Source: Field Survey, 2009

According to the responses, of those who are not getting credit, about 67.82% of them do not need it; they seem to be, economically better off compared to the borrowers. The important reasons for not getting the credit are: i) have no assets; since this should not matter for the widest reach of MF, the reply suggests that urban MF is not known to cater to the ultra-poor, a potentially important issue for the future. ii) Are not permanent resident at the community; iii) having vulnerable income1; iv) to avoid dishonor. For one or more of these reasons, they are not getting credit from the MFIs.

5.1.2 MF Involvement The trend of microfinance involvement is upward sloppingâ&#x20AC;&#x201D;over the years, it has increased remarkably. However, the majority of the borrowers in the study areas are found to be involved with MFIs since 2003; before that year, only about 15% HHs were member of the MFIs. The trend of microfinance involvement is shown in the following figure 5.1. Since 2003, the trend continues to rise very sharply. In a single year 2008, the membership has increased to 905, which is just double that of the previous year. However, there are some members having longstanding involvement ;since 1987 in Dhaka city, since 1989 in Bogra, since 1992 in Dinajpur, since 1994 in Chittagong, since 1995 in Barishal, Khulna, since 1996 in Brahmanbaria, Mymemsing, Rajshahi, Sylhet, since 1998 in Rangpur, since 1999 in Comilla, Kushtia.

Vulnerable income is defined as the income from begging, shoe repairing, or some other occupations that can not ensure an amount on regular basis.

Figure 5.1: Trend of microfinance involvement

number of borrowers

905

453 345

191

1985

1990

48 11 17 30 31

1995

73 52

2000

97 92

137

2005

2010

year

5.1.3 Overlapping of MFIs More than one MFI have been seen working at each community in the study areas; the mean number of MFIs is more than 3. Overlapping rate is comparatively high in Dhaka, Khulna, Rajshahi, Bogra,Rangpur, Dinajpur and Kushtia compared to the other cities. Table 5.3: The number of MFI attached No. of working MFI Valid 0

Frequency 859

Percent 33.1

Valid Percent 33.4

Cumulative Percent 33.4

1490

57.5

57.9

91.3

193

7.4

7.5

98.8

1.0

99.8

100.0

2573

99.2

100.0

Total Missing Total

System

2593

100.0 Source: Field Survey, 2009

At present, 33.4% MFI-members in the study areas have been found to be nonborrowers; they took loans from MFIs earlier and have successfully paid the installments. Another about 58% respondents take loans only from one MFI. However, the rest about 9% takes loans from more than one MFI. The major working FMIs in the urban areas of the study areas are shown in table 5.4 below.

Table 5.4: Borrower involved with more than one MFI No. of MFI

Mean year of involvement

Mean income earner

Average present loan

HH earlier income

HH present income

2004

18482

9970

13586

2004

20923

15396

22600

2004

25500

22000

35500

The borrowers involved with more than one MFI are attached since 2004 on average and the number of income earners of the HH is two. The total amount of the loans increases with the number of loans in decreasing rate. But income increases more with higher number of loans.

5.2 CREDIT, BORROWERS AND MFIs

5.2.1 Frequency of Loans Borrowed In the study areas, the number of loan taken by the borrowers has varied from one to twenty times. About 36% borrowers are found to take loan for the first time and additional 19.3% takes twice. However, 95% of the borrowers take credit up to 9 times; the remaining take more than 9 times. Details have been shown in the appendix 5.

5.2.2 Loan Sizes Different loan sizes have been found, and are usually determined by the MFIs. Depending on the borrowers, and the number of previous loans taken by the borrowers, the current loan size is estimated. In the study areas, the loan sizes start at Tk. 2,000 and end at Tk. 200,000---but 98% of them are Tk. 50,000 or less. However, about 28.62% of the loans lie below or equal to Tk. 10,000, and about 49% of the loans fall in Tk. 10,001 to 20,000. Others amount are bigger than the mentioned ranges. There are some single loans which represent the bigger percentage: 13.43% loans is concentrated at Tk 10,000; 11.13% at Tk. 15,000; and 12.43% at Tk. 20,000. The following figure 5.2 depicts the loan sizes and their comparative frequency.

Figure 5.2: Loan sizes 467

370

frequency

268

175 138 108 68

5000 or less

50018000

800110000

1000115000

1500120000

2000125000

2500130000

3000140000

4000150000

more than 50000

Loan sizes depend upon some factors such as households’ present income, number of loans borrowed, home ownership, and total savings. The occupations of the HHH or the assets owned by the borrowers do not have any significant influence on the amount of loans. The following regression shows the level of significance of different variables. Source | SS df MS -------------+-----------------------------Model | 1.2549e+11 5 2.5097e+10 Residual | 2.1790e+11 1647 132300749 -------------+-----------------------------Total | 3.4339e+11 1652 207861072

Number of obs F( 5, 1647) Prob > F R-squared Adj R-squared Root MSE

= = = = = =

1653 189.70 0.0000 0.3654 0.3635 11502

amount _loan | Coef. Std. Err. T P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------hh_inccme | .09769 .0158596 6.16 0.000 .066583 .1287971 # of loan | 741.6025 107.1504 6.92 0.000 531.4371 951.7679 hmown(rented) | 3047.05 813.0938 3.75 0.000 1452.243 4641.857 hmown(self) | 4390.258 847.1682 5.18 0.000 2728.617 6051.898 totalsavings | 1.400977 .0616112 22.74 0.000 1.280133 1.521822 _cons | 4288.924 832.5868 5.15 0.000 2655.884 5921.964

The amount of loans depends significantly on HH income, number of loans borrowed by the individuals, house ownership (e.g. seflf house, rented house, or house on public land, etc.). Therefore, If the HH income is denoted by I, frequency of loan by n, house ownership by γ, and savings by s, then the amount of loans can be formulated by the following derivative: Loan size, δ= .098I+741n+ γ+C (if the home ownership is self, γ=4390 and if it is rented then, γ=3047) It also tells that the increase of HH’s income by about Tk. 100 will also increase the loan amount by about Tk. 10. On the other hand, the average difference of loan size is

about Tk. 742 from the previous one and the borrowers living in their own homes get about Tk. 1400 more than those who are living in rented houses. It also reveals that the borrowers deposit Tk. 100 on average against the loan of Tk.140.

5.2.3 Size and Nature of the Installment The average number of installments for each loan issued by MFIs is 44 but varies from 10 to 52 but mostly being between 37 and 46. And the installment size depends on the loan amount. Table 5.3 below shows the installment sizes, usually determined by the MFIs, of the different amount of loans. The data reveal that more than half of the borrowers are paying installments amounting between Tk. 350 to Tk. 500 per week. Table 5.5: Installment sizes against loans Loan size (Tk.) <5000

Average installment size(Tk.) 105

Percentage of borrowers 3.9

5000-8000 8001-10000

208 287

3.8 10.1

10001-15000

350/375

27.7

15001-20,000 20,001-25000

400/450/500 550/600/625

22.5 10.0

25,001-30,000

650/700/750

9.0

30,001-40,000

800/900/1000

5.6

Source: Field Survey, 2009

Typically, the payment of installments is weekly but there also prevails daily or monthly payment systems, available in some MFIs. About 64% borrowers pay weekly installment to refund the loans. The type of installment varies with the communities, borrowers and the MFIs. The Appendix: 5 (II) shows the nature of installment of different MFIs. The monthly installment has been found only in Dhaka and Bogra. The MFIs collecting this installment are: ASA, BRAC, Shakti and TMSS in Dhaka. Two installments per month have been rarely found in Chittagong and Dhaka and the MFIs providing the services are ASA Shakti, BRAC, BURO, Sajida and some local MFIs.

5.2.4 Savings with the MFI Each borrower attached with the MFI is obliged to save a minimum amount---it is true for almost all NGOs. Although the amount varies but minimum amount is mandatory for getting a specific amount of loan. Additionally, they have to save weekly amout with the installment. The average savings of the borrowers is Tk. 181 per month, however, it increases with the size of the loan. The average savings, against loan size up to Tk. 5000, is Tk. 122, whereas it is Tk. 183, against loan size between Tk 10,000 to Tk. 20,000.

Figure 5.3: Savings per month against loan sizes 300

amount of savings (Tk)

250

Dhaka outside Linear (Dhaka) Linear (outside) 202

255 229 203

192

200

145

150 121 100 100

0 up to 5000

5001-10000

10001-20000

20001-50000

loan sizes

The amount of savings also varies between Dhaka and outside. Table 5.3 illustrates the comparative amount of savings. However, it reveals that the amount is always higher in Dhaka city against all loan sizes when compared with he outside

Payback Period and Rules Figure 5.4: Payback period (in month)

6, 7% 18, 21%

8, 9%

9, 10%

13, 15% 10, 11%

12, 14%

11, 13%

The amount endorsed by the MFIs to the borrowers has a repayment schedules. The average payback period of each loan is about eleven and a half month, but, it varies from 8 to 13 months, mostly within 10 to 13 months. However, about 68% of the loansâ&#x20AC;&#x2122; recovery period is twelve months. The figure 5.4 shows the payback period of different loans in the study areas. The payback rules vary from one MFI to another. On the basis of responses from the borrowers, it seems that some MFIs are strict and some are flexible. Additionally, one MFI plays different attitude towards borrowers in different working areas. However, in the study areas, out of 2499 respondents, about 80% replied that the payment rules are strict and the remaining told that it was flexible. Again, about 75% borrowers in Dhaka city feel the rules are strict whereas, the percentage is 85% in outside. The study shows that most of the big MFIs such as BURO, ASA, are strict in loan reimbursement. However, among the big MFIs Proshika and BRAC are comparatively flexible, in the sense that they are willing to consider special problems faced by the borrowers.

5.3 CHALLENGES However, according to the responses, 61% of the borrowers do not face any challenge in loan reimbursement but 39% face hardship. Those who are facing hardships

either have irregular incomes or face strict MFI rules or are burdened by the nature of installment (e.g daily, weekly,) and payback period. Figure 5.5 shows the factors which contribute to loan default and their percentages. Figure 5.5: Challenges to pay back

time constraints 6% strict rules 20% nothing 61%

irregular income 13%

5.4 PURPOSES OF TAKING LOANS People take loans for a number of purposes. The reasons may be, to invest in income generating activities, to make ornaments/furniture for houses, to give dowry at the time of a daughterâ&#x20AC;&#x2122;s marriage, to pay for medical treatment, or even to buy food, but, they have been separated into some categories. However, out of a total of 2593 respondents in the study areas, 1713 did not hesitate to tell the specific purpose of their loans, others remained reluctant to answer. Table 5.6: Purpose of the present loan ---------------------------------------------------------------------------Prpos_Presnt_Ln |

Freq.

Percent

Cum.

----------------------------------------+----------------------------------Missing data |

876

33.81

Invested in existing business |

1,022

39.44

33.81 73.25

Invested in medical treatment |

1.54

74.80 84.25

Invested in new venture |

245

9.46

Invested in non-income generating activ |

164

6.33

90.58

Others |

244

9.42

100.00

----------------------------------------+----------------------------------Total |

2,591

100.00

---------------------------------------------------------------------------Source: Field Survey, 2009

According to the responses, 39.44% of the borrowers used it in their existing businesses. Other usage of the loans are: 1) investment in new business; 2) medical treatment; 3) non-income generating activities, e.g. to buy ornament, furniture; and 4)

others, include for going abroad, spending on daughterâ&#x20AC;&#x2122;s marriage, etc. Table 5.4 shows that a significant percentage of loans has been used for the investment in new venture.

5.6 INFORMAL GUARANTEE REQUIRED BY THE MFIS Usually, the MFIs require no legal document for loan disbursement. But, most of them like the potential borrowers to have some informal assets, like home, existing businesses, etc., in most of the cases. In the study areas, about 26% of the borrowers have to have at least one of such guarantee for receiving loans, the other 74% did not need to show any guarantee. Table 5.7: guarantee presented by the borrowers --------------------------------------------------Grntee_Presnt_Ln | Freq. Percent Cum. ---------------------+----------------------------------. | 98 3.78 3.78 Business |

344

13.27

17.05

Home Asset |

791

30.52

47.57 73.80

Nothing |

680

26.23

Permanent Resident |

217

8.37

82.18

Savings |

1.97

84.14

Through other people |

411

15.86

100.00

---------------------+----------------------------------Total | 2,592 100.00 --------------------------------------------------------Source: Field Survey, 2009

Of those who showed a guarantee, 12% of them are permanent residents, and an additional 43% owned home assets. About 19% had an existing business. The remaining others are those who had nothing to present but a leader of the group or someone reliable to the MFIs who helped getting the loan--but, it should be noticed that they had to deposit weekly or daily until a certain amount had been raised. Figure 5.6 shows the percentage of borrowers who showed guarantee to the lender. Most of the borrowers living outside Dhaka got loans for their permanent residence and home assets, whereas, in Dhaka, for existing businesses.

Figure 5.6: guarantees presented by the borrowers

introduced by other people 23%

business 19%

savings with MFIs 3% residency 12%

home 43%

There are two types to lending: lending by going door to door, and lending through group. In lending through group, membership is given on the recommendation of other member, where all members share responsibility for any kind of irregularity, i.e. if a member be unable to pay an installment, others help manage it, or even if any member flees, others held responsibility to reimburse his/her amount given by the MFI. 5.7 REASONS FOR LEAVING MFI Almost 80% HHs (according to the respondents in the study areas) of the poor communities are attached [diff from being a member?]with MFIs. They are people who are involved with MFIs for a long time. On the other hand, they also leave MFIs for a number of reasons. Some accuse those in charge of the MFIâ&#x20AC;&#x2122;s of irregularities and some feel their own fault. From those who gave a reason for leaving the MFI, we have the following table , which illustrates the reasons for which people leave the MFIs. Table 5.8: Reasons for which people leave MFIs ----------------------------------------------------------------------Rsns_Leave_MFI | Freq. Percent Cum. -------------------------------------+----------------------------------. | 1 0.04 0.04 Don't know | 1,198 46.22 46.26 For additional service charge of MFI | 23 0.89 47.15 Irregular income to pay installment | 579 22.34 69.48 Loss in business | 161 6.21 75.69 Migration | 154 5.94 81.64 No need | 213 8.22 89.85 Other reasons | 23 0.89 90.74 Strict rules of MFI | 240 9.26 100.00 -------------------------------------+----------------------------------Total | 2,592 100.00 ------------------------------------------------------------------------Source: Field Survey, 2009

According to the respondents, majority leave while their income becomes irregular. This causes them to feel the MFIsâ&#x20AC;&#x2122; regulation or repayment system to be very

strict. Consequently, they feel forced to cut off their membership. On the other hand, a significant portion of the respondents said that when the peopleâ&#x20AC;&#x2122;s businesses failed, their incomes were drastically reduced, and hence they left the MFIs. However, a portion of the respondents said that after taking the loan, their economic condition improved and thus caused them to leave. On the other hand, some people complain that MFIs realize additional service charges for different headings. Therefore, all the factors require much attention to the policy makers, the government and the MFIs, so that optimal trade off between borrowers and MFIs is ensured.

CHAPTER SIX

INVESTMENT AND ENTERPRISES

6.1 ENTERPRISES AND INVESTMENT In the study areas, about 40% of the people are involved in SME. Most of them are doing their businesses using a very small amount of investment which starts at Tk. 500; the average amount of investment is Tk. 21,150, 90% of the investorsâ&#x20AC;&#x2122; amount is limited up to Tk. 82,000. The largest 5% of investment goes beyond Tk. 170,000 or more. Figure 6.1 shows the cumulative percentage of investment.

investment .4 .6

Figure 6.1: Cumulative percentage of investment (Tk.)

1000000

2000000

3000000

* ninety percent investment is below or equal to Tk. 82,000

A variety of different businesses are conducted by the MFI members. They range from recycling scrap-materials to restaurants. The amount of investments differs among businesses as well as in cities. However, some businesses are common to all the regions; the average investment has been shown in the following table 6.1. Depending on the business, the amount of investment varies. The most favorite small businesses are: petty shop keeping, fish selling, vegetable selling, cloth/shari business, and tea stalls. Among them, investment in cloth/shari is the highest, about Tk. 30,000 and, in petty shop keeping is about Tk. 25,000. Several lie on around Tk. 20,000 but the investment in tea stalls which cost only 13,000 on average. Details of average investment have been shown in Appendix 6.

The capital investment in the businesses is almost inversely related to the loan sizes. It seems that those who are taking big loans are investing small amount in the enterprises. The figure 6.2 illustrates the clear relationship between loan sizes and different amount of investment. It may be occurred because enterpreneures of small businesses started getting microcredit since long time ago---now they are getting more than those who started recently. Figure 6.2: Relation between loan size and amount of investment

5000 4000 3000 2000

amount of investment (Tk.)

6000

(downward sloping curve)

5000

10000

15000 20000 amount of loans (Tk.)

25000

* loan size is less or equal to Tk. 30,000 & amount of investment is less or equal to 80,000 have been considersed as they cover 90% of the borrowers

But, there are some businesses like fish selling, furniture business, ornament making (gold smith), hotel business, making shoes, pan-biri business, petty shop keeping, tea stall, vegetable business, where the average investment is less than Tk. 8,000. The borrowers of these businesses invested the total amount whichever they took from the MFIs. The owners of the rest of the others businesses invested less amount than they

borrowed. On the other hand, the borrowers of the big amount of loans used it for purposes other than the business itself. Nonetheless, those who invested more higher amounts than what they borrowed from the MFIs, collected money from different sources such as neighbors, relatives, professional money lender, bank loan, and personal savings (Figure 4.8). On the other hand, those who invested less than the borrowed amount invested in other sectors like medical treatment, non-income generating fields, e.g. furniture, ornaments, incidental expenses, etc. Although more than 95% borrowers are female, very few of them are using the loans. In the study areas, only about 30% of the borrowers themselves use the loans for their purposes. The other loans users are family members. The percentages of loans users have been shown in the figure 6.2. The principal loan users are the husbands of the borrowers and the sons. However, other than the borrowers, very insignificant percentage of users is are female users comprise of a very insignificant percentage. Figure 6.3: Loan users

other mother 2% 1%

son 5%

brother 1% father 1%

self 30%

husband 60%

6.2 SAVINGS The borrowers save a percentage of amount from their income. The average savings of the 90% borrowers is Tk. 2273. However, it varies from Tk. 10 to Tk. 50,000. Only two households (HHs) are found to have no savings at all. The lower 10% savings fall in the amount less than Tk. 563 and the upper 10% go above Tk. 7400.

2000

4000

6000

8000

Figure 6.4: Savings on households' income

5000

10000

15000

20000

25000

income Fitted values

saving

* ninety percent household's income & savings have been considered

However, the figure 6.4 depicts the savings against income of the HHs. It tells us that savings have been increasinged as the HHsâ&#x20AC;&#x2122; income increaseds; at least, it is true for the income, up to Tk. 18,000. Thereaftern, with furtherthe increase of income, savings have the propensity to decline.

6.3 INSURANCE Figure 6.5: Knowledge of insurance

know well 45%

know little 40%

not at all 15%

More than fifty percent of the borrowers do not have propergood knowledge on insurance. The remaining others have a better understanding of insurance, especially, on the health and life insurances, but, all do notnone have anythe insurance. Figure 6.5 shows the percentage of people regarding their knowledge about insurance. Only 32% of the borrowers are found to have insurance either with the MFIs or some insurance organizations operating in the market. The main MFIs providing insurance are: ASA, 87

BURO, Sajida, Shakti, Proshika, TMSS and BRAC; all are big and old MFIs . Among the MFIs, ASA covers the most. A detailed list of organizations proving insurance is shown in Appendix: 6 (III). The borrowers were asked about which financial product helps most:: loan, insurance or savings. In response to the question, about 82% of the borrowers said that loans helped the most when compared to the other two. The others 17% were in favor of savings. On the other hand, while they were asked about which one might help in the future, about 65% were in favor of savings and only 14% advocated for loans.

6.4 SUCCESS Of Tthose who took loans for different purposes from the MFIs, 50% of them are successful because they could use properly the amount properly. The other 10% of borrowers are partially successfulâ&#x20AC;&#x201D; as they could not spend whatever they wanted. The remaining 40% borrowers are found to be using the loan for income smoothing rather than income generation. The borrowers, who were successful, were asked about the reasons behind their success. The Majority (about 60%) of them said that they invested big amounts in their businesses and the loans acted as the catalyst in the investment. The other factors which they perceived as the influencing factors are: sincerity of the borrowers, usage in reasonable fields, cautious to spend only on non-income generation, etc. On the other hand, those who were unsuccessful could identify the following reasons for failure: the inadequate amount of loans, spending on unsustainable fields of investment, loss of business, etc. The fact that loans were to be of inadequate amount probably is the obverse of the fact that overlapping was, on the average, profitable.

6.5 WHETHER THE BORROWERS ARE CAPABLE TO HANDLE BIG AMOUNT The poor HHs often suffer a lot from lack of capital. In the study areas, about 72.9% HHs are capable to operate more amount of loan but they do not get the money as they require. In case of HHs located in B.Baria, highest percentage (85%) seeks more amounts but in Sylhet, the scenario is oppositeâ&#x20AC;&#x201D;only 33% HHS claimed that they are capable to operate more amounts.

The amount they can operate varies from Tk. 3,000 to 1 crore. The average amount required to operate is about Tk. 78,070; if loans above 100,000 are eliminated, then the average estimated amount will be Tk. 38,305. 43. Table 6.2 shows the average loan size and required loans in the study areas. Table 6.2: Average loan size and required loan City

Average loan size

Average amount required

Barishal Bogra Brahmanbaria Chittagong Comilla Dhaka Dinajpur Khulna Kushtia Mymensingh Rajshahi Rangpur Sylhet

15742.86 17230.77 17728.81 19699.39 15782.61 22393.56 15179.49 11565.89 11453.13 13217.39 11558.82 14409.64 14627.91

45521.13 48579.71 42384.62 41852.95 42358.02 119746.31 46287.67 26021.74 27227.27 23090.91 24536.23 187910.45 25030.30

Source: Field Survey, 2009

Those who require larger amounts are also getting more amount of loan now. Amount between 100,000 to 500,000 are demanded in the divisional cities except Rajshahi. The average loan size is also bigger in those cities. There are a number of fields oin which the membersy want to invest the loanamount, and they believe that they are able to improve their financial condition, if the access to credit for larger amounts is ensured. Of those who require more amount, 18.5% wants to expand their existing businesses, 20.1% are interested in any type of business and a significant portion (3.5%) wants to establish small grocery shops. The Oother 3% wants to invest on building or expansion of home for secured earning and about 2.7% want to buy land for agricultureal activities, or vacant land to benefit from capital gains.

6.6 POTENTIAL OF SMALL BUSINESS ENTERPRISES The data reveals that about one-fifth (17.6%) of the HHs in the study areas ca notcannot find any potential small businesses yet. But, the remaining majority is hopeful about a number of small business enterprises. They are: embroidery/Boutique, beauty parlor, bakery, CD/VCD, AD, Cloth-Shari business, candle (manufacturing), egg business, phone/fax/flexi load, and so on (details can be seen in Appendix) and the type is diversified in big cities and also in communities, particularly in Dhaka. However, more

than one-tenth of the HHs are already doing a number of small businesses and trying to expand it; of some 311 HHs, 118 are not satisfied receiving the amount of the present loans, are seeking more amount of credit from MFIs. The average amount required for those businesses is Tk. 95,231, if the upper 10% is eliminated. Some businesses require lesser amounts than Tk.50,000, such as: selling cloth-shari, selling vegetables, wood business, selling, selling fish, grocery shop, rickshaw renting, rickshaw garage, tea stall, tailoring, cooking meals for mess memberspeople etc. All those require less amount but the borrowers hope to make an average profit of Tk. 8,009 per month. And if the capital is bigger (> Tk.50000- â&#x2030;¤ Tk.100,000), then people find interest in some additional businesses like building a housemaking home for rent, hotel/restaurant business, transport business (e.g. buying reconditioned cars for renting, or giving dawn-payment to buy the new one, etc.). Moreover, some find more fields forof investment in the construction of buildings, buying and selling land or properties, dairy farm business, going abroad, requires greater amount of capital (> Tk.100,000 to â&#x2030;¤ Tk.500,000). A number of factors may affect choosing the business types. However, education and skills presumably have greater impact on choosing a business. The data reveals that those who have no more than eight years of schooling prefer fish selling, tea stall, rickshaw pulling or renting, hotel business, wood business, etc. On the other hand, people with more than eight years of schooling, prefer to choose businesses in the transport sector, construction, etc. or they want to go abroad. Again, it varies community to community or district to district depending on availability of raw materials and marketing facilities of the products. For instances, Ad business, karchupi, garments business, leather business, etc. are found to be the popular potential small business enterprises, whereas fish selling and daily-newspapers selling are in Barishal. The table below shows some of the potential entrepreneurships, average required capital, and average monthly profit (estimated).

Table 6.3: Potential small business enterprises Potential small business enterprises Advertisement

business

Average capital requirement 100000

Average estimated profit/month 70000

Average estimated profit/Year 840000

Profit Rate per Year (%) 840.00

Average Person Requirement 5

Boutique/ Embroidery shop

69393

13075

156900

226.10

Bag business

48333

11000

132000

273.11

Bamboo business

60000

53500

642000

1070.00

Beauty parlor business

51666

10333

123996

240.00

Brick/sand business

60000

9333

111996

186.66

Business(Halim+Chotpoti)

80000

16000

192000

240.00

Buying machine for juice

20000

7000

84000

420.00

Buying rickshaw for renting

32257

10297

123564

383.06

Buying pushcart

17500

5000

60000

342.86

Buy van

15000

3125

37500

250.00

CD/ VCD shop

58750

9000

108000

183.83

Candle factory

100000

15000

180000

180.00

Cattle business

38600

8550

102600

265.80

sugarcane

Churi business

30000

3000

36000

120.00

Cloth /Shari business

48338

8963

107556

222.51

Contract bidding

100000

5000

60000

60.00

Cosmetics business

85000

13000

156000

183.53

Dairy farm

61428

6857

82284

133.95

Egg business

40000

10000

120000

300.00

Electric shop

70500

13300

159600

226.38

Fish selling

62105

13960

167520

269.74

Flexi load business

50000

5000

60000

120.00

Flower business

50000

10000

120000

240.00

Fruits business

47000

13725

164700

350.43

Furniture shop

66250

13500

162000

244.53

Garment business

62500

18750

225000

360.00

General store

55250

11666

139992

253.38

Generator business

60000

5000

60000

100.00

Gold refiner shop

60000

8000

96000

160.00

Grocery shop

62710

10783

129396

206.34

Hen selling

37500

9500

114000

304.00

Hotel business

62678

14111

169332

270.16

Ice business

50000

6000

72000

144.00

Investment in transport

72580

13096

157152

216.52

Rod/Iron/Tin shop

67500

12500

150000

222.22

Jewelry shop

100000

20000

240000

240.00

Jharu business

25000

12000

144000

576.00

Juice factory

100000

25000

300000

300.00

Karchupi business

50000

5000

60000

120.00

Land (broker) business

55555

6157

73884

132.99

Land lease

51153

8769

105228

205.71

Laundry shop

100000

12000

144000

144.00

Leather business

50000

40000

480000

960.00

Library shop

50000

5000

60000

120.00

Masrum business

50000

5000

60000

120.00

business

Potential small business enterprises Meal business

Average capital requirement 44000

Average estimated profit/month 8400

Average estimated profit/Year 100800

Profit Rate per Year (%) 229.09

Average Person Requirement 2

Meat business

65833

14500

174000

264.31

Motor parts business

73333

14166

169992

231.81

Nursery business

10000

70000

840000

8400.00

Oven factory

100000

20000

240000

240.00

Pan/Biri business

37500

8750

105000

280.00

Paper business

75000

8000

96000

128.00

Pharmacy

42500

7500

90000

211.76

Phohe/fax

68333

10611

127332

186.34

Pitha business

40000

6000

72000

180.00

Plastic business

64000

11200

134400

210.00

Poultry farm

69411

11000

132000

190.17

Raw material business

45400

7616

91392

201.30

Rice selling

61818

14272

171264

277.05

Rickshaw garage

65357

14142

169704

259.66

Rickshaw renting business

40484

12506

150072

370.69

Saloon business

48333

5416

64992

134.47

Scrap material business

73333

14333

171996

234.54

Shoe shop

83333

13333

159996

192.00

Tailoring shop

54285

11057

132684

244.42

Tea stall

36038

7442

89304

247.81

Tiler khaza business

20000

5000

60000

300.00

Vegetable business

45714

11857

142284

311.25

Want to go abroad

51100

10555

126660

247.87

Wood business

50500

24925

299100

592.28

Workshop

52500

8333

99996

190.47

From the above table it is observed that for all business in which people are interested to invest, the rate of per year profit is more than 100%. It seems that all the business will be run successfully. In Nursery Business the average required capital is 10000 Taka but according to respondent per year profit rate is highest (8400%) in this business. On the other hand from Nursery Business 3 to 4 persons can be employed. Besides Nursery Business, a high rate of profit will be earned from Bamboo Business, Leather Business, AD Business and the rates are 1070%, 960%, and 840% respectively. From the above table it is also evident that a small capital required for Buy van (15000 TK), Pushcart business (17500 Tk), Juice business (20000 Tk), Tiler Khaza business (20000 Tk), Jhaur business (25000 Tk), Churi business (30000 Tk). But from these businesses small profit will be earned and the range of per year profit rate is 200% to 400%. At the same time it is assumed that 1 person will be employed by the van business, pushcart business, Tiler khaza business, Tea stall and 2 persons will be employed by Juice business, Jharu business, Churi business etc. from the table another findings is found that a

large number of employment will be created through Hotel business, Poultry business, Garment, Juice factory, oven factory, Dairy farm etc. Because all these business will be required minimum 5 persons or more. So it is possible to create employment opportunities through these businesses. If a project initiates to increase employment opportunities through Nursery business, it is more profitable business in all these above business. So if this project invest in 50 Nursery businesses so it is possible to create 50 employment opportunities. On the other hand in another project which invests in Hotel or Poultry business may create more job opportunity. So if this project invests in 10 Hotels or 10 Poultry businesses, it is possible to create 50 employment opportunities. The range of profit rates is striking and is well above the interest rates changed by any MFI. Such high anticipated rates of profit- which were generally confirmed by interviews with established shops- show the existence of a significant gaf in the capital market which MF is meant to fill.

CHAPTER SEVEN

URBAN MICROFINANCE: IMPACT

7.1 INTRODUCTION There are lots of good things happening through microfinance across the world, but it is also true that, a lot of questions have been raised against its impact on borrowers. This study has tried to comprehend the microfinance impact on urban borrowers:,both monetary and non-monetary. And it should be noted here that, the changes of different aspects of the members, who became involved with MFIs in 2005 or earlier, have been taken into consideration for impact analysis. Additionally, the changes of non-members have been taken for comparability. A members’ socio-economic condition, both monetary and non-monetary, depends on a number of factors, and so, considering all these factors, selection of criteria for control was a difficult part of the field survey. However, some suitable criteria were picked up so that reasonable comparability among two groups can be drawn. They are: the same communities, income range, and occupation. Based on these three criteria, the enumerators collected data on non-members at the communities, where the number of members’ HHs was 8 or more, so that at least two non-members’ HHs could be selected from a single community. Finding comparable controls for our survey was one of the more challenging tasks we had to face. In talking to the communities we sampled we were struck with the scarcity of individuals who had never been members of an NGO [one has to be careful about the use of the common word ‘samity’ since in some areas we found that ‘samity’ meant a community organization. After what was almost a house to house query in some communities we did find about ten percent of the population to be never members. Since almost all HH are members it is very hard to distinguish between local and global effects. This is a point requiring careful consideration by any future scholars. In total, 1000 control HHs (40% of the sample) were selected for the study. The average HH size of the non-members is 4.19 and the ratio of male to female is 1.38. About 94% of the non-members’ HH are headed by male; the average age of HHH is 39.6 years and the average year of schooling is 7.75. However, the average length of years in a 94

community is 17 years. The average number of dependent members of a HH is 2.71; among them, adult dependents are 1.7

Occupation and income of non-member Like members, major portion of the non-borrower’s HHHs is involved in small businesses, other occupations remains at proportional rate. The table 7.1 below shows the details of the occupation of the non-members. Table 7.1: Occupation of the non-members HHH ----------------------------------------------------------Pry_Occ_HHH | Freq. Percent Cum. -----------------------+----------------------------------Agricultural labor | 4 0.40 0.40 Day labour | 25 2.50 2.90 Housewife | 26 2.60 5.49 Rickshaw puller | 67 6.69 12.19 Service | 176 17.58 29.77 Small Businessman | 436 43.56 73.33 Without any occupation | 30 3.00 76.32 others | 237 23.68 100.00 -----------------------+----------------------------------Total | 1,001 100.00

-------------------------------------------------------------------------------Source: Field Survey, 2009

The average income earners of a HH are 1.48 and the income is Tk. 9,431. However, very few of the incomes is less than Tk. 5000; more than a half of the HH’s income falls between Tk. 5,000 and Tk 10,000, and about one third of it lies in-between Tk. 10000 to Tk. 20,000. Only about 10% income is more than Tk. 20,000.

Land and assets of non-member The average homestead land of the non-members’ HH is 2.65 decimal in urban area, and 4.68 decimal at bari. On the other hand, the average agricultural land at bari is 16.8 decimal, but they do not hold a single amount of land in urban area. However, the average amount of total assets of the non-members is Tk. 565,933.

Housing of non-member The average number of rooms used by a non-merber’s HH is two. However, 39% of the HHs lives in their own houses and 49% live in rented houses, the average rent of the

house is Tk. 1,882. Others are living either on government land or others unclassified tenure. Moreover, 26% of them are living in pucca houses; among them, 50% houses are built on their own land. But, 13% are living in a miserable condition in kutcha/ jhupri houses. The remaining 60% or so of HHs live in semi-pucca houses, that are made of CI tin and brick, bamboo or soil.

7.2 MONETARY IMPACT

7.2.1 Income The members who became involved with MFIs in 2005 or earlier, 66.61% of them could improve their economic condition and 22.52% remained unchanged. But, the condition of about 11.50% HHsâ&#x20AC;&#x2122; deteriorated. However, 433 HHs in the study areas could improve their economic condition and at present, their average income is Tk. 12,035 per month. But four years ago, it was Tk. 8,211. So, the average income increment is Tk 3,824. On the other hand, of the 28 HHsâ&#x20AC;&#x2122; whose income declined, the average decrease is Tk. 2,704. City wise income increase and decrease is shown in Appendix:7. In most of the cities, there are mixed income variations: increased, unchanged and declined. Only in Comilla and Mymensingh, no HHâ&#x20AC;&#x2122;s income have decreased. Figure 7.1 plots the income variations of the HHs over the periods between before taking loans and now; the income increments have largely fluctuated from Tk. 10,000 to -10,000, particularly in the upper region. On the other hand, income of 72.45% non-members increased, 18.5% declined and 9% remained unchanged. The statistics shows that the average income increment of the non-members (66%of the HHs), having the same level of income (Tk. 3000- Tk. 20000) as the members in 2005 or earlier, is Tk. 3,640. Conversely, the average income decline of the non-members is Tk. 2,155.

-10000

-5000

5000

10000

Figure 7.1: Scatter plot of income variation (between 2005/earlier & 2009 )

5000

10000

15000 20000 HH income (present)

income difference

25000

fitted values

Both the member and non-member HHs in the study areas improved their economic condition. But mean income increment of MFI members is about Tk. 1,000 more than the non-members. And t-test of mean differences shows significant income increase of MFI-members compared to non-member. . ttest hh_incm_diff (income change of members) == var146 (non-members), unpaired unequal Two-sample t test with unequal variances -----------------------------------------------------------------------------Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------hh_inc~f | 2296 3074.583 285.6207 13685.97 2514.481 3634.684 var146 | 974 2133.214 149.9719 4680.47 1838.908 2427.519 ---------+-------------------------------------------------------------------combined | 3270 2794.187 205.5825 11756.01 2391.104 3197.271 ---------+-------------------------------------------------------------------diff | 941.3692 322.6 308.8431 1573.895 -----------------------------------------------------------------------------diff = mean(hh_incm_diff) - mean(var146) t = 2.9181 Ho: diff = 0 Satterthwaite's degrees of freedom = 3167.1 Ha: diff < 0 Pr(T < t) = 0.9982

Ha: diff != 0 Pr(|T| > |t|) = 0.0035

Ha: diff > 0 Pr(T > t) = 0.0018

Table 7.2: GDP Growth Rate & Inflation Rate (2006-2009)

Year 2006 – 07 2007 – 08 2008 – 09

GDP growth rate Current price Constant price 13.65 6.43 15.52 6.19 12.66 5.88

Inflation rate 7.22 9.93 6.66

The average income increments of the two groups are Tk. 3,074 and Tk. 2133 respectively. However, the average annual growth rate of income increment of the borrowers over the years is about 9.3% which is higher than both national growth and the

average inflation rate as well (please see table 7.2). Therefore, it is easy to say that the loans have made the members to become economically better off.

7.2.2 Nature of Income The income of the poor is frail in nature because the sources are very weak. In the study areas, at present, about 90.4% of the membersâ&#x20AC;&#x2122; income is regular, but before taking loans, it was 85.4%. So, the nature of income of 5% of the members shifted from irregular to regular after taking loan. Table 7.3 shows the comparative statistics of the nature of income between the members and non-members. By contrast,, the situation remained unchanged for almost all of the non-members; about 98.7% of the non-membersâ&#x20AC;&#x2122; income is regular and five years ago, it was 98.5%. this is significant to judge vulnerability of the poor and the benefit of MF. Table 7.3: Nature of income Present Nature Regular Irregular Total

Members Frequency

Before

Non-members Frequency %

Members Frequency

Non-members Frequency %

2345

90.4

986

98.5

2215

85.4

988

98.7

248

9.6

1.5

378

14.6

1.3

100

Source: Field Survey, 2009

There are some occupations e.g. pitha making, karchupi, shoe polish etc., mostly seasonal, which do not give guarantee of regular income to meet the cost of daily basic needs. These incomes condition termed as insecure in terms of income has been reduced by 56.4% of the members and 63.1% of the non-members, while it remains unchanged for 36.3% and 17.3% respectively. Table 7.4 represents the frequency and percentage of changes of the nature of income for both the members and non-members. Table 7.4: Seasonality of income

slightly reduced

Borrower Frequency 815

% 31.4

Non-borrower Frequency % 121 12.1

reduced

648

25.0

511

51.0

unchanged

942

36.3

173

17.3

slightly increased

3.1

4.1

increased

107

4.1

155

15.5

Total

2593

100.0

1001

100.0

Source: Field Survey, 2009

7.2.3 Assets The average assets value of the members, who got them involved with MFIs in 2005 or earlier, is Tk. 407,469 and it was Tk. 318,991 before taking loan (upper 10% and lower 10% values have been eliminated and rest of the assets fall between Tk. 20,000 to 1,700,000 ). Therefore, the average increment of assets value of the members is Tk. 88,478, at the annual growth rate of 5.5%. But out of 416 HHs that are found to be suitable for comparison between two periods, 210 HHs’ assets (80% of HHs whose assets value increased) increased on an average of Tk. 28,097 and it varies between Tk. 5,000 and Tk. 105,000. Yet again, 20 HHs’ assets (80% of HHs whose assets value declined) decreased on an average of Tk. 56,700. However, the other HHs’ assets remained unchanged. The figure 7.2 depicts the overall variation of assets value of the members with respect to the assets before taking loans.

-400000 -200000

200000

400000

Figure 7.2: Changes of assets value

500000 1000000 value of assets before taking loans Fitted values

1500000

variation in assets value

The changes of assets values scatter widely on the assets values of earlier period, particularly on up to Tk. 400,000. The fitted line indicates that the changes of assets are comparatively higher of those having more assets; however, changes are almost flat. The statistics reveals that the value of total assets of the members change mostly in the range of Tk. 2,00,000 or less. But the assets have been increased of members who had assets amounted up to Tk. 50,000 (31.4% members had the assets in this range). On the other hand, considering the selection criteria of the members for impact, the average income increment of non-borrower’s HHs, located in the same communities, is 99

Tk. 178,249, which is more than double compared to members. Therefore, if the data were reliable and consistent, we might infer that microfinance might have no or negative impact on urban members in those selected communities. For the reasons described at length (please see the assets of the borrowers in Appendix: 1), we cannot use the data on assets for such comparative purposes.

7.2.4 Impact on Children Figure 7.5 shows the average expenditure of the households (HHs) on children. The members’ expenditure is always higher on food, education and healthcare. But, nonmembers spend more on children communication Figure 7.5: Comparative expenses (Tk.) on children

food, 1390

education, 1091 food, 1032 education, 866

healthcare, 573

communication, 340

borrowers

communication, 374 healthcare, 333

non-borrowers

In the study areas, about 22% of the borrower’s HH do not have dependent children---they neither have child nor spend for the purpose. On the other hand about 39% of the non-borrower’s HH have no dependent children. But, for those who have dependents, the average number is 1.74 for members, and 1.65 for non-members, however, it ranges between 1 and 6. The table 7.3 shows both the members and nonmembers’ expenditure on education, communication, health, and food.

100

Table 7.5: Changes of children expenditure Education Impact Slightly increased Increased Unchanged Decreased missing

Borrower 32 (15.53%) 70 (33.98%) 39 (18.93%) 7 (3.40%) 57 (27.67)

Nonborrower 46 (27.06%) 92 (54.12%) 9 (5.29%) 4 (2.35%) 19 (11.18)

206

170

Total

Communication NonBorrower borrower 12 14 (5.83%) (8.24%) 13 11 (6.31%) (6.47%) 19 16 (19.22%) (9.41%) 4 (1.94%) 158 129 (76.70) (75.88) 206

170

Health Borrower 21 (10.19%) 32 (15.53%) 57 (27.67%) 4 (1.94%) 91 (44.17) 206

Food

Nonborrower 31 (18.24%) 20 (11.76%) 18 (10.59%) 2 (1.18%) 99 (58.24)

Borrower 22 (10.68%) 69 (33.50%) 46 (22.33%) 2 (.97%) 67 (32.52)

Nonborrower 40 (23.53%) 71 (41.76%) 12 (7.06%) 2 (1.18%) 45 (26.47)

170

206

170

Source: Field Survey, 2009

7.2.4.1 Education About half of the members’ expenditure on children’ education has increased, but this percentage is lower than the non-members.

On the other hand, expenditure

declined of more members; moreover, a significant portion of HHs’ expenditure remained unchanged. The figure 7.6 shows the percentage of HHs of both groups whose expenditure increased.

7.2.4.2 Communication The communication expenditure of 81.1% members and 74.8% non-members is zero. They neither have child nor spend for the purpose. However, out of those HHs who are spending on communication, about 19.22% members and 9.41% non-members’ expenditure remains unchanged. Expenditure has been decreased of few HHs of both groups. On the other hand, 5.83% members and 8.24% non-members’ expenditure has to some extent been increased, while 6.31% members and 6.47% non-members’ expenditure has been increased notably.

101

Figure 7.6: Percentage of HHs whose expenditure inceased education 54.12

food 41.76 education 33.98 food 33.5

healthcare 15.53 communication 6.31

borrower

healthcare 11.76 communication 6.47

non-borrower

7.2.4.3 Healthcare About 56.1% of the members and 70.1% of the non-members don’t spend at all on healthcare of their children. The average expenditure of the remaining HHs is Tk. 883 and Tk. 357 respectively. Expenditure remained unchanged of 27.67% members and 10.59% non-members. Very few HHs’ expenditure has decreased. And it has slightly increased for a major portion of both groups but non-members’ percentage is higher than the members.

7.2.4.4 Daily Food Intake About fifty percent of the HHs of both groups has been found to spend nothing on children food; they neither have child nor spend separately for them. It seems that they are living under the poverty all the year round which does not allow them to feed their child. However, the average expenditure on food of members is Tk. 1,205 and of non-members is 971. Expenditure remained unchanged of 22.33% of the members and 7.06% of the nonmembers. It has been declined of very few HHs of both members and non-members. But a significant percentage of HHs’ expenditure has been increased. However, about 20% more of non-members’ HHs raised their expenditure on food. The figure 7.6 gives an idea about the comparative rise of the expenditures.

102

7.3 NON-MONETARY IMPACT The study did find a significant monetary impact of microfinance on its clients .Next we study the non-monetary impact, without which, the study might be substantially incomplete. Hence, changes in both the materials and non-material aspects of the members have been tried to find out as the means to comprehend the indirect effects on membersâ&#x20AC;&#x2122; livelihood.

7.3.1 Housing Materials Housing is one of the fundamental rights of the people of Bangladesh. However, improvement of financial condition of the HHs usually has effect on housing. So, what happed over the last four years in improvement of housing materials? Let see the table 7.6. Table 7.6: Improvement of housing materials

Slightly Improved

Improvement of housing material Members Non-members frequency % frequency % 68 3.25 20 2.0

Improved

3.5

8.3

Unchanged

2422

91.13

875

87.4

Deteriorated

2.0

2.3

1001

100.0

Others

.13

Total

2593

100.0

Source: Field Survey, 2009

The table illustrates that housing materials remained unchanged for about 90% of both the members and non-members. Very small percentages of HHs of both groups could improve their housing materials; comparatively more non-membersâ&#x20AC;&#x2122; housing materials were improved. The remaining othersâ&#x20AC;&#x2122; condition deteriorated.

7.3.2 Healthcare Facility The table 7.7 illustrates the changes in healthcare facility received both by members and non-members. The received services of almost whole percentage of members and non-members remained unchanged. However, only 2.63% of the members and .4% of the non-members could receive better services than the earlier.

103

Table 7.7:

Improvement of healthcare facility received

Impact Slightly Improved

Improvement of healthcare facility Members Non-members frequency % frequency % 23 1.13 -

Improved

1.5

Unchanged

2537

97.0

995

99.4

Deteriorated

.63

Total

2593

100.0

1001

100.0

Source: Field Survey, 2009

It would be useful if it could be separated by those MFIâ&#x20AC;&#x2122;s offering healthcare, but data are not available.

7.3.3 Sanitation Condition Sanitation condition also remained unchanged of majority of the HHs of both groups; about 97.75% of the members and 93.3% of the non-members could not improve their sanitation condition. Additionally, .25% and .8% of the respective groupâ&#x20AC;&#x2122;s condition deteriorated. Only 1.25% members and 4.5% non-members could improve their condition. The figure 7.3 shows the comparative statistics of the sanitation condition. Figure 7.3: Changes of sanitation condition borrowers b 93.3

percentage

97.75

0.75

1.4

slightly Improved

1.25

4.5

improved

0.25 unchanged

0.8

deteriorated

7.3.4 Sources of Drinking Water Here is no significant improvement of drinking water sources of the members. Those HHs of both groups could improve their sources, among them the percentage of non-members is three times greater than the members. However, about 98% of the 104

members and 93.8% of the non-members’ sources of drinking water remains unchanged. Figure 7.4 show that the situation of non-members is better compared to the members. Figure 7.4: Changes of drinking water sources 120

borrowers non-borrowers 98

100

93.8

percentage

20 1.4

1.1

0.2

4.7

0.5

0.4

0 Slightly Improved

Improved

Unchanged

Deteriorated

7.3.5 Utility Services After involvement with MFI since 2005 or earlier, majority of the members couldn’t improve the utility services like gas, electricity which remain unchanged. Very few HHs of the members could connect themselves with the services which is about two times less than the non-members. Table 7.8 shows the comparative statistics. Table.7.8: Improvement in utility services

Impact Slightly Improved

Improvement of utility services Members Non-members frequency % frequency % 41 1.6 18 1.8

Improved

2.9

7.5

Unchanged

2468

95.2

903

90.2

Deteriorated

Other

Total

2593

100.0

1001

100.0

Source: Field Survey, 2009

7.3.6 Daily Food Intake and Clothing The daily food intake about of 97% members and 99% of no-members’ remains unchanged, while 2.6% members and .6% non-members’ condition improved. It might be true that, the non-members’ daily food intake was an improved one for since many years,

105

so the condition remains unchanged. However, only .3% of the membersâ&#x20AC;&#x2122; condition deteriorated. Clothing situation remains unchanged of 97.1% of the members and 90.2% of the non-members. The situation of few HHs deteriorated in both groups. However, improvement of non-members is four times higher than the members. Table 7.9: Improvement of clothing

Impact Slightly Improved Improved

Improvement of clothing Members Non-members frequency % frequency 25

1.0

% 1.8

1.5

7.5

Unchanged

2518

97.1

903

90.2

Deteriorated

Others Total

2593

100.0

1001

100.0

Source: Field Survey, 2009

7.3.7 Women empowerment Bringing women in to the mainstream of development has become an important issue. To understand the situation, their involvement in daily activities needs to be known and it is thought that to become empowered, some factors must be ensured. Three tangible factors have been considered to be important to make a woman empowered. These factors are: their role in decision making, whether expenditure and mobility increased. The changes of empowerment have been considered here in between before and after taking the loan. While all three factors have been changed positively, then it is assumed that the woman has been empowered, if any one or two factors are seen to be changed, then they said to be empowered little.

106

Fgure 7.7: Change in women empowerment

empowered 8%

unchanged 89%

empowered little 3%

Statistical data shows that only few women have been empowered—8.0% women became empowered and additional 3.0% empowered little. The rest of the women’s condition remains unchanged. The figure shows the overall condition of the country. In Chittagong, Sylhet, Mymensingh, Comilla, Bogra, no women were found to be empowered. The exception is Rajshahi—33.77% women have been empowered followed by Dinajpur and Rangpur by 19% and 17% respectively. A skeptic might say that this occurred because: firstly, the women already were empowered before taking loan; and secondly, the situation has not been changed by the MF. Appendix:7 shows the detail statistics of the members’ condition before taking loans. More women in Comilla, Brahmanbaria, Chittagong were empowered rather other districts of the country. On the other hand very few women were empowered in Barishal, Bogra and Rangpur, and none in Khulna and Kushtia.

7.3.8 Awareness of Education There is credo widely held that education is the key to success. But poverty stricken people are neither sufficiently aware to send their children to school nor does their poor financial condition does not allow them to do so. The data reveal that 45.5% of the HHs became aware in this regard after taking loans and remaining hhs’ condition remains unchanged or decreased little. About 64.78% hhs in Barishal became aware to send their children to school which is the highest percentage among the districts. In Comilla and B,Baria, there is no change of the state of awarenes. The situation has even deteriorated in

107

some districs: Dhaka, Chittagong, Barishal, Khulna and Kushtia—among the four districts, the worst condition is in Chittagong, where the 4.54% HHHs’s awareness of 4.5% HH deteriorated.

7.3.9 State of Happiness Happiness studies are now a standard part of the literature in developed countries and it was thought useful to make a beginning in Bangladesh. The state of happiness varies from person to person depending on a number of aspects of the members in the study areas: assets, occupation income, education, social environment, etc. In the case of overall statement of happiness of the MF members, it can be said from the data that the percentage of happy people is high compared to unhappy. In the study area, about 38.08% HHs are happy and another 44.41% are moderately happy. On the other hand, about 3% are either unhappy or moderately unhappy. The remaining 14.51% hhs are neither happy nor unhappy in their respective situation. However, in Chittagong, Sylhet, Brahmanbaria, Mymensingh, and Comilla area above 90% HH are happy. Interestingly, in Sylhet no unhappy HH is found. In the case of Bogra, the percentage is the lowest, 71%. The picture of 13 districts can be found in Appendix:7. It is fact that the households earning less or equal to Tk 10,000, are less “happy”, the percentage varies from 24% to 31.25%, compared to higher income groups whose percentages varies between 44 and 72. The people of the lower income groups tell that they are “neither happy nor unhappy” but people having income less than 2000, more than 6% of them are “unhappy” whereas a small fraction of the higher income groups are unhappy. Interestingly, more non-members are happy than the members. According to the respondents, about 61% non-members are happy, and further about 28% are moderately happy. The remainders are either moderately unhappy or completely unhappy.

7.3.10 Hopefulness Questions about hope have not been introduced in the literature but seem relevant for assessing the psychological impact of MF. The MF members are asked about changes in their hopefulness before and after taking the loans. According to their responses, half of

108

the households’ hope has been increased—they are more hopeful now than before taking loans. Other two-fifth of them remains unchanged and very few (6.8%) became hopeless. This seems to be the overall condition of the members across the country. The figure below depicts the percentage of state of members’ hope in the study areas. Figure 7.8: Change in hope

became hopeless 4%

unchanged 40%

declined little 3%

increased 30%

increased little 23%

But hopefulness varies from place to place and man to man—the ways of surviving with hardships and coping up capabilities are different. Additionally, it also depends on other means to overcome hardship available in the areas: opportunities of earning to secured necessary daily needs, infrastructure facilities, availability of tools1 in hand to reduce or resist vulnerability, access to necessary credit, etc. The appendix: 7 shows the state of hope in different cities in the study areas. The data reveals that members’ hope has been increased a lot in Barishal, Bogra, Mymensingh, Dhaka, and Kushtia at the rate of 72%, 69%, 67%, 60.13%, and 58%, respectively. On the other hand, it remains unchanged in most of the cities but largely in Chittigong, Comilla, Rangpur and Rajshahi at the rate of 84%, 60%, 46% and 41.72%, respectively. Very few hh’s hope has been decreased in the areas under the study.

Monetary tools e.g. savings, secured income, assets etc. & Non- monetary e.g. strength, occupation, skills, education, etc. mean to resist vulnerability in the emergency period.

109

7.4 CONCLUSION Significant impact has been found on HHâ&#x20AC;&#x2122;s income change of the MFI members. And insignificant impact also has been found on businesses, improvement of sanitation condition, hopefulness, and the state of happiness. Indeed, no significant impact has been fount on other condition of the MFI members. The length of MF involvement, the frequency of loans borrowed or the amount of loans received have no significant impact on assets, expenditure on children, awareness of education, and improvement of housing materials, healthcare services, sources of drinking water, utility services, clothing and food.

110

Part 4: POTENTIAL AND CONCLUSION

111

POTENTIAL

The final task of this initial report on urban MF was to explore how MFI’s can add value in the future. This can happen either by doing something better, looking for more venues within Bangladesh or getting ideas from abroad and domesticating them. It is important to remind ourselves that MF has gotten to its present status by quietly refusing to accept the status quo and by being willing to experiment. It is in this spirit, and after extensive consulatations with knowledgable people that the following suggestions are proposed. In looking at new directions for MF to operate in BD, we can break down the issues into several parts. 1. Deficiencies or gaps in Capital Markets 2. Managing Infrastructure and Housing 3. Coordination among MFI’s We begin by looking at missing capital markets. We see extensive examples of such gaps in the capital market by looking at the expected profit rates compiled earlier; virtually all were above 150-200%. Compare this with the highest MFI interest rates never more than 40% in annualized terms and we see how there is much scope for capital markets to increase output,productivity and employment. Rather than make general comments which may not lead to action we suggest that four specific areas be explored by MFI’s; · Rickshaws for the pullers---creating an independent working class · Education loans with the parents of matriculating children as security · Nurses: for female empowerment, for male liberation, for domestic needs and for export of skills · NRB’s: importing the Vittana and Kiva models focused upon NRB’s

112

1. RICKSHAWS

MF is meant to fill in the gaps that exist in capital markets. Since these gaps are most prevalent for the poor, such activity automatically becomes a part of poverty alleviation. So MF activities can be broken down into two parts; first, identifying failures in the capital market, and second, devising ways to overcome such failures. One area where capital market failures are evident is the market for rickshaws---and, by extension, that for vans, thela garis and even that for ‘baby’ taxis. My experience began in 1978 when I did a study of the Rickshaw industry for the BIDS and I have kept up a continuing interest in them. Last year I wrote an article for the NEW AGE which encapsulates all my principal arguments and I will borrow heavily from it below. What can be done to help rickshaw wallahs under present circumstances? In Dhaka, an old rickshaw costs about 7000 and a new one about 12000. If we calculate a joma of taka 70 a day for an old rickshaw, or of 2100 per month, then an old rickshaw can be bought with about four months joma; a license costs about 15000, or another 7 months joma. In one year or so, a rickshaw can belong completely to the rickshaw wallah. Even an old rickshaw will last for three years. So the second and third year consist of pure profit. But this is too exacting a calculation. The cost of a license should be figured differently, since a license costs nothing to keep and its value keeps on increasing. It is an appreciating fixed cost, but not a sunk cost. In other words it is an item that is very appropriate for an MFI to pick up. So the most effective scheme will have an NGO buy the license and ‘rent’ it out to a rickshaw wallah. This means that an old rickshaw can be completely purchased in four months. A poor man thus saves an extra 100 taka per day or rather gains Taka 3000 per month---just by overcoming a capital market flaw. As we total the cost of such a scheme, we note that while rickshaws cost about the same all over BD, the cost of a license in Dinajpur is only 1000. So a total cost of some 7000 in Dinajpur will suffice to set up an independent rickshaw. It’s not the money. What is it that holds back the large scale success of micro-credit in the rickshaw industry? After many years of truly backbreaking labor, a rickshaw wallah has no health benefits and no pension. A car driver for Parjatan, by contrast, whose last salary is 8000 taka, gets a pension after twenty five years of about 14 lakhs. No wonder Government jobs are so

113

prized. Ordinary citizens will be unable to move without the rickshaws ---they provide employment and they are clean and efficient for short distances. It is in everyone’s interest to have an environmentally clean and domestic resource based source of employment and transportation. But we must plan our housing and towns and cities around the constraints posed by using such vehicles. Why should we worry about this particular category of working poor? Have you seen a healthy rickshaw wallah? Why do those who work so hard look so weak and emaciated? For the last eight months I have been looking for the healthy ones. They are not all thin. I have been driven by two healthy rickshaw wallahs and I saw a third. But I have been on a hundred rickshaw rides and probably seen a thousand of them---so it seems that less than 1% are healthy. I asked students and researchers to find me some healthy ones and give me photos. Some of them were surprised that I even expected the rickshaw wallah’s to be healthy. I first studied the Rickshaw industry some 30 years ago. BD has gone from 3% to 6% growth, but how have the rickshaws fared in this time? What do we learn about BD economic growth by watching rickshaws? What will it take to find healthy rickshaw wallahs, and how does BD get there? There are a few who are healthy and their lives are instructive. One of them has driven a rickshaw for about 25 years. He saved up and bought one. He drives for only one bela or time-period each day. The rickshaw is stored in his home. Once he rented out his rickshaw when he was not driving. It soon got stolen. So now he just lets it sit idle. He has two children; the son works in a professional capacity in an office and the daughter runs a canteen in a hospital. ‘Go and visit her’, he proudly told me. By working only one bela he has saved his health and by investing in education he has ensured his family’s future. This forward looking approach---not going for the maximum he can make each day, but seeing to the long-run, is essential for sustainable economic growth. But it is always a gamble. Who knows what catastrophe tomorrow can bring? So saving and foresight involve faith in the future. How can we spread such a hopeful attitude? The ‘ordinary’ rickshaw wallah rents a rickshaw for either one or two ‘belas’---the morning or evening shifts. The great majority I talked to rented all day. They work till lunch, then nap for an hour or two and begin again around 4, before ending at about 8 or 9 pm. For a days rental they pay between 70 to 90 taka; after paying the rent they save about 250-300 taka per day. This would make for taka 7500 -9000 a month if they worked for all

114

thirty days. Fortunately, they do not. The recent pattern is somewhat as follows: they work for about twenty days, making about taka 5000-6000, of which they can save some 3000 to take home. Keeping the family at their village home, they save on many expenses, and they can look after parents and children. A simple, frugal life. But, what hopes light up such lives? Some of the rickshaw wallahs worked for garment industries. They found the rickshaw more paying and less exhausting---at least they have control over their lives. Not a happy thought when we consider that the RMG’s have now been the pride of BD for over twenty five years. If we polled the RMG workers, would they say they were in the same position as the rickshaw wallahs? Do the RMG workers have more permanent gains to show than the rickshaw wallahs? Some rickshaw wallahs worked as masons helpers. Of the three helpers, two of them left in disgust because the contractor refused to pay them their due. Another sad fact about everyday life. When a newcomer reaches Dhaka, he looks for someone from his ‘gram’, or failing that, from his district. The strength of local ties is strong with us. Relying on the assurance of someone from his gram, the new rickshaw wallah gets a rickshaw, finds a garage, and a mess in which to live and to eat. It is a simple, reliable system. I have heard of rickshaw wallahs who were cheated in business by their brothers, but not one reported of fellow villagers who refused to be their contact to help them get started. It is the same bond that leads the rickshaw wallahs to travel to Dinajpur every 20 days, or to Munshiganj every week, all depending upon the distance of their bari. It is what empties out Dhaka when Eid visits. All in all, a rickshaw wallah can be the owner in under a year; thereafter the rickshaw wallah becomes an independent owner-operator. What a world of difference it makes for ones dignity and self-respect. The few rickshaw wallahs I asked about this possibility said they could not raise the cash to buy a rickshaw. Given the great social benefit, this is a ripe market for micro-credit. Indeed, I was impressed with the number of NGO’s that began rickshaw buying programs in the 1980’s. Why have the NGO’s not been able to affect the structure of the rickshaw industry in Dhaka? Even today almost 95% of the rickshaws are owned by ‘mahajans’. Here there is great scope for cooperation, coordination and the enabling hand of Government. Imagine an industry dominated by owner operated rickshaws; in order to secure their futures the rickshaw wallahs will form

115

cooperatives, which in turn will arrange for health benefits, savings schemes and education for children.

116

2. EDUCATION LOANS AND NURSING The sytematic provision of education loans can be a new and effective activity for the MFI’s. Secondary Schools can be utilized as mediating with the Financial Institution where the guardians of students are the potential clients. The MFI need not send employees door to door to engage in transactions. Both in urban and rural areas such move seems to be viable for the following reasons: 1.management cost will be decreased because teachers and students may work as employees; 2. repayment of defaulter may be insured through the governing body or school teacher of the school; and 3. senior students may get some financial help giving service in management of the MF program. Ultimately, parents should be encouraged to send their children for secondary education. One specific area of education that deserves attention by MFI’s is that of Nursing. Bangladesh should have some 3 nurses for every doctor; instead it has about 1.5 doctors for every nurse. While the recent upgrading of nursing as a government post is welcome and the subsidized education provided by the Nursing colleges will go some way in curing the shortage, it is unlikely to be satisfactory on its own. According to the report of Bangladesh Health Watch titled “The State of Health in Bangladesh, 2007″, the country lacks at least 2.80 lakh nurses. The number of registered nurses is only 22,555 and the nurse to population ratio now stands at 1:6342, according to Bangladesh Nursing Council (BNC) reports. Since there is a valuable world market for nurses even now, and since the nurse shortage is likely to continue as a global phenomenon because of the ageing populations of Europe and Japan, the export of nurses is virtually a sure market into the indefinite future. To utilise such an opportunity, BD needs to produce qualified nurses in large numbers; this will both satisfy the domestic need and provide desirable export earnings. The most important reason for the low supply of nurses is unanimously agreed upon to be the lack of respect given to the profession. This is a question of social attitude and one that MFI’s are quite practised in addressing. As most nurses are female, the empowerment of females is involved. But we can go further, since there is no logical reason why males may not also train to be nurses. So the ‘liberation’ of both sexes can be effected by providing both social and financial help to nurses.

117

3. INFRASTRUCTURE AND HOUSING Infrastructure is an issue of central importance in providing a better life and in increasing productivity. One has only to see the headlines whenever gas or electricity or water is in short supply----and as for items like sanitation, there is considerable public benefit in ensuring that the slums are clean. Infrastructure is also an issue where both economies of scale and agglomeration economies are of significance, and large fixed costs are involved. The simple-minded approach would suggest that most of the fixed costs be borne by some body other than the local community, and that maintenance and operation be left in local hands. This solution requires local communities that see and appreciate the benefits of infrastructure and are sufficiently civic minded to organise themselves into permanent organisations which both operate and fund the infrastructure. In a practical sense, this is the crux of the problem, as we need to find modes of local organisation that can sustain local infrastructure. There is a further issue in the use of scarce land---the provision of appropriate housing. Studies show that there is much room for increasing housing in the slum areas with planning. We can have compact towns within cities---or urban habitation centers in the conceptual language proposed long ago by the finance minister--- and there are more complex psychological and sociological issues involved in the development of slums into compact towns. As infrastructure involves several items, such as water, power, sanitation, wastewater and solid waste, there are several issues involved in the provision of infrastructure. Who are the local bodies that can be entrusted with such services? Is a uniform organizational form required or can they be broken up by function, so that power remains in public control but waste management is farmed out to private entrepreneurs?[van Horen] India has been experimenting with a variety of

ways to

provide infrastructure to the poor and perhaps something can be learned from the experience there.[Tewari et al] Studies show that the provision of items such as water are subject to high levels of corruption and hence waste and inefficiency; studies also show that NGOâ&#x20AC;&#x2122;s tend to suffer less from these defects than Government organizations. [Davis] The extensive review of Davis tells us that there are no general lessons to be extracted from the experience of Public-Private partnerships thus far, and all seems to rest on the details of any arrangement. Taking it for granted that the public provision of infrastructure will be the 118

norm for say water and sanitation in the near future; we have to ask what local body can be entrusted to continue the operation and maintenance in sustainable form. There are two alternatives to the typical BD style of MF program, requiring lesser amounts of MFI involvement, called SHG’s and VSLA’s. The popularity of SHG’s is really an Indian contribution with small groups of less than 30members ,typically, women providing mutually supportive savings, loans and emergency financial support group. They frequently set up a small business such as basket making or a dairy. The SHG typically receives its initial form , encouragement, and skills from a NGO, and the NGO may add services, such as HIV/AIDS awareness, as needed. The SHG is linked with a bank where it must maintain an account for a minimum of six months before a SHG can become eligible for credit. The SHG members can use funds from the groups common fund upon terms decided on by the group itself. The following description is taken from the Case Studies 1. “First, the NGO promoter identifies potential clients and selects 5 to 30 women to form a SHG. The group members are generally residents of the same area and have an affinity of some kind, such as caste, occupation, or economic status. 2. The NGO then explains the benefits of becoming part of a SHG, and stresses the importance of ideas like savings, effective utilization of credit, training, and entrepreneurship. 3. Next, the SHG is recommended to a bank in which the group opens a joint account. In the case of Microsate, opening a bank account with Indian Bank is not compulsory. SHGs can open group accounts in any branch of any bank, as per the convenience of their members. 4. Before becoming eligible for receiving a loan, a SHG must save for a period of three to six months. 5. Loans are typically granted based on the rating of the SHG done by the bank manager, which is done on a 3-category 00-point scale. Groups become eligible for loan based on score achieved by the group on the said rating tool.”

119

Being a SHG member requires dedication, since a typical group saves money, decides upon loaning savings to its own members, keeps accounts, monitors activities and meets regularly to take decisions. This may be exhausting and is also time consuming for urban salaried workers. But it corresponds to a level of decentralized democracy that is admirable. An alternative to the SHG is to have an organization that is set up and functions independently of an NGO and is known as a VSLA or, in urban areas, as a USLA. The Char Livelihood Project is an instance and there is a second project in Jamalpur, now entering its second year. Both attempts appear to be successful. They have an even lower level of monitoring since a field officer is capable of running about 500-600 members. Such organizations seem to be running smoothly in other countries also, for example, Egypt [personal communication from Edward Abbey of PLAN]. The question is whether there is active need for a replication in BD? When asked such a question, two MFIâ&#x20AC;&#x2122;s responded that they had tried out such experiments but found that the groups soon lost both leadership and motivation. Such an opinion can also be tested by the following data from the CLP

Table 1: Savings deposits, return on savings and share-out Annualized Savings per Member (Tk.)

Annualized Return on Savings (%)

Projected Annualized Shareout per Member (Tk.)

GUK

518

658

MMS

762

1059

NDP

458

627

SKS

791

1020

RSDA

998

1547

Average

705

982

ATP Beneficiaries

Non-ATP Sajida foundation

931

1238

120

Table2: Loan size and Loan fund Utilization Average Outstanding Loan Size per Borrower (Tk.)

Percentage of Members with Active Loans(%)

Loan Fund Utilization Rate(%)

ATP Beneficiaries GUK

483

MMS

654

NDP

451

SKS

714

RSDA

727

Average

606

81 Non-ATP

Sajida foundation

735

The point of relevance to us is that the MFI, Sajida Foundation, does well on all counts and is in all but the very best in every category. The conclusion appears to be that low levels of monitoring for community organizations may work well, such as SHGâ&#x20AC;&#x2122;s in the culture of India, but do not seem to have any special advantages over the MFI directed activities in Bangladesh. However it is also true that Sajida would not have undertaken the VSLA without support from the donors.[personal communication]. Hence it seems that a final decision depends upon particular circumstances which direct the choice between community control, oversight and cost. In recent years various agencies, government organizations such as LGED are initiating a new program to improve the economic conditions of the poor through their own initiatives, planning and resource mobilization. The aim of these programs is to enhance the poor for saving generation as well as capital accumulation for productive investment or even to face various uncertainties. In these programs the community people plan the scheme, manage and monitor the whole programs but the agencies only act as a facilitator. The project has provided technical and financial support to the beneficiaries but the main works like improving their livelihoods are done by themselves. Under this scheme all the members of the community will save a small percentage of their income per week as their personal savings. After a certain period when a large amount of money will be accumulated then the community people can get loan from this fund with a small interest rate for various purposes. In micro financing the NGOs earn the interest rate but

121

here the community will earn the interest rate. The positive part of this program is to enhance local resources by community partnership. But there are some limitations in this system. It has been seen that after the completion of the project the system can not be run for a long time for various reasons. So it is a challenge for the project to make a self reliant community. Self reliant community is such kind of community where local people can mobilize their own resources for improving their social and economic conditions. Such programs however seem to fade away once a project ends. This raises the question --Why does the social development program, facilitated by the agency, not continue after termination of the project period? The answer seems to lie in the fact that when a program is implemented by an agency (e.g. LGED, UNDP, etc.) both financial and logistic supports are also provided. These contributions appear to be crucial. The agency recruits a facilitator who is deployed to run the program for the specific duration of the project. So s/he usually does not stay over his/her period of services. After the period, no one is responsible of its future/maintenance. For instance, if a CBO is formed for solid waste management, facilitated by LGED in cooperation with Paurashova, LGED provides a Community Mobilization Expert for the specific duration, say for five years. It also provides a number of logistic supports (e.g. Vans and other necessary tools) for the purpose. But when project period is over, nobody takes up the responsibility. The following measures may be taken to create a self reliant community. •

It is important to enhance the leadership characteristics among the community people.

•

To enhance mutual trust among the community people.

•

To increase the awareness among the community people about the benefit of the projects.

•

The system should be registered as co-operative under the ministry of Social Welfare so that the members will be complied to follow the rules and regulations properly.

•

After completion of the project the agencies can recruit someone from the community to monitor the project.

122

These requirements seem to be most naturally filled by NGO’s/MFI’s

and

constitute a direction for urban MFI’s to consider. While many urban activities point towards the upper tranches of laons, and hence towards Microenterprises, the contribution that MFI’s can make towards poverty amelioration by supporting infrastructure is considerable. This is particularly so in encouraging longer run investments in both physical and human capital by altering ‘impatience’ through improving public health and education. Housing is one item that is not properly infrastructural, and yet since all infrastructural items have to be ‘housed’ in an abode, housing is intimately linked with the long term provision of infrastructural services. We see that x amount is GoB land in the slums and a further y amount is private. Let us look ahead 30 years and ask, how will these slums grow, what can be done to make them safer and more productive? Once we adopt the long-run point of view, and take into account the projection that the population is expected to increase by another 50% in 50 years, it should be evident that the slums need to be made into flat based housing areas. How is such a goal to be accomplished? One method is to tell the residents something like the following If you give up your land rights and let the GoB build a 4 storey flat on your land, you will be recompensed by being granted 2 flats as compensation. The remaining flats will belong to the GoB who will either rent or sell those flats in order to cover their costs. By this means the GoB can provide long term infrastructure in financially feasible fashion and you get to live in a flat with full infrastructural facilities. Upon being presented with such an option, the residents were very curious---they asked lots of questions, but always ended up by saying, “But we cannot lose ownership of our land”. We pointed out that lakhs of families have given up just such ownership in order to live in flats, where the land becomes the joint property of all the flat owners while the individual flats are only private property. The residents had no answer to this but just insisted that they did not want to lose ‘malikana’. This is clearly a point of psychological importance worth probing. In many situations we find those who work closely with the poor to find that the poor are unable to visualize radically new arrangements that can benefit them.[case studies SEWA?] So we asked the MFI’s why the poor had such beliefs. The opinion we got was that the belief arose out of a distrust of local government. They had been made false promises too often and had now adopted a position of complete 123

negation to direction from above on such questions. Actually we even found a rich family, living next to a NGO on 10 kathas of prime land that refused all offers to build and develop. While such actions by the rich cause negligible social impact, it is otherwise with the poor, who are driven by circumstance to act together if they wish to enjoy infrastructural benefits. Here is a great opportunity for MFIâ&#x20AC;&#x2122;s who generally speaking already have the trust of the people. If the housing loans and infrastructural plans are mediated through the MFI, it is just possible that the residents can be persuaded to see their long run self interest. The above is a plan for areas like Bawnia where there are already planned blocks, brick roads and the possibility of quick provision of all infrastructures. In less structured areas like Shimrail kandi in Brahmanbaria the sites and services scheme, whereby the Gob will provide infrastructure on condition that the households surrender say 10% of their land to finance the work and to allow the development of schools, parks etc in the future. This too is a plan that needs to be mediated by a local organization whom the community trusts. The critical prerequisite is trust. At this moment, such trust is most readily given to NGOâ&#x20AC;&#x2122;s and MFIâ&#x20AC;&#x2122;s---hence they have the obligation to look to advancing positive changes in the slums into the near future.

124

4. LESSONS FROM ABROAD SEWA: pensions and insurance SEWA Bank has pioneered individual lending in the city of Ahmedabad for over 30 years, thus making it one of the oldest of modern MFI’s. It is also remarkable for having been financially self-sustainable since the beginning of its operations. By substituting rigorous residency and character requirements it has been able to dispense worth the group based lending mechanism made popular by Grameen and now has a delinquency rate below 4% Since group based lending is likely to become difficult in urban areas due to the high time value of clients, their transient nature and the anonymity that city life provides, it is worth examining the careful process of SEWA. “First, clients must demonstrate regularity of savings for about 6 months before they can take out their first loan, thus developing a financial discipline and history, and establishing their creditworthiness. Second, loan size increases from cycle to cycle-allowing clients to establish their credit history and regularity of repayment. Typically, a first loan would be in the amount of Rs 11,7004 while the second loan is Rs 17,000. …Third, although the loan is individual, SEWA Bank relies heavily on social networks to manage risk and prevent defaults, through guarantors and community leaders. In order to take out a loan, the borrower must find two guarantors to co-sign the loan, one of which can be a family member. Finally, SEWA Bank’s loan officers maintain daily contact with the clients, offering savings collection and loan services, thus closely monitoring the financial situation of the borrower. In addition, a good data management and information system allows SEWA Bank to keep track of its portfolio and promptly notify irregular borrowers. SEWA Bank also conducts financial literacy trainings, which educate clients about the importance of saving, planning, maintaining financial discipline and repaying.”

To carry out this intensive screening and social monitoring SEWA uses a system of Sathis or companions. These are experienced and reliable SEWA clients, whom the community knows and trusts, who are entrusted with things like attracting new clients, sanctioning loans, and monitoring collection in each area. They also need to be literate, and have good numeric skills. Further safeguards keep an eye on the Sathis. They must keep a substantial security deposit in the bank; and limits are placed on the availability of funds to the Sathis in order to ensure that Sathis will not misuse their powers. SEWA has not only introduced two new financial products, the daily savings and the daily loan, to meet the needs of its urban clients, ---the latter to displace the persistent need

125

for moneylenders--- it also possesses two particularly interesting products --- its pension and its insurance schemes In the pension scheme, installments (sums in multiples of Rs. 50) are collected by SEWA Bank and invested through a UTI mutual fund in government securities and stocks in a ratio 60:40 with an expected return of 10% per annum. The pension scheme is open to all clients, including new clients, between the ages of 18 and 55 who commit to a minimum duration of 15 years. Installments are collected (in multiples of 50) and invested in government securities and stocks in a ratio of 60:40, with an expected return of 10% per annum. On reaching 58 years, the pension can be either a lump sum or an annuity. Already in 2006, some 30,000 women had enrolled in the SEWA Bank pension scheme. In 1981 SEWA Bank started its first life insurance scheme in partnership with LIC. The premium was Rs. 6 for a payout of Rs. 1,000. Since insurance companies were expected to be hesitant about the regularity of premiums, SEWA formulated an ingenious payment method. It set up an insurance-linked fixed depositâ&#x20AC;&#x201D;the premium was automatically paid by the interest gained on a Rs. 100 fixed deposit with interest rate of 6%. This system has not only worked, it has now extended its reach to cover accident, sickness, maternity, and death in the household;. Remarkably, the numbers involved are not particularly large, with some 32,000 beneficiaries in 2007.

126

5. UTILISING NRB’S THROUGH THE WEB Non –Resident Bangladeshis or NRB’s can be more actively involved in helping poor Bangladeshis by using models like those of Kiva and Vittana in the US. Vittana makes it possible for young people around the world to get student loans directly from people who want to lend. It works closely with microfinance organizations around the world to develop student loan programs in developing countries. Combining the collective decades of experience of its board and advisors, partnerships with innovative microfinance organizations around the world, and months of conversations with students and families, it has a good idea of what works and what doesn’t (Source: Vittana website): •

vocational school and college degrees — real, employable skills;

•

high school graduates — demonstrated commitment to education;

•

mom (or another close relative) as co-signer — a clear, direct way to make sure nothing goes wrong;

•

urban areas — near good jobs and good schools; and,

•

specific financial structures for the loan and other best practices. The diagram below shows how VITTANA works

127

How a student gets loan Students apply to microfinance partner for a student loan. If the student’s application is accepted, the microfinance partner disburses the loan, creates a profile on Vittana, and uploads a picture. Lenders like to browse the students’ profiles and choose someone to lend to. All transactions are handled through PayPal, securely and safely. After a student’s loan is fully funded, Vittana readies that loan for transfer to microfinance partner. To minimize administrative and transaction overheads, Vittana aggregates loans for all fully-funded profiles and transfers them on a pre-determined day of the month to microfinance partner. Using loan, the student attends college, graduates and gets a degree within 12 months. After the student graduates and gets a job, she begins repaying her loan. As the student makes repayments, VITTANA deposits lender’s share of that month’s repayment into PayPal account. For example, let’s say a lender contributed $75 (15%) toward a $500 loan where, after a 4-month grace period, the student is expected to repay over 10 months. In this case, the student would repay $50 every month and Vittana would deposit $7.50 (15%) into lender’s PayPal account every month. After being repaid, lender can choose to re-lend funds to another student on Vittana or withdraw your funds from PayPal (Source: Vittana website).

128

6. COORDINATION The need for coordination among NGO’s is an old issue and one that has been raised many times before. It was specifically noted in the ADB study of Urban NGO activity. To make such coordination meaningful, it has to accord with the interest of the NGO’s as well as that of the public. To turn Microcredit into meaningful Microfinance requires the addition of both savings and insurance to each MFI list of activities. While Savings can often be individually managed, it is otherwise with insurance, where large numbers and pooled information is always a benefit. This requires a cooperative attitude among the NGO’s. by taking small initial steps in this direction the MFI’s may reap substantial long-run gains. To some extent, both the PKSF and the donor organizations can ensure the beginnings of such joint efforts at the pooling of information and possibilities, and then time and experience can guide the rest. Three specific ideas came to mind in setting up such a joint venture. First, entrepreneurship and Skill training. The high profit rates in most occupations perceived as open to slum dwellers is remarkable. The fact that many do not exit in such a situation suggests that more is involved than just the making of money---there must be issues of saving, planning, management and marketing. Studies in East Africa by Peter Kilby showed long ago that accounting, marketing and management are aspects which many budding entrepreneurs stumble upon. More recent volunteer efforts by marketing students in East Africa produced enthusiastic responses from potential businessmen. The assumption of economists that all individuals are born with the ability to perform economic calculations is mistaken. There are even simpler issues with the skills of our workers. The RMG’s have long complained about the fact that most workers lack skills, and when some initial training was given to poor females this resulted in a tripling of their income ---from 2000 to 6000 per month---within six months. There is clearly a social gain in having such training generally spread, but in whose incentive is it to provide such skills? If the MFI’s are to engage in lending money for enterprises it is in their interest to provide the groundwork in accounting, revenues, investments and trates of return that are essential for a successful business. Given the mobility of individuals and the prospects of taking multiple loans, it is not in the interest of any single MFI to engage in providing such entrepreneurship training centers in urban areas---but it is in their joint interest, since

129

every MFI will gain by having a pool of trained entrepreneurs at hand. How does one assign the costs? If there is no coordinating body for the MFI’s to achieve this by internal agreement, then a simple rule will be for the PKSF or the MRA to entrust an independent body---chosen from a governing body of IBA and private university faculty---and then ‘tax’ each urban NGO in proportion to the numbers of their clients. A second problem involving coordination is that of minimum efficient scale. We have heard many clients complain about the fact that the loans are not tailored to their specific needs. Thus , a rickshaw may cost 7000 but the loan given is only for 5000. After thinking how to make the 5 into 7 for a while, the family ends up spending the money on something other than the original intent. We see the list of potential enterprises that the poor consider within their reach to be quite large, but the scale of entry for each is also quite variable. Furthermore, there cannot be a uniform answer to the question of the initial cost of setting up a laundry in any basti---it has to vary by city and by location. How will it pay an individual MFI to collect and disseminate such statistics. If the figures are inaccurate, they should not be used; but if they are accurate, everyone gains by knowing all the different employments that can be started in Bawnia. Those who made no effort to collect, scrutinize and disseminate the information gain. Once again there is a problem of collective action which can be directly addressed by the PKSF or the MRA. A third area of coordination is the calculation of externalities. For example, Slum areas are typically dirty. So we can suppose that they spread disease to some extent. Do we have numbers on how many diseases they do spread, and what is the cost to non-slum dwellers? This should measure roughly what society should be willing to pay to clean slums. It would make sense that some approximate idea of the cost of having the slums with unhygienic surroundings would be calculated by an interested body. But, surprisingly, neither the ICDDRB, nor the World Bank, nor UNDP, nor any of the Private universities have such a calculation. It may be that people believe the age of severe infectious diseases, particularly the water-borne infections, is over and that we are now facing primarily chronic health problems. But the issue surely needs to be addressed. Is slum hygiene a private health question or does it require policy action from the Public Health program? If the latter, then how can we get the value of such Public Health activities? The slum dwellers do not have the vision or the clout to provide such information, but the MFI’s who are concerned with all aspects of poverty can take a stand

130

and either through collective organization among themselves or by pressuring the Government, start acquiring such data.

131

CONCLUSION The need for an extended study of urban microfinance in Bangladesh arises from two sources: the increasing importance of the urban poor, and the possibilities for innovative research and extension activities in this area. This study was meant to be a first step in contributing to such an extended longitudinal study. This paper has attempted a pioneering start at examining urban MF in BD. It was written with the thought “what would we have wanted to see if we were the second researchers in this area” Such a motive has led us to examine as much of the history as we could and to give the reader a guide to available and potential references. We began by arguing for the importance of such a study then went on to illustrate the history with a selection of consolidated tables. To find out about the current state of MF in BD we used a survey of some 2500 MFI members and a control group of 1000 non-members. The sample chosen was reasonably large but future work needs more grounding in a more systematic framework, such as an update of the sampling frame provided by the CUS 2005 study. Finding a comparison group of non-MFI members was challenging since between 80 to 90 per cent of the inhabitants in the low income communities we studied were, or had been, members of an MFI. The survey produced some evidence of MFI impact on non-economic variables like women’s empowerment schooling but none of the gains was particularly remarkable. The evidence regarding economic variables is more noteworthy. A significant proportion of MFI members own considerable assets. It suggests that MFI’s are trending away from loaning only to the poorest. Many loans are not used for generating incomes--shows how MF is moving considerably into ISL rather than IGL. Fairly large loans are given to those who are successful, indicating how MFI’s are trending into ME. Income growth is significant and members have more gains than non-members The clients tell us that, to them, MF begins with loans, not savings or insurance Use of MFI loans are constrained both by loan size and by weekly repayment schedules.

132

Overlapping is beneficial, and those who overlap most seem to make the most money. The survey innovated, both in the BD literature and in the MF literature at large by asking questions about ‘happiness’ and ‘hope’. The last section of the Report studied new ways for urban MF to turn to. It found no serious areas where MF should close down, apart from the hints given above about increased flexibility in loan terms and loan amounts. The potential for expansion of MF is seen in many ways, but they should be layered according to urban incomes, because MF is not one thing but many. The range of new activities runs from infrastructure management to rickshaws to education loans to coordinated MFI activities. Garages and rickshaw loans to create an independent working class. Education loans, particularly to nurses, both for national need and for female empowerment. The involvement of NRB’s using models such as that of Kiva or Vittana. Housing development in low income communities is an area of great impact . Coordination of selected actions---entrepreneurial training for example---will save costs, increase political clout and have even greater impact Since the benefits of Rural MF have been largely reaped already, there are probably greater benefits from a careful longitudinal study of UMF than from a focused study of any other branch of MF.

133

APPENDICES

134

LIST OF APPENDICES

Appendix 1: Resurvey data of assets of both MFI members and non-members Appendix: 2 I. Average number of school going children, children not attending school, dependent and number of income earner II. Number of School going children III. Number of School going children in Dhaka IV. Dependent & family size Appendix-3 A. Housing types B. Home ownership C. Room size D. Utility Services Appendix: 4 I. Occupation of the HHH II. Occupation of the HHH III. Small businesses in the study areas III. Income of HH in different cities (up to Tk. 10,000) IV. Average income at different communities in Dhaka city V. Other sources of money Appendix: 5 I. number of loan borrowed II. Installment types Appendix: 6 I. list of small businesses in the study areas II. Average investment against businesses in different cities III. Organization providing insurance Appendix:7 I. Average increase of income II. Average decline of income III. Table: the state of empowerment before taking loans IV. Table: Hopefulness V. Table: the state of happiness Appendix 8: General Information of the communities

Appendix 1: Resurvey data of assets of both MFI members and non-members Jatrabari (MFI)

Unproduc tive

1. 010809 (MFI)

Type of Asset Prod ucti ve

Unproductiv e

010819 (MFI)

Prod ucti ve

Unproductiv e

010818 (MFI)

Prod ucti ve

Unproduct ive

010815 (MFI)

Prod ucti ve

Unproduc tive

010795 (MFI)

Pr od uc ti ve

7. 010792 (MFI)

Prod ucti ve

Unproduct ive

010794 (MFI)

Product ive

Name of Asset

Amount/No

Value per Unit

Homestead Land(Urban) Home Shop TV Refrigerator Mobile Bed Almira Showcase Homestead Land(Urban) home Shop TV Refrigerator Mobile Bed Almira Weardrof Sofa Showcase Homestead Land(Urban) Home Shop TV Refrigerator Mobile

1 Katha

30 lakh per katha

Bed Almira Showcase Weardrof Homestead Land(Urban) Home Sewing Machine TV

2 1 1 1 2 katha

Mobile Bed Almira Sofa Weardrof

2 2 1 1 set 1

2200+3000 3000+3000

5200 6000 4000 6000 4000

Homestead Land(Rural) Shop TV Mobile Bed Almira Showcase Sofa Homestead Land(Urban) Land (Rural) -Agri Rural Homestead TV Refrigerator Mobile Bed Almira Weardrof Sofa Homestead Land(Urban) Land (Munsiganj)

18 katha

30,000 per katha

5,40,000

1 1 1 1 2 2 1 1 3 katha 1 1 1 1 2 1 1 1 1 set 1 1 Katha 1 1 1 1 3

6000+3500 8000+10000 12 lakh per katha

6000+4500

30 lakh per katha

4000+5000+25 00 5000+7000

20 lakh per katha

1 1 1

1 1 2 1 1 1 1 set 1.5 katha 10 katha 2 katha 1 1 2 2 1 1 1 set 4 katha 20 katha

Total estimated Amount(tk.) 30lakh 2 lakh 2 lakh 15000 20000 9500 18000 8000 6000 36 lakh 3 lakh 1 lakh 16000 21000 10500 12000 9000 8500 15000 4500 30 lakh 2 lakh 3 lakh 12000 20000 11500 12000 7000 3500 5000 40 lakh 2 lakh 5000 10000

5000+3000

5 lakh per katha 10,000 per katha 30,000 per katha 4500+2600 7000+5000

2 lakh per katha 25,000 per katha

1 lakh 16,000 8000 6000 7000 5000 7000 7.5 lakh 1 lakh

Total

Survey Data

34 lakh

30 lakh

76,500

70,000

40 lakh

30 lakh

96,500

70,000

35 lakh

30 lakh

71,000

70,000

42 lakh and 5 thousan d 35,200

40 lakh

6,40,00 0

7 lakh

49,000

50000

9,10,00 0

30,000

10 lakh

60,000 12,000 14,000 7100 12,000 6500 8000 10000 8 lakh

69,600

50,000

18 lakh

10 lakh

5 lakh

Unproduct ive Unproduc tive

010817 (MFI)

Prod ucti ve

Unproducti ve

010803 (MFI)

Produ ctive

10.

Unproduc tive

Productive

010810 (MFI)

Unproducti ve

010813 (MFI)

Prod ucti ve

11.

-Agri Rural â&#x20AC;&#x201C; (Munsiganj) Homestead Shop TV Mobile

5 katha 1 1 4

50,000 per katha

12000+4000+30 00+2600 14000+9000

2.5 lakh 2.5 lakh 12000 21,600

Bed Almira Showcase Weardrof Homestead Land(Urban) Home Business TV Mobile Bed Almira Show case Refrigerator

2 1 1 1 4 katha

Homestead Land(urban)

1.5 katha

Home TV Mobile Bed

1 1 2 3

Almira Refrigerator Wear drof Homestead Land(Urban)

1 1 1 1.5 katha

Land (Narayanganj) - Homestead Home

4 katha 1

1.5 lakh

Sewing Machine

5000

TV Refrigerator Mobile Bed Almira Show case Homestead Land(Urban) Home Shop TV Mobile

1 1 1 1 1 1 3 katha

16,000 20,000 3500 7000 5000 4000 75 lakh

Bed Almira Showcase Refrigerator

2 1 1 1

25 lakh per katha

1 1 1 2 1 1 1

1 1 1 3

5000+7000

35,lakh per katha

7000+3000 5000+6000+40 00

12 lakh per katha 5 lakh per katha

25 lakh per katha

6000+4000+35 00 8000+7000

23000 5000 6000 8,000 1 crore 3 lakh 50,000 23000 5000 12000 7000 3000 28000 52.5 lakh

7 lakh 12,000 10000 15000 7000 20000 6000 18 lakh

75,600

40,000

1,35,00 ,000

1 crore

78,000

70,000

59.5 lakh

60 lakh

70,000

39.55 lakh

40 lakh and 5 thousand

55,500

70,000

94 lakh

1 crore

68500

50,000

20 lakh

7 lakh 12 lakh 12000 13500 15000 5000 3000 20000

iii

Jatrabari-Non MFI ID

Type of Asset

Unpr oduc tive Unpr oduc tive

100108 (NonMFI))

Produc tive

100106 (NonMFI)

Pr od uc ti ve

Unproduc tive

100107 (NonMFI))

Pr od uc ti ve

7. 100116 (NonMFI))

Product ive

Unproductiv e

100114 (NonMFI))

Product ive

Unpr oduc tive

100122 (Non-MFI)

Product ive

Unproductive

100109 (NonMFI)

Productive

Name of Asset

Amount/No

Value per Unit

Homestead Land(Urban) Home Sewing Machine Mobile Bed Showcase Shop Land (Rural) -Agri Rural -Homestead

1 katha

15 lakh per katha

Mobile

2000

Bed

500

Homestead Land(Urban) Home TV Mobile Bed

2.5 katha

Almira Showcase Homestead Land(Urban) Rural (Homestead)

1 1 3 katha

Rural (agri)

20 katha

Home Shop TV Mobile

1 1 1 3

Bed

Almira Show case Refrigerator Weardrof Sofa

1 1 1 1 1 set

1 1 2 1 1 1 60 decimal 8 decimal

1 1 2 3

Total estimated Amount(tk. ) 15 lakh 3 lakh 5000 5200 4000 3000 30000 3 lakh

3000+2200

5000 per decimal 10,000 per decimal

12 lakh per katha

30 lakh

2 katha

Home Shop Tv Mobile Bed Almira Shop

1 1 1 2 3 1 1

Home

Urban-Homestead

3 katha

Mobile

Bed Sofa Almira Show case TV Refrigerator Homestead Land(Urban)

2 1 set 1 1 1 1 3 katha

Rural(Agri)

10 katha

1 lakh 5000 5500 15000 5000 3000 45 lakh

15 lakh per katha 50, 000 per katha 10,000 per katha

18 lakh

15 lakh

17,200

15,000

4,10,00 0

3.5 lakh

2500

2000

31 lakh

30 lakh

33,500

25,000

60 lakh

55 lakh

107500

1 lakh

3 lakh 2 lakh

5000+4500+35 00 9000+6000+50 00+5000

Homestead Land(Urban)

Survey data

80,000

3000+2500 6000+4000+50 00

6 katha

Total (taka)

5 lakh 5 lakh 12000 13000 25000 6500 5000 20000 8000 18000

15 lakh per katha

5000+3500 5000*2+4000

30 lakh

5 lakh 1 lakh 5000 8500 14000 3000 2 lakh 3.5 lakh

30 lakh per katha 4500+4000+30 00 6000+5000

15 lakh per katha 10,000 per katha

36 lakh

40 lakh

30,500

25,000

95.5 lakh

1 crore

75,000

70,000

51.5 lakh

50 lakh

90 lakh 13000 12000 12000 7000 3000 16000 12000 45 lakh

1 lakh

Unproductive

Rural (homestead land) Shop TV Mobile

5 katha

Bed

Almira Showcase Refrigerator Sofa

1 1 1 1 set

1 1 3

50,000 per katha 4500+5000+35 00 4000+3500+30 00

2.5 lakh 3 lakh 10000 14500

74,500

70,000

10500 4000 3500 22000 10000

Rampura (MFI) ID

Type of Asset

Unproduc tive

010545 (MFI)

Product ive

12.

14.

010546 (MFI)

Unproduc tive

15.

P r o d u c

Unproduct ive

010564 (MFI)

Produc tive

Unproductiv e

010497 (MFI)

Pr od uc ti ve

13.

Unproductive

010576 (MFI)

Prod ucti ve

16.

U n p r o

010513 (MFI)

Pr od uc ti ve

17.

Name of Asset

Amount/No

Value per Unit

Homestead Land(rural) Rural land(agri) Shop Rickshaw TV Refrigerator Mobile Bed Almira Showcase Homestead Land(Urban) Shop TV Refrigerator Mobile

2 katha

2 lakh per katha 50000 per katha

Bed

Almira Showcase Homestead Land(rural) Land rural(agri) Shop TV Refrigerator Mobile Bed Almira Sofa Showcase Home shop TV Refrigerator Mobile Bed Almira Dressing table

1 1 7 katha

5 katha 1 1 1 1 2 2 1 1 40 kora 1 1 1 4

3 bigha 1 1 1 2 2 1 1 set 1 1 1 1 1 2 1 1 1

Homestead Land(urban) Home Shop TV Refrigerator Mobile

3 katha

Bed

Almira Showcase Weardrof Homestead Land(Urban) Home TV Refrigerator

2 1 2 6 katha

1 1 1 1 4

1 1 1

3500+2200 5000+6000 25000 per kora

2600+2200+ 3000+4000 4000*2+500 0 2 lakh per katha 5lakh per bigha

7000+3500 15000+9000

7000+2600

12 lakh per katha

2600+4000+50 00+6500 16000+8000+6 000*2 8000+6000 3000*2 10 lakh per katha

Total estimated Amount(tk.) 4 lakh

Total

Survey data

7,08,000

6.8 lakh

53,700

50,000

10.5 lakh

10 lakh

50,800

50000

30 lakh

94500

1 lakh

5.5 lakh

7.6 lakh 60000

2.5 lakh 50,000 8000 10000 15000 5700 11000 7000 5000 10 lakh 50000 8000 10000 11800 13000 4000 4000 14 lakh 15 lakh 1 lakh 14000 20000 10500 24000 10000 12000 4000 1.5 lakh 4 lakh 16000 20000 9600 8000 9000 5000

67,600

36 lakh

41 lakh

42 lakh

3 lakh 2 lakh 13000 20000 18100

1,10,100

1 lakh

70 lakh

30 lakh

43,500

15000

36000 14000 3000 6000 60 lakh 10 lakh 12000 15000

Pr od uc ti ve Unproduct ive

010556 (MFI)

Prod ucti ve

Unprod uctive

010550 (MFI)

Mobile Bed Almira Showcase Homestead Land(urban) Shop Mobile Dressing table Bed CD player Showcase Homestead land(Narayanga nj) shop TV Refrigerator Mobile Bed CD player Showcase Almira

1 1 1 1 3 katha 1 2 1 1 1 1 6 katha 1 1 1 2 1 1 1 1

20 lakh per kataha 6000+2500

30,000 per katha

4000+2500

2500 7000 3000 4000 60 lakh 3 lakh 8500 6000 8000 2000 12000 1,80,000 50000 5000 7000 6500 5000 1500 3500 9000

63 lakh

50 lakh

36,500

40,000

2,30,000

6 lakh

37,500

50,000

Rampura (Non MFI)

100047 (Non-MFI)

Productiv e

Unpr oduc tive

100046 (NonMFI)

Prod ucti ve

Unproduc tive

100043 (NonMFI))

Produc tive

Unproduc tive

100057 (NonMFI))

Pr od uc ti ve

Unproductive

100036 (Non-MFI)

Type of Asset Prod ucti ve

Name of Asset

Amount/No

Value per Unit

Homestead Land(Urban) Home Shop Tv Refrigerator Mobile

6 katha

12 lakh per katha

Bed Almira Sofa Weardrof Showcase Shop Urban-Homestead

4 2 1 set 2 1 1 2 katha

Mobile Bed Weardrof Almira TV Refrigerator Rural(Agri)

2 2 1 1 1 1 100 decimal 20 decimal

Rural (homestead land) Almira Mobile Bed Showcase Refrigerator Sofa Urban-Homestead

1 1 1 1 5

1 1 1 1 1 1 set 6 katha

4500+5000*2 +3500+2200 5000*3+9000 5000+6000 4000+5000 4 lakh per katha 5000+3000 3000+5000

10000 per decimal 40000 per decimal

12 lakh per katha

Total estimated Amount(tk.) 72 lakh 3 lakh 3 lakh 10000 18000 20200 24000 11000 12000 9000 4000 3 lakh 8 lakh 8000 8000 8000 10000 10000 20000 10 lakh

Total (taka)

Survey data

78 lakh

40 lakh

1,08,200

1 lakh

11 lakh

20 lakh

64000

35,000

18 lakh

15 lakh

49,500

50,000

79 lakh

80.5 lakh

26700

20,000

8 lakh 9500 3000 11000 6000 15000 5000 72 lakh

Shop Home Mobile Bed Showcase TV

1 1 2 4 1 1

4000+2200 3000*4

2 lakh 5 lakh 6200 12000 2500 6000

Homestead Land(Urban)

2 katha

20 lakh per katha

40 lakh

Homestead Land(Gazipur)

10 katha

1 lakh per katha

10 lakh

55 lakh

50 lakh

Unproduc tive Product ive Unproductive

100062 (NonMFI))

Home Tv Refrigerator Mobile Bed Almira Sofa Shop

1 1 1 2 2 1 1 set 1

Home

Urban-Homestead

2 katha

Mobile

Bed

Showcase Almira TV card Refrigerator Sofa Computer Dyning table

2 3 1 1 1 set 1 1

5000+6000 6000+8000

5 lakh 10000 20000 11000 14000 6000 20000 1 lakh 50, 000

13 lakh per katha 3000+5000+ 4500 6000+7000+ 4000 5000+4000 3*4000

81,000

90,000

27.5 lakh

20 lakh

1,14,500

1 lakh

26 lakh 12500 17000 9000 12000 3000 20000 12000 25000 4000

vii

Name of Asset

Amount/No

Value per Unit

010843 (MFI)

Homestead Land(Rural)

16 katha

50000 per katha

Land-agri (Rural) Poultry farm Tv Mobile Bed Alna

3 katha

1 lakh per katha

Shop

Home

Urban-Homestead

4 katha

Sewing Machine

Mobile Bed DVD Steel Almira TV Refrigerator Home Urban (homestead land) Shop Sewing Machine Refrigerator TV Mobile Bed

2 1 1 1 1 1 1 4 katha

Showcase Almira Weardrof Sofa Rural-Homestead

1 1 1 1 6 katha

Rural-Agri land

10 katha

Micro bus

Mobile Bed Showcase TV Refrigerator

1 2 1 1 1

4000 3000+4000

4000 7000 1500 8000 20000

Homestead Land(Rural)

4 katha

40,000 per

1.6 lakh

Homestead Land(urban)

5 katha

2 lakh per katha

10 lakh

Land-agri (Rural) Tv Mobile Bed Refrigerator

1 bigha

3 lakh per bigha

3 lakh

Showcase Almari Weardrof Dressing table Urban-Homestead

1 1 1 1 5 katha

Home

Unprod uctive

010829 (MFI)

Productive

Unproductive

010860 (MFI)

Produc tive

Unproduc tive

010862 (MFI)

Productive

Unproduc tive

Producti ve

Uttara (MFI) ID Type of Asset

010851 (MFI)

Prod ucti ve

Unproductive

Productive

010879 (MFI)

1 1 2 1 1

1 1 1 1 1 2

1 2 1 1

6500+4500

Total estimated Amount(tk.) 8 lakh

Survey data

12 lakh

10 lakh

49000

50000

82,53,0 00

60 lakh

56,000

70,000

1,03,53 ,000

1 crore

71,500

80,000

15.3 lakh * The client has bought a new car. 40,500

6 lakh

14.6 lakh

12 lakh

81,200

1 lakh

1,05,00 ,000

1 crore

3 lakh 1 lakh 21000 11000 16000 1000 50000 2 lakh

20 lakh per katha

Total (taka)

80 lakh 3000

4500+6500

25 lakh per katha

6000+5000

30000 per katha 10000 per katha 6 lakh+4 lakh+2.5 lakh

2500+5000

20 lakh per katha

11000 8000 4000 9000 16000 8000 1.5 lakh 1 crore 2 lakh 3000 8000 16000 4500 11000 5000 7000 8000 12000 1,80,000 1,00,000 12.5 lakh

13500 7500 2200 24000 14000 9000 5000 6000 1 crore 5 lakh

viii

30,000

Unproduct ive Prod ucti ve Unproductive

010830 (MFI)

Unpro ducti ve

010841 (MFI)

Produ ctive

Unproductiv e

010845 (MFI)

Producti ve

Unproductiv e

010846 (MFI)

Prod ucti ve

Unproductive

Productive

010882 (MFI)

Mobile

Bed Steel Almira TV Refrigerator Sofa

4 1 1 1 1 set

Home Urban (homestead land) Shop Refrigerator TV Mobile

1 3 katha

Bed

Dining table

15,000

Showcase Almira Weardrof Sofa computer Bed Showcase TV Refrigerator

1 1 1 1 set 1 2 1 1 1

5000 12000 7000 35,000 30000

Homestead Land(Rural)

6 katha

30,000 per katha

1,80,000

Homestead Land(urban)

2 katha

4 lakh per katha

8 lakh

Land-agri (Rural)

2 bigha

2 lakh per bigha

4 lakh

Sewing machine Tv Mobile Bed Refrigerator

1 1 2 3 1

Sofa Almari

1 set 1

Dressing table

Urban-Homestead

5 katha

Home

Mobile Bed Steel Almira TV Refrigerator Sofa Showcase Home Urban (homestead land) Homestead Land(Rural) Sewing machine Refrigerator TV Mobile Bed

2 2 1 1 1 1 set 1 1 2 katha

Showcase Sofa Homestead Land(Rural) Land-agri (Rural)

1 1 set 4 katha

Shop TV

1 1

50000 12000

Mobile Bed Almari

1 2 1

5000 12000 4000

1 1 1 5

5 katha 1 1 1 3 2

20 katha

8000+3500+79 00+2600 3*4000

30 lakh per katha

12000+8000+600 0+5000+3500 4*6000

8000+4000 8000+6000*2

22,000

89,000

1 lakh

1 crore and 12 lakh

40 lakh

2,12,500

2 lakh

13, 83,000

12 lakh

93,000

1 lakh

72.5 lakh

60 lakh

73,000

70000

44,03,00 0

40 lakh

88,000

80,000

8.5 lakh

8 lakh

33000

30000

12000 2000 17,000 23,000 13,000 20 lakh 90 lakh 2 lakh 30000 20000 34,500 24000

3000 15000 12000 20000 20000 10,000 12,000 4,000

14lakh per katha

70 lakh 2.5 lakh

4500+3000 5000*2

20 lakh per katha 20000 per katha

7000+5500+3500 5000+7000

50,000 per katha 30,000 per katha

5000+7000

7500 10000 9000 12000 20000 12000 2500 3 lakh 40 lakh 1 lakh 3000 20,000 15,000 16,000 12000 5000 20000 2 lakh 6,00,000

Uttara (Non-MFI)

Unproductive

100086 (NonMFI)

Product ive

Unproduct ive

100084 (NonMFI)

Prod ucti ve

Unprod uctive

100091 (Non-MFI

Produc tive

Unproduc tive

100092 (Non-MFI

Prod ucti ve

Unproducti ve

100090 (NonMFI)

Type of Asset

Productive

U Productive n p r o

100081 (Non-MFI

Name of Asset

Amount/No

Value per Unit

Total estimated Amount(tk.)

Total (taka)

Survey data

Homestead Land(urban)

2 katha

30 lakh per katha

60 lakh

1,21,10 ,000

1 crore

Homestead Land(urban)

4 katha

15 lakh per katha

60 lakh

home Shop Showcase Mobile Bed Refrigerator

1 1 1 2 2 1

54,000

70,000

Sofa Almari Urban-Homestead

1 1 3 katha

45.75 lakh

40 lakh

Rural (homestead) Mobile Bed Steel Almira TV Dressing table Sofa Home Urban (homestead land) Homestead Land(urban) Mobile Bed Almira Showcase Wear drop Homestead Land(urban) home Shop refrigerator Mobile

1.5 katha

50,500

45000

1,20,30 ,000

1 crore

26,500

40000

1 creore and 9.5 lakh 82,000

1 crore

Bed

Almari Sofa

1 1 set

Homestead Land(urban)

6 katha

1 crore and 4 lakh

1 crore

home Shop Showcase tv Mobile

1 1 1 1 4

1,69,00 0

1.5 lakh

Bed Refrigerator

4 1

Sofa Almari Dressing table Dining table Urban-Homestead

2 set 2 1 1 2 katha

1,20,30 ,000

1 crore

Urban (homestead)

4 katha

Home

Mobile

47500

50,000

2 1 1 1 1 1 set 1 2 katha 4 katha 1 1 1 1 1 7 katha

1 3

6000+4000 4500*2

15 lakh per katha 50000 per katha 4000+3500

30 lakh per katha 15 lakh per katha

15lakh per katha

7500+4000+80 00 15000+7000+1 2000

30000 80000 5000 10000 9000 12000 8000 10000 45 lakh 75,000 7500 12000 6000 7000 3000 15000 30000 60 lakh 60 lakh 7000 4500 7000 3000 5000 1,05,00,000 2.5 lakh 2 lakh 30000 19500

1 lakh

34000 8500 20000

16 lakh per katha

12000+9000+5 000+4000 12000+8000*2 20000+15000 8000+4000

30 lakh per katha 15 lakh per katha

96 lakh

3 lakh 5 lakh 8000 20000 30,000 28000 22000 35000 12000 6000 8000 60 lakh 60 lakh 30000

8000+3000

11000

Pr od uc ti ve Unproduct ive

100075 (Non-MFI

Bed Steel Almira TV weardrop Home Urban (homestead land) Mobile Bed TV Refrigerator weardrop Almari Dressing table

1 1 1 1 1 2.5 katha 2 1 1 1 1 1 1

25 lakh per katha 3000+5000

5000 7500 16000 8000 1 lakh 62.5 lakh 8000 4000 16000 25000 4500 6000 2500

63.5 lakh

50 lakh

66,000

70,000

Appendix: 2A I. Average number of school going children, children not attending school, dependent and number of income earner ----------------------------------------------------------------------------City | Child. going child. not dependent income earner | schl attending schl -------------+--------------------------------------------------------------Barishal | 0.80 0.61 1.56 1.98 Bogra | 0.69 0.39 1.63 1.55 Brahmanbaria | 0.86 0.95 1.38 1.83 Chittagong | 0.77 0.65 1.36 1.87 Comilla | 0.88 0.75 1.13 2.01 Dhaka | 0.81 0.53 1.32 1.96 Dinajpur | 0.78 0.36 1.45 1.89 Khulna | 0.89 0.41 1.46 1.73 Kushtia | 0.77 0.55 1.16 1.51 Mymensingh | 0.68 0.84 1.41 1.49 Rajshahi | 0.69 0.37 1.61 1.64 Rangpur | 0.69 0.44 1.84 1.69 Sylhet | 0.62 0.90 1.30 1.35 -----------------------------------------------------------------------------

II. Number of School going children -------------------------------------------------------------------------------City | 0 1 2 3 4 | Total -------------+-------------------------------------------------------+---------Barishal | 18 46 14 2 0 | 80 | 22.50 57.50 17.50 2.50 0.00 | 100.00 | 3.47 4.60 3.43 3.23 0.00 | 4.01 -------------+-------------------------------------------------------+---------Bogra | 19 44 11 1 0 | 75 | 25.33 58.67 14.67 1.33 0.00 | 100.00 | 3.66 4.40 2.70 1.61 0.00 | 3.76 -------------+-------------------------------------------------------+---------Brahmanbaria | 27 29 24 3 0 | 83 | 32.53 34.94 28.92 3.61 0.00 | 100.00 | 5.20 2.90 5.88 4.84 0.00 | 4.16 -------------+-------------------------------------------------------+---------Chittagong | 54 76 41 9 2 | 182 | 29.67 41.76 22.53 4.95 1.10 | 100.00 | 10.40 7.60 10.05 14.52 40.00 | 9.13 -------------+-------------------------------------------------------+---------Comilla | 23 37 15 7 0 | 82 | 28.05 45.12 18.29 8.54 0.00 | 100.00 | 4.43 3.70 3.68 11.29 0.00 | 4.11 -------------+-------------------------------------------------------+---------Dhaka | 188 450 174 27 1 | 840 | 22.38 53.57 20.71 3.21 0.12 | 100.00 | 36.22 45.00 42.65 43.55 20.00 | 42.13 -------------+-------------------------------------------------------+---------Dinajpur | 15 38 20 0 0 | 73 | 20.55 52.05 27.40 0.00 0.00 | 100.00 | 2.89 3.80 4.90 0.00 0.00 | 3.66 -------------+-------------------------------------------------------+---------Khulna | 26 89 35 4 2 | 156 | 16.67 57.05 22.44 2.56 1.28 | 100.00 | 5.01 8.90 8.58 6.45 40.00 | 7.82 -------------+-------------------------------------------------------+---------Kushtia | 16 52 11 1 0 | 80 | 20.00 65.00 13.75 1.25 0.00 | 100.00 | 3.08 5.20 2.70 1.61 0.00 | 4.01 -------------+-------------------------------------------------------+---------Mymensingh | 37 31 14 3 0 | 85 | 43.53 36.47 16.47 3.53 0.00 | 100.00 | 7.13 3.10 3.43 4.84 0.00 | 4.26 -------------+-------------------------------------------------------+---------Rajshahi | 22 53 17 1 0 | 93 | 23.66 56.99 18.28 1.08 0.00 | 100.00 | 4.24 5.30 4.17 1.61 0.00 | 4.66 -------------+-------------------------------------------------------+---------Rangpur | 23 33 15 2 0 | 73 | 31.51 45.21 20.55 2.74 0.00 | 100.00 | 4.43 3.30 3.68 3.23 0.00 | 3.66 -------------+-------------------------------------------------------+----------

xii

Sylhet | 51 22 17 2 0 | 92 | 55.43 23.91 18.48 2.17 0.00 | 100.00 | 9.83 2.20 4.17 3.23 0.00 | 4.61 -------------+-------------------------------------------------------+---------Total | 519 1,000 408 62 5 | 1,994 | 26.03 50.15 20.46 3.11 0.25 | 100.00 100.00 | 100.00 100.00 100.00 100.00 100.00 |

-----------------------------------------------------------------------

III. Number of School going children in Dhaka ----------------------------------------------------------------------------------------Nm_Cmnty | 0 1 2 3 4 | Total ----------------------+-------------------------------------------------------+---------Badda | 9 26 11 1 0 | 47 | 19.15 55.32 23.40 2.13 0.00 | 100.00 | 4.79 5.78 6.32 3.70 0.00 | 5.60 ----------------------+-------------------------------------------------------+---------Gabtali-Aminbazar | 15 33 12 0 0 | 60 | 25.00 55.00 20.00 0.00 0.00 | 100.00 | 7.98 7.33 6.90 0.00 0.00 | 7.14 ----------------------+-------------------------------------------------------+---------Goran/ Bashabo | 23 30 13 4 0 | 70 | 32.86 42.86 18.57 5.71 0.00 | 100.00 | 12.23 6.67 7.47 14.81 0.00 | 8.33 ----------------------+-------------------------------------------------------+---------Gulshan/Banani | 7 24 8 1 0 | 40 | 17.50 60.00 20.00 2.50 0.00 | 100.00 | 3.72 5.33 4.60 3.70 0.00 | 4.76 ----------------------+-------------------------------------------------------+---------Hazaribagh/Kamrangirc | 13 35 14 2 0 | 64 | 20.31 54.69 21.88 3.13 0.00 | 100.00 | 6.91 7.78 8.05 7.41 0.00 | 7.62 ----------------------+-------------------------------------------------------+---------Jatrabari | 10 15 10 1 0 | 36 | 27.78 41.67 27.78 2.78 0.00 | 100.00 | 5.32 3.33 5.75 3.70 0.00 | 4.29 ----------------------+-------------------------------------------------------+---------Kamlapur/Gopibagh | 8 26 10 3 0 | 47 | 17.02 55.32 21.28 6.38 0.00 | 100.00 | 4.26 5.78 5.75 11.11 0.00 | 5.60 ----------------------+-------------------------------------------------------+---------Lalbagh | 10 29 5 2 0 | 46 | 21.74 63.04 10.87 4.35 0.00 | 100.00 | 5.32 6.44 2.87 7.41 0.00 | 5.48 ----------------------+-------------------------------------------------------+---------Mirpur | 11 46 14 2 0 | 73 | 15.07 63.01 19.18 2.74 0.00 | 100.00 | 5.85 10.22 8.05 7.41 0.00 | 8.69 ----------------------+-------------------------------------------------------+---------Mohakhali | 11 31 11 1 0 | 54 | 20.37 57.41 20.37 1.85 0.00 | 100.00 | 5.85 6.89 6.32 3.70 0.00 | 6.43 ----------------------+-------------------------------------------------------+---------Mohammadpur/ Adabor | 11 22 11 3 0 | 47 | 23.40 46.81 23.40 6.38 0.00 | 100.00 | 5.85 4.89 6.32 11.11 0.00 | 5.60 ----------------------+-------------------------------------------------------+---------Rampura | 12 40 11 1 0 | 64 | 18.75 62.50 17.19 1.56 0.00 | 100.00 | 6.38 8.89 6.32 3.70 0.00 | 7.62 ----------------------+-------------------------------------------------------+---------Razarbagh/Malibagh | 14 31 14 3 0 | 62 | 22.58 50.00 22.58 4.84 0.00 | 100.00 | 7.45 6.89 8.05 11.11 0.00 | 7.38 ----------------------+-------------------------------------------------------+---------Savar | 18 33 15 1 1 | 68 | 26.47 48.53 22.06 1.47 1.47 | 100.00 | 9.57 7.33 8.62 3.70 100.00 | 8.10 ----------------------+-------------------------------------------------------+---------Uttara/Khilkhet | 16 29 15 2 0 | 62 | 25.81 46.77 24.19 3.23 0.00 | 100.00 | 8.51 6.44 8.62 7.41 0.00 | 7.38 ----------------------+-------------------------------------------------------+---------Total | 188 450 174 27 1 | 840 | 22.38 53.57 20.71 3.21 0.12 | 100.00 | 100.00 100.00 100.00 100.00 100.00 | 100.00 -----------------------------------------------------------------------------------------

xiii

IV. Dependent & family size City

Average family size

Average total dependent

Average adult dependent

Average no. of child. not attending school

Average no. of child. attending school

Barishal

5.08

2.97

1.56

0.61

0.80

Bogra

4.31

2.71

1.63

0.39

0.69

Brahmanbaria

5.12

3.19

1.38

0.95

0.86

Chittagong

4.74

2.79

1.36

0.65

0.77

Comilla

4.90

2.76

1.13

0.75

0.88

Dhaka

4.72

2.66

1.32

0.53

0.81

Dinajpur

4.59

2.59

1.45

0.36

0.78

Khulna

4.51

2.77

1.46

0.41

0.89

Kushtia

4.03

2.48

1.16

0.55

0.77

Mymensingh

4.51

2.93

1.41

0.84

0.68

Rajshahi

4.29

2.67

1.61

0.37

0.69

Rangpur

4.64

2.97

1.84

0.44

0.69

Sylhet

4.21

2.82

1.30

0.90

0.62

xiv

Appendix:3 A. Housing types -------------------------------------------------------------------------------City | Kutcha/Jh Others Pucca Semipucc-II Semipucc-I| Total -------------+-------------------------------------------------------+---------Barishal | 11 0 7 6 76 | 100 | 11.00 0.00 7.00 6.00 76.00 | 100.00 | 3.81 0.00 1.85 0.54 9.37 | 3.86 -------------+-------------------------------------------------------+---------Bogra | 1 0 6 54 39 | 100 | 1.00 0.00 6.00 54.00 39.00 | 100.00 | 0.35 0.00 1.58 4.89 4.81 | 3.86 -------------+-------------------------------------------------------+---------Brahmanbaria | 37 0 5 25 33 | 100 | 37.00 0.00 5.00 25.00 33.00 | 100.00 | 12.80 0.00 1.32 2.26 4.07 | 3.86 -------------+-------------------------------------------------------+---------Chittagong | 77 0 28 100 45 | 250 | 30.80 0.00 11.20 40.00 18.00 | 100.00 | 26.64 0.00 7.39 9.05 5.55 | 9.65 -------------+-------------------------------------------------------+---------Comilla | 19 0 4 35 42 | 100 | 19.00 0.00 4.00 35.00 42.00 | 100.00 | 6.57 0.00 1.06 3.17 5.18 | 3.86 -------------+-------------------------------------------------------+---------Dhaka | 17 5 286 545 238 | 1,091 | 1.56 0.46 26.21 49.95 21.81 | 100.00 | 5.88 62.50 75.46 49.32 29.35 | 42.09 -------------+-------------------------------------------------------+---------Dinajpur | 22 0 0 33 45 | 100 | 22.00 0.00 0.00 33.00 45.00 | 100.00 | 7.61 0.00 0.00 2.99 5.55 | 3.86 -------------+-------------------------------------------------------+---------Khulna | 75 0 5 37 83 | 200 | 37.50 0.00 2.50 18.50 41.50 | 100.00 | 25.95 0.00 1.32 3.35 10.23 | 7.72 -------------+-------------------------------------------------------+---------Kushtia | 11 0 1 12 76 | 100 | 11.00 0.00 1.00 12.00 76.00 | 100.00 | 3.81 0.00 0.26 1.09 9.37 | 3.86 -------------+-------------------------------------------------------+---------Mymensingh | 0 0 7 48 45 | 100 | 0.00 0.00 7.00 48.00 45.00 | 100.00 | 0.00 0.00 1.85 4.34 5.55 | 3.86 -------------+-------------------------------------------------------+---------Rajshahi | 4 0 20 94 33 | 151 | 2.65 0.00 13.25 62.25 21.85 | 100.00 | 1.38 0.00 5.28 8.51 4.07 | 5.83 -------------+-------------------------------------------------------+---------Rangpur | 15 3 2 31 49 | 100 | 15.00 3.00 2.00 31.00 49.00 | 100.00 | 5.19 37.50 0.53 2.81 6.04 | 3.86 -------------+-------------------------------------------------------+---------Sylhet | 0 0 8 85 7 | 100 | 0.00 0.00 8.00 85.00 7.00 | 100.00 | 0.00 0.00 2.11 7.69 0.86 | 3.86 -------------+-------------------------------------------------------+---------Total | 289 8 379 1,105 811 | 2,592 | 11.15 0.31 14.62 42.63 31.29 | 100.00 | 100.00 100.00 100.00 100.00 100.00 | 100.00 --------------------------------------------------------------------------------

B. Home ownership --------------------------------------------------------------------City |Govt. land Other Rented Self | Total -------------+--------------------------------------------+---------Barishal | 13 1 50 36 | 100 | 13.00 1.00 50.00 36.00 | 100.00 | 3.64 1.82 3.74 4.27 | 3.86 -------------+--------------------------------------------+---------Bogra | 3 6 23 68 | 100 | 3.00 6.00 23.00 68.00 | 100.00 | 0.84 10.91 1.72 8.07 | 3.86 -------------+--------------------------------------------+---------Brahmanbaria | 6 1 33 60 | 100 | 6.00 1.00 33.00 60.00 | 100.00 | 1.68 1.82 2.47 7.12 | 3.86 -------------+--------------------------------------------+---------Chittagong | 21 1 135 93 | 250 | 8.40 0.40 54.00 37.20 | 100.00 | 5.88 1.82 10.10 11.03 | 9.65 -------------+--------------------------------------------+---------Comilla | 17 0 31 52 | 100 | 17.00 0.00 31.00 52.00 | 100.00 | 4.76 0.00 2.32 6.17 | 3.86 -------------+--------------------------------------------+---------Dhaka | 63 19 779 230 | 1,091 | 5.77 1.74 71.40 21.08 | 100.00 | 17.65 34.55 58.26 27.28 | 42.09 -------------+--------------------------------------------+---------Dinajpur | 37 4 10 49 | 100 | 37.00 4.00 10.00 49.00 | 100.00 | 10.36 7.27 0.75 5.81 | 3.86 -------------+--------------------------------------------+---------Khulna | 73 4 93 30 | 200 | 36.50 2.00 46.50 15.00 | 100.00 | 20.45 7.27 6.96 3.56 | 7.72 -------------+--------------------------------------------+---------Kushtia | 74 0 10 16 | 100 | 74.00 0.00 10.00 16.00 | 100.00 | 20.73 0.00 0.75 1.90 | 3.86 -------------+--------------------------------------------+---------Mymensingh | 33 9 51 7 | 100 | 33.00 9.00 51.00 7.00 | 100.00 | 9.24 16.36 3.81 0.83 | 3.86 -------------+--------------------------------------------+---------Rajshahi | 12 5 24 110 | 151 | 7.95 3.31 15.89 72.85 | 100.00 | 3.36 9.09 1.80 13.05 | 5.83 -------------+--------------------------------------------+---------Rangpur | 5 5 27 63 | 100 | 5.00 5.00 27.00 63.00 | 100.00 | 1.40 9.09 2.02 7.47 | 3.86 -------------+--------------------------------------------+---------Sylhet | 0 0 71 29 | 100 | 0.00 0.00 71.00 29.00 | 100.00 | 0.00 0.00 5.31 3.44 | 3.86 -------------+--------------------------------------------+---------Total | 357 55 1,337 843 | 2,592 | 13.77 2.12 51.58 32.52 | 100.00 | 100.00 100.00 100.00 100.00 | 100.00 ---------------------------------------------------------------------

xvi

C. Room size ----------------------------------------------City | 100 or mo <100 sqf | Total -------------+----------------------+---------Barishal | 90 10 | 100 | 90.00 10.00 | 100.00 | 4.02 2.83 | 3.86 -------------+----------------------+---------Bogra | 80 20 | 100 | 80.00 20.00 | 100.00 | 3.57 5.67 | 3.86 -------------+----------------------+---------Brahmanbaria | 100 0 | 100 | 100.00 0.00 | 100.00 | 4.47 0.00 | 3.86 -------------+----------------------+---------Chittagong | 240 10 | 250 | 96.00 4.00 | 100.00 | 10.72 2.83 | 9.65 -------------+----------------------+---------Comilla | 98 2 | 100 | 98.00 2.00 | 100.00 | 4.38 0.57 | 3.86 -------------+----------------------+---------Dhaka | 896 195 | 1,091 | 82.13 17.87 | 100.00 | 40.02 55.24 | 42.09 -------------+----------------------+---------Dinajpur | 83 17 | 100 | 83.00 17.00 | 100.00 | 3.71 4.82 | 3.86 -------------+----------------------+---------Khulna | 192 8 | 200 | 96.00 4.00 | 100.00 | 8.58 2.27 | 7.72 -------------+----------------------+---------Kushtia | 96 4 | 100 | 96.00 4.00 | 100.00 | 4.29 1.13 | 3.86 -------------+----------------------+---------Mymensingh | 94 6 | 100 | 94.00 6.00 | 100.00 | 4.20 1.70 | 3.86 -------------+----------------------+---------Rajshahi | 125 26 | 151 | 82.78 17.22 | 100.00 | 5.58 7.37 | 5.83 -------------+----------------------+---------Rangpur | 71 29 | 100 | 71.00 29.00 | 100.00 | 3.17 8.22 | 3.86 -------------+----------------------+---------Sylhet | 74 26 | 100 | 74.00 26.00 | 100.00 | 3.31 7.37 | 3.86 -------------+----------------------+---------Total | 2,239 353 | 2,592 | 86.38 13.62 | 100.00 | 100.00 100.00 | 100.00 -----------------------------------------------

xvii

D. Utility Services

1. Water Supply -----------------------------------------------City | . No Yes | Total -------------+---------------------------------+---------Barishal | 0 82 18 | 100 | 0.00 82.00 18.00 | 100.00 | 0.00 6.43 1.40 | 3.86 -------------+---------------------------------+---------Bogra | 7 82 11 | 100 | 7.00 82.00 11.00 | 100.00 | 20.00 6.43 0.86 | 3.86 -------------+---------------------------------+---------Brahmanbaria | 0 91 9 | 100 | 0.00 91.00 9.00 | 100.00 | 0.00 7.14 0.70 | 3.86 -------------+---------------------------------+---------Chittagong | 0 118 132 | 250 | 0.00 47.20 52.80 | 100.00 | 0.00 9.25 10.30 | 9.65 -------------+---------------------------------+---------Comilla | 0 82 18 | 100 | 0.00 82.00 18.00 | 100.00 | 0.00 6.43 1.40 | 3.86 -------------+---------------------------------+---------Dhaka | 5 128 958 | 1,091 | 0.46 11.73 87.81 | 100.00 | 14.29 10.04 74.73 | 42.09 -------------+---------------------------------+---------Dinajpur | 0 98 2 | 100 | 0.00 98.00 2.00 | 100.00 | 0.00 7.69 0.16 | 3.86 -------------+---------------------------------+---------Khulna | 0 200 0 | 200 | 0.00 100.00 0.00 | 100.00 | 0.00 15.69 0.00 | 7.72 -------------+---------------------------------+---------Kushtia | 0 100 0 | 100 | 0.00 100.00 0.00 | 100.00 | 0.00 7.84 0.00 | 3.86 -------------+---------------------------------+---------Mymensingh | 0 69 31 | 100 | 0.00 69.00 31.00 | 100.00 | 0.00 5.41 2.42 | 3.86 -------------+---------------------------------+---------Rajshahi | 23 69 59 | 151 | 15.23 45.70 39.07 | 100.00 | 65.71 5.41 4.60 | 5.83 -------------+---------------------------------+---------Rangpur | 0 83 17 | 100 | 0.00 83.00 17.00 | 100.00 | 0.00 6.51 1.33 | 3.86 -------------+---------------------------------+---------Sylhet | 0 73 27 | 100 | 0.00 73.00 27.00 | 100.00 | 0.00 5.73 2.11 | 3.86 -------------+---------------------------------+---------Total | 35 1,275 1,282 | 2,592 | 1.35 49.19 49.46 | 100.00 | 100.00 100.00 100.00 | 100.00 ---------------------------------------------------------Note: . indicates missing data

II. Water supply in Dhaka -------------------------------------------------------------------

xviii

Nm_Cmnty | . No Yes | Total ----------------------+---------------------------------+---------Badda | 0 6 54 | 60 | 0.00 10.00 90.00 | 100.00 | 0.00 4.69 5.64 | 5.50 ----------------------+---------------------------------+---------Gabtali-Aminbazar | 1 22 58 | 81 | 1.23 27.16 71.60 | 100.00 | 20.00 17.19 6.05 | 7.42 ----------------------+---------------------------------+---------Goran/ Bashabo | 0 4 87 | 91 | 0.00 4.40 95.60 | 100.00 | 0.00 3.13 9.08 | 8.34 ----------------------+---------------------------------+---------Gulshan/Banani | 1 2 55 | 58 | 1.72 3.45 94.83 | 100.00 | 20.00 1.56 5.74 | 5.32 ----------------------+---------------------------------+---------Hazaribagh/Kamrangirc | 0 1 80 | 81 | 0.00 1.23 98.77 | 100.00 | 0.00 0.78 8.35 | 7.42 ----------------------+---------------------------------+---------Jatrabari | 0 2 42 | 44 | 0.00 4.55 95.45 | 100.00 | 0.00 1.56 4.38 | 4.03 ----------------------+---------------------------------+---------Kamlapur/Gopibagh | 0 2 57 | 59 | 0.00 3.39 96.61 | 100.00 | 0.00 1.56 5.95 | 5.41 ----------------------+---------------------------------+---------Lalbagh | 1 0 60 | 61 | 1.64 0.00 98.36 | 100.00 | 20.00 0.00 6.26 | 5.59 ----------------------+---------------------------------+---------Mirpur | 1 9 93 | 103 | 0.97 8.74 90.29 | 100.00 | 20.00 7.03 9.71 | 9.44 ----------------------+---------------------------------+---------Mohakhali | 0 1 79 | 80 | 0.00 1.25 98.75 | 100.00 | 0.00 0.78 8.25 | 7.33 ----------------------+---------------------------------+---------Mohammadpur/ Adabor | 0 1 59 | 60 | 0.00 1.67 98.33 | 100.00 | 0.00 0.78 6.16 | 5.50 ----------------------+---------------------------------+---------Rampura | 0 14 68 | 82 | 0.00 17.07 82.93 | 100.00 | 0.00 10.94 7.10 | 7.52 ----------------------+---------------------------------+---------Razarbagh/Malibagh | 0 1 69 | 70 | 0.00 1.43 98.57 | 100.00 | 0.00 0.78 7.20 | 6.42 ----------------------+---------------------------------+---------Savar | 1 55 25 | 81 | 1.23 67.90 30.86 | 100.00 | 20.00 42.97 2.61 | 7.42 ----------------------+---------------------------------+---------Uttara/Khilkhet | 0 8 72 | 80 | 0.00 10.00 90.00 | 100.00 | 0.00 6.25 7.52 | 7.33 ----------------------+---------------------------------+---------Total | 5 128 958 | 1,091 | 0.46 11.73 87.81 | 100.00 | 100.00 100.00 100.00 | 100.00 ------------------------------------------------------------------Note: . indicates missing data

xix

III. Gas Connection ---------------------------------------------------------City | . No Yes | Total -------------+---------------------------------+---------Barishal | 0 99 1 | 100 | 0.00 99.00 1.00 | 100.00 | 0.00 7.03 0.09 | 3.86 -------------+---------------------------------+---------Bogra | 7 81 12 | 100 | 7.00 81.00 12.00 | 100.00 | 20.00 5.75 1.04 | 3.86 -------------+---------------------------------+---------Brahmanbaria | 0 44 56 | 100 | 0.00 44.00 56.00 | 100.00 | 0.00 3.13 4.87 | 3.86 -------------+---------------------------------+---------Chittagong | 0 144 106 | 250 | 0.00 57.60 42.40 | 100.00 | 0.00 10.23 9.23 | 9.65 -------------+---------------------------------+---------Comilla | 0 30 70 | 100 | 0.00 30.00 70.00 | 100.00 | 0.00 2.13 6.09 | 3.86 -------------+---------------------------------+---------Dhaka | 5 202 884 | 1,091 | 0.46 18.52 81.03 | 100.00 | 14.29 14.35 76.94 | 42.09 -------------+---------------------------------+---------Dinajpur | 0 100 0 | 100 | 0.00 100.00 0.00 | 100.00 | 0.00 7.10 0.00 | 3.86 -------------+---------------------------------+---------Khulna | 0 200 0 | 200 | 0.00 100.00 0.00 | 100.00 | 0.00 14.20 0.00 | 7.72 -------------+---------------------------------+---------Kushtia | 0 100 0 | 100 | 0.00 100.00 0.00 | 100.00 | 0.00 7.10 0.00 | 3.86 -------------+---------------------------------+---------Mymensingh | 0 83 17 | 100 | 0.00 83.00 17.00 | 100.00 | 0.00 5.89 1.48 | 3.86 -------------+---------------------------------+---------Rajshahi | 23 128 0 | 151 | 15.23 84.77 0.00 | 100.00 | 65.71 9.09 0.00 | 5.83 -------------+---------------------------------+---------Rangpur | 0 100 0 | 100 | 0.00 100.00 0.00 | 100.00 | 0.00 7.10 0.00 | 3.86 -------------+---------------------------------+---------Sylhet | 0 97 3 | 100 | 0.00 97.00 3.00 | 100.00 | 0.00 6.89 0.26 | 3.86 -------------+---------------------------------+---------Total | 35 1,408 1,149 | 2,592 | 1.35 54.32 44.33 | 100.00 | 100.00 100.00 100.00 | 100.00 ----------------------------------------------------------

Note: . indicates missing data

IV. Gas Connection in Dhaka ------------------------------------------------------------------Nm_Cmnty | . No Yes | Total ----------------------+---------------------------------+---------Badda | 0 3 57 | 60 | 0.00 5.00 95.00 | 100.00 | 0.00 1.49 6.45 | 5.50 ----------------------+---------------------------------+---------Gabtali-Aminbazar | 1 4 76 | 81 | 1.23 4.94 93.83 | 100.00 | 20.00 1.98 8.60 | 7.42 ----------------------+---------------------------------+---------Goran/ Bashabo | 0 3 88 | 91 | 0.00 3.30 96.70 | 100.00 | 0.00 1.49 9.95 | 8.34 ----------------------+---------------------------------+---------Gulshan/Banani | 1 1 56 | 58 | 1.72 1.72 96.55 | 100.00 | 20.00 0.50 6.33 | 5.32 ----------------------+---------------------------------+---------Hazaribagh/Kamrangirc | 0 3 78 | 81 | 0.00 3.70 96.30 | 100.00 | 0.00 1.49 8.82 | 7.42 ----------------------+---------------------------------+---------Jatrabari | 0 0 44 | 44 | 0.00 0.00 100.00 | 100.00 | 0.00 0.00 4.98 | 4.03 ----------------------+---------------------------------+---------Kamlapur/Gopibagh | 0 4 55 | 59 | 0.00 6.78 93.22 | 100.00 | 0.00 1.98 6.22 | 5.41 ----------------------+---------------------------------+---------Lalbagh | 1 0 60 | 61 | 1.64 0.00 98.36 | 100.00 | 20.00 0.00 6.79 | 5.59 ----------------------+---------------------------------+---------Mirpur | 1 31 71 | 103 | 0.97 30.10 68.93 | 100.00 | 20.00 15.35 8.03 | 9.44 ----------------------+---------------------------------+---------Mohakhali | 0 52 28 | 80 | 0.00 65.00 35.00 | 100.00 | 0.00 25.74 3.17 | 7.33 ----------------------+---------------------------------+---------Mohammadpur/ Adabor | 0 22 38 | 60 | 0.00 36.67 63.33 | 100.00 | 0.00 10.89 4.30 | 5.50 ----------------------+---------------------------------+---------Rampura | 0 5 77 | 82 | 0.00 6.10 93.90 | 100.00 | 0.00 2.48 8.71 | 7.52 ----------------------+---------------------------------+---------Razarbagh/Malibagh | 0 10 60 | 70 | 0.00 14.29 85.71 | 100.00 | 0.00 4.95 6.79 | 6.42 ----------------------+---------------------------------+---------Savar | 1 53 27 | 81 | 1.23 65.43 33.33 | 100.00 | 20.00 26.24 3.05 | 7.42 ----------------------+---------------------------------+---------Uttara/Khilkhet | 0 11 69 | 80 | 0.00 13.75 86.25 | 100.00 | 0.00 5.45 7.81 | 7.33 ----------------------+---------------------------------+---------Total | 5 202 884 | 1,091 | 0.46 18.52 81.03 | 100.00 | 100.00 100.00 100.00 | 100.00 -------------------------------------------------------------------Note: . indicates missing data

xxi

V. Electricity ---------------------------------------------------------City | . No Yes | Total -------------+---------------------------------+---------Barishal | 0 1 99 | 100 | 0.00 1.00 99.00 | 100.00 | 0.00 0.82 4.07 | 3.86 -------------+---------------------------------+---------Bogra | 7 6 87 | 100 | 7.00 6.00 87.00 | 100.00 | 20.00 4.92 3.57 | 3.86 -------------+---------------------------------+---------Brahmanbaria | 0 7 93 | 100 | 0.00 7.00 93.00 | 100.00 | 0.00 5.74 3.82 | 3.86 -------------+---------------------------------+---------Chittagong | 0 8 242 | 250 | 0.00 3.20 96.80 | 100.00 | 0.00 6.56 9.94 | 9.65 -------------+---------------------------------+---------Comilla | 0 1 99 | 100 | 0.00 1.00 99.00 | 100.00 | 0.00 0.82 4.07 | 3.86 -------------+---------------------------------+---------Dhaka | 5 14 1,072 | 1,091 | 0.46 1.28 98.26 | 100.00 | 14.29 11.48 44.02 | 42.09 -------------+---------------------------------+---------Dinajpur | 0 18 82 | 100 | 0.00 18.00 82.00 | 100.00 | 0.00 14.75 3.37 | 3.86 -------------+---------------------------------+---------Khulna | 0 23 177 | 200 | 0.00 11.50 88.50 | 100.00 | 0.00 18.85 7.27 | 7.72 -------------+---------------------------------+---------Kushtia | 0 21 79 | 100 | 0.00 21.00 79.00 | 100.00 | 0.00 17.21 3.24 | 3.86 -------------+---------------------------------+---------Mymensingh | 0 0 100 | 100 | 0.00 0.00 100.00 | 100.00 | 0.00 0.00 4.11 | 3.86 -------------+---------------------------------+---------Rajshahi | 23 5 123 | 151 | 15.23 3.31 81.46 | 100.00 | 65.71 4.10 5.05 | 5.83 -------------+---------------------------------+---------Rangpur | 0 18 82 | 100 | 0.00 18.00 82.00 | 100.00 | 0.00 14.75 3.37 | 3.86 -------------+---------------------------------+---------Sylhet | 0 0 100 | 100 | 0.00 0.00 100.00 | 100.00 | 0.00 0.00 4.11 | 3.86 -------------+---------------------------------+---------Total | 35 122 2,435 | 2,592 | 1.35 4.71 93.94 | 100.00 | 100.00 100.00 100.00 | 100.00 ---------------------------------------------------------Note: . indicates missing data

xxii

VI. Electricity in Dhaka

------------------------------------------------------------------Nm_Cmnty | . No Yes | Total ----------------------+---------------------------------+---------Badda | 0 0 60 | 60 | 0.00 0.00 100.00 | 100.00 | 0.00 0.00 5.60 | 5.50 ----------------------+---------------------------------+---------Gabtali-Aminbazar | 1 0 80 | 81 | 1.23 0.00 98.77 | 100.00 | 20.00 0.00 7.46 | 7.42 ----------------------+---------------------------------+---------Goran/ Bashabo | 0 0 91 | 91 | 0.00 0.00 100.00 | 100.00 | 0.00 0.00 8.49 | 8.34 ----------------------+---------------------------------+---------Gulshan/Banani | 1 0 57 | 58 | 1.72 0.00 98.28 | 100.00 | 20.00 0.00 5.32 | 5.32 ----------------------+---------------------------------+---------Hazaribagh/Kamrangirc | 0 2 79 | 81 | 0.00 2.47 97.53 | 100.00 | 0.00 14.29 7.37 | 7.42 ----------------------+---------------------------------+---------Jatrabari | 0 2 42 | 44 | 0.00 4.55 95.45 | 100.00 | 0.00 14.29 3.92 | 4.03 ----------------------+---------------------------------+---------Kamlapur/Gopibagh | 0 2 57 | 59 | 0.00 3.39 96.61 | 100.00 | 0.00 14.29 5.32 | 5.41 ----------------------+---------------------------------+---------Lalbagh | 1 0 60 | 61 | 1.64 0.00 98.36 | 100.00 | 20.00 0.00 5.60 | 5.59 ----------------------+---------------------------------+---------Mirpur | 1 1 101 | 103 | 0.97 0.97 98.06 | 100.00 | 20.00 7.14 9.42 | 9.44 ----------------------+---------------------------------+---------Mohakhali | 0 0 80 | 80 | 0.00 0.00 100.00 | 100.00 | 0.00 0.00 7.46 | 7.33 ----------------------+---------------------------------+---------Mohammadpur/ Adabor | 0 1 59 | 60 | 0.00 1.67 98.33 | 100.00 | 0.00 7.14 5.50 | 5.50 ----------------------+---------------------------------+---------Rampura | 0 1 81 | 82 | 0.00 1.22 98.78 | 100.00 | 0.00 7.14 7.56 | 7.52 ----------------------+---------------------------------+---------Razarbagh/Malibagh | 0 1 69 | 70 | 0.00 1.43 98.57 | 100.00 | 0.00 7.14 6.44 | 6.42 ----------------------+---------------------------------+---------Savar | 1 1 79 | 81 | 1.23 1.23 97.53 | 100.00 | 20.00 7.14 7.37 | 7.42 ----------------------+---------------------------------+---------Uttara/Khilkhet | 0 3 77 | 80 | 0.00 3.75 96.25 | 100.00 | 0.00 21.43 7.18 | 7.33 ----------------------+---------------------------------+---------Total | 5 14 1,072 | 1,091 | 0.46 1.28 98.26 | 100.00 | 100.00 100.00 100.00 | 100.00 ------------------------------------------------------------------Note: . indicates missing data

VII. Sewerage

xxiii

---------------------------------------------------------City | . No Yes | Total -------------+---------------------------------+---------Barishal | 0 100 0 | 100 | 0.00 100.00 0.00 | 100.00 | 0.00 5.06 0.00 | 3.86 -------------+---------------------------------+---------Bogra | 7 84 9 | 100 | 7.00 84.00 9.00 | 100.00 | 20.00 4.25 1.55 | 3.86 -------------+---------------------------------+---------Brahmanbaria | 0 100 0 | 100 | 0.00 100.00 0.00 | 100.00 | 0.00 5.06 0.00 | 3.86 -------------+---------------------------------+---------Chittagong | 0 247 3 | 250 | 0.00 98.80 1.20 | 100.00 | 0.00 12.51 0.52 | 9.65 -------------+---------------------------------+---------Comilla | 0 100 0 | 100 | 0.00 100.00 0.00 | 100.00 | 0.00 5.06 0.00 | 3.86 -------------+---------------------------------+---------Dhaka | 5 533 553 | 1,091 | 0.46 48.85 50.69 | 100.00 | 14.29 26.99 95.02 | 42.09 -------------+---------------------------------+---------Dinajpur | 0 100 0 | 100 | 0.00 100.00 0.00 | 100.00 | 0.00 5.06 0.00 | 3.86 -------------+---------------------------------+---------Khulna | 0 200 0 | 200 | 0.00 100.00 0.00 | 100.00 | 0.00 10.13 0.00 | 7.72 -------------+---------------------------------+---------Kushtia | 0 100 0 | 100 | 0.00 100.00 0.00 | 100.00 | 0.00 5.06 0.00 | 3.86 -------------+---------------------------------+---------Mymensingh | 0 100 0 | 100 | 0.00 100.00 0.00 | 100.00 | 0.00 5.06 0.00 | 3.86 -------------+---------------------------------+---------Rajshahi | 23 114 14 | 151 | 15.23 75.50 9.27 | 100.00 | 65.71 5.77 2.41 | 5.83 -------------+---------------------------------+---------Rangpur | 0 97 3 | 100 | 0.00 97.00 3.00 | 100.00 | 0.00 4.91 0.52 | 3.86 -------------+---------------------------------+---------Sylhet | 0 100 0 | 100 | 0.00 100.00 0.00 | 100.00 | 0.00 5.06 0.00 | 3.86 -------------+---------------------------------+---------Total | 35 1,975 582 | 2,592 | 1.35 76.20 22.45 | 100.00 | 100.00 100.00 100.00 | 100.00 ----------------------------------------------------------

Note: . indicates missing data

xxiv

VIII. Sewerage in Dhaka city ------------------------------------------------------------------Nm_Cmnty | . No Yes | Total ----------------------+---------------------------------+---------Badda | 0 10 50 | 60 | 0.00 16.67 83.33 | 100.00 | 0.00 1.88 9.04 | 5.50 ----------------------+---------------------------------+---------Gabtali-Aminbazar | 1 23 57 | 81 | 1.23 28.40 70.37 | 100.00 | 20.00 4.32 10.31 | 7.42 ----------------------+---------------------------------+---------Goran/ Bashabo | 0 46 45 | 91 | 0.00 50.55 49.45 | 100.00 | 0.00 8.63 8.14 | 8.34 ----------------------+---------------------------------+---------Gulshan/Banani | 1 3 54 | 58 | 1.72 5.17 93.10 | 100.00 | 20.00 0.56 9.76 | 5.32 ----------------------+---------------------------------+---------Hazaribagh/Kamrangirc | 0 37 44 | 81 | 0.00 45.68 54.32 | 100.00 | 0.00 6.94 7.96 | 7.42 ----------------------+---------------------------------+---------Jatrabari | 0 38 6 | 44 | 0.00 86.36 13.64 | 100.00 | 0.00 7.13 1.08 | 4.03 ----------------------+---------------------------------+---------Kamlapur/Gopibagh | 0 3 56 | 59 | 0.00 5.08 94.92 | 100.00 | 0.00 0.56 10.13 | 5.41 ----------------------+---------------------------------+---------Lalbagh | 1 5 55 | 61 | 1.64 8.20 90.16 | 100.00 | 20.00 0.94 9.95 | 5.59 ----------------------+---------------------------------+---------Mirpur | 1 72 30 | 103 | 0.97 69.90 29.13 | 100.00 | 20.00 13.51 5.42 | 9.44 ----------------------+---------------------------------+---------Mohakhali | 0 69 11 | 80 | 0.00 86.25 13.75 | 100.00 | 0.00 12.95 1.99 | 7.33 ----------------------+---------------------------------+---------Mohammadpur/ Adabor | 0 17 43 | 60 | 0.00 28.33 71.67 | 100.00 | 0.00 3.19 7.78 | 5.50 ----------------------+---------------------------------+---------Rampura | 0 65 17 | 82 | 0.00 79.27 20.73 | 100.00 | 0.00 12.20 3.07 | 7.52 ----------------------+---------------------------------+---------Razarbagh/Malibagh | 0 7 63 | 70 | 0.00 10.00 90.00 | 100.00 | 0.00 1.31 11.39 | 6.42 ----------------------+---------------------------------+---------Savar | 1 77 3 | 81 | 1.23 95.06 3.70 | 100.00 | 20.00 14.45 0.54 | 7.42 ----------------------+---------------------------------+---------Uttara/Khilkhet | 0 61 19 | 80 | 0.00 76.25 23.75 | 100.00 | 0.00 11.44 3.44 | 7.33 ----------------------+---------------------------------+---------Total | 5 533 553 | 1,091 | 0.46 48.85 50.69 | 100.00 | 100.00 100.00 100.00 | 100.00 ------------------------------------------------------------------Note: . indicates missing data

xxv

Appendix: 4 I. Occupation of the HHH -------------------------------------------------------------------------------------------------------------------------

City | Agri.labor Day labor Housewife Rickshaw Service Small buss. No occu others | Total -------------+----------------------------------------------------------------------------------------+---------Barishal | 0 2 3 13 17 50 3 12 | 100 | 0.00 2.00 3.00 13.00 17.00 50.00 3.00 12.00 | 100.00 | 0.00 2.30 3.85 6.84 3.63 4.84 3.45 1.88 | 3.86 -------------+----------------------------------------------------------------------------------------+---------Bogra | 1 0 6 10 19 37 0 27 | 100 | 1.00 0.00 6.00 10.00 19.00 37.00 0.00 27.00 | 100.00 | 8.33 0.00 7.69 5.26 4.06 3.59 0.00 4.23 | 3.86 -------------+----------------------------------------------------------------------------------------+---------Brahmanbaria | 1 0 3 9 8 41 6 32 | 100 | 1.00 0.00 3.00 9.00 8.00 41.00 6.00 32.00 | 100.00 | 8.33 0.00 3.85 4.74 1.71 3.97 6.90 5.02 | 3.86 -------------+----------------------------------------------------------------------------------------+---------Chittagong | 0 7 7 7 56 108 7 58 | 250 | 0.00 2.80 2.80 2.80 22.40 43.20 2.80 23.20 | 100.00 | 0.00 8.05 8.97 3.68 11.97 10.47 8.05 9.09 | 9.65 -------------+----------------------------------------------------------------------------------------+---------Comilla | 1 3 1 6 16 37 1 35 | 100 | 1.00 3.00 1.00 6.00 16.00 37.00 1.00 35.00 | 100.00 | 8.33 3.45 1.28 3.16 3.42 3.59 1.15 5.49 | 3.86 -------------+----------------------------------------------------------------------------------------+---------Dhaka | 5 45 41 48 234 420 50 248 | 1,091 | 0.46 4.12 3.76 4.40 21.45 38.50 4.58 22.73 | 100.00 | 41.67 51.72 52.56 25.26 50.00 40.70 57.47 38.87 | 42.09 -------------+----------------------------------------------------------------------------------------+---------Dinajpur | 0 1 0 16 14 36 1 32 | 100 | 0.00 1.00 0.00 16.00 14.00 36.00 1.00 32.00 | 100.00 | 0.00 1.15 0.00 8.42 2.99 3.49 1.15 5.02 | 3.86 -------------+----------------------------------------------------------------------------------------+---------Khulna | 0 16 6 16 38 48 9 67 | 200 | 0.00 8.00 3.00 8.00 19.00 24.00 4.50 33.50 | 100.00 | 0.00 18.39 7.69 8.42 8.12 4.65 10.34 10.50 | 7.72 -------------+----------------------------------------------------------------------------------------+---------Kushtia | 0 7 4 13 9 39 2 26 | 100 | 0.00 7.00 4.00 13.00 9.00 39.00 2.00 26.00 | 100.00 | 0.00 8.05 5.13 6.84 1.92 3.78 2.30 4.08 | 3.86 -------------+----------------------------------------------------------------------------------------+---------Mymensingh | 0 1 3 17 6 65 2 6 | 100 | 0.00 1.00 3.00 17.00 6.00 65.00 2.00 6.00 | 100.00 | 0.00 1.15 3.85 8.95 1.28 6.30 2.30 0.94 | 3.86 -------------+----------------------------------------------------------------------------------------+---------Rajshahi | 3 1 2 6 28 61 2 48 | 151 | 1.99 0.66 1.32 3.97 18.54 40.40 1.32 31.79 | 100.00 | 25.00 1.15 2.56 3.16 5.98 5.91 2.30 7.52 | 5.83 -------------+----------------------------------------------------------------------------------------+---------Rangpur | 0 0 2 8 16 41 2 31 | 100 | 0.00 0.00 2.00 8.00 16.00 41.00 2.00 31.00 | 100.00 | 0.00 0.00 2.56 4.21 3.42 3.97 2.30 4.86 | 3.86 -------------+----------------------------------------------------------------------------------------+---------Sylhet | 1 4 0 21 7 49 2 16 | 100 | 1.00 4.00 0.00 21.00 7.00 49.00 2.00 16.00 | 100.00 | 8.33 4.60 0.00 11.05 1.50 4.75 2.30 2.51 | 3.86 -------------+----------------------------------------------------------------------------------------+---------Total | 12 87 78 190 468 1,032 87 638 | 2,592 | 0.46 3.36 3.01 7.33 18.06 39.81 3.36 24.61 | 100.00 | 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 | 100.00 -----------------------------------------------------------------------------------------------------------------

xxvi

II. Occupation of the HHH ------------------------------------------------------------------------------------------------------------------------------------

Nm_Cmnty | Agricultu Day labou Housewife Rickshaw Service Small Bus Without a others | Total ----------------------+----------------------------------------------------------------------------------------+---------Badda | 1 2 0 1 18 29 0 9 | 60 | 1.67 3.33 0.00 1.67 30.00 48.33 0.00 15.00 | 100.00 | 20.00 4.44 0.00 2.08 7.69 6.90 0.00 3.63 | 5.50 ----------------------+----------------------------------------------------------------------------------------+---------Gabtali-Aminbazar | 1 1 5 6 18 22 1 27 | 81 | 1.23 1.23 6.17 7.41 22.22 27.16 1.23 33.33 | 100.00 | 20.00 2.22 12.20 12.50 7.69 5.24 2.00 10.89 | 7.42 ----------------------+----------------------------------------------------------------------------------------+---------Goran/ Bashabo | 0 5 0 6 19 37 2 22 | 91 | 0.00 5.49 0.00 6.59 20.88 40.66 2.20 24.18 | 100.00 | 0.00 11.11 0.00 12.50 8.12 8.81 4.00 8.87 | 8.34 ----------------------+----------------------------------------------------------------------------------------+---------Gulshan/Banani | 0 0 2 1 20 27 1 7 | 58 | 0.00 0.00 3.45 1.72 34.48 46.55 1.72 12.07 | 100.00 | 0.00 0.00 4.88 2.08 8.55 6.43 2.00 2.82 | 5.32 ----------------------+----------------------------------------------------------------------------------------+---------Hazaribagh/Kamrangirc | 0 6 0 3 13 41 3 15 | 81 | 0.00 7.41 0.00 3.70 16.05 50.62 3.70 18.52 | 100.00 | 0.00 13.33 0.00 6.25 5.56 9.76 6.00 6.05 | 7.42 ----------------------+----------------------------------------------------------------------------------------+---------Jatrabari | 0 2 0 0 8 28 0 6 | 44 | 0.00 4.55 0.00 0.00 18.18 63.64 0.00 13.64 | 100.00 | 0.00 4.44 0.00 0.00 3.42 6.67 0.00 2.42 | 4.03 ----------------------+----------------------------------------------------------------------------------------+---------Kamlapur/Gopibagh | 0 3 4 5 10 19 1 17 | 59 | 0.00 5.08 6.78 8.47 16.95 32.20 1.69 28.81 | 100.00 | 0.00 6.67 9.76 10.42 4.27 4.52 2.00 6.85 | 5.41 ----------------------+----------------------------------------------------------------------------------------+---------Lalbagh | 1 0 0 2 16 37 0 5 | 61 | 1.64 0.00 0.00 3.28 26.23 60.66 0.00 8.20 | 100.00 | 20.00 0.00 0.00 4.17 6.84 8.81 0.00 2.02 | 5.59 ----------------------+----------------------------------------------------------------------------------------+---------Mirpur | 1 5 8 8 11 33 11 26 | 103 | 0.97 4.85 7.77 7.77 10.68 32.04 10.68 25.24 | 100.00 | 20.00 11.11 19.51 16.67 4.70 7.86 22.00 10.48 | 9.44 ----------------------+----------------------------------------------------------------------------------------+---------Mohakhali | 0 2 8 3 16 32 6 13 | 80 | 0.00 2.50 10.00 3.75 20.00 40.00 7.50 16.25 | 100.00 | 0.00 4.44 19.51 6.25 6.84 7.62 12.00 5.24 | 7.33 ----------------------+----------------------------------------------------------------------------------------+---------Mohammadpur/ Adabor | 0 0 1 2 18 26 2 11 | 60 | 0.00 0.00 1.67 3.33 30.00 43.33 3.33 18.33 | 100.00 | 0.00 0.00 2.44 4.17 7.69 6.19 4.00 4.44 | 5.50 ----------------------+----------------------------------------------------------------------------------------+---------Rampura | 0 3 3 2 8 18 3 45 | 82 | 0.00 3.66 3.66 2.44 9.76 21.95 3.66 54.88 | 100.00 | 0.00 6.67 7.32 4.17 3.42 4.29 6.00 18.15 | 7.52 ----------------------+----------------------------------------------------------------------------------------+---------Razarbagh/Malibagh | 0 4 3 1 26 17 0 19 | 70 | 0.00 5.71 4.29 1.43 37.14 24.29 0.00 27.14 | 100.00 | 0.00 8.89 7.32 2.08 11.11 4.05 0.00 7.66 | 6.42 ----------------------+----------------------------------------------------------------------------------------+---------Savar | 1 3 2 4 11 34 12 14 | 81 | 1.23 3.70 2.47 4.94 13.58 41.98 14.81 17.28 | 100.00 | 20.00 6.67 4.88 8.33 4.70 8.10 24.00 5.65 | 7.42 ----------------------+----------------------------------------------------------------------------------------+---------Uttara/Khilkhet | 0 9 5 4 22 20 8 12 | 80 | 0.00 11.25 6.25 5.00 27.50 25.00 10.00 15.00 | 100.00 | 0.00 20.00 12.20 8.33 9.40 4.76 16.00 4.84 | 7.33 ----------------------+----------------------------------------------------------------------------------------+---------Total | 5 45 41 48 234 420 50 248 | 1,091 | 0.46 4.12 3.76 4.40 21.45 38.50 4.58 22.73 | 100.00 | 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 | 100.00 ---------------------------------------------------------------------------------------------------------------------------------------

xxvii

Note: . indicates missing data

xxviii

xxix

xxx

Sylhet --------------------------------------------------------------Small_bussi | Freq. Percent Cum. ---------------------------+----------------------------------. | 85 85.00 85.00 electric shop | 1 1.00 86.00 petty shopkeeping | 3 3.00 89.00 rice selling | 1 1.00 90.00 selling sola,muri | 2 2.00 92.00 tea stall | 4 4.00 96.00 vegetable business | 4 4.00 100.00 ---------------------------+----------------------------------Total | 100 100.00 ---------------------------------------------------------------

xxxi

III. the income of HH in different cities (up to Tk. 10,000) ---------------------------------------------------------------------------------------------------------------

City | 2001-3000 3001-4000 4001-5000 5001-6000 6001-8000 8001-1000 <2000 | Total -------------+-----------------------------------------------------------------------------+---------Barishal | 2 4 10 18 11 15 0 | 60 | 3.33 6.67 16.67 30.00 18.33 25.00 0.00 | 100.00 | 1.67 2.48 3.98 7.66 2.72 4.72 0.00 | 3.94 -------------+-----------------------------------------------------------------------------+---------Bogra | 3 6 8 10 17 12 3 | 59 | 5.08 10.17 13.56 16.95 28.81 20.34 5.08 | 100.00 | 2.50 3.73 3.19 4.26 4.21 3.77 9.38 | 3.88 -------------+-----------------------------------------------------------------------------+---------Brahmanbaria | 0 4 5 10 16 9 0 | 44 | 0.00 9.09 11.36 22.73 36.36 20.45 0.00 | 100.00 | 0.00 2.48 1.99 4.26 3.96 2.83 0.00 | 2.89 -------------+-----------------------------------------------------------------------------+---------Chittagong | 5 13 21 22 47 39 2 | 149 | 3.36 8.72 14.09 14.77 31.54 26.17 1.34 | 100.00 | 4.17 8.07 8.37 9.36 11.63 12.26 6.25 | 9.80 -------------+-----------------------------------------------------------------------------+---------Comilla | 0 4 6 10 14 13 0 | 47 | 0.00 8.51 12.77 21.28 29.79 27.66 0.00 | 100.00 | 0.00 2.48 2.39 4.26 3.47 4.09 0.00 | 3.09 -------------+-----------------------------------------------------------------------------+---------Dhaka | 11 16 48 74 151 149 3 | 452 | 2.43 3.54 10.62 16.37 33.41 32.96 0.66 | 100.00 | 9.17 9.94 19.12 31.49 37.38 46.86 9.38 | 29.72 -------------+-----------------------------------------------------------------------------+---------Dinajpur | 6 8 9 16 20 18 1 | 78 | 7.69 10.26 11.54 20.51 25.64 23.08 1.28 | 100.00 | 5.00 4.97 3.59 6.81 4.95 5.66 3.13 | 5.13 -------------+-----------------------------------------------------------------------------+---------Khulna | 18 38 57 21 37 10 5 | 186 | 9.68 20.43 30.65 11.29 19.89 5.38 2.69 | 100.00 | 15.00 23.60 22.71 8.94 9.16 3.14 15.63 | 12.23 -------------+-----------------------------------------------------------------------------+---------Kushtia | 12 12 21 13 18 13 1 | 90 | 13.33 13.33 23.33 14.44 20.00 14.44 1.11 | 100.00 | 10.00 7.45 8.37 5.53 4.46 4.09 3.13 | 5.92 -------------+-----------------------------------------------------------------------------+---------Mymensingh | 12 15 22 10 12 5 5 | 81 | 14.81 18.52 27.16 12.35 14.81 6.17 6.17 | 100.00 | 10.00 9.32 8.76 4.26 2.97 1.57 15.63 | 5.33 -------------+-----------------------------------------------------------------------------+---------Rajshahi | 19 11 24 11 37 17 4 | 123 | 15.45 8.94 19.51 8.94 30.08 13.82 3.25 | 100.00 | 15.83 6.83 9.56 4.68 9.16 5.35 12.50 | 8.09 -------------+-----------------------------------------------------------------------------+---------Rangpur | 7 6 5 11 15 16 1 | 61 | 11.48 9.84 8.20 18.03 24.59 26.23 1.64 | 100.00 | 5.83 3.73 1.99 4.68 3.71 5.03 3.13 | 4.01 -------------+-----------------------------------------------------------------------------+---------Sylhet | 25 24 15 9 9 2 7 | 91 | 27.47 26.37 16.48 9.89 9.89 2.20 7.69 | 100.00 | 20.83 14.91 5.98 3.83 2.23 0.63 21.88 | 5.98 -------------+-----------------------------------------------------------------------------+---------Total | 120 161 251 235 404 318 32 | 1,521 | 7.89 10.59 16.50 15.45 26.56 20.91 2.10 | 100.00 | 100.00 100.00 100.00 100.00 100.00 100.00 100.00 | 100.00 ---------------------------------------------------------------------------------------------------------------

-----------------------------------------------

xxxii

V. Other sources of money ------------------------------------------------------------------------------------------City | . Bank Loan neighbor savings moneylender relatives| Total -------------+------------------------------------------------------------------+---------Barishal | 61 2 0 28 1 7 | 99 | 61.62 2.02 0.00 28.28 1.01 7.07 | 100.00 | 3.65 18.18 0.00 4.76 1.43 4.93 | 3.83 -------------+------------------------------------------------------------------+---------Bogra | 52 2 9 26 10 1 | 100 | 52.00 2.00 9.00 26.00 10.00 1.00 | 100.00 | 3.11 18.18 8.91 4.42 14.29 0.70 | 3.87 -------------+------------------------------------------------------------------+---------Brahmanbaria | 67 1 0 28 1 3 | 100 | 67.00 1.00 0.00 28.00 1.00 3.00 | 100.00 | 4.00 9.09 0.00 4.76 1.43 2.11 | 3.87 -------------+------------------------------------------------------------------+---------Chittagong | 210 0 17 16 0 7 | 250 | 84.00 0.00 6.80 6.40 0.00 2.80 | 100.00 | 12.55 0.00 16.83 2.72 0.00 4.93 | 9.67 -------------+------------------------------------------------------------------+---------Comilla | 84 0 4 6 3 3 | 100 | 84.00 0.00 4.00 6.00 3.00 3.00 | 100.00 | 5.02 0.00 3.96 1.02 4.29 2.11 | 3.87 -------------+------------------------------------------------------------------+---------Dhaka | 619 6 32 320 33 75 | 1,085 | 57.05 0.55 2.95 29.49 3.04 6.91 | 100.00 | 37.00 54.55 31.68 54.42 47.14 52.82 | 41.97 -------------+------------------------------------------------------------------+---------Dinajpur | 64 0 2 20 9 5 | 100 | 64.00 0.00 2.00 20.00 9.00 5.00 | 100.00 | 3.83 0.00 1.98 3.40 12.86 3.52 | 3.87 -------------+------------------------------------------------------------------+---------Khulna | 156 0 5 28 8 3 | 200 | 78.00 0.00 2.50 14.00 4.00 1.50 | 100.00 | 9.32 0.00 4.95 4.76 11.43 2.11 | 7.74 -------------+------------------------------------------------------------------+---------Kushtia | 69 0 2 25 1 3 | 100 | 69.00 0.00 2.00 25.00 1.00 3.00 | 100.00 | 4.12 0.00 1.98 4.25 1.43 2.11 | 3.87 -------------+------------------------------------------------------------------+---------Mymensingh | 73 0 3 8 0 16 | 100 | 73.00 0.00 3.00 8.00 0.00 16.00 | 100.00 | 4.36 0.00 2.97 1.36 0.00 11.27 | 3.87 -------------+------------------------------------------------------------------+---------Rajshahi | 114 0 1 30 4 2 | 151 | 75.50 0.00 0.66 19.87 2.65 1.32 | 100.00 | 6.81 0.00 0.99 5.10 5.71 1.41 | 5.84 -------------+------------------------------------------------------------------+---------Rangpur | 58 0 1 39 0 2 | 100 | 58.00 0.00 1.00 39.00 0.00 2.00 | 100.00 | 3.47 0.00 0.99 6.63 0.00 1.41 | 3.87 -------------+------------------------------------------------------------------+---------Sylhet | 46 0 25 14 0 15 | 100 | 46.00 0.00 25.00 14.00 0.00 15.00 | 100.00 | 2.75 0.00 24.75 2.38 0.00 10.56 | 3.87 -------------+------------------------------------------------------------------+---------Total | 1,673 11 101 588 70 142 | 2,585 | 64.72 0.43 3.91 22.75 2.71 5.49 | 100.00 | 100.00 100.00 100.00 100.00 100.00 100.00 | 100.00

---------------------------------------------------------------------------------

xxxiii

Appendix:5

I. number of loan borrowed -----------------------------------------------No_Ln_Borrd | Freq. Percent Cum. ------------+----------------------------------1 | 930 35.88 35.88 2 | 500 19.29 55.17 3 | 397 15.32 70.49 4 | 226 8.72 79.21 5 | 149 5.75 84.95 6 | 88 3.40 88.35 7 | 62 2.39 90.74 8 | 70 2.70 93.44 9 | 52 2.01 95.45 10 | 43 1.66 97.11 11 | 17 0.66 97.76 12 | 25 0.96 98.73 13 | 16 0.62 99.34 14 | 1 0.04 99.38 15 | 8 0.31 99.69 16 | 2 0.08 99.77 17 | 2 0.08 99.85 18 | 1 0.04 99.88 20 | 3 0.12 100.00 ------------+----------------------------------Total | 2,592 100.00 ------------------------------------------------

xxxiv

II. Installment types ---------------------------------------------------------------------------------------------------Nm_MFI_Attch | . Daily Monthly Other 2 times/Month weekly | Total ----------------------+------------------------------------------------------------------+---------ASA | 200 0 2 0 1 607 | 810 | 24.69 0.00 0.25 0.00 0.12 74.94 | 100.00 | 23.04 0.00 10.53 0.00 3.13 36.41 | 31.26 ----------------------+------------------------------------------------------------------+---------Association For Reali | 2 0 0 0 0 1 | 3 | 66.67 0.00 0.00 0.00 0.00 33.33 | 100.00 | 0.23 0.00 0.00 0.00 0.00 0.06 | 0.12 ----------------------+------------------------------------------------------------------+---------BRAC | 120 1 8 0 28 230 | 387 | 31.01 0.26 2.07 0.00 7.24 59.43 | 100.00 | 13.82 25.00 42.11 0.00 87.50 13.80 | 14.94 ----------------------+------------------------------------------------------------------+---------BURO | 147 1 0 0 1 118 | 267 | 55.06 0.37 0.00 0.00 0.37 44.19 | 100.00 | 16.94 25.00 0.00 0.00 3.13 7.08 | 10.30 ----------------------+------------------------------------------------------------------+---------CAP | 0 0 0 0 0 1 | 1 | 0.00 0.00 0.00 0.00 0.00 100.00 | 100.00 | 0.00 0.00 0.00 0.00 0.00 0.06 | 0.04 ----------------------+------------------------------------------------------------------+---------CARITAS Bangladesh | 0 0 0 0 0 1 | 1 | 0.00 0.00 0.00 0.00 0.00 100.00 | 100.00 | 0.00 0.00 0.00 0.00 0.00 0.06 | 0.04 ----------------------+------------------------------------------------------------------+---------DSK | 0 0 0 0 0 9 | 9 | 0.00 0.00 0.00 0.00 0.00 100.00 | 100.00 | 0.00 0.00 0.00 0.00 0.00 0.54 | 0.35 ----------------------+------------------------------------------------------------------+---------Muslim Aid-UK | 0 0 0 0 0 1 | 1 | 0.00 0.00 0.00 0.00 0.00 100.00 | 100.00 | 0.00 0.00 0.00 0.00 0.00 0.06 | 0.04 ----------------------+------------------------------------------------------------------+---------Others | 145 2 7 0 1 283 | 438 | 33.11 0.46 1.60 0.00 0.23 64.61 | 100.00 | 16.71 50.00 36.84 0.00 3.13 16.98 | 16.90 ----------------------+------------------------------------------------------------------+---------Proshika | 16 0 0 0 0 94 | 110 | 14.55 0.00 0.00 0.00 0.00 85.45 | 100.00 | 1.84 0.00 0.00 0.00 0.00 5.64 | 4.25 ----------------------+------------------------------------------------------------------+---------RIK | 7 0 0 0 0 4 | 11 | 63.64 0.00 0.00 0.00 0.00 36.36 | 100.00 | 0.81 0.00 0.00 0.00 0.00 0.24 | 0.42 ----------------------+------------------------------------------------------------------+---------Sajida Foundation | 67 0 0 1 1 90 | 159 | 42.14 0.00 0.00 0.63 0.63 56.60 | 100.00 | 7.72 0.00 0.00 100.00 3.13 5.40 | 6.14 ----------------------+------------------------------------------------------------------+---------Shakti Foundation | 154 0 1 0 0 204 | 359 | 42.90 0.00 0.28 0.00 0.00 56.82 | 100.00 | 17.74 0.00 5.26 0.00 0.00 12.24 | 13.86 ----------------------+------------------------------------------------------------------+---------Society Development C | 1 0 0 0 0 0 | 1 | 100.00 0.00 0.00 0.00 0.00 0.00 | 100.00 | 0.12 0.00 0.00 0.00 0.00 0.00 | 0.04 ----------------------+------------------------------------------------------------------+---------Thengamara Mohila Sab | 9 0 1 0 0 23 | 33 | 27.27 0.00 3.03 0.00 0.00 69.70 | 100.00 | 1.04 0.00 5.26 0.00 0.00 1.38 | 1.27 ----------------------+------------------------------------------------------------------+---------Village Education Res | 0 0 0 0 0 1 | 1 | 0.00 0.00 0.00 0.00 0.00 100.00 | 100.00 | 0.00 0.00 0.00 0.00 0.00 0.06 | 0.04 ----------------------+------------------------------------------------------------------+---------Total | 868 4 19 1 32 1,667 | 2,591 | 33.50 0.15 0.73 0.04 1.24 64.34 | 100.00 | 100.00 100.00 100.00 100.00 100.00 100.00 | 100.00 ----------------------------------------------------------------------------------------------------

Note: . indicates missing data

xxxv

Appendix: 6

Note: . indicates missing data

xxxvi

xxxvii

xxxviii

xxxix

III. Organizations providing insurance ------------------------------------------------------------------------------------> city = Barishal Ins_Org | Freq. Percent Cum. ----------------------------------------+----------------------------------ASA | 5 13.89 13.89 BURO | 2 5.56 19.44 Delta life insuravnce | 1 2.78 22.22 Don't know | 2 5.56 27.78 Golden life insurance | 1 2.78 30.56 Islami Life Insurance | 5 13.89 44.44 Jibon Bima Corporation | 3 8.33 52.78 Meghna Life Insurance | 3 8.33 61.11 National Life Insurance | 1 2.78 63.89 Padma life ins. | 1 2.78 66.67 Popular Life Insurance | 8 22.22 88.89 Sujon life insurance | 1 2.78 91.67 Sunlife insurance | 2 5.56 97.22 can't say | 1 2.78 100.00 ----------------------------------------+----------------------------------Total | 36 100.00 ------------------------------------------------------------------------------------> city = Bogra Ins_Org | Freq. Percent Cum. ----------------------------------------+----------------------------------ASA | 2 9.52 9.52 Delta life ins. | 1 4.76 14.29 Delta life insurance | 1 4.76 19.05 Fareast Islami life insurance | 2 9.52 28.57 Forest islami life insurance | 1 4.76 33.33 Islami life ins. | 5 23.81 57.14 Jibon Bima Corporation | 2 9.52 66.67 National life insurence | 2 9.52 76.19 Popular Life Insurance | 1 4.76 80.95 can't say | 3 14.29 95.24 not known | 1 4.76 100.00 ----------------------------------------+----------------------------------Total | 21 100.00

xli

xlii

xliii

-----------------------------------------------------------------------------city = Dinajpur Ins_Org | Freq. Percent Cum. ----------------------------------------+----------------------------------ASA | 2 13.33 13.33 Delta Life Insurance | 2 13.33 26.67 Islamic Life Insurance | 3 20.00 46.67 Jibon Bima Corporation | 1 6.67 53.33 Padma Life Insurance | 5 33.33 86.67 Padma Life Insurance, Islamic Life Insu | 1 6.67 93.33 Popular Life Insurence | 1 6.67 100.00 ----------------------------------------+----------------------------------Total | 15 100.00 -----------------------------------------------------------------------------city = Khulna Ins_Org | Freq. Percent Cum. ----------------------------------------+----------------------------------ASA | 4 6.78 6.78 Akota | 1 1.69 8.47 BRAC | 5 8.47 16.95 Boyra Islami Insurance | 1 1.69 18.64 CSS | 1 1.69 20.34 Delta ins. | 1 1.69 22.03 Delta, padma | 1 1.69 23.73 Griha bima | 2 3.39 27.12 Islamic life | 1 1.69 28.81 Jibon Bima Corporation | 14 23.73 52.54 Meghna ins. | 1 1.69 54.24 National Life Insurance | 2 3.39 57.63 Podma life insurance | 1 1.69 59.32 Popular life insurance | 10 16.95 76.27 Popular, ALICO | 1 1.69 77.97 Sandhani life insurence | 10 16.95 94.92 Sonalilife insurence | 1 1.69 96.61 don't know | 2 3.39 100.00 ----------------------------------------+----------------------------------Total | 59 100.00 ------------------------------------------------------------------------------------city = Kushtia Ins_Org | Freq. Percent Cum. ----------------------------------------+----------------------------------ASA | 8 20.00 20.00 BRAC | 1 2.50 22.50 BRAC-Delta Life Insurance | 1 2.50 25.00 Grameen bank | 3 7.50 32.50 Islamic life Insurance | 2 5.00 37.50 Popular life insurence | 2 5.00 42.50 Prime | 1 2.50 45.00 Sandhani Life Insurance | 18 45.00 90.00 Sun life insurence | 3 7.50 97.50 can't say | 1 2.50 100.00 ----------------------------------------+----------------------------------Total | 40 100.00 ------------------------------------------------------------------------------------> city = Mymensingh Ins_Org | Freq. Percent Cum. ----------------------------------------+----------------------------------ASA | 22 57.89 57.89 BRAC | 3 7.89 65.79 Shakti | 12 31.58 97.37 sonali bank dps | 1 2.63 100.00 ----------------------------------------+----------------------------------Total | 38 100.00

xliv

------------------------------------------------------------------------------------> city = Rajshahi Ins_Org | Freq. Percent Cum. ----------------------------------------+----------------------------------ASA | 2 4.35 4.35 Delta life insurence | 1 2.17 6.52 Golden life ins. | 2 4.35 10.87 Islami life insirance | 22 47.83 58.70 Jibon bima | 1 2.17 60.87 Meghna life insurance | 1 2.17 63.04 Popular life insurance | 7 15.22 78.26 Progostip ins. | 2 4.35 82.61 Progoti life insurance | 1 2.17 84.78 can't say | 1 2.17 86.96 don't know | 1 2.17 89.13 grameen bima prokolpo | 1 2.17 91.30 meghna life insurance | 1 2.17 93.48 popular life insurance | 1 2.17 95.65 prottasha life | 2 4.35 100.00 ----------------------------------------+----------------------------------Total | 46 100.00 ------------------------------------------------------------------------------------> city = Rangpur Ins_Org | Freq. Percent Cum. ----------------------------------------+----------------------------------ALICO, Jibon Bima | 1 5.26 5.26 Grameen bank | 1 5.26 10.53 Jibon Bima Corporation | 2 10.53 21.05 MSS | 1 5.26 26.32 Megna life insurance | 1 5.26 31.58 National life insurance | 4 21.05 52.63 Padma life insurance | 2 10.53 63.16 Podokhep | 1 5.26 68.42 Popular life insurence | 4 21.05 89.47 Progoti Life Insurence | 1 5.26 94.74 can't say | 1 5.26 100.00 ----------------------------------------+----------------------------------Total | 19 100.00 ---------------------------------------------------------------------------city = Sylhet Ins_Org | Freq. Percent Cum. ----------------------------------------+----------------------------------ASA | 21 53.85 53.85 BURO | 16 41.03 94.87 Islami life insurance | 2 5.13 100.00 ----------------------------------------+----------------------------------Total | 39 100.00

xlv

Appendix:7 i. Average increase of income ----------------------------------------------------------------------city = Barishal Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 30 3503.333 3164.51 500 15000 ----------------------------------------------------------------------city = Bogra Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 16 3687.5 2555.223 1500 12500 ----------------------------------------------------------------------city = Brahmanbaria Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 11 6109.091 6669.101 800 19500 ----------------------------------------------------------------------city = Chittagong Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 53 3828.302 3306.949 100 20700 ----------------------------------------------------------------------city = Comilla Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 23 3834.783 1790.025 1000 8500 ----------------------------------------------------------------------city = Dhaka Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 174 5149.138 3819.862 500 19000 ---------------------------------------------------------------------city = Dinajpur Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 28 3241.786 2086.908 1000 7500 ---------------------------------------------------------------------city = Khulna Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 31 2370.968 1516.178 500 7000 ----------------------------------------------------------------------city = Kushtia Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 19 2489.474 1956.725 400 7200 ----------------------------------------------------------------------city = Mymensingh Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 2 2500 2121.32 1000 4000 ----------------------------------------------------------------------city = Rajshahi Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 21 2476.19 1279.025 500 6200 -----------------------------------------------------------------------

xlvi

city = Rangpur Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 14 3726.429 2692.856 1000 10000 ----------------------------------------------------------------------city = Sylhet Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 7 4671.429 3764.622 500 10000

II. Average decline of income ----------------------------------------------------------------------city = Barishal Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 5 -2160 477.4935 -3000 -1800 ---------------------------------------------------------------------city = Bogra Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 1 -1500 . -1500 -1500 ----------------------------------------------------------------------city = Brahmanbaria Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 1 -4200 . -4200 -4200 ----------------------------------------------------------------------city = Chittagong Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 1 -3500 . -3500 -3500 ----------------------------------------------------------------------city = Comilla Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 0 ----------------------------------------------------------------------city = Dhaka Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 10 -4100 2503.331 -9000 -500 ----------------------------------------------------------------------city = Dinajpur Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 2 -2500 2121.32 -4000 -1000 ----------------------------------------------------------------------city = Khulna Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 5 -820 432.435 -1500 -500 ----------------------------------------------------------------------city = Kushtia Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 1 -4100 . -4100 -4100

xlvii

----------------------------------------------------------------------city = Mymensingh Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 0 ----------------------------------------------------------------------city = Rajshahi Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 1 -1000 . -1000 -1000 ---------------------------------------------------------------------city = Rangpur Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------hh_incm_diff | 1 -500 . -500 -500 ----------------------------------------------------------------------city = Sylhet Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------

III. Table: the state of empowerment before taking loans City

Missing data

Empowered

Empowered little

Not Empowered

Other

Total

Barishal % Bogra % Brahmanbaria

0 0.00 5 5.00 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 1 0.50 0 0.00 0 0.00 23 15.23 0 0.00 0 0.00 29 1.12

1 1.00 3 3.00 58 58.00 123 49.20 61 61.00 245 22.46 8 8.00 0 0.00 0 0.00 8 8.00 7 4.64 2 2.00 4 4.00 520 20.06

94 94.00 86 86.00 33 33.00 90 36.00 29 29.00 611 56.00 83 83.00 198 99.00 96 96.00 61 61.00 115 76.16 64 64.00 71 71.00 1,631 62.92

5 5.00 6 6.00 8 8.00 27 10.80 10 10.00 35 3.21 8 8.00 1 0.50 4 4.00 30 30.00 0 0.00 10 10.00 25 25.00 169 6.52

0 0.00 0 0.00 1 1.00 10 4.00 0 0.00 200 18.33 1 1.00 0 0.00 0 0.00 1 1.00 6 3.97 24 24.00 0 0.00 243 9.38

100 100.00 100 100.00 100 100.00 250 100.00 100 100.00 1,091 100.00 100 100.00 200 100.00 100 100.00 100 100.00 151 100.00 100 100.00 100 100.00 2,592 100.00

Chittagong % Comilla % Dhaka % Dinajpur % Khulna % Kushtia % Mymensingh % Rajshahi % Rangpur % Sylhet % Total %

xlviii

IV. Table: Hopefulness City Barishal (%) Bogra (%) Brahmanbaria (%) Chittagong (%) Comilla (%) Dhaka (%) Dinajpur (%) Khulna (%) Kushtia (%) Mymensingh (%) Rajshahi (%) Rangpur (%) Sylhet (%) Total (%)

Missing data 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 5 5.00 5 0.19

Became hopeless 0 0.00 1 1.00 0 0.00 21 8.40 0 0.00 44 4.03 4 4.00 16 8.00 0 0.00 0 0.00 5 3.31 2 2.00 1 1.00 94 3.63

Decreased little 6 6.00 1 1.00 3 3.00 8 3.20 0 0.00 51 4.67 0 0.00 3 1.50 3 3.00 1 1.00 4 2.65 1 1.00 1 1.00 82 3.16

Slightly increased 28 28.00 60 60.00 10 10.00 5 2.00 2 2.00 273 25.02 36 36.00 51 25.50 22 22.00 43 43.00 23 15.23 25 25.00 15 15.00 593 22.88

Increased

Unchanged

Other

Total

44 44.00 9 9.00 16 16.00 6 2.40 38 38.00 383 35.11 21 21.00 65 32.50 42 42.00 25 25.00 56 37.09 26 26.00 38 38.00 769 29.67

22 22.00 28 28.00 71 71.00 210 84.00 60 60.00 327 29.97 39 39.00 65 32.50 33 33.00 31 31.00 63 41.72 46 46.00 39 39.00 1,034 39.89

0 0.00 1 1.00 0 0.00 0 0.00 0 0.00 13 1.19 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 1 1.00 15 0.58

100 100.00 100 100.00 100 100.00 250 100.00 100 100.00 1,091 100.00 100 100.00 200 100.00 100 100.00 100 100.00 151 100.00 100 100.00 100 100.00 2,592 100.00

V. Table: the state of happiness

City

Happy

Moderately Happy

Barishal

19 19.00 28 28.00 52 52.00 101 40.40 56 56.00 497 45.55 22 22.00 46 23.00 24 24.00 45 45.00 16 10.60 32 32.00 49 49.00 987 38.08

57 57.00 44 44.00 44 44.00 134 53.60 39 39.00 443 40.60 47 47.00 83 41.50 46 46.00 47 47.00 81 53.64 44 44.00 42 42.00 1,151 44.41

Bogra Brahmanbaria Chittagong Comilla Dhaka Dinajpur Khulna Kushtia Mymensingh Rajshahi Rangpur Sylhet Total

Neither happy nor unhappy 22 22.00 17 17.00 3 3.00 13 5.20 3 3.00 118 10.82 26 26.00 67 33.50 26 26.00 7 7.00 46 30.46 19 19.00 9 9.00 376 14.51

Moderately unhappy

Unhappy

Total

1 1.00 9 9.00 1 1.00 2 0.80 1 1.00 18 1.65 2 2.00 2 1.00 1 1.00 1 1.00 8 5.30 3 3.00 0 0.00 49 1.89

1 1.00 2 2.00 0 0.00 0 0.00 1 1.00 15 1.37 3 3.00 2 1.00 3 3.00 0 0.00 0 0.00 2 2.00 0 0.00 29 1.12

100 100.00 100 100.00 100 100.00 250 100.00 100 100.00 1,091 100.00 100 100.00 200 100.00 100 100.00 100 100.00 151 100.00 100 100.00 100 100.00 2,592 100.00

xlix

Appendix 8: General Information of the communities

5.5 2

4. 88

6.6 7

8581.6 7

15. 0

55.0

30.0

1.10

21.75

99,565

Housewife 4%, Small Businessman 60%, Service hoder4.0%, Day labour 4.0%, Others 24%, Rickshaw puller 4.0% Jobless- 4.2%, Small Businessman16.7%, Service58.3%, Others-16.7% Rickshaw puller4.2% Jobless-4.5%, Housewife-4.5%, SmallBusinessman40.9% Service-36.4%, Others-9.1%, Rickshaw puller4.5% Housewife-2.9%, Small Businessman38.2%,Service14.7%,Day labour2.9%, Others-41.2%

1.84

11220. 00

pucca-0% Semi-pucca 28.3%, Kutcha/Jhupri71.7% Pucca-8%, Semi-pucca-72% Kutcha/Jhupri20%

92.0

8.0

21.71

10.20

453,700

1.83

11416. 67

Pucca-20.8%, Semi pucca-79.2%

50. 0

41.7

8.4

18.25

16.33

1,102,50 0

1.82

11140. 91

Pucca- 4.5% Semi pucca-59.1%, Kutcha/Jhupri36.4%

27. 3

72.7

5.23

14.68

1,367,10 5

1.76

11716. 18

Pucca- 11.8%, Semi pucca-64.7% Kutcha/Jhupri23.5%

55. 9

44.1

4.27

14.47

1,700,42 3

1.33

9512.5 0

Pucca-29.2%, Semi pucca-70.9%

58. 3

41.7

11.70

15.33

759,500

Without any occupation-4.2%, Small Businessman29.2%, Service37.5%, Others20.8%,Rickshaw puller-8.3%

% of HHs having sanitary latrine % of HHs having insuranc e % of women empowere d State of happines s ( %)

1.98

Sewe rage

13 1,408,25 0

working MFIs

Avg. asset value (Tk.)

Avg. living years

Avg. amount of land owned by

Avg. income (Tk.) of HH

Avg. no. of income

12 20.88

Gas

4. 76

11 2.84

Wate r supp l Elec tric ity

5.8 8

10 0

15 38.2

16 97.1

17 20.6

18 0

19 79.4

20 35.3

21 85.3

22 97.1

18.3

91.7

16.7

5.0

100

16.0

79.2

100

66.7

4.2

87.5

40.9

95.8

91.7

86.4

95.5

90.9

54.5

42.9

86.4

76.5

100

41.2

2.9

76.5

9.4

91.2

33.3

100

45.8

79.2

29.2

79.2

100

BURO, BRAC, Ghasful,

4. 77

9 20.6

ASA, BRAC, Shakt i Prosh

7.6 8

8 79. 4

ASA, Sajida

5. 04

7 73.5% semipucca, 11.8% pucca, 14.7% katcha

BRAC, Sajida, Proshik a

4.5 3

6 14085. 29

ASA, Sajida

4. 68

5 2.09

ASA, BRAC, Sajida, Shakti, Proshik

5.1 5

4 Businessman-67.6%, Service holder20.6% Jobless-5.9%, Daily labour-2.9% Other occupation2.9% 48.3% businessman, 10% service, 31.7% others.

Type of house

% of HHs having utility connection

BRAC, Shakti Foundati on, Proshika

4. 33

othe rs

3 5.4 5

rent ed

2 5. 09

Major share of occupation of HHHs

Home ownership Own

Pahartol i (east & west Nasiraba

Monsura bad (24 Bylane, Pachimp

Madarbari (Marissapa ra)

Eidgah (Rampur a, Madazza

Bottali Railway colony

Jamto li (14 no,

1 no Suparipa ra (Dewan

Avg. no. of family Avg. years of schoolin

Communit y name

1. Chittagong City Corporation

Sadar ghat (Jelepara, Vagolpara) 1

2 4. 78 3 4.2 8

Avg. no. of family Avg. years of schoolin

Communit y name

4 Housewife-3.7% Small Businessman 29.6% Service-22.2% Day labour-14.8% Others-25.9% Rickshaw puller -3.7% 5 2.04

Avg. income (Tk.) of HH

Avg. no. of income

Major share of occupation of HHHs 6 11525. 93 8 22. 2 9 77.8 10 0 11 4.09 12 11.89 13 771589.4 7

BURO, BRAC, Shakti Foundation Proshika

15 40.7 17 29.6

16 100

% of HHs having utility connection

18 3.7

% of HHs having sanitary latrine % of HHs having insuranc e % of women empowere d State of happines s ( %)

Sewe rage

Gas

Wate r supp l Elec tric ity

working MFIs

Avg. asset value (Tk.)

Avg. living years

Avg. amount of land owned by

othe rs

7 Pucca-18.5% Semi-pucca-51.8% Kutcha/Jhupri29.6%

rent ed

Type of house Own

Home ownership

19 18.5 20 3.8 21 70.4

li 22 88.9

11 9.51

12 13.2 8

4.30

5.6 8

2.48

15707. 41

Pucca-14.8% Semi-pucca51.8% Kutcha/Jhupri -33.3%

59. 3

37.0

3.7

6.83

16.1 1

15707.41

5.09

6.2 5

Jobless-3.7%, Housewife-3.7% Businessman-33.3% Service-11.1% Others-37.0% Rickshaw puller 11.1% Businessman-72.7% Day labour-9.1% Others-18.2%

1.82

19109. 09

Semi pucca90.9% , Kutcha/Jhupri9.1%

81. 8

9.1

5.91

20.0 9

1905714.29

5.62

5.0 0

Businessman-46.2% Service-7.7% Others- 46.2%

2.23

17219. 23

Semi pucca61.5% Kutcha/Jhupri -38.5%

30. 8

69.2

1.62

23.4 6

674666.67

4.57

8.3 3

Businessman-71.4% Service-14.3% Others-14.3%

1.86

30028. 57

Semi pucca 100%

71. 4

28.6

7.85

14.1 4

3105000.00

3.83

5.0 0

Agricultural labor-16.7% Businessman-33.3% Others-50.0%

1.50

8400.0 0

Semi pucca-100%

100

1.00

10.1 7

57750.00

15 20.7

16 100

17 93.1

18 0

19 37.9

20 41.4

21 89.7

22 100

7.4

100

66.7

25.9

33.3

81.5

96.3

45.5

100

54.5

18.2

63.6

90.9

100

23.1

100

69.2

7.7

76.9

92.3

76.9

100

85.7

57.1

28.6

100

85.7

83.3

16.7

100

Shak ti

ASA, BRAC

ASA, BRAC Shakti

ASA, BRAC, Shakti

ASA, BURO, BRAC, Sh kti

PAGE

Suvopu r Dharma pur/ Durgap ur

13 1456045.45

State of happiness ( %)

10 0

% of women empowered

9 62.1

% of HHs having sanitary l t i % of HHs having insurance

8 37. 9

Sewer age

7 Semi pucca96.6%, Kutcha/Jhupri3.4%

Gas

6 9653.4 5

Elect ricit y

5 1.72

% of HHs having utility connection Water suppl y

4 Businessman-10.3%, Service-34.5%, Day labour-6.9%, Others-37.9% Rickshaw puller10.3%

Type of house

working MFIs

Avg. asset value (Tk.)

other s

Avg. living years

rente d

Avg. amount of land owned by HHs

Own

Avg. income (Tk.) of HH

Avg. no. of income earners

3 7.4 2

Bagi cha Gaon Chanp ur

Major share of HHHâ&#x20AC;&#x2122;s occupation

Home ownership

2 4.24

Shashan gacha

Ashoktol a

Avg. no. of family members Avg. years of schooling

Community name

2. Comilla Paurashova

lii

5. 89

4.5 3

7. 64

4.3 7

3. 84

Goran/ Bashabo Uttara/K hilkhet GabtaliAminbazar Gulshan / Banani Hazariba gh/ Kamrangi rchar Jat rab ari Kamlapur / Gopibagh

Businessman-62.2% Service-17.8% Day labour-4.4% Others-15.6% Jobless-1.7% Housewife-6.8% Businessman-32.2% Service-16.9% Day labour-5.1% Others-28.8% Rickshaw puller-8.5%

Pucca-35.2% Semi pucca64.8%

11.0

89. 0

30.8 7

21.13

141153.8 5

15781. 88

Pucca-27.5%, Semi pucca67.6% Kutcha-5%

18.8

72. 5

8.8

25.9 6

10.96

1842509. 62

1.81

22000. 77

Pucca-37% Semi pucca59.2% Others-3.7%

48.1

44. 4

7.4

26.8

24.06

1480323. 08

1.88

24287. 93

Pucca-19% Semipucca– 81.0%

15.5

82. 8

1.7

51.7 9

17.76

2258854. 55

1.94

16564. 20

Pucca-45.7% Semi pucca48.2% Kutcha -6.2%

16.0

84. 0

17.4

11.58

1008343. 14

2.04

19362. 22

Pucca- 51.1% Semi pucca48.9%

22.2

77. 8

19.9 6

11.18

1470657. 89

1.64

16525. 42

Pucca-50.8% Semi pucca20.3% Kutcha-5.1% Others-3.4%

20.3

78. 0

1.7

24.6 1

19.24

1039245. 61

Wa te r

worki ng MFIs

Avg. asset value (Tk.)

Avg. livin g

Avg. amoun t of land

ot he rs

re nt ed

Ow n

Avg. incom e (Tk )

13 139897.0 6

Se we ra

4.8 3

12 17.28

El ec tr Ga s

7. 81

11 19.7 3

15 90

16 100

17 95

18 83. 3

19 65.0

20 53.3

21 93.3

22 80

95.6

100

96 .7

49. 5

57.1

39.6

83.5

81 .3

96. 3

86 .3

23. 8

68.8

25.0

93 .8

72.5

100

71. 3

81.3

28.4

96.3

81 .5

96.5

100

98 .2

94. 7

73.7

32.8

89.7

91 .4

98.8

97. 5

96 .3

54. 3

90.1

42.0

84.0

98 .8

95.6

95. 6

10 0

13. 3

86.7

37.8

93.3

95 .6

96.6

96. 6

93 .2

94. 9

74.6

30.5

96.6

84 .7

ASA, BURO, Sajida, Shakti

4.3 4

1.89

10 0

ASA, BURO, BRAC, Sajida, Shakti

4. 42

13749. 45

9 93. 3

ASA, BURO, BRAC, Sajida, Shakti

4.7 4

1.96

8 6.7

ASA, BURO, BRAC, Shakti, Proshika, DSK TMSS

7. 73

Type of house 7 Pucca-11.7% Semi pucca88.3%

ASA, BURO, BRAC, Sajida

4.5 6

6 15604. 17

ASA, BRAC, Sajida, Shakti,, RIK SDC

5. 31

5 1.97

% of HHs having utility connection

ASA , Saj ida

4.7 3

Major share of HHH’s occupation 4 Agricultural labor1.7%, Businessman48.3%, Service-30%, Day labour-3.3% Others-15.0% Rickshaw puller-1.7% Jobless-2.2%, Small Businessman-40.7%, Service-20.9%, Day labour-5.5% Others-24.2% Rickshaw puller-6.6% Jobless-10% Housewife-6.3%, Businessman-25%, Service-27.5%, Day labour-11.3% Others-15% Rickshaw puller-5% Jobless-1.2% Housewife-6.2% Agricultural labor1.2% Businessman-27.2% Service-22.2% Day labour-1.2% Others-33.3% Rickshaw puller-7.4% Jobless-1.7% Housewife-3.4% Businessman-46.6% Service-34.5% Others-12.1% Rickshaw puller-1.7% Jobless-3.7% Businessman-50.6% Service-16% Day labour-7.4% Others-18.5% Rickshaw puller-3.7%

ASA, BURO, BRAC Sajida, Shakti

3 6. 21

Avg. no. of incom e

Avg. years of

2 4.6 0

Badda

Avg. no.

Commu nity name

Home ownership

% of HHs havin g % of HHs havin g % of women empow ered State of happi

3. Dhaka City Corporation

liii

3. 79

Mohakhali Mohammadpu r/ Adabor Rampura Savar

16.73

948369.2 3

11852. 43

Pucca-8.7% Semi pucca89.4% Kutcha-1.9%

18.4

60. 2

21. 3

48.5 5

17.80

706385.4 8

1.94

11708. 75

Pucca-2.5% Semi pucca97.5%

16.3

33. 8

9.48

17.74

169627.6 6

2.22

21763. 33

Pucca-28.3% Semi pucca71.7%

26.7

68. 3

10.4 5

24.17

214194.4 4

1.88

12347. 17

Pucca- 31.7% Semi pucca67% Kutcha-1.2%

11.0

89. 0

15.5 4

7.34

576961.5 4

1.96

10401. 23

Pucca- 4.9% Semi pucca93.8% Kutcha-1.2%

44.4

54. 3

1.2

20.8 4

20.15

962333.3 3

2.17

% of HHs havin g % of HHs havin g % of women empow ered State of happi

27.0 3

Wa te r

17. 1

worki ng MFIs

Avg. asset value (Tk.)

Avg. livin g

Avg. amoun t of land

Avg. incom e (Tk )

70. 0

Se we ra

4.9 6

12.9

15781. 43

El ec tr Ga s

6. 69

Pucca-17.1% Semi pucca81.4% Kutcha-1.4%

6.16

14 ASA, BURO, BRAC, Sajida, Shakti

4.3 2

13 228258.0 6

15 100

16 100

17 10 0

18 91. 7

19 83.3

20 39.3

21 90.2

22 86 .9

98.6

98. 6

85. 7

68.6

30.0

98.6

91.2

69 .6

29. 4

43.1

38.8

82.5

92 .2

98.8

10 0

13. 8

32.5

82.5

72 .5

98.3

98 .3

63 .3

71. 7

53.3

83.3

83 .3

82.9

98 .8

93 .9

20. 7

74.4

29.3

90.2

95 .1

31.3

98 .8

33 .8

3.8

33.3

35.8

65.4

76 .5

ASA, BURO, BRAC, Shakti

4. 97

12 27.56

ASA, BURO, BRAC, Shakti, Proshika, DSK ARBAN

5.3 0

11 20.0 7

ASA, BURO, BRAC Shakti, CAP Proshika

5. 49

10 3.2

Type of house 7 Pucca-39.3% Semi pucca61.7%

% of HHs having utility connection

ASA, BURO, Sajida, Shakti, RIK

4.6 1

9 70. 5

6 17931. 97

ASA, BURO, BRAC, Sajida, Shakti, Proshika

4. 20

8 26.2

5 2.02

ASA, BURO, BRAC, Shakti, Proshika, VERC

4.7 4

ot he rs

6. 16

re nt ed

5.1 3

Major share of HHHâ&#x20AC;&#x2122;s occupation 4 Agricultural labor1.6% Businessman-60.7% Service-26.2% Others-8.2% Rickshaw puller-3.3 Housewife-4.3% Businessman-24.3% Service-37.1% Day labour-5.7% Others-27.1% Rickshaw puller-1.4% Jobless-10.7% Housewife-7.8% Agricultural labor1% Businessman-32% Service-10.7% Day labour-4.9% Others-25.2% Rickshaw puller-7.8% Jobless-7.5% Housewife-10% Small Businessman40% Service-20% Day labour-2.5% Others-16.3% Rickshaw puller-3.8% Without any occupation-3.3% Housewife-1.7% Small Businessman43.3% Service-30% Others-18.3% Rickshaw puller-3.3% Jobless-3.7% Housewife-3.7% Small Businessman22% Service-9.8% Day labour-3.7% Others-54.9% Rickshaw puller-2.4% Jobless-14.8% Housewife-2.5% Agricultural labor1.2% Small Businessman42% Service-13.6% Day labour-3.7% Others-17.3% Rickshaw puller-4.9%

Ow n

3 5. 25

Avg. no. of incom e

Avg. years of

2 5.0 5

Mirpur

Razarba gh/ Malibag h

Lalbagh

Avg. no.

Commu nity name

Home ownership

33.3

liv

23. 5

76.5

11.71

23.82

327000.0 0

3. 84

Businessman-52.6%, Service-26.3%, Others-10.5%, Rickshaw puller-10.5%

2.00

10242.1 1

Semi pucca94.7% Kutcha-5.3%

68. 4

31.6

4.63

18.32

133058.8 2

5.62

3. 85

Housewife-7.7% Businessman-76.9% Service-15.4%

2.08

9923.08

Semi pucca 100%

69. 2

30.8

27.62

23.38

270416.6 7

4.13

1. 63

Businessman-25%, Service-25%, Others-25% Rickshaw puller-25%

1.50

7937.50

Semi pucca 75% Kutcha-25%

37.5

62.5

1.38

22.13

36416.67

4.13

6. 38

Housewife-12.5%, Businessman-50%, Service-37.5%

2.00

15462.5 0

Pucca-50% Semi pucca 50%

100

17.88

16.75

279000.0 0

BUR O, Sha kti

5.05

% of women empowere State of happines s ( %)

Pucca-5.9% Semi pucca82.4% Kutcha-11.8%

% of HHs having insuranc e

33452.9 4

5. 00

Sewer age

2.12

5.53

Elect ricit y Gas

13 122470.5 9

Water suppl y

12 20.47

working MFIs

11 4.52

15 0

16 100

17 0

18 0

19 23.5

20 41.2

21 10 0

22 88. 2

64.7

100

41.2

23.5

88 .2

88. 2

100

15.8

31.6

94 .7

73. 7

100

30.8

23.1

10 0

61. 5

100

10 0

87. 5

100

12 .5

62.5

10 0

87. 5

ASA, BRAC Shakt i

10 11.8

ASA, BURO, BRAC

9 58.8

ASA, BURO BRAC, Shakti

8 29. 4

ASA Shakt i

7 Pucca -5.9% Semipucca82.4% Kutcha-11.8%

Type of house

% of HHs having utility connection

ASA , BRA C, Sha

Avg. asset value (Tk.)

other s

Avg. living years

rente d

Avg. amount of land owned by

Own

Bisic, East Kaunia

Avg. income (Tk.) of HH 6 11223.5 3

k.d.c balugh at Pol ash pur

Avg. no. of income earners 5 2.12

3 3. 06

Stadi um math, Bango TTC Col y, CNB

Major share of occupation 4 Jobless-11.8%, Businessman-29.4%, Day labour-5.9%, Others-29.4%, Rickshaw puller-23.5% Jobless-5.9% Businessman-58.8%, Service-17.6%, Day labour-5.9% Rickshaw puller-11.8%

2 5.24

Banga bandh u refui

Avg. no. of f il Avg. years of schoolin

Communit y name

Home ownership

% of HHs having sanitary latrine

4. Barishal Pauroshava

4.5 0

1.6 7

3.9 2

2.5 4

Semi pucca84% Kutcha-16%

2.32

15.88

55368.42

1.53

7905.8 8

Semi pucca100%

23 .5

76.5

1.69

22.65

102562.5 0

1.45

6460.0 0

Semi pucca95% Kutcha-5%

5.0

10 .0

85.0

1.88

26.30

84529.41

1.83

8366.6 7

100

4.04

34.33

214166.6 7

1.38

5788.4 6

Semi pucca83.3% Kutcha-16.7% Semi pucca69.2% Kutcha-30.8%

7.7

92.3

.48

17.38

42291.67

State of happine ss ( %)

5636.0 0

% of HHs having i % of women empower ed

1.64

Se we ra % of HHs having

13 94090.91

El ec tr Ga s

12 19.14

Wa te r

11 7.33

working MFIs

Avg. asset value (Tk.)

Avg. living years

Avg. amount of land

Avg. income (Tk.) of HH

10 64.3

15 0

16 10 0

17 0

18 0

19 42. 9

20 57.1

21 92.9

22 78.6

35 .3

41. 2

17.6

100

10 0

45.0

100

10 0

16. 7

33.3

100

83.3

30 .8

15. 4

15.4

100

53.8

ASA, BURO, Shakti

3.4 0

9 14 .3

ASA, BURO, Shakti, Muslim Aid UK

4.1 5

8 21. 4

ASA, BURO, BRAC

2.8 2

7 Semi pucca78.6% Kutcha-7.1%

% of HH having utility connection

ASA, BURO, BRAC Shakt i

4.1 2

6 6021.4 3

BR AC

3.4 2

Type of house

5 1.29

ASA, BRAC,

3.7 2

4 Businessman-21.4%, Service-7.1%, Day labour-7.1, others-50% Rickshaw puller14.3% Jobless-4% Housewife-12% Businessman-36% Service-12% Day labour-8%, others-20%, Rickshaw puller-8% Housewife-5.9% Businessman-47.1%, Service-11.8%, others-11.8%, Rickshaw puller23.5% Businessman-20% Service-10%, Day labour-15%, Others-45%, Rickshaw puller-10% Businessman-66.7, Day labour-16.7% Others-16.7% Small Businessman61.5% Others-15.4% Rickshaw puller23.1%

re nt ed ot he rs

3 4.2 9

Avg. no. of income earners

Avg. years of

2 4.0 7

Major share of occupation

Home ownership

Ow n

housi ng C & D block

Ch eu ri a

Char thana para

Char mill para

Char amlapara

Arshad nagar

Avg. no. of

Communi ty name

5. Kushtia Pauroshava

lvi

5.13

4.6 9

5.25

3.7 8

Shimrail Kandi Vadu ghar

12 8.33

13 1865650.0

1.7 1

19376. 47

Pucca- 11.8% Semi pucca58.8% Kutcha-29.4%

52.9

41.9

5.2

5.16

16.76

1277142.86

2.0 0

17542. 86

Semi pucca100%

85.7

14.3

2.00

21.14

1161857.14

1.9 0

15920. 83

Pucca-4.2% Semi pucca64.6% Kutcha-31.3%

62.5

35.4

2.1

14.53

19.17

2094547.62

1.9 4

21268. 75

Semi pucca18.8% Kutcha-81.3%

56.3

12.5

31.3

8.00

16.63

538884.62

% of HHs having i % of HHs having i % of women empower d State of happine ( %)

11 28.54

Sew era ge

10 0

Gas

9 50

15 0

16 100

17 83.3

18 0

19 33.3

20 66.7

21 91.7

22 100

17.6

100

47.1

17.6

23.5

82.4

94.1

85.7

100

28.6

14.3

85.7

97.9

60.4

12.5

45.8

91.7

97.9

62.5

12.5

6.3

12.5

100

93.8

ASA, BRAC

8 50

Wat er sup l Ele ctr ici

Avg. asset value (Tk.)

Avg. living years

Avg. amount of land

Own

Avg. income (Tk.) of HH

Avg. no. of

7 Pucca-8.3% Semi pucca58.4% Kutcha-33.3%

Type of house

AS A, BR C

4.0 0

Pa ik pa

6.00

6 12550. 00

ASA, BRAC Shakti

3.2 5

5 1.5 0

% of HHs having utility connection

ASA

5.29

4 Jobless-8.3% Small Businessman50% Others-25% Rickshaw puller -16.7% Jobless -11.8% Small Businessman29.4%, Service-5.9%, Others-47.1%, Rickshaw puller-5.9% Housewife-14.3% Small Businessman57.1%, others-28.6% Jobless -4.2% Housewife-4.2% Agricultural labor2.1%, Businessman41.7%, Service-8.3%, others-31.3%, Rickshaw puller-8.3% Jobless -6.3% Businessman-37.5%, Service-18.8%, others-25%, Rickshaw puller12.5%

oth ers

3 4.7 8

Major share of HHHâ&#x20AC;&#x2122;s occupation

ren ted

2 4.17

Medda (east & )

Khaiyas ar

Avg. no. of f il Avg. years of

Communi ty name

Home ownership

working MFIs

6. Brahmanbaria Pauroshava

lvii

2.13

11 .28

12 29.78

13 43333.33

1.17

5279.1 7

Semi pucca 100%

100

30.17

37500.00

1.67

7216.6 7

Semi pucca 100%

4. 2

16. 7

79.1

.208 3

29.92

23000.00

1.32

6232.2 6

Pucca-6.5% Semi pucca93.5%

3. 2

35. 5

61.3

.16

28.10

41666.67

Sew era ge

10 8.9

workin g MFIs

Avg. asset value (Tk.)

Avg. living years

Avg. amount of land

Own

Avg. income (Tk.) of HH

Avg. no. of i

9 80

Gas

4.3 9

Jobless -4.2%, Housewife- 8.3% , Businessman-66.7%, Service-4.2%, Others4.2% Rickshaw puller12.5% Jobless -3.2% Businessman-51.6%, Service-9.7%, Day labour-3.2%, others-9.7%, Rickshaw puller22.6%

8 11 .1

Wat er sup Ele ctr ici

2.00

7 Pucca-11.1% Semi pucca 88.9%

Type of house

ASA Shakti

4.5 8

6 7148.3 3

AS A

5.17

5 1.51

ASA, BRAC Shakti

4.5 0

4 Housewife-2.2% Businessman-73.3%, Service-4.4%, others 4.4%, Rickshaw puller15.6% Businessman-83.3% Others-16.7%

% of HH having utility connection

15 25. 6

16 100

17 10.3

18 0

19 77.8

20 62.2

21 82.2

22 88.9

33. 3

100

66.7

83.3

66. 7

100

54.2

29.2

62.5

100

9.7

100

41.9

9.7

58.1

90.3

ASA, BRAC, Shakti

3 4.65

oth ers

2 4.5 6

Major share of occupation

ren ted

36 Bari Colony & Bash bari Colony

Kristapur

Dhup khol a (Dou

Charpar a & Charpar a

Avg. no. of Avg. years of h l

Commun ity name

Home ownership

% of HHs having i % of HHs having i % of women empowe d State of happin (

7. Mymensingh Pauroshava

lviii

Fulbar Dhauap i ara (karig (North orpara ) Malgr am Sheuzgari North Maloti Chalop nagar ara

Semi pucca 100%

100

13.75

26.30

521000

1.53

8720.0 0

Semi pucca 100%

53. 3

26. 7

10.18

28.53

263200

1.29

8379.1 7

Pucca- 12.5% Semi pucca87.5%

91. 7

8.3

17.32

15.06

414875

1.90

20170

Pucca- 20% Semi pucca-80%

100

3.2

924300

1.53

7900

33. 3

26 .7

19.03

29.60

310433.33

1.82

9590.9 1

Pucca- 6.7% Semi pucca86.6% Kutcha6.7% Semi pucca-100%

27. 3

54. 5

18 .2

16.32

17.73

167818.18

% of HHs having it % of HHs having i % of women empower d State of happine ( %)

11700. 00

Sew era ge

1.40

Gas

13 394000

Wat er sup Ele ctr ici

12 23.10

working MFIs

11 1.70

ASA, Shak ti, TMSS

4.00

10 0

15 40

16 80

17 50

18 40

19 70

20 20

21 100

22 70

100

6.7

93.3

100

11.8

52.4

33.3

100

86. 7

26.7

13.3

100

46.7

81. 8

18.2

27.3

9.1

90.9

TMS S

4.0 9

9 60

ASA , BRA C,

4.60

8 40

ASA, BRAC Shakt i

4.6 7

7 Semi pucca 100%

BRA C

8.10

6 14635. 00

% of HH having utility connection

ASA , BRA C

4.2 0

5 1.80

ASA, BRAC, TMSS Shakti

6.33

Avg. asset value (Tk.)

3.7 9

Avg. living years

5.73

Avg. amount of land

4.2 7

oth ers

7.3

ren ted

4.3 0

4 Housewife-10% Businessman-30% Service-30% Others-30% Housewife-10% Businessman-40%, Service-30%, Others20% Businessman-40% Others-33.3% Rickshaw puller26.7% Agricultural labor4.2% Businessman-37.5%, Service-25%, Others-33.3% Businessman-60%, Service-10%, Others10% Rickshaw puller20% Housewife-6.7% Businessman-26.7% Service-26.7% Others-40% Housewife-18.2%, Businessman-18.2%, Service-9.2%, Others-18.2%, Rickshaw puller36.4%

Type of house

Own

3 6.20

Major share of HHHâ&#x20AC;&#x2122;s occupation

Avg. income (Tk.) of HH

2 4.8 0

Home ownership Avg. no. of income

Avg. no. of family Avg. years of schooli

1 Chok lokm an

Communi ty name

8. Bogra Pauroshava

lix

3.00

7093.7 5

Pucca- 12.5% Semi pucca87.5%

18. 8

81. 3

0.5

33.00

44666.67

1.47

3896.8 8

Pucca- 3.1% Semi pucca96.9%

40. 6

59. 4

.0625

29.50

37888.89

1.29

5104.7 6

Pucca- 9.5% Semi pucca90.5%

42. 9

57. 1

.095

28.38

1.40

7200.0 0

Pucca- 10% Semi pucca-90.%

.40

30.30

15 9. 5

16 100

17 0

18 0

19 57.1

20 47.6

21 90.5

22 100

68 .8

100

43.8

12.5

68.8

100

34 .4

100

65.6

59.4

71.9

81.3

9. 5

100

9.5

47.6

14.3

61.9

90.5

100

Sew era ge

1.19

Gas

13 49625

Wat er sup Ele ctr ici

12 29.48

working MFIs

Avg. asset value (Tk.)

Avg. living years

Avg. amount of land owned by

Avg. income (Tk.) of HH

Avg. no. of income

11 .45

% of women empowere d State of happines s ( %)

4.1 0

10 0

% of HHs having sanitary l t i % of HHs having insuranc

3.29

9 95. 2

ASA, BURO, BRAC

4.0 5

8 4.2

ASA , BUR O, BRA

3.56

7 Pucca- 9.5% Semi pucca90.5%

ASA, BURO, BRAC

4.2 8

6 5285.7 1

ASA, BURO, BRAC

2.19

5 1.33

Type of house

% of HH having utility connection

ASA, BRAC

4.4 4

4 Businessman-47.6%, Service-9.5%, Day labour-4.8% Others-19% Rickshaw puller-19% Businessman-43.8%, Day labour-6.3%, Others-25%, Rickshaw puller-25% Jobless -3.1% Agricultural labor3.1% Businessman59.4%, Service-6.3% others-6.3% Rickshaw puller21.9% Businessman-38.1% Service-9.5% Day labour-4.8% Others-23.8% Rickshaw puller23.8% Jobless-10% Businessman-50% Service-10% Day labour-10% Others-10% Rickshaw puller-10%

oth ers

3 4.90

Home ownership ren ted

2 4.1 4

Major share of occupation

Own

Mirer Moidan

Mendiba g

Masimpur

Kali bari

Halde r para/ Hawal

Avg. no. of Avg. years of schoolin f

Communit y name

9. Sylhet Pauroshava

2.00

4.00

5.60

5.07

6.60

4.20

6.90

Horg Ding Dhorompur ram a Doba Kaj la Poli Mirjapur Keshobpur ce line Bad S i r o Tikapara Talaim ari (ranin agar

309375

100

2.454

42.20

178583.3 3

11250

Pucca-12.5% Semi pucca87.5%

3.58

30.88

495250

1.29

6576.4 7

Semi pucca-100%

76. 5

23. 5

2.55

27.14

257705.8 8

1.83

5075

Pucca- 8.3% Semi pucca91.7%

2.03

31.17

213000

1.08

6384.6 2

Semi pucca92.3% Kutcha-7.7% Semi pucca-100%

100

2.13

30.00

235416.6 7

100

97.6

20.00

579800

Pucca- 46.7% Semi pucca53.3%

86. 7

13. 3

4.49

26.67

685142.8 6

Pucca- 20% Semi pucca-80%

1.34

28.50

255200

6666.6 7

2.00

Jobless -10% Businessman-30% Service-30% Others-20% Rickshaw puller-10%

1.40

12300

2.00

10033. 33

2.00

6480

% of HHs having it % of HHs having i % of women empowe d State of happin ess (

32.86

Wa te r El ec tr

2.25

workin g MFIs

Avg. asset value (Tk.)

1.92

Avg. living years

100

6062.5 0

Ow n

Avg. income (Tk.) of HH

Pucca-25% Semi pucca-50% Kutcha-25% Semi pucca-100%

1.13

Se we ra

3.85

1124466. 67

Ga s

2.75

32.60

% of HHs having utility connection

15 10

16 90

17 0

18 10

19 100

20 33.3

21 100

22 80

46 .7

93. 3

46.7

100

85.7

37.5

100

62.5

100

41.7

100

83.3

62 .5

87. 5

37.5

100

21 .4

100

21.4

85.7

23.5

100

76.5

33 .3

83. 3

33.3

83.3

100

92.3

100

8 0 7 3 . 3 1 0 0

100

46.7

100

ASA , BRA C Sha

4.08

4.86

ASA, Proshik a Shakti

2.14

6.7

7753.3 3

AS A

3.94

1.80

AS A, Pr os hi

6.00

93. 3

Jobless -6.7%, Businessman-40%, Service-13.3% Others-33.3% Rickshaw puller6.7% Businessman-62.5%, Service-12.5%, Others-25% Businessman-41.7%, Day labour-8.3% Others-50% Housewife-12.5%, Businessman-25%, Service-25%, Others-37.5% Housewife-5.9% Agricultural labor17.6%, Businessman23.5%, Service11.8%, Others-41.2% Businessman-25%, Service-50%, Others-16.7%, Rickshaw puller8.3% Businessman-38.5%, Service-15.4%, Others-46.2% Businessman-80%, Service-20% Businessman-53.3% Service-20% Others-26.7%

ASA , TMS S

5.75

13 175800

ASA, BRAC Shakti

1.00

12 27.90

ASA, TMSS Shakt i

4.17

11 2.181

AS A

5.57

10 26. 7

Type of house 7 Pucca-6.7% Semi pucca86.7% Kutcha-6.7% Pucca-13.3% Semi pucca86.3%

A S A ,

4.13

9 13. 3

6 7473.3 3

ASA , Sha kti Pro

4.00

8 60

5 1.87

ASA, Prosh ika

4.53

ot he rs Avg. amount of land

3 3.30

Home ownership re nt ed

2 4.00

Bok hti ara bad

Major share of occupation 4 Businessman-46.7% Service-6.7% Others-46.7%

Avg. no. of income

Commun ity name Avg. no. of family member Avg. years of h l

10. Rajshahi City Corporation

lxi

4.81

6.17

New Adarshapa ra Nurpur

12800.00

1.18

6245.45

1.77

12123.23

20.7 3

34400.00

Semi pucca 100%

25.000 0

25.4 0

191400.0 0

Semi pucca 85.7% Kutcha/ Jhupri7.1% Others-7.1% Semi pucca 54.5% Kutcha/ Jhupri36.4% Others-9.1%

71. 4

28. 6

16.11

20.2 1

462000.0 0

18. 2

45. 5

36. 4

10.77

16.3 6

238590.9 1

Pucca -3.2% Semi pucca87.1% Kutcha/ Jhupri9.7%

64. 5

29. 0

6.5

24.177 4

32.0 0

559000.0 0

% of women empowere d State of happines s ( %)

8.8636

Sewer age

27. 3

15 16.7

16 75

17 0

18 8.3

19 25

20 16.7

21 100

22 66.7

100

9.1

18.2

90.9

100

57.1

92.9

7.1

35.7

21.4

85.7

78.6

9.1

54.5

9.1

18.2

90.9

63.6

6.5

80.6

22.6

71.0

74.2

Gas

36. 4

working MFIs

Avg. asset value (Tk.)

Avg. living years

Avg. amount of land owned by HHs

36. 4

Own

Avg. income (Tk.) of HH

Avg. no. of i 1.79

13 861583.3 3

Elect ricit y

4.36

Businessman35.7% Others-57.1% Rickshaw puller- 7.1% Businessman36.4% Service-18.2% Others-18.2% Rickshaw puller-27.3% Without any occupation-3.2% Housewife-6.5% Small Businessman38.7% Service-19.4% Others-22.6% Rickshaw puller-9.7%

12 21.0 8

Water suppl y

4.55

11600.00

11 6.7083

ASA BRAC Pros hika

5.07

1.60

10 0

Proshika

4.21

5190.91

9 25

Pro shi ka

3.40

1.73

8 75

7 Semi pucca 83.3% Kutcha/ Jhupri16.7% Semi pucca 63.6% Kutcha/ Jhupri36.4%

ASA BRAC Prosh ika

4.00

6 10975.00

ASA BRAC Proshik a

4.36

5 1.83

Type of house

% of HHs having utility connection

ASA BRAC Proshika

4.36

4 Businessman33.3% Service-16.7% Others-50% Jobless -9.1% Businessman27.3% Service-9.1% Others-45.5% Rickshaw puller-9.1% Businessman-20% Service-20% Others-60%

other s

3 5.83

Major share of HHHâ&#x20AC;&#x2122;s occupation

rente d

2 5.00

Mohad ebpur

Kamarp Islampur/ ara unumantala

Ador sha Para

Avg. no. of f il Avg. years of schoolin f

Communit y name

Home ownership

% of HHs having sanitary l t i % of HHs having insuranc

11. Rangpur Pauroshava

lxii

Pakpa ra 1 2 4.93 3 6.67 4 Businessman80% Service holder20% 5 1.73

Avg. income (Tk.) of HH

Avg. no. of i

Major share of HHHâ&#x20AC;&#x2122;s occupation 6 9146.67 7 Pucca -6.7% Semi pucca -80% Kutcha/ Jhupri6.7% Others-6.7% 9 0 10 0 11 33.522 0 12 34.6 0 13 582600.0 0 15 6.7 16 93.3 17 0

% of HHs having utility connection

18 0 19 13.3

lxiii

20 6.7

% of women empowere d State of happines s ( %)

% of HHs having sanitary l t i % of HHs having insuranc

Sewer age

Gas

Elect ricit y

Water suppl y

working MFIs

Avg. asset value (Tk.)

Avg. living years

Avg. amount of land owned by HHs

other s

8 100

rente d

Own

Type of house

TMSS

Avg. no. of f il Avg. years of schoolin f

Communit y name

Home ownership

21 80 22 73.3

3.3 3

4.2

5.6 7

4.7 7

2.5 4

4.7 5

5.8 8

5.4 4

4.0 0

Businessman-7.7% Service- 15.4% Others-69.2% Rickshaw puller7.7% Businessman-50% Service-12.5% Others-37.5% Jobless-5.6% Businessman72.2% Others-11.1% Rickshaw puller11.1%

2.0 0

13797.50

Semi pucca 83.3 Kutcha/Jhupr i- 16.7%

100

6.83

30.6 7

232333.3 3

1.8 5

3275.00

Semi pucca 20% Kutcha/Jhupr i-80%

100

1.00

14.7 9

122800.0 0

1.6 3

7725.00

Semi pucca 100%

87. 5

12.5

6.19

26.3 8

558625.0 0

2.1 7

7916.67

Semi pucca 100%

4.21

37.8 3

117000.0 0

1.2 0

3300.00

100

2.40

13.8 0

522000.0 0

2.6 2

6876.92

Semi pucca 20% Kutcha/Jhupr i-80% Semi pucca 100%

46.2

53.8

1.40

34.3 1

136230.7 7

1.7 5

8550.00

Semipucca 100%

100

1.912 5

33.7 5

173362.5 0

2.0 0

13697.22

Semi pucca 94.4% Kutcha/Jhupr i-5.6%

55. 6

5.6

38.9

29.9 4

399277.7 8

% of HHs having sanitar % of HHs having i % of women empower d State of happine

265100.0 0

Se we ra

14.0 0

Ga s

20.63

El ec tr

Wa te r

working MFIs

Avg. asset value (Tk.)

Avg. living years

Avg. amount of land

ot he rs

Businessman-50% Service-33.3% Rickshaw puller1-6.7% Service-20% Others-80%

re nt ed

Avg. income (Tk.) of HH

Avg. no. of

Semi pucca 100%

15 0

16 40

17 0

18 0

19 40

20 0

21 100

22 60

100

83. 3

33.3

100

37.5

100

16.7

83.3

100

83.3

100

7.7

61.5

53.8

23.1

100

46.2

87.5

100

88.9

22.2

5.6

88.9

Sha kti

5.3 3

14720.00

1.7 0

BRAC Shakt i

3.6 3

13 257400.0 0

MBSK

4.5 0

12 23.4 0

ASA., Palli shri

3.5 5

11 3.70

AS A BR AC

4.3 5

10 20

ASA

4.3 3

9 20

6 5520.00

Pal lis hri

4.3 3

8 60

5 1.2 0

% of HH having utility connection

ASA

6.5 0

Type of house 7 Semi pucca 100%

4 Businessman -20% Others-20% Rickshaw puller60% Service-40% Day labour-10% Others-30% Rickshaw puller20% Businessman66.7% Others-16.7% Rickshaw puller16.7% Businessman-20% Service-10% Others-45% Rickshaw puller25% Businessman- 75% Service-25%

AS A TM SS

4.0 0

Home ownership

BRAC

3 2.6 0

Major share of occupation

Ow n

Rajba ri Sh ei kh pa ra West Baluada nga

Avg. years of

2 3.2 0

Gha shi par a

Ful bag an, raj bat

Do pt or ip ar

Balub ari

Balua danga

8 no uposo hor

8 no rai l gon

Avg. no. of

Communi ty name

12. Dinajpur Pauroshava

lxiv

13. Khulna City Corporation

4.00

1.17

4.00

.20

4.43

3.00

4.14

2.86

Ca mp 1 Charerhat, khalishpur Goa lka li Gob orc hak a Ho us in g Jo ra ga te Khali shpur

1.50

10 0

.06 82

33.14

48625

6983. 33

Pucca -10% Semi pucca83.3% Kutcha/Jhupri6.7%

7.2 4

22.13

125814

1.50

4416. 67

Semi pucca-100%

10 0

1.6 7

35.33

40833.33

2.38

7125. 00

3.2 6

58750.00

Jobless-5.9% Businessman-17.6% Service holder17.6% Day labour23.5% Others-29.4% Rickshaw puller5.9% Service-55.6% Day labour-22.2% Others-22.2%

1.65

4470. 59

Semi pucca -75% Kutcha/Jhupri25% Semi pucca 17.6% Kutcha/ Jhupri82.4%

94. 1

5. 9

1.7 6

27.59

48700.00

1.22

4833. 33

22. 2

33. 3

44 .4

7.0 6

20.44

41777.78

Businessman-50% Service holder16.7% Others-33.33% Businessman-40% Day labour-20% Others-40% Housewife-28.6% Businessman-28.6% Others-42.9% Service holder14.3% Others-71.4% Rickshaw puller14.3%

2.33

4750. 00

Semi pucca 55.6% Kutcha/Jhupri44.4% Semi pucca-50% Kutcha-50%

100

1.3 3

30.17

34600.00

1.20

5780. 00

Semi pucca-100%

100

.90

14.40

105000

1.14

5357. 14

Semi pucca-100%

10 0

2.5 7

33.29

38285.71

1.71

5185. 71

Semi pucca42.9% Kutcha-57.1%

57. 1

42 .9

4.5 7

25.29

70333.33

17 0

18 0

19 6.3

20 12. 5

21 100

22 75

100

4.5

27. 3

100

40. 9

100

36. 7

100

86. 7

100

33. 3

100

83. 3

100

87. 5

100

87. 5

41.2

5.9

11. 8

100

47. 1

88.9

11. 1

100

33.3 3

16. 7

100

42. 9

100

71. 4

85.7

71. 4

100

Se we ra

16 75

El ec tr i Ga s

15 0

Wa te r

worki ng MFIs

Avg. asset value (Tk.)

Avg. livin g years

re nt ed ot he rs Avg. amoun t of l d

Ow n

Avg. incom e (Tk )

Avg. no. f

% of women empow d State of happi

6.33

Pucca -4.5% Semi pucca 68.2% Kutcha/Jhupri27.3%

% of HHs havin

3.33

4963. 64

% of HHs havin

3.76

13 50636

ASA BRAC

3.94

12 29.06

BRAC Shakti

6.50

11 7.8 8

ASA BRAC

4.75

10 0

ASA BRA C

4.33

9 100

AS A, BR AC Sh

8 0

ASA BRAC Shakti

5.03

Type of house 7 Semi pucca 31.3% Kutcha/Jhupri68.8%

BRI DGE

4.37

6 5956. 25

% of HH having utility connection

BRA C, Pro shi ka

2.05

5 2.06

AS A

5.73

Major share of HHHâ&#x20AC;&#x2122; soccupation 4 Businessman-31.3% Service-18.8% Others-37.5% Rickshaw puller12.5% Jobless-4.5% Housewife-9.1% Businessman-18.2% Service-13.6% Day labour-4.5% Others-18.2% Rickshaw puller31.8% Jobless - 6.7% Housewife-3.3% Businessman-36.7% Service-23.3% Day labour-3.3% Others-26.7% Businessman-16.7% Service-33.3% Day labour-16.7% Others-33.3% Service-12.5% Others-87.5%

AS A, BU RO BR

3 3.38

ASA BRAC

2 4.75

Boy ra

Bastuhara Baitul Coloney camp Boyra

falah Alamnaga r Porabari Mosque

Avg. no. of famil Avg. years of schoo

Commu nity name

Home ownership

lxv

4.20

2.00

5.20

3.73

4.00

4.50

Khora bosti sonadanga Khu dra shi lpo Nay aba ri Port bondho gate Son ada nga

9 16. 7

10 83 .3

11 .00

12 22.67

13 56250.00

Semi pucca12.5% Kutcha-87.5%

93. 8

6. 3

4.4 2

21.38

53400.00

5342. 86

Semi pucca71.4% Kutcha-28.6%

28. 6

57. 1

14 .3

15. 78

24.00

102000

1.40

4810. 00

Semi pucca-80% Kutcha-20%

5.8 0

29.40

97750.00

1.73

6000. 00

Semi pucca73.3% Kutcha-26.7%

6.7

93 .3

.46

34.20

106250.0 0

1.88

5106. 25

Semi pucca-50% Kutcha-50%

62. 5

37 .5

1.2 5

26.75

53937.50

% of women empow d State of happi

% of HHs havin

Se we ra

El ec tr i Ga s

Wa te r

worki ng MFIs

Avg. asset value (Tk.)

Avg. livin g years

1.57

re nt ed ot he rs Avg. amoun t of l d

7937. 50

Ow n

Avg. incom e (Tk )

Avg. no. f 2.13

8 0

15 0

16 100

17 0

18 0

19 100

20 0

21 100

22 100

87.5

12.5

43. 8

100

68. 8

85.7

14.3

100

71. 4

100

62.5

12. 5

100

37. 5

Unn aya n, LPU PAP

4.86

Type of house 7 Pucca -16.7% Semi pucca 83.3%

ASA,BRAC, Proshika

4.00

6 4566. 67

ASA

3.19

5 1.67

BRI DGE

4.50

Major share of HHHâ&#x20AC;&#x2122; soccupation 4 Jobless-16.7%, Businessman-16.7%, Service holder-50%, Others-16.7% Jobless-12.5%, Businessman-31.3%, Service holder12.5%, Day labour6.3% Others-18.8% Rickshaw puller18.8% Businessman-28.6% Service holder28.6%, Others-42.9% Businessan-30%, Service holder-20%, Others-30%, Rickshaw puller-20% Jobless-6.7% Small Businessman-6.7%, Service holder13.3%, Day labour26.7% Others-46.7% Jobless-12.5%, Housewife-12.5%, Small Businessman25%, Others-50%

% of HH having utility connection

ASA BRAC

3 4.33

ASA , BRA C Pro

2 4.83

Khe ma1, ref

Avg. no. of famil Avg. years of schoo

Commu nity name

Home ownership

lxvi