Regional Disparities in India

61 Pages·2014·0.77 MB·Indonesian

SPECIAL ARTICLE
Regional Disparities in India
A Moving Frontier
Sanchita Bakshi, Arunish Chawla, Mihir Shah
Among the various axes of inequality in India, regional Overview 1
disparities have acquired greater salience in recent times, The Eleventh Plan period saw states with the lowest per
capita income (PCI) register relatively higher rates of
with demands being made for special status for certain
growth. Bihar, Odisha, Uttar Pradesh, Madhya Pradesh
states on this basis. What has been completely and Rajasthan had the lowest PCI in the Eighth Plan. All of these
overlooked in the process is that regional backwardness have gradually improved their growth rates, particularly in
in India is a moving frontier with the most intense forms the Eleventh Plan. The average gross domestic product (GDP)
growth rate of these states increased from 4.6% in the Eighth
of poverty and deprivation getting increasingly
Plan to 6.76% in the Tenth Plan and 8.58% in the Eleventh Plan.
concentrated within enclaves of backwardness,
Table 1 provides growth rates of states across plan periods.
especially those inhabited by adivasi communities. This These show several convergence trends. First, the average GDP
paper reports on a recent exercise within the Planning growth rate of states with lowest PCI over the last three plans
is increasing continuously and during the Eleventh Plan, it
Commission that tries to capture this dynamic of
exceeded the average growth rates of general category states.
regional backwardness in India.
Table 1: Growth Rates in State Domestic Product across Plan Periods
(% per annum)
Sl No States/Union Territories Eighth Plan Ninth Plan Tenth Plana Eleventh Plan
1 Andhra Pradesh 5.4 4.6 6.7 8.33
2 Bihar 2.2 4.0 4.7 12.11
3 Chhattisgarh NA NA 9.2 8.44
4 Goa 8.9 5.5 7.8 9.02
5 Gujarat 12.4 4.0 10.6 9.59
6 Haryana 5.2 4.1 7.6 9.10
7 Jharkhand NA NA 11.1 7.27
8 Karnataka 6.2 7.2 7.0 8.04
9 Kerala 6.5 5.7 7.2 8.04
10 MP 6.3 4.0 4.3 8.93
11 Maharashtra 8.9 4.7 7.9 9.48
12 Odisha 2.1 5.1 9.1 8.23
13 Punjab 4.7 4.4 4.5 6.87
14 Rajasthan 7.5 3.5 5.0 7.68
15 Tamil Nadu 7.0 6.3 6.6 8.32
Annexures A (“List of districts in descending order of backwardness 16 UP 4.9 4.0 4.6 6.90
based on the index”) and B (“List of sub-districts in descending order of 17 West Bengal 6.3 6.9 6.1 7.32
backwardness based on the index”) are posted on the EPW website Special category states
along with this article. 18 Arunachal Pradesh 5.1 4.4 5.8 9.42
19 Assam 2.8 2.1 6.1 5.50
The authors gratefully acknowledge inputs received from
20 Himachal Pradesh 6.5 5.9 7.3 5.50
Montek Singh Ahluwalia, B K Chaturvedi, Siddharth Coelho-Prabhu,
Kishore Chandra Deo, Radhicka Kapoor, Jairam Ramesh, Abhijit Sen 21 J&K 5.0 5.2 5.2 4.40
and P S Vijayshankar. The authors also acknowledge the inputs of the 22 Manipur 4.6 6.4 11.6 4.60
members of the Advisory Council of the Ministry of Rural Development’s 23 Meghalaya 3.8 6.2 5.6 7.50
India Rural Development Report 2014, which will carry a modifi ed 24 Mizoram NA NA 5.9 8.70
version of this paper. 25 Nagaland 8.9 2.6 8.3 3.50
Sanchita Bakshi (sanchita.bakshi @gmail.com) is Young Professional, 26 Sikkim 5.3 8.3 7.7 12.20
Planning Commission, Government of India; Arunish Chawla (arunish. 27 Tripura 6.6 7.4 8.7 8.00
[email protected]) is Joint Secretary (Expenditure), Ministry of 28 Uttarakhand NA NA 8.8 9.30
Finance, Government of India; Mihir Shah ([email protected]) is a Average of 2002-03 to 2005-06 for all states except J&K, Mizoram, Nagaland (2002-03 to
Secretary, Samaj Pragati Sahayog. 2004-05) and Tripura (2002-03 to 2003-04).
Source: Twelfth Five-Year Plan, Volume 1, Chapter 11.
44 january 3, 2015 vol l no 1 EPW Economic & Political Weekly
SPECIAL ARTICLE
Second, these also exceeded the growth rates of all states Figure 3: Growth during 2001-10 and Income in 2001
(including special category) during the Eleventh Plan. Third, 0 10
the ratio of average growth rates of states with lowest PCI, as 001-1 y =R 3². 2=3 02.60 x9 6- 88.08
against those of fi ve highest PCI states, increased from 57% DP 2
(Eighth Plan) to 94% (Eleventh Plan). Fourth, the coeffi cient a NS 8
of variation indicating the extent of inequality in growth rates apit
amongst different states also shows an increasing convergence per c
of gross state domestic product (GSDP) growth rates over suc- ate of 6
cessive plan periods. This can be seen in Table 2 and Figure 1. wth r
o
T able 2: Convergence of GDP Growth Rates in SuEcicgehtshs iveN Pinltahn sTenth Eleventh ual gr 4
n
Plan Plan Plan Plan e an
Average GDP growth of top five states, g
Raatmioo onfg a gveernaegrea lg craotwegtho royf sbtoattetosm (% ) 8.02 5.00 7.00 9.10 Avera 23.7 3.9 4.1 4.3 4.5 4.7
five states to that of all India 0.68 0.75 0.87 1.08 PCI (log Scale)
Source: Twelfth Five-Year Plan, Volume 1, Chapter 11.
Ratio of average growth of bottom five states
to that of non-special category states 0.73 0.84 0.96 1.02
in income levels, the relationship would have been downward
Ratio of average growth rate of bottom
sloping. But as Figure 3 shows, the relationship is upward sloping.
five states with that of top five
(general category states) 0.57 0.82 0.96 0.94 States with higher initial per capita NSDP on average grew
faster, suggesting that the inequality across states is actually
Figure 1: Convergence of GDP Growth Rates during Successive Plans
increasing.2 Of course, it is important to clarify that although
1.1
1.08
Ratio of average growth rates of bottom five states to that of all-India we see no unconditional convergence (reducing dispersion of
1 0.96 income), there still might be conditional convergence. Conditional
0.9 0.94 convergence can be consistent with divergence in PCIs over a
0.82 0.87
certain period of time. It is possible that Indian states are
0.8
converging to increasingly divergent steady states.
0.7 0.75
0.68 Ratio of average growth rates of bottom five states to that of top five states Disparities in Human Development
0.6
0.57 Human development indicators show greater convergence than
0.5 incomes across states. The India Human Development Report
Eighth Plan Ninth Plan Tenth Plan Eleventh Plan 2011 (IHDR-2011), which estimates the Human Development
(1992-97) (1997-2002) (2002-07) (2007-12)
Table 3: Human Development Index (1999-2000 and 2007-08)
Figure 2: Weighted Gini Coefficient (Per capita GSDP, current prices)
State HDI HDI Change in Percentage
0.26 (2007-08) (1999-2000) HDI Change
Uttarakhand 0.49 0.339 0.151 44.54
Kerala 0.79 0.677 0.113 16.69
0.24
Assam 0.444 0.336 0.108 32.14
Jharkhand 0.376 0.268 0.108 32.14
0.22 Andhra Pradesh 0.473 0.368 0.105 28.53
North-east (excluding Assam) 0.573 0.473 0.100 21.14
MP 0.375 0.285 0.090 31.58
0.2 Tamil Nadu 0.57 0.48 0.090 18.75
Karnataka 0.519 0.432 0.087 31.64
Odisha 0.362 0.275 0.087 31.64
0.18
All India 0.467 0.387 0.080 20.72
Chhattisgarh 0.358 0.278 0.080 28.78
0.16 Bihar 0.367 0.292 0.075 25.68
1980- 1983- 1986- 1989- 1992- 1995- 1998- 2001- 2004- 2007- 2010- 2011-
81 84 87 90 93 96 99 02 05 08 11 12 Himachal Pradesh 0.652 0.581 0.071 12.22
Maharashtra 0.572 0.501 0.071 14.17
Growing Disparities in Per Capita Incomes
West Bengal 0.492 0.422 0.070 16.59
However, as Ahluwalia (2011) has shown, convergent growth J&K 0.529 0.465 0.064 13.76
rates have not translated into equalising incomes across states. UP 0.38 0.316 0.064 20.25
An update of the Ahluwalia computation is provided in Figure 2. Punjab 0.605 0.543 0.062 11.42
Gujarat 0.527 0.466 0.061 13.09
The coeffi cient of variation of per capita net state domestic
product (NSDP) has increased from around 28% in the early Haryana 0.552 0.501 0.051 10.18
Rajasthan 0.434 0.387 0.047 12.14
1980s to 36% in 2004-05 and further to 41% in 2011-12.
Goa 0.617 0.595 0.022 3.70
Figure 3 plots the growth rate of the states for the period
Delhi 0.75 0.783 -0.033 -4.21
2001-10 against the log of PCI in 2001. If there was convergence Source: India Human Development Report, 2011.
Economic & Political Weekly EPW january 3, 2015 vol l no 1 45
SPECIAL ARTICLE
Index (HDI) for states at beginning of the decade and for of our “hungriest” districts. These include East Godavari,
the year 2007-08, allows us to compare HDI across states Khammam and Mahbubnagar in Andhra Pradesh, Fatehabad
and over time. The top fi ve ranks in HDI in both years are and Hissar in Haryana, Gulbarga in Karnataka, Malappuram,
occupied by Kerala, Delhi, Himachal Pradesh, Goa and Palakkad, Thiruvananthapuram and Thrissur in Kerala and
Punjab. At the other end of the spectrum are states such as Kolhapur, Ratnagiri, Satara and Sindhudurg in Maharashtra.
Chhattisgarh, Odisha, Bihar, Madhya Pradesh, Jharkhand, Uttar This is also true of other indicators such as infant mortality
Pradesh and Rajasthan. These states have shown tremendous and literacy.
improvement in their HDI and its component indices over
time, leading to a convergence in HDI across states. The coef- A New Exercise for Identifying Backward Districts
fi cient of variation of the HDI for states in 2000 was 0.313. and Sub-Districts
This fell sharply to 0.235 in 2008. Furthermore, the IHDR-2011 It is against this backdrop that the Planning Commission
fi nds that the absolute improvements in health and education undertook a new exercise to identify the most backward
indices for low PCI states such as Chhattisgarh, Jharkhand, districts and sub-districts in the country as part of the
MP and Odisha have been better than for all India, with their restructuring of the Backward Regions Grant Fund (BRGF)
gaps with the all-India average narrowing over time. In six of mandated by the Twelfth Five-Year Plan. The Twelfth Plan
the low HDI states – Bihar, Andhra Pradesh, Chhattisgarh, has recognised that backwardness is a dynamic phenomenon
MP, Odisha and Assam – the improvement in HDI (in absolute and, therefore, the selection of districts under BRGF must be
terms) is considerably more than the national average. In updated with time. Independent evaluations have also
fact, if we look at absolute changes in HDI over the decade pointed out that district identifi cation for fund allocation
(Table 3, p 45), the conclusion that the poorer states are catch- under BRGF has not always been related to backwardness,
ing up with the national average is strengthened. and there is an urgent need to devise robust and transparent
criteria for identifi cation of backward districts. It has also
Intrastate Disparities been suggested that an index be devised that appropriately
However, the remarkable characteristic of regional disparities captures the multidimensional character of backwardness.
in India is the presence of backward areas even within states Another aspect emphasised by the Ministries of Panchayati
that have grown faster and are at relatively high income levels Raj, Tribal Affairs and Rural Development, as also by several
on average. Debroy and Bhandari (2003) list districts that fall state governments, is the need to address intra-district
in the bottom 25% under various categories such as head count inequality to ensure that the truly backward sub-districts of
ratio (HCR) poverty, food suffi ciency, infant mortality rate the state receive adequate support
(IMR) and literacy rate. On examining this dataset, we fi nd
that the most backward districts in terms of these parameters Methodology
lie not just in the undivided BIMARU (Bihar, Madhya Pradesh, Backwardness is multidimensional and there is no single vari-
Rajasthan and Uttar Pradesh) states, but also in states that able that captures all its dimensions. Many committees
have grown faster and are at a relatively high income level on in the past have assessed backwardness at the state and
average. District-level poverty estimates confi rm that the sub-state levels. Prominent among these are the Planning
poorest districts in India lie not only in undivided BIMARU Commission Study Group set up for the Fourth Plan, Wanchoo
states and Odisha, but also in rich states such as Maharashtra, Committee set up by National Development Council in 1968,
Karnataka and Tamil Nadu. The disparity across districts in National Committee on Development of Backward Areas
HCR is stark in the case of Maharashtra. At one end of the spec- 1978, Inter-Ministry Task Group on Redressing Growing
trum, there are districts with poverty HCR between 40% and Regional Imbalances in 2005 and the Raghuram Rajan Com-
48% such as Wardha, Washim, Akola, Amravati, Bhandara, mittee set up by Ministry of Finance in 2013. All these expert
Buldhana, Dhule, Gondia, Nanded and Nandurbar. At the committees used a multitude of variables for identifying
other extreme are districts such as Mumbai and Pune with backwardness at the state and sub-state levels. There has
HCR of 11.3% and 14.1%, respectively. Similarly in the case of never been a consensus on what variables should be used
Karnataka, there are districts with extremely high poverty for this purpose.
HCRs, such as Bellary, Gulbarga, Koppal and Raichur, while In the academic literature also, there has been debate on
there are also districts with extremely low percentage of poor the use of indices for the measurement of underdevelopment,
such as Kodagu and Bangalore. In Tamil Nadu, too, we fi nd a more so after the publication of HDI. This has been researched
very wide range in district-level HCR – Tiruvannamalai at for both the US and Latin American countries. Unfortunately,
60.2% and Thoothukudi at 3.3%. The fact that these three PCI is not available, through reliable sources, at the district
states have lower poverty HCRs than the national average and and sub-district levels. Most of the indicators used by expert
yet have some of the poorest districts in India is an indicator of committees for measuring backwardness at the state and
the extent of intrastate inequalities. regional levels are also not reliably available at the district
Intrastate disparities are not just in terms of income, but also and sub-district levels. This makes the task of fi nding reliable
non-income indicators such as hunger (defi ned in National backwardness variables at the district and sub-district levels
Sample Survey (NSS) terms). India’s richest states include some all the more challenging. We used the following criteria to
46 january 3, 2015 vol l no 1 EPW Economic & Political Weekly
SPECIAL ARTICLE
shortlist variables for the new backwardness index at the based reporting of these variables makes them unreliable.
district and sub-district levels: The next step was to apply the correlation criteria, and
correlation matrix for these seven variables is reproduced
(1) Data Source Criteria: This was the fi rst most important below in Table 4.
fi lter. Data collected by independent agencies using standardised
Table 4: Correlation Matrix
methodologies is more reliable than data collected by imple-
Agri Female Electricity Water- Banking SC Pop ST Pop
menting ministries or their agencies. Data collected in a Workers Literacy Sanitation
Rate
census format is likely to have only measurement errors,
Agri workers 1.000
whereas data collected purely through samples will have
Female literacy rate 0.635 1.000
both measurement and sampling errors. Further, even when
Electricity 0.522 0.560 1.000
both conditions are met, data collected by hearsay is going to
Water-sanitation 0.735 0.636 0.501 1.000
be less reliable than data collected by direct evidence, for
Banking 0.423 0.314 0.334 0.424 1.000
example, any household’s reporting of its personal assets to SC pop 0.054 0.044 0.016 0.024 -0.195 1.000
an enumerator is going to be less reliable, than say electrifi ca- ST pop 0.258 0.079 0.072 0.244 0.346 -0.614 1.000
tion status which is visible to the naked eye, particularly in an
environment where interviewees suspect their answers could The fi rst fi ve variables passed the correlation test and were
cause them a loss. selected as explanatory variables for backwardness at the
district and sub-district levels. The distribution of scheduled
(2) Sensitivity Criteria: This was the second fi lter. Any vari- caste (SC) and scheduled tribe (ST) population was found to be
able selected should be able to differentiate between backward uncorrelated with the fi ve selected variables, but they were
and less backwardness entities, whether the ranking is cardi- negatively correlated with each other, indicating their geo-
nal or ordinal. Variables which cluster too many entities graphical concentration is signifi cantly different from each
around a particular measure, or which give results perverse to other. Thus, the distribution of SC and ST population could not
common sense must be dropped. be included as structural variables.3
It is also important to identify the structural relationships,
(3) Correlation Criteria: Variables selected should ideally and what the latent and observed variables are. We took the
have correlation coeffi cients between 0.30 and 0.90, that is proportion of workers who derive their livelihood from the
neither uncorrelated, nor so tightly correlated that they virtu- agriculture sector as an indicator of the absence of economic
ally lose their independence as explanatory factors. diversifi cation, hence a proxy for economic backwardness.
To fi nd variables at the district as well as sub-district We added the total agriculture workers, that is main and
level, which satisfy all these criteria was not an easy task. marginal cultivators and labourers and then divide them by
At the state level, we have the luxury of choosing from the total number of workers in that geographical unit. The
amongst many variables, including per capita income. Unfor- HDI would have been a good choice for human development
tunately, at the district and sub-district levels, the options but the components of HDI are not reliably available at the
were limited. Fortunately, Census 2011 has been completed district and sub-district levels. However, literacy rates are
and data has been made available. We considered all the accurately measured by census right down to the village level.
variables used in the census questionnaire, and found that In the literature, female literacy rate has been found to be
following seven variables satisfy both data source and closely related to education, health and nutrition outcomes.
sensitivity criteria: Using this tradition, we use the census female literacy rate
(1) Agriculture workers as a proportion of total workers (for age 7-plus years) as a proxy for the level of human develop-
(2) Female literacy rate ment at the district and sub-district levels. Quality of infra-
(3) Households without access to electricity structure is another important dimension of backwardness at
(4) Households without drinking water and sanitary latrine any regional level. While many variables are available at the
within premises state level, there is a real paucity of reliable data sources at
(5) Households without access to banking facility the district and sub-district levels. We selected household
(6) Percentage SC population electrifi cation as the fi rst indicator of quality of infrastructure
(7) Percentage ST population. services at this level. Availability of drinking water and sani-
We also tried agricultural labourers as a percentage of total tary latrines within premises (clearly crucial for health as
workers, but this was dropped as distribution of labourers, well) was taken as the next observed variable for infrastruc-
sharecroppers and cultivators greatly varies across the ture services. In the absence of household level identifi ers,
geographical terrain (valley plains, rain-fed and mountain- simple average of the percentage availability of these services
ous areas) and these structural differences in the agricultural at the household level was taken as the indicator variable.
economy precluded its use as a reliable indicator of back- Last but not the least, fi nancial infrastructure is important,
wardness at the district and sub-district levels. Household and availability of banking services at the household level
assets also had to be dropped for the reasons mentioned was taken as the third indicator variable for the quality of
above. At the district and sub-district levels, the hearsay infrastructure services.
Economic & Political Weekly EPW january 3, 2015 vol l no 1 47
SPECIAL ARTICLE
The relationship between the observed and latent variables that an emerging characteristic of regional disparities in India
is summarised in Table 5. is the presence of underdeveloped regions even within higher
Table 5: Relationship between Latent and Observed Variable income states. We fi nd that the most backward regions in
Latent Variable Observed Variable India lie not just in the undivided BIMAROU states, but also in
Latent Variable Observed Variable states such as Gujarat, Haryana, Maharashtra and Karnataka.
Economic Diversification Agriculture workers as a % of total workers Dohad and Dang in Gujarat, Mahbubnagar, Srikakulam and
Human Development Female illiteracy rate (7+ years) Vizianagaram in Andhra Pradesh, Mewat in Haryana and
Quality of Infrastructure Households without electricity
Yadgir, Raichur and Chamarajnagar in Karnataka are all
Without drinking water, sanitation facilities
examples of districts in the advanced states that have ap-
Without access to banking services
peared in the bottom quartile of the most backward districts.
Next we addressed the question of the relative weights to be But what is even more remarkable, within relatively devel-
assigned to each variable. We tried three different formulations: oped districts, we also fi nd pockets of intense backwardness in
some of their sub-districts. And conversely, some backward
Model 1: Equal Weights Formulation: We provide equal districts can have some of the most developed sub-districts. In
weights to variables at every level. The three components fact we fi nd many districts, which include the most backward
of infrastructure services receive one-third weight each. and most developed sub-districts of India. And these districts
Similarly, the three components of backwardness, economic, can themselves be either among the most developed or
human development and infrastructure receive equal weight most backward. “Developed” districts like Thane, Vadodara,
each. To make them comparable, all the variables are normal- Ranchi, Visakhapatnam, Raipur have some of the most back-
ised by computing the classical z scores, that is value of an ward sub-districts. Conversely, “backward” districts like
observation minus the variable mean, divided by its standard Koraput, Kandhamal, Mayurbhanj have some of the most
deviation. developed sub-districts.
Model 2: Principal Component Analysis: In this formulation, Evidence of Polarisation at District Level
we do not impose any weights. We do a stepwise principal In fact we have as many as 27 districts which have sub-districts
component analysis, that is fi rst at the level of infrastructure that are both in the top 10% and bottom 10% in the list of
services and then at the level of backwardness index, for the sub-districts. Furthermore, we have 92 districts that include
three components at each level. The weights so derived are sub-districts from both the top 20% and bottom 20%
then used to construct the backwardness index using the nor- sub-districts. And fi nally, when we look at the top 30% and
malised variables as described above. bottom 30% of the sub-districts in the country, they coexist
in as many as 166 districts of India. We may call these the
Model 3: Ordinal Rankings: This is simply a variant of Model 1. “polarised” districts of India (Table 6).
We compute the infrastructure index as above, and then rank
the districts in the order of economic, human development Pockets of Tribal Concentration
and infrastructure backwardness. The rank sum produces a What we also fi nd is that the backward sub-districts of these
backwardness ranking of all the districts in the country. highly polarised districts are overwhelmingly tribal. In the
After implementing the three models, we correlated the 27 districts that have sub-districts in the top and bottom 10%,
backwardness ranking of districts across them. We got the fol-
Table 6: Extremes of Development (Polarisation) within the Same District
lowing results: Districts with Number Illustrative Names Backward Sub-districts
Rank Correlations Sub-districts in of Such of Such Districts with High Tribal Share
Districts (>20%) in These Districts
(cid:129) Model 1 vs Model 2: 0.992
Category I – 27 Adilabad, Guntur, 78 out of 102
(cid:129) Model 1 vs Model 3: 0.994 Top 10% and Visakhapatnam, Raipur, sub-districts in 27
(cid:129) Model 2 vs Model 3: 0.990 Bottom 10% Vadodara, Ranchi, Thane, districts (76%)
Koraput, Mayurbhanj,
This made us cast our vote in favour of Model 1 as more
Sundargarh, Jodhpur
complex models do not offer any advantage as shown by rank
Category II – 92 Anantapur, Karimnagar, 445 out of 483
correlations above. We chose Model 1 as it is simple and easy to Top 20% and Gaya, Patna, Korba, Raigarh, sub-districts in
understand. The rest of the work in this paper is based on Bottom 20% Valsad, Banaskantha, Kathua, 92 districts (92%)
Palamu, Gwalior, Nashik,
Model 1.
Nayagarh, Udaipur,
South 24 Parganas
Results of the Exercise Category III – 166 Krishna, Kurnool, Murshidabad, 944 out of 993
Top 30% and Bhagalpur, Nalanda, Bastar, sub-districts (95%)
The list of India’s 640 districts in descending order of back-
Bottom 30% Bilaspur, Kachchh, Surat, Palwal,
wardness, based on our index, is given in Annexure A (posted Anantnag, Bokaro, Kodarma,
on the EPW website along with this paper). Similarly the list of Bellary, Balaghat, Vidisha,
Gadchiroli, Dhule, Anugul,
India’s 5,955 sub-districts in descending order of backward-
Kendhujhar, Ajmer, Bikaner,
ness, based on our index, is given in Annexure B (also posted Allahabad, Jhansi, Malda,
on the EPW website along with this paper). Our results show Murshidabad
48 january 3, 2015 vol l no 1 EPW Economic & Political Weekly
SPECIAL ARTICLE
78 out of 102 sub-districts (76%) have a tribal percentage of yawning gap between tribal and other women. Only 18%
more than 20%. In the 92 districts with the top 20% and bot- of ST women deliver in medical facilities, much below an
tom 20% sub-districts, of the total 483 backward sub-districts all India average of 39%. When we look at the child health
445 (92%) have more than 20% tribal population. And fi nally, indicators, we fi nd that a disproportionately high number of
the 166 districts with the top and bottom 30% sub-districts, child deaths are concentrated amongst the tribals, especially
have a total of 993 backward sub-districts of which 944 (95%) children under 5. STs make up to 8-9% of the population, but
have more than 20% tribal population. account for about 14% of all under-5 deaths, and 23% of deaths
This correlation of backwardness with preponderance of in the 1-4 age group in rural areas (World Bank, Policy Paper
tribal concentration also shows up more directly in our data 2010). While it is true that we have made progress in child sur-
sets. When we begin to analyse the features of the most back- vival over the years, the fact remains that children born in
ward sub-districts of India, we fi nd a remarkable correlation tribal areas are at a much higher risk of dying that those in
with the proportion of ST population within the sub-district. other places.
As shown in Table 7 below, we fi nd the share of ST population in
the bottom 100 sub-districts Table 7: High Adivasi Concentration in Tribal Demography Reinforcing Disadvantage
of the country at 72%. At the Most Backward Sub-districts So what explains the deprivation faced by India’s tribals? We
Sub-districts ST Population
bottom 250 sub-districts, the begin to get a clue when we examine the distinctive demography
(%)
tribal percentage fi gure goes Bottom 100 sub-districts 72 of tribals in India. As shown in Shah et al (1998), tribals in
down but it is still more than Bottom 250 sub-districts 51 most districts of India (outside the north-east) form a minority
half the total population Bottom 500 sub-districts 42 of the district population. From the 2011 Census, we estimate
(about 51%). With the 500 Bottom 1,000 sub-districts 36 that in 74% of the districts, Table 8: Frequency Distribution
most backward sub-districts, Bottom 1,500 sub-districts 38 tribal population is below of Districts by Share of Tribals in
Total Population
the share of the ST population is as high as 42%. Overall, when 20% and these districts to-
Category Tribal Number of % of
we classify the most backward sub-districts into different gether account for nearly (%) Districts Districts
classes (at bottom 100, 250, 500, 1,000) we fi nd a very high 40% of total tribal popula- Nil 0 58 9
preponderance of ST population. tion in the country. Table 8 Low 1-10 347 54
Important 10-20 70 11
We need therefore to look for an explanation of this extra- also reveals that in as many
Significant 20-50 79 12
ordinary correlation. At the same time we need an explanation as 554 (86%) of the 640 dis-
High 50-80 46 7
for this amazing coexistence of development and underdeve- tricts, tribals are in a minor-
Dominant >80 40 6
lopment in such a large number of districts in the country. As ity. Tribals living in these
Total 640 100
we shall show, a remarkable convergence emerges between districts constitute 76% of Source: Census India 2011.
these two explanations. the national tribal population. Thus, as shown in Table 8,
nearly three-quarters of India’s tribals live in districts where
Indicators of Tribal Deprivation they form a minority of the population.
A variety of indicators of development clearly show that the ST In fact what is even more remarkable is that this spatial dis-
population is perhaps the most disadvantaged segment of tribution pattern of clustering and concentration applies even
India’s population. STs are at the bottom of all indicators of when the area unit of analysis
Table 9: Incidence of Tribal Population
living conditions and household amenities and assets as per becomes smaller. Sub-district in Sub-Districts
the Census 2011. Only one-tenth of the ST households have level patterns, given in Percentage of Number of % of
Tribal Population Sub-districts Sub-districts
houses with concrete roofs, one-fourth have tap water, less Table 9 below, reveal that
Nil 196 4
than a quarter have latrine facility within their premises, and within districts, tribals are
Up to 4.9 2,918 49
only half of them have electricity in their houses. The quality concentrated in a few sub-
5 to 9.9 660 11
of life in an ST household is dismal. districts. From Table 9 we 10 to 19.9 624 10
STs trail behind the rest of the society in human development can see that nearly 75% sub- 20 to 49.9 659 11
indicators, with health and education remaining a signifi cant districts in India have a 50 to 74.9 375 6
challenge. In the context of education, data from the NSS 66th tribal proportion of less than More than 75 523 9
round reveals that STs have the highest illiteracy rates in both 20%. There are only 15% Total 5,955 100
Source: Census of India 2011.
rural and urban areas (47% in rural and 22% in urban) com- sub-districts in India where
pared to other social groups. Equally worrying are the high tribal population constitutes 50% or more. This explains the
dropout rates among ST children. As per the Statistics of School essentially enclave character of the demography of tribal
Education 2010-11, 70.6% of ST boys and 71.3% of ST girls drop communities in India.
out of school before fi nishing Class X, as compared to 50.4% of
boys and 47.9% of girls from other groups. Confinement to Forest and Ecologically Difficult Areas
On other key health indicators, STs show a dismal record. Shah et al (1998) propose that this very distinctive “enclavement”
National Family Health Survey data reveals a very high (nearly of the tribes is a result of long drawn-out historical encounters
70%) prevalence of anaemia in ST women. On a range of indi- involving the subjugation of the tribes by more dominant com-
cators related to access to maternal healthcare, the data fi nds a munities. Tribes have been driven over centuries, further and
Economic & Political Weekly EPW january 3, 2015 vol l no 1 49
SPECIAL ARTICLE
further away from the alluvial planes and fertile river basins south and the neglected tribal regions in the north. The major-
in what has been described as “refuge zones” – the hills, forest, ity of the tribal population is concentrated towards the north
arid and semi-arid tracts. Whatever be the exact historical in sub-districts of Palghar, Dahanu, Vikramgadh, Talasari,
process that led to tribals occupying these regions in India Mokhada and Wada. This area is part of a contiguous tribal
(and this has indeed been a matter of debate and disagree- stretch covering districts of Dadra and Nagar Haveli, Daman
ment among scholars), the undeniable fact (as we show below) and Diu, and parts of Gujarat and Rajasthan.
is that they do inhabit some of the harshest ecological regions
of the country today. Growth Poles or Polarised Development?
We have put together data on three kinds of ecological In a large number of polarised districts, where the majority of
zones, which we fi nd important from the point of view of geo- the population in the district is non-tribal, we do not just fi nd a
graphical location of tribal communities in India. The ecologi- high concentration of tribals in the backward sub-districts, we
cal zones are: also discover evidence of this enclavement around centres of
(cid:129) Forests (where >15% of district area is under forest); growth and development. In Korba and Raigarh districts of
(cid:129) Hilly areas (1 to 3, 7, 8 and 11 of the 14 physiographic zones Chhattisgarh, Valsad of Gujarat, Paschmi Singhbhum and Purbi
classifi ed by the Forest Survey of India’s State of the Forests Singhbhum of Jharkhand, Kendujhar, Koraput and Mayurbhanj
Report, 2009); and of Odisha, we fi nd that the most advanced sub-districts are
(cid:129) Drylands (as defi ned in Shah et al 1998).4 fl anked by the most underdeveloped tribal sub-districts. Thus,
For the purpose of this analysis, we consider only those far from the ideal pattern of development expanding in con-
districts where the tribal population is at least as high as the centric circles around growth poles, we fi nd a growing diver-
national average. We call Table 10: Distribution of Tribal gence of development leading to a high degree of polarisation
these the “tribal districts” of Districts and Population by within different, even adjacent parts of the same district. In
Ecological Zones (2011)
India. In 2011, there were 257 fact, in spatial terms, the extent of divide in these districts
Ecological Region Districts % of Districts
such tribal districts (40% of Forests 193 75 manifests itself as a core-periphery contrast.
the total 640 districts of In- Dry 98 38 The most important consequence of the minority status and
dia). Table 10 gives a break- Hilly 77 30 enclavement of tribals in India has been that it has prepared
up of this tribal population Hilly and forests 72 28 the objective basis for the process of internal colonialism and
in different combinations of Dry or hilly or forests 230 90 resource emasculation that tribal areas have often been
hilly, forest and dry areas. All-India 257 100 subject to (Shah et al 1998). It could even be suggested that in
We can see that of the 257 districts with tribal concentra- many instances, the development of the larger region of which
tion, 237 are either forested, or hilly or dry and these together the tribals are a part itself becomes a source of underdevelop-
account for 80% of the total tribal population of the country. ment of the tribals. Typically tribal areas are mineral and
forest-rich and the extraction of these resources tends to be a
Regions of Contiguity one-way street with little benefi t fl owing to the tribal people.
We must also recognise that these tribal areas transcend the At the same time, the fact that many of India’s remaining large
static administrative borders of districts and states. Indeed, dam sites are found in areas of high tribal concentration, has
tribal concentration mirrors the ecological continuity of these also led to massive displacement of tribal people from their
areas, in terms of their being hilly, forested or dry. Our tribal original habitats. As stated in the Twelfth Five-Year Plan in the
sub-districts belong to a larger contiguous backward region or chapter on Land Issues:
tribal belt, that goes beyond the frozen administrative catego-
Independent estimates place the number of people displaced following
ries of state, district and sub-district. In fact mapping of these
development projects in India over the last sixty years at 60 million, and
predominantly tribal concentrated sub-districts suggests a only a third of these are estimated to have been resettled in a planned
continuum of pockets of underdevelopment that are connected manner. This is the highest number of people uprooted for develop-
to one another and to the larger development processes ment projects in the world. Most of these people are the asset-less
rural poor, marginal farmers, poor fi sher-folk and quarry workers.
around them.
Around 40% of those displaced belonged to Adivasis and 20% to Dal-
A brief illustration of this can be provided with reference
its. Given that 90% of our coal, more than 50% of most minerals and
to the districts of Gwalior, Visakhapatnam and Thane. In most prospective dam sites are in Adivasi regions, there is likely to be
Gwalior, the backward sub-district of Bhitarwar is adjoining continuing contention over issues of land acquisition in these areas,
Shivpuri district in the south. This larger area is part of the inhabited by some of our most deprived people (Planning Commission,
2013, Vol I, p 196).
contiguous Sahariya5 tribal belt that moves from Baran in
Rajasthan in the west, towards the east to Sheopur, Shivpuri, Because the tribals are surrounded by large areas of
Gwalior and Bhind across Madhya Pradesh. Similarly, in non-tribals and because they form such a small proportion of
Visakhapatnam we fi nd the backward sub-districts of Peda the district or state population, they are often unable to infl u-
Bayalu, G Madugula, Chintapalle all concentrated in the ence the mainstream political agenda. The political leader-
north, adjoining the tribal-dominated KBK (Koraput, Balangir ship that arises, for the most part, projects them only symboli-
and Kalahandi) region of Odisha. In Thane too, we fi nd wide cally and strategically in political parties. It has a limited
variations in the levels of development between the prosperous voice in effecting power sharing between the state and tribal
50 january 3, 2015 vol l no 1 EPW Economic & Political Weekly
SPECIAL ARTICLE
areas, which for many is a critical step to improve the lives of people, thanks to the complete absence of requisite health and
tribals in India.6 education facilities in their areas, they are deeply disadvan-
Before we end the presentation of our fi ndings, we need to taged in being able to benefi t from the possibilities of growth
also draw attention to the fact that while a preponderance of our in these regions. This not only points to the infi rmities and
polarised districts include tribal pockets within them, there inadequacies of the prevailing regional development strate-
are others which do not exhibit this feature. Thus, for example, gies, but also raises pertinent questions about the nature of
the districts of Gaya, Katihar and Patna in Bihar; Bilaspur and development taking place around the so-called “growth
Durg in Chhattisgarh; Banaskantha and Surendranagar in poles”. Our data reveals that development coexists with
Gujarat; Hazaribagh and Koderma in Jharkhand; Bharatpur, underdevelopment in a large number of districts in India. It
Jodhpur and Bikaner in Rajasthan; Birbhum, South 24 Parganas may even be speculated that the development and under-
in West Bengal and the district of Ujjain in Madhya Pradesh, development of subregions within the same region could be of
do not include any signifi cant pocket of tribal concentration one piece. Establishing this is beyond the scope of the present
within them. The explanation for their being polarised lies paper but forms an extremely useful line of further enquiry
somewhere other than the explanation proffered above. Indeed, and research.
we need perhaps to speak of a typology of dynamics of deve-
lopment that would adequately explain differing patterns of Conclusions
regional development and underdevelopment in India. As Hirschman and Rothschild presciently warned 40 years ago,
In the early stages of rapid economic development, when inequalities
Need for a New Theorisation
in the distribution of income among different classes, sectors and re-
This spatial dimension of uneven development in these polarised gions are apt to increase sharply, it can happen that society’s tolerance
districts calls for a re-examination of some of the conventional for such disparities will be substantial. To the extent that such toler-
ance comes into being, it accommodates, as it were, the increasing in-
theories of development planning. Mainstream regional eco-
equalities in an almost providential fashion. But this tolerance is like a
nomic planning entails a growth pole strategy designed with
credit that falls due at a certain date. It is extended in the expectation
the expectation of favourable spin-off impacts for the larger that eventually the disparities will narrow again. If this does not oc-
region. Advocates for the strategy argue that all regions do not cur, there is bound to be trouble and, perhaps, disaster (Hirschman
possess equal capacity to grow, and deliberate focusing of and Rothschild, 1973, p 545).
investment on a limited numbers of centres would satisfy a The “initial gratifi cation” caused by the hope-inducing “tun-
necessary condition for development.7 Typically, the strategy nel effect” that Hirschman and Rothschild drew attention to
involves concentration of investment at a limited number of has long since run its course in tribal India, which is increas-
locations, in an attempt to encourage economic activity and ingly gripped by a sense of alienation and disenchantment
thereby improve the standards of living within a broader region. with the national mainstream.9 There is an urgent need to re-
A growth pole is viewed as a “set of expanding industries think strategies of development for these regions with a greater
located in an urban area and inducing further development of focus on sustainable and equitable natural resource manage-
economic activity throughout its zone of infl uence” (Boudeville ment, within a framework of greater devolution of powers and
1966 as quoted in Parr 1999). It is generally assumed that early participatory development planning. A focus on the sub-dis-
development within a region would initially generate increas- trict would be a natural starting point for a new strategy for
ingly large differentials in income and development, but grad- these regions. It is heartening to see that the budget speech of
ually as the core prospers, inter-regional income inequality the union fi nance minister makes a mention of this intent in
after reaching a maximum level, would subsequently decline, the context of the BRGF, which is a step in exactly the right di-
in the manner of an inverted U, so-called Kuznets Curve.8 rection. We are also encouraged that the intensive participatory
According to Williamson (1965): planning exercises and multidisciplinary cluster facilitation
teams to support gram panchayats to implement Mahatma
Somewhere during the course of development, some or all of the dise-
Gandhi National Rural Employment Guarantee Act, recently
quilibrating tendencies diminish, causing a reversal in the pattern of
interregional inequality. Instead of divergence in interregional levels initiated by the Union Ministry of Rural Development in 2,500
of development, convergence becomes the rule, with the backward most backward sub-districts, is based on our list.
regions closing the gap between themselves and the already industri-
alised areas. The expected result is that a statistic describing regional
inequality will trace out an inverted ‘U’ over the national growth path.
Our fi ndings directly contradict this sanguine view that
available at
dominates mainstream development economics literature. It is
clear that while the growth pole could be regarded as a neces- Oxford Bookstore-Mumbai
sary condition for growth of the region, it is by no means Apeejay House
suffi cient for the purpose. Contrary to this perception of a 3, Dinshaw Vacha Road
distributive core, we fi nd that increasingly the deprivation of Mumbai 400 020
Ph: 66364477
the tribals happens around the growth pole. What is more,
given the abysmal levels of human development of the tribal
Economic & Political Weekly EPW january 3, 2015 vol l no 1 51
SPECIAL ARTICLE
Notes
Bundelkhand region of Uttar Pradesh and Boude Ville, J R (1966): Problems of Regional Eco-
Madhya Pradesh. nomic Planning (Edinburg h: Edinburgh Uni-
1 This section draws upon and extends further
the analysis contained in the chapter on 5 One of India’s most deprived, Particularly versity Press).
Regional Equality in the Twelfth Five-Year Plan. Vulnerable Tribal Groups (PVTGs), earlier Debroy, B and L Bhandari (2003): “District Level
called Primitive Tribal Groups (PTGs). Deprivation in the New Millennium”, RGICS
2 Of course, it is important to clarify that
although we see no unconditional convergence 6 For more on lack of tribal leadership, see Roy and Indicus Analytics.
(reducing dispersion of income), there still Burman (1989). Das, M B, S Kapoor and D Nikitin (2010): “A Closer
might be conditional convergence. Conditional 7 The concept of the growth pole derives directly Look at Child Mortality among Adivasis in
convergence can be consistent with divergence from the work of Perroux (1950), in whose India”, World Bank Policy Research Working
in PCIs over a certain period of time. It is pos- words, “Growth does not appear everywhere at Paper Series, available at SSRN: http://ssrn.
sible that Indian states are converging to in- the same time; it appears at points or poles of com/abstract=1565992
creasingly divergent steady states. At this growth with varying intensity; it spreads along Hirschman, A and M Rothschild (1973): “The
point, we do not have state-level data on rele- various changes and with differing overall Changing Tolerance for Income Inequality in
vant variables to estimate the growth equation effects on the whole economy”. the Course of Economic Development”, The
to take this analysis forward. 8 We say “so-called” because, as argued in Shah Quarterly Journal of Economics, Oxford Univer-
3 Although not directly relevant for the purposes (2014), the popular attribution of this trend to sity Press, Vol 87, No 4, pp 544-66.
of this paper, it should be mentioned here that Kuznets may not be correct. Perroux, F (1950): “Economic Space: Theory and
these variables were included as special com- 9 We fi nd incredible anticipation of this by Application”, The Quarterly Journal of Economics,
ponent variables in the BRGF exercise. The Hirschman and Rothschild: “For the tunnel 64, pp 89-104, Oxford University Press.
most backward among these districts were in- effect to be strong (or even to exist), the group Planning Commission (2013): Twelfth Five Year
cluded as a top-up, over and above the dis- that does not advance must be able to em- Plan, Government of India, New Delhi.
tricts/sub-districts selected through the use of pathise, at least for a while, with the group that Parr, J B (1999): “Growth Pole Strategies in Regional
structural variables. This also satisfi es a does. In other words, the two groups must not Economic Planning: A Retrospective View”,
policy requirement of the planning exercise, be divided by barriers that are or are felt as im- (Part 1, Origins and Advocacy), Urban Studies,
called the SC and ST sub-plan. passable. . . If, in segmented societies, econom- 36, pp 1195-215
4 Dryland districts are those that are located in ic advance becomes identifi ed with one partic- Roy Burman, B K (1989): Tribes In Perspective (New
Agro-Ecological Sub-Regions 1.1 to 9.2, 10.1, ular language or ethnic group or with members Delhi: Mittal Publications).
10.2, 10.3 and 12.3; have Length of Growing of one particular religion or region, then those Shah, M, D Banerji, P S Vijayshankar and P Ambasta
Period (LGP) <180 days; and have a GIA/GSA who are left out and behind are unlikely to ex- (1998): India’s Drylands: Tribal Societies and
less than 40% to 50%. In addition, we have in- perience the tunnel effect” (pp 553-54). Development Through Environmental Regenera-
cluded all districts which have been identifi ed tion (New Delhi: Oxford University Press).
under DPAP/DDP programmes except those Shah, M (2014): “Fairy Tale Capitalism”, The Indian
which get excluded on the basis of our irriga- References Express, 24 April, see: http://indianexpress.com/
tion criterion. In the case of districts which article/opinion/columns/fairy-tale-capitalism/
have only a few blocks under DPAP, we have Ahluwalia, M S (2011): “Regional Balance in Indian Williamson, J (1965): “Regional Inequality and the
included them only if more than half the blocks Planning”, accessed from http://planningcom- Process of National Development: A Descrip-
of the district are under DPAP/DDP. In addition mission.nic.in/aboutus/history/spe_region- tion of the Patterns”, Economic Development
to this, we have also included all districts in the al1206.pdf and Cultural Change, 13, pp 3-45.
Decentralisation and Local Governments
Edited by
T R R
AGHUNANDAN
The idea of devolving power to local governments was part of the larger political debate during the Indian national
movement. With strong advocates for it, like Gandhi, it resulted in constitutional changes and policy decisions in the
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The introduction discusses the milestones in the evolution of local governments post-Independence, while providing an
overview of the panchayat system, its evolution and its powers under the British, and the stand of various leaders of the
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52 january 3, 2015 vol l no 1 EPW Economic & Political Weekly
SPECIAL ARTICLE
Annexure A: List of Districts in Descending Order of Backwardness Based on the Index
Backwardness District State Backwardness District State Backwardness District State
Ranking Ranking Ranking
1 Bijapur Chhattisgarh 58 Jamtara Jharkhand 116 Sawai Madhopur Rajasthan
2 Nabarangapur Orissa 59 Balrampur Uttar Pradesh 117 Janjgir - Champa Chhattisgarh
3 Alirajpur Madhya Pradesh 60 Singrauli Madhya Pradesh 118 Bhind Madhya Pradesh
4 Dakshin Bastar 61 Surguja Chhattisgarh 119 Nagaur Rajasthan
Dantewada Chhattisgarh 62 Sidhi Madhya Pradesh 120 Tamenglong Manipur
5 Malkangiri Orissa 63 Dindori Madhya Pradesh 121 Ramban Jammu & Kashmir
6 Jhabua Madhya Pradesh 64 Ashoknagar Madhya Pradesh 122 Sitapur Uttar Pradesh
7 Narayanpur Chhattisgarh 65 Samastipur Bihar 123 Reasi Jammu & Kashmir
8 Madhepura Bihar 66 Balangir Orissa 124 Raichur Karnataka
9 Araria Bihar 67 Jashpur Chhattisgarh 125 East Nimar Madhya Pradesh
10 Purnia Bihar 68 Mewat Haryana 126 Uttar Bastar Kanker Chhattisgarh
11 Kurung Kumey Arunachal Pradesh 69 Panna Madhya Pradesh 127 Datia Madhya Pradesh
12 Katihar Bihar 70 Nandurbar Maharashtra 128 Dhubri Assam
13 Nuapada Orissa 71 Guna Madhya Pradesh 129 Vaishali Bihar
14 Godda Jharkhand 72 Kabeerdham Chhattisgarh 130 Muzaffarpur Bihar
15 Koraput Orissa 73 Dhar Madhya Pradesh 131 Ratlam Madhya Pradesh
16 Saharsa Bihar 74 Giridih Jharkhand 132 Gopalganj Bihar
17 Kishanganj Bihar 75 Baudh Orissa 133 Bundi Rajasthan
18 Sitamarhi Bihar 76 Kandhamal Orissa 134 Deoghar Jharkhand
19 Kalahandi Orissa 77 Kaushambi Uttar Pradesh 135 Umaria Madhya Pradesh
20 Rayagada Orissa 78 Jaisalmer Rajasthan 136 Bara Banki Uttar Pradesh
21 Supaul Bihar 79 Palamu Jharkhand 137 Bargarh Orissa
22 Pashchim Champaran Bihar 80 Sheikhpura Bihar 138 Mandla Madhya Pradesh
23 Pakur Jharkhand 81 West Nimar Madhya Pradesh 139 Mahoba Uttar Pradesh
24 Shrawasti Uttar Pradesh 82 Arwal Bihar 140 Kendujhar Orissa
25 Pratapgarh Rajasthan 83 Jhalawar Rajasthan 141 Mahasamund Chhattisgarh
26 Banka Bihar 84 East Kameng Arunachal Pradesh 142 Kheri Uttar Pradesh
27 Madhubani Bihar 85 Lohardaga Jharkhand 143 Fatehpur Uttar Pradesh
28 Barwani Madhya Pradesh 86 Chitrakoot Uttar Pradesh 144 Chittaurgarh Rajasthan
29 Purba Champaran Bihar 87 Chhatarpur Madhya Pradesh 145 Chamarajanagar Karnataka
30 Sheopur Madhya Pradesh 88 Lalitpur Uttar Pradesh 146 Vizianagaram Andhra Pradesh
31 Garhwa Jharkhand 89 Shajapur Madhya Pradesh 147 Neemuch Madhya Pradesh
32 Bastar Chhattisgarh 90 Puruliya West Bengal 148 The Dangs Gujarat
33 Mon Nagaland 91 Siddharthnagar Uttar Pradesh 149 Upper Subansiri Arunachal Pradesh
34 Dumka Jharkhand 92 Anjaw Arunachal Pradesh 150 Damoh Madhya Pradesh
35 Sahibganj Jharkhand 93 Karauli Rajasthan 151 Hardoi Uttar Pradesh
36 Latehar Jharkhand 94 Uttar Dinajpur West Bengal 152 Udaipur Rajasthan
37 Bahraich Uttar Pradesh 95 Mayurbhanj Orissa 153 Srikakulam Andhra Pradesh
38 Sheohar Bihar 96 Mahbubnagar Andhra Pradesh 154 Rewa Madhya Pradesh
39 Shivpuri Madhya Pradesh 97 Kanshiram Nagar Uttar Pradesh 155 Shahjahanpur Uttar Pradesh
40 Tikamgarh Madhya Pradesh 98 Nalanda Bihar 156 Saran Bihar
41 Budaun Uttar Pradesh 99 Lawngtlai Mizoram 157 Dungarpur Rajasthan
42 Pashchimi Singhbhum Jharkhand 100 Subarnapur Orissa 158 Raigarh Chhattisgarh
43 Banswara Rajasthan 101 Banda Uttar Pradesh 159 Longleng Nagaland
44 Barmer Rajasthan 102 Tonk Rajasthan 160 Baran Rajasthan
45 Khunti Jharkhand 103 Gonda Uttar Pradesh 161 Vidisha Madhya Pradesh
46 Rajgarh Madhya Pradesh 104 Kaimur (Bhabua) Bihar 162 Sirohi Rajasthan
47 Darbhanga Bihar 105 Debagarh Orissa 163 Kokrajhar Assam
48 Nawada Bihar 106 Gaya Bihar 164 Kulgam Jammu & Kashmir
49 Yadgir Karnataka 107 Morena Madhya Pradesh 165 Begusarai Bihar
50 Khagaria Bihar 108 Lakhisarai Bihar 166 Narmada Gujarat
51 Dohad Gujarat 109 Jehanabad Bihar 167 Aurangabad Bihar
52 Chatra Jharkhand 110 Changlang Arunachal Pradesh 168 Tapi Gujarat
53 Gajapati Orissa 111 Mahrajganj Uttar Pradesh 169 Chirang Assam
54 Simdega Jharkhand 112 Tirap Arunachal Pradesh 170 Bhagalpur Bihar
55 Jamui Bihar 113 Dhemaji Assam 171 Sonbhadra Uttar Pradesh
56 Gumla Jharkhand 114 Kishtwar Jammu & Kashmir 172 Shahdol Madhya Pradesh
57 Jalor Rajasthan 115 Mandsaur Madhya Pradesh 173 Dhaulpur Rajasthan
Economic & Political Weekly EPW january 3, 2015 vol L no 1 1