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Levels of Living and Poverty Patterns: A District-Wise Analysis for India

Most of the contemporary studies of level of living and poverty concentrate only on state-level averages. In view of the growing divergence both between and within the states, disaggregated studies are necessary for accurate identification of the critical areas calling for policy intervention. In the National Sample Survey Organisation's Consumer Expenditure Survey held in 2004-05, the sample design had taken districts as strata in both the rural and urban sectors, which makes it possible to get unbiased estimates of parameters at the district level. This paper presents a profile of levels of living, poverty and inequality for all the districts of the 20 major states of India. An attempt has also been made to map poverty in the districts to examine their spatial disparity within and across the states.

SPECIAL ARTICLE

Levels of Living and Poverty Patterns: A District-Wise Analysis for India

Siladitya Chaudhuri, Nivedita Gupta

Most of the contemporary studies of level of living and poverty concentrate only on state-level averages. In view of the growing divergence both between and within the states, disaggregated studies are necessary for accurate identification of the critical areas calling for policy intervention. In the National Sample Survey Organisation’s Consumer Expenditure Survey held in 2004-05, the sample design had taken districts as strata in both the rural and urban sectors, which makes it possible to get unbiased estimates of parameters at the district level. This paper presents a profile of levels of living, poverty and inequality for all the districts of the 20 major states of India. An attempt has also been made to map poverty in the districts to examine their spatial disparity within and across the states.

The authors are grateful to an anonymous referee of this journal for comments on an earlier version. The organisation to which the authors belong is in no way responsible for the observations and comments drawn in this paper.

Siladitya Chaudhuri (siladityachaudhuri@yahoo.com) and Nivedita Gupta (nivedita_03@yahoo.co.in) are working in the National Sample Survey Organisation.

N
umerous studies have been made in recent years on the trends of poverty, inequality and level of living in Indian states during the 1990s. Some have highlighted the reduction in poverty (Sundaram and Tendulkar 2003; Bhanumurthy and Mitra 2004) while some others have expressed anguish over the rising economic inequality (Deaton and Dreze 2002; Sen and Himanshu 2004; Krishna 2004).

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.
  • 4.3 Mapping of Poverty in Indian Districts

    The district-level HCR, an absolute measure comparable across the country irrespective of any exogenous influences, has been portrayed graphically here to summarise the performances of the districts in terms of the most tangible measurement of pov-From the Table 7R (p 99) we observe the following: erty. This exercise enables easy identification of critically poor (a) While in rural India at the state level the average MPCE of the pockets, that demand more focused attention. It also depicts best state (Kerala) was 2.5 times that of the worst (Orissa), within the variability in the poverty ratio across state divergence in the level of l iving was no

    Table 5: Frequency of Districts by RSE Level

    the districts. less alarming. In Chhattisgarh, Gujarat and

    RSE Level (%) Frequency of Districts

    The critically high HCR districts were Rural Urban Karnataka, the average MPCE for the best dis

    concentrated in states like Orissa, Chhattisgarh, Jharkhand, Bihar, Madhya Pradesh and eastern Uttar Pradesh. On the other hand, almost zero-poverty districts were mainly from HP, J&K, Gujarat and Assam. Again, in the rural sector, more than half of about 500 districts had HCR of 30% or less, while in 16% of districts HCR was 50% or more.

    In case of the urban sector, high poverty districts were clustered in the states of Orissa, Chhattisgarh, Karnataka, Maharashtra, Bihar, etc. Low urban poverty districts were found

    < 5 129(25.4) 59(11.6)

    5-10 262(51.6) 148(29.0)

    10-20 98(19.3) 213(41.8)

    20 and above 19(3.7) 90(17.6)

    Total 508 510

    The figures in brackets indicate percentage occurrences.

    Table 6: Percentage Distribution of Districts over Different HCR Classes

    % Poor (HCR) Percentage of Districts

    Rural Urban

    Less than 1.0 2.5 3.2

    1.0-10.0 17.4 15.5

    10.0-30.0 39.8 29.1

    30.0-50.0 24.4 30.0

    50.0-75.0 13.8 20.0

    trict was almost thrice that of the worst. The gap between best and worst districts was n arrow only in case of two eastern states, i e, Assam and West Bengal.

    (b) Among all the rural districts of the 20 major states of the country, Gurgaon, H aryana (Rs 1,559) had the highest average level of living while Dantewada, Chhattisgarh (Rs 218) had the lowest. The gap between the two was too wide even in spite of interstate price differences.

    (c)In Chhattisgarh, Orissa, MP, Jharkhand and Bihar there were districts, some of

    mainly in states like Haryana, HP, J&K and 75.0-100.0 2.1 2.3 which had average MPCE around Rs 300 or

    Punjab in the north and Assam in the east. Also, the percentage of urban districts in the higher ranges of HCR was always greater than that in its rural counterpart and in about 22% of districts urban HCR was more than 50%. This highlights the grim urban poverty scenario that needs to be reckoned with due importance.

    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
  • Economic & Political Weekly

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    february 28, 2009 vol XLIV No 9

    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.

  • (1) Ogive analysis was made to graphically represent the interstate disparity in distribution against a fixed set of MPCE percentile classes as also to indicate that some of the states have very little representation in the extreme end all-India MPCE classes. At sub-state level, the problem gets aggravated with the district-level distributions being farther away from the central ogive. There were 425 instances in rural India and 558 in the urban, where one or more of the all-India MPCE percentile classes did not have any representation from a particular district. The problem can be addressed through the use of state-level percentile classes. This paper suggests that in addition to the all-India MPCE percentile classes, useful for country level analysis and interstate comparisons, state-level MPCE percentile classes be used for more realistic analysis at the state and sub-state level. Although there is no precondition that state-level MPCE classes would have to be identical to the all-India MPCE classes i e, at 5%, 10%, 20% … 80%, 90%, 95% annexure, etc, it was only for better comparability with the official results that an identical composition of state level percentile classes has been made.
  • (2) In rural India at the state-level, there has been an indication of a trade-off between prosperity and inequality with rich states having high level of inequality as against a low Lorenz ratio in the poor states. But the situation is a lot more complicated in the urban sector where many of the poor states also suffer from high level of inequality.
  • (3) In urban India, in about half of the states, RSE of average MPCE estimate at state-level was more than 5% while in the rural
  • sector almost all the states had RSE less than 5% or so.

  • (4) There has been an intense rural-urban divide even at the district-level but the pattern has not been very predictable in either of the sectors. A district with excellent indicators in terms of any of the parameters under study in one sector often failed to perform at the same level in the other sector.
  • (5) From the district-level estimates of average level of living, poverty and inequality we find that the range of disparity at the sub-state level within a state was often more serious than the disparity between the states. Thus there was wide spatial disparity in the level of living of the Indian districts, both within and across the states.
  • (6) In both the sectors, in almost all the states, there were some districts with higher within district inequality as compared to the level of inequality at the state-level.
  • (7) The mapping of poverty across the districts of 20 major states enables easy identification of the pockets of critical poverty which require urgent focused attention. This also adequately reveals the grim urban poverty scenario in spite of high average urban level of living.
  • (8) There was adequate evidence of concentration of affluence or poverty in certain pockets of the country depicting polarisation in the level of living across the districts within the states.
  • (9) For about a quarter of the rural districts and in more than half of the urban districts the RSE of average MPCE was higher than 10%. But that need not deter us from using these sub-state level natural estimates adequately supported by the sample design, for in-depth analysis of within state variability. Further effective improvement can be made in these estimates through “model assisted” as well as “model independent” procedures. Developing the greg using these initial estimates and their RSE is a simple and viable option.
  • (10) In the NSS 2004-05 survey, in a good number of cases, low sample size resulted in high RSE of the district-level e stimates especially in the urban sector. The number of sample observations needs to be suitably augmented in the future s urveys, to arrive at more reliable and conclusive district- level estimates.
  • 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

    EPW
    Economic & Political Weekly

    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
    EPW
    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
    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)
    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
    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)
    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
    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)
    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
    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)
    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
    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) 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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