ส่วนที่ 4 4 1 measuring inequality of income distribution in thailand with many index by Office of Border Economy and Logistics Study

Measuring Inequality with Multiple Indexes: An Evidence from Empirical of Thai Household Survey. Paponpat Taveeapiradeecharoen Abstract Economists have developed many measuring index of inequality so as to create the most accurate measurement. There have been substantially increased in tools for doing so. This research are conducted by using many of those tools of inequality to decompose the inequality index into two parts. First is the evaluation of income distribution within group (or within cluster) and between group. In this sense, we are able to understand the inequality of income distribution of Thailand from a different perspective. Index we applied1 are Atkinson, Generalized Entropy Families, Extended Gini Coefficient and Stratification Index. The primary aim is to identify the inequality of income distribution in subgroups for Gender Gap, Age, Region and Rural-Urban. Data used in this working-paper is collected by National Statistic Office (NSO) of Thailand. We focus mainly on the distribution of income in 2015 where all observation 33,869 were calculated. We found that by using maximum BGI developed by Elbers, Lanjouw, Mistiaen and Ozler (2005) showed that the city of Songkhla are largely not equal when compare income both Rural and Urban group, whereas Phuket has the widest range of different in income distribution in gender gap (male and female). Cities in Bangkok Metropolitan Regions which are extremely high correlated to the proportion of GDP growth, only Nonthaburi has a wide gap of inequality between male and female in gender decomposition. The additional empirical results are illustrated in appendix B.

Lecturer at Mae Fah Luang University, Office of Border Economy and Logistics Study (OBELS) Researcher.

1. Introduction and Data Configuration According to many recent research on inequality index has proved that the gap of income distribution in Thailand was steadily decreased over time during 1992 until 2011 by using Gini coefficient see more details S.W. Paweenawat, R, McNown (2014). However, the consideration of decomposing the sub-group by using modern inequality indexes such as stratification index or extended Gini by Yitzhaki and Lerman (1991). In order to understand the current circumstances about inequality index of Thailand. It is therefore crucial for us to decompose the sub-clusters in both within group and between groups of data. The data is household survey2 across regions from all over Thailand. The duration of years are during the 2007-2015 biyearly. In 2015, observations are simulated in total number of 33,869 households from five regions, Bangkok Metropolis3, Central, North, Northeast and South. For further details about cities in each region, the reader are referred to the appendix A. Before getting in the computational process for each measurement, it is worth to discuss the transformation of the matrix first. Let yij be the individual income of i in group j . Like mention in above about how inequality index can be computed separately between within group and between group, then we need to stack data in order to match individual income to each group. The group we would decompose are Gender Gap (Male and Female), Area (Rural and Urban)4, Regions (Bangkok Metropolis, Center, North, Northeast and South). To illustrate this more clearly, the data can be organized in the following:

 yi  j  y k data   i 1  ... ...   yn 

j j   k  k  ...



...    

Where n number of the last observation in each group,  j ,  j ,  j are clusters between group and { j, k , ...} are noted as sub-group or within cluster. Please note that every sub-group from each group do not have the same dimension, it depends on how the data is collected5. The reason we manage to stack data like above is due to the different dimension on each group and sub-group. For instance, the frequency of income observations collected from capital city of Thailand (Bangkok) is higher in the number and average of data which means we cannot stack all data and compute it at once. We need to process it separately. 2. A Brief of Selected Measurement 2.1 Atkinson’s index Atkinson’s index is obtained using functional form by underlying between social welfare function approach and welfare weight6. Suppose each household’s form of unity function is:

yi(1e) U ( yi )  a  b 1 e 2

(1)

We as the OBELS team would like to thank you to National Statistical Office for providing the available data of this household survey. As this generosity, we are able to squeeze the best result from them and to create a new potential evidence to the better upcoming policy in Thailand. 3 The reason we separate the Bangkok metropolis as one region is due to the highest frequency of 8 percent from all survey. 4 All household survey data were collected and identified about the area of observer, municipal=urban and non-municipal area=rural. 5 See more detail about each sub-cluster at Appendix. 6 See also Atkinson (1970) On the Measurement of inequality.

Where e = epsilon which we prefer not to be “1”. If e[0,1] is satisfied, then the unity function can be written:

U ( y)  ln( y)

(2)

e is a measure of the level of inequality aversion, on the other words, it is referred as the sensitivity to transfers at a different income rank. If e is higher means the more weights are transferred from the higher level of income distribution to the lower. To put it simply, we would set the higher epsilon when we think most households in specific country are developing country due to higher number of low to medium income level. The atkinson’s index can be derived by two steps. First is to obtain the equalized social wealfare where everyone in the observation has the same level of income distribution, We .

 yi(1e )  1 n We  a  b    U  ye   U ( ye ) n i 1 i 1  1  e  For every income ye , n

(3)

1 n 1e ye    yi1e   n i 1 

(4)

Second is to contrast between the equalized income distribution and mean income    . The Atkinson’s index can now be derived as follow: 1

 1 n  y 1e 1e ye Atkinson ' s index  1   1     i    n i 1        Where Atkinson ' sindex (0,1)

(5)

2.2 Entropy Measuring Families Entropy measurement is based on the probability of distribution theory by assuming that every event’s probabilities in every specific set of those events can be calculated. Let us suppose that there are n events numbered 1, 2, 3, … n. which of those are expected to have their own probability of occurrence p1 , p2 , p3 ,..., pn , these number must be non-negative due to the impossibility of occurrence from data set, and

p

1.

The concepts are simple. If events numbered “1” is more likely to be frequent ( p1 is closed to 1), then this information is relatively low h( p1 ) . Conversely, if this event has low probability to happen then we got a higher of h( p1 ) . Another assumption is that if two events are statistically independent, probabilities of event 1 and event 2 would be pi p j , therefore the condition met the requirement and will be written as:

h( pi p j )  h( pi )  h( p j ) where i  j

(6)

Now, the one function above satisfies the decreasing information content property. The individual event now can be written:

h( pi )  ln(

1 ) pi

(7)

The average content of information (finalized entropy), of circumstance H ( pi ) is done by using sum

of all individual event

 h( p ) , now the formula becomes: i 1

i 1

H ( p)   pi h( pi )   pi ln(

1 ) pi

(8)

The interpretation from the equation above is to measure the probability of different varieties of occurrence are equal or not. On other words, the smaller of the differences in probabilities as pi becomes closer to 1/n. Most of the inequalities index in the past were able to analyze inequality only the data itself, where the source or the type of how data were collected are not in the part of the process until Shorrocks (1980) and among others, proving that the Generalized Entrypy Class can be rewritten as follow to evaluate the between group and within groups separately.

GE 

n  y  1 fi ( i )c  1  c(c  1) i 1   

for c  0,1

(9)

According to Cowell, F, A (2009). If c  0 , the measurement will also be known as Theil-L or Mean Log Deviation indexes.

MLD 

y 1 n ln i  n i 1 

(10)

If c  1 the measurement will be known as Theil’s index (T) which evaluate the distinction between the maximum and the average content of information of events.

T  ln(n)  H ( p)

(11)

Rewrite the equation above and it becomes, n 1   T   p ln(n)  ln( )  n  i 1 i 

Now transform pi into the term of income inequality measurement by replace pi 

(12)

yi n

y i 1

Where yi is individual income of household number i, and n is number of observation, then the finalized Theil index is:

T

y 1 n yi ln( i )  n i 1  

(13)

2.3 Extended Gini Coefficient Gini coefficient is the most popular among others for measuring inequality using income distribution. The logic behind this is because this measurement satisfies both of statistical and social welfare axioms. - Mean Independence: the gini coefficient is not changed when all household’s income is doubled. - Population size independence: on other words, ceteris paribus, which means that if the population size changes. The measurement remains unchanged.

Pigou-Dalton Transfer Sensitivity: inequality index means calculating how income is distributed by telling us in number, by this it means that if the income is to be transferred from higher to lower income. The inequality index must be decreased too. Symmetry: if two individual observation with different income is swap, the final index is unchanged. From Elbers C., Lanjouw P., Mistiaen J.A., Ozler B. (2005) stated the maximum of betweengroup inequality as 100% of total inequality share mean cannot be possible in realistic possibility. Under only two circumstance which the statement above is not true, however, is whether each household constitutes a separate “group” or there were no difference of per capital income between all individual households within each category. It is therefore advisable to generalize the index from a different category to be identical by using the following formula.

Rb ' 

BGI total inequality  Rb max imum BGI max imum BGI

(14)

Where BGI is between group inequality index, Maximum BGI is the maximum possible BGI which can strictly calculated by using Elbers and Alii method (2005). As Cowell and Jenkins (1995) noted that the between group inequality can never be exceeded total inequality, the same notation goes to Rb cannot be higher than Rb’. The interpretation from Rb and Rb’ is that the closer of Rb’ get to Rb the less inequality inter-group become. The reason is simple, if Rb’ get closer to Rb, it proves that the between inequality is significantly near to the value of total inequality.

2.4 Stratification Index The development of Yitzhaki and Lerman7 (1991) for the stratification of inequality index. The objective for creating this index is to identify the distribution of between group and within group of data. To put it in simpler words, suppose there are two groups in provided data, showing that the number of books that each manufacturing cluster can produce. By comparing this, means that we are analyzing the divergence of how much manufacturer’s productions are. Other inequality index can only point out the probability of inequality such as Atkinson’s index. However, for stratification from YL it is able to separate each class of each individual within group and between groups are. First, let yij be the data where i  individual data of group j . Assume there are L j groups.

L

 A , Pj 

Lj A

where Pj is straightforward for itself as number proportion of group j from all

observation. The concept of stratification procedure is to find the covariance between yi among members of group j only. The following is the example of stratification of group j in the case there are only two clusters:

CO j 

Cov j [( Fj  Fnj ), y ] Cov j ( Fj , y )

, where 1  CO j  1

(15)

Formula above represents the covariance over group j which determined by the difference between the ranking of a member from within each individual in group j . Hence Fj ( yij ) 

We will denote this index as YL index hereafter.

Rank ( yij ) (Lj )

is the

cumulative distribution of individual i within group j only. On the other hand, Fnj ( yij ) 

Rank ( yij ) ( A  Lj )

is the same value but for individual that is not in group j . After the covariance measurement, we can move on to the next step by using the decomposable stratification index. For the sake of brevity, we will not present the full details of how this specific index can be reformulated to be decomposable, interested authors are referred to Income Stratification and Income Inequality (1991). The decomposable stratification index can be written as follow:

G   S j G j   S j G j CO j ( Pj  1) 

2Cov( y j , Fj )

(16)

Where S j is the data share of group j ;

y is the mean data of all observation; y j is the mean data in specific group j ;

CO j is the covariance mentioned above; The first component of the RHS is determined as within inequality proportions whereas the second term is represented as intra-group variability in overall ranks. On other words, how overall ranks will be changed due to the divergence on individual data alteration. The last term is Gini index across all between groups. After the decomposition process, as stated by YL’s work that this decomposition index is more complicated than the other index such as Theil. Speaking loosely, each component has no identical distribution to each other. For instance, if there is changes in CO j for only group j would leave the Gini and unchanged and affect only the impact of stratification or intra-group variability (second component). In addition to this, if there is alteration in covariance means and means of rank of specific cluster, it leave overall gini index unchanged and only last term of RHS would be affected. 3. Empirical Results Table 3.1 Inequality in HFC implied by Grouping Decomposable Atkinson's index. Decomposed Decomposed Factors Groups e=0.20 e=0.25 8 0.003 0.149 0.004 0.187 Age Child

e=0.30 0.005 0.226

e=0.35 0.006 0.265

2.2%

97.8%

2.2%

97.8%

2.1%

97.9%

2.1%

97.9%

Male

0.000

0.152

0.001

0.190

0.001

0.229

0.001

0.269

Female

0.3%

99.7%

0.3%

99.7%

0.3%

99.7%

0.3%

99.7%

Buddhism

0.000 0.1%

0.152 99.9%

0.000 0.1%

0.190 99.9%

0.000 0.1%

0.229 99.9%

0.000 0.1%

0.269 99.9%

0.006 4.2%

0.146 95.8%

0.008 4.1%

0.184 95.9%

0.000 0.1%

0.229 99.9%

0.000 0.1%

0.269 99.9%

Teenage Adult Middle Adult Eldery Gender Religion

Christian Islam Others Region

BKKMETRO Central North

The clustering are done by using general psychology where 1-12=child, 13-19=teenage, 20-39=adult, 40-64=middle adult and above 65=eldery.

Table 3.1 Inequality in HFC implied by Grouping Decomposable Atkinson's index. Decomposed Decomposed Factors Groups e=0.20 e=0.25

e=0.30

e=0.35

Northeast South Municipal Area Hour works Cities

Urban

Rural

0.006 4.2%

0.146 95.8%

0.009 4.4%

25 groups

0.014

0.139

(2-12)

9.4%

90.6%

77 groups

0.012

0.141 92.0%

8.0% Source: author’s calculation by household survey.

0.184 95.6%

0.009 4.1%

0.222 95.9%

0.011 4.0%

0.018

0.176

9.4%

90.6%

0.015

0.178

7.9%

92.1%

0.261 96.0%

0.022

0.213

0.026

0.250

9.3%

90.7%

9.3%

90.7%

0.018

0.215

0.021

0.253

7.9%

92.1%

7.8%

92.2%

Seven groups are decomposed from the Atkinson’s index based on the income distribution in 2015. Four of epsilons are chosen so as to investigate how sensitive of weight when transferring them from higher income per month to lower income group. These results quite speak for itself as the more epsilon increase, means that lower income dominates and have more effect to overall inequality index. Despite the fact mentioned above, some decomposed clusters remain unchanged when epsilons are altered. For example, Age, Gender and Region. With given e = 0.2 over all inequality goes up to 0.152 (15.2%) and this counts as 99.7% of overall Atk’s index. When e increase to 0.35 Atk rises to 0.269. In addition to this, there are 99.7% of overall inequality came from within-group (Male and Female) rather than 0.3% of between-group index. This could lead to a possible result that gender gap during this globalizational era are narrower compared to the past century. However, the decrease between gender gap thus lead to the increase between the same sex within group instead. The further estimate to confirm this should be the Gender decomposition of income during the previous period should be calculated. On the other hand, the most sensitive to the weight changed in this index is Region cluster. Although, there is only moderately changes in Atkinson’s index during epsilon 0.2-0.25, but it is clear that the transmission from e 0.25-0.3, the share of decomposition between-group and within-group raises from 95.9 – 99.9% (almost 100%), this implied that lower income distribution somehow overlapped with the middle and high income distribution and thus the more sensitive of each cluster to overall index. Table 3.2 Theil's index and the mean logarithmic deviation (MLD) Decomposed Factors Age

Theil

Decomposed Groups

MLD

Between

Within

Between

Within

Child

0.002

0.750

0.892

1.413

Teenage

0.3%

99.7%

38.7%

61.3%

Male

0.017

0.735

0.016

1.399

Female

2.2%

97.8%

1.2%

98.8%

Buddhism

0.001

0.751

0.001

1.414

Christian

0.1%

99.9%

0.1%

99.9%

0.028 2.0%

1.387 98.0%

Adult Middle Adult Eldery Gender Religion

Islam Others Region

BKKMETRO

0.033

0.719

Central

4.4%

95.6%

North

Table 3.2 Theil's index and the mean logarithmic deviation (MLD) Decomposed Factors

Theil

Decomposed Groups

MLD

Northeast South Rural

0.034

0.718

Urban

4.5%

95.5%

0.036 2.6%

1.379 97.4%

groups9

0.07136 9.5%

0.68058 90.5%

0.08201 5.8%

1.333219 94.2%

0.06209 8.3%

0.6898567 91.7%

0.06135 4.3%

1.353888 95.7%

Municipal Area Hour works

(2-12) Cities

77 groups10

Source: author’s calculation by household survey.

Theil and MLD index are generalized entropy families, where Theil is calculated using alpha = 1, MLD’s alpha = 0 in equation (10) The most prominent result is that the difference between these two indexes in Age clustering, among these groups, MLD tells us that just above half of measures are from within, where the rest 38.7% resulted from between. One possible logic behind this approach is that the mean logarithm process which we replace alpha as zero led to the extremely low value in an extreme distribution where the income from child and teenage are really low from the given data set. Therefore, the deviation is changed a lot based only on alpha. Both Table 3.1 and 3.2 are the representative for inequality index which loose assumption about considering of the ranking from each individual from clustered group. For example, if we are talking about the economist who can publish many articles online. Then we can evaluate inequality index of those article that get published by calculating atkinson’s index or generalized entropy families. Suppose there are three clusters, Atkinson and generalized entropy families never evaluate the individual rank, which means that rank of economists within group and overall population are missed. Until the work of stratification index developed by Yitzhaki S., Lerman. R. (1991). To put it simply, other index are evaluated using “how different of each individual data within, between group and overall”, Conversely, for stratification index by YL are based on “How similar of each individual within cluster, between group and overall”. It seems awkward at the first place but it provides huge benefit to evaluate inequality index and we are able to identify the type of distribution in specific group of data. The following table are based on the stratification index calculation. Table 3.3 Extended Gini with stratification index by YL. (Gender Gap Decomposition). Sgini Gender Cluster Male Female

Mean income

Population Share

Income Share

Group Gini

Group Stratification

10,946.51

45.8%

57.6%

0.667

-0.008

9,577.91

54.2%

42.4%

0.587

0.012

1.00

0.621

Total Within inequality

0.620

Between inequality

0.002

Stratification

-0.001

BGI Maximization Source: author’s calculation by household survey.

Hour works are per day unit, started from two hrs/day till twelve hrs/day. For further details, see Appendix A.

0.425

To interpret table above, let’s start with the first column from the left, this is the Gender Gap clustered by stratification measurement between Male and Female, the average income for male is 10k whereas women earned in 2015 approximate 9.5k. Third column referred to number of observation in each cluster. 45.8 goes to male and the rest goes to female. The Group Gini is showing us within subgroup, how stratification about inequality demonstrate to us in a number. For example, there are 66.7 percent of male gender who owns most of income, whereas 58.7 percent in female cluster. The last column is group stratification, this provide a benefit to us about how inequality is measured by not looking at only the extended Gini index (col=5), but rather the distribution of each cluster’s data. To read this column, if stratification = 1 ( CO j  1 ) means that for specific of group j , there is no rank of individual data in that group duplicate in other groups. In this case, YL stated that it is an perfect strata or one specific horizontal layer are booked by one group only. The lower of CO j leads to the less perfect strata, in this sense, other group’s rank are in the same horizontal rank of group j . However if CO j drops until zero, this shows that the individual’s rank in group j is the same as when compare rank in overall population or comparing every data at once. On other words, it never form strata from the first place. In addition to this, as close as CO j get to -1, it proves that one cluster of j can be composed into two identical group if it reaches -1. In this case the divergence of rank within group j is higher than overall population. The stratification of overall observation is -0.001 which is really close to zero, in this case, we can conclude that most of the rank of individual data in group j is the same as the rank when compare everyone at once. Hence, the within group of extended Gini largely account in within inequality measurement, roughly 99.7%. For the sake of brevity we will not present every Sgini table here. Now we will move on to the maximum between group index which is implied and developed by Elbers, Lanjouw, Mistiaen and Ozler11 (results are on Appendix B, Figure B.1 and B.2). As described above that the more divergence between the Rb ' and Rb the higher inequality in different sub-clusters. In the figure B1, Rural and Urban are decomposed by extended Gini and plotted so as to see if there is any inequality between subclusters and within cluster itself. It is worth concluding that the highest inequality presented is Songkhla city which is located in South region in Thailand. Please note that in capital city (Bangkok) have no difference between Rb ' and Rb since this is not a big city and every part of the city is accounted as urbanized province. It is thus no different between sub-clusters. On the other hand, on the gender income gap between male and female is in figure B.2 (for further details at appendix B), the city which has most inequality between both sex is Phuket, follow by Yasothorn, Nongkai and Nonthaburi. Surprisingly, for Bangkok Metropolitan Region12, there is only one city that has excess inequality between both gender (Nonthaburi). Ranked third among all 77 cities in Thailand whereas the expectation for the rest provinces in BMR should be exponentially high relative to others. The possible calculation can be claimed by two reasons. First is the observation collected in each cities are not sufficient and cannot be representative to reflect all of inequality for each. Second, for the last century the educations are developed not for male side only, there are tremendous increase in the number of female who graduate a higher degree especially Master and PhD. Hence, this could lead to the narrower in income gap between both genders. 4. FURTHER VISION ON INEQUALITY DISTRIBUTION IN THAILAND. It’s pretty obvious that the data that I have used to calculate the inequality distribution is still small relatively to the number of population in each city. For instance, population in Bangkok has 11

Re-Interpreting Sub-Group Inequality Decompositions (2005). Bangkok Metropolitan Region contains 6 cities (Bangkok, Nakhonpathom, Nonthaburi, Pathumthani, SamutPrakan, Samutsakhon). 12

officially reported that there are 8.28 million people. However, the sample that we collected was only 2 thousand. Statistically speaking, this is not such an ideal sample to be the representative of all population. In order to tackle this problem efficiently, we need to design the structure process of how we approach the data. In addition to this and most importantly, the algorithm used to apply the complex model must be specified since the start. Otherwise, the system will constantly adjusted during the collecting period and thus lead to a greater problems. APPENDIX A. NO.

Cities

NO.Obs

Proportion

NO.

Cities

NO.Obs

Proportion

Angthong

424

Phayao

246

Aumnadcharoen

421

Phetchabun

341

Bangkok

2,795

Phetchaburi

478

Buengkan

297

Phichit

306

Burirum

376

Phitsanulok

296

Chachoengsao

506

Phrae

427

Chainat

413

Phranakhonsi

476

Chaipum

386

Phuket

368

Chanthaburi

364

Prachinburi

411

ChiangMai

404

PrachuapKhirikhan

478

ChiangRai

167

Ranong

397

Chonburi

604

Ratchaburi

555

Chumphon

343

Rayong

551

Kamphangphet

669

Roiet

338

Kanchanaburi

401

Sakaeo

401

Kanlasin

241

Sakonnakhon

365

Khonkaen

562

SamutPrakan

667

Krabi

405

Samutsakhon

623

Lampang

494

Samutsongkhram

352

Lamphun

466

Saraburi

513

Lay

212

Satun

359

Lopburi

437

Singburi

524

MaeHongSon

304

Songkhla

447

Mahasarakam

392

Srisaket

400

Mukdahan

373

Sukhothai

388

Nakhonnayok

345

Suphanburi

262

NakhonPathom

513

SuratThani

382

Nakhonrachasima

582

Surin

423

Nakhonsawan

436

Tak

366

Nakhonsithammarat

490

Trang

411

Nakonpanom

344

Trat

362

Nan

302

Ubon

392

Naratiwat

426

Udon

312

Nongbua

211

Uthaithani

286

Nongkai

345

Uttaradit

298

Nonthaburi

658

Yala

232

Yasothorn

415

37 Pathumthani 734 2% Source: National Statistical Office Thailand (NSO).

APPENDIX B. (EMPIRICAL RESULTS) Table B.1 Extended Gini with stratification index by YL. (Age Decomposition). Sgini Age Cluster

Mean income

Population Share

Income Share

Group Gini

Child

8,416.080

32.2%

0.626

0.020

Teenage

8,794.664

13.3%

0.666

-0.052

Adult

10,811.731

30.8%

0.561

0.105

Mid adult

13,635.182

18.9%

0.634

-0.069

Eldery

10,948.849

4.8%

0.637

-0.055

Total

0.621

Within inequality

0.612

Between inequality

0.011

Stratification

-0.001

BGI Maximization Source: author’s calculation by household survey.

Group Stratification

0.578

Table B.2 Extended Gini with stratification index by YL. (Religion Decomposition). Sgini Religion Cluster

Mean income

Population Share

Income Share

Group Gini

Group Stratification

10,402.494

94.1%

95.1%

0.619

0.036

Christian

8,556.681

5.4%

4.2%

0.573

0.038

Islam

11,575.209

0.5%

0.6%

0.661

0.001

Others

25,270.417

0.0%

0.1%

0.766

0.000

Buddhism

Total

0.621

Within inequality

0.621

Between inequality

0.001

Stratification

0.000

BGI Maximization Source: author’s calculation by household survey.

0.110

Table B.3 Extended Gini with stratification index by YL. (Region Decomposition) Sgini Region Cluster

Mean income

Population Share

Income Share

Group Gini

Group Stratification

BKK Metro

20,003.162

8.3%

32.7%

0.473

0.286

Central

9,428.967

35.6%

15.7%

0.599

0.020

North

8,756.437

18.3%

20.2%

0.658

-0.045

Northeast

9,526.886

21.8%

15.1%

0.657

-0.010

South

10,096.575

16.1%

0.626

-0.014

Total

0.621

Within inequality

0.603

Between inequality

0.035

Stratification

-0.017

BGI Maximization Source: authorâ&#x20AC;&#x2122;s calculation by household survey.

0.584

Table B.4 Extended Gini with stratification index by YL. (Rural-Urban Decomposition) Sgini Area Cluster

Mean income

Population Share

Income Share

Group Gini

Group Stratification

Rural

6,879.66

36.8%

24.2%

0.667

-0.018

Urban

12,275.55

63.2%

75.8%

0.587

0.110

Total

0.621

Within inequality

0.607

Between inequality

0.031

Stratification

-0.016

BGI Maximization Source: authorâ&#x20AC;&#x2122;s calculation by household survey.

0.353

Table B.5 Extended Gini with Stratification index by YL. (Cities Decomposition) City Cluster Bangkok

Mean income 20,003.16

Sgini Population Share 8.3%

Income Share 16.3%

Group Gini 0.163

Group Stratification 0.604

SamutPrakan

9,931.51

2.0%

1.9%

0.019

0.697

Nonthaburi

16,709.97

1.9%

3.2%

0.032

0.473

Pathumthani

11,128.38

2.2%

2.4%

0.024

0.649

Phranakhonsi

10,713.95

1.4%

1.5%

0.015

0.725

Angthong

8,935.89

1.3%

1.1%

0.011

0.512

Lopburi

12,470.86

1.3%

1.6%

0.016

0.690

Singburi

9,552.68

1.5%

1.4%

0.014

0.666

Chainat

8,646.85

1.2%

1.0%

0.010

0.663

Saraburi

9,894.80

1.5%

0.015

0.649

Chonburi

11,840.07

1.8%

2.1%

0.021

0.555

Rayong

11,027.11

1.6%

1.7%

0.017

0.366

Chanthaburi

6,825.77

1.1%

0.7%

0.007

0.616

Trat

8,792.47

1.1%

0.9%

0.009

0.781

Chachoengsao

8,678.68

1.5%

1.3%

0.013

0.723

Prachinburi

9,894.73

1.2%

0.012

0.643

Nakhonnayok

11,836.33

1.0%

1.2%

0.012

0.591

Sakaeo

9,609.36

1.2%

1.1%

0.011

0.570

Nakhonrachasima

11,724.71

1.7%

1.9%

0.019

0.669

Burirum

8,524.34

1.1%

0.9%

0.009

0.702

Surin

10,236.36

1.2%

1.3%

0.013

0.579

Srisaket

10,273.31

1.2%

0.012

0.655

Ubon

10,337.22

1.2%

0.012

0.513

Yasothorn

8,722.61

1.2%

1.1%

0.011

0.686

Chaipum

10,077.82

1.1%

0.011

0.697

Aumnadcharoen

6,006.93

1.2%

0.7%

0.007

0.581

Buengkan

8,661.33

0.9%

0.7%

0.007

0.667

Nongbua

9,491.51

0.6%

0.006

0.636

Khonkaen

10,318.10

1.7%

0.017

0.599

Udon

8,330.07

0.9%

0.7%

0.007

0.704

Lay

10,711.60

0.6%

0.006

0.610

Nongkai

8,740.24

1.0%

0.9%

0.009

0.595

Mahasarakam

11,293.93

1.2%

1.3%

0.013

0.606

Roiet

9,074.96

1.0%

0.9%

0.009

0.561

Kanlasin

8,830.76

0.7%

0.6%

0.006

0.641

Sakonnakhon

8,324.30

1.1%

0.9%

0.009

0.438

Nakonpanom

11,374.38

1.0%

1.1%

0.011

0.418

Mukdahan

8,056.51

1.1%

0.9%

0.009

0.705

ChiangMai

4,575.29

1.2%

0.5%

0.005

0.518

Lamphun

5,833.85

1.4%

0.8%

0.008

0.716

Lampang

10,312.02

1.5%

0.015

0.643

Uttaradit

12,484.24

0.9%

1.1%

0.011

0.686

Phrae

8,107.15

1.3%

1.0%

0.010

0.653

Nan

10,977.67

0.9%

0.009

0.682

Phayao

7,307.16

0.7%

0.5%

0.005

0.519

ChiangRai

8,405.59

0.5%

0.4%

0.004

0.707

MaeHongSon

12,593.25

0.9%

1.1%

0.011

0.510

Nakhonsawan

10,028.72

1.3%

0.013

0.382

Uthaithani

9,516.79

0.8%

0.008

0.611

Kamphangphet

4,041.00

2.0%

0.8%

0.008

0.615

Tak

12,089.25

1.1%

1.3%

0.013

0.569

Sukhothai

8,713.74

1.1%

0.9%

0.009

0.747

Phitsanulok

12,127.80

0.9%

1.1%

0.011

0.549

Phichit

11,425.45

0.9%

1.0%

0.010

0.710

Phetchabun

6,778.36

1.0%

0.7%

0.007

0.597

Ratchaburi

6,220.60

1.6%

1.0%

0.010

0.664

Kanchanaburi

5,040.00

1.2%

0.6%

0.006

0.546

Suphanburi

6,480.86

0.8%

0.5%

0.005

0.736

NakhonPathom

8,249.48

1.5%

1.2%

0.012

0.646

Samutsakhon

5,035.67

1.8%

0.9%

0.009

0.545

Samutsongkhram

5,769.28

1.0%

0.6%

0.006

0.626

Phetchaburi

8,689.62

1.4%

1.2%

0.012

0.674

PrachuapKhirikhan

7,638.50

1.4%

1.0%

0.010

0.566

Nakhonsithammarat

7,650.70

1.4%

1.0%

0.010

0.667

Krabi

10,802.91

1.2%

1.3%

0.013

0.698

Phangnga

10,710.54

0.9%

0.8%

0.008

0.677

Phuket

11,687.46

1.1%

1.3%

0.013

0.522

SuratThani

9,775.00

1.1%

1.0%

0.010

0.632

Ranong

8,746.93

1.2%

1.0%

0.010

0.607

Chumphon

11,703.59

1.0%

0.010

0.667

Songkhla

11,825.67

1.3%

1.5%

0.015

0.606

Satun

10,645.08

1.1%

0.011

0.590

Trang

8,932.23

1.2%

1.0%

0.010

0.664

Phatthalung

7,226.18

1.1%

0.8%

0.008

0.629

Pattani

6,502.24

1.5%

0.8%

0.008

0.527

Yala

18,196.23

0.7%

1.2%

0.012

0.798

Naratiwat

11,449.08

1.3%

1.2%

0.012

0.699

Total

0.621

Within inequality

0.584

Between inequality

0.061

Stratification

-0.024

BGI Maximization Source: authorâ&#x20AC;&#x2122;s calculation by household survey.

0.614

Figure B.1

Rural and Urban Decomposed using extended Gini coefficient by YL calculation Yasothorn Yala Uttaradit Uthaithani Udon Ubon Trat Trang Tak Surin SuratThani Suphanburi Sukhothai Srisaket Songkhla Singburi Satun Saraburi Samutsongkhram Samutsakhon SamutPrakan Sakonnakhon Sakaeo Roiet Rayong Ratchaburi Ranong PrachuapKhirikhan Prachinburi Phuket Phranakhonsi Phrae Phitsanulok Phichit Phetchaburi Phetchabun Phayao Phatthalung Phangnga Pattani Pathumthani Nonthaburi Nongkai Nongbua Naratiwat Nan Nakonpanom Nakhonsithammarat Nakhonsawan Nakhonrachasima NakhonPathom Nakhonnayok Mukdahan Mahasarakam MaeHongSon Lopburi Lay Lamphun Lampang Krabi Khonkaen Kanlasin Kanchanaburi Kamphangphet Chumphon Chonburi ChiangRai ChiangMai Chanthaburi Chaipum Chainat Chachoengsao Burirum Buengkan Bangkok Aumnadcharoen Angthong

0.1

0.2

0.3

0.4

Rb'

0.5

0.6

0.7

0.8

Figure B.2

Gender Gap decomposed using extended Gini coefficient by YL calculation Yasothorn Yala Uttaradit Uthaithani Udon Ubon Trat Trang Tak Surin SuratThani Suphanburi Sukhothai Srisaket Songkhla Singburi Satun Saraburi Samutsongkhram Samutsakhon SamutPrakan Sakonnakhon Sakaeo Roiet Rayong Ratchaburi Ranong PrachuapKhirikhan Prachinburi Phuket Phranakhonsi Phrae Phitsanulok Phichit Phetchaburi Phetchabun Phayao Phatthalung Phangnga Pattani Pathumthani Nonthaburi Nongkai Nongbua Naratiwat Nan Nakonpanom Nakhonsithammarat Nakhonsawan Nakhonrachasima NakhonPathom Nakhonnayok Mukdahan Mahasarakam MaeHongSon Lopburi Lay Lamphun Lampang Krabi Khonkaen Kanlasin Kanchanaburi Kamphangphet Chumphon Chonburi ChiangRai ChiangMai Chanthaburi Chaipum Chainat Chachoengsao Burirum Buengkan Bangkok Aumnadcharoen Angthong

0.02

0.04

0.06

0.08 Rb

Rb'

0.1

0.12

0.14

0.16

0.18

Acknowledgement Special thanks go to the National Statistic Office of Thailand for the generosity to provide the household survey of income. In addition, I send appreciation to Mae Fah Luang University and Office of Border Economy and Logistics Study (OBELS). References Atkinson A.B. (1970). On the Measurement of Inequality. Journal of Economic Theory, 2,pp.244263.

Shorrocks (1980). The Class of Addictively Decomposable Inequality Measures. Econometrica, Vol. 48 No. 3, Apr. 1980. Blackorby C., Donaldson D., Auersperg M. (1981). A new procedure for the measurement of inequality within and among population subgroups. Canadian Journal of Economics, 14, pp.665-685. Elbers C., Lanjouw P., Mistiaen J.A., Ozler B. (2005). Re-Interpreting Sub-Group Inequality Decompositions. World Bank, World Bank Policy Research Working Paper 3687, 42 p. Schechtman E., Yitzhaki S. (2008). Calculating the Extended Gini Coefficient from Grouped Data: A Covariance Presentation. Bulletin of Statistics & Economics, 2(S08), pp.64-69. Yitzhaki S., Lerman R. (1991). Income Stratification and Income Inequality. Review of Income and Wealth, 37(3), pp.313-29.

Cowell and Jenkin. (1995). How Much Inequality Can We Explain? A Methodology and an Application to the United States. Economic Journal. Vol. 105 issue. 429, 421-30. Cowell F.A. (2000). Measurement of Inequality. In Atkinson A.B., Bourguignon F.(Eds.) Handbook of Income Distribution. Elsevier, Vol. 1, pp.87-166.

Cowell, F, A. (2009). Measuring Inequality. Oxford University Press. S.W. Paweenawat, R. McNown (2014). The determinants of income inequality in Thailand: A synthetic cohort analysis. Journal of Asian Economics 31-32, pp.10-21.