1 Introduction

There is a common feeling that although there has been some overall improvement in the average level of living of people across the majority of states, those which were already on a better footing could reap the advantages of the economic reform in the 1990s and experience fast growth, while there was no tangible improvement for the poorest few. Again, the rural-urban expenditure gap, believed to have widened over time, needs meticulous scrutiny. There is a strong indication that the improvement in the level of living might not have been distributed well and certain pockets of the states might have remained impoverished in spite of their overall growth. Thus, dealing merely with state-level aggregates may not reveal the true extent of disparity prevailing and there has been a serious dearth of studies on these issues at the sub-state level. It is also necessary to examine how far the assumption of states as homogeneous units for socioeconomic studies, is tenable.

Very few studies have been attempted any district level analysis. Again, most of them were based on a small segment of the country. Sastry (2003) had discussed the feasibility of using the National Sample Survey (NSS) Consumer Expenditure Survey (CES) data for district-level poverty estimates in its entirety based on the NSS 1999-2000 (55th round) survey. But the main bottleneck that refrained researchers from generating sub-state or district-level estimates from NSS data was the nature of sampling design.1 It was only in the 61st round survey of NSS (2004-05) that the sampling design defined rural and urban parts of districts as strata for selection of sample villages and urban blocks respectively. This has paved the way for generating unbiased estimates of important socio-economic parameters at the district-level a dequately supported by the sample design.

The paper is divided into five sections. In Section 2 an ogive analysis2depicts the wide interstate disparity in population distribution over the all-India monthly per capita consumption expenditure (MPCE) classes, which is perfectly adequate for c ountry level analysis or for comparison among states. But use of state-level percentile MPCE classes3 has been suggested

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Figure 1R: Ogive Analysis – Rural

(Per cent distribution of population over different expenditure classes)

0 10 20 30 40 50 60 70 80 90 100 0 235 270 320 365 410 455 510 580 690 890 1155 1155 & more Bihar Chhattisgarh Orissa all Punjab Kerala Himachal Pradesh

MPCE (in Rs)

a dditionally for more realistic analysis at state/sub-state-level with adequate representation across the MPCE percentile classes. Section 3 discusses the state-level estimates of major parameters for subsequent comparison with the corresponding estimates at

In Figures 1R and 1U (p 96) the ogives for some of the most poor/rich states are plotted against the central ogive for the country as a whole. For the remaining states, the ogives lie somewhere within the band. If we look at the extreme end percentile classes in rural India (Figure 1R), we find that for the bottom 10 percentile class of the country (with MPCE of Rs 270 or less), the share of population varied widely from state to state. Orissa had more than 30% of its people in this class as against less than 1% of population in a state like Punjab. At the other end of the spectrum, was the top 10 percentile class all-India (MPCE more than Rs 890), where Kerala and Punjab had about a third of their population as against less than 4% in Chhattisgarh and Orissa.

Again, an extremely lopsided distribution of sample households in different states over the all-India MPCE percentile classes is evident from Tables 1R and 1U. In rural Punjab only nine sample households belonged to the bottom 10 percentile class. Such low sample sizes at state-level in these all-India percentile classes would certainly affect the reliability of the estimates at MPCE class-level even for the state-level analysis.

In urban India, the situation was no better either (see Figure 1U or Table 1U). Bihar and Orissa were the two most impoverished

the district level. Average MPCE4, head states with more than 25% of their popula-

Table 1R: Population Share of Poorest and Richest States count ratio (HCR) using state-specific pov-in the All-India Percentile Classes (Rural) tion in the bottom 10 percentile class of

erty lines,5 Lorenz ratio using state-level States Population in the Population in the Top 10 the country (i e, MPCE less than Rs 395)

Bottom 10 Percentile Classes Percentile Classes

percentile classes (LR-S)6 and the relative whereas Punjab and Himachal Pradesh

(i e, MPCE ≤ Rs 270) (i e, MPCE ≥ Rs 890)

standard errors (RSEs) of average MPCE Orissa 31.1% (926) * 3.7% (265) had less than 2% of their people in this

were the major parameters under consid-Chhattisgarh 24.1% (325) 3.3% (182) category. In terms of distribution of sam

eration. However, the main focus of the study is on district-level estimates of the parameters and their level of divergence, which is discussed in Section 4 with four sub-sections. The first sub-section dis-

Kerala 2.3% (50) 37.5% (1598)

Punjab 0.5% (9) 31.9% (1005)

* The figures in brackets give the number of sample households falling in the respective percentile classes.

Table 1U: Population Share of Poorest and Richest States in the All-India Percentile Classes (Urban)

ple households over the MPCE classes, Himachal Pradesh had as few as six samples in the bottom 10 percentile class.

Thus, although all-India MPCE percentile classes are useful for the interstate compar

cusses the methodology of obtaining States Population in the Population in the Top 10 isons, yet they often affect the estimates

Bottom 10 Percentile Classes Percentile Classes

d istrict-level estimates, followed by broad and their reliability at the state x MPCE class

(i e, MPCE ≤ Rs 395) (i e, MPCE ≥ Rs 1880)

observations on the salient features of Bihar 28.2% (436) * 3.4% (48) level due to inadequate sample size. For

detail district estimates. In the third sub-Orissa 24.6% (344) 3.2% (58) district-level estimates the problem gets

section, a graphical presentation of the Punjab 1.3% (45) 13.6% (280) more serious, especially when we find some

district-level pattern in terms of the HCR Himachal Pradesh 1.7% (6) 19.1% (99) of the districts not h aving any sample in

* The figures in brackets give the number of sample households falling in the

has been made to map the pockets of pov-one or more all-India MPCE percen tile

respective percentile classes.

erty across the country. The last subsection examines the spatial disparity among the districts both within and across the states. Section 5 summarises the findings, discusses the limitations of the present exercise and explores the ways of improvement.

2 Distribution of Population in States over Expenditure Classes – Ogive Analysis

In the NSS 61st round survey reports, detail analysis was carried out by classifying the population into 12 percentile classes (at 5%, 10%, 20%,..., 80%, 90%, 95%) of MPCE at the all-India level, separately for the rural and urban sectors, which was necessary for the analysis of survey results at the country level or for the comparisons among states against the same set of MPCE classes. An ogive analysis has been attempted here to study the divergence of the distribution in the states from the all-India MPCE percentile class distribution.

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classes, as evident from Table 2 (p 96).

Out of 508 rural districts of the 20 major states of the country, more than a third of the districts did not have any sample in the first (i e, the bottom 5%) MPCE class. Again out of 510 urban districts, as many as 149 districts did not have any sample in the top five percentile classes. In all there were 425 instances in rural India and 558 in the urban, where a district did not have any representation in an all-India MPCE percentile class. In some of the extreme cases (as given in Table 3, p 96), we found that only four samples in a particular district were in the bottom 50 percentile class. However, as in the case of Ambala in Haryana and Pathanamthitta in Kerala, such a problem can be addressed through the use of state-level percentile classes for analysis at state/districtlevel as indicated in Table 3.

Therefore, it appears appropriate that, in addition to all-India MPCE classes used for country-level analysis and interstate c omparison, state-level MPCE percentile classes be used for

Figure 1U: Ogive Analysis – Urban

(Per cent distribution of population over different expenditure classes)

0 10 20 30 40 50 60 70 80 90 100 Bihar Chhattisgarh Orissa Himachal Pradesh Punjab all Kerala

0 335 395 485 580 675 790 930 1100 1380 1880 2540 2540 & more

MPCE (in Rs)

obtaining more reliable estimates at state x MPCE classes for the purpose of state or sub-state level analysis. For better comparability with the official results, an identical composition (i e, 5%, 10%, 20%, etc) of state-level percentile classes has been advocated. Accordingly, the lower and upper limits of the state-level MPCE percentile classes have been derived for the 20 major states of the country for 2004-05, separately for the rural and the urban s ectors (see Table A1.R and A1.U at Annexure, p 101).

3 Overview of State-Level Estimates of Major Parameters

Before moving on to the district-level estimates of the parameters let us have a quick look at the corresponding state-level estimates for the 20 major states of India including the three newly created states of Jharkhand, Chhattisgarh and Uttarakhand. More than 98% of the country’s rural population and about 94% of urban population reside in these 20 states. In Table 4 (p 97), a summary of state-level estimates of the parameters – average MPCE, the HCR and Lorenz ratio – has been given which together reflect the level of living. The RSE of average MPCE estimates have also been indicated. These would be useful for comparison with the corresponding estimates at the district level. For J&K, state-level estimates suffer from certain limitations owing to non-coverage of some of the districts7 of the state in the NSS survey (2004-05).

In rural India the average MPCE was the lowest in Orissa (Rs 399) and the highest in Kerala (Rs 1,013). The RSE of average statelevel MPCE was found to be low (less than 5%) except for rural Haryana. All-India rural HCR was around 28%. States like Punjab and J&K had less than 10% poor while Orissa and Jharkhand, each had more than 46% of their population below the respective poverty lines. For better comparability with the districts, the level of inequality in the states has been calculated using statelevel percentile classes (LR-S) although these do not vary much from the usual LR using all-India percentile classes. Inequality was found to be low in states like Assam (0.1964) and Bihar (0.2054) where average level of living was also low. On the other hand, the two best average MPCE states in the rural part, i e, Kerala (Rs 1,013) and Haryana (Rs 863) were the two most unequal states with LR-S 0.3748 and 0.3347, respectively. Thus in

96 rural India there was some indication of a trade-off between prosperity and inequality at state level.

Average urban MPCE again varied from Rs 696 and Rs 757 in Bihar and Orissa, respectively, to more than Rs 1,300 in Punjab and Himachal Pradesh (HP). Orissa had the highest urban poverty (45%) while it was less than 4% in HP and Assam. The most critical position was that of urban Chhattisgarh which had the highest inequality (0.4308), coupled with high poverty (42.2%) and low average MPCE. Urban inequality was also high in Kerala (0.4307) and Punjab (0.3936), the states which were placed at the third (Rs 1,291) and second (Rs 1,326) highest position respectively, in terms of average per capita expenditure. Thus, the high urban inequality in the better-off states as well as in some of the poor states made the issue more complex. Another notable feature was that, in half of the states the RSE of MPCE estimates was more than 5% in the urban sector.

4 Level of Living in Indian Districts

This section first discusses some of the methodological issues.

4.1 Methodological Issues

As already indicated, NSS 61st round survey (2004-05) enabled district-level estimation mainly through its stratification scheme. The survey design followed was the usual stratified multi-stage sampling scheme but in this particular round districts were taken as strata for selection of first stage units (FSU) in both the rural and urban sectors. Further sub-stratification was done within the strata (ie, districts) as per the following rule:

If “r” be the sample size allocated for a rural stratum, the number of sub-strata formed was “r/2”. The villages within a district as per frame were first arranged in ascending order of population and each sub-stratum comprised of a group of villages having more or less equal population. In urban sector the substratification scheme was almost similar to that of rural area. Here the towns in a district were arranged in ascending order of population. Finally, the FSUs were drawn following Probability Proportional to Size with Replacement (PPSWR) scheme in rural area and Simple Random Sampling Without Replacement (SRSWOR)

Table 2: Instances of No Sample Representation

Number of Districts Not Having Any Sample in All-India MPCE Percentile Class

MPCE Classes (Rs) 0-5 5-10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-95 95+ Total Cases

Rural 162 114 51 22 8 3 3 3 0 4 25 30 425

Urban 96 49 13 11 22 19 23 28 34 33 81 149 558

Table 3: Sample Households in the Districts Falling in All-India and State Percentile Classes

Using All-India Using State Specific Percentile Classes Percentile Classes

State District Item Bottom 50 Top 50 Bottom 50 Top 50 Percentile Percentile Percentile Percentile Class Class Class Class

(1) (2) (3) (4) (5) (6) (7)

Rural Haryana Ambala Population share 3.9% 96.1% 38.9% 61.1%

No of samples

4 76 28 52

Kerala Pathanamthitta Population share 5.2% 94.8% 45.1% 54.9%

No of samples 4 156 51 109

Urban Himachal Bilaspur Population share 13.8% 86.2% 38.7% 61.3%

Pradesh

No of samples

7 33 18 22

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in urban area. This was a significant deviation in the sampling the sub-state (i e, district) level. For measurement of HCR at the design from the earlier NSS rounds.8 d istrict-level, state-specific poverty lines have been used. The state-

In the NSS 1999-2000 survey, i e, the previous large sample level MPCE percentile classes have been utilised for calculating CES, the selection of first stage units in the rural area was done Lorenz ratio for the districts. The number of sample observations using the circular systematic sampling scheme taking districts as and the estimated RSE of average MPCE have been given to indicate strata while in the urban area, selection was done following the reliability and robustness of the estimates. Table 4: State Level Estimates of Average MPCE, Headcount Ratio and Lorenz Ratio in 2004-05 Although the parameters (i e, average

State		Rural			Urban			MPCE, HCR and LR-S) have been estimated
% of All-India Population Andhra Pradesh 7.4	Average MPCE (Rs) 586	RSE of Average % MPCE Poor 1.50 10.5	Lorenz Ratio-S 0.2896	% of All-India Population 7.5	Average RSE of Average MPCE (Rs) MPCE 1,019 3.72	% Poor 27.4	Lorenz Ratio-S 0.3693	for all the districts of the 20 major states of India, no attempt has been made to
Assam	3.1	543	1.36	22.1	0.1964	0.9	1,058	6.2	3.6	0.3154	a nalyse in detail the pattern of these para-
Bihar	9.1	417	0.95	42.6	0.2054	2.7	696	5.76	36.1	0.3289	meters in each of the districts, rather the
Chhattisgarh	2.5	425	2.98	40.8	0.2927	1.3	990	11.28	42.2	0.4308	figures have been allowed to speak for
Gujarat HaryanaHimachal Pradesh J & K	4.2 2.2 0.8 0.7	596 863 798 793	2.03 9.23 2.69 1.57	18.9 13.3 10.5 4.3	0.2696 0.3347 0.305 0.2442	6.6 2.3 0.2 0.7	1,115 1,142 1,390 1,070	2.85 5.15 9.65 1.81	13.3 14.5 3.2 7.4	0.3059 0.3603 0.3217 0.2465	themselves. Nevertheless, certain broad features emerged. (a) There were perceptible differences
Jharkhand	2.8	425	1.61	46.2	0.2247	1.6	985	5.58	20.3	0.351	between the rural and urban areas of many
Karnataka	4.7	508	2.89	20.7	0.2619	6.1	1,033	3.28	32.6	0.3638	districts in terms of one or more parame-
KeralaMadhya Pradesh Maharashtra Orissa	3.2 6.3 7.5 4.4	1,013 439 568 399	2.30 1.51 1.75 1.68	13.2 36.8 29.6 46.9	0.3748 0.2643 0.3078 0.2816	2.9 5.7 15.0 2.0	1,291 904 1,148 757	4.73 5.62 2.41 5.6	20 42.7 32.1 44.7	0.4037 0.3921 0.3723 0.3489	ters. A district with excellent performance in either average MPCE or in percentage poor or in Lorenz ratio in one sector often
Punjab	2.1	847	1.90	9.0	0.2903	3.0	1,326	10.2	6.3	0.3936	failed to put up a matching record in the
Rajasthan	5.9	591	1.36	18.3	0.2461	5.0	964	10.33	32.3	0.3658	other sector.
Tamil Nadu	4.7	602	3.36	23	0.3163	8.7	1,080	2.33	22.5	0.3562	(b) In some of the states, a majority of
Uttar Pradesh	18.1	533	1.23	33.3	0.2807	13.0	857	4.96	30.1	0.323	the districts had MPCE much below the
Uttarakhand West Bengal All India	0.9 8.1 100.0	647 562 559	4.49 2.02 0.54	40.7 28.4 28.3	0.2859 0.2696 -	0.8 7.8 100.0	978 1,124 1,052	6.0 3.1 1.14	36.5 13.5 25.6	0.364 0.3786 -	state-level MPCE and only a few very high MPCE districts were responsible for pulling

For calculating per cent poor (HCR) state-specific poverty lines released by Planning Commission have been used and for Lorenz Ratio (LR-S) state-specific up the state averages.

percentile classes as given in the Annexure.

SRSWOR where strata were formed using town size class within NSS regions, and not with districts as strata. Thus, while in the 1990-2000 survey, districts were taken as homogeneous units in the rural sector, in NSS (2004-05) high population variability at the district-level was assumed and was taken care of through substratification into similar size villages expected to have more homogeneous consumption pattern. Even the second stage s tratifications of CES (2004-05) were different from that of CES (1999-2000).

The RSE9 of average MPCE, has been calculated using subsample variations of estimates at sub-stratum level, as given in the official estimation procedure of NSS 61st round.10 Sastry (2003) had worked out average RSE of MPCE for different MPCE classes at district level for the 1999-2000 survey and then p robably combined them to obtain district-level average RSE without presenting the district-wise MPCE estimates. But the average RSEs given there were not strictly comparable to the RSEs computed here for the reasons stated in the previous paragraph.

4.2 Estimates for All Districts within the States

In order to get a good understanding of the level of living prevailing in the districts, we need to study the estimates for all the major parameters (average level of living, poverty and inequality) together and not in isolation from one another. The district-level estimates of the parameters for all the districts of 20 major states of India have been derived and presented in Table A2 (p 102) in the annexure. The two sets of estimates for rural and urban sectors are placed side by side to indicate the magnitude of the rural-urban divide even at

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(c) The number of sample observations was too small for many of the districts in the urban sector. Often low sample size or high RSE of the estimates restricted us from making conclusive remarks about the estimates. This was particularly true for urban Orissa and Chhattisgarh.

(d) The range of RSE for the district-level estimates of MPCE is summarised in Table 5 (p 98).

About 25% of the districts yielded RSE lower than 5%, and 77% of districts had less than 10% RSE in the rural areas. In the urban areas the corresponding figures were 12% and 41%, respectively. Thus, about one-fourth of the rural districts and more than half of the urban districts had RSE of MPCE more than 10%, which was often due to low sample size.

(e) In spite of incidents of high RSE of MPCE estimates, it is indeed useful to look at these natural estimates at the districtlevel supported by the sample design. These estimates can be used for further refinement through “model assisted” as well as “model independent” procedures. A Generalised Regression Estimate (greg)11 method may be one of the simplest ways of improving upon these initial estimates.

(f) In both the sectors, there were some districts in almost all the states for which within district inequality (Lorenz ratio) was higher than the inequality at state level.

4.4 State-wise Best and Worst Districts

A summary of best and worst districts within each state in terms of average MPCE or poverty (HCR) is presented here to i ndicate the spatial disparity among the districts within and across the states.

Figure 2R: Mapping of Poverty in Districts of 20 Major States (Rural)

less (i e, Rs 10 per capita per day). Barring MP and Chhattisgarh, in all these states the average MPCE even in the best districts was less than Rs 600 (Rs 20 per capita per day). Such low level of living all over a state is a matter of grave concern. In contrast, in rich states like Kerala, Haryana and HP, the average MPCE in any of the districts was not less than Rs 600.

(d) In terms of rural poverty, the scenario was quite intriguing. In the states of Bihar, Chhattisgarh, Gujarat, Jharkhand, MP, Orissa and UP, in a number of districts, the HCR was as high as 75% or more. On the other hand, in states like Assam, Gujarat, Himachal Pradesh, J&K and Karnataka, in one or more districts there was “zero poverty”.

Figure 2U: Mapping of Poverty in Districts of 20 Major States (Urban)

Percentage of poor 75 to 100 50 to 75 30 to 50 10 to 30 1 to 10 0 to 1 all others Percentage of poor 75 to 100 50 to 75 30 to 50 10 to 30 1 to 10 0 to 1 all others

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Table 7R: State-wise Best and Worst Districts in Terms of Average MPCE and HCR in Rural India

districts within each state was far

State Avrg Best MPCE District Avrg Worst MPCE District Avrg Least Poor District % Most Poor District %

more glaring. In at least four

MPCE MPCE MPCE Poor Poor

(Rs) (Rs) (Rs) states, i e, Haryana, Chhattisgarh,

Andhra Pradesh 586 Warangal 752 Adilabad 400 Warangal 0.9 Adilabad 26.1 Karnataka and Gujarat the aver-Assam 543 Sibsagar 650 Karimganj 444 Dhemaji 0.0 Dhubri 42.4

age MPCE for the best district had

Bihar 417 Saharsa 586 West Champaran 320 Madhepura 7.7 West Champaran 76.9

been more than four times that of

Chhattisgarh 425 Korba 627 Dantewada 218 Kawardha 16.9 Dantewada 88.2

the worst. In four other states (MP,

Gujarat 596 Gandhinagar 1012 Dangs 349 Junagadh 0.0 Dangs 88.4

Maharashtra, UP and AP) the ratio

Haryana 863 Gurgaon 1559 Faridabad 634 Kurukshetra 2.4 Faridabad 37.6 Himachal Pradesh 798 Lahul and Spiti 1076 Chamba 646 Lahul & Spiti 0.0 Chamba 20.7

of best and worst was still more

J&K 793 Pulwama 1008 Udhampur 542 Pulwama 0.0 Kupwara 13.1 than three. Only in Himachal

Jharkhand 425 Dhanbad 540 Lohardaga 310 Dhanbad 19.3 Lohardaga 81.6 Pradesh and J&K, the ratio was

Karnataka 508 Udupi 966 Raichur 339 Udupi 0.0 Raichur 59.2

found to be less than two.

Kerala 1013 Thiruvananthpuram 1442 Kannur 656 Idukki 3.4 Kannur 35.4

(b) For the country as a whole,

Madhya Pradesh 439 Dewas 749 Dindori 278 Neemuch 0.2 Umaria 76.4

Kurukshetra, Haryana was the best

Maharashtra 568 Pune 871 Gadchiroli 352 Sindhudurg 2.3 Gadchiroli 65.0

MPCE district (Rs 2,851) follo wed by

Orissa 399 Cuttack 578 Nowarangpur 255 Jajpur 4.9 Nowarangpur 80.6

Gandhinagar, Gujarat (Rs 2,422).

Punjab 847 Fatehgarh Sahib 1136 Muktsar 571 Jalandhar 0.9 Muktsar 28.3 Rajasthan 591 Jhunjjuna 756 Banswara 423 Jaisalmer 3.3 Banswara 50.1 At the other extreme was Banka,

Tamil Nadu 602 Nilgiri 864 Salem 460 Nilgiri 4.0 Thiruvannamalai 43.2 Bihar with lowest average MPCE

Uttarakhand 533 Nainital 919 Champawat 494 Rudraprayag 8.7 Champawat 72.1 of Rs 355, followed by Raichur, Uttar Pradesh 647 Faizabad 917 Chitrakoot 348 G Buddha Nagar 2.6 Chitrakoot 81.5

Karnataka (Rs 407).

West Bengal 562 Hooghly 664 Murshidabad 428 Kochbihar 11.2 Murshidabad 55.9

(c) In HP, the average MPCE in

All India 559 Gurgaon, Haryana 1559 Dantewada, 218 0.0 Dangs, Gujarat 88.4 Chhattisgarh was more than Rs 1,000, while in

For calculating % poor (BER) state-specific poverty lines released by Planning Commission have been used. none of the districts of urban

Bihar the average MPCE could

Table 7U: State-wise Best and Worst Districts in Terms of Average MPCE and HCR in Urban India

State Avrg Best MPCE District Avrg Worst MPCE District Avrg Least Poor District % Most Poor District % reach that level.

MPCE MPCE MPCE Poor Poor

(d) The urban poverty scenario

(Rs) (Rs) (Rs)

Andhra Pradesh 1,019 Vishakhapatnam 1,734 Medak 568 Prakasam 15.6 Medak 54.5 was more grim. Most abject pov-

Assam 1,058 Dibrugarh 1,608 North Cachar Hill 656 Morigaon 0 Karimganj 14.3 erty could be found in Gajapati,

Bihar 696 Saharsa 939 Banka 355 Saharsa 1.4 Banka 88.4 Orissa with more than 90% peo-Chhattisgarh 990 Rajnandgaon 1,934 Dantewada 418 Surguja 15.7 Dantewada 84

ple below the state poverty line.

Gujarat 1,115 Gandhinagar 2,422 Kheda 604 Gandhinagar 0.6 Kachchh 52.9

The second poorest urban dis-

Haryana 1,142 Kurukshetra 2,851 Sonipat 615 Ambala 0 Sonipat 56.3

trict was Raichur (88.6%) in

Himachal Pradesh 1,390 Mandi 1,612 Hamirpur 1,020 Shimla 0 Hamirpur 27.7

K arnataka. In four other states,

J & K 1,070 Jammu 1,330 Badgam 844 Doda 0 Barmula 11.4

i e, Bihar, Chhattisgarh, Maha-

Jharkhand 985 Hazaribagh 1,286 Paschim Singhbhum 555 Giridihi 1.9 Paschim Singhbhum 51.3 Karnataka 1,033 Dakshin Kannad 1,761 Raichur 407 Bangalore Urban 7.9 Raichur 88.6 rashtra and Madhya Pradesh

Kerala 1,291 Thiruvananthapuram 1,867 Kannur 824 Thiruvananthapuram 6.0 Kannur 39.4 there were one or more districts

Madhya Pradesh 904 Indore 1,648 Shivpuri 479 Shahdol 12.6 Shivpuri 77.4 with HCR higher than 75%. Maharashtra 1,148 Greater Mumbai 1,570 Bid 474 Greater Mumbai 11.7 Bid 80.4

(e) At the other extreme were

Orissa 757 Jajpur 1,048 Boudh 490 Rayagada 21.8 Gajapati 91.2

the districts with “zero” or “near-

Punjab 1,326 Ludhiana 1,835 Faridkot 887 Kapurthala 0.2 Muktsar 22.8

zero” HCR in the states of Assam,

Rajasthan 964 Kota 1,477 Hanuman Garh 501 Dungarpur 3.0 Hanuman Garh 68.3

Haryana, HP, J&K and Punjab.

Tamil Nadu 1,080 Chennai 1,596 Ramnathapuram 618 Chennai 8.7 Perambalur 57.3 Uttarakhand 857 Almora 1,455 Champawat 706 Tehri Garhwal 1.4 Champawat 64.4

Assam and J&K had less than 15%

Uttar Pradesh 978 Agra 1,393 Banda 436 Shahjahanpur 3.3 Chaundli 74.5 poverty in all of their districts.

West Bengal 1,124 Kolkata 1,520 Birbhum 591 Kolkata 2.3 Puruliya 36.9 From the discussion above, it is

All India 1,052 Kurukshetra, 2,851 Banka, Bihar 355 0.0 Gajapati, Orissa 91.2

apparent that the sub-state level

Haryana

For calculating % poor (HCR) state-specific poverty lines released by Planning Commission have been used.

(e) In Gujarat we found the district Dangs, which had been the poorest rural district of the country with 88% population below state-specific poverty line, while in the same state at least three districts J unagadh, Jamnagar and Porbandar had “zero poverty”.

In urban India the intra-state disparity in terms of MPCE and poverty was of higher dimension as compared to the interstate differences. Table 7U reveals the following:

(a) While the best state average MPCE (HP, Rs 1,390) was just about double the worst (Bihar, Rs 696), the disparity among the

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estimates are extremely useful

in identifying pockets of impoverishment or prosperity across the length and the breadth of the country. Even in a state like Gujarat with commendable growth performance in terms of level of living, poverty or inequality, we find a district like Dangs, which was among the most critically poor regions of India in 2004-05. Such incidents would have escaped our attention had we restricted ourselves to state-level averages only. The study also revealed major indications of polarisation in the level of living within and across the states.

5 Conclusions

This paper attempts to cater to the long felt need for generation of district-level estimates of major socio-economic parameters to facilitate more focused analysis. The results obtained strongly indicate the serious limitations of seeing the “state” as a homogeneous socio-economic unit for poverty or inequality analysis. In fact, it is felt that state-level aggregates may often mislead us and draw away our attention from some imminent areas of concern.

The district-level estimates are found to be absolutely necessary for a complete understanding of the level of living prevailing in any part of the country. The other major observations are as mentioned below.

sector almost all the states had RSE less than 5% or so.

Notes

1 The two-stage stratified sampling design followed in NSS surveys prior to its 61st round (2004-05) did not use districts as strata in the urban sector and thus allowed generation of unbiased estimates of population parameters at most at NSS region level.

2 In the Ogive Analysis the cumulative proportions of persons per 1,000 in each state had been plotted against the MPCE cut-off points for the (12) all-India percentile classes on unequal scale.

3 Usually, 12 MPCE classes (corresponding to 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% and 100%) are formed for the country as a whole from the distribution of persons by MPCE separately for rural and urban sectors. This paper examines the need for undertaking similar exercise at state level for obtaining state-specific percentile classes.

4 Average MPCE at national or state (or region) level is the aggregate consumer expenditure of the relevant population divided by the corresponding population.

5 HCR is the ratio of population below poverty line and the total population of a particular region (ie, proportion of population with MPCEless than the specified poverty line). The official poverty lines for India and its states are based on a calorie norm of 2,400 calories per capita per day for rural areas and 2,100 calories per capita per day for urban areas. State wise poverty lines (2004-05) used here were released by the Planning Commission in its press note in March 2007.

6 The Lorenz Ratio has been obtained from the cumulated expenditure share of each MPCE class in the aggregate consumer expenditure against the cumulated population shares of these MPCE classes. The term LR-S has been used here to denote the Lorenz ratio computed for each of the major states or its districts using the state-specific MPCE percentile classes.

7 Two districts of Jammu and Kashmir (Leh and Kargil) were out of survey coverage in 2004-05. In three more districts (Doda, Poonch and

Rajouri) survey could not be conducted due to insurgency problem. 8 The estimates from 61st round for CES were gen

erated using the formula as given below First Stage Unit (FSU): village for rural area and urban block for urban area.

s = subscript for s-th stratum, t = subscript for t-th sub-stratum, m = subscript for sub-sample (m =1, 2), i = subscript for i-th FSU [village/block], j = subscript for j-th second stage stratum in an FSU/hamlet group(hg)/sub-block(sb) (j=1, 2 or 3), k = subscript for k-th sample household under a particular second stage stratum within an FSU/ hg/sb D = total number of hg’s/sb’s formed in the s ample village/block D* = 1 if D = 1 = D/2 for any FSUs (village/urban block) with D>1 Z = total size of a rural sub-stratum (= sum of sizes for all the FSUs of a rural sub-stratum), z = size of

february 28, 2009 vol XLIV No 9

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sample village used for selection, N = total no of s tratum ‘s´’ and sub-stratum ‘t and (^

’R) is a ratio urban blocks, n = number of sample village/ estimator. And blocks surveyed, H = total number of households listed in a second-stage stratum of a village/ R^R)= √M^

SE(^^

SE (^R) × 100

block/hamlet-group/sub-block of sample FSU, R h = number of households surveyed in a second-10 For detail estimation procedures for CES (2004-05)

stage stratum of a village/block/hamlet-group/ and CES (1999-2000) one may visit www.mospi. sub-block of sample FSU for a particular schedule. gov.in and see NSS report No 508 on Level and For Rural: P attern of Consumer Expenditure, 2004-05.

1 Z nj 1*⎡ Hi 1j hi 1j Hi 2 j hi 2 j ⎤ 11 Generalised Regression Estimate (greg) is aYˆ = ∑∑∑∑ ∑ Di ⎢∑ yi 1jk + ∑ yi 2 jk ⎥

st 2 m j nji =1 z hk =1 hk =1 s ynthetic regression method, which involve esti

i ⎣⎢ i 1ji 2 j ⎦⎥

For Urban: mating the common regression coefficient using nj hi 1j hi 2 j survey data coming from each sub-domain

ˆ1 N *⎡ Hi 1j Hi 2 j ⎤ Y = ∑∑∑∑ ∑ Di ⎢∑ yi 1jk + ∑ y jk ⎥ (district) in a domain (state). The GREG estimate

st 2 m j nji =1 ⎢ hi 1jk =1 hi 2 jk =1 i 2 ⎥

⎣⎦ of simple form can be as follows. For dth district

Y the GREG estimate is tgd = 1/2* (tg(1) + tg(2)) with R) of the ratio ( )will be

Ratio estimate (^R = ) t(m) = t(y) + b(m) ( X– t(x)) and where m

X gmqmYˆ denotes the subsample and t(y) is the estimator

m

obtained as Rˆ = X . for mth subsample, b is the regression coefficient

ˆ q

and q assumes a suitable form of inclusion 9 Estimates of RSE for a Ratio Estimator (^ auxiliary

R) for p robability, X is the suitably chosen s tratum (s´): variable.

^^

)2 + ^)2

M^SEs (^R)= Σ —1 [(^R2(^

4 Ys´t1 – Ys´t2Xs´t1 – Xs´t2t

R (^^^References

–2^)(^)]

Ys´t1 – Ys´t2Xs´t1 – Xs´t2Ahluwalia, Montek S (2000): “Economic Performance where ^ and ^ are the estimates for sub-of States in Post-Reform Period”, Economic &

Ys´t1Ys´t2 sample 1 and sub-sample 2, respectively, for Political Weekly, 6 May.

Table A1.R: The Lower and Upper Limits of the State Level MPCE Percentile Classes for the Rural Sector

Bhanumurthy, N R and A Mitra (2004): “Economic Growth, Poverty and Reforms in Indian States”, DEG, Working Paper Series No E/247/2004.

Deaton, A and J Dreze (2002): “Poverty and Inequality in India: A Re-examination”, Economic & Political Weekly, 3 September.

Ghosh, B, S Marjit and C Neogi (1998): “Economic Growth and Regional Divergence in India, 1960 to 1995”, Economic & Political Weekly, Vol 33, No 26.

Himanshu (2007): “Recent Trends in Poverty and Inequality: Some Preliminary Results”, Economic & Political Weekly, 10 February.

Krishna, K L (2004): “Patterns and Determinants of Economic Growth in Indian States”, ICRIER, D iscussion Paper No 144, New Delhi.

Report on Small Area Estimation of Socio-Economic Variables-November (2000): A Study conducted by Indian Statistical Institute in Collaboration with National Sample Survey Organisation.

Sastry, N S (2003): “District Level Poverty Estimates: Feasibility of Using NSS Household Consumption Expenditure Survey Data”, Economic & Political Weekly, 25 January.

Sen, A and Himanshu (2004): “Poverty and Inequa lity

– I and II, Widening Disparities during the 1990s”, Economic & Political Weekly, 18 and 25 September.

Sundaram, K and S D Tendulkar (2003): “Poverty in India in the 1990s – An Analysis of Changes in 15 Major States”, Economic & Political Weekly, 5 April.

MPCE Percentile Classes in the State (Lower and Upper Limits in Rs) Rural

State 0-5% 5-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% (90-95)% Andhra Pradesh 0-249 249-289 289-342 342-389 389-441 441-488 488-546 546-621 621-726 726-921 921-1,151 Assam 0-291 291-325 325-376 376-420 420-467 467-514 514-559 559-606 606-668 668-769 769-894 Bihar 0-228 228-251 251-286 286-319 319-345 345-379 379-415 415-458 458-513 513-608 608-729 Chhattisgarh 0-179 179-215 215-257 257-290 290-320 320-345 345-381 381-423 423-498 498-625 625-771 Gujarat 0-268 268-304 304-359 359-408 408-455 455-508 508-572 572-644 644-758 758-970 970-1,195 Haryana 0-328 328-386 386-461 461-536 536-592 592-674 674-757 757-870 870-1,020 1,020-1,291 1,291-1,889 Himachal Pradesh 0-338 338-388 388-459 459-521 521-571 571-631 631-714 714-816 816-973 973-1,243 1,243-1,600 J & K 0-400 400-457 457-516 516-561 561-607 607-666 666-751 751-861 861-1,034 1,034-1,272 1,272-1,469 Jharkhand 0-222 222-250 250-282 282-314 314-343 343-378 378-412 412-464 464-526 526-640 640-774 Karnataka 0-257 257-287 287-321 321-357 357-391 391-426 426-464 464-516 516-592 592-747 747-937 Kerala 0-336 336-398 398-487 487-569 569-656 656-744 744-852 852-1012 1,012-1,253 1,253-1,716 1,716-2,265 Madhya Pradesh 0-200 200-227 227-265 265-303 303-339 339-377 377-420 420-474 474-551 551-713 713-876 Maharashtra 0-235 235-266 266-319 319-364 364-409 409-459 459-519 519-594 594-701 701-934 934-1,226 Orissa 0-171 171-197 197-233 233-265 265-301 301-335 335-377 377-423 423-502 502-666 666-809 Punjab 0-372 372-420 420-484 484-548 548-612 612-693 693-805 805-910 910-1,084 1,084-1,382 1,382-1,804 Rajasthan 0-290 290-330 330-381 381-429 429-471 471-515 515-558 558-622 622-707 707-881 881-1,107 Tamil Nadu 0-259 259-292 292-340 340-382 382-425 425-469 469-526 526-597 597-699 699-920 920-1,181 Uttarakhand 0-309 309-340 340-394 394-430 430-474 474-522 522-590 590-667 667-763 763-980 980-1,312 Uttar Pradesh 0-242 242-274 274-318 318-354 354-394 394-437 437-486 486-550 550-648 648-834 834-1,069 West Bengal 0-267 267-297 297-344 344-389 389-429 429-474 474-528 528-591 591-673 673-841 841-1,069 Table A1.U: The Lower and Upper Limits of the State Level MPCE Percentile Classes for the Urban Sector MPCE Percentile Classes in the State (Lower and Upper Limits in Rs) Urban State 0-5% 5-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90-95% Andhra Pradesh 0-363 363-418 418-481 481-564 564-645 645-748 748-864 864-1,032 1,032-1,280 1,280-1,728 1,728-2,314 Assam 0-410 410-456 456-521 521-668 668-748 748-899 899-974 974-1,116 1,116-1,435 1,435-1,807 1,807-2,278 Bihar 0-269 269-308 308-368 368-402 402-459 459-542 542-643 643-753 753-895 895-1,217 1,217-1,558 Chhattisgarh 0-286 286-319 319-395 395-471 471-532 532-698 698-787 787-1,018 1,018-1,189 1,189-1,723 1,723-2,144 Gujarat 0-438 438-497 497-609 609-685 685-804 804-933 933-104 1,041-1,218 1,218-1,519 1,519-1,887 1,887-2,323 Haryana 0-376 376-438 438-564 564-665 665-757 757-871 871-101 1,014-1,186 1,186-1,447 1,447-1,987 1,987-2,580 Himachal Pradesh 0-584 584-632 632-668 668-846 846-984 984-1139 1,139-1311 1,311-1,520 1,520-1,832 1,832-2,317 2,317-2,817 J & K 0-476 476-607 607-670 670-751 751-853 853-949 949-1,059 1,059-1,197 1,197-1,435 1,435-1,695 1,695-2,019 Jharkhand 0-312 312-363 363-448 448-557 557-662 662-807 807-942 942-1,097 1,097-1,331 1,331-1,773 1,773-2,204 Karnataka 0-331 331-378 378-483 483-573 573-670 670-764 764-933 933-1,104 1,104-1,417 1,417-1,937 1,937-2,453 Kerala 0-368 368-442 442-561 561-664 664-768 768-903 903-1,092 1,092-1,320 1,320-1,626 1,626-2,267 2,267-3,118 Madhya Pradesh 0-286 286-333 333-406 406-471 471-551 551-641 641-759 759-920 920-1,130 1,130-1,552 1,552-2,244 Maharashtra 0-349 349-416 416-528 528-637 637-753 753-863 863-1,019 1,019-1,211 1,211-1,475 1,475-2,074 2,074-2,671 Orissa 0-238 238-294 294-358 358-426 426-491 491-580 580-725 725-857 857-1,106 1,106-1,354 1,354-1,664 Punjab 0-446 446-499 499-604 604-706 706-808 808-932 932-1081 1,081-1,305 1,305-1,582 1,582-2,027 2,027-2,653 Rajasthan 0-361 361-395 395-472 472-545 545-612 612-708 708-820 820-965 965-1,167 1,167-1,615 1,615-2,200 Tamilnadu 0-372 372-428 428-529 529-606 606-690 690-819 819-954 954-1,152 1,152-1,435 1,435-1,965 1,965-2,557 Uttarakhand 0-400 400-448 448-505 505-580 580-669 669-794 794-929 929-1,034 1,034-1,244 1,244-1,559 1,559-2,063 Uttar Pradesh 0-294 294-345 345-409 409-482 482-552 552-636 636-749 749-899 899-1,077 1,077-1,516 1,516-1,993 West Bengal 0-355 355-415 415-493 493-591 591-686 686-833 833-1017 1,017-1,195 1,195-1,513 1,513-2,063 2,063-2,831 Economic & Political Weekly february 28, 2009 vol XLIV No 9

95-100% ≥ 1,151 ≥ 894 ≥ 729 ≥ 771 ≥ 1,195 ≥ 1,889 ≥ 1,600 ≥ 1,469 ≥ 774 ≥ 937 ≥ 2,265 ≥ 876 ≥ 1,226 ≥ 809 ≥ 1,804 ≥ 1,107 ≥ 1,181 ≥ 1,312 ≥ 1,069 ≥ 1,069 95-100% ≥ 2,314 ≥ 2,278 ≥ 1,558 ≥ 2,144 ≥ 2,323 ≥ 2,580 ≥ 2,817 ≥ 2,019 ≥ 2,204 ≥ 2,453 ≥ 3,118 ≥ 2,244 ≥ 2,671 ≥ 1,664 ≥ 2,653 ≥ 2,200 ≥ 2,557 ≥ 2,063 ≥ 1,993 ≥ 2,831 101

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Table A2: District-Wise Population Proportion, MPCE, HCR and LR-S for Rural and Urban Sector within States
			Rural						Urban
District Name	Proportional	No of Sample	MPCE	RSE of	%	Lorenz	Proportional	No of Sample	MPCE	RSE of	%	Lorenz
	Population	Households	(Rs)	MPCE	Poor	Ratio(S)	Population	Households	(Rs)	MPCE	Poor	Ratio(S)
Adilabad	3.3	200	400	6.87	26.1	0.202	3.8	120	665	6.54	38.2	0.226
Nizamabad	3.2	200	416	4.38	23.1	0.199	1.2	60	775	5.79	43.2	0.305
Karimnagar	5.0	280	565	7.68	7.2	0.287	2.8	90	893	12.36	30.2	0.279
Medak	3.5	240	537	8.27	9.3	0.278	1.3	69	568	5.22	54.5	0.198
Hyderabad	-						17.7	392	1,296	11.47	22.7	0.422
Ranga Reddy	2.8	160	575	11.04	10.9	0.293	0.7	279	743	8.69	47.6	0.316
Mahboob nagar	5.6	317	617	5.17	11.8	0.329	1.9	58	933	13.94	22.4	0.281
Nalgonda	4.7	279	596	4.36	5.4	0.234	2.0	80	687	7.09	31.7	0.194
Warangal	4.8	280	752	6.55	0.9	0.283	3.1	80	976	12.89	26.0	0.296
Khammam	3.8	200	530	7.07	13.1	0.270	2.3	80	793	3.06	27.8	0.272
Srikakulam	3.8	240	624	5.30	6.0	0.269	2.8	40	819	13.78	31.4	0.285
Vizianagaram	3.2	200	590	7.60	4.7	0.282	2.5	70	811	10.57	41.4	0.333
Vishakhapatnam	4.3	240	585	8.08	18.9	0.341	9.0	229	1,734	11.91	16.1	0.436
East Godavari	7.7	320	652	4.43	3.3	0.257	6.2	159	946	7.72	20.1	0.303
West Godawari	5.3	280	729	6.97	4.4	0.262	3.9	110	866	14.37	26.2	0.330
Krishna	5.5	279	687	4.10	2.8	0.246	8.1	200	1,194	6.63	16.3	0.322
Guntur	5.5	320	644	6.98	3.9	0.257	6.4	190	865	3.66	26.6	0.278
Prakasam	4.7	280	616	6.44	9.9	0.281	3.2	80	870	12.40	15.6	0.250
Nellore	3.9	200	498	4.90	14.1	0.269	3.4	80	776	5.72	24.5	0.235
Cuddapah	3.7	200	702	14.51	5.4	0.333	2.9	60	695	17.17	46.9	0.271
Kurnool	5.3	280	442	3.92	24.6	0.259	3.9	90	806	12.36	35.9	0.307
Anantpur	5.1	280	471	6.65	20.2	0.274	6.3	150	784	10.59	44.8	0.331
Chittoor	5.2	280	481	7.23	15.9	0.261	4.6	110	826	4.75	31.0	0.288
Andhra Pradesh	100.0	5,555	586	1.50	10.5	0.290	100.0	2876	1,019	3.72	27.4	0.369
Kokrajhar	3.0	110	479	6.30	35.7	0.220	1.5	40	854	11.98	3.0	0.241
Dhubri	5.9	190	455	5.47	42.4	0.190	4.9	30	701	9.92	4.2	0.199
Goalpara	2.7	120	495	7.87	33.9	0.194	1.8	40	808	8.13	6.8	0.240
Bongaigaon	3.3	120	448	5.77	33.0	0.177	3.2	40	838	18.30	0.9	0.223
Barpeta	6.8	190	492	5.84	39.9	0.211	3.2	40	713	3.57	6.0	0.180
Kamrup	6.8	180	531	5.40	22.3	0.206	24.3	110	1,272	8.78	2.9	0.268
Nalbari	4.8	160	542	5.00	15.0	0.155	0.9	20	897	20.97	0.8	0.258
Darrang	6.7	200	620	2.69	0.1	0.097	2.5	40	925	10.51	0.0	0.163
Morigaon	3.5	120	529	10.52	21.5	0.202	2.2	20	1,580	20.32	0.0	0.153
Nowgong	8.1	240	557	5.38	25.3	0.208	7.5	40	787	2.80	9.1	0.221
Sonitpur	7.8	200	601	5.26	3.6	0.148	5.8	40	851	6.82	0.7	0.307
Lakhimpur	3.9	120	636	3.04	1.4	0.118	1.2	40	832	3.60	1.2	0.201
Dhemaji	2.3	80	640	8.09	0.0	0.140	0.6	20	758	8.99	0.0	0.272
Tinsukia	4.2	160	628	7.29	14.4	0.204	6.0	40	1,209	10.49	2.6	0.254
Dibrugarh	4.9	160	576	8.51	19.2	0.192	9.9	40	1,608	26.06	3.9	0.438
Sibsagar	3.8	160	650	6.85	20.3	0.257	1.9	40	1,167	10.16	7.1	0.236
Jorhat	3.1	120	593	7.77	27.5	0.242	5.7	40	1,184	21.39	3.8	0.308
Golaghat	4.0	120	539	6.04	25.5	0.216	1.5	40	896	9.46	8.1	0.263
Karbiaglong	3.2	120	448	5.16	26.5	0.123	2.0	40	815	14.70	0.0	0.205
N Cachar Hills	0.6	40	484	1.94	6.1	0.094	1.7	40	656	5.44	3.1	0.186
Cachar	5.0	200	481	6.48	33.5	0.188	7.2	40	748	15.44	0.7	0.224
Karimganj	4.0	160	444	5.47	40.9	0.158	3.0	40	758	10.17	14.3	0.272
Hailakandi	1.7	80	512	5.16	7.0	0.118	1.5	20	671	5.24	2.6	0.215
Assam	100.0	3,350	543	1.36	22.1	0.196	100.0	900	1,058	6.20	3.6	0.315
West Champaran	3.5	159	320	4.28	76.9	0.162	0.8	40	450	20.48	71.7	0.276
East Champaran	5.8	200	474	2.80	20.1	0.163	2.9	40	592	17.27	35.2	0.213
Sheohar	1.0	40	484	4.90	14.8	0.114	0.3	20	604	5.56	32.5	0.230
Sitamari	4.0	160	451	5.22	28.1	0.170	1.0	40	587	8.67	39.3	0.238
Madhubani	4.5	200	356	2.36	59.2	0.163	1.1	40	629	16.42	41.2	0.331
Supaul	1.8	118	543	4.55	20.0	0.193	1.1	20	503	13.24	35.3	0.216
Araria	2.4	120	362	3.70	54.6	0.142	0.9	40	649	6.55	35.6	0.251
Kishanganj	1.5	80	363	3.92	62.3	0.173	0.7	40	769	22.62	30.6	0.304
Purnea	3.8	120	495	7.62	29.0	0.217	1.6	40	815	14.29	8.6	0.243
Katihar	3.3	120	426	5.50	36.5	0.194	1.5	40	884	18.39	13.3	0.305
Madhepura	1.5	80	563	6.60	7.7	0.158	0.7	20	509	33.92	37.1	0.270
	(Continued)
102	february 28, 2009	vol XLIV No 9		Economic & Political Weekly

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Table A2: District-Wise Population Proportion, MPCE, HCR and LR-S for Rural and Urban Sector within States (Continued)
Rural		Urban
District Name	Proportional	No of Sample	MPCE	RSE of	%	Lorenz	Proportional	No of Sample	MPCE	RSE of	%	Lorenz
	Population	Households	(Rs)	MPCE	Poor	Ratio(S)	Population	Households	(Rs)	MPCE	Poor	Ratio(S)
Saharsa	1.6	80	586	9.91	21.1	0.253	0.6	40	939	19.23	1.4	0.230
Darbhanga	3.7	160	428	5.11	42.2	0.241	2.2	40	628	11.34	40.7	0.292
Muzaffarpur	4.5	200	383	6.04	65.3	0.233	3.8	40	546	21.23	56.3	0.335
Gopalganj	2.5	118	445	5.73	27.4	0.196	1.9	38	646	9.24	28.6	0.283
Siwan	3.6	160	455	2.49	30.2	0.180	1.2	40	634	14.00	41.4	0.262
Saran	3.7	160	382	4.65	55.9	0.199	2.8	40	701	16.30	34.7	0.341
Vaishali	3.6	120	411	5.28	41.6	0.214	2.1	40	526	12.10	54.3	0.287
Samastipur	4.2	200	388	3.26	52.3	0.201	1.0	40	480	2.16	62.1	0.240
Begusarai	2.8	120	370	3.20	56.7	0.149	2.7	40	496	17.41	47.6	0.247
Khagaria	1.6	80	495	3.09	16.7	0.157	0.3	20	617	11.98	4.0	0.150
Bhagalpur	2.7	119	382	2.57	45.2	0.173	5.8	40	687	8.58	14.9	0.200
Banka	2.3	80	362	5.42	59.8	0.165	0.6	20	355	2.57	88.4	0.114
Munger	1.2	40	437	2.76	35.6	0.157	3.3	40	601	16.44	44.2	0.255
Lakhisarai	1.1	40	457	7.99	38.6	0.189	0.7	40	591	8.81	41.7	0.262
Sheikpura	0.5	40	433	4.41	28.6	0.191	0.8	20	506	11.83	39.3	0.160
Nalanda	2.5	120	398	4.11	44.8	0.167	5.3	40	526	4.35	39.6	0.203
Patna	3.7	160	420	6.09	44.7	0.236	31.1	120	908	12.61	25.8	0.344
Bhojpur	2.5	120	399	4.06	41.6	0.188	4.0	40	553	7.62	43.6	0.249
Buxar	1.9	80	354	3.31	54.2	0.151	1.1	40	552	8.53	33.3	0.237
Bhabua	1.6	80	388	2.31	42.0	0.179	0.6	20	662	1.31	21.7	0.185
Rohtas	3.0	120	407	5.74	34.6	0.168	5.2	40	440	5.57	62.1	0.205
Jehanabad	1.9	80	373	10.95	54.2	0.205	2.2	40	464	8.14	57.1	0.211
Aurangabad	2.2	120	372	7.46	55.4	0.242	1.8	40	648	16.65	53.6	0.374
Gaya	4.1	160	434	7.02	37.5	0.224	3.8	40	890	30.72	33.5	0.423
Nawada	2.0	120	431	2.37	38.8	0.194	1.7	40	563	7.01	48.7	0.232
Jamui	1.7	80	390	3.44	46.3	0.164	0.9	20	402	2.59	68.1	0.179
Bihar	100.0	4,354	417	0.95	42.6	0.205	100.0	1398	696	5.76	36.1	0.329
Koriya	2.4	40	384	14.37	49.7	0.241	1.7	40	1036	29.88	46.8	0.448
Surguja	10.1	200	334	3.67	49.7	0.160	3.2	40	965	13.61	15.7	0.209
Jashpur	4.0	80	373	7.31	35.0	0.154	1.3	40	897	19.12	33.8	0.262
Raigarh	6.3	120	431	5.53	23.6	0.179	3.4	40	654	12.53	61.8	0.291
Korba	3.6	80	627	20.00	22.7	0.383	5.6	80	1179	17.32	32.8	0.364
Janjgir-Champa	7.4	157	486	8.74	29.8	0.285	4.3	40	638	5.83	50.4	0.262
Bilaspur	10.5	200	434	6.37	34.8	0.255	20.7	80	802	2.95	42.5	0.334
Kawardha	3.6	80	465	10.10	16.9	0.263	1.4	40	699	16.49	39.6	0.266
Rajnandgaon	6.1	120	322	2.62	58.6	0.163	5.8	40	1,934	60.64	36.3	0.524
Durg	9.4	200	414	5.25	35.5	0.239	20.2	80	1,310	32.52	35.6	0.485
Raipur	14.3	240	520	8.72	31.2	0.342	19.9	80	835	11.92	41.1	0.372
Mahasamund	4.9	80	602	24.32	21.4	0.359	2.5	40	1,057	9.72	39.9	0.466
Dhamtari	3.2	80	451	15.00	38.5	0.265	3.2	40	613	4.58	70.8	0.272
Kanker	3.7	80	358	8.92	53.1	0.211	1.1	40	629	18.57	57.0	0.364
Bastar	6.5	160	316	16.98	80.6	0.334	4.7	40	845	42.64	57.1	0.438
Dantewada	4.0	80	218	12.16	88.2	0.223	1.2	39	418	13.34	84.0	0.351
Chhattisgarh	100.0	1997	425	2.98	40.8	0.293	100.0	799	990	11.28	42.2	0.431
Kachchh	3.9	80	520	7.34	20.0	0.216	1.2	30	812	23.12	52.9	0.317
Bans Kantha	7.4	120	448	7.93	26.0	0.187	1.1	40	893	5.51	5.2	0.188
patan	3.3	80	424	8.44	42.4	0.209	0.9	40	805	6.70	22.8	0.210
Mahesana	4.2	120	516	7.02	27.3	0.233	3.4	40	804	14.72	26.3	0.225
Sabar Kantha	6.1	120	497	6.04	20.2	0.190	0.6	40	770	2.98	20.5	0.234
Gandhinagar	3.0	80	1,012	17.20	5.2	0.274	2.2	37	2,422	20.53	0.6	0.338
Ahmedabad	4.5	80	726	6.99	11.3	0.263	22.3	349	1,203	4.97	11.2	0.305
Surendranagar	3.6	80	530	12.96	20.5	0.231	1.8	40	758	20.14	26.4	0.222
Rajkot	4.6	120	715	2.92	10.4	0.214	10.5	160	1,058	6.58	8.6	0.238
Jamnagar	2.3	80	690	9.78	0.0	0.161	2.4	80	756	2.26	11.9	0.142
Porbandar	0.6	40	709	12.78	0.0	0.150	1.1	40	712	4.57	17.8	0.162
Junagadh	5.5	120	749	9.56	0.0	0.259	2.5	80	890	8.40	13.4	0.231
Amreli	3.1	80	719	5.40	0.5	0.213	1.8	40	716	13.41	12.6	0.190
Bhavnagar	4.7	120	632	4.98	1.2	0.160	5.4	111	927	6.50	18.6	0.268
Anand	4.2	80	517	7.63	13.6	0.204	2.4	40	692	4.06	43.6	0.226
				(Continued)
Economic & Political Weekly		february 28, 2009	vol XLIV No 9				103

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Table A2: District-Wise Population Proportion, MPCE, HCR and LR-S for Rural and Urban Sector within States (Continued)
			Rural						Urban
District Name	Proportional	No of Sample	MPCE	RSE of	%	Lorenz	Proportional	No of Sample	MPCE	RSE of	%	Lorenz
	Population	Households	(Rs)	MPCE	Poor	Ratio(S)	Population	Households	(Rs)	MPCE	Poor	Ratio(S)
Kheda	5.0	120	446	6.33	42.4	0.204	1.6	40	604	9.75	50.8	0.217
Godhra	5.3	120	489	13.54	38.3	0.276	2.2	40	861	19.74	25.2	0.261
Dohad	5.4	120	416	6.61	41.4	0.212	1.5	40	714	15.23	33.8	0.257
Vadodara	6.4	120	602	4.40	5.6	0.214	11.0	190	1,519	6.98	8.1	0.331
Narmada	1.4	40	624	18.16	24.5	0.298	0.1	40	1,030	25.97	18.7	0.310
Bharuch	3.1	80	676	11.21	17.1	0.328	1.0	40	1,144	11.31	13.1	0.248
Surat	5.7	120	693	8.64	23.1	0.336	17.4	318	1,121	7.52	7.6	0.243
Dangs	0.7	40	349	12.32	88.4	0.271	-
Navasari	2.9	80	793	13.44	6.5	0.263	1.6	40	1,036	13.06	3.1	0.235
Valsad	3.0	80	745	10.04	3.4	0.206	4.2	40	1,307	13.08	2.1	0.212
Gujarat	100.0	2,320	596	2.03	18.9	0.270	100.0	1955	1,115	2.85	13.3	0.306
Panchkula	1.5	40	950	17.60	4.3	0.252	4.1	40	1,328	19.02	5.7	0.373
Ambala	5.1	80	836	7.18	3.1	0.218	5.2	40	1,156	13.15	0.0	0.224
Yamuna Nagar	4.6	80	1,011	23.59	7.6	0.324	8.7	80	1,208	9.69	0.6	0.250
Kurukshetra	3.6	80	1,039	4.26	2.4	0.255	2.9	40	2,851	42.85	5.7	0.416
Kaithal	5.4	80	768	8.46	12.4	0.222	2.5	40	1,052	17.35	8.3	0.244
Karnal	6.1	80	798	12.07	5.9	0.264	4.1	40	1,894	8.21	1.8	0.267
Panipat	4.2	80	839	14.03	22.7	0.366	4.1	80	1,399	25.45	6.5	0.343
Sonipat	6.2	120	718	8.29	24.5	0.306	4.9	40	615	16.10	56.3	0.363
Jind	6.8	80	869	3.98	14.6	0.364	4.1	40	1,163	23.14	17.3	0.395
Fatehabad	4.2	80	795	13.87	13.2	0.286	2.4	40	958	14.26	26.8	0.356
Sirsa	5.2	80	712	4.82	9.4	0.248	5.0	40	1,050	7.75	19.5	0.350
Hisar	7.0	120	702	6.27	15.2	0.224	6.8	80	894	12.37	17.7	0.277
Bhilwani	7.3	120	670	3.93	18.3	0.261	5.2	40	822	7.06	35.5	0.323
Rohtak	3.9	80	803	6.80	6.0	0.204	5.9	40	855	14.63	25.1	0.316
Jhajjar	4.1	80	791	9.95	6.6	0.218	3.2	40	832	5.67	11.1	0.232
Mahendragarh	4.0	80	719	8.11	8.4	0.209	1.5	40	886	9.76	25.8	0.245
Rewari	4.0	80	790	12.19	16.8	0.338	2.0	40	1,591	60.31	26.7	0.648
Gurgaon	10.2	120	1,559	39.90	6.2	0.466	5.9	80	1,292	17.60	16.8	0.349
Faridabad	6.7	120	634	9.17	37.6	0.285	21.6	160	1,042	10.05	7.5	0.282
Haryana	100.0	1680	863	9.23	13.3	0.335	100.0	1040	1,142	5.15	14.5	0.360
Chamba	7.9	160	646	11.32	20.7	0.312	5.3	40	1,273	7.42	3.6	0.274
Kangra	23.2	400	813	6.68	11.4	0.309	10.5	40	1,124	7.81	9.9	0.276
Lahul and Spiti	0.6	40	1,076	24.51	0.0	0.325	-
Kullu	6.4	160	655	9.01	16.8	0.250	6.1	40	1,311	6.11	1.2	0.244
Mandi	13.9	354	695	3.81	10.0	0.238	7.6	40	1,612	29.44	1.4	0.348
Hamirpur	7.0	160	937	5.82	6.3	0.317	5.5	40	1,020	13.54	27.7	0.381
Una	8.0	160	929	14.10	6.1	0.347	6.4	40	1,423	15.29	0.8	0.305
Bilaspur	6.0	116	816	7.87	6.9	0.328	2.5	40	1,344	10.56	5.5	0.263
Solan	7.9	155	878	7.64	4.7	0.295	31.6	40	1,456	27.97	0.0	0.368
Siramour	6.9	160	785	6.51	7.7	0.282	6.3	40	1,436	6.29	1.0	0.233
Shimla	11.1	238	812	8.75	13.2	0.293	18.1	40	1,489	13.08	0.0	0.266
Kinnaur	1.1	40	963	5.67	7.0	0.263	-
Himachal Pradesh	100.0	2143	798	2.69	10.5	0.305	100.0	400	1,390	9.65	3.2	0.322
Kupwara	8.8	70	582	0.75	13.1	0.147	1.0	10	887	0.00	0.0	0.154
Barmula	13.1	310	666	2.45	6.0	0.191	7.5	120	932	1.65	11.4	0.236
Srinagar	4.1	120	656	5.91	6.1	0.165	47.1	157	956	2.41	10.2	0.222
Badgam	10.1	189	764	3.07	2.9	0.226	1.7	20	844	3.42	7.2	0.112
Pulwama	10.6	218	1,008	5.16	0.0	0.219	2.6	40	1,150	2.17	2.2	0.174
Anantnag	15.9	255	911	1.87	0.0	0.232	4.7	48	1,135	2.00	2.4	0.193
Doda	-						0.8	10	990	0.00	0.0	0.138
Udhampur	11.1	200	542	4.07	9.3	0.144	3.6	80	941	4.73	4.8	0.195
Jammu	17.3	320	946	4.80	1.8	0.257	27.5	359	1,330	4.52	4.4	0.263
Kathus	9.1	200	833	6.59	5.0	0.229	3.5	40	1,021	6.55	2.0	0.193
J & K	100.0	1882	793	1.57	4.3	0.244	100.0	884	1,070	1.81	7.4	0.247
Garhwa	4.7	120	404	3.37	38.6	0.157	0.7	40	596	17.69	38.3	0.285
Palamau	9.6	200	379	3.52	54.3	0.171	1.6	40	852	31.64	29.2	0.357
Chatra	3.4	80	398	8.38	55.2	0.191	0.7	40	989	19.12	28.9	0.420
Hazaribagh	8.8	200	486	3.06	28.3	0.202	7.5	80	1,286	26.76	15.9	0.379
	(Continued)
104	february 28, 2009	vol XLIV No 9		Economic & Political Weekly

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Table A2: District-Wise Population Proportion, MPCE, HCR and LR-S for Rural and Urban Sector within States (Continued)
Rural		Urban
District Name	Proportional	No of Sample	MPCE	RSE of	%	Lorenz	Proportional	No of Sample	MPCE	RSE of	%	Lorenz
	Population	Households	(Rs)	MPCE	Poor	Ratio(S)	Population	Households	(Rs)	MPCE	Poor	Ratio(S)
Kodarma	2.2	30	403	3.96	38.1	0.144	1.3	40	988	35.23	30.7	0.519
Giridihi	8.1	190	467	5.86	30.5	0.203	1.2	40	851	10.05	1.9	0.196
Deoghar	5.1	120	417	12.07	58.7	0.259	3.6	40	722	20.64	38.8	0.298
Godda	5.5	120	516	14.22	41.3	0.317	1.5	40	625	5.82	37.8	0.301
Sahibganj	3.6	120	382	5.66	63.7	0.190	1.0	40	808	2.31	29.9	0.272
Pakur	3.4	80	319	2.36	75.6	0.167	0.6	40	902	16.13	6.7	0.236
Dumka	6.9	160	373	1.86	55.4	0.164	1.6	40	1,204	13.37	4.2	0.234
Dhanbad	6.0	120	540	3.93	19.3	0.220	20.1	120	1,065	11.86	21.6	0.382
Bokaro	4.6	120	414	5.60	52.4	0.244	12.9	80	943	10.49	9.2	0.258
Ranchi	8.7	200	494	3.28	23.2	0.187	14.5	80	799	16.89	18.6	0.296
Lohardaga	1.7	40	310	4.84	81.6	0.134	0.9	40	816	12.93	30.2	0.339
Gumla	5.2	160	328	4.69	68.6	0.180	0.6	40	616	42.53	45.2	0.364
Paschim Singhbhum	7.8	199	406	4.61	53.8	0.227	7.5	80	555	13.97	51.3	0.305
Purbi Singhbhum	4.7	120	394	8.34	58.4	0.265	22.1	120	1,212	8.01	12.2	0.304
Jharkhand	100.0	2,379	425	1.61	46.2	0.225	100.0	1040	985	5.58	20.3	0.351
Belgaum	10.3	160	570	15.08	12.0	0.285	5.8	119	768	7.42	42.0	0.257
Bagalkote	3.3	120	487	11.34	18.1	0.231	1.6	70	536	4.85	79.7	0.171
Bijapur	4.0	120	489	3.60	20.0	0.195	3.5	40	704	12.66	43.6	0.257
Gulbarga	6.5	160	372	2.72	39.4	0.144	4.8	119	649	9.21	60.0	0.303
Bidar	2.7	120	406	7.30	31.0	0.181	1.0	39	664	2.63	40.1	0.223
Raichur	3.0	120	339	8.74	59.2	0.186	2.6	40	407	15.45	88.6	0.255
Koppal	2.6	80	427	2.62	3.7	0.089	0.7	40	557	30.40	70.3	0.295
Gadag	2.3	40	404	8.60	6.4	0.124	2.8	40	682	22.32	54.0	0.264
Dharwad	1.9	80	482	3.30	9.7	0.158	5.1	120	1,083	8.75	36.5	0.389
Uttar Kannad	3.2	80	423	12.03	47.6	0.246	3.0	40	627	17.21	66.4	0.288
Haveri	3.4	80	408	8.59	55.1	0.302	1.6	40	567	20.91	83.8	0.342
Bellary	3.7	120	409	5.59	40.0	0.211	2.7	80	519	7.82	84.1	0.271
Chitradurga	3.6	120	404	7.89	24.8	0.177	1.5	40	596	10.81	62.4	0.263
Davanagere	3.2	120	364	4.17	42.2	0.136	1.9	60	586	10.38	72.1	0.249
Shimoga	3.1	80	557	10.88	7.8	0.217	4.2	100	899	7.07	23.3	0.264
Udupi	2.8	80	966	26.79	0.0	0.379	0.2	40	747	15.55	63.2	0.286
Chikmagalur	2.6	80	629	4.69	2.0	0.236	1.0	40	837	17.12	52.2	0.281
Tumkur	6.3	160	487	5.23	20.6	0.202	3.0	80	1,141	12.65	8.0	0.260
Kolar	5.3	160	500	3.57	12.9	0.205	3.4	80	1,062	20.23	33.0	0.352
Bangalore Urban	2.8	80	718	22.97	6.6	0.349	35.2	600	1,395	4.91	7.9	0.321
Bangalore Rural	3.7	120	501	4.33	17.4	0.223	1.4	40	921	18.86	32.0	0.319
Mandya	4.7	120	508	4.58	15.3	0.214	1.1	40	643	9.70	58.7	0.239
Hassan	3.9	120	486	4.86	5.1	0.172	1.6	40	901	1.75	37.6	0.275
Dakshin Kannad	3.5	120	731	8.60	11.2	0.306	2.7	80	1,761	22.03	14.4	0.390
Kodagu	1.4	40	718	8.46	4.6	0.253	0.3	40	1,111	11.39	19.1	0.284
Mysore	4.3	120	592	21.70	14.2	0.317	6.3	120	1,046	13.86	24.4	0.293
Chamarajnagar	2.1	80	520	6.21	13.8	0.204	0.8	40	707	6.65	52.8	0.227
Karnataka	100.0	2,880	508	2.89	20.7	0.262	100.0	2,227	1,033	3.28	32.6	0.364
Kasargod	4.1	150	725	10.77	22.6	0.314	2.2	80	874	9.61	34.2	0.319
Kannur	4.7	120	656	8.21	35.4	0.327	9.1	280	824	4.65	39.4	0.330
Wayanad	3.3	120	790	7.81	22.2	0.339	0.3	40	1,153	19.69	10.6	0.364
Kozhikode	7.5	220	715	6.53	25.3	0.310	13.0	240	918	9.07	36.2	0.365
Malapuram	14.1	470	901	8.74	19.3	0.397	5.4	80	938	20.10	31.6	0.391
Palakkad	8.2	320	868	4.77	11.2	0.312	5.6	80	1,762	43.85	20.5	0.544
Trichur	9.3	280	1,049	6.82	13.1	0.385	9.7	200	1,112	6.09	15.3	0.318
Ernakulam	8.2	200	1,018	6.27	12.5	0.360	21.9	280	1,419	6.83	16.3	0.393
Idukki	4.5	160	1,156	6.35	3.4	0.335	0.5	40	1,557	10.96	14.2	0.326
Kottayam	7.3	270	1,218	7.21	6.9	0.352	3.4	80	1,774	11.91	6.0	0.354
Alappuzha	6.4	210	1,259	15.08	4.4	0.443	8.0	160	1,200	10.37	14.1	0.389
Pathanamthitta	4.7	160	1,165	8.19	5.2	0.356	2.2	30	1,243	1.49	6.1	0.277
Kollam	8.9	320	1,014	4.95	7.0	0.318	5.7	120	1,270	7.75	12.2	0.308
Thiruvananthapuram	8.8	300	1,442	6.12	3.7	0.332	12.9	240	1,867	10.59	6.0	0.378
Kerala	100.0	3,300	1,013	2.30	13.2	0.375	100.0	1950	1,291	4.73	20.0	0.404
Sheopur	1.0	40	481	27.76	37.6	0.274	0.6	40	790	18.79	49.2	0.402
				(Continued)
Economic & Political Weekly		february 28, 2009	vol XLIV No 9				105

EPW

Table A2: District-Wise Population Proportion, MPCE, HCR and LR-S for Rural and Urban Sector within States (Continued)
			Rural						Urban
District Name	Proportional	No of Sample	MPCE	RSE of	%	Lorenz	Proportional	No of Sample	MPCE	RSE of	%	Lorenz
	Population	Households	(Rs)	MPCE	Poor	Ratio(S)	Population	Households	(Rs)	MPCE	Poor	Ratio(S)
Morena	2.8	120	469	4.27	20.8	0.184	1.6	40	645	10.56	42.1	0.203
Bhind	2.3	80	567	12.16	16.4	0.238	3.5	40	596	23.37	69.1	0.302
Gwalior	1.4	40	502	18.16	20.5	0.190	5.4	80	941	28.71	46.8	0.408
Datia	1.2	40	542	18.10	14.7	0.210	0.6	40	698	6.49	64.0	0.296
Shivpuri	2.3	120	361	5.14	38.7	0.156	1.7	40	479	15.50	77.4	0.273
Guna	2.6	120	444	6.03	16.6	0.170	2.5	40	665	19.84	58.4	0.307
Tikamgarh	2.4	80	358	4.75	44.1	0.174	0.8	40	653	14.89	58.4	0.221
Chhatarpur	2.8	80	354	6.85	52.8	0.169	1.2	40	496	5.17	62.2	0.210
Panna	1.6	80	376	8.21	49.6	0.250	0.7	40	589	13.81	48.2	0.233
Sagar	3.1	120	377	6.43	55.7	0.274	4.1	40	551	11.21	67.5	0.288
Damoh	2.4	80	378	3.73	49.0	0.264	1.2	40	486	25.19	70.2	0.358
Satna	3.6	120	508	10.01	19.8	0.234	3.2	40	646	13.56	45.0	0.251
Rewa	3.7	120	405	7.15	43.1	0.269	1.4	40	773	23.82	46.5	0.352
Umaria	1.1	40	289	1.09	76.4	0.187	0.4	40	972	23.52	20.9	0.287
Shahdol	2.7	120	333	2.98	64.4	0.221	3.1	40	961	14.50	12.6	0.253
Sidhi	4.0	120	366	8.86	57.6	0.274	2.4	40	1,121	26.85	19.4	0.285
Neemuch	1.0	40	668	12.35	0.2	0.180	0.9	40	933	11.62	32.7	0.292
Mandsaur	1.9	79	566	10.09	15.5	0.226	1.0	40	1,043	4.32	18.0	0.262
Ratlam	2.2	80	416	3.54	17.1	0.162	4.2	40	565	16.03	61.7	0.260
Ujjain	2.1	80	566	8.85	28.9	0.304	4.8	79	1,542	24.58	25.5	0.470
Shajapur	2.4	80	483	11.69	29.0	0.289	1.4	39	725	21.76	48.0	0.332
Dewas	2.1	80	749	15.98	17.7	0.335	2.4	40	577	6.65	53.4	0.258
Jhabua	3.3	120	350	7.29	56.9	0.195	0.8	40	778	10.20	42.3	0.321
Dhar	3.4	119	589	8.46	23.9	0.301	0.6	39	654	16.87	44.5	0.309
Indore	1.7	80	535	17.13	21.8	0.310	12.3	119	1,648	23.52	20.2	0.419
West Nimar	3.0	120	475	8.35	14.1	0.174	1.2	40	708	15.59	54.9	0.274
Barwani	1.8	80	438	4.58	6.3	0.107	0.6	40	627	16.14	58.0	0.179
East Nimar	2.8	120	504	3.84	4.7	0.136	3.7	40	701	3.62	37.7	0.215
Rajgarh	2.8	80	599	6.95	11.9	0.241	1.2	39	893	11.26	25.9	0.255
Vidisha	1.7	80	416	6.06	51.3	0.253	1.5	40	817	8.47	56.8	0.411
Bhopal	0.7	40	421	12.69	34.5	0.233	8.2	120	856	11.14	34.8	0.295
Sehore	1.8	80	373	5.76	39.1	0.167	1.0	40	632	4.55	48.6	0.247
Raisen	2.1	80	327	7.51	58.1	0.234	1.1	40	627	17.25	50.9	0.232
Betul	2.6	80	350	8.36	53.7	0.191	1.3	40	960	10.79	54.1	0.463
Harda	0.9	40	468	19.20	37.2	0.329	0.6	40	1,076	35.70	50.6	0.528
Hoshangabad	1.8	80	470	9.22	37.2	0.289	4.2	40	855	18.54	39.3	0.331
Katni	2.0	80	375	12.36	48.9	0.244	1.5	40	640	18.31	56.9	0.289
Jabalpur	2.0	80	459	9.43	33.3	0.243	5.4	80	871	13.21	33.9	0.290
Narsimhapur	1.7	80	394	5.60	36.6	0.174	0.8	40	681	24.93	58.1	0.307
Dindori	1.2	40	278	13.49	72.0	0.186	0.1	40	637	13.91	55.8	0.287
Mandla	1.8	80	312	7.62	73.7	0.233	0.4	40	669	8.12	52.8	0.318
Chhindwara	3.0	120	462	6.46	30.9	0.234	2.8	40	859	29.71	60.1	0.408
Seoni	2.7	80	349	9.12	60.0	0.282	0.8	40	621	11.06	59.8	0.282
Balaghat	2.5	120	368	7.48	53.5	0.212	0.9	40	644	11.10	52.3	0.310
Madhya Pradesh	100.0	3,838	439	1.51	36.8	0.264	100.0	2075	904	5.62	42.7	0.392
Nandurbar	2.1	120	450	15.58	49.4	0.335	0.4	40	932	27.32	55.5	0.384
Dhule	2.4	120	488	9.80	38.2	0.255	0.9	40	727	15.63	47.9	0.243
Jalgaon	4.6	240	577	6.41	22.8	0.276	3.2	120	1,037	14.94	44.8	0.361
Buldana	3.1	160	557	6.98	31.0	0.298	1.1	80	764	7.53	52.0	0.300
Akola	1.7	80	565	4.86	23.4	0.264	1.2	80	713	15.70	59.2	0.324
Washim	1.6	80	545	7.28	23.8	0.242	0.4	40	827	17.88	35.8	0.294
Amaravati	3.0	160	434	4.42	39.5	0.207	2.3	120	718	12.77	60.9	0.277
Wardha	1.8	80	674	10.56	20.9	0.312	0.6	40	676	9.64	55.2	0.253
Nagpur	2.6	120	492	5.76	39.3	0.244	7.4	315	1,078	9.82	36.5	0.391
Bhandara	1.7	76	419	8.27	51.2	0.236	0.3	40	921	12.27	46.4	0.301
Gondiya	2.0	117	491	3.77	47.0	0.294	0.4	38	931	23.70	28.5	0.320
Gadchiroli	1.7	78	352	11.77	65.0	0.297	0.2	40	632	13.13	58.3	0.297
Chandrapur	2.2	118	671	13.03	30.1	0.374	2.1	77	892	14.32	33.3	0.272
Yavatmal	3.4	200	502	12.29	42.1	0.299	0.8	80	640	9.30	75.1	0.338
	(Continued)
106	february 28, 2009	vol XLIV No 9		Economic & Political Weekly

EPW

SPECIAL ARTICLE

Table A2: District-Wise Population Proportion, MPCE, HCR and LR-S for Rural and Urban Sector within States (Continued)
Rural		Urban
District Name	Proportional	No of Sample	MPCE	RSE of	%	Lorenz	Proportional	No of Sample	MPCE	RSE of	%	Lorenz
	Population	Households	(Rs)	MPCE	Poor	Ratio(S)	Population	Households	(Rs)	MPCE	Poor	Ratio(S)
Nanded	4.2	199	438	5.36	42.8	0.238	1.8	80	597	6.86	70.1	0.254
Hingoli	1.5	80	713	16.30	25.9	0.409	0.4	40	672	12.78	64.7	0.206
Parbhani	2.1	80	401	6.85	52.2	0.192	1.3	80	792	13.71	50.3	0.333
Jalna	2.1	120	615	27.76	35.8	0.425	0.6	40	788	40.30	64.1	0.387
Aurangabad	3.2	160	390	4.31	46.5	0.183	2.7	120	688	17.17	67.8	0.384
Nashik	4.5	240	423	4.49	48.0	0.244	4.3	237	875	8.34	50.1	0.363
Thane	4.1	192	622	11.52	40.3	0.387	14.7	754	1,281	4.82	18.8	0.321
Greater Mumbai	-						28.1	1,136	1,570	5.81	11.7	0.359
Raigarh	3.1	154	665	11.64	26.6	0.347	0.9	79	1,291	11.07	16.1	0.317
Pune	5.4	240	871	8.44	6.7	0.280	11.2	518	1,177	3.66	25.9	0.320
Ahmadnagar	5.5	240	654	8.39	10.3	0.265	1.5	119	862	13.66	51.3	0.299
Bid	3.5	160	414	6.13	55.0	0.262	1.0	40	474	20.11	80.4	0.253
Latur	3.2	160	492	6.86	53.9	0.363	1.1	80	749	13.08	63.2	0.363
Osmanabad	2.3	120	757	14.45	10.3	0.348	0.6	40	597	8.38	64.4	0.209
Solapur	4.8	240	689	5.76	11.0	0.305	3.3	160	735	7.20	49.7	0.285
Satara	4.1	200	670	4.98	4.9	0.221	1.2	40	1085	4.37	27.3	0.301
Ratnagiri	2.5	160	541	4.51	16.9	0.202	0.3	40	944	6.71	43.2	0.237
Sindhudurg	1.5	80	575	2.57	2.3	0.127	0.1	40	666	12.48	59.6	0.213
Kolhapur	4.7	240	628	6.03	8.4	0.225	2.0	120	771	6.22	45.1	0.221
Sangli	3.6	200	555	7.08	17.5	0.219	1.5	80	575	8.73	70.9	0.179
Maharashtra	100.0	5,014	568	1.75	29.6	0.308	100.0	4,993	1,148	2.41	32.1	0.372
Baragarh	4.2	159	351	5.95	61.7	0.234	1.2	40	891	33.29	44.7	0.427
Jharsuguda	1.2	40	441	39.52	58.7	0.406	3.9	39	756	33.44	57.5	0.396
Sambalpur	2.3	80	275	6.41	79.5	0.224	4.6	39	652	4.89	46.9	0.320
Deogarh	0.9	40	285	7.25	73.4	0.233	0.3	20	697	4.24	35.3	0.231
Sundargarh	3.6	160	308	7.22	69.9	0.224	13.0	80	768	8.83	28.7	0.296
Keonjhar	4.4	160	430	8.98	46.1	0.304	4.8	40	648	4.65	58.5	0.303
Mayurbhanj	6.6	200	428	5.61	52.5	0.324	3.3	40	915	17.45	30.4	0.346
Baleshwar	5.9	200	491	5.30	28.3	0.280	4.4	40	620	13.72	67.0	0.344
Bhadrak	4.1	160	534	8.65	22.9	0.288	3.5	40	993	27.44	27.3	0.332
Kendrapara	3.8	160	404	3.17	31.5	0.193	1.2	40	517	7.11	69.4	0.262
Jagatsinghpura	2.9	120	412	7.92	37.3	0.224	1.3	40	762	14.70	41.6	0.284
Cuttack	5.3	160	578	10.58	14.0	0.281	11.9	70	832	17.07	25.9	0.268
Jajpur	4.8	200	513	5.20	4.9	0.175	1.1	40	1,048	8.33	25.2	0.297
Dhenkanal	3.0	119	356	11.27	57.1	0.219	2.3	40	650	11.87	54.5	0.277
Angul	3.2	120	358	6.27	53.0	0.199	3.9	39	647	23.63	49.6	0.300
Nayagarh	2.5	120	364	7.06	47.0	0.208	1.0	20	661	10.67	35.3	0.169
Khurda	3.3	160	470	7.54	27.8	0.235	13.8	80	809	23.94	50.2	0.395
Puri	4.4	160	417	5.82	27.0	0.193	4.9	40	616	18.69	51.3	0.243
Ganjam	7.9	240	435	4.96	33.6	0.233	5.6	80	758	15.20	45.3	0.314
Gajapati	1.5	78	347	16.03	61.4	0.317	1.1	20	503	40.63	91.2	0.285
Phulbani	1.9	80	295	17.45	76.6	0.266	1.0	20	784	50.61	39.0	0.406
Boudh	1.1	40	303	9.70	70.5	0.188	0.5	20	490	0.33	85.6	0.310
Sonepur	1.5	80	350	10.29	51.3	0.233	0.7	20	529	15.06	63.8	0.288
Bolangir	4.0	160	341	6.56	66.3	0.248	2.2	40	704	15.46	48.3	0.320
Nuapara	1.8	80	315	9.96	70.1	0.230	0.7	20	527	30.24	62.3	0.253
Kalahandi	4.0	160	304	6.17	70.5	0.250	1.9	40	741	40.42	60.3	0.536
Rayagada	2.4	80	307	11.30	67.1	0.315	1.9	40	918	15.97	21.8	0.280
Nowarangpur	3.1	120	255	7.73	80.6	0.232	0.8	40	563	29.09	87.7	0.429
Koraput	2.7	120	277	13.34	74.2	0.268	2.6	40	971	55.53	61.0	0.528
Malkangiri	1.5	80	307	22.01	67.9	0.310	0.6	20	593	21.35	70.8	0.355
Orissa	100.0	3,836	399	1.68	46.9	0.282	100.0	1,187	757	5.60	44.7	0.349
Gurdaspur	9.7	240	1,017	10.03	2.3	0.330	7.6	120	1,348	13.20	7.7	0.377
Amritsar	10.5	240	711	4.06	8.7	0.221	13.8	270	917	5.44	3.8	0.223
Kapurthala	3.3	80	818	7.99	4.2	0.228	2.5	80	1,418	6.31	0.2	0.300
Jalandhar	6.6	160	951	5.98	0.9	0.249	12.3	158	1,170	10.37	5.7	0.282
Hoshiarpur	7.5	160	938	5.04	1.7	0.281	2.9	80	1,197	7.50	6.1	0.300
Nawanshehar	3.0	80	884	8.82	1.2	0.246	0.9	40	1,336	3.07	2.3	0.249
Rupnagar (Ropar)	5.5	120	969	6.18	2.4	0.278	5.2	80	1,491	37.89	9.1	0.433
				(Continued)
Economic & Political Weekly		february 28, 2009	vol XLIV No 9				107

EPW

Table A2: District-Wise Population Proportion, MPCE, HCR and LR-S for Rural and Urban Sector within States (Continued)
			Rural						Urban
District Name	Proportional	No of Sample	MPCE	RSE of	%	Lorenz	Proportional	No of Sample	MPCE	RSE of	%	Lorenz
	Population	Households	(Rs)	MPCE	Poor	Ratio(S)	Population	Households	(Rs)	MPCE	Poor	Ratio(S)
Fatehgarh Sahib	2.5	80	1,136	14.04	6.2	0.347	1.6	40	996	11.83	21.0	0.313
Ludhiana	8.4	200	831	5.20	8.9	0.271	22.6	359	1,835	30.77	4.3	0.504
Moga	4.3	117	715	6.56	25.2	0.314	1.8	40	1,452	8.14	2.2	0.278
Firozpur	7.8	197	626	4.96	17.9	0.238	5.3	110	948	13.70	7.9	0.350
Muktsar	3.6	80	571	4.76	28.3	0.179	2.2	39	928	5.84	22.8	0.288
Faridkot	2.0	79	741	13.56	23.9	0.340	1.6	39	887	13.45	14.4	0.246
Bhatinda	5.1	120	762	2.83	23.1	0.299	6.2	80	1,003	20.11	9.8	0.320
Mansa	3.6	80	709	5.33	16.6	0.262	1.2	40	984	28.78	16.5	0.285
Sangrur	8.4	200	887	4.69	6.2	0.278	6.7	120	1,130	6.89	2.8	0.276
Patiala	8.2	200	994	7.02	2.6	0.286	5.7	160	1,819	20.38	5.5	0.446
Punjab	100.0	2,433	847	1.90	9.0	0.290	100.0	1,855	1,326	10.20	6.3	0.394
Ganganagar	3.3	118	673	11.10	22.8	0.312	4.6	39	950	10.63	27.4	0.344
Hanumangarh	3.1	120	621	6.08	27.2	0.301	3.2	40	501	21.17	68.3	0.273
Bikaner	2.3	79	573	17.78	35.4	0.352	4.5	80	680	9.69	48.8	0.255
Churu	3.4	116	731	8.13	13.6	0.346	3.4	79	794	10.97	33.1	0.241
Jhunjjuna	3.6	120	756	6.56	3.6	0.232	3.3	40	779	12.02	36.7	0.273
Alwar	5.5	159	681	5.94	9.9	0.228	2.2	40	911	31.38	42.9	0.378
Bharatpur	4.8	119	600	3.63	16.6	0.214	3.4	38	855	14.68	21.5	0.256
Dholpur	1.9	80	744	12.17	8.7	0.331	1.2	39	719	10.81	38.8	0.296
Karauli	2.4	80	539	5.44	6.4	0.154	0.9	40	913	15.18	21.4	0.287
Sawai Madhopur	1.9	80	562	5.41	18.5	0.172	2.1	40	715	15.48	38.3	0.224
Dausa	2.5	119	565	10.01	19.6	0.245	1.5	40	707	8.04	47.3	0.249
Jaipur	5.9	157	617	6.08	12.5	0.230	22.2	157	1,147	37.89	42.3	0.469
Sikar	3.9	158	593	6.34	10.5	0.202	3.3	39	740	16.08	40.6	0.252
Nagaur	4.8	159	548	4.76	31.8	0.244	2.2	40	762	2.62	23.3	0.201
Jodhpur	4.5	160	537	4.50	23.9	0.220	7.2	80	1073	6.17	12.9	0.298
Jaisalmer	1.1	40	502	6.49	3.3	0.119	0.6	40	915	7.15	8.8	0.169
Barmer	4.5	160	552	2.22	13.3	0.196	1.1	40	1,279	35.62	29.9	0.395
Jalor	2.9	120	523	1.86	13.4	0.158	0.5	40	900	10.42	52.0	0.354
Sirohi	1.7	80	505	7.13	27.0	0.191	1.6	40	785	15.29	26.3	0.215
Pali	3.4	120	504	4.22	27.2	0.228	3.3	40	920	18.23	11.2	0.263
Ajmer	2.8	119	644	4.02	7.4	0.206	7.6	79	1,193	18.86	18.4	0.380
Tonk	2.4	79	494	4.70	24.8	0.189	2.0	40	790	20.54	53.3	0.324
Bundi	1.6	80	595	6.60	3.5	0.154	0.9	40	640	12.23	51.6	0.189
Bhilwara	3.6	120	632	6.97	18.5	0.260	2.8	40	798	11.85	23.7	0.254
Rajsamand	2.1	80	690	15.92	24.9	0.329	0.6	40	897	8.86	36.8	0.330
Udaipur	5.1	160	546	5.56	20.9	0.226	5.2	80	993	4.61	26.4	0.277
Dungarpur	2.6	80	535	8.16	25.2	0.244	0.7	40	1,380	33.53	3.0	0.337
Banswara	3.7	120	423	4.04	50.1	0.179	0.9	40	856	7.81	16.5	0.246
Chittaurgarh	3.3	119	640	10.28	15.5	0.256	1.3	40	904	6.31	38.7	0.354
Kota	1.7	80	541	4.47	3.9	0.133	3.8	80	1,477	23.32	8.9	0.343
Baran	1.7	80	626	8.86	6.5	0.206	0.8	40	626	9.99	45.4	0.237
Jhalawar	2.3	80	498	13.22	18.2	0.189	1.1	40	673	5.74	27.5	0.124
Rajasthan	100.0	3,541	591	1.36	18.3	0.246	100.0	1630	964	10.33	32.3	0.366
Tiruvallur	3.6	160	546	4.56	23.4	0.234	8.4	240	1,055	5.53	12.0	0.275
Chennai	-						18.1	479	1,596	5.59	8.7	0.358
Kancheepuram	3.9	160	706	17.10	20.2	0.391	6.8	240	1,121	7.75	13.8	0.324
Vellore	5.5	240	628	8.79	26.2	0.359	4.9	200	968	17.10	36.8	0.400
Dharampuri	7.5	240	749	29.88	40.3	0.510	1.4	80	976	27.77	38.5	0.415
Thiruvannamalai	4.6	200	464	5.17	43.2	0.272	1.0	80	958	12.20	38.1	0.383
Villupuram	7.0	240	476	5.18	34.8	0.225	1.2	80	859	8.98	29.9	0.296
Salem	4.8	200	460	5.47	37.4	0.258	5.6	200	965	10.14	28.4	0.375
Namakkal	2.8	120	575	7.28	18.5	0.256	1.7	80	1,086	12.68	15.2	0.308
Erode	4.1	159	562	6.15	16.9	0.229	3.1	200	1,024	9.35	18.2	0.356
Nilgiri	1.0	40	864	13.79	4.0	0.233	1.2	80	1,029	13.04	21.0	0.289
Coimbatore	4.7	160	686	5.97	12.4	0.290	10.8	439	1,085	7.22	20.2	0.349
Dindigul	3.4	160	693	11.26	10.3	0.289	1.8	120	908	8.52	35.8	0.374
Karur	1.8	80	607	10.68	10.2	0.230	0.9	40	748	9.16	26.2	0.223
Tiruchirapalli	3.6	160	531	5.51	19.8	0.213	4.1	159	1,111	9.02	22.3	0.317
	(Continued)
108	february 28, 2009	vol XLIV No 9		Economic & Political Weekly

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Table A2: District-Wise Population Proportion, MPCE, HCR and LR-S for Rural and Urban Sector within States (Continued)
Rural		Urban
District Name	Proportional	No of Sample	MPCE	RSE of	%	Lorenz	Proportional	No of Sample	MPCE	RSE of	%	Lorenz
	Population	Households	(Rs)	MPCE	Poor	Ratio(S)	Population	Households	(Rs)	MPCE	Poor	Ratio(S)
Perambalur	1.1	40	483	13.66	34.4	0.220	0.2	40	656	23.41	57.3	0.315
Ariyalur	1.7	80	506	6.70	11.0	0.210	0.1	40	802	9.48	19.9	0.226
Cuddalore	4.1	200	596	9.25	14.0	0.264	2.4	120	722	7.30	42.5	0.253
Nagapattinam	3.3	160	863	17.25	7.0	0.390	1.1	40	1,052	14.51	19.6	0.310
Tiruvarur	2.8	120	664	7.91	11.3	0.262	1.0	40	972	2.93	11.5	0.237
Thanjavur	4.3	160	700	10.67	7.5	0.284	3.2	120	992	9.26	17.0	0.296
Pudukottai	3.5	160	521	4.11	18.6	0.203	0.8	40	919	13.09	28.7	0.277
Sivgangai	2.2	120	634	14.07	13.1	0.304	1.0	30	858	7.44	26.1	0.299
Madurai	3.0	120	579	7.29	18.6	0.247	5.4	240	1,025	6.73	17.5	0.282
Theni	1.4	80	745	33.22	16.0	0.416	1.7	80	720	6.53	31.2	0.229
Virudhu Nagar	2.7	120	532	5.90	22.9	0.241	2.5	120	769	6.84	32.7	0.257
Ramnathapuram	2.6	120	466	3.54	36.7	0.237	1.0	40	618	13.13	56.2	0.245
Tuticorin	2.5	120	726	11.78	33.2	0.448	3.4	110	665	5.35	47.1	0.261
Tirunelveli	4.5	160	503	5.37	23.6	0.222	4.0	200	715	6.51	44.3	0.306
Kannyakumari	1.7	80	549	12.60	19.8	0.296	1.3	160	816	6.72	38.1	0.328
Tamil Nadu	100.0	4159	602	3.36	23.0	0.316	100.0	4137	1,080	2.33	22.5	0.356
Uttarkashi	4.7	80	745	24.32	19.5	0.303	1.3	40	1,094	0.86	4.7	0.151
Chamoli	4.3	79	593	10.76	35.7	0.179	2.1	40	912	11.26	28.9	0.286
Rudraprayag	3.9	40	670	6.55	8.7	0.134	0.1	40	1,325	7.13	5.3	0.264
Tehri Garhwal	8.1	110	501	6.28	61.2	0.191	1.1	30	1,296	6.05	1.4	0.234
Dehradun	9.2	160	677	8.24	30.3	0.252	28.7	120	1,114	17.32	40.9	0.378
Garhwal	9.2	156	620	4.71	31.8	0.213	4.8	40	725	15.64	52.6	0.255
Pithoragarh	5.9	120	554	3.77	44.3	0.219	1.9	40	824	9.17	29.5	0.230
Bageshwar	4.1	80	704	13.88	33.7	0.299	0.4	40	789	12.16	48.2	0.253
Almora	9.0	160	574	4.64	44.1	0.213	2.0	40	1,455	20.66	6.3	0.260
Champawat	3.0	40	494	27.24	72.1	0.243	1.2	40	706	15.76	64.4	0.269
Nainital	6.6	120	919	32.70	40.5	0.453	9.6	80	760	8.42	46.5	0.262
Udham Singh Nagar	15.2	160	714	14.24	45.7	0.339	21.9	80	746	9.86	48.9	0.257
Hardwar	16.6	160	615	4.19	44.4	0.251	24.8	120	1,132	7.72	19.1	0.277
Uttarakhand	100.0	1,465	647	4.49	40.7	0.281	100.0	750	978	6.00	36.5	0.323
Saharanpur	1.7	120	665	6.55	14.6	0.291	1.8	40	783	10.07	29.0	0.292
Muzaffarnagar	2.1	160	602	9.21	30.6	0.296	5.5	40	667	17.41	21.8	0.232
Bijnor	1.6	150	618	7.16	17.9	0.245	2.1	40	868	7.23	12.7	0.219
Moradabad	2.0	160	723	6.48	17.1	0.323	2.1	40	952	16.64	25.9	0.303
Rampur	1.3	80	547	7.63	31.7	0.276	1.6	40	593	4.64	42.2	0.203
MJ Phule nagar	0.9	80	675	10.93	4.7	0.232	1.7	40	628	9.15	39.8	0.227
Meerut	1.1	80	725	14.27	6.5	0.298	3.2	119	897	9.32	16.0	0.275
Baghpat	0.8	80	634	8.85	28.2	0.289	0.4	40	748	3.97	13.2	0.218
Ghaziabad	1.1	70	637	7.19	14.9	0.290	4.8	40	640	11.02	33.9	0.230
G Buddha nagar	0.6	40	689	6.72	2.6	0.224	3.7	40	1,046	16.25	4.5	0.234
Bulandshahr	1.8	119	781	4.22	14.9	0.342	2.3	39	1,053	12.48	24.7	0.363
Aligarh	1.8	118	665	14.69	19.8	0.330	2.4	39	784	6.81	28.4	0.271
Hathras	0.8	79	546	9.68	31.5	0.245	1.0	39	623	1.11	28.0	0.218
Mathura	1.1	80	489	7.47	41.0	0.275	1.7	39	518	22.10	60.9	0.296
Agra	1.5	120	598	6.39	22.1	0.250	4.9	120	1,393	37.00	29.6	0.496
Firozabad	1.0	79	609	7.50	26.5	0.294	1.6	38	817	29.77	34.1	0.357
Etah	1.8	159	516	9.53	30.8	0.292	1.0	40	796	14.22	41.9	0.360
Mainpuri	1.2	80	484	5.94	22.9	0.177	0.6	40	612	10.84	28.7	0.217
Budaun	2.2	160	472	5.04	28.8	0.193	1.2	40	640	3.52	45.8	0.283
Bareilly	1.9	160	519	7.55	30.2	0.255	3.1	80	1,121	14.24	24.2	0.381
Pilibhit	0.9	80	523	2.59	27.3	0.243	0.6	40	539	18.13	46.8	0.211
Shahjahanpur	1.5	120	439	4.15	37.4	0.184	1.2	40	822	5.02	3.3	0.136
Kheri	2.1	160	552	7.52	21.5	0.240	0.8	39	708	2.69	34.0	0.276
Sitapur	2.7	199	676	9.37	27.6	0.354	1.5	38	571	14.66	53.4	0.308
Hardoi	2.5	160	502	6.60	34.2	0.243	1.4	40	593	12.87	42.1	0.242
Unnao	1.8	160	576	10.53	24.1	0.292	1.1	40	569	19.62	50.3	0.344
Lucknow	1.1	80	616	19.94	35.6	0.368	7.3	160	1,329	23.69	14.7	0.412
Rai Bareli	1.8	160	385	3.41	54.4	0.186	1.0	39	699	11.98	40.5	0.304
Farrukhabad	1.1	80	480	8.75	28.5	0.185	0.8	40	629	9.11	43.7	0.257
Kannauj	1.0	80	464	3.74	25.4	0.150	0.5	40	504	9.43	73.3	0.356
				(Continued)
Economic & Political Weekly		february 28, 2009	vol XLIV No 9				109

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Table A2: District-Wise Population Proportion, MPCE, HCR and LR-S for Rural and Urban Sector within States (Continued) Rural Urban District Name Proportional No of Sample MPCE RSE of % Lorenz Proportional No of Sample MPCE RSE of % Lorenz Population Households (Rs) MPCE Poor Ratio(S) Population Households (Rs) MPCE Poor Ratio(S) Etawah 0.8 79 543 9.85 32.3 0.265 0.5 40 949 18.76 17.7 0.314 Auraiya 0.7 80 566 5.67 28.8 0.290 0.7 40 536 20.63 62.8 0.311 Kanpur Dehat 1.2 80 493 11.72 35.6 0.239 0.3 40 574 29.63 61.5 0.340 Kanpur Nagar 1.1 80 577 7.33 28.6 0.279 7.7 160 1224 16.04 15.0 0.386 Jalaun 0.8 80 817 27.78 15.3 0.421 0.8 40 471 17.53 68.1 0.305 Jhansi 0.9 80 589 10.74 19.8 0.276 2.5 40 743 16.84 24.1 0.251 Lalitpur 0.7 40 472 5.09 42.7 0.235 0.5 40 704 9.54 34.9 0.307 Hamirpur 0.6 40 488 21.32 44.1 0.269 0.5 40 552 6.64 54.5 0.286 Mohoba 0.5 40 500 6.46 23.2 0.231 0.3 40 610 9.43 49.1 0.266 Banda 0.8 79 431 8.82 52.8 0.238 0.7 40 436 13.13 71.6 0.290 Chitrakoot 0.6 40 348 2.32 81.5 0.123 0.3 40 773 30.90 54.0 0.331 Fatehpur 1.5 120 518 6.28 31.1 0.252 0.5 39 663 12.80 49.2 0.320 Pratapgarh 1.7 158 369 7.29 65.2 0.236 0.5 40 933 17.47 23.3 0.356 Kaushumbi 0.8 80 507 19.41 45.5 0.364 0.3 40 516 7.02 53.2 0.191 Allahabad 2.9 200 512 8.27 34.5 0.269 3.8 79 731 18.16 35.6 0.313 Bara Banki 1.9 160 687 7.38 14.2 0.251 0.4 40 869 10.87 30.3 0.312 Faizabad 1.6 80 917 14.95 25.0 0.454 0.9 40 892 29.39 37.9 0.419 Ambedkar Nagar 1.5 120 440 8.75 50.4 0.261 0.6 40 451 4.98 70.6 0.235 Sultanpur 2.0 160 516 8.08 28.5 0.228 0.3 40 828 8.98 13.2 0.213 Bahraich 1.5 120 442 9.24 43.7 0.218 0.4 40 683 14.30 36.8 0.276 Shravasthi 0.8 80 377 9.75 56.1 0.254 0.1 40 586 3.65 48.7 0.246 Balrampur 0.9 80 481 6.25 18.6 0.187 0.3 40 801 17.50 28.1 0.349 Gonda 1.9 160 444 12.28 39.0 0.256 0.4 40 651 3.69 43.9 0.283 Sidhartha nagar 1.4 120 359 6.64 66.3 0.218 0.3 40 607 10.77 36.7 0.329 Basti 1.5 120 648 14.25 23.2 0.354 0.4 40 964 12.80 36.3 0.370 S Kabir Nagar 1.0 80 364 4.51 58.0 0.178 0.3 40 525 4.22 69.3 0.258 Maharajganj 1.5 120 397 6.19 53.4 0.211 0.3 40 511 9.96 67.5 0.266 Gorakhpur 2.2 160 420 5.41 56.5 0.228 1.6 40 604 16.05 54.8 0.270 Kushi Nagar 2.2 160 417 7.00 54.8 0.239 0.5 40 564 24.54 57.1 0.289 Deoria 2.0 160 440 4.40 41.9 0.213 0.8 40 506 26.27 59.7 0.274 Azamgarh 2.7 190 509 5.75 29.5 0.244 0.8 40 903 5.90 12.3 0.260 Mau 1.0 80 476 6.06 39.5 0.221 1.0 40 557 14.59 36.3 0.182 Ballia 1.7 160 447 5.93 51.5 0.239 0.5 40 869 12.69 19.6 0.221 Jaunpur 2.7 200 529 5.96 27.9 0.254 1.5 40 939 13.35 7.7 0.244 Ghazipur 2.1 159 380 4.36 53.7 0.209 0.7 40 611 31.72 46.5 0.344 Chaundli 1.1 70 510 8.82 36.0 0.241 0.6 40 519 18.60 74.5 0.275 Varanashi 1.4 120 495 3.81 33.1 0.230 3.0 119 837 10.00 23.7 0.319 S Ravidas Nagar 0.8 80 467 6.35 30.6 0.191 0.2 39 657 11.27 45.5 0.290 Mirzapur 1.4 120 481 5.71 28.6 0.210 0.8 40 532 9.73 53.0 0.206 Sonbadra 0.6 80 447 2.63 24.8 0.136 0.8 40 623 9.39 33.3 0.204 Uttar Pradesh 100.0 7,868 533 1.23 33.3 0.286 100.0 3345 857 4.96 30.1 0.364 Darjeeling 1.8 80 644 16.43 14.7 0.267 2.0 70 913 15.16 9.6 0.329 Jalpaiguri 4.6 240 492 5.93 29.0 0.208 1.2 80 873 11.13 18.5 0.319 Kochbihar 3.5 200 598 4.80 11.2 0.197 1.2 40 847 12.79 22.4 0.249 North Dinajpur 3.7 200 456 8.97 49.0 0.260 1.6 40 763 25.99 31.0 0.309 South Dinajpur 2.4 120 442 9.43 48.9 0.238 0.6 40 1,005 2.58 9.8 0.247 Maldha 5.1 270 547 12.62 46.0 0.353 0.9 40 1,287 9.90 11.7 0.383 Murshidabad 9.1 440 428 3.99 55.9 0.233 4.9 120 891 12.33 36.7 0.387 Birdhum 5.2 240 474 4.66 39.2 0.201 2.1 40 591 18.54 30.9 0.255 Burdwan 7.7 400 606 4.80 20.3 0.255 11.2 320 824 7.55 26.1 0.331 Nadia 6.2 320 576 3.63 18.3 0.225 4.5 120 794 9.56 16.5 0.299 24-Parganas North 7.8 360 608 5.37 20.6 0.256 21.5 560 1,261 8.31 9.1 0.372 Hooghly 5.6 280 664 7.44 21.1 0.274 7.0 240 1,057 7.75 14.2 0.336 Bankura 4.9 280 582 3.71 28.5 0.265 1.7 40 630 6.11 28.3 0.245 Puruliya 4.0 200 461 4.94 31.2 0.199 0.9 40 846 10.92 36.9 0.372 Midnapur 14.0 638 654 9.22 21.8 0.329 3.8 110 991 7.24 7.4 0.276 Howrah 3.7 200 526 5.03 21.6 0.180 6.8 280 1,023 9.53 12.2 0.332 Kolkata -21.4 549 1,520 6.38 2.3 0.393 24-Parganas South 10.7 520 588 3.88 18.5 0.244 6.6 160 1,121 9.87 10.2 0.365 february 28, 2009 vol XLIV No 9 Economic & Political Weekly 110 West Bengal 100.0 4,988 562 2.02 28.4 0.270 100.0 2889 1,124 3.10 13.5 0.379

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