Regional Income Maps of bangladesh

8/10/2019 Regional Income Maps of bangladesh

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BANGLADESH RESEARCH PUBLICATIONS JOURNAL

ISSN: 1998-2003 Volume: 1, Issue: 1, Page: 22-37, April - June 2008

REGIONAL DIVERGENCE OF INCOME AND ALLOCATION OF

PUBLIC FACILITIES IN BANGLADESHMd. Shohel Reza Amin*TP1PT, Mrs. Umma Tamima TP2PT

Abstract

Bangladesh is one of the least urbanized countries in south Asia andfeatures densely populated rural areas composed of clustered villages.Imbalanced industrial development and agrarian agony has drawn

regional divergence of income distribution. This paper examines theextent of asymmetrical distribution of income among the districts ofBangladesh and its impact on the provision of public facilities in the year2001. One of the main areas of innovation in this research work is that ofthe methodological instruments used to accomplish the objectives suchas spatial autocorrelation (Morans I), Location Quotient method, GiniIndex, and Discrimination or Dissimilarity Index. The result attained revealsthat the level of disparity varies considerably between districts, which leadto discrimination in the provision of public services in sixty-four districts. Thecomparative evaluation among income distribution and discriminationindex of facilities provision reveals that districts within low-income groupsare usually lowly and moderately deprived from provision of publicfacilities. This is because households of these districts are mainly involved inrural based income generating activities and almost deprived of urbanfacilities. Furthermore, most of the incomes generating establishments areconcentrated in some selected districts. This sort of divergence of incomeis leading to disproportional regional growth resulting lower nationalgrowth as a whole. The regional convergence in terms of income andpublic facilities is required for the overall development of Bangladesh.

Key words: Regional divergence, Discrimination Index, Gini Index, LocationQuotient Method, and Spatial autocorrelation.

Introduction

Bangladesh is a country of 130 million population with population density of

840 persons/sq.km (BBS, 2001). Bangladesh economy is not big enough to support

such a vast population and high incidence of poverty is the ultimate result.

Though the country is making good progress in the socio-economic field in

TP

1PTCorresponding Author

P

1PAssistant Professor, Department of Urban and Regional Planning, Jahangirnagar

University, Savar, Dhaka-1342. e-mail: [email protected], Cell: 8801717159382TP

2PTLecturer, Department of Urban and Regional Planning, Bangladesh University of

Engineering and Technology, BUET, Dhaka-1000, e-mail: [email protected]


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Reg iona l Divergenc e of Inc om e

http://www.bdresearchpublications.com/journal/

23

increasing the literacy rate, improving expectation of life, increasing food

production and decreasing infant mortality and total fertility but progress of

poverty reduction is very slow. For a number of reasons the current distribution of

income in the districts of Bangladesh is worthy of analysis. Unequal distribution of

income boosts up the poverty and hinders inter-regional migration along with

deprivation of public facilities and services and income generation

establishments.

On the other hand, provision of basic services greatly influences the quality

of human development and economic activities. Efficient and equal delivery of

essential services is critical for reducing poverty and improving welfare.

Investment in improving the delivery of services can make significant contribution

towards raising productivity and accelerating the pace of economic growth

(ADB, 2005).

Nevertheless in case of service provision the regions are more or less

discriminated on the issue of income, race and religion. A common complaint is

that people in the low-income part are unfairly treated compared to the rests

regarding deteriorating service levels, small resources and poor infrastructure.

The objectives of this project are to explore the spatial distribution of income

inequality in the districts of Bangladesh, and to determine the significance of

district-wise income inequality on the provision of facilities in different districts.

Research methodology

In this research the impact of spatial income inequality on the provision of

facilities and income generating establishments in sixty-four districts of Bangladesh

was investigated using data from the last Population census of Bangladesh: Zila

Series (2001) and Preliminary Report on Household Income and Expenditure

Survey 2005. Three public facilities such as safe drinking water, hygienic sanitation

system and electricity supply along with income generating establishments per

10000 were taken into account for this research based on the availability of data.

Later on, the spatial correlation of income of households of different

districts of Bangladesh was calculated by using Morans I in order to determine

the spatial dependency of districts regarding household income.


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Tam im a a nd Tam im a


24

Moran introduced in 1950 the first measure of spatial autocorrelation in

order to study stochastic phenomena, which are distributed in space in two or

more dimensions. Morans I is used to estimate the strength of this correlation

between observations as a function of the distance separating them

(correlograms). Like a correlation coefficient the values of Morans I range from

+1 (meaning strong positive spatial autocorrelation) to 0 (meaning a random

pattern) and to 1 (indicating strong negative spatial autocorrelation). Values

near +1 indicate similar values tend to cluster; values near 1 indicate dissimilar

values tend to cluster; values near -1/(n-1) (which goes to 0 as n gets large)

indicate values tend to be randomly scattered (T

Zimeras and Tsimbos, n.d.)T

.

The definition of Morans I(Anselin, 1995) for a spatial proximity matrix wBijBfor

a variable yat location i is defined below as:

=

=

= =

ji

ij

n

i

i

j

n

i

i

n

j

ij

wyy

yyyywn

I

1

2

1 1

)(

)()(

(1)

Nevertheless, before performing these tests it is necessary to define a

spatial weight matrix Wto capture the strength of the interdependence between

each pair of districts i andj. a first option is to use the concept of first order-

contiguity, according to which wBijB=1 if districts i andj are physically adjacent and

0 otherwise (Lpez-Bazo e t a l ., 1999; Rey and Montouri, 1999). Usually, the

proximity matrix wBijBis everywhere 0 except for contiguous locations i andj where it

takes the value 1.

The extent of concentration of district-wise households provided by public

facilities was determined by applying Location Quotient (LQ) method. An idea

about the extent of concentration of public facilities in different parts of the city

can be obtained if we consider the distribution of population in our analysis. In this

context the use of location Quotient method could be helpful. This method can

be use to measure the extent to which public facilities households in different

districts are in balance. (Pasha, 1991).

For calculating the location quotient (LQ) for households of a particular

facility i in a particular district, the following formula has been used:

L.Q = (nBiB/p)/(NBiB/P) (2)


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Where,

nBiB= No. of households of each facility iin a given district

p= Households of the concerned districtNBiB=No. of households of each facility iin BangladeshP= Total households in Bangladesh

Using the above formula location quotients for the households of selected

public facilities for districts has been prepared. These quotients identify the

concentration or deconcentration of households in different districts based on

facility provision. If the value of the quotient for a particular facility in a particular

district exceeds 1 (one), concentration is indicated. An indication of deficiency is

given by a value of less than 1 while a value of 1 or close to 1 indicates self-

sufficiency.

On the basis of the values of LQ the concentration of households of each

facility are grouped below:

Range Rank Range Rank

0.00-0.49 Highly Deficient 1.21-2.00 ModeratelyConcentrated

0.50-0.85 Moderately Deficient >2.00 Highly concentrated0.86-1.20 Self-sufficient

Thereafter, district-wise inequality of facilities provided households was

measures by Gini Index. In order to determine the spatial disparity with respect to

various public facilities a special type of cumulative frequency graph - known as

Lorenze Curve - is used (Pasha, 1991). This curve is commonly used for measuring

the inequality in the distribution of facility provided households (Bahauddin, 1989).

For this purpose the districts have been grouped in order of the values of their

location quotient and the disparities existing among the different groups of

districts have been computed.

The computation of Gini Index is simplified by calculating twice the

concentration area and dividing this by twice the area under E (Figure 1). The

area of a trapezoid such as WXYZ (called T) is

2

cbabT += (3)

Doubling this and rearranging for convenience, we have: \

{ })(2

)2(2

22

caabT

cabT

cbabT

++=

+=

+=

(4)


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Where bis the sum of the percent of households of districts under a certain group,

ais the cumulative percentage of households of a particular facility and (a+c) is

the cumulative percentage of facilitated households of a group plus all

preceding groups.

Since area under E is the area of the square or, 10000, the Gini Index, G is

given by,

( ){ }10000

10000 iiii caabG++

= where, i= 1, 2, 3.. (5)

After that, Discrimination or Dissimilarity Index was applied to determine

which districts are in advantaged condition in case of facilities provision.

There is a large literature on how to measure discrimination, which primarily

focuses on residential discrimination across urban areal units. Massey and Denton

(1988) distinguished five dimensions of residential discrimination: evenness,

exposure, concentration, centralization and clustering. Each is conceptually

distinct, picking up different aspects of the phenomenon. Concentration,

centralization and clustering are all explicitly spatial in nature that is hardly ever

quantifiable. Hence, the focus is on evenness.

Figure 1: Lorenze curve


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Evenness refers to the differential distribution of two social groups in a city

(Massey and Denton, 1988). An uneven distribution of an income group across

urban areas results in discrimination of that group. Following Duncan and Duncan

(1955), the most widely used measure of evenness is the index of dissimilarity; D.

So, this study picks up on evenness dimension of discrimination by using D.

Massey and Denton (1988) discuss this and other measures in detail, setting

out their advantages and disadvantages. No single measure captures all aspects

of discrimination and all have some statistical shortcomings.

Nevertheless, this index is most widely used. The dissimilarity index was

discussed in detail by Duncan and Duncan (1955) and used for example more

recently by Culter e t. a l. (1999) for the US. The discrimination index ranges from -1

to 1.

In this study, the discrimination in case of public facilities by the households

for the districts was assessed. The formula of dissimilarity index D for each district is

given by the following

( )

=

jT

ij

jT

ij

iHHALL

HHALL

HH

HHD

)(2

1 (6)

By combining all the households provided by the selected three facilities,

the discrimination index for each district will be

( )

=

=

3

1 )(2

1

j T

i

T

i

iHHALL

HHALL

HH

HHD (7)

In this case, the value of Discrimination Index was categorized into

(i)

Lowly deprived (0 -(-0.3));

(ii)

Moderately deprived ((-0.3) - (-0.6));

(iii)

Highly deprived ((-0.6) (-1));

(iv)

Lowly benefited (0 - 0.3);

(v)

Moderately benefited (0.3 - 0.6); and

(vi) Highly benefited (0.6 1).

Finally a comparison was drawn between spatial distribution of income and

combined Discrimination Index of the selected facilities and establishments in the

sixty-four districts of Bangladesh.


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Spatial dependency of monthly income

The district-wise monthly income (million TK) of households were

categorized into three groups high ( 88.5661 ), medium (2983.73-5661.87) and

low (305.58-2983.72). From the analysis it is revealed that fourty-four districts arewithin the category low-income group while seventeen are within medium

income group. The rest Mymensingh, Chittagong and Dhaka are within high-

income group. Among the low-income group the gross monthly income of Barisal,

Bandarban, Lalmonirhat, Netrokona, Meherpur, Rangamati, Khagrachari, Narail

and Jhalakathi are very low comparing to that of other districts. On the other

hand, within the middle income, the gross monthly income of Comilla, Tangail,

Khulna, Gazipur, Narayangonj, Rajshahi and Sirajgonj is considerable higher than

rest of the districts (Map 1).

Table 1 summarizes the result of spatial autocorrelation tests by using

Morans I(Equation 1). It can be seen that the standardized Morans Istatistics is

positive (0.328) and statistically significant (0.008). This is clear evidence of the

existence of a pattern of positive spatial association. We can therefore conclude


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that, in the Bangladesh setting, spatially adjacent districts tend on the whole to

exhibit a similar degree of income dispersion.

Table 1: Calculation of Morans Iof Household Monthly Income (Million TK)

Model

UnstandardizedCoefficients

Standardized

Coefficients

t Sig.

B Std. Error Beta

1 (Constant) 2411.382 192.204 12.546 .000Monthly Income(Million TK)

.156 .057 .328 2.736 .008

a Dependent Variable: income_Y

Spatial concentration and disparity of public facilities

The extent of concentration of district-wise households provided by public

facilities was determined by applying Location Quotient (LQ) method (Equation

2). Considering the safe drinking water supply, the concentration of households is

highly deficient in Rangamati and Bandarban districts and moderately deficient

in Khagrachari, Bagerhat, Sylhet, Narail, Moulvibazar and Pirojpur. While in rest

fifty-six districts the concentration of households is self-sufficient (Appendix 1).

In case of hygienic sanitation facility, eleven districts of northwest and

southeast region of Bangladesh are highly deficient districts while Patuakhali and

Comilla are highly concentrated districts. On the other hand, seventeen districts

are self-sufficient districts apropos of hygienic sanitation facilities (Appendix 2).

Correspondingly fifteen districts are highly deficient regarding the

concentration of households facilitated by electricity supply. On the contrary,

fourteen districts are self-sufficient. The electricity-facilitated households are highly

concentrated in Narayangonj and Dhaka districts (Appendix 3)

Furthermore, spatial disparity of household for each facility was measured

by using Gini Index (3, 4 and 5). The Gini Indices of safe drinking water, hygienic

sanitation and electricity facility are 0.014, 0.636 and 0.493 respectively which

reveal that the inter-district disparity is very high in case of hygienic sanitation

facility and electricity supply. On the inter-district disparity is less in case of safe

drinking water (Appendix 4).


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Inter-district discrimination in facility provision

The result of discrimination among the districts based on each of selected

three public services categorized the districts into advantaged and

disadvantaged groups (Equation 6) and reveals that twenty-three districts are in

disadvantaged group comparing to others in case of safe drinking water supply.

Although the discrimination indexes value of Chittagong, Meherpur and Sherpur is

almost equal to zero i.e. negligible (Appendix 5 and Map 2).

In the same way, twenty-six districts are in advantaged group in case of

hygienic sanitation facility. Nevertheless, within this group the index value is

comparatively high for Gazipur, Narayangonj, Jhalokathi, Khulna, Barisal, Dhaka,

Feni, Patuakhali, and Comilla districts (Appendix 5 and Map 3).

In case of electricity supply, seventeen districts are in advantaged group

(Appendix 5 and Map 4).

Comparative Evaluation

Finally a combined Discrimination Index of three facilities was derived for

the sixty-four districts. The combined index reveals that Bandarban, Rangamati

and Khagrachari districts are in moderately deprived group, while lowly deprivedgroup consists of forty-two districts (Map 5).

On the other hand, Narayangonj, Feni, Dhaka and Comilla are in

moderately benefited group and rests of the district are in lowly benefited group

(Map 5).

Finally, the comparison between income distribution and combined

discrimination index reveals that most of the districts of the low-income groups are

within lowly and moderately deprived group. While the high income districts e.g.

Dhaka and Chittagong are categorized as moderately and lowly benefited

districted respectively. The reason behind this scenario is that the major share of

monthly income of these low-income districts is rural income and the rural

communities are highly deprived of public facilities (Appendix 6). The lion share of

these rural households are involved in agriculture based activities who are usually

deprived in Bangladesh. Another reason is that most of the income generating

establishments are located only a selected number of districts e.g. Dhaka,Chittagong, Narayangonj, Rajshahi, Sylhet, Joypurhat, Feni, Khulna, Bagerhat,


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Jhenaidah, Chuadanga and Dinajpur (Map 6). This type of regional disparity

causes various problems like inter-district migration, inefficient utilization of local

resources and unplanned development, which lead excessive pressure on some

districts leaving rest of the districts undeveloped.

Conclusion

The paper examined the regional unequal distribution of income in

Bangladesh. The study carried out shows that inequality levels vary considerably

across regions. Nevertheless, the presence of positive spatial dependence in

regional inequality levels was detected in this study. This means that, in

Bangladesh, income is not randomly distributed in space, and therefore,

neighboring regions tend to register similar degrees of income dispersion. On the

other hand, the spatial disparity is very high in case of hygienic sanitation and

electricity supplies especially in the northern districts, Chittagong Hill Tracts and

coastal belt districts that are highly disadvantaged. The similar case is obvious in

case of provision of safe drinking water. Later on, comparative evaluation among

income distribution and discrimination index distribution reveals that those districts

that are within low-income groups are usually lowly and moderately deprived


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from provision of public facilities. This is because households of these districts are

mainly involved in rural based income generating activities and almost deprived

of urban facilities. This sort of divergence of income is leading to disproportional

regional growth resulting lower national growth as a whole. Therefore, The

regional convergence in terms of income and public facilities is required for the

overall development of Bangladesh.

References

Anselin, L. (1995) Local indicators of spatial association LISA, Geog raph i ca l

A na lysis,27(2).

Bahauddin, M. (1989) The Spatial Distribution of Physical Facilities in Bangladesh

unpublished Undergraduate project, Urban and Regional Planning

Department, Bangladesh University of Engineering and Technology,, Dhaka.

Bangladesh Bureau of Statistics (2001) Ba ng la d e sh Sta t ist ic a l Ye a r Bo o k. Dhaka:

Bangladesh Bureau of Statistics.

Culter, D. M., Glaseser, E. L. and Vidgor, J. L. (1999) The rise and decline of the

American ghetto Journa l of Polit ic a l Ec o nom y,107(3): 455-506.

Duncan, O. D. and Duncan, B. (1955) A methodological analysis of discrimination

indexes, Am er ic a n So c iolog ic a l Re vie w , 20:210-217.

Lpez-Bazo E, Vaa E, Mora, A, Suriach, J. (1999) Regional economic dynamics

and convergence in the European Union, An na ls o f Reg ion a l Sc ien c e, 33:

343-370.

Massey, D. S. and Denton, N. A. (1993) Am er ic a n Ap a rthe id : Disc rim ina t ion a nd

the M a king of the Unde rc la ss. Cambridge, MA: Harvard University Press.

Pasha, K. (1991) Spatial Distribution of Socio-economic Facilities in Dhaka City,

unpublished Undergraduate project, Urban and Regional Planning

Department, Bangladesh University of Engineering and Technology,, Dhaka.

T

Rey, S. and Montouri, B. (1999) US regional income convergence: a spatial

econometric perspective Reg io na l Stud ie s, 33: 143-156.

Zimeras, S. and Tsimbos, C. (n.d.) Modeling the Spatial Distribution of the

Immigrant Population in Greece viewed 12P

thP

February 2008


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Appendix 1: Calculation of Location Quotient of the households facilitated byservices

DistrictTotal

household

Households

facilitate

d by safedrinkingwater

Location

Quotient

Households

provided

bysanitaryfaculties

Location

Quotient

Householdsprovided by

electricity

Location

Quotient

No ofestablishme

nts per10000

Location

Quotient

Bagerhat 323505 205534 0.71 107636 0.88 73201 0.75 796 1.25

Bandarban 60141 26042 0.49 6009 0.26 8638 0.48 1175 9.93

Barguna 179968 149700 0.93 80263 1.18 19964 0.37 303 0.86

Barisal 48596 42049 0.97 30016 1.63 13764 0.94 20 0.21

Bhola 328670 297413 1.02 87732 0.71 30514 0.31 382 0.59

Bogra 688367 645502 1.05 209042 0.80 170766 0.82 643 0.47

Brahmanbaria 402681 378192 1.05 186439 1.22 128498 1.06 410 0.52

Chandpur 433768 372301 0.96 228529 1.39 133468 1.02 464 0.54

Chittagong 1039690 914152 0.99 556284 1.41 554424 1.77 3573 1.75Chudanga 225830 213593 1.06 47917 0.56 66358 0.97 455 1.02

Comilla 808998 742710 1.03 806621 2.63 357050 1.46 526 0.33

Cox'sbazar 296109 265269 1.01 93440 0.83 55528 0.62 375 0.64

Dhaka 1438685 1292841 1.01 1021695 1.88 1152912 2.66 7531 2.66

Dinajpur 579929 539662 1.04 89623 0.41 123759 0.71 1058 0.93

Faridpur 349458 330660 1.06 158426 1.20 73208 0.69 550 0.80

Feni 193049 201621 1.17 140841 1.93 104948 1.80 468 1.23

Gaibandha 493282 450008 1.02 55158 0.30 53721 0.36 438 0.45

Gazipur 448258 412107 1.03 248358 1.46 235769 1.74 682 0.77

Gopalganj 221986 206002 1.04 97392 1.16 33990 0.51 249 0.57Habiganj 322037 266934 0.93 88537 0.73 76230 0.78 495 0.78

Jamalpur 481235 439087 1.02 93080 0.51 78233 0.54 572 0.60

Jessore 524126 495221 1.06 193895 0.98 187037 1.18 743 0.72

Jhalokati 144923 124186 0.96 83681 1.53 27971 0.64 81 0.28

Jhenaidah 333396 319024 1.07 66230 0.52 77534 0.77 612 0.93

Joypurhat 204317 194304 1.07 42685 0.55 53731 0.87 430 1.07

Khagrachhari 109190 56674 0.58 17357 0.42 14604 0.44 1144 5.33

Khulna 499324 430639 0.97 295596 1.56 216593 1.44 2843 2.89

Kishoreganj 534770 489723 1.03 133107 0.66 102772 0.64 808 0.77

Kurigram 397465 372585 1.05 122140 0.81 29928 0.25 543 0.69

Kustia 379504 359698 1.06 136855 0.95 125749 1.10 671 0.90

Lakshmipur 288736 247335 0.96 127807 1.17 68843 0.79 294 0.52

Lalmonirhat 85264 74060 0.98 29228 0.91 4352 0.17 139 0.83

Madaripur 231655 215810 1.05 41245 0.47 72621 1.04 246 0.54

Magura 163949 157052 1.08 34179 0.55 32005 0.65 251 0.78

Manikganj 276932 262470 1.06 115533 1.10 78137 0.94 392 0.72

Maulvibazar 292889 208875 0.80 93657 0.84 74426 0.84 352 0.61

Meherpur 117382 102355 0.98 22141 0.50 33488 0.95 158 0.69

Munshiganj 214529 200267 1.05 109972 1.35 115896 1.79 219 0.52

Mymensingh 1001476 910541 1.02 228742 0.60 164327 0.54 654 0.33

Naogaon 540222 490862 1.02 88536 0.43 95741 0.59 1000 0.94Narail 141071 97856 0.78 62110 1.16 26114 0.61 150 0.54

Narayanganj 453627 419438 1.04 254456 1.48 360949 2.64 887 0.99


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Narsingdi 385361 356431 1.04 153346 1.05 170623 1.47 672 0.89

Natore 337311 312043 1.04 88608 0.69 84471 0.83 353 0.53

Nawabganj 275707 259837 1.06 40671 0.39 55963 0.67 362 0.67

Netrokona 118954 107014 1.01 17558 0.39 13094 0.36 59 0.25

Nilphamari 335178 287441 0.96 46022 0.36 45355 0.45 429 0.65

Noakhali 460394 412199 1.01 198870 1.14 136489 0.98 296 0.33Pabna 448290 420682 1.05 115587 0.68 137299 1.02 716 0.81

Panchagarh 178957 147372 0.92 41668 0.62 15180 0.28 310 0.88

Patuakhali 272984 244988 1.01 211686 2.05 40176 0.49 353 0.66

Pirojpur 232962 167369 0.81 90502 1.03 46642 0.66 451 0.98

Rajbari 191492 183114 1.07 59948 0.83 27658 0.48 304 0.81

Rajshahi 503036 483977 1.08 146830 0.77 143279 0.94 2706 2.73

Rangamati 102820 42837 0.47 19854 0.51 22269 0.72 2044 10.11

Rangpur 579902 529883 1.03 95431 0.43 108130 0.62 721 0.63

Satkhira 390745 330517 0.95 140609 0.95 72442 0.61 410 0.53

Shariatpur 213677 193574 1.02 78996 0.98 29182 0.45 344 0.82Sherpur 301706 267809 1.00 90435 0.79 33453 0.37 325 0.55

Sirajganj 562708 525340 1.05 132177 0.62 130352 0.77 462 0.42

Sunamganj 349558 267018 0.86 72424 0.55 35185 0.33 377 0.55

Sylhet 423670 271512 0.72 179632 1.12 145020 1.13 834 1.00

Tangail 723111 655876 1.02 283617 1.04 184220 0.84 566 0.40

Tharkurgaon 257816 242489 1.06 26434 0.27 39753 0.51 229 0.45

Source: calculated by authors, 2008

Appendix 2: Calculation of Gini Index

b c a+c a+(a+c) b*(a+(a+c)) Total Gini Index

0.68 0.32 0.32 0.32 0.218

6.36 4.72 5.04 5.36 34.129

Safe drinking watersupply

92.96 95.28 100.32 105.68 9824.191

9858.538 0.014

14.96 5.79 5.79 5.79 86.618

34.93 23.50 29.29 49.89 1742.54

23.39 24.66 53.95 38.35 896.918

22.21 34.82 88.77 37.17 825.266

Hygienic watersupply

4.52 11.24 100.01 19.48 88.015

3639.357 0.636

15.1 5.55 5.55 5.55 83.805

42.46 29.05 34.60 40.15 1704.949

19.55 20.14 54.74 60.29 1178.47814.99 24.30 79.04 84.59 1267.98

Electricity supply

7.90 20.96 100.00 105.55 834.115

5069.327 0.493

22.17 8.43 8.43 8.43 186.893

42.80 27.95 36.38 44.81 1918.15

17.20 16.31 52.69 61.12 1051.113

6.50 10.27 62.96 71.39 463.987

Income generatingestablishments

11.33 37.03 99.99 108.42 1228.524

4848.668 0.515



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Appendix 3: Calculation of Discrimination Index

DistrictsDiscrimination

Index forwater

DiscriminationIndex forsanitation

DiscriminationIndex forelectricity

CombinedDiscrimination

IndexRanking

Bandarban -0.229 -0.140 -0.080 -0.448 2

Khagrachhari -0.187 -0.110 -0.080 -0.381 2Rangamati -0.238 -0.090 -0.040 -0.374 2

Sunamganj -0.064 -0.090 -0.100 -0.253 1

Gaibandha 0.011 -0.140 -0.100 -0.224 1

Nilphamari -0.017 -0.120 -0.080 -0.224 1

Panchagarh -0.034 -0.070 -0.110 -0.217 1

Netrokona 0.004 -0.120 -0.100 -0.208 1

Bagerhat -0.129 -0.020 -0.040 -0.191 1

Tharkurgaon 0.025 -0.140 -0.070 -0.189 1

Naogaon 0.009 -0.110 -0.060 -0.164 1

Rangpur 0.012 -0.110 -0.060 -0.157 1Bhola 0.007 -0.060 -0.110 -0.155 1

Lalmonirhat -0.011 -0.020 -0.130 -0.155 1

Jamalpur 0.011 -0.090 -0.070 -0.154 1

Maulvibazar -0.090 -0.030 -0.020 -0.144 1

Nawabganj 0.026 -0.120 -0.050 -0.141 1

Mymensingh 0.010 -0.080 -0.070 -0.141 1

Dinajpur 0.020 -0.110 -0.050 -0.140 1

Sherpur -0.002 -0.040 -0.100 -0.138 1

Pirojpur -0.087 0.010 -0.050 -0.133 1

Kurigram 0.024 -0.040 -0.120 -0.128 1Narail -0.099 0.030 -0.060 -0.127 1

Habiganj -0.031 -0.050 -0.030 -0.117 1

Meherpur -0.009 -0.100 -0.010 -0.113 1

Kishoreganj 0.013 -0.070 -0.060 -0.109 1

Magura 0.034 -0.090 -0.050 -0.105 1

Jhenaidah 0.034 -0.090 -0.040 -0.093 1

Barguna -0.030 0.030 -0.100 -0.092 1

Satkhira -0.023 -0.010 -0.060 -0.091 1

Sirajganj 0.022 -0.070 -0.040 -0.087 1

Cox'sbazar 0.003 -0.030 -0.060 -0.087 1

Sylhet -0.127 0.020 0.020 -0.083 1

Shariatpur 0.008 0.000 -0.080 -0.080 1

Rajbari 0.033 -0.030 -0.080 -0.079 1

Joypurhat 0.030 -0.090 -0.020 -0.075 1

Madaripur 0.021 -0.100 0.010 -0.075 1

Natore 0.017 -0.060 -0.030 -0.067 1

Chudanga 0.028 -0.080 0.000 -0.060 1

Bogra 0.024 -0.040 -0.030 -0.042 1

Pabna 0.024 -0.060 0.000 -0.035 1

Gopalganj 0.019 0.030 -0.070 -0.026 1

Lakshmipur -0.017 0.030 -0.030 -0.017 1Rajshahi 0.036 -0.040 -0.010 -0.016 1

Tangail 0.008 0.010 -0.020 -0.009 1


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Reg iona l Divergenc e of Inc om e 37

Faridpur 0.028 0.040 -0.050 0.019 4

Noakhali 0.002 0.030 0.000 0.027 4

Jhalokati -0.017 0.100 -0.050 0.028 4

Kustia 0.029 -0.010 0.020 0.035 4

Manikganj 0.029 0.020 -0.010 0.039 4

Jessore 0.028 0.000 0.030 0.051 4Chandpur -0.017 0.080 0.000 0.062 4

Brahmanbaria 0.025 0.040 0.010 0.076 4

Barisal -0.013 0.120 -0.010 0.098 4

Narsingdi 0.017 0.010 0.070 0.099 4

Patuakhali 0.003 0.200 -0.080 0.126 4

Khulna -0.014 0.110 0.070 0.162 4

Chittagong -0.006 0.080 0.120 0.197 4

Munshiganj 0.022 0.070 0.120 0.210 4

Gazipur 0.015 0.090 0.110 0.218 4

Narayanganj 0.017 0.090 0.250 0.362 5Feni 0.077 0.180 0.120 0.376 5

Comilla 0.014 0.320 0.070 0.406 5

Dhaka 0.004 0.180 0.270 0.446 5


Regional Income Maps of bangladesh

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