1 Introduction

T

The steep rise in inter-state inequality has been recognised in policy circles and the approach paper to the Eleventh Five-Year Plan adopted in 2006 forcefully articulates the urgency for “bridging the gaps”.

The strategy of inclusive growth proposed in this paper can command broad-based support only if growth is seen to demonstrably bridge d ivides and avoid exclusion or marginalisation of large segments of our population. These divides manifest themselves in various forms: between the haves and the have-nots; between rural and urban areas; between the employed and the under-unemployed; between different states, districts and communities; and finally between genders.1

The discussion about disparities or inequality is not new. The well-known Kuznets curve (Kuznets 1955) relating economic d evelopment with regional disparities predicted that during the initial stages of development, inter-regional disparities tend to increase, which broadly conforms to the experience of the developing countries, including India (Figure 1, p 225). The Kuznets curve has an inverted U-shape which implies that after an initial rise in inequalities, they narrow down. However, several recent studies in development economics point out that developing countries are characterised by various kinds of heterogeneity (socio-cultural and religious) that could defy the predictions implied by the Kuznets curve. It is increasingly being realised that the long-term economic standing of the households are also influenced by historical forces – multicultural, multiethnic and multireligion – that have shaped income levels and the possessions of households. Several studies underline the sharp differences in standards of living across geographical domains, across castes and religious communities within and across states.

june 27, 2009 vol xliv nos 26 & 27

INTER AND INTRA-STATE DISPARITIES

While a large body of literature is devoted to inter-regional disparities, intra-regional disparities have received scant attention. One of the main reasons appears to be the unavailability of comparable information on variables of interest. As a result there are very few studies which deal with intra-regional disparities. Among the earliest studies is that by Jain et al (1988). They examined disparities across regions identified by the National Sample Survey Organisation (NSSO).2 They calculated six interrelated characteristics of poverty in about 56 NSS regions and reported the COV of poverty incidence to be around 47% in 1972-73, which was nearly two and half times the COV of the HCR across major states in 1973-74 (Appendix Table 1).

Among the more recent studies, Dubey and Gangopadhyay (1998) also look at intra-state disparities in the incidence of poverty at the NSS region level. They find that there are several states in India where the incidence of poverty (calculated from NSS consumption expenditure data) across regions within a state is very high. For example, they reported that in 1993-94, among the seven NSS regions in Madhya Pradesh, poverty incidence varied from one of the lowest in the country in the western region to one of the highest in the eastern region (now Chhattisgarh).

The analysis of disparities across districts (clearly demarcated, smallest administrative units) in India is a more recent phenomenon. Using data on a set of variables ranging from the incidence of poverty to immunisation rates of children at the district level, Borooah and Dubey (2007) identified the 100 most backward districts, which fall into several states in India.3 But none of the studies have systematically analysed intra-state disparities even in larger states.

As pointed out, in this paper I have chosen three indicators at the district level to focus on intra-state disparities in five states. The rest of the paper is organised in the following fashion. In the next section, issues related to the data used in this paper have been discussed. This is followed by a discussion of inter-state disparities (across major states only) in Section 3. In Section 4, the intra-state disparities among the five states chosen in this study are discussed. The findings of the paper have been summarised in Section 5.

2 Data and Methodological Issues

Despite well-grounded criticism of monetary indicators of wellbeing in recent times, levels of income or expenditure at the household level and the proportion of population below a prespecified poverty norm continue to be the main indicators (of well-being). In this paper I have mainly used the consumption expenditure survey (CES) data collected by the NSSO during two quinquennial rounds of surveys, the 50th (1993-94) and the 61st (2004-05). The NSSO CES data have been in the public domain for some time now. Therefore, it is assumed that its scope, sampling design, and limitations are well known.4

One of the data-related issues that has relevance to this paper is the calculation of some of the characteristics at the district level from the NSSO data. Since, the district is emerging as the b asic unit for implementing and monitoring progress of these d evelopment-related programmes, it is strongly felt that there should be some basic district-level indicators of well-being. B esides being able to monitor the progress of well-being at the smallest administrative unit, the district, for informed policy

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Figure 1: Kuznets Curve

Inequality

Income per capita

i ntervention, this would also facilitate the study of intra-state disparities. As a result, there have been several attempts to provide estimates of well-being indicators, including poverty incidence, at the district level.5

For estimating indicators of well-being, some researchers use the small-area estimation method. In these studies, the sub-state or NSS region level poverty ratios are used to estimate districtlevel poverty in a given NSS region (Parikh and Radhakrishna 2002). A basic flaw in the small-area estimation technique is that the estimation is carried out using the “average values” of indicators from a large area. The estimates of characteristics for the smaller units thus have an obvious bias.

In the last few years there have been attempts to overcome this problem through directly estimating district-level poverty by increasing the sample size at the district level. Murgai et al (2003) have tried to estimate district-level poverty incidence by combining central and state sample consumption expenditure data c ollected by the NSSO and state directorate of economics and statistics.6 Similarly, Bhandari and Dubey (2003) produced district-level poverty estimates by combining employment and un employment data with consumption expenditure data of the 55th round.

While the problem of sample size is taken care of to some extent by pooling the two data sets (state and central samples) by Murgai et al (2003), the main criticism of this exercise is that the data collection agencies are different (state and central). Consequently, there could be problem of data quality bringing in what Sastry (2003) refers as “agency bias”.7 The strategy used by Bhandari and Dubey (2003) does provide a reasonable sample size for a fairly large number of districts, but the major issue with this exercise has been non-comparability of household expenditure distribution in the two surveys. Besides these, the NSSO sampling design in earlier years has been such that the urban sample was decided at the NSS region level. As a result, some districts went unrepresented in urban areas, thereby introducing bias in the district-level poverty estimate.

Suggestions from Sastry (2003) and others (Indira et al 2002) have been to use only the central sample for estimation. Sastry (2003) has examined the relative standard error (RSE) of average monthly per capita expenditure (MPCE) at the district level for 1999-2000. He reports that out of nearly 490 districts in the 1999-2000 NSS sampling frame, the RSE is within the 0-5% range for the rural population of 451 districts. However, bias remains in the estimates because of the region-level stratification of u rban areas.

INTER AND INTRA-STATE DISPARITIES

Clearly, for calculating district-level poverty, the major concern of researchers is whether the expenditure distribution of the p opulation provided by the NSS surveys will be adequate. Put d ifferently, the central issue is whether we have enough sampled households at the district level to obtain reliable estimates. A r elated problem is the constant reorganisation of districts over time, especially during the 1980s and 1990s.8 With the splitting up of districts, the sample size further shrinks and getting reliable estimates at the district level becomes that much more difficult.

The problem of urban bias has been addressed by the NSSO in its last round of CES survey (2004-05) as rural and urban areas in

Table 1: Number of NSS Regions and Districts

States	Number of NSS Regions	Total Number of Districts	Number of Districts
			Available for Calculation
Gujarat	5	24	13
Haryana	2	19	5
Kerala	2	14	10
Orissa	3	30	10
Punjab	2	17	9

Source: Tabulated from unit record CES data for 2004-05 by the author.

each of the districts in the sampling frame have been considered. In this paper, I have tried to circumvent the second problem (constant reorganisation of the districts) by merging the contiguous districts within an NSS region in a state. In most cases, the newly created districts have been merged with the districts from which they were carved out.

The total number of districts and the number of districts for which calculation of relevant characteristics was carried out is reported in Table 1 (districts in the sampling frame of the NSSO in the 2004-05 survey are listed in Appendix Table 2, p 230). It is to be mentioned here that the number of sampled households in each one of the “pseudo” districts in the last column is sufficient for calculation of poverty and other characteristics as the estimated standard e rrors are within acceptable limits in almost all the cases. The table suggests that though the incidence of poverty and other related characteristics is calculated for fewer districts than the number of districts within the states, the number of “pseudo” districts is large enough to provide useful insights into intra-state disparities.

As indicated earlier, the characteristics used for investigation of the disparities within each of the states are the incidence of poverty, the mean consumption and the widely used measure of inequality, the Gini coefficient. For calculating poverty incidence, the state and sector-wise poverty lines published by the Planning Commission have been used. In comparing consumption expenditure, the 2004-05 data has been deflated using implicit price d eflators in each state and s ector, derived from the state and s ector poverty lines (PLs).

3 Trends in Inter-State Disparities

Interstate disparities calculated from per capita state income have been investigated earlier; see for example, Mathur (1983), which pointed out the existence of the disparities but without any definite trend. One of the reasons for such findings could have been the overall inertia of the Indian economy during the 1960s and 1970s. This is in part corroborated by the COV of poverty incidence across 15 major states reported in Appendix Table 1. In addition, Debroy and Bhandari (2007) report that the consumption-based

226 Gini coefficient has been relatively stable between 1983 and 1993-94. The COV of the HCR for major states for 1973-74, 1977-78, 1983 and 1987-88 are 19.8%, 27.7%, 33.4% and 31.9%, respectively. Inter-state disparities were not just low but increased at a moderate rate between 1973-74 and 1983 and stagnated during 1983 and 1993-94.

In Table 2, the incidence and change of poverty (HCR) for two years, 1993-94 and 2004-05, along with the annualised growth of gross state domestic product (GSDP) during the same period is reported. There are several points to be noted. First, there is a large variation in the HCR across states in both years. Second, the COV of the HCR has increased by about 12 percentage points b etween 1993-94 and 2004-05. Observe that there is no change in the COV during the 1980s. Third, the inter-state variation in a nnualised GSDP growth is over 21% but the COV of decline in poverty incidence is close to 55%. Fourth, the large difference in the COV of reduction in poverty and annualised GSDP seems to indicate that GSDP growth is very poorly associated with poverty reduction, which is confirmed by the extremely low correlation between the two (-0.06).

The substantial increase in the COV of the HCR requires a much closer examination which is beyond the scope of this paper. But it could be mentioned in passing that inequalities have started playing a role now and the apparent disconnect between GSDP growth and poverty reduction could be explained by looking at the level and change in the Gini coefficient.

4 Intra-State Disparities

The brief analysis of inter-state disparities in the last section suggests that after being stagnant for over a decade during the 1980s, inter-regional disparities in poverty incidence have risen much faster than observed during the 1970s. In the rest of this paper, I examine intra-state disparities among the states in the sample, Gujarat, Haryana, Kerala, Orissa and Punjab.

As relatively large geographical units, these states have different levels of development that have been conditioned by their

Figure 2: Gini Coefficient and Per Capita GSDP for 2004-05 (Large States) The mean PCTE ranges from Rs 423 in Rewari to Rs 632 in

F aridabad. But the incidence of poverty ranges from 5.1% in the Ambala group to over 18% in the Rewari group. Despite having the highest mean PCTE, the poverty ratio in Faridabad is higher Per capita GSDP, 2004-05 Source: Debroy and Bhandari (2007). .4 .3 .25 ●Asm ●Bih ● Raj ●JK ●●WB ●AP ●Kar ●TN ●

Gini (Household Consumption Expenditure), 2004-05

●Ori ●MP ●HP ●Guj ●Har ●Mah●Ker

| | |

10000 20000 30000

i nequality indicated by the Gini coefficient (0.431).

The COV of the HCR is more than two times the COV of the real

PCTE. The COV of the HCR increased by about 10 percentage points over 1993-94, indicating that there is an increase in disparities in the incidence of poverty. In 1993-94, poverty incidence was the highest in Gurgaon and Faridabad at close to 34%, which declined by 18.7 percentage points. The largest decline in poverty is for the group of districts clubbed along with Ambala. Though Faridabad and Gurgaon have the highest PCTE, inequality in

agro-climatic conditions and locations. While Haryana and P unjab in the north are leaders in improving the well-being of the population with one of the highest levels of income among the major (large) states in India, Kerala leads the list in achievements other than income indicators of development, namely, health and education. Between 1973-74 and 2004-05, Kerala improved its rank in poverty incidence from 11 to 4 (Appendix Table 1). Gujarat is considered the most investment friendly state in recent times and has attracted large investments. Its relative ranking in the HCR has stagnated at around 4 in the same period (Appendix T able 1). Orissa is at the bottom in most of the development i ndicators and considered the poorest among the 17 major states, ranked 15th throughout except in 1993-94, when its rank was 14.

Improvement in the well-being of households is quite varied. For example, with similar levels of poverty incidence in 1993-94 and identical GSDP growth, Gujarat and Haryana show quite different

results as far as decline in poverty is concerned. Similarly, Orissa

and Punjab had similar GSDP growth and reduction in p overty dur

ing 1993-94 and 2004-05 but the poverty levels in these states are

the highest and lowest, respectively, among the major states.

An analysis of intra-regional disparities could, therefore, provide some insight into which areas in each of these states are l agging. In this section, intra-regional disparities are reported separately for each one of the five states in our sample.

4.1 Haryana and Punjab

These two states in northern India are considered the most developed. They are typically a textbook case of economic development. Initial public investment in agriculture (irrigation and i nfrastructure) improved yields and incomes, which created a booming consumer market. Since 1973-74, both the states have had lowest levels of incidence of poverty, except in 1993-94 when Haryana slipped to the fourth rank only to regain its position in 2004-05.

4.1.1 Inter-District Inequalities in Haryana

Table 3 (p 228) reports three indicators, the real (at 1993-94 prices) per capita total expenditure (PCTE), the Gini coefficient calculated from real PCTE and the incidence of poverty in five r egions within Haryana. The names of the districts appearing in column 1, along with those in column 2, form the five regions for which the three indicators have been calculated.

these districts is also the highest. The level of vertical inequality as captured by the Gini coefficient of PCTE puts these two dis-tricts (Gurgaon and Faridabad) of Haryana among the districts that have most unequal distribution. 4.1.2 Inter-District Inequalities in Punjab In Table 4 (p 228), the three indicators, the mean PCTE, the Gini c oefficient and the HCR are reported for nine pseudo districts in Punjab. Unlike Haryana where the sample sizes at the district level were relatively smaller and fewer pseudo districts could be formed combining contiguous districts, there are five districts in Punjab where the sample size has been found to be sufficient for calculation of these characteristics. From Table 4, it becomes apparent that the districts in Haryana and Punjab are not too different as far as disparities in mean PCTE and inequality measures are concerned. But it is in the case of HCR that Punjab turns out to be the most heterogeneous. The HCR ranges from 3.1% in Kapurthala to 19.7% in Firozpur. The only other district in the state that has an HCR in double digits is B hatinda (18.3%). Thus, the south-western region has the highest incidence of poverty within the state. In this region (Bhatinda and Firozpur), poverty incidence actu-ally increased significantly in 2004-05 compared to 1993-94. The other feature of intra-regional disparity in Punjab is the ex-istence of a very high level of inequality. Since the Gini coefficient has been calculated from CES data, the Gini at 0.469 could be one of the highest in the country. It is to be noted that inter-country comparison of income based on Gini coefficients suggest that Figure 3: Change in Gini Coefficient and Per Capita GSDP (Large States) (between 1993-94 and 2004-05)● Asm ●UP ●MP ●Ori ●Pun ●TN ●Bih ●JK ● Raj ●HP Kar●●Har ●Guj .o8 .o6 .o4 .o2 0 –.02 Change in per capita GDP Source: Debroy and Bhandari (2007).

Change in Gini

●Mah ●AP ●Ker

| | | | |

3 4 5 6 7

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June 27, 2009 vol xliv nos 26 & 27

INTER AND INTRA-STATE DISPARITIES

Table 3: Mean PCTE, Gini and HCR among Districts in Haryana in 2004-05 observed. Compared to 1993-94, the COV of the HCR increased by

02 Ambala 01 Panchkula, 03 Yamunanagar, substantially higher than the all-India average while four districts 572 5.4

04 Kurukshetra, 05 Kaithal 600 0.319 had an HCR less than 10%. At 3.5%, Bhavnagar had the lowest HCR 06 Karnal 07 Panipat, 08 Sonipat,

14 Rohtak, 15 Jhajjar 680 472 0.325 15.7 19 Faridabad 18 Gurgaon 480 632 0.431 15.2 served in the mean PCTE, the Gini and the HCR are among the larg17 Rewari 13 Bhiwani, 16 Mahendragarh 400 423 0.315 18.1 est. Thus, among the relatively better-off states, Gujarat has a fairly 09 Jind 10 Fatehabad, 11 Sirsa, 12 Hisar 560 453 0.313 14.7

4.3 Kerala

District Code Merged District(s) Sample Mean Gini

gregate level, there has been constant improvement in the rank

and Name Code and Name Size PCTE Coefficient HCR

ing of Kerala in the case of poverty incidence. It was ranked 11

01 Gurdaspur 360 620 0.359 3.8

position. There were a total of 14 districts in Kerala in the NSSO

04 Jalandhar 318 585 0.273 3.2

sampling frame in 2004-05. With limited reorganisation of dis

tricts where merging of neighbouring districts was not required.

14 Bathinda 13 Faridkot, 15 Mansa 438 453 0.309 18.3

The incidence of poverty ranges from 4.4% in Thiruvanan

Punjab 4,288 558 0.347 8.1

thapuram to about 30% in Kasaragod (Table 6, p 229). Of 10 dis-

From Table 2, it can be noted that even with modest growth in

the GSDP (annualised growth of 5.7%) Kerala appears to have Gujarat in western India is rated as the most investor-friendly state done well in improving income and substantially reducing povin India. In NSS surveys, it is divided into five regions. This is the erty. But recent growth in income has come at a cost – it has inonly state in India where a number of districts fall into two or more creased intra-regional disparities. Another interesting feature of NSS regions based on cropping, agro-climatic conditions and de-inequality in Kerala is that the level of consumption inequality is mographic patterns. There are large disparities in the state’s agro-highest among the five states considered in this paper. climatic regions. Consequently, the creation of pseudo districts has

District Code Merged District(s) Sample Mean Gini states, I have stuck to contiguity of the districts in combining these and Name Code and Name Size PCTE Coefficient HCR

even if a district falls into two or more NSS regions. From a total of 10 Jamnagar 09 Rajkot 440 477 0.237 8.1 08 Surendranagar 01 Kachch 230 322 0.249 23.2

14 Bhavnagar 231 417 0.229 3.5 this paper. There are five d istricts with a large urban population, 02 Banas Kantha 03 Patan 280 271 0.229 29.5

which have stood alone in all the calculations in this paper. 06 Gandhinagar 04 Mahasena, 05 Sabarkantha 437 431 0.367 18.7

Table 5 reports the mean PCTE, the Gini coefficient and the HCR 17 Panchmahals 18 Dohad 320 286 0.281 38.1 07 Ahmedabad 429 592 0.318 11.3

19 Vadodara 310 577 0.379 6.8 across districts in all three indicators is not surprising. The highest 21 Bharuch 20 Narmada 200 404 0.335 18.8

level of PCTE is in Ahmedabad and it is about 2.2 times higher than 22 Surat 438 531 0.306 13.6

the figure estimated for Banas Kantha district. Compared to two of 25 Valsad 23 The Dangs, 24 Navasari 280 497 0.292 10.1 Gujarat 4,275 434 0.328 17.0

COV 25.7 19.1 65.2 Punjab. However, it is in the case of the HCR that large variation is Source: As in Table 1.

228 june 27, 2009 vol xliv nos 26 & 27

4.4 Orissa

Orissa is one of the poorest states in India and on a few indicators it is comparable to some of the most underdeveloped areas in the world. With a persistently high level of poverty for the last few decades, Orissa is considered one of the toughest challenges for development economists. As apparent from Appendix Table 1, it has remained the poorest state in India for most of the 30 years since 1973-74. During an era of unprecedented economic growth and modest annualised growth of GSDP, the slowest reduction in poverty in more than 10 years (between 1993-94 and 2004-05) was seen in Orissa (only 2.2 percentage points). During the same period, the annualised GSDP growth in Orissa at 4.5% per annum was marginally higher than in Punjab.

With this background information, one would expect a large inter-state variation in the PCTE and the Gini coefficient. But contrary to expectations, Table 7 suggests that the mean PCTE ranges from Rs 195 in Phulabani to Rs 319 in Baleshwar, that is, the highest PCTE in the state is only 1.6 times compared to the lowest. In the other four states included in this study, the ratio of the mean PCTE between the poorest and richest districts is more than 2. It is, therefore, not surprising that the COV of PCTE in Orissa is among the lowest at about 17%. The COV of the Gini coefficient is fairly low, only marginally higher than the COV of the Gini c oefficient in Kerala.

As said earlier, Orissa has had the highest level of poverty i ncidence since 1973-74. It ranged from close to 21% in Cuttack to about 72% in Koraput in 2004-05. The COV of the HCR is among the lowest in Orissa at 35.5%, which has increased marginally by about 6 percentage points from its level in 1993-94. Thus, Orissa is characterised by low levels of regional disparities in all the measures used here but continues to have the highest levels of deprivation and poverty among the five states considered in this paper.

5 Conclusions and Policy Implications

In this paper I have considered intra-state disparities in Gujarat, Haryana, Kerala, Orissa and Punjab. For investigating them, three indicators, the mean PCTE, the Gini coefficient and the i ncidence of poverty, have been considered. Taken together, these three variables indicate the level of well-being that is a result of the inter-play of a large number of economic and policy variables. One of the advantages of the indicators used in this paper for

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i ntra-state and inter-state comparisons is that all three have been calculated from the same data source, thus minimising biases.

A comparison of the three characteristics at the state level throws up a big surprise. Among the five states considered in this paper, Kerala has the highest mean PCTE, even higher than the most prosperous states in India. In the rural sector, the mean PCTE in Kerala was highest in 2004-05 among the major states. However, this comes at a price – the highest vertical inequality is also observed in Kerala as indicated by the consumption e xpenditure-based Gini coefficient.

On intra-state inequalities, the picture that emerges is that the highest level of disparities in poverty incidence is in Punjab, followed by Gujarat and Kerala. Haryana has the least disparities, only marginally lower than that in Orissa. Compared to 1993-94, disparities in poverty incidence have increased in all the states but the highest increase is in Kerala where the COV of the HCR has increased by close to 40 percentage points. The second highest increase is seen in Punjab, by 33 percentage points. The COV in Gujarat increased by about 25 percentage points. The lowest i ncrease was in Orissa (6%), which was lower than the increase in Haryana (10%).

The increase in the COV observed in 2004-05 is accompanied by a substantial increase in the COV of the mean PCTE compared to 1993-94. In 1993-94, the COV of the mean PCTE across d istricts in Gujarat, Haryana, Kerala, Orissa and Punjab was 13.4%, 11.8%, 14.8%, 13.8% and 13.6%, respectively. As apparent from these n umbers, the level of intra-regional disparities was similar in all the states in the sample. However, over the last 10 years, along with an increase in the GSDP, the mean PCTE in most cases has

Table 7: Mean PCTE, Gini and HCR among Districts in Orissa

Source: As in Table 1.

i ncreased but the rates have been different among the districts in each state. As a result, the COV of the mean PCTE increased in all the states but the quantum of increase is modest.

In sum, the analysis on three indicators carried out in this p aper suggests that intra-state disparities, which were modest in 1993-94, have increased. The highest increase in disparity is found in case of the HCR. Inter-district disparity in real mean PCTE in 2004-05 has also increased in each of the states considered in this paper but the quantum of increase is modest, in the range of 6 to 12 percentage points.

INTER AND INTRA-STATE DISPARITIES

1 Towards Faster and More Inclusive Growth: An Approach to the Eleventh Five-Year Plan, Plan-

4 Details about the NSSO CES data are available in

the reports brought out by the Ministry of Statistics and Programme Implementation, Govern-Punjab 28.2 1 19.3 1 16.2 1 13.2 1 11.8 1 8.1 1 ment of India.

Haryana 35.4 2 29.6 2 21.4 2 16.6 2 25.1 4 13.6 2

Andhra Pradesh 48.9 5 39.3 4 28.9 3 25.9 3 22.2 2 14.8 3

district level indicators on hunger, poverty, school enrolment, child mortality and immunisation. Kerala 59.8 11 52.2 8 40.4 7 31.8 5 25.4 5 14.8 4 Another source for district level information is

Gujarat 48.2 4 41.2 5 32.8 4 31.5 4 24.2 3 17.0 5

Assam 51.2 6 57.2 11 40.5 8 36.2 7 40.9 12 20.4 6

(2001) report some selected district level agricul-Rajasthan 46.1 3 37.4 3 34.5 5 35.2 6 27.4 6 21.4 7 tural outputs.

Tamil Nadu 54.9 9 54.8 9 51.7 12 43.4 12 35.0 8 22.8 8

6 The consumption expenditure data collected by the NSSO is known as the central sample. There is Karnataka 54.5 8 48.8 6 38.2 6 37.5 8 33.2 7 24.3 9

at least a matching sample size (number of house-West Bengal 63.4 14 60.5 12 54.9 13 44.7 13 35.7 9 24.7 10 holds) that each state or union territory collects

Maharashtra 53.2 7 55.9 10 43.4 9 40.4 9 36.9 10 30.6 11

independently in each round of NSS survey. It is, therefore, possible to pool the data from two Uttar Pradesh* 57.1 10 49.1 7 47.1 10 41.5 10 40.9 11 33.0 12 sources. But to the best of our knowledge, com-

Madhya Pradesh* 61.8 12 61.8 14 49.8 11 43.1 11 42.6 13 38.9 13

bining state and central sample data to calculate district level HCR has been attempted in only two Bihar* 61.9 13 61.6 13 62.2 14 52.1 14 55.0 15 42.0 14

states, Karnataka and Uttar Pradesh. Most of the Orissa 66.2 15 70.1 15 65.3 15 55.6 15 48.6 14 46.6 15 states do not even validate the state sample data.

Mean 52.7 49.2 41.8 36.6 33.6 24.9

SD 10.4 13.6 13.9 11.7 11.2 11.2

COV 19.8 27.7 33.3 31.9 33.3 45.2

*These are undivided states, they include Uttarakhand, Chhattisgarh and Jharkhand, respectively.

Source: Planning Commission, Government of India (1997, 2007).

Census, 17 new districts were carved out from the existing districts in Orissa. Appendix Table 2: Name and Code of Districts in the States 9 UNDP (2006).

District Name Code District Name Code District Name Code District Name Code

Gujarat Punjab Jind 9 Jharsuguda 2

Kachch 1 Fatehabad 10 Sambalpur 3

Bhalla, G S and Gurmail Singh (2001): Indian Agriculture: Four Decades of Development (Sage Publica-Bans Kantha 2 Amritsar 2 Sirsa 11 Debagarh 4 tion: New Delhi). Kapurthala 3 Sundargarh 5

Patan 3 Hisar 12

India”, GCIS Working Paper, Rajiv Gandhi Insti- Hoshiarpur 5 Rohtak 14

tute for Contemporary Studies, New Delhi. Nawanshahr 6 Baleshwar 8

Borooah, V and Amaresh Dubey (2007): “Measuring Bhadrak 9

Children in the Districts of India”, Margin – The Fatehgarh Sahib 8 Rewari 17 Journal of Applied Economic Research, Vol 1 (4), Surendranagar 8 Jagatsinghapur 11

403-440. Rajkot 9 Cuttack 12

Debroy, Bibek and Laveesh Bhandari (2004): “District Faridabad 19

Level Deprivation in the New Millennium” (New Firozpur 11 Kerala

Delhi: Rajiv Gandhi Institute for Contemporary Porbandar 11 Kasaragod 1

Studies). Anugul 15

Junagadh 12 Kannur 2

– (2007): “Exclusive Growth–Inclusive Inequality?”, Faridkot 13 Nayagarh 16 FISME Occasional Paper 1, September. Amreli 13 Bathinda 14 Wyanad 3

Bhavnagar 14 Mansa 15 Kozhikode 4

Analytical Report, No 1, February.Anand 15 Sangrur 16 Ganjam 19

– (2006): “Towards Faster and More Inclusive Palakkad 6

Kheda 16 Patiala 17 Gajapati 20

– (2007): “Poverty Estimates for 2004-05”, Planning Dohad 18 Baudh 22 Commission, Press Information Bureau, March. Ambala 2 Idukki 9 Sonapur 23

Indira, A, M Rajeev and V Vyasulu (2002): “Estimation Yamunanagar 3 Kottayam 10 Balangir 24 of District Income and Poverty in Indian States”, Narmada 20

Jain, L R, K Sundaram and S D Tendulkar (1988): Kaithal 5 Pathanamthitta 12 Kalahandi 26 “D imensions of Rural Poverty: An Inter-regional Surat 22

The Dangs 23 Nabarangapur 28

ber, 2395-2408. Panipat 7 Thiruvananthapuram 14 Kuznets, S (1955): “Economic Growth and Income Navsari 24 Sonipat 8 Orissa Koraput 29 I nequality”, American Economic Review, Vol 45. Bargarh 1 Malkangiri 30 Mathur, Ashok (1983): “Regional Development and Income Disparities in India: A Sectoral Analysis”, Source: GoI (2004).

230 june 27, 2009 vol xliv nos 26 & 27