Does Bank Credit Enhance Inequality? An Empirical Analysis of Spatial and Sectoral Distribution of Bank Advances in Bangladesh Abstract: Financial intermediaries play an important role in the economic development of the country. In Bangladesh, banks are the main vehicles for mobilizing invisible funds and channeling those funds to faster the growth in productive sectors of the economy in the absences of healthy capital market. In this paper, a study has been conducted to investigate whether asymmetry in bank advances have any effect on the regional inequality within the country. The paper finds that the banks were inclined towards urban areas in comparison to rural areas. Regression analysis shows that bank advance plays a role in enhancing inequality amongst the different regions of the country. Furthermore, the paper also finds that there exists skewed-ness in the concentration level of advances and over the years there has not been much improvement. Private bank advances shows higher concentration level then national banks. All these investigations indicate that bank advances does have an impact on regional disparity, especially private bank I. Introduction: The general idea that economic growth is related to financial development and structure can be traced back, at least to Schumpeter (1911), who highlights the possibility that financial institutions could actively spur innovations and growth by identifying and funding productive investments. For many years, theoretical discussions about the importance of financial development and the role that financial intermediaries play in economic growth have occupied a key position in the literature of development finance. Gurley and Shaw (1955), Goldsmith (1969), Mckinnon (1973) and Shaw(1973) all suggested financial development can foster economic growth by raising saving, improving allocation efficiency of loanable funds, and promoting capital accumulation. Later works include that of Greenwood and Jovanovic (1990), Levine (1991), Bencivenga and Smith (1991) and Saint-Paul (1992), which involved theoretical models, wherein an efficient financial market raises the quality of investments, thus leading to economic growth. Specifically, Greenwood and Jovanovic (1990) built in their model a financial sector whose main objective it to direct funds to high-yielding investments with the assistance of information. This then would lead to economic growth, which would in turn enable the implementation of costly financial structures. In his model, Levine (1991) explains how stock markets influence growth by improving firm efficiency. Furthermore, Bencivenga and Smith (1991) explain in their study, that a well-functioning financial system would improve the level of investment towards non-liquid objects, which will be beneficial to the economy. Saint-Paul (1992) on the other hand, explains the role of the financial sector in helping business enterprises in specialization by allowing investors to hedge by holding a diversified portfolio. Balckburn and Hung (1996) found that in a developed financial system, the task of monitoring projects can be undertaken by financial intermediaries, lowering transaction costs and channeling greater savings towards new investments, thus boosting economic growth. Thus different financial institution plays role in enhancing economic growth of which banks are the most significant.
Deidda and Fattouh (2002) found out that reduction in bank concentration has a positive and a negative effect on economic growth. In one hand by increasing efficiency it increases economic growth but it also results in duplication of fixed cost which inflicts negative effect on growth. They also found that bank concentration has negative association with industrial growth. This paper is going to focus the impact of concentration of private bank advances on regional inequality of Bangladesh. Traditionally, if there is a rise in inequality while the economy is growing, this may not only offset the poverty-reducing effects of growth, but may also retard subsequent growth through an increased emphasis on redistribution in favor of non-accumulable factors. The literature on the effect of inequality on growth has gained momentum since the influential work of Alesina and Rodrik (1994), Persson and Tabellini (1994). The difference between Alesina and Persson arises from the fact that Alesina consider infinitely lived agents, while Persson consider an overlapping generations (two-period) model. However, they share the underlying logic that there is a redistributive role for the government to combat inequality within a democratic set-up. Suggesting that there is heterogeneity among the cross-section of countries, Partridge (1997) empirically examines the validity of the arguments put forward by both Alesina and Persson for the states within the US and finds that there is a positive relationship between initial inequality and subsequent growth. The author hypothesizes that a number of factors (e.g., free inter-regional mobility of physical and human capital, effect of non-political considerations on income distribution and growth, characteristics specific to sample countries in Alesina) may be responsible for this positive relationship, though he does not formally examine the validity of any. Sugata Ghosh and Sarmistha Pal (2002) found out that rural inequality is more important to explain growth of total output per capita and there is an inverse relationship between the two. There is also evidence of rural-urban dichotomy: higher rural inequality lowers growth of agricultural output per capita while higher urban inequality seems to enhance economic growth of manufacturing output per capita. In Bangladesh, banks are the main vehicles for mobilizing invisible funds and channeling those funds to faster the growth in productive sectors of the economy in the absences of healthy capital market. Until the early 1980s, the Government owned, controlled and directed Bangladesh’s financial system with the objective of allocating funds to priority sectors. Loan recovery was not emphasized because loans were collateralized and considered ultimately collectable. The quality of financial intermediation, judged by loan recovery rates, was dismal. Bangladesh had virtually no private banking and role the private sector was considered secondary. In the early 1980s, the Government began to reform the financial sector. Interest rates on deposits were raised to provide a positive real return on deposits, private banks were allowed commercial banks to operate side by side with the public banks to start a meaningful and constructive competition in the banking sector. There are four types of scheduled banks Bangladesh-National commercial banks, private commercial banks, foreign banks and specialized banks. The introduction of the Financial Sector Reform Project (FSRP), which included liberalization of interest rates, improvement of monetary policy, abolishing priority sector lending, broadening capital market development, in 1990 has created a competitive environment by increasing the relative credit share of private commercial banks than those of nationalized commercial banks. The private banks have about 70% of its branches located in urban areas and almost 80% of its advances are channeled towards urban areas. Private Banks have remained highly confined to regions having higher gross regional product. Economic growth is not enhancing to its potential level due to lack of lowering in the level of concentration by private banks. Due to higher
concentration towards high income regions private bank is contributing to the existing regional disparity. The rest of the paper is organized as follows Section II outlines some of the facts of scheduled banks (National and Private Commercial Banks) advances of Bangladesh. Section III discusses the relationship between bank advances and regional growth. Section IV exhibits the Sectoral and spatial concentration of bank advance of National and Private Banks of Bangladesh while section V contains concluding remarks of this study. II.
Some Facts About Advances by National and Private Banks of Bangladesh: 2.1: An overview of Banking System: The banking sector of Bangladesh comprises four categories of scheduled banks: National commercial banks, specialized banks, local private banks and foreign banks. In addition, one national Co-operative Bank, one Ansar-VDP Bank, one Karmasangsthan Bank and one Grameen bank and some non-scheduled banks are also in operation. Table 2.1 summarizes the structure of the banking system in Bangladesh according to their categories: Table 2.1: Structure of the Banking System in Bangladesh (June 2007) Types of No. No. of % of total %of total % of total Banks Branches asset deposits advances NCBs 4 3383 36.54 35.36 28.80 SBs 5 1359 6.80 6.71 8.78 PCBs 30 1804 47.13 52.24 54.64 FCBs 09 50 9.53 5.69 7.78 Total 48 6596 100.00 100.00 100.00 National commercial banks continue to exert oligopolistic market power over the banking system. NCBs have helped to promote the Government’s socioeconomic objectives by expanding their rural branch network and lending to agriculture, lending to small scale and cottage industries and funding state-owned enterprises. On the other hand, private banks have been able to increase their market shares, over the years. Domestic private banks offer higher interest rates to attract deposits and charge higher rates on loans. Their service is considered better than NCBs and they have expanded into fee-based and international services. These banks compete for a limited number of creditworthy borrows, but not with NCBs for priority or public sector lending. Private Banks hold 52.24 % and 54.64% of bank’s deposits and advances respectively. Though PCBs hold higher percentage of deposits and advances, NCBs still dominate the banking system considering the number of NCBs to PCBs. 2.2: Deployment of Bank Branches: Although PCBs show better performance according to different criteria such as capital adequacy, quality of assets and expenditure-income ratio, the common people throughout the country have easy access to NCBs because of their locations. PCBs have branches outside the urban area but it is only 27.49 percent of their total number of branches. On the contrary, 63.41 percent of the total branches of NCBs are located in different sub-urban and rural areas of Bangladesh respectively. The NCB’s drive to expand their deposits arises from the continual need to provide fresh funds in an environment of poor loan recovery. As deposits are clearly associated with branch numbers, while advances are industry and location
specific, the need to expand deposits leads directly to expanding branch networks. Urban branches are better sources of deposits than rural branches so the primary objective is to increase urban banshees. However, central bank policy required that the rural branch network be expanded at the same time, to provide banking services in such areas. Taken together these two factors led the NCBs to increase their branch network rapidly Table 2.2.1: Distribution of Bank Branches in Different Regions (June 2007): Types of Banks NCBs PCBs
No. of branches Urban 1238 1308
Rural 2145 496
As % of the total branches
Total 3383 1804
Urban 36.59 72.51
Rural 63.41 27.49
2.3: Sectoral Distribution of Bank Advances: Ahmed and Sarker has found that private sector banking remained confined to industry and trade business ignoring agriculture and small-scale industry while national banks are more inclined towards extending loans to government bodies, sector corporations and private enterprises. To uphold the role of agricultural sector and rural areas in the overall socialeconomic development of the country, the government has been continuing the distribution programmers of the agriculture and rural credit through NCBs. But over the years, share of agricultural sector for both NCB and PCB has been declining. NCB on average has been distributing 12.39 percent of its total credit in agricultural share while PCB has been distributing 1.63 percent of its total credit, which is negligible. Chart 2.3.1: Sectoral Share of Advances in Agriculture, Fishing and Forestry: Timeline of Sectoral Share of Advances in Agriculture, Fishing & Forestry for NCBs, PCBs and All Banks 25 20
Agriculture, Fishing & Forestry-NCB
15
Agriculture, Fishing & Forestry-PCB
10
Agriculture, Fishing and Forestry-All Banks
5
20 06
20 04
20 02
20 00
19 98
19 96
19 94
19 92
19 90
0
Recently, NCBs have reduced the rate of interest from 10-12.50 percent to 9 percent in the thrust industrial sector e.g. textiles, agro-based industry, computer software etc to boost up industrial investments. The government has taken up programs to provide financial assistance to expand SMEs along side of large-scale industry through commercial banks. National banks, hence, have contributed a lump-sum portion of its total credit disbursement on industrial sector. Loan disbursement in working capital for both the scheduled banks
(national and private) has increased over the years. But the growth of PCBs is higher than NCBs. Chart 2.3.2: Sectoral Share of Advances in Industry: Timeline of Industrial(including working capital) share for NCBs, PCBs & All Banks 60 50
Industry(including working capital)-NCBs
40
Industry(including working capital)-PCBs
30 20
Industry(including working capital)-All Banks
10
06 20
04 20
02 20
00 20
98 19
19 96
94 19
19
19
90
92
0
Private bank has been playing the dominate role in distributing loans in trading sectors compared to other banks. A huge amount of its credit goes in import financing and wholesale trade. The Sectoral share of trade of private banks has been on a declining. It share in 2007 is 41.63 percent in comparison to 58.65 percent in1990. NCBs sectoral share of trade has declined but not in the same rate as private bank. Chart 2.3.3: Sectoral Share of Advances in Trade. Timeline of Sectoral Share of Advances in Trade for NCBs, PCBs & All Banks 70 60 50
Trade-NCB
40
Trade-PCB Trade-All banks
30 20 10
20 07
20 06
20 05
20 04
20 03
20 02
20 01
20 00
19 99
19 98
19 97
19 96
19 95
19 94
19 93
19 92
19 91
19 90
0
An overall share of advances made by both the scheduled banks to different economic purposes for 4 years has been shown in Appendix (Chart 1&2). Advances distributed on storage sector by NCBs have increased, conversely for that of PCBs has declined. Both the scheduled banks have declined its share of credit disbursement on electricity, water and sanitary services. Credit disbursement on transport and communication sector is recently being subjugated by private commercial banks. Both the scheduled banks has improved their shares of total credit disbursement on miscellaneous sector which includes social services like private welfare and development activities, special credit programs, professional services, poverty alleviation program etc. This is due to the fiscal reforms taken by the government.
2.4 Share of Deposits & Advances in Urban & Rural Areas: The NCBs have expanded credit to priority sectors in response to government directives without due regard to quality, often at interest rates below the bank’s cost of funds. This had led to inefficient resource allocation and widespread bad loan. However, Rahman (2004) showed that, the three indicators of financial development such as domestic credit to the private sector by banks to GDP ratio, total deposits to GDP ration and broad money (M2) to GDP ratio for Bangladesh economy displayed steady increase trend during 1976-2005, indicating widening and deepening of the financial system in Bangladesh overtime with a structure break in 1991.Both the deposits to GDP ratio and Credit to GDP ratios has increased over time. But there is asymmetry in the advance to deposits ratio in urban and rural areas of the scheduled banks. The ratio of advance to deposits in urban areas for both NCBs and PCBs is higher than 70 percent. Though in recent time both the scheduled bank has been observed a declination in the ratio, but PCBs has maintained the ratio above 80 percent (Chart 2.4.1).On the contrary, the advance/deposits ratio in the rural areas for the national banks has been declining since 1996. Whilst in recent time this ratio for PCBs in rural areas has increased. In current time period the advance to deposit ratio for national banks and private banks are below 40% and 30% respectively. This implies that credit disbursements in rural areas are inadequate (Chart 2.4.2) Chart 2.4.1: Ratio of Advances to Deposits of Urban Areas for All Banks, NCBs & PCBs Ratio of Advance/Deposits of Urban areas for NCBs, PCBs & All banks 120.00% 100.00% 80.00% NCBs Urban
60.00%
PCBs Urban All Banks-Urban
40.00%
00 6 -6 -2
30
30
-6 -2
00 4
00 2 -6 -2
0 30
-6 -0 30
99 8
30
-6 -1
99 6 -6 -1
30
30
-6 -1
99 2 -6 -1
30
30
-6 -1
99 0
0.00%
99 4
20.00%
Chart 2.4.2: Ratio of Advances to Deposits of Urban Areas for All Banks, NCBs & PCBs
Ratio of Advance/Deposits of rural areas for NCBs, PCBs & All Banks 120.00% 100.00% 80.00%
NCBs Rural
60.00%
PCBs Rural
40.00%
All Banks Rural
30-6-2007
30-6-2006
30-6-2005
30-6-2004
30-6-2003
30-6-2002
30-6-01
30-6-00
30-6-1999
30-6-1998
30-6-1997
30-6-1996
30-6-1995
30-6-1994
30-6-1993
30-6-1992
30-6-1991
0.00%
30-6-1990
20.00%
In further analysis it has been found out that the ratio of credit disbursement in rural areas by Ratio of Rural/Total Advances for NCB & PCB NCBs has shown a declining trend. On average 17 percent of the total credit disbursement has been channeled toward rural areas by the national banks. Private bank’s credit 25.00% disbursement in rural areas though remains constant but its contribution is almost negligible. (Chart 2.4.3) 20.00% Chart 2.4.3: Ratio of Rural to Total Advances for NCBs & PCBs 15.00%
Rural/Total-NCB Rural/Total-PCB
10.00% 5.00%
2007
2006
2005
2003
2002
2001
2000
1999
1998
1996
1995
1994
1993
1992
1991
1990
0.00%
III. Bank Advances and Regional Growth: 3.1: The Model and Empirical Specifications: To investigate the impact of bank advance on growth, a straightforward growth mode, where growth is a function of per capita income and bank advances has been used. The exogenous growth model (neo-classical growth model) explains an economy’s growth rate in terms of saving, productivity of capita, factors of production, time-varying technology. It states that the rate of growth slows as diminishing returns take effect and the economy converges to a constant “steady-state” growth rate while in long run growth is exogenously determined. On the other hand, endogenous growth models developed by Lucas-Romer extend the old neoclassical model by emphasizing the role of endogenous factors (i.e., human capital stock and R&D activities) as the main engines of economic growth. While early neo-classical models assume total factor productivity growth (or technical progress) as exogenously given, the newer endogenous growth models attribute this component of growth to the ‘learning by doing’ effect occurring between physical and human capital, which result in increasing returns to scale in production technology (Lucas, 1988). The theories of endogenous economic growth stress the point that the opening up of the investment opportunities under a liberalized market-friendly economy brings about high economic growth. Besides, the
financing gap model of the World Bank which is offered as an alternative policy framework for growth believes that growth of real output is related to total investment, where investment is considered as one of the demand factors in determining growth. This brings another crucial point in the endogenous growth literature linking the relationship between the evolution of the financial system and development of the real economy, which suggests that financial development may promote productivity growth as a result of better screening and monitoring. One of the key measures of financial market deepening is domestic credit to the private sector that can affect economic activity in many ways. In many developed and developing countries, bank advances has played a critical role by efficiently allocating resources for investment and is considered to be an engine of economic growth. Historically, economists who have focused on banks e.g., Bagehot (1873) and Schumpeter (1911) have emphasized the critical importance of the banking system in economic growth and have highlighted circumstances when banks can actively drive innovation and future growth by identifying and funding productive investments (Levive and Zervos, 1998) Traditional argument suggests that departures from perfect competition are detrimental for growth insofar they are bound to generate inefficiencies in all the allocation mechanism provided by the credit market. Younus (2007) used different econometric technique such as unrestricted VAR, Granger Causality and found out that private sector credit has no real effect on economic growth but is inflationary. Economic growth, on the other hand, has positive impact on real private sector credit growth reflecting higher credit demand emanating from increased economic activities. Her result was consistent with the conventional belief that when an economy starts to grow it creates immediate additional demand for financial services and helps grow a better financial system. At early stage, the positive impact of financial development on economic growth could be modest or negligible. Rahman has shown that in the long run financial development results higher investment and output growth based on structural vector auto-regression (SVARs) model. Mackinnon (1993) used a model showing that growth was a function of per-capita income, investment and literacy rate. Mankiw, Romer and Weil (1992) showed that growth was a function of investment as a percentage of GDP and population growth. In this paper the model used is a modified version of MRW and Mackinnon models to suit the purpose of this paper. Instead of taking percentage of investment to GDP as a variable, ratio of bank credit to GDP has been taken. Bank credit can be used as the proxy of investment as a large portion of investment is distributed through bank credit. On the other hand, Mackinnon model suggest that initial income is a function of growth. Thus the growth model for this paper includes to variables- bank advance and initial income i.e. g = f (pci, adv) Here, g is used to denote economic growth (log differences of real per capita GDP), pci stands for log of per-capita initial real GDP and adv indicates the log ratio of change in bank advances to nominal GDP. The model is shown below: g = Îą +Îą1 (pci) + Îą2 (adv) The paper also used another similar model to see the impact of bank advances on human poverty index. Instead of using economic growth as dependent variable change in human poverty index (hpi) has been used. hpi =f( pc, cadv) where pc is per capita income and cadv indicates ratio of change in bank advance to GDP. 3.2: Data
The sample includes yearly bank advance data of 64 districts from Scheduled Banks Statistics books of Bangladesh. The 64 districts where further rearranged into 19 former districts of Bangladesh (see Appendix). GDP data of 19 districts has been collected from different Statistical Yearbooks of Bangladesh. This study has been conducted by breaking the time frame of 1990-2000 into two periods i.e. from 1990-1995 and 1995-2000.The time frame could not be extended to present period due to lack availability of district level data. A dummy variable (D90) has been used as two time period has been taken. Also the model has been tested for the whole 10 year period. The test has been performed on 19 former districts of Bangladesh. As it has been found out that Dhaka district was an outlier, the model has been estimated without and with Dhaka for the different time frames. In the other model, measuring the impact of per-capita and bank advance on Human Poverty Index, per capita real GDP of 64 districts has been taken. Sen and Ali (2003) calculated Human Poverty Index using three variables: deprivation in longevity (P1), deprivation in knowledge (P2) and deprivation in economic provisioning (P3). That Human Poverty Index data, calculated by Sen and Ali has been used in this paper. 3.3: Empirical Result: The basic growth equation model estimated is: g = Îą +Îą1(pci) + Îą2 (adv) The estimation in Table 3.1 & 3.2, indicates that ratio of change in bank advance to GDP has statistically significant positive impact on the economic growth of the country for the different time periods. A one percent increase in the ratio of change in bank credit to real GDP will generate about 0.047261 per cent positive impact on the economic growth (GDP) in case of 5 years including Dhaka. The per capita income is negatively associated with growth. Interestingly, when economic growth is taken as a function of only per capita income, there is positive association between per capita and growth (Table 3.3 & 3.4). That is, when bank advances are controlled, the per capita income has a positive association with growth implying that there is convergence of income among the different regions of the country. But when advances are not controlled there exist negative relationship between growth and per capita income, signifying that there will be divergence of income amongst the regions. Thus, it is clear that bank advances does play a role in the regional income inequality. Sen and Ali (2003) illustrated that spatial inequality in social development in Bangladesh is evident from the district level data. It has been found that there is a positive correlation between change Human Poverty index and change in private bank advances. From Table 3.5 it is seen that private bank has a positive marginal significant association with human poverty index. Thus it can be stated that bank credit does help to lower Human Poverty Index (HPI). But due to lack of appropriate policy and proper channeling of credit disbursement bank advances has not been that effective in lower HPI. If suitable policy and proper channel for credit disbursement is introduced then lower income regions will be able to reduce the existing poverty. Banks in Bangladesh does play an important role to mobilizing credit for investment, through which it is affecting growth. On the other hand, due to lack policy and proper channeling of credit disbursement these financial institutions are also playing a role in enhancing the present inequality among the different regions of Bangladesh. Table 3.1: Pooled Cross Section Estimation of the Growth Model for1990-1995 &
1995-2000: Dependent variable: Economic Growth (g) With Dhaka Without Dhaka
Variables Adv Pci Adv Pci
Coefficient 0.047261 -0.118233 0.043912 -0.110416
t-statistics 3.287493 -1.850231 2.330439 -1.644319
P-value .0024 .0730 .0455 .1099
R2 0.618232 0.600059
Table 3.2: Cross Section estimation of the growth model, 1990-2000: Dependent variable: Economic Growth (g) With Dhaka Without Dhaka
Variables Adv Pci Adv Pci
Coefficient 0.378984 -0.191223 0.597279 -1.662906
t-statistics 2.644890 -1.601807 2.560094 -2.176854
P-value 0.0177 0.1034 0.0218 0.0459
R2 0.337317 0.345512
Table 3.3: Cross Section Estimated of the Growth Model for 1990-1995, 1995-2000: Dependent variable: Economic Growth (g)
With Dhaka Without Dhaka
Variables
Coefficient
t-statistics
P-value
pci pci
0.946163 0.811059
1.174526 0.971445
0.2483 0.3386
Table3.4: Cross Section Estimation of the Growth Model for 1990-2000: Dependent variable: Economic Growth (g) Variables
Coefficient
t-statistics
P-value
With Dhaka
pci
0.801034
1.006468
0.3292
Without Dhaka
pci
0.742433
0.921583
0.3696
Table 3.5: Cross Section Estimation of Measuring the Impact of Per-capita Income and Bank Advance on HPI: Dependent variable: Human Poverty Index Variables Cadv Pc
Coefficient 5.968558 -8.20E-06
t-statistics 1.669325 -0.404797
IV. Sectoral and Spatial Concentration of Bank Advances:
P-value 0.1002 0.6870
Deidda and Fattouh(2003) presented that an OLG endogenous growth model in which a reduction in the level of concentration in the banking industry exerts two opposite effects on economic growth. On the one hand, it induces economies of specialization which enhances intermediation efficiency and thereby economic growth. On the other hand, it results in duplication of fixed costs which is detrimental for efficiency and growth. The trade off between the two opposing effects is ambiguous and can vary along with the dynamic process of financial and economic development. Using cross country industry data they found that banking concentration is negatively associated with industrial growth only in low income countries while there is no such association in high income countries. The empirical findings support the prediction of their model, that there exist a different relationship between banking concentration and growth depending on the level of economic development. In this study the concentration level of the bank advances of NCBs and PCBs in different regions of Bangladesh has been investigated, to provide further evidence that asymmetry in credit disbursement of these banks is enhancing the existing regional inequality. An interesting finding of Sarker and Rahman (1996) was that only 2 to 5 percent of total borrowers were responsible for more than 35 percent of total outstanding loans, while about 70 percent of total borrowers were responsible for only 10 to 15 percent of the total outstanding advances, in Bangladesh. In their study they found that the directors of a private bank hold directly or indirectly the highest portion of its total advances and they do not repay the loans in most of the cases. The four National Commercial Banks dominates the banking system. Thus this scheduled bank can be hold as proxy of all banks. Private Commercial Banks conversely has been expanding its market share, over the years. It has the highest percentage of deposits and advances. PCBs have shown better performance in management and services than NCBs. But PCBs has remained confined to urban areas and certain sectors causing inaccessibility for the common people. To examine the concentration level of advances for both national and private commercial banks herfindhal index and coefficient of variation has been used for former 19 districts and new 64 districts of Bangladesh. Chart 4.1 and 4.2 suggest that private bank has a higher level of concentration, as herfindhal index lies between 0.40 to 0..55. These values, implies that there is a lower level of competition between the regions and some regions hold higher market power e.g. Dhaka, Chittagong and Khulna. On the other hand, the herfindhal index of NCBs lies between .25 to .35, indicating a lower level of concentration, higher competition and lower market power of certain regions. Chart 4.1: Herfindhal Index Bank Advances of 19 districts (former) in Bangladesh:
Chart 4.2: Herfindhal Index of Bank Advances of 64 districts in Bangladesh: Herfindhal Index of 64 districts 0.6 0.5 0.4 0.3 0.2
NCB 64-H Pvt 64-H
0.1
30 -6 30 -1 9 -6 90 30 -1 9 -6 91 30 -1 9 -6 92 30 -1 9 -6 93 30 -1 9 -6 94 30 -1 9 -6 95 30 -1 9 -6 96 30 -1 9 -6 97 30 -1 9 -6 98 -1 30 999 -6 30 -0 0 30 -6-6 0 1 30 -2 0 -6 02 30 -2 0 -6 03 30 -2 0 -6 04 30 -2 0 -6 05 30 -2 0 -6 06 -2 00 7
0
The Coefficient of Variation (Chart 4.3), a measure of agglomeration has declined over the years for both NCBs and PCBs. This measure shows that private banks are more inclined towards urban and sub-urban areas than national banks. It has been observed that the private banks are more inclined towards districts having higher gross regional product. In many of the rural areas private bank has not disbursed any advances nor even distribution of deposits. These rural areas belong to Brahmanbaria, Rangamati, Habibganj, Faridpur, Goplaganj, Madaripur, Rajbari, Shariatpur. Sherpur, Jhenaidah, Magura, Meherpur, Narail , Shatkhira, Nilphamari, Panchagarth. But in the urban areas of these regions both advance disbursement and distribution of deposits are taking place, thus imposing credit disparity among urban and rural areas of the same region by the private banks. Chart 4.3: Coefficient of Variation of 19 districts of Bangladesh:
Coefficient of Variation of 19 districts 3 2.5 2
CV-Pvt
1.5
CV-NCB
1 0.5 30-6-2006
30-6-2005
30-6-2004
30-6-2003
30-6-2002
30-6-01
30-6-00
30-6-1998
30-6-1996
30-6-1995
30-6-1994
30-6-1993
30-6-1992
30-6-1991
30-6-1990
0
In addition, as the NCBs have expanded credit to priority sectors in response to government directives without due regard to quality, often at interest rates below the bank’s cost of funds. This had led to inefficient resource allocation and widespread bad loan. The esteem of these problems reduces the level of investment, the productivity of capital and the volume of savings. This results in reduced economic growth and employment opportunities. As a whole, though banks as a financial institution, increases efficiency and enhance economic growth, it also enhances the regional inequality in Bangladesh, due to lack of appropriate policy and proper distribution channels. V. Conclusion: Financial development is a prerequisite for economic development. Given the very low levels of both domestic and national savings, and together with rapid population growth, Bangladesh has the need to mobilize its financial resources more effectively to make resources available for investment. The establishment of a market driven private banking system with the presence of national banks was linked to achieving higher employment, increased productivity, more efficient use of resources and more rapid economic growth in Bangladesh. The intention of this paper is to investigate the affect of bank advances on regional disparity in Bangladesh. An overall view of the credit disbursement has been described. Private banks have remain confined to urban areas and certain sectors, on the contrary , national banks due to government regulation have more branches in rural areas, making banking system more accessible to the common people. Sectoral performances of both public and private scheduled banks illustrated that, private banks play a dominate role in trade sector and its share in industrial sector is rapidly growing. In opposition, NCBs share in agricultural sector is declining, whereas in industrial sector it is growing at a slow pace. A modified version of MRW (1992) growth model has been used to see the impact of bank advances on economic growth. It has been found out that there exists positive relationship between growth and bank credit, but a negative relationship between per capita income and growth, yet again when bank advance is controlled; there exists a positive association between growth and per capita income. This emphasizes the fact that in presence of constant bank advance, per capita income of different regions in Bangladesh is converging, conversely
when bank advances is not controlled there is an income divergence amongst the regions. Another model measuring the impact of per capita income and bank advance on human poverty index implied that bank advance has positive association with HPI, with marginal significance. Lack of appropriate policy and proper distribution channel bank advances had restricted bank advances from contributing to the nation’s drive to reduce poverty. To further verify the hypothesis of this paper, herfindhal index and coefficient of variation has been used to see the concentration level of both private and national banks. Private Banks are more concentrated towards comparatively developed areas. There are still areas where private banks have not channeled any credit disbursement, further implying the discrepancy of credit disbursement by the private bank. These results therefore support the main hypothesis of the study that bank’s discrepancy in credit disbursement is causative to regional disparity within Bangladesh, especially by private banks. Also, economic growth is not enhancing to its potential level due to lack of proper policy and higher level of concentration in public and private banks respectively. Reference: Alesina, A. and D. Rodrick, (1994) “Distributive Politics and Economic growth”, Quarterly Journal of Economics, 109(2), 465-469. Deidda, L. and B. Fattouh, (2002), “Concentration in the Banking Industry and Economic Growth”. H, Saidul and A. Baten, (2005), “Performance of Nationalized and Private Commercial Banks in Bangladesh”, Journal of Applied Science, 1814-1818. Rahman, H (2004), “Financial Development- Economic Growth Nexus: A Case Study of Bangladesh”, The Bangladesh Development Studies, Vol. XXX, Nos.3 & 4,113-128. Rajan, R.G. and L. Zingales (1998), “Financial development and Growth”, American Economic Review, 88, 559-586. Sarker and M. Rahman, (1996), “Credit management of commercial banks: A comparative study of public and private sector banks”, The Bureau of Business Research, Faculty of Business Studies. University of Dhaka, Bangladesh. Sen, B. and Z..Ali, (2003), “Spatial Inequality in Social Progress in Bangladesh”. World Bank Wa, H. N,(2005), “ Bank Credit and Economic Growth in Macao”. Younus, S. (2007), “Nexus amoung Output, Inflation and Private Sector Credit in Bangladesh”. Bangladesh Bank. Persson, T. and G. Tabelliini (1994), “Is Inequality Harmful for Growth?”, American Economic Review,84(3), 600-621.
Appendix: Chart 1: Advances classified by Economic Purposes for National Commercial Banks (NCBs) & Private Commercial Banks (PCBs) Years
NCBs
PCBs
1990
1995
•
. Agriculture, Fishing and Forestry, Industry, Working Capital, Construction, Electricity, Gas, Water & Sanitary service, Transport & Communication, Storage, Trade, Miscellaneous.
NCB-2000
Pvt-2000
Chart 2.3.5: Advances classified by Economic Purposes for National Commercial Banks (NCBs) & Private Commercial Banks (PCBs) Years
NCBs
PCBs
2000
2005
•
Agriculture, Fishing and Forestry, Industry, Working Capital, Construction, Electricity, Gas, Water & Sanitary service, Transport & Communication, Storage, Trade, Miscellaneous
Name of former 19 districts of Bangladesh:
NCB-2000
1. Dhaka, 2. Mymensigh, 3. Faridpur, 4. Tangail, 5. Jamalpur, 6. Chittagong 7. Comilla, 8. Noakhali, 9. Khulna, 10. Khustia, 11. Jessore, 12. Rajshahi, 13. Rangpur, 14. Bogra, 15. Dinajpur, 16. Pvt-2000 Pabna, 17. Sylhet, 18. Barisal, 19. Patuakhali
Cross Section Estimation of Growth Model for 1990-1995, 1995-2000 (without Dhaka): Dependent Variable: g Method: Least Squares Date: 03/19/09 Time: 19:58
Sample: 1 36 Included observations: 36 Variable
Coefficie nt Std. Error
t-Statistic
Prob.
0.067150 0.261396 0.018843
-1.644319 2.081341 2.330439
0.1099 0.0455 0.0262
0.008442
-6.879788
0.0000
D90
0.110416 0.544054 0.043912 0.058080
R-squared Adjusted R-squared
0.600059 0.562565
Mean dependent var S.D. dependent var
S.E. of regression
0.023699
Akaike info criterion
Sum squared resid Log likelihood Durbin-Watson stat
0.017973 85.76183 1.986180
Schwarz criterion F-statistic Prob(F-statistic)
PCI C ADV
0.052997 0.035832 4.542324 4.366377 16.00394 0.000002
Cross Section Estimation of Growth Model for 1990-1995, 1995-2000 (with Dhaka): Dependent Variable: g Method: Least Squares Date: 03/19/09 Time: 20:06 Sample: 1 38 Included observations: 38 Variable
Coefficie nt Std. Error
t-Statistic
Prob.
0.063902 0.245349 0.014376
-1.850231 2.351169 3.287493
0.0730 0.0247 0.0024
0.007981
-7.162221
0.0000
D90
0.118233 0.576856 0.047261 0.057163
R-squared Adjusted R-squared
0.618232 0.584547
Mean dependent var S.D. dependent var
S.E. of regression
0.023244
Akaike info criterion
Sum squared resid Log likelihood Durbin-Watson stat
0.018370 91.13823 1.943839
Schwarz criterion F-statistic Prob(F-statistic)
PCI C ADV
0.055023 0.036062 4.586223 4.413845 18.35313 0.000000
Cross Section Estimation of Growth Model for 1990-2000 (without Dhaka): Dependent Variable: g Method: Least Squares Date: 03/19/09 Time: 19:27 Sample: 1 18 Included observations: 18
Variable
Coefficie nt Std. Error
t-Statistic
Prob.
PCI C ADV
1.662906 0.763903 5.515758 2.873971 0.597279 0.233304
-2.176854 1.919211 2.560094
0.0459 0.0742 0.0218
R-squared Adjusted R-squared
0.345512 0.258247
Mean dependent var S.D. dependent var
S.E. of regression
0.201131
Akaike info criterion
Sum squared resid Log likelihood Durbin-Watson stat
0.606804 4.968401 2.479634
Schwarz criterion F-statistic Prob(F-statistic)
1.015522 0.233533 0.218711 0.070316 3.959346 0.041616
Cross Section Estimation of Growth Model for 1990-2000 (with Dhaka): Dependent Variable: g Method: Least Squares Date: 03/22/09 Time: 19:31 Sample: 1 19 Included observations: 19 Variable
Coefficie nt Std. Error
t-Statistic
Prob.
PCI C ADV
1.377541 0.733111 4.298626 2.713726 0.378984 0.143289
-1.879034 1.584031 2.644890
0.0786 0.1328 0.0177
R-squared Adjusted R-squared
0.337317 0.254482
Mean dependent var S.D. dependent var
S.E. of regression
0.203542
Akaike info criterion
Sum squared resid
0.662871
Schwarz criterion
1.000898 0.235736 0.201947 0.052825
Log likelihood Durbin-Watson stat
4.918500 2.592366
F-statistic Prob(F-statistic)
4.072145 0.037192
Cross Section Estimation of Growth Model for 1990-1995, 1995-2000(with Dhaka): Dependent Variable: G1 Method: Least Squares Date: 03/22/09 Time: 19:15 Sample: 1 38 Included observations: 38 Variable LOGPC C R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat
Coefficie nt Std. Error 0.946163 0.805570 4.781506 2.954031 0.038992 0.010727 0.341403 3.962905 11.36404 1.486106
t-Statistic
Prob.
1.174526
0.2483
-1.618638
0.1148
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion
1.312564 0.343249 0.742447 0.830420
F-statistic Prob(F-statistic)
1.379511 0.248345
Cross Section Estimation of Growth Model for 1990-1995, 1995-2000(without Dhaka) Dependent Variable: G1 Method: Least Squares Date: 03/22/09 Time: 19:22 Sample: 1 36 Included observations: 36 Variable
Coefficie nt Std. Error
t-Statistic
Prob.
C LOGPC
4.299904 3.059441 0.811059 0.834899
-1.405454 0.971445
0.1695 0.3386
R-squared Adjusted R-squared S.E. of regression Sum squared resid
0.028646 0.001709 0.346791 3.848448
Mean dependent var
1.328385
S.D. dependent var Akaike info criterion Schwarz criterion
0.346495 0.776834 0.866620
Log likelihood Durbin-Watson stat
11.20617 1.508181
F-statistic Prob(F-statistic)
0.943706 0.338613
Cross Section Estimation of HPI: Dependent Variable: HPI Method: Least Squares Date: 03/17/09 Time: 14:30 Sample: 1 64 Included observations: 64 Variable CADV C PC R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat
Coefficie nt Std. Error 5.968558 3.575431 4.005970 0.459448 -8.20E-06 2.03E-05 0.043812 0.012462 2.256699 310.6541 141.3656 1.328966
t-Statistic
Prob.
1.669325
0.1002
-8.719094 -0.404797
0.0000 0.6870
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion
3.748750 2.270893 4.511424 4.612622
F-statistic Prob(F-statistic)
1.397509 0.255014