Final Project Report submitted to the Ministry of Statistics & Programme Implementation,

Government of India, New Delhi

By

Buddhadeb Ghosh Economic Research Unit Indian Statistical Institute 203, B. T. Road, Kolkata

E: buddhadeb_ghosh@yahoo.com Mobile: 09433164711

March 2010

Contents Pages List of Tables, Figures, Appendices, Charts & Maps iii-x 1. The Background: Idea of Social Development Index & Basic

Geographical Unit of Analysis 1-16 2. Social Development Index: Data & Methodology 17-61

3. Inter-Temporal Transition of Districts between 1991 and 2001 62-98 4. In Search of the Best & Worst Districts from Social and

Economic Factors 99-141

5. People’s Responses: Perception Survey 142-165

Reference 166-170

iii

List of Tables, Figures, Appendices Charts & Maps

List of Tables

1. Table 2.1: PCA Factor Scores of 44 Indicators for 29 States (Rural)

2. Table 2.1a: Factor Loadings of 44 Indicators for 29 States (Rural)

3. Table 2.2: PCA Factor Scores 42 Indicators for 29 States (Rural)

4. Table 2.2a: Factor Loadings of 42 Indicators for 29 States (Rural)

5. Table 2.3: State-specific Poverty Lines in 2004-05

6. Table 2.4 : Indices-wise Selection of Indicators or Attributes 2001 Census (Districts)

7. Table 2.5: Descriptive Statistics of the Districts in 2001 & 2004-05: Rural

8. Table 2.6: Descriptive Statistics of the Districts in 2001 & 2004-05: Urban

9. Table 2.7: Indicator-wise CVs for States and Districts, 2001 and 2004-05

10. Table 2.8: Nature of Distribution of the District-wise Values of Indices in 2001 & 2004-

05

11. Table 3.1a. Descriptive Statistics of Common Rural Districts in 1991

12. Table 3.1b. Descriptive Statistics of Common Rural Districts in 2001

13. Table 3.2a. Descriptive Statistics of Common Urban Districts in 1991

14. Table 3.2b. Descriptive Statistics of Common Urban Districts in 2001

iv

15. Table 3.3. : Pearson's Correlation Coefficients of Relevant Indicators of Common Rural

Districts between 1991 & 2001

16. Table 3.4. : Pearson's Correlation Coefficients of Relevant Indicators of Common Urban

Districts between 1991 & 2001

17. Table 3.5. : Chi-square Significance Test among Districts between 1991 & 2001

18. Table 3.5a: Chi-Square Formula with Degrees of Freedom

19. Table 4.1: District-wise Pearson's Correlation between relevant Indices for Rural

Districts in 2001 and 2004-05

20. Table 4.2: District-wise Pearson's Correlation between relevant Indices for Urban

Districts in 2001 and 2004-05

21. Table 4.3: Rural Versus Urban District-wise Correlation in 2001 and 2004-05

22. Table 4.4: Names of Best 25 Rural Districts in India in Social & Economic Indicators,

2001 & 2004-05

23. Table 4.5: Names of Worst 25 Rural Districts in India in Social & Economic Indicators,

2001 & 2004-05

24. Table 4.6: Names of Best 25 Urban Districts in India in Social & Economic Indicators,

2001 & 2004-05

25. Table 4.7: Names of Worst 25 Urban Districts in India in Social & Economic Indicators,

2001 & 2004-05

26. Table 5.1: People's Perception (Rural Poor)

v

27. Table 5.2: People's Perception (Rural Non- Poor)

28. Table 5.3: People's Perception (Urban Poor)

29. Table 5.4: People's Perception (Urban Non-Poor)

30. Table 5.5: People's Perception about Land acquisition (Rural Poor)

31. Table 5.6: People's Perception about Land acquisition (Rural Non-Poor)

List of Charts

1. Chart 3.1: Nature of Distribution of the Common Districts in 1991 and 2001

List of Appendices

1. Appendix 1.1: Infrastructure & Spatial Development

2. Appendix 2.1. Composition of Social Development Indices for Rural & Urban Districts

in 2001 & 2004-05

3. Appendix 3.1. Indicators used for Common Districts in 1991 and 2001, Rural & Urban

Areas.

4. Appendix 3.2: Names of Common Districts in 1991 & 2001

5. Appendix 4.1: List of District Names for 2001 & 2004-05

List of Figures

1. Figure 2.1: Distribution (Normal) of SDIR4 among Districts (Rural) 2001

2. Figure 2.2: Distribution (Normal) of SDIR6 among Districts (Rural) 2001 & 2004-05

3. Figure 2.3: Distribution (Normal) of SDIR7 among Districts (Rural) 2001 & 2004-05

4. Figure 2.4: Distribution (Normal) of SDIR7_W among Districts (Rural) 2001 & 2004-05

5. Figure 2.5: Distribution (Normal) of WPIR1 among Districts (Rural) 2001

vi

6. Figure 2.6: Distribution (Normal) of HCIR1among Districts (Rural) 2001

7. Figure 2.7: Distribution (Normal) of HHIR1 among Districts (Rural) 2001

8. Figure 2.8: Distribution (Normal) of TCIR1 among Districts (Rural) 2001

9. Figure 2.9: Distribution (Normal) of HCRR5 among Districts (Rural) 2004-05

10. Figure 2.10: Distribution (Normal) of PPRRD5 among Districts (Rural) 2004-05

11. Figure 2.11: Distribution (Normal) of GINIRD5 among Districts (Rural) 2004-05

12. Figure 2.12: Distribution (Normal) of SCMWPMR1 among Districts (Rural) 2001

13. Figure 2.13: Distribution (Normal) of STMWPMR1 among Districts (Rural) 2001

14. Figure 2.14: Distribution (Normal) of SCMWPFR1 among Districts (Rural) 2001

15. Figure 2.15: Distribution (Normal) of STMWPFR1 among Districts (Rural) 2001

16. Figure 2.16: Distribution (Normal) of SXR06RD1 among Districts (Rural) 2001

17. Figure 2.17: Distribution (Normal) of SDIU4 among Districts (Urban) 2001

18. Figure 2.18: Distribution (Normal) of SDIU6 among Districts (Urban) 2001 & 2004-05

19. Figure 2.19: Distribution (Normal) of SDIU7 among Districts (Urban) 2001 & 2004-05

20. Figure 2.20: Distribution (Normal) of SDIU7_W among Districts (Urban) 2001 & 2004-

05

21. Figure 2.21: Distribution (Normal) of WPIU1 among Districts (Urban) 2001

22. Figure 2.22: Distribution (Normal) of HCIU1 among Districts (Urban) 2001

23. Figure 2.23: Distribution (Normal) of HHIU1 among Districts (Urban) 2001

24. Figure 2.24: Distribution (Normal) of TCIU1 among Districts (Urban) 2001

25. Figure 2.25: Distribution (Normal) of HCRUD5 among Districts (Urban) 2004-05

26. Figure 2.26: Distribution (Normal) of PPRUD5 among Districts (Urban) 2004-05

27. Figure 2.27: Distribution (Normal) of GINIUD5 among Districts (Urban) 2004-05

vii

28. Figure 2.28: Distribution (Normal) of SXR06UD1 among Districts (Urban) 2001

29. Figure 3.1: Distribution (Normal) of HCIR9 among Districts (Rural) 1991

30. Figure 3.2: Distribution (Normal) of HCIR1 among Districts (Rural) 2001

31. Figure 3.3: Distribution (Normal) of HHIR9 among Districts (Rural) 1991

32. Figure 3.4: Distribution (Normal) of HHIR1 among Districts (Rural) 2001

33. Figure 3.5: Distribution (Normal) of SDIR9 among Districts (Rural) 1991

34. Figure 3.6: Distribution (Normal) of SDIR1 among Districts (Rural) 2001

35. Figure 3.7: Distribution (Normal) of HCIU9 among Districts (Rural) 1991

36. Figure 3.8: Distribution (Normal) ofHCIU1 among Districts (Rural) 2001

37. Figure 3.9: Distribution (Normal) of HHIU9 among Districts (Rural) 1991

38. Figure 3.10: Distribution (Normal) of HHIU1 among Districts (Rural) 2001

39. Figure 3.11: Distribution (Normal) of SDIU9 among Districts (Rural) 1991

40. Figure 3.12: Distribution (Normal) of SDIU1 among Districts (Rural) 2001

41. Figure 3.13: Scatter Plot between WPIR9 & WPIR1 for Common Rural Districts

42. Figure 3.14: Scatter Plot between HCIR9 & HCIR1 for Common Rural Districts

43. Figure 3.15: Scatter Plot between HHIR9 & HHIR1 for Common Rural Districts

44. Figure 3.16: Scatter Plot between SDIR9 & SDIR1 for Common Rural Districts

45. Figure 3.17: Scatter Plot between SXR06RD9 & SXR06RD1 for Common Rural Districts

46. Figure 3.18: Scatter Plot between WPIU9 & WPIU1 for Common Urban Districts

47. Figure 3.19: Scatter Plot between HCIU9 & HCIU1 for Common Urban Districts

48. Figure 3.20: Scatter Plot between HHIU9 & HHIU1 for Common Urban Districts

49. Figure 3.21: Scatter Plot between SDIU9 & SDIU1 for Common Urban Districts

viii

50. Figure 3.22: Scatter Plot between SXR06UD9 & SXR06UD1 for Common Urban Districts

51. Figure 4.1: Scatterplot between SDIR4 & HCRRD5

52. Figure 4.2: Scatterplot between SDIR4 & PPRRD5

53. Figure 4.3: Scatterplot between SDIR4 & GINIRD5

54. Figure 4.4: Scatterplot between SDIR4 & SDIU4

55. Figure 4.5: Scatterplot between HCIR1 & HCIU1

56. Figure 4.6: Scatterplot between HHIR1 & HHIU1

57. Figure 4.7: Scatterplot between TCIR1 & TCIU1

58. Figure 4.8: Scatterplot between HCRUD05 & HCRRD5

59. Figure 4.9: Scatterplot between PPRUD05 & PPRRD5

60. Figure 4.10: Scatterplot between GINIUD05 & GINIRD5

61. Figure 4.11. Vertical Illusion versus Horizontal Rift: Problems of Capability &

Entitlement: Standard India Rural 62. Figure 4.12: Vertical Illusion versus Horizontal Rift: Problems of Capability &

Entitlement in Kerala (Rural) in 2004-05 63. Figure 4.13: Vertical Illusion versus Horizontal Rift: Problems of Capability &

Entitlement in Orissa (Rural) in 2004-05 64. Figure 4.14: Vertical Illusion versus Horizontal Rift: Problems of Capability &

Entitlement in Kurukshetra District (Rural) of Haryana in 2004-05 65. Figure 4.15: Vertical Illusion versus Horizontal Rift: Problems of Capability &

Entitlement in Dantewada District (Rural) in Chhattisgarh in 2004-05 66. Figure 4.16: Vertical Illusion versus Horizontal Rift: Problems of Capability &

Entitlement in Medinipur District (Rural) in West Bengal in 2004-05 67. Figure 5.1: Percentage of BPL Card Holder Chosen from by the Authority as Reported by

RP, 2007-08 68. Figure 5.2: Percentage of Reporting Illiteracy as Cause of Poverty, 2007-08

ix

69. Figure 5.3: Percentage of Surveyed felt Majority in the Neighbourhood are Poor, 2007-08

70. Figure 5.4: Percentage of Surveyed Reported Local Politicians Prefer Uneducated Voters,

2007-08 71. Figure 5.5: Percentage of Surveyed Reported Having no Idea of Poverty Eradication

Programme, 2007-08 72. Figure 5.6: Percentage of Surveyed Support Land Acquisition for Development Purposes,

2007-08 73. Figure 5.7: Percentage of Surveyed Reported Poverty as a Cause of a Person Being

Antisocial, 2007-08 74. Figure 5.8: Percentage of Rural Poor Surveyed Reported that Justice Depend on Money/

Connection, 2007-08 75. Figure 5.9: Percentage of Rural Non-Poor Surveyed Desire Educated Politician, 2007-08 76. Figure 5.10: Percentage of Rural Non-Poor Surveyed Reported Local Politicians Prefer

Uneducated Voters, 2007-08 77. Figure 5.11: Percentage of Rural Non-Poor Surveyed Support Land Acquisition for

Development Purposes, 2007-08

78. Figure 5.12: Percentage of Rural Non-Poor Surveyed Having no Trust on Police, 2007-08

79. Figure 5.13: Percentage of BPL Card Holder Chosen from Urban Poor by Authority as Reported by UP, 2007-08

80. Figure 5.14: Percentage of Urban Poor Reported Illiterate as Cause of Poverty, 2007-08

81. Figure 5.15: Percentage of Urban Poor Surveyed Reported Local Politicians Prefer

Uneducated Voters, 2007-08

82. Figure 5.16: Percentage of Urban Poor Surveyed Reported Having no Idea of Poverty Eradication Programme, 2007-08

83. Figure 5.17: Percentage of Urban Poor Surveyed Reported that Justice Depends on

Money/ Connection, 2007-08

84. Figure 5.18: Percentage of Urban Non-Poor Surveyed Reported Having Satisfied with Govt. Infrastructure Projects, 2007-08

x

List of Maps

1. Map-1: Map of India showing District-wise Rural Poverty: 2004-05

2. Map-2: Map of India showing District-wise Urban Poverty: 2004-05

3. Map-3: Map of India showing District-wise Rural Social Development: 2001

4. Map-4: Map of India showing District-wise Urban Social Development: 2001

1

Chapter 1. The Background: Idea of Social Development Index & Basic

Geographical Unit of Analysis The literature on development index is not new. In one of the oldest scripts of economic

administration of ancient India in third century BCE, Arthashastra, Arya Chanakya (popularly

known as Kautilya) had mentioned in his book some advices for the Maurya rulers regarding (1)

territorial control and proper administration of the state, and (2) wholesome administration for

the general benefits of the inhabitants. Adam Smith in his book, The Wealth of Nations, has

discussed the role of government in case of market failures. He has emphasized the need for

public investment in education and other social goods as market may fail to deliver the required

goods and services. In sharp contrast to such liberal idea, the proponents of Social Darwinism

particularly Herbert Spencer was in favour of ruthless social competition thereby leading to a

socially benign state of affairs (Galbraith, 1958). Myrdal (1957) and Hirschman (1958) in

more recent period have researched on the distinction between market failures and government

failures. They both proposed substantial role of government intervention in terms of developing

infrastructure facilities in backward regions. The main belief underlying these pioneering

research works is that private rationality guided by profit motive may not always serve the social

purpose, and in such situations the role of the state becomes obligatory. In order to implement

future developmental policy across diverse regions in a heterogeneous country like India, the

Government needs to have some a priori idea about the relative levels of development and

backwardness among the constituent regions. In order to get such idea, there is no alternative to

estimating some kind of development indices across the constituent regions of a country. But one

must be aware of the caveats before undertaking such job.

First, construction of a representative index involves the traditional problems of scaling and

weighting. This lacks solid welfarist foundations of economic analysis particularly because of,

among other things, distributional assumption and varying intensities of the need of specific set

of social facilities for different classes of the society in different neighbourhoods. But national

comparisons across constitutional regions (in case of India, ‘states’ or ‘districts’) and

international comparisons across countries of social, economic, political, environmental and

development performances are useful instruments at the hands of the policy makers for planning

2

future course of development. Equalization among the regions and hence the people is, therefore,

at the core of such endeavour. Policy makers, public persons and even politicians are very often

guided by the concept of rankings of their respective regions and countries. In democratic

societies, it is a basic right for all to know the relative rakings of the constituent regions and the

level of deprivation of the ‘excluded’ people living in lagging regions. In multilateral settings,

international organizations like UNO, ILO, WB, ADB and others are also guided by the

rankings of both regions and countries for future decisions regarding allocation of social

development funds. Apart from these, researchers often use these indices in order to

econometrically test existing hypotheses concerning both static and inter-temporal linkages

between social and economic factors across spaces. Questions may be raised about the methods

and choice of indicators in order to make the indices more representative particularly in a

heterogeneous country like India. After 60 years of Independence it is actually late to visualize

the issue of India’s ‘national integration’ in terms of relative levels of development across

regions as well as people living in smaller in remote and smaller geographical areas, which have

failed to grow compared to the state capitals and urban areas across the states. Importance of

such indices can not be undermined even with some degree of value judgment regarding the

distributional assumption and choice of indicators.

Second, the conventional literature speaks of two major groups of indicators, which are assumed

to represent the well-being of the people of a nation. The ‘positive indicators’ include income,

expenditure and other overhead infrastructure facilities like health, education, environment, rule

of law, transport, banking, and the like, which are generally the targets of public policy so that

they increase over time and across regions. The ‘negative indicators’ include crime, lack of rule

of law, lack of competition, political corruption, partiality, environmental pollution, other health

hazards and the like, which affects human abilities negatively irrespective of time and space.

Hence, the target of public is to minimize these.

Third, development performance of an emerging global power like India with large magnitude of

vulnerable people can (must) not be judged by, among many other things, the average income

and expenditure of the people living in the country and average supply of public infrastructure

facilities across the regions without considering accessibility. Given the federal constitutional

status and the existing diversity and disparity in their varied manifestations, performance of India

3

as a whole and by any averaging method does not make much sense. Even sub-state level

disparities are difficult to capture by state level averages. Any average value of an indicator for

the nation as a whole and states in particular essentially conceals the justification for policy

intervention. So the task would be to review such performance in terms of average per capita

consumption and overhead availability of infrastructure facilities at least at the sub-state level,

and not only for the districts as a whole, but separately for rural and urban areas. Disaggregating

such basic achievements below the state level is doubtless limited by availability of required

information. Legitimate questions may be raised against going down to the districts as the

districts fall under the jurisdiction of the head of the state. But given widespread failure by most

states to ensure uniform intra-state development and distribution, we should not be scared much

to face the eventual truth.

Fourth, performance in ‘per capita’ terms may not be complete if an economy has significant

degree of inequity measured by either income or consumption. It is a common perception among

development economists, planners and policy experts advising national and world organizations

that human development index (HDI) is a reasonably condensed measure of welfare of the

people in a developing society. As is well known, such an index is based on a broader concept of

economic development capturing income along with health and education that have been the

most traditional yardsticks of social wellbeing. The process of construction of such an index

involves calculating respective deprivation indices for each of the components. Some measure of

per capita income or expenditure for capturing the capability of the family, some measure health

like infant mortality and some measure of schooling or literacy rate are generally combined to

reach the HDI. The indices are normalized in such a way that it lies between zero and one so that

in a sense it becomes a deprivation index. The basic methodology and its chronological

improvements are available in UNDP Reports from 1990 to 2008. More theoretical oriented

research works on this area are discussed later.

Fifth, questions may be raised for not choosing a much more comprehensive index, called social

development index (SDI), replacing the established index of HDI. There is no conflict in

conceptualizing SDI as a more comprehensive and more inclusive measure of HDI in situations

where the bulk mass of the population are living in extremely deprived conditions in all spheres

of life and death away from the state capitals and much beyond the idea of income, education

4

and health. For example, reliable income or expenditure data may not be available below the

state over a reasonable period of time. The limitation of such measure becomes much more

obtrusive if state level average fails to represent inter-district disparities within a state. This is

indeed the case among the districts within most states in India. On the other hand, observations

on infant mortality alone may not be able to summarize the nature and extent of health care

services available in the concerned regions particularly if the region is a lesser geographical unit

below the state, and more so, if it is a poor area. It is true that a high infant mortality rate gives

good signal for poor health conditions of a region, but a low rate may hide awfully poor health

facilities open to the adult and to people in higher ages particularly for those who do not have

any idea of social security or pension or accident insurance. The bulk of Indians living in the

unorganized sector amounting to about 95% falls in this category. Moreover, gender disparity in

health services may not be captured by infant mortality measure as such. Third, in so far as

education indicator is concerned, there is no single representative trait, which can safely be used

as a proxy for quality and/or quantity of services available for the bulk mass of our poor,

illiterate and unemployed masses in vast rural regions away from the state capitals (Vyas, 2004).

Heterogeneity is this facility is the most prominent characteristic at the sub-state level. In some

states like Kerala, HP, Punjab and Haryana, spread of educational facilities below the level of the

state may not be scarce. But in most other states, it is so substandard that literacy rate alone can

not capture the intensity of human deprivation in any conceptualization of human capital.

Moreover, gender disparity in educational achievement away from the state capitals is extremely

high among all other areas of social statistics.

Sixth, what about other social aspects of life, which are not substitutes of these three indicators

universally chosen, and are inseparable elements of living with dignity in any locality- developed

or backward? One can raise more elementary issues like ‘political freedom’, which is essential in

present Indian reality to get involved in any social process and to live with minimum dignity in

any region. Even judicial freedom is inextricably linked to broader political freedom for any

group of people in any region. Prevalence of ‘rule of law’ for all the inhabitants in any

neighbourhood without any discrimination is another major indicator of freedom or justice.

Beyond all, in such an age of man made global environmental disorder, one has to think of

linking environmental factors like air pollution, water pollution, sound pollution (mainly in urban

5

areas), health, hygiene, sanitary conditions, drinking water and a whole host of other hazards

with standard social development index. In a sense, therefore, human development index can not

be a substitute of social development index.

Seventh, apart from these, a more profound dilemma lies in constructing an SDI as a true

reflector of quality of life. Beyond the mundane index, one may bring in the debate on ‘whose

society’ we are talking about. An SDI constructed exclusively for a tribal zone in India will be

altogether different from the one, which is commonly referred by people living in established

urban areas. Not even urban areas at large, which urban areas we are talking of beyond the

metropolis? How to compare Kolkata South with North? So is Kolkata Central versus West.

What about quality and security? Let us forget the Metros and capital cities. How to compare the

rural areas of Nabarangapur and Kandamal with those of Bhubaneswar and Cuttack? How to

compare Dantewada with Raipur? In what conceivable way one ought to juxtapose on the same

plane the districts of Hooghly bordering with Medinipur, and also Purulia, Murshidabad and

Malda (all from West Bengal)? Imagine where one would land if all the districts of Maharashtra

are compared among themselves, though nobody questions the comparison between the state and

Gujarat. If one is elegant enough to extend it to challenge the ‘conventional wisdom’ of what is

development and what is not, satisfactory answer is difficult to flow in by which to construct an

universally acceptable index of development across varied states of India. Economists try to be

satisfied with modes of aggregation undermining the constituent parts or over-emphasizing the

importance of a particular group. If different communities, however defined, visualize the

process of development fairly differently from one another, a homogenous HDI will be of just

mundane scholastic value. This calls for a kind of dis-aggregation and comprehensiveness of

indicators unprecedented both as a concept and as an analytical tool. To look for such categories,

and generate appropriate data is not only an immense task in the short run, but defer all short run

developmental programmes thereby multiplying the ‘excluded’ class and endangering national

integration real. Therefore, while one has to be very careful in selecting indicators at

disaggregated level, but going below the level of the state is obligatory for understanding the

actual conditions of living of the people. Where availability of expected data set is a genuine

short run problem, one can try to encompass those attributes from the available set, which are not

substitutes (Escober, 1995).

6

Eight, the recent nationwide exercise of preparing state level HDR has essentially raised the

questions discoursed above, which had so far been buried under the Constitutional provision of

state as the second stage of India’s administratively governed spatial unit of analysis. One such

universal problem pertains to the conceptual nature of state level HDI using different indicators

by different governments at the district level. This has also brought to the forefront the typical

problems relating to availability of the right kind of indicators comparable among the districts for

estimating state level development index as well as the appropriate needs of the Central

disbursing authorities for future course of actions. It is all too known that national data in India

are of high quality but there are many reservations about lower level data from the state to the

local level crossing the borders of the districts. This has become a national constraint for

comparative analysis of sub-national HDR. The relevance of such scrutiny becomes all the more

important, among others, for linking district level poverty, inequality and purchasing power with

social, economic and political (read ‘governance’) development. It has now theoretically become

necessary for all practical purposes to posit the districts of India on the same plane since the

Panchayat Act was passed by the 73rd and 74th Constitutional Amendments brought about for

establishing the three-tier governance system from the Union to the Local level through the

State. Here also, the process of transition from the two-tier to the three-tier system of governance

differs to a large extent across the states, because devolution is a state responsibility under the

present Constitutional provisions (Indira, Rajeev and Vyasulu, 2002). Those who believe in

democratic decision making for removal of poverty and all-round development of extreme areas,

they wish the government to organize necessary data for implementation of development

programmes on the living conditions of those whose voices have not found any platform. This

could improve the honest efforts by the top layers of administration. The question of

methodology is certainly important. No sensible analyst would deny it. But far more important is

the real time reliable information, which would help the government to fulfill the target oriented

employment guarantee programmes and other economic actions announced and implemented in

the name of the Prime Minister. Even with same methodology, it is almost impossible to

compare district-wise HDI between two independent but adjacent states, because ‘agency biases’

with so-called local government departments in the states are very often intractable and

formidable.

7

Ninth, our combined indexation does not tell us anything about what communities perceive as

social development. One way out will be to target the appropriate group or community, interact

with them extensively to understand what quality of life means to them, how much unhappiness

they are strained to endure in their neighbourhood for mere survival and also for overall

betterment of the nation. Typically, there are common factors affecting them all, and there are

neighbourhood specific factors as well. Micro level works with an elaborate statistical

mechanism like that NSSO could only quantitatively address such problems thereby eventually

reaching nationally consensus index.

Finally, the trouble of finding an aggregative index overlooks the examples of the extremely

separated areas even in a district. The classic example is: a recently electrified village will care

less for a ten hour power failure in a day than an urban metropolis, which is dependent on

electric power as an inseparable part of life. A community that can not simply imagine sending

their children to college will never be worried about the problems of higher education and

centers for GRE, GMAT, TOEFEL tests. In general, people, who live from hand to mouth, will

have very dissimilar perception about development from those, who are much better placed and

accustomed to global competition. Conventional HDI can not certainly serve the purpose. But

the ultimate question is whether the SDIs constructed for various communities will be

incorporated in a Rawalsian index of some kind or in more conservative welfarist assimilation.

While an aggregative index is useful in evaluating the effectiveness of a particular

developmental programme, it may still require some foundational groundwork to be defined as a

meaningful comprehensive index for an extremely heterogeneous nation like India, which

accommodates many nations within and represent habits and cultures which are stretched back

for few thousand years (Marjit and Ghosh, 2000 & 2004). They have found that district level

disparity in the state of West Bengal was quite high during last two decades, and had been rising

during the 1990s. In a sense, therefore, we have to search for more disaggregated relationship

between disparity in infrastructure, social development and economic levels of living rather than

trying to test prima facie the theory of convergence. Unless such concept of spatial development

at sub-state levels is charted out, the distinction between market failure and policy failure can not

be separated out. This may help the policy makers to detect the exact spaces for target oriented

allocation of development funds; it may also guide private investment in backward areas, where

8

marginal productivities may be higher and cost lower. The problem becomes much more

pressing indeed under the present process of globalization when increasing withdrawal of

government from many spheres of economic activities has not automatically paved the way for

private initiatives. Eleventh Five Year Plan (2007-12, p. 136) in Chapter-7 on Spatial

Development and Regional Imbalances has clearly admitted this: “As the Eleventh Plan

commences, a widespread perception all over the country is that disparities among States, and

regions within States, between urban and rural areas, and between various sections of the

community, have been steadily increasing in the past few years and that the gains of the rapid

growth witnessed in this period have not reached all parts of the country and all sections of the

people in an equitable manner. That this perception is well founded is borne by available

statistics on a number of indicators. Though there is some evidence to indicate a movement

towards convergence on human development indicators across the States, one of the reasons for

this convergence could also be that most human development indicators have a value cap.

However, widening income differentials between more developed and relatively poorer States is

a matter of serious concern.” There is hardly any doubt that growing India is visible in terms of

in many pockets of the mega cities. But India lives in villages still. This study seeks to explore

relative levels of development across the districts in all spheres of life in terms of parameters of

social and economic development. The conventional literature on infrastructure and regional has

dealt with ‘state’ as a unit of analysis. The major finding is commonly shared by all researchers.

That is that there is a tendency towards divergence among the states in the long run. In a sense,

therefore, the sub-state level myrrh has remained buried under these studies. The major works in

this field are reported in Appendix 1.1.

Under such a backdrop, the present study seeks to estimate a comprehensive measure of social

development index (SDI and its possible segregated components at the dis-aggregated regional

level separately for rural and urban areas. The broader purpose is to understand the inter- and

intra-state relationship between social and economic parameters. The economic parameters are

those, which could be estimated from NSSO data on consumer expenditure, namely poverty,

inequality and purchasing power. It also attempts to test inter-temporal transition of districts

between 1991 and 2001 Census. It also explores the inter- and intra-state purchasing power

differentials in rural and urban areas corresponding to the proportion of people thereof in each of

9

the states. The significance of such enquiry becomes all the more important in view of the fact

that the major thrust of the present Central Government is rightly placed on the ‘inclusion’ of the

‘excluded people’.

1. Whether regions with higher social development at the time of economic reform have

paved the way for faster growth of the backward regions thereby resulting in

‘convergence’?

2. Whether regions with higher social development have been successful in reducing

poverty?

3. Whether regions with higher human capital, health & housing as well as technology have

been able to record lower poverty and inequality?

4. Whether regions with higher social development have achieved higher purchasing power,

lower poverty and lower inequality?

5. Whether better off regions are better in terms of both social and economic parameters of

developments?

6. Where are the people in major expenditure classes spiraling over time from before the

economic reform till now?

7. Whether inter-district disparities (within each state) in these developmental parameters

are higher than those among the states?

8. Is the neighbourhood trap in poverty has given rise to spatial backwardness syndrome of

poverty and social development among Indian districts?

We seek to answer these specific questions as far transparently as possible without inviting any

functional complexity and with the help of some crucial official statistics at the district level,

separately for rural and urban areas.

The Analytics

The present study focuses on 575 rural districts and 573 urban districts separately for each of the

states in India for which detailed information are available from Census of India, National

Sample Survey Organization and other official sources. Wherever necessary, this data set was

supplemented by Ghosh & De (2005b). The period of study is stretched from 1991 to 2004-05

10

through 2001 at the district as well as state levels. Social development indices are here divided

into (i) work participation (male and female separately), (ii) human capital, (iii) health and

housing, and (iv) transport and communication for rural and urban districts separately. Economic

indicators are classified into poverty or head count ratio (HCR), purchasing power (Monthly Per

Capita Consumer Expenditure, i.e., PP) and inequality (Gini coefficient). Three methods of

indexation are tested for the construction of development indices, namely principal component

analysis, specific distribution based indexation and UNDP method. Apart from analyzing

different aspects of social backwardness and the interrelationship between economic and social

factors, an attempt is made to study the performance of a common set of districts identified

between 1991 and 2001 with the help of transition matrix. An intensive empirical examination

across various expenditure classes has enabled us to trace the cluster of the poor and rich in

absolute terms in different economic classes across the districts separately for rural and urban

areas.

Then we have identified 100 most developed districts in terms of human capital, health &

housing, transport & communication, overall social development as well as purchasing power,

poverty, inequality. Also, 100 worst districts are searched out. Along with this, the top 25%,

bottom 25%, upper 25% and lower 25% districts are marked on India’s district level maps in

different colours to have an instant geographical visualization of the districts.

We have also estimated the number and proportion of people living across four crucial MPCE

classes with corresponding share of endowment in rural and urban areas. These are bottom 30%,

called ‘poorest of the poor’ (PP), (30-50) %, called ‘vulnerable middle’ (VM), (50-80) %,

called ‘upper middle class’ (UMC), and top 20 %, or ‘rich’ (R). This is a unique way to

understand the spatial distribution of the people with different purchasing power in a nation with

intense heterogeneity across regions in terms of levels of living, culture and agro-climatic

classification but with same heritage and same cultural mindsets. In absolute terms, the number

of people living in the bottom two classes is assumed to be half a billion for India as a whole.

But they are as disproportionately distributed across the districts as more than 90% in many

districts and close to zero in some. Such study helps the policy makers to prioritize allocation in

varying order even across the districts.

11

As revealed from our analysis, quality of governance has appeared to be the most dominant

factor for projecting the future course of development. Unless one comes down from the state

level to smaller administrative cum geographical units at least like ‘district’, it is not possible to

get people’s responses about ‘governance’. This is captured by a limited nationwide survey of

the extreme districts from 22 states organized by our team, which is called “Perception

Survey”.

Perception Survey

Apart from the secondary data set to be used in the study as discussed above, we have organized

a nationwide survey, which is called “Perception Survey”. This survey covers selected extreme

districts from 22 states. By extreme district is meant the poorest and the richest as well as the

nearest and the farthest district from the state capital. The sample size is doubtless small

amounting to only 2676 in total for rural and urban areas taken together. But the strength of the

approach is that the sample audience is divided into four economically defined classes: rural poor

(RP), rural rich (RR), urban poor (UP) and urban rich (UR). The state-specific poverty line given

by the Planning Commission for NSS 61st Round Survey (2004-05) guided us as the

benchmark to identify the poor and non-poor households. Thus, limitation of sample size is

partly compensated by selecting homogeneous groups on the basis of expenditure classes. Even

then, we have not used this survey based data set for any parametric or non-parametric

estimation. It better served the purpose of this research by raising some crucial issues like BPL

syndrome, land acquisition, voting and democracy, local governance, quality of local leadership,

efficacy of election, corruption, criminality, religion and impact of public sector infrastructure

projects and many more. There is no way to get to know this set of information from official

statistics. The findings are very suggestive with regard to public responses to those questions.

We hope that such questions should be included in future information system of the Central

Government.

i

*Acknowledgement:

Working with Indian regions particularly the districts and more so with rural and urban divisions

is a really daunting task. We have ventured to do this precisely because of liberal research scopes

offered by the Ministry of Statistics & Programme Implementation (MOSPI), GOI and the

working environment at Indian Statistical. There is no doubt that without the financial and

technical supports by the MOSPI, GOI, this research would not have been possible. More

specifically, official support from Shri Pranab Sen (Secretary & CSI, MOSPI) , Shri S. K. Das

(DG, CSO), S. C. Seddey (DG & CEO, NSSO), J. Kar (Monitoring Officer of the project,

Mahalanobis Bhavan), J. Dash (ADG, SSD, CSO), Ms. Madhu Bala (ADG, CAD, CSO), Vijay

Kumar (ADG, ESD, CSO), V. K. Malhotra (ADG, Ministry of Health & Family Welfare), Ms.

Jeyalakshmi (DDG, Ministry of WCD), Ashis Kumar (DDG, Ministry of Social Justice &

Empowerment), P. C. Mohanan (DDG, NSC), N. J. Kurian (CSD), Ms. Savita Sharma (DDG,

NAD, CSO), T. V. Raman (DDG, SSD, CSO), M. R. Meena (DDG, SSD, CSO), R. K. Khurana

(DDG, NAD, CSO), S. N. Singh (DDG, CPD, NSSO), P. H. Khopkar (DDG, Computer Centre),

K. D. Maiti (DDG, NASA, CSO), S. Chakraborty (Director, SSD, CSO), S. Dhar (Director, SSD,

CSO), I. J. Singh (Director, SSD, CSO), R. C. Aggarwal (Director, SSD, CSO), H. Borah

(Director, SSD, CSO), M. Singh (Director, SSD, CSO), Ms. Ruchika Gupta (Deputy Director,

Ministry of HRD), Suresh Kumar (DD, SSD, CSO), Niyati joshi (Assistant Director, SSD, CSO)

& many other officers have played significant role in facilitating this research project. Technical

help from Shri Samiron Mallick at every stage of the work in dealing with NSSO unit level data

as they are is gratefully acknowledged. Earlier versions of the report at various stages of

preparation have been presented at ISI Kolkata, French Institute (Delhi) and NSSO Kolkata

(Mahalanobis Bhavan). Fruitful suggestions from DDGs of SDRD, FOD, NAD and EPD at

Mahalanobis Bhavan have been gratefully acknowledged. We are delighted to report that very

instrumental roles have been played by Bimal Roy, Amita Majumdar, Snigdha Chakraborty and

Sandip Mitra of ISI, Shubhashis Gangopadhyay of Finance Ministry (GOI) and IDF, Amitav

Kundu (JNU), Ashok Kotwal of UBC (Canada), and Sugata Marjit (CSSSC), who have helped

us dare such an extensive and complex task. Effective suggestions from Arijit Chaudhury,

Dipankor Coondoo, P. P. Choudhury, Madhura Swaminathan, V. K. Ramchandran (all ISI),

Bashudeb Choudhury, Himansu and others (Centre de Humaines, French Embassy), Anjan Roy

ii

(FICCI), Subrata Gupta (Northeaster University, UK), Dipak Rag Basu (Nagasaki University),

Bipul Chatterjee (CUTS), L. C. Jain (Bangalore) and others have helped us in perfecting the

report the way we are capable of. ISI Director has tried to facilitate the work in his best possible

ways. Staffs of ERU, ISI Kolkata library, ISI Delhi Library and Reprography Unit have provided

their excellent services as and when we required. Experts and friends from other organizations in

different states have spent their valuable times in lending technical support. It would not be

possible to organize the primary survey without the local supports in numerous Indian districts

from 22 states by K. J. Joseph from CDS (Trivandrum), Subrata Sarkar from IGIDR (Mumbai),

Keshab Das and Gani Menon from GIDR (Ahmedabad), Meenakshi Rajiv from ISI Bangalore,

Siddhartha Mitra from GIPE (Pune), Gurudas Das from NIT (Silchar), Hrudananda Pal (Bureau

of Statistics, Orissa), Binoy John and Najma John from NORMA (Trivandrum), and many others

from across India. Simple thanks are not enough for two social activists from Himachal Pradesh,

who took us to regions, where tourists certainly fail to turn up. We can not forget the sincere

services of about 40 field investigators across the states, which have rendered their services

much more than the remuneration they got. On the whole, this survey helped us perceive an India

moving across about 350 districts, which can not be captured by any empirical measure. The

committed computer expertise and service of Shri Amiya Das (ASU, ISI), Mrs. Swagata Gupta

(PSU, ISI) and B. Stat. students of ISI dealing with NSS Unit level data and Perception Survey

data would not have been possible but for their dedicated research motivation. Computing

supports from Sanchary Joardar, Swati Dutta, Sambit Hazra, Pratip Dutta, Pritam Dutta, Abinash

Adhikary and many others were simply indispensable. To mention about the family’s sacrifice is

to subsidize the privilege compared to the landless agricultural labourers, marginal farmers and

other assetless people living in misfortunate geographical spaces with scanty social facilities with

whom we have spent days, weeks, months and year even in the most fertile regions of Bengal

and the most unfriendly geographies beyond. I alone remain responsible for the remaining errors

and omissions.

Indian Statistical Institute Buddhadeb Ghosh

Kolkata March 2010

12

Appendix 1.1. Infrastructure & Spatial Development

A major thrust of Indian economic policy, since the beginning of the planning process way back

in the 1950s, was to foster `balanced' regional development with active support for

industrialization in backward regions as well as through minimizing inter-regional disparities in

costs and prices. The well known policy of `freight-equalization' and subsidies to industries in

backward regions point towards the commitments of the planners for harmonious spatial

development of the nation. There was an implicit universal understanding among all that

‘regions’ refer to the ‘states’, and attention on state level balanced development would

automatically take care of inter-district disparities within each of the states. As a result, no

serious need for focusing on what is going on at the sub-state levels was as intensely felt as it is

today. But this consensus assumption terribly failed everywhere except Kerala, Punjab, Haryana,

Himachal Pradesh and partially Gujarat.

The world literature on regional development within a nation is not of very old origin. Myrdal

(1957) and Hirschman (1958) have focused attention on the causes of concentration of

economic activities in a particular location or region. According to Myrdal (1957), although, in

the long run, the "crowding out" effects may exert negative impact on further development,

given the phenomenon of "historical accidents" and "cumulative causation hypothesis",

market forces normally tend to increase rather than decrease the inequalities between the

competing regions. There is adequate literature at the provincial level to show that prevailing

infrastructure facilities always favour the better off regions. Even the movements of labour,

capital, goods and other services generate ever-increasing internal and external economies in the

preferred regions which have very strong "backwash effects" on the unlucky regions. Thus,

"backwash effects" exert a retarding pull on other regions. There are diseconomies of

agglomeration and "spread effects" to other regions too. So it is not always possible to predict

at any particular point of time which effects will dominate. The whole outcome depends on the

relative strengths of natural forces and impacts of man made policy interventions. In fact,

Hirschman (1958) strongly propagated the case for governmental initiatives to counteract the

"polarization effects" of free market forces in order to mitigate the misfortune of the unlucky

regions. Here the caveat is: the Western idea of public policy and its implementation is altogether

13

different from the present need of a nation, where lack of accountability of public servants and

elected representatives has made ‘tolerance’ and ‘conformity’ as the best weapon for the masses,

while rebellion for the few.

With the advent of the endogenous growth theory, convergence in per capita income of the

nations and then for the regions infused renewed interest among the theoreticians to try out

empirical vindication of theoretical judgments. In a theoretical plane, following Barro and Sala-

i-Martin (1995), several authors such as Quah (1993, 1996), Shioji (1992), Sala-i-Martin

(1996) and others have extensively investigated the issues of regional growth and convergence in

different countries. Sala-i-Martin (1996) nicely surveys the literature and its current standing in

terms of the empirical results obtained so far. The major finding is: the simple Solovian idea

that a region with lower per-capita income should grow faster tends to hold good for all the

developed countries experimented so far. United States, Canada, Japan and Europe clearly show

the required ‘negative’ relationship between initial per capita income and annual average growth

rate of the regions over a long period of time. The point that within a national boundary the

poorer regions have grown faster than the richer ones is well taken. However, this does not

resolve entirely the statistical controversies raised by Quah (1993) who concludes that the

constant estimates of “two percent per year” convergence could just be a statistical illusion since

a collection of random walks estimated in a cross section could deliver such an outcome. Also,

Quah (1993) argues that Barro-regressions suffer from 'Galton' fallacy. It is quite possible that

the negative relationship between per capita income and growth rate just depict the stationary

distribution, and there may not be any ‘long run’ tendency toward convergence. Sala-i-Martin

(1996) partially agrees with it (Quah, 1996), though not fully.

With the recent revival of interest in neoclassical growth theory, researchers all over the world

have been talking about `endogenous’ explanation of `converging’ or `diverging’ national

growth rates across the world. While the major part of this research has been focusing on the

differential per capita growth rates among different groups of nations over a considerable length

of time, a subset of them has been preoccupied with the question of convergence in regional

growth rates within a specified geographical boundary. The idea of convergence is nothing new;

it was buried in the conventional treatment of growth model a la Robert Solow. But the issues,

14

which have been made transparent through recent research, seem to be quite interesting for

understanding courses of development, and have opened up avenues for further work in this area.

The main economic foundation for convergence even among the districts is quite well perceived

by Barro and Sala-i-Martin (1995) in terms of a national assumption. One key property of the

neoclassical growth model is its prediction of conditional convergence. The main point is that an

economy that starts out proportionately further below its own steady-state position tends to grow

faster. But there are some underlying assumptions which facilitate this prediction. These are

similarity in taste, technology, institutional set up, legal system, and the like. In the words of

Barro and Sala-i-Martin (1995, p.382), “Although differences in technology, preferences, and

institutions do exist across regions, these differences are likely to be smaller than those across

countries. Firms and households of different regions within a single country tend to have access

to similar technologies and have roughly similar tastes and cultures. Furthermore, the regions

share a common central government and therefore have similar institutional setups and legal

systems. This relative homogeneity means that absolute convergence is more likely to apply

across regions within countries than across countries.” Still another assumption is that factors of

production tend to be more mobile across regions within countries than across countries because

legal, cultural, linguistic and institutional barriers to factor movements tend to be smaller across

the regions within a country.

Literature on Infrastructure & Development

The linkage between infrastructure and economic growth is multiple and complex, because not

only does it affect production and consumption directly, but it also creates many direct and

indirect externalities, and involves large flows of physical inputs and expenditures thereby

creating additional employment. It generates a continuous flow of opportunities for people living

in different layers and spaces within a country as well as across countries. Most of the empirical

studies on macroeconomic impact of infrastructure were generated after the 1980s. These studies

suggest that infrastructure does contribute towards a hinterland’s output, income and

employment growth as well as quality of life (Looney and Frederiksen, 1981; Aschauer, 1989;

Munnell, 1990; Kessides, 1996; Looney, 1997). Unlike the developed nations, unequal

distribution of basic infrastructure facilities across different regions within a developing country

15

like India may generally be so pervasive as to nullify the operation of the law of diminishing

returns to particular factor in specific regions. And ultimately, economies of agglomeration

create a “backwash effect” against the waning regions. Because, the productive individuals in the

unfortunate regions never stops moving away from the origin towards better off regions thereby

strengthening both inter-regional and rural versus urban disparity. In fact, much before the

recent resurgence of the theory of convergence, the pioneering works of Myrdal (1958) and

Hirschman (1958) showed why economic activities starting from “historical accident” are

concentrated in a particular region.

Applied Empirical Indian Literature

Extreme diversity among the constituent regions and our emotional predilection towards national

integration on cultural lines may be substantially responsible for our failure to promote objective

analysis of the interrelationship among the economic and extra-economic factors of development

in a general economic framework at the sub-state level. Such negligence over last 60 years

appears to have not only strengthened the dissecting forces, which prefer a weak central

government, but also encouraged the tendency towards intense economic corruption, which

generates rents within the present decentralized set up. In recent period, the research works done

on India have mostly focused solely on state level analysis, though there are intense district level

disparities in all conceivable indicators of development. The following works among others have

dealt with the details of inter-state disparities and divergence in India: Dutta Roy Choudhury

(1993), Dholakia (1994), Marjit and Mitra (1996), Nagaraj, Varaudakis and Veganzones

(2000), Ghosh, Marjit and Neogi (1998), Ghosh and De (1998, 2000a, 2000b, 2001, 2002,

2003, 2004, 2005a, 2005b, 2005b), Ahluwalia (2000), Dasgupta et al. (2000), De and Ghosh

(2003) and others.

Some studies addressing the problems of regional disparity in India during the last two decades

have hardly dealt with infrastructure and regional inequality. One of the earliest works on

conventional regional disparity was done by Heston (1967) though he did not deal directly with

infrastructure. Barnes and Binswanger (1986), Elhance and Lakshmanan (1988),

Binswanger, Khandkur and Rosenwing (1989), De and Ghosh (2003), Ghosh and De (1998,

2000a, 2000b, 2001, 2002, 2004, 2005a, 2005b, 2005c), Ghosh, Marjit and Neogi (1998), Datt

16

and Ravallion (1998), Krishna (2003) and Marjit and Ghosh (2000, 2004) deal directly with

infrastructure and income in order to understand inter-state disparity. Topalova (2005) in a

recent work on Indian districts find that trade liberalization has led to an increase in poverty and

poverty gap in the rural districts, where industries more exposed to liberalization are

concentrated. The effect is quite substantial.

It would be nice to work with agro-climatic regions of NSS, but our national information system

beyond NSS does not collect data on the basis of these NSS regions. For details on the

justification of NSS district level poverty estimates and NSS regions, see NSS Regions and also

Jain, Sundaram and Tendulkar (1988), Dube and Gangopadhyay (1998) and Sastry (2003).

17

Chapter 2. Social Development Index: Data & Methodology

UNDP Approach

The United Nations Development Program (UNDP) has been publishing in each year an annual

Human Development Report (HDR) since 1990 in order to review the progress of some specific

and broad indicators of well being of the people across the countries of the world. Interested

readers may consult the works of McGrahaman et al. (1972), Morris (1979), Streeten et al.

(1981), Morris and McAlpin (1982), Stewart (1985), Sen (1985, 1992), Kelly (1991), Desai

(1991), McGillivray (1991), Srinivasan (1994), Anand and Sen (1995a and 1995b),

Ravallion (1997), Bardhan and Klasen (1999), Dowrick et al. (2003) and related works for the

origin and evolution of this approach. There are many instances where income does not work as

a good indicator of well being. Distributional bias works against it. What is more, income data

are not available in many countries including India. On the other hand, health and education may

be much more reliable proxy of well being in the absence of income and expenditure data. Thus,

the Index is shown in relative terms between 0 and 1. It is actually estimated in such a way that it

becomes essentially a ‘deprivation index’ ranging between zero and one.

Efforts in India:

Following the UNDP’s Human Development Report, the Planning Commission of India has

started publishing similar reports since 2001. It stated that:

“Following the UNDP’s human development framework, the National Human Development

Report seeks to put together indicators and composite indices to evaluate development process in

terms of ‘ex-post outcomes’ rather than only in terms of available ‘means’ or ‘inputs’. The

Report, recognizing the broad based consensus that exists on the three critical dimensions of

well-being, focuses on identifying the various contextually relevant indicators on each of them.

These dimensions of well-being are related to:

• Longevity — the ability to live long and healthy life;

• Education — the ability to read, write and acquire knowledge; and

18

• Command over resources — the ability to enjoy a decent standard of living and have a socially

meaningful life.”

The Planning Commission essentially followed the HDI method and estimated state level

development index over the period from 1980s to 1990s taking into account many more factors

than by UNDP’s HDI dividing the states into rural and urban areas. This report is very

comprehensive. But it stopped short of trying to link the development indices with growth

performance of the states at disaggregated levels. Sub-state level performance differentials could

not be compared across board.

National Council of Applied Economic Research (NCAER) in 1993–94 initiated a major

research project on Human Development on behalf of the Planning Commission. The objective

was to construct a human development profile for major states in the country through data

available from secondary sources and also from a large primary survey. The NCAER–HDI

sample survey – 1994, covered 33,200 rural households spread over 1765 villages in 195 districts

of 15 major states and the North-Eastern region. The data generated through the survey enabled

NCAER to construct about 100 indicators of progress in four broad areas of social concern, viz.

material well-being, health, education and basic amenities. The NCAER data are, therefore,

useful in building HDI highlighting the inter-state differences in different aspects of social well

being. As in all previous endeavours, this study too did not pay much heed to the sub-state level

disparities.

Another attempt by Kundu et al. (2002) on behalf of NCAER has derived from the NCAER

database state level development index with the help of both HDI method as well as principal

component analysis. They used 41 indicators from four broad areas of social concern namely

economic development, health, education and basic amenities. A more recent study by Debroy

and Bhandari (2005) have derived with the help of modified HDI method an interesting index

called Economic Freedom Index for the states of India. This study eventually used 26 indicators

for 20 states of India. Ghosh and De (1998, 2000a, 2004 and 2005a) have independently

attempted similar works at the state level. They have divided infrastructure development index

into three parts such as economic overhead capital index, social overhead capital index and

financial overhead capital index with the help of principal component analysis at the state level

19

for four different points since the 1970s. But they have taken into account only 10 major

indicators of development along with port dummy, which has been proved to be an important

man made factor (given geographical limitation) for explaining state level disparities.

These works have certainly begun the process of empirical analysis of the conditions of living of

the people at the state level. But none of these aggregate indices signals us anything about what

actual communities living in a lower geographical unit within the states perceive as social

development.

Methods of Indexation:

1. Principal Component Analysis There are various methods of estimating development indices across regions within a country.

One of the oldest statistical measures is the Principal Component Analysis (PCA). It is the well-

known multivariate technique of factor analysis or principal component analysis used primarily

for reducing number of attributes or indicators from a large basket (Fruchter, 1967). The

development index is a linear combination of the unit free values of the individual development

indicators chosen by the researcher such that

Development Indexij = ∑Wk j Xk i j (2.1)

where Indexij = development index of the i-th region in j-th time, Wkj = weight of the k-th

indicator in j-th time, and Xkij = unit free value of the k-th facility for the i-th region in j-th time

point.

In the PCA approach, the first principal component is that linear combination of the weighted

facilities that explain the maximum of variance across the observations at a point of time. The

rationale for using PCA is that it helps one to reach an aggregate representation from various

individual indicators with varying weights. Its overall objective is consistent with homogenizing

the overall requirements for the individual development indicators across the regions within a

common constitutional set up. Before multiplying by the respective weights derived from

20

“rotated factor loading” the raw individual attributes have been converted into “unit-free” values

dividing by the column-wise (indicator-wise) standard deviation, or the maximum value to

neutralize the heterogeneity due to varied units.

In spite of its numerical impartiality, the mathematical logic of PCA method is not guided by the

economic rationality of a national policy maker. It is not possible for such a numerically sound

statistical methodology to understand the need and priority of the people. It very often comes out

with such results, which assign negative weights to important indicators such as education,

health and the like. We have here estimated the relative weights of all the individual attributes

across both states and districts with the help of Principal Component Analysis. But the weights

here appear to go against economic logic of development across regions. This is why we have

eventually discarded the method in the final analysis. Applying the PCA for reducing the

number of attributes also did not give much satisfactory results. A better alternative may be to

verify the cross-correlation matrix with possible large set of indicators taken from Census of

India. The details of the process of selection of appropriate indicators will be analyzed below.

Let us briefly discuss the inconsistencies of the weights derived from PCA for 29 states with 44

and 42 indicators respectively. The factor weights are produced in Table 2.1a and Table 2.2a.

The estimated weighted scores of the states from these factors are respectively presented in

Table 2.1 and Table 2.2. Note that all data are normalized in terms of either region or population

as required.

Some interesting findings may be noted from the first two tables.

(i) For both 44 and 42 indicators, literacy rates for males and females, number of schools and

colleges, life expectancy at birth, number of factories, main worker proportions, household using

electricity as source of light, and scheduled caste and scheduled tribe worker proportions

(separately for male and female) have not got significant weights in explaining the total variance

across the states. Moreover, some of the indicators have been attributed negative weights by

PCA. By any rational thinking, these indicators are granted to be the most decisive factors for

policy induced development strategy for both backward regions and backward communities.

21

(ii) State-wise ranks in the two cases are not significantly different. For example, in both cases,

Delhi achieved the highest score, followed by Goa, Kerala, Punjab, Himachal Pradesh and

Haryana. In the other end, Jharkhand is the worst performing state followed by Bihar,

Chhattisgarh, Madhya Pradesh and Orissa. So using PCA for deriving a series of weights for

reaching a composite index does not apparently create much conflict with common sense

perception with regard to regional performance across Indian states. But the unwarranted weights

give wrong priorities from the viewpoints of the policy makers as regards relative state-wise

performance. Moreover, it does not help us to regroup the chosen indicators in terms of various

social sectors on the basis of a priori notion. For example, we would like to decompose SDI into

work participation, human capital (read ‘education’), health and housing, transport &

communication, and finally, economic indicators. One could, of course, normalize the PCA

derived weights between zero and one, even if they are negative. But doing that would not add

any better property. That is possible with the help of a specific distribution based methodology,

which is also tested here.

(iii) Still another is that even if different regions have same priorities, different communities may

not have. As our purpose is to explore sub-state level performance in an universal way primarily

because of the fact nothing is known in advance about varying priorities, we refrain from

focusing further on state level analysis except occasionally referring to the same for better

understanding of sub-state level differences, which are the root cause for concern of the Central

Government in the present state of widespread social instability.

2. Distribution Based Indexation

Another method, which has gained in importance in theoretical studies, relates to the method of

indexation on the basis of specific distribution applied to various attributes depending on the

nature of the statistical properties of the attributes (Mallikarjuna, Chandrashekhar and

Reddy, 2008). Theoretically, it is adequately sound. But it also suffers from the limitation that

one has to fix a specific distributional function on the whole set of observations, say districts, for

a particular indicator. This also fails to accommodate the extreme observations. For real life

22

policy analysis by capturing extreme cases of development and backwardness as in Indian

districts, this type of method may not be of much help.

The values of indices obtained from this method for both states and districts are compared to

those estimated by UNDP method as used here. Pearson’s correlation coefficients for all the

components and also for SDI in general are as high as 0.90 and above. That is, there is no

significant difference between these two methods of indexation. This has motivated us to stick to

the more general approach followed by UNDP in calculating HDI. One could also try for some

suitable parametric method. But such approach would essentially either ignore the extreme

observations as outliers, or give less weight to these observations, or prioritize among various

indicators, which is not desirable in the present context of extremely contrasting development

among districts.

3. UNDP Method

Finally, the most popular method as is used by UNDP for deriving its HDI is both the simplest

and most comprehensive, yet it captures the extremes of development and backwardness across

the regions under scrutiny without any special treatment for either attribute or observation.

Moreover, if one is not satisfied with only three main development indicators, one is free to add

as much as is demanded by empirical reality. There is hardly any doubt that this is a highly

generalized measure for capturing the deprivation in society not only across regions but also

across communities. In this study, we have experimented with all these methods, but ultimately

worked with the UNDP method because of its all-encompassing nature. Giving equal weights to

all indicators is not certainly desirable. But assigning unwarranted weights to different factors is

also not justified. In a situation, where regional preferences are not definitely known, it may be

less undesirable to assign equal weights.

In essence thus, it converts each region’s achievement in a scale from zero to one thereby

removing the problem arising out of units of measurement of the original attribute. No extra

effort is required for normalizing the variables for extreme inter-regional variance. It is also

23

suitable for understanding inter-temporal performance differential with suitable adjustment of the

maximum and minimum values.

Quantitatively, it is the average distance of each region from the lowest performing one as

expressed in terms of unit level of extreme regions, best and worst. It is derived by the formula

SDIij = )Minimum - (Maximum)Minimum - (Own value

ijij

ijij

1∑=

n

i…..(2.2)

Here SDI is the social development index of any kind. Here we have used four components of

SDI such as work participation index (WPI), human capital index (HCI), health and housing

index (HHI), and transport & communication index (TCI) separately for the rural and urban

parts of the districts. These group indices are then averaged to derive the social development

index (SDI) by simple mean. This is the first version of SDI, which is designated as SDI4 as it is

computed from four components.

We originally began with 80 indicators at the district level without applying any preconceived

notion depending on availability. By common sense logic, we realized that some indicators were

substitutes. Then guided by the Pearson’s correlation matrix with very high level of significance

coupled with occupational pattern of the informal sector, we have chosen 16 indicators for WPI,

seven for HCI, 13 for HHI, and five for TCI for the rural parts of the districts in 2001. For

urban areas, four items came to be recognized as irrelevant, which were not included. These are:

agricultural labour (male), agricultural labour (female), cultivator (male) and cultivator (female).

As life expectancy at birth is not separately available for rural and urban areas, the same is used

for both. Appendix 2.1 presents the details of these indices along with their composition.

Four variants of SDI are estimated. The first is SDIR4 and SDIU4 respectively for rural and

urban areas using only the social indicators from Census of India for 2001. The second variant

includes two economic indicators additionally from NSSO 61st Round (2004-05), namely

inverse of poverty ratio (HCRR5i for rural and HCRU5i for urban), and MPCE, which is called

purchasing power real (PPRR5 for rural and PPRU5 for urban areas). These are used as fifth

24

and sixth components of social development index: SDIR6 for rural and SDIU6 for urban. The

third variant additionally includes inverse of inequality, here Gini coefficient, with the second

variant: SDIR7 for rural and SDIU7 for urban areas.

Many experts are of the opinion that economic inequality may rise at the initial stage of

economic development through which India is believed to be passing now. A small minority also

hold that inequality is very intrinsic by nature itself. Others strongly believe that is a social vice

for sustainable development. However, Generality demands that we should include it, although

there is no a priori evidence so far to judge whether inequality is the main culprit, or vulnerable

population. This will be tested later. The fourth variant of SDI is not common. We have ventured

to experiment with it because there is a prevalent belief that regions with high work participation

rate in informal and unorganized sectors are essentially very backward and poor in terms of both

social and economic opportunities. And work participation by definition as provided by Census

of India does not necessarily mean a reasonably better opportunity of earning. As a result,

inclusion of various indicators of work participation might unnecessarily inflate the values of

SDI for these districts. This suggestion by the Ministry is taken care of appropriately. Another

suggestion is that ignoring the proportion of SC and ST population in work force would take us

away from the reality. We have also included in work participation index the shares of both SC

and ST population so that one may choose between these two types of indices. This is essentially

equivalent to the fourth variant of SDI excluding work participation index (WPI) from both rural

and urban sectors: SDIR7_W for rural and SDIU7_W for urban areas. It now remains to be seen

how deprived communities particularly scheduled caste and scheduled tribe are related to these

social and economic indices at the sub-state level.

Crime data could not be separately available for rural and urban parts of the districts. Moreover,

behaviour of recorded crime data is not consistent either. That is, the best performing states have

recorded higher overhead crime rate of all types compared to those of worst performing states

due mainly to varying nature of reporting system and also post-recording consequences on the

family of the victim. This is why it was not possible to incorporate crime information into the

indexation. Nature and rate of crime in rural and urban areas have altogether different roots and

ramifications.

25

Economic Parameters

A few words need to be at the outset on the use of NSSO data on poverty, inequality and

purchasing power. Objects of any society is to see the largest share of its citizens enjoying a high

and stable standard of living with reasonably low poverty and inequality (social and economic)

irrespective of the magnitude of the poverty line. This simple mission is not easy to achieve in

the short run and in a country with a billion people way back in 2004-05.

(1) In so far as economic parameters are concerned, poverty ratio (HCR) is estimated at the

district level separately for rural and urban areas on the basis of state specific poverty lines (PL)

given by the Planning Commission for NSSO 61st Round, 2004-05.

(2) Mean MPCE is also estimated for both rural and urban sectors. Price data for deflating

MPCE in order to make district level comparison meaningful across India are generated by

transforming state specific poverty lines into all-India base. For example, if all India rural PL of

Rs.356.10 is equated to 1.00, then the Gujarat rural PL of Rs.353. 93 = 0.9933. For urban areas,

if all-India PL of Rs.538.60 is equated to 1.00, then the Gujarat urban PL of Rs.541.16 = 0.9933.

This is presented in Table 2.3. Table 2.4 lists the details of indicators according to the estimated

indices.

(3) In the absence of any panel data for such a large number of districts to find out the sequence

of causality, the best we can deliver is to enquire the strength of various pair of social indices

pertaining to the year 2001 and economic parameters for the year 2004-05 with the help of

Pearson’s correlation coefficients. One may argue how we venture to link the parameters of two

different years, Census 2001 and NSSO 2004-05. Apparently, it may appear to be inconsistent.

Note that our purpose is to find out the district level disparities with the help of both social and

economic indicators. Given Indian official information system, we are lucky that social

indicators are available for a time point, which is prior to economic indicators by a couple of

years, here just about three years, with the given mismatch of months between Census and

NSSO. In classic development literature, it is hypothesized that social development facilities are

26

part of social infrastructure largely created by governmental capital formation, which, with some

time lag, creates positive externalities for economic performance of the people living in specific

regions. In this case, a gestation lag of three years, even if small, is in the right direction. If it

were in the other way round, it would be difficult for any analyst to use them simultaneously.

This will be analyzed in Chapter 4 in the context of the relationship between social and

economic parameters, between rural and urban areas, and also among the indices themselves.

Descriptive Statistics of the Indices

The required descriptive statistics of the computed indices across the districts speak a lot. Table

2.5 and Table 2.6 present the mean, maximum, minimum, CV and other relevant statistics for

rural and urban areas respectively. Let us first concentrate on two columns, maximum/minimum

and CV2, which is derived from dividing the SD by lower quartile value, whereas CV1 is SD

divided by all districts mean.

(1) In all the rural districts, the ratio of the indices between the extreme districts is

significantly high.

(i) It is appalling in case of transport and communication index (TCIR1), reaching about 41

times. Its coefficient of variation is also very high (63%). It suggests that the disparity is not

limited to the best and the worst districts only. It is true across board. Similarly, literacy rate for

female population has also recorded very high disparity across the districts. The same is true for

poverty ratio, which, albeit, is known to all. Though the max/min ratio for Gini coefficient is

extremely high (12.35), the coefficient of variation of Gini across the districts is relatively low.

This means that it is almost a universal across the districts. Even if the max/min ratio for all other

indices lies between two to four times, CV2 of none is too high except TCI, poverty and female

literacy. That is, there does not appear to have unusually high disparity in social indices except

transport and communication, although economic disparity is much more glaring. In sharp

contrast to popular belief, the fact that disparity in sex ratio is also not high suggests that it is also

a universal feature.

27

(ii) Even with such apparent homogeneity with highly normalized indices, two important issues

must be mentioned here. First, difference between the lower and upper quartile is high in case

human capital, health & housing, transport & communication, social development index itself,

and poverty and purchasing power. Second, if one compares the coefficients of variation

estimated by overall mean, two unusual findings are worth noting here. In many of the individual

indicators from each of the indices, the values of CV are alarmingly high. But the pattern is same

for both states and districts.

(2) What about the basic features of the indices for the urban districts? As in rural districts,

so also in urban districts, extreme districts lie apart in TCI with moderately high CV. Among the

rest, high disparity is observed only in poverty ratio and purchasing power real across the urban

districts. Therefore, unlike rural districts, inter-district disparities in economic parameters are

stronger among the urban parts of the districts compared to the rural areas. In a sense, therefore,

economic disparities among urban areas in India are really glaring, while rural parts of the

districts are generally lagging behind across board.

There is a widespread misconception nurtured at different layers of government regarding the

comparability between states and districts, which has intruded into some layers of academic

spheres too. It is widely circulated that inter-district disparities should not be placed in the same

platform with inter-state disparities as the former would be essentially higher in any development

indicators- economic or social. Nobody has perhaps enquired into the matter with pure statistical

impersonality. We try to be statistically unbiased here. The preliminary results are presented here

in Table 2.7. This table reports the coefficients of variations of the relevant indicators selected

by us under each of the SDI components separately for the states and districts. This enables one

to verify several misconceptions as prevalent in different layers of the government regarding

socio-economic disparities at state and district levels. Many interesting features hitherto hidden

become precise here.

(1) Let us begin with TCI for rural areas. It is composed of five indicators, namely (i) household

with telephone connection, (ii) household with radio, transistor, etc., (iii) household having bi-

cycle, (iv) household having scooter, moped, etc., and (v) number of factories per one lakh (0.10

28

Million) of population. In all the indicators under TCI, CV is high for both states and districts.

Out of these, the first four indicators are direct and positive function of family income at a point

of time in any particular region. But they too are outcomes of overall development of a region,

which is guided by altogether different sets of factors, which could best be captured by public

infrastructure facilities and available opportunities of the regions. The last among these, that is,

number of factories is one of those factors, which can be taken as creating opportunities for

general economic activities even for those who do not directly work in the factories. Both

forward and backward linkages of industries create multiplier chain in the long run. Backward

area development programme is some such policy. The fact that vast areas are lacking such

facilities is more a government failure than a case of market failure. In all these five indicators,

both inter-state and inter-district level disparities are high across board except the “group of five”

(Kerala, Punjab, Himachal, Haryana and Gujarat). The picture is almost similar in urban areas

except the fact that in TCI itself the value of CV is lower in urban areas for both states and

districts. Therefore, disparities are widespread and intense for most of the indicators under

transport and communication (TCI) in rural areas at both state and district levels, while there are

small differences in urban areas. For example, district level disparity is high in telephone density

compared to that at state level. And unlike other indicators, radio, TV, etc. is more equally

distributed in urban areas compared to rural areas at both state and district levels. These

differences are expected, given the nature of information contained in these indicators.

(2) For all the indicators under rural and urban WPI, there is no significant difference of the

relative strength of the CV between the states and districts. More specifically, in rural areas,

values of CV are reasonably low in both states and districts for male main workers, male

cultivators and male marginal workers. That is, both states and districts characterize equal

pattern of work participation with no major disparities. For rest of the indicators under rural

WPI, variations are equally high for both states and districts. Again, there are no significant

discrepancies between states and districts. The same pattern is observed for the indicators under

urban WPI except that non-agricultural workers (both male and female) records low variations

again for both states and districts. Thus, whether it is high or low, the pattern is precisely same

for both states and districts.

29

(3) What about the indicators under human capital index (HCI)? The same pattern as with WPI

is observed with the indicators under HCI for both states and districts in both rural and urban

areas except households availing banking services (HABS). It is quite natural that variations are

high in banking services in rural areas for both states and districts. On the whole, in most of the

indicators under HCI, there is no significant discrepancy between states and districts.

(4) Inconsistencies are also not very common for the indicators under health and housing (HHI)

between rural and urban areas. In 12 out of 13 indicators, the pattern of variations is similar

across states and districts in rural areas. In households with no exclusive room (HWNER), the

value of CV is high for both rural and urban areas of the districts, but low for the states. Apart

from this, variations are very high for number of hospitals and dispensaries per one lakh (0.10

Million) population (NHD1L) and households using LPG cylinder among urban districts, but

low among urban states. This means that relative disparities in these public utility services are

high among urban areas compared to rural areas at disaggregated levels. In one sense, rural areas

are uniformly unequal at both state and district levels, while urban districts display more unequal

than urban states. Note that for hospital and dispensaries, supply side factor plays a pre-dominant

role compared to de facto data, while for cooking gas, both demand and supply side factors

matters for available information.

(5) Finally, and most importantly, patterns of distribution of economic indicators, namely

poverty, purchasing power and inequality are quite similar between rural and urban areas

irrespective of level of aggregation, that is, state or district. For example, there are wide

variations in poverty ratio at both state and district levels. But in purchasing power and economic

inequality, there is no significant disparity between states and districts in both rural and urban

areas. It is all too known that poverty ratio varies between zero percent and about 90% at district

level, whereas it lies between zero and about 46% at state level. This finding in itself is an

extremely crucial justification to focus attention away from the states and at the districts level.

Unlike poverty ratio, purchasing power and inequality data do not display any significant

disparities between state and district irrespective of rural and urban areas. However, such wide

variation of poverty ratio at the district level creates an intense headache for us, because it is

30

likely to cause significant differences in SDI with or without poverty ratio. We have to wait till

Chapter 5 to visage the importance of this simple information.

Nature of Distribution of the Indices

Given the purpose of this study, it would be unjustified not to look through the nature of

distribution of the indices across the districts. This also helps understand what is meant by the

mainstream and outliers, even if indices are all-encompassing. The fitted normal distribution

along with the frequency bars and the corresponding values of Chi-square are presented

separately in 28 diagrams from Figure 2.1 to Figure 2.28. The main observations are condensed

in Table 2.8. The table shows the values of Chi-squares with corresponding degrees of freedom

in brackets against fitting normal distribution. The definite comments about the nature of

distribution of the indices across the districts are reported in separate column. In practice,

expected frequencies are calculated on the basis of a preconceived hypothesis, which is called

the Null Hypothesis, H0. If under this hypothesis, the computed value of χ2 (called Chi-square)

given by equation (2.3) or (2.4) is greater than some critical value (such as χ2.95 or χ2

.99, which

are the critical values of the 0.05 and 0.01 significance levels, respectively), we would conclude

that the observed frequencies differ significantly from the expected frequencies, and would reject

the Null Hypothesis at the corresponding level of significance; otherwise, we would accept it (or,

at least, not reject it, to be precise statistically). This commonly used procedure is called the Chi-

square test. If the value of Chi-square happens to lie close to zero, we should look at it

suspiciously, because it is a rare event that observed frequencies agree too well (that is, 100%)

with expected frequencies. However, such ideal results are not very common. A measure of the

discrepancy existing between the observed (o) and expected (e) frequencies is given by the

statistic,

χ2 (read ‘Chi-square’) = j

jjk

jk

kk

eeo

eeo

eeo

eeo 2

1

2

2

222

1

211 )()(

.....)()( −∑=

−++

−+

−=

…..(2.3)

where if the total frequency is N,

31

then Neo jj =∑=∑ . An equivalent expression is

χ2 = Neo

j

j −∑ 2

2

…..(2.4)

The major findings are briefed here.

(1) Out of 19 variables, there are eight development indices and 11 development indicators for

both rural and urban districts. The most prominent observation is that in eight out of these 19

variables, there is no rural versus urban harmony. In all the variants of SDI, the rural and urban

have divergent tendencies. Specifically, in SDI4 there is high dispersion among the rural

districts, that is, the districts are not normally distributed, whereas there is perfect normal

distribution among the urban districts. The same observation is true for SDI6, SDI7 and

SDI7_W. But in SDI6, this divergence is slightly weak.

(2) The same diverging pattern between rural and urban districts are confirmed for the

components of SDI, namely WPI, HCI and TCI except HHI. That is, health and housing

conditions are equally disperse and unequal in both rural and urban segments. For more clear

understanding, one can simultaneously look at Figure 2.1 (SDIR4), Figure 2.6 (HCIR1),

Figure 2.7 (HHIR1), Figure 2.8 (TCIR1), and Figure 2.16 (SXR06RD1) for highly biased

distribution of the rural districts in 2001. On the other hand, for extremely high disharmony in

distribution among the urban districts, one can look at Figure 2.19 (SDIU7), Figure 2.20

(SDIU7_W), Figure 2.21 (WPIU1), Figure 2.23 (HHIU1) and Figure 2.28 (SXR06UD1).

(3) The results of distribution in male and female working population from SC and ST are not

presented. They can be gauzed from Table 2.8. The said distributions across the districts are

extremely biased. But it is at best a natural phenomenon.

(4) What about the performance distribution of the districts in the three economic

indicators? It is doubtless that the districts are not normally distributed in poverty ratio and

purchasing power in both rural and urban areas. But the picture is fairly different in economic

32

inequality between rural and urban districts. While the rural districts display a close to normal

distribution in inequality, the urban districts do not. Figure 2.9 (HCRR5), Figure 2.10

(PPRRD5) and Figure 3.11 (GINIRD5) respectively present the frequency distributions for

poverty, purchasing power and inequality in rural districts. The corresponding urban pictures are

given in Figure 2.25 (HCRUD5), Figure 2.26 (PPRUD5) and Figure 2.27 (GINIUD5).

Therefore, it may be concluded that there are higher disparities among the urban districts in

economic inequality in contrast to rural districts, whereas the rural in general is deprived

universally across India except the “group of five”.

We would return to the functional details of these findings, and point out more precise

relationship among these indicators and also between rural and urban districts in Chapter 4.

33

Appendix 2.1. Composition of Social Development Indices for Rural & Urban Districts in 2001 & 2004-05

I(a). WPIR1 (Work Participation Index for Rural District in 2001) consists of following indicators:

1. MWTPMRD1: Percentage of Male Main Worker to Total Male Population in Rural part of a District in 2001.

2. MWTPFRD1: Percentage of Female Main Worker to Total Female Population in

Rural part of a District in 2001. 3. ALTWMRD1: Percentage of Male Agricultural Labourers to Total Male Workers in

Rural part of a District in 2001. 4. ALTWFRD1: Percentage of Female Agricultural Labourers to Total Female

Workers in Rural part of a District in 2001. 5. CLTWMRD1: Percentage of Male Cultivators to Total Male Workers in Rural part

of a District in 2001. 6. CLTWFRD1: Percentage of Female Cultivators to Total Female Workers in Rural

part of a District in 2001.

7. NATWMRD1: Percentage of Male Non-Agricultural Workers to Total Male Workers in Rural part of a District in 2001.

8. NATWFRD1: Percentage of Female Non-Agricultural Workers to Total Female

Workers in Rural part of a District in 2001.

9. HITWMRD1: Percentage of Male Household Industry Workers to Total Male Workers in Rural part of a District in 2001.

10. HITWFRD1: Percentage of Female Household Industry Workers to Total Female

Workers in Rural part of a District in 2001. 11. MRTPMRD1: Percentage of Male Marginal Workers to Total Male Population in

Rural part of a District in 2001. 12. MRTPFRD1: Percentage of Female Marginal Workers to Total Female Population

in Rural part of a District in 2001. 13. SCMWPMR1: Percentage of SC Male Main Workers to Total SC Male Population

in Rural part of a District in 2001.

34

14. SCMWPFR1: Percentage of SC Female Main Workers to Total SC Female Population in Rural part of a District in 2001.

15. STMWPMR1: Percentage of ST Male Main Workers to Total ST Male Population in

Rural part of a District in 2001. 16. STMWPFR1: Percentage of ST Female Main Workers to Total ST Female

Population in Rural part of a District in 2001.

I(b). WPIU1 (Work Participation Index for Urban District in 2001) is composed of all the

above indicators as used for rural areas except agricultural labourers (male and female) and cultivators (male and female).

II(a). HCIR1 (Human Capital Index for Rural District in 2001) consists of following

indicators:

1. ASHHRD1: Average Size of Households in Rural Part of a District in 2001.

2. LRMRD1: Literacy Rate of Male Person in Rural Part of a District in 2001.

3. LRFRD1: Literacy Rate of Female Person in Rural Part of a District in 2001.

4. SXR06RD1: Sex Ratio of 0-6 Age Group in Rural Part of a District in 2001.

5. SXRAARD1: Sex Ratio of All Age Group in Rural Part of a District in 2001. 6. NSC1LRD1: Number of Schools and Colleges per One Lakh Population in Rural Part of a

District in 2001. 7. HABSRD1: Households Availing Banking Services in Rural Part of a District in 2001. II(b) HCIU1 (Human Capital Index for Urban District in 2001) consists of all the above

indicators as in rural districts. III(a). HHIR1 (Health and Housing Index for Rural District in 2001) consists of the

following indicators:

1. HUESLRD1: Percentage of Households using Electricity as Source of Light in Rural part of a District in 2001.

2. HWBFRD1: Percentage of Households having Bathroom Facility within house in Rural

part of a District in 2001.

35

3. HWCDRD1: Percentage of Households with Closed Drainage within the house in Rural

part of a District in 2001.

4. NHD1LRD1: Number of Hospitals, Dispensaries etc. per 1 Lac Population in Rural part of a District in 2001.

5. HULPGRD1: Percentage of Households using LPG as fuel for cooking in Rural part of a

District in 2001.

6. DWWHRD1: Percentage of Households having Drinking Water within the premises (House) in Rural part of a District in 2001.

7. HWFLMRD1: Percentage of Households with Floor Materials Mud in Rural part of a

District in 2001.

8. HWRMCRD1: Percentage of Households with Roof Materials Concrete in Rural part of a District in 2001.

9. HWRGTRD1: Percentage of Households with Roof Materials Grass, Thatch etc. in

Rural part of a District in 2001.

10. HWNERRD1: Percentage of Households with No Exclusive Room in Rural part of a District in 2001.

11. HW1RRD1: Percentage of Households with One Room in Rural part of a District in

2001.

12. POCHRD1: Percentage of Owned Census Houses to total houses in Rural part of a District in 2001.

13. LEBTD1: Life Expectancy at Birth for the Total District as a whole in 2001. As LEB is

not available for all the districts, it was substituted by values from similar districts. III(b). HHIU1 (Health and Housing Index for Urban District in 2001) consists of all the above indicators except Households with No Exclusive Room as in rural district. IV(a). TCIR1 (Transport & Telecommunication Index for Rural District in 2001) consists of the following indicators:

1. HWTCRD1: Percentage of Households having Telephone Connection in Rural part of a District in 2001. 2. HWRTRD1: Percentage of Households having Radio, Transistor etc. in Rural part of a District in 2001.

36

3. HHBRD1: Percentage of Households having Bicycle in Rural part of a District in 2001. 4. HHSMRD1: Percentage of Households having Scooter, Moped etc. in Rural part of a District in 2001. 5. NF1lRD1: No. of Factories per One Lakh Population in Rural part of a District in 2001.

IV(b). TCIU1 (Transport & Tele-Communication Index for Urban District in 2001) consists of all the above indicators as in rural district. V(a). SDIR4 (Social Development Index averaging the four individual indices for Rural Districts in 2001) consists of the above four individual rural indices as follows:

SDIR4 = 4

)1111( TCIRHHIRHCIRWPIR +++

V(b). SDIU4 (Social Development Index averaging the four individual indices for Urban Districts in 2001) consists of the above four individual urban indices as follows:

SDIU4 = 4

)1111( TCIUHHIUHCIUWPIU +++

VI. Economic Variables (NSSO): 2004-05 Using the following economic indicators we have redefined SDI as follows:

1. PPR: Purchasing Power Real (Rs), or Monthly Per Capita Consumer Expenditure

(MPCE)

2. HCR: Head Count Ratio, or percentage of people living below poverty line.

3. GINI: Gini Coefficient, or Inequality Index.

For rural districts, these are symbolized as PPRR, HCRR and GINIR. For urban districts these

are PPRU, HCRU and GINIU.

37

SDIR6 = 6

)551111( PPRRiHCRRTCIRHHIRHCIRWPIR +++++

SDIR7 = 7

)5551111( iGINIRPPRRiHCRRTCIRHHIRHCIRWPIR ++++++

SDIR7_W = 6

)555111( iGINIRPPRRiHCRRTCIRHHIRHCIR +++++ ,

where HCRR5i and GINIR5i are respectively their inverse values. And the final SDI namely SDIR7_W is estimated excluding work participation index. For urban districts, the same set of social development indices are estimated following the same methodology.

SDIU6 = 6

)551111( PPRUiHCRUTCIUHHIUHCIUWPIU +++++

SDIU7 = 7

)5551111( iGINIUPPRUiHCRUTCIUHHIUHCIUWPIU ++++++

SDIU7_W = 6

)555111( iGINIUPPRUiHCRUTCIUHHIUHCIU +++++ ,

where HCRU5i and GINIU5i are respectively their inverse values. And the final SDI namely SDIU7_W is estimated excluding work participation index.

Table 2.1 PCA Factor Scores of 44 Indicators for 29 States (Rural)

Factor 1 RankSN States Scores PCA 44 Scores PCA 44 SDIR4N2 RSDIR4N21 AP -0.303 17 0.494 92 ARP -0.182 16 0.452 143 ASS -0.549 21 0.351 224 BIH -1.266 28 0.196 295 CHA -1.077 27 0.300 246 DEL 2.630 1 0.618 37 GOA 2.228 2 0.775 18 GUJ -0.051 12 0.429 179 HAR 0.521 6 0.475 1010 HP 0.944 5 0.612 411 JHA -1.342 29 0.220 2812 JK 0.090 9 0.522 813 KAR -0.058 14 0.433 1614 KER 1.935 3 0.661 215 MP -1.021 25 0.286 2616 MAH -0.063 15 0.396 1917 MAN -0.361 19 0.473 1118 MEG -0.632 22 0.439 1519 MIZ -0.055 13 0.557 520 NAG -0.310 18 0.537 721 ORI -1.061 26 0.247 2722 PUN 1.392 4 0.553 623 RAJ -0.535 20 0.395 2024 SIK 0.366 7 0.464 1225 TN -0.015 10 0.452 1326 TRI -0.644 23 0.290 2527 UP -0.764 24 0.313 2328 UR 0.228 8 0.389 2129 WB -0.044 11 0.412 18

38

Table 2.1a Factor Loadings of 44 Indicators for 29 States (Rural)

SN Indicators Factor 1 RFactor1 Factor 2 RFactor21 MWTPMR1 0.186 24 -0.308 312 MWTPFR1 -0.123 33 -0.820 443 ALTWMR1I 0.483 20 -0.557 394 ALTWFR1I 0.471 21 -0.482 375 CLTWMR1 -0.580 44 -0.590 406 CLTWFR1 -0.208 36 -0.729 427 NATWMR1 0.872 2 0.155 158 NATWFR1 0.757 7 0.430 89 HITWMR1 -0.207 35 0.620 2

10 HITWFR1 -0.062 31 0.549 311 MRTPMR1 -0.218 37 0.072 1712 MRTPFR1 -0.491 43 -0.181 2713 SCMWPMR1 0.060 28 -0.335 3314 SCMWPFR1 0.073 26 0.018 1815 STMWPMR1 -0.386 41 -0.387 3516 STMWPFR1 -0.260 39 -0.668 4117 ASHHR1I 0.254 23 -0.140 2618 SXR06R1 -0.491 42 -0.315 3219 SXRAAR1 -0.141 34 -0.063 2120 LRMR1 0.660 13 -0.072 2321 LRFR1 0.705 12 -0.277 2922 NSC1LR1 -0.006 30 -0.768 4323 HABSR1 0.736 9 0.170 1324 HUESLR1 0.741 8 -0.262 2825 HWNSLR1I 0.018 29 0.453 726 HWNERR1I 0.073 27 0.380 927 HWBFR1 0.803 4 -0.136 2528 HWCDR1 0.153 25 0.277 1029 NHD1LR1 0.504 19 -0.403 3630 HULPGR1 0.820 3 0.165 1431 DWWHR1 0.636 14 0.462 532 HWFLMR1I 0.604 15 -0.287 3033 HWRMCR1 0.539 17 0.456 634 HWRGTR1I 0.403 22 0.083 1635 HW1RR1 -0.234 38 0.262 1236 POCHRD1 -0.386 40 0.537 437 LEBTD1 0.536 18 -0.121 2438 HWTCR1 0.943 1 0.002 1939 HWRTR1 0.718 10 -0.011 2040 HHBR1 -0.107 32 0.798 141 NF1LR1 0.763 5 -0.067 2242 HHSMR1 0.715 11 0.263 1143 UPPRRS5 0.760 6 -0.347 3444 UHCRRS5I 0.564 16 -0.506 38

Expl.Var 11.849 .. 7.324 ..Prp.Totl 0.269 .. 0.166 ..

39

Table 2.2 PCA Factor Scores 42 Indicators for 29 States (Rural)

Scores PCA 42 Scores PCA 42SN States Factor 1 RFactor 1 Factor 2 RFactor 21 AP -0.374 16 0.066 142 ARP -0.430 17 -1.795 283 ASS -0.517 19 0.355 94 BIH -1.090 28 1.480 25 CHA -1.006 27 -0.047 176 DEL 2.751 1 0.822 77 GOA 2.183 2 -0.367 218 GUJ -0.039 13 0.007 169 HAR 0.501 6 0.658 810 HP 0.850 5 -1.341 2611 JHA -1.184 29 1.117 512 JK -0.024 12 0.248 1113 KAR -0.066 14 -0.572 2314 KER 1.980 3 0.024 1515 MP -0.958 26 0.226 1216 MAH -0.024 11 -0.514 2217 MAN -0.431 18 0.134 1318 MEG -0.803 24 -0.772 2419 MIZ -0.347 15 -2.546 2920 NAG -0.656 23 -0.850 2521 ORI -0.889 25 0.946 622 PUN 1.472 4 1.190 423 RAJ -0.583 21 -0.158 1924 SIK 0.270 8 -1.466 2725 TN 0.008 10 -0.207 2026 TRI -0.534 20 0.311 1027 UP -0.648 22 1.409 328 UR 0.408 7 -0.098 1829 WB 0.180 9 1.739 1

40

Table 2.2a Factor Loadings of 42 Indicators for 29 States (Rural)

SN Indicators Factor 1 RFactor1 Factor 2 RFactor21 MWTPMR1 0.156 23 -0.354 302 MWTPFR1 -0.223 35 -0.804 423 ALTWMR1I 0.398 19 -0.567 384 ALTWFR1I 0.393 20 -0.487 375 CLTWMR1 -0.658 42 -0.485 366 CLTWFR1 -0.298 36 -0.679 407 NATWMR1 0.884 2 0.051 158 NATWFR1 0.791 4 0.360 99 HITWMR1 -0.141 32 0.649 210 HITWFR1 -0.010 29 0.573 411 MRTPMR1 -0.199 34 0.076 1312 MRTPFR1 -0.500 40 -0.136 2213 SCMWPMR1 0.042 27 -0.377 3214 SCMWPFR1 0.101 25 -0.057 1815 STMWPMR1 -0.409 39 -0.395 3416 STMWPFR1 -0.334 38 -0.661 3917 ASHHR1I 0.269 21 -0.252 2718 SXR06R1 -0.520 41 -0.276 2819 SXRAAR1 -0.124 31 -0.112 2020 LRMR1 0.674 13 -0.204 2521 LRFR1 0.676 12 -0.382 3322 NSC1LR1 -0.074 30 -0.802 4123 HABSR1 0.766 5 0.049 1624 HUESLR1 0.698 10 -0.342 2925 HWNSLR1I 0.089 26 0.411 526 HWNERR1I 0.119 24 0.376 627 HWBFR1 0.764 6 -0.203 2428 HWCDR1 0.209 22 0.211 1129 NHD1LR1 0.447 17 -0.449 3530 HULPGR1 0.843 3 0.055 1431 DWWHR1 0.697 11 0.364 832 HWFLMR1I 0.570 15 -0.366 3133 HWRMCR1 0.595 14 0.367 734 HWRGTR1I 0.429 18 -0.004 1735 HW1RR1 -0.167 33 0.224 1036 POCHRD1 -0.327 37 0.588 337 LEBTD1 0.523 16 -0.206 2638 HWTCR1 0.941 1 -0.120 2139 HWRTR1 0.707 9 -0.085 1940 HHBR1 -0.006 28 0.800 141 NF1LR1 0.750 7 -0.166 2342 HHSMR1 0.735 8 0.184 12

Expl.Var 11.071 .. 6.938 ..Prp.Totl 0.264 .. 0.165 ..

41

State/Uts Rural Urban Rural Urban Rural UrbanAndhra Pradesh 292.95 542.89 82.22 100.8 0.8222 1.00797Arunachal Pradesh 387.64 378.84 108.8 70.34 1.08796 0.70338Assam 387.64 378.84 108.8 70.34 1.08796 0.70338Bihar 354.36 435 99.46 80.76 0.99456 0.80765Chattishgarh 322.41 560 90.49 103.97 0.90488 1.03973Delhi 410.38 612.91 115.18 113.8 1.15178 1.13797Goa 362.25 665.9 101.67 123.64 1.0167 1.23635Gujarat 353.93 541.16 99.33 100.48 0.99335 1.00475Haryana 414.76 504.49 116.41 93.67 1.16408 0.93667Himachal Pradesh 394.28 504.49 110.66 93.67 1.1066 0.93667Jammu & Kashmir 391.26 553.77 109.81 102.82 1.09812 1.02817Jharkhand 366.56 451.24 102.88 83.78 1.0288 0.8378Karnataka 324.17 599.66 90.98 111.34 0.90982 1.11337Kerala 430.12 559.39 120.72 103.86 1.20718 1.0386Madhya Pradesh 327.78 570.15 92 105.86 0.91996 1.05858Maharashtra 362.25 665.9 101.67 123.64 1.0167 1.23635Manipua 387.64 378.84 108.8 70.34 1.08796 0.70338Meghalaya 387.64 378.84 108.8 70.34 1.08796 0.70338Mizoram 387.64 378.84 108.8 70.34 1.08796 0.70338Nagaland 387.64 378.84 108.8 70.34 1.08796 0.70338Orissa 325.79 528.49 91.44 98.12 0.91437 0.98123Punjab 410.38 466.16 115.18 86.55 1.15178 0.8655Rajasthan 374.57 559.63 105.13 103.9 1.05128 1.03905Sikkim 387.64 378.84 108.8 70.34 1.08796 0.70338Tamil Nadu 351.56 547.42 98.67 101.64 0.9867 1.01638Tripura 387.64 378.84 108.8 70.34 1.08796 0.70338Uttar Pradesh 365.84 483.26 102.68 89.73 1.02678 0.89725Uttarakhand 478.02 637.67 134.16 118.39 1.34162 1.18394West Bengal 382.82 449.32 107.44 83.42 1.07443 0.83424All-India 356.3 538.6 100 100 1 1

42

Proportion of State PLwith All-India = 1.00

Table 2.3: State-specific Poverty Lines in 2004-05(Rs. Per Capita Per Month) MPCE Deflator

Table 2.4 : Indices-wise Selection of Indicators or Attributes 2001 Census (Districts) Rural

WPI HCI HHI TCI SDI4 SDI6 SDI7S.N.

1 MWTPMR1 ASHHR1 HUESLR1 HWTCR1 WPIR WPIR WPIR2 MWTPFR1 SXR06R1 HWBFR1 HWRTR1 HCIR HCIR HCIR3 ALTWMR1 SXRAAR1 HWCDR1 HHBR1 HHIR HHIR HHIR4 ALTWFR1 LRMR1 NHD1LR1 HHSMR1 TCIR TCIR TCIR5 CLTWMR1 LRFR1 HULPGR1 NF1LR1 PPRR PPRR6 CLTWFR1 NSC1LR1 DWWHR1 HCRR HCRR7 NATWMR1 HABSR1 HWFLMR1 GINIR8 NATWFR1 HWRMCR19 HITWMR1 HWRGTR110 HITWFR1 HWNERR111 MRTPMR1 HW1RR112 MRTPFR1 POCHR113 SCMWPMR1 LEBT114 SCMWPFR115 STMWPMR116 STMWPFR1

S.N. Urban1 MWTPMU1 ASHHU1 HUESLU1 HWTCU1 WPIU WPIU WPIU2 MWTPFU1 SXR06U1 HWBFU1 HWRTU1 HCIU HCIU HCIU3 NATWMU1 SXRAAU1 HWCDU1 HHBU1 HHIU HHIU HHIU4 NATWFU1 LRMU1 NHD1LU1 HHSMU1 TCIU TCIU TCIU5 HITWMU1 LRFU1 HULPGU1 NF1LU1 PPRU PPRU6 HITWFU1 NSC1LU1 DWWHU1 HCRU HCRU7 MRTPMU1 HABSU1 HWFLMU1 GINIU8 MRTPFU1 HWRMCU19 SCMWPMU1 HWRGTU110 SCMWPFU1 HWNERU111 STMWPMU1 HW1RU112 STMWPFU1 POCHU113 LEBT1

Note : In case of districts, we originally began with 44 and 42 indicators, but eventually founda strong substitute relationship between HWNSLR1 (Household with no sources of light) and some other indicators chosen for the category 'Health and Housing Index' (HHI).

43

Table 2.5: Descriptive Statistics of the Districts in 2001 & 2004-05: RuralLower Upper SE of

Indices Valid N Mean Median Minimum Maximum Max/Min Quartile Quartile SD Mean CV1 CV2WPIR1 575 0.354 0.350 0.161 0.564 3.50 0.310 0.400 0.065 0.003 0.18 0.21HCIR1 575 0.441 0.438 0.219 0.732 3.35 0.372 0.489 0.093 0.004 0.21 0.25HHIR1 575 0.415 0.404 0.260 0.690 2.66 0.355 0.462 0.078 0.003 0.19 0.22TCIR1 575 0.244 0.239 0.015 0.607 40.84 0.164 0.304 0.103 0.004 0.42 0.63SDIR4 575 0.364 0.355 0.218 0.571 2.62 0.326 0.392 0.060 0.002 0.16 0.18SDIR6 575 0.405 0.409 0.198 0.652 3.28 0.346 0.455 0.084 0.004 0.21 0.24SDIR7 575 0.363 0.366 0.198 0.642 3.24 0.314 0.407 0.072 0.003 0.20 0.23

SDIR7_W 575 0.364 0.364 0.157 0.692 4.40 0.310 0.409 0.080 0.003 0.22 0.26HCRRD5 575 28.04 22.31 0.00 88.43 Indefinite 8.71 38.82 20.00 0.83 0.71 2.30PPRRD5 575 558.76 536.71 240.79 1464.14 6.08 448.50 660.01 169.96 7.09 0.30 0.38GINIRD5 575 0.24 0.23 0.04 0.51 12.35 0.19 0.28 0.07 0.00 0.28 0.34LRMRD1 575 71.47 72.9 35.5 97.3 2.74 64.4 79.6 11.67 0.49 0.16 0.18LRFRD1 575 47.30 46.7 15.4 94.3 6.12 36.3 57.4 15.31 0.64 0.32 0.42

SXR06RD1 575 933.26 946 757 1038 1.37 917 965 48.13 2.01 0.05 0.05SXRAARD1 575 944.11 947 744 1189 1.60 908 982 61.37 2.56 0.06 0.07SCMWPMR1 575 43.57 42.87 0 100 Indefinite 38.30 46.74 13.09 0.55 0.30 0.34STMWPMR1 575 39.76 43.17 0 100 Indefinite 37.57 47.25 14.95 0.62 0.38 0.40SCMWPFR1 575 17.36 14.97 0.00 55.56 Indefinite 9.11 24.32 11.10 0.46 0.64 1.22STMWPFR1 575 21.27 21.70 0.00 80.00 Indefinite 11.65 30.48 13.00 0.54 0.61 1.12

Notes: 1. CV1 is SD divided by mean of all districts.

2. CV2 is SD divided by mean of lower quartile of all districts.

44

Table 2.6: Descriptive Statistics of the Districts in 2001 & 2004-05: UrbanLower Upper SE of

Indices Valid N Mean Median Minimum Maximum Max/Min Quartile Quartile SD Mean CV1 CV2WPIU1 573 0.32 0.32 0.22 0.51 2.30 0.29 0.34 0.04 0.00 0.14 0.15HCIU1 573 0.51 0.52 0.28 0.82 2.95 0.45 0.57 0.09 0.00 0.17 0.19HHIU1 573 0.55 0.54 0.30 0.77 2.58 0.48 0.61 0.09 0.00 0.17 0.20TCIU1 573 0.39 0.38 0.09 0.72 7.59 0.33 0.46 0.10 0.00 0.27 0.32SDIU4 573 0.44 0.44 0.28 0.59 2.09 0.40 0.48 0.05 0.00 0.12 0.14SDIU6 573 0.44 0.44 0.23 0.65 2.89 0.38 0.50 0.08 0.00 0.18 0.20SDIU7 573 0.42 0.42 0.26 0.58 2.22 0.36 0.47 0.07 0.00 0.16 0.19SDIU7_W 573 0.43 0.43 0.25 0.63 2.51 0.37 0.50 0.08 0.00 0.18 0.21HCRUD5 573 25.91 28.64 0.00 91.21 Indefinite 11.27 46.85 21.92 0.92 0.85 1.94PPRUD5 573 1052.38 880.91 365.55 3043.96 8.33 684.92 1119.11 368.18 15.38 0.35 0.54GINIUD5 573 0.29 0.28 0.10 0.65 6.20 0.24 0.33 0.07 0.00 0.26 0.31LRMUD1 573 85.54 86.50 54.50 97.50 1.79 82.40 90.10 6.48 0.27 0.08 0.08LRFUD1 573 70.58 70.80 31.20 97.20 3.12 63.90 77.50 10.03 0.42 0.14 0.16SXR06UD1 573 911.42 922.00 751.00 1061.00 1.41 887.00 948.00 50.91 2.13 0.06 0.06SXRAAUD1 573 906.33 904.00 327.00 1286.00 3.93 870.00 951.00 77.18 3.22 0.09 0.09SCMWPMU1 573 41.56 41.08 0.00 100.00 Indefinite 37.46 44.48 10.77 0.45 0.26 0.29STMWPMU1 573 38.78 40.95 0.00 100.00 Indefinite 34.48 46.56 15.80 0.66 0.41 0.46SCMWPFU1 573 10.78 9.47 0.00 100.00 Indefinite 6.51 13.15 7.23 0.30 0.67 1.11STMWPFU1 573 12.67 12.23 0.00 100.00 Indefinite 7.05 17.16 9.64 0.40 0.76 1.37

Notes:1. CV1 is SD divided by mean of all districts.

2. CV2 is SD divided by mean of lower quartile of all districts

45

Table 2.7: Indicator-wise CVs for States and Districts, 2001 and 2004-05Sr. No.

Indicators States Districts Indicators States DistrictsWPI Low Low WPI Low Low

1 MWTPMR1 Low Low MWTPMU1 Low Low2 MWTPFR1 High High MWTPFU1 High High3 ALTWMR1 High High NATWMU1 Low Low4 ALTWFR1 High High NATWFU1 Low Low5 CLTWMR1 Low Low HITWMU1 High High6 CLTWFR1 High High HITWFU1 High High7 NATWMR1 High High MRTPMU1 Low Low8 NATWFR1 High High MRTPFU1 High High9 HITWMR1 High High SCMWPMU1 High High10 HITWFR1 High High SCMWPFU1 High High11 MRTPMR1 Low Low STMWPMU1 High High12 MRTPFR1 High High STMWPFU1 High High13 SCMWPMR1 High High14 SCMWPFR1 High High15 STMWPMR1 High High16 STMWPFR1 High High

HCI Low Low HCI Low Low1 ASHHR1 Low Low ASHHU1 Low Low2 SXR06R1 Low Low SXR06U1 Low Low3 SXRAAR1 Low Low SXRAAU1 Low Low4 LRMR1 Low Low LRMU1 Low Low5 LRFR1 Low Low LRFU1 Low Low6 NSC1LR1 High High NSC1LU1 High High7 HABSR1 High High HABSU1 Low Low

HHI Low Low HHI Low Low1 HUESLR1 High High HUESLU1 Low Low2 HWBFR1 High High HWBFU1 Low Low3 HWCDR1 High High HWCDU1 High High4 NHD1LR1 High High NHD1LU1 Low High5 HULPGR1 High High HULPGU1 Low High6 DWWHR1 High High DWWHU1 Low Low7 HWFLMR1 Low Low HWFLMU1 Low Low8 HWRMCR1 Low Low HWRMCU1 High High9 HWRGTR1 Low Low HWRGTU1 Low Low10 HWNERR1 Low High HWNERU1 Low High11 HW1RR1 Low Low HW1RU1 Low Low12 POCHR1 Low Low POCHU1 Low Low13 LEBT1 High High LEBT1 High High

TCI High High TCI Low Low1 HWTCR1 High High HWTCU1 Low High2 HWRTR1 High High HWRTU1 Low Low3 HHBR1 High High HHBU1 High High4 HHSMR1 High High HHSMU1 High High5 NF1LR1 High High NF1LU1 High High

ECONOMIC INDICATORS ECONOMIC INDICATORS1 PPRR5 Low Low PPRU5 Low Low2 HCRR5 High High HCRU5 High High3 GINIR5 Low Low GINIU5 Low Low

46

Rural Urban

Table 2.8: Nature of Distribution of the District-wise Values of Indices in 2001 & 2004-05

Sr. No. Indices/ Indicators

Chi-Square (Degree ofFreedom)

Comments Indices/ Indicators

Chi-Square (Degree ofFreedom)

Comments

1 SDIR4 67.72 (4) Not Normal Distribution (R ≠ U) SDIU4 9.59(8) Normal Distribution

2 SDIR6 9.54 (6) Normal Distribution (R ≈ U) SDIU6 22.65 (9) Close to Normal Distribution

3 SDIR7 15.37 (5) Normal Distribution (R ≠ U) SDIU7 30.17 (10) Not Normal Distribution

4 SDIR7_W 4.74 (3) Normal Distribution (R ≠ U) SDIU7_W 32.14 (9) Not Normal Distribution

5 WPIR1 7.74 (4) Normal Distribution (R ≠ U) WPIU1 41.94 (6) Not Normal Distribution

6 HCIR1 57.77 (5) Not Normal Distribution (R ≠ U) HCIU1 15 (7) Close to Normal Distribution

7 HHIR1 58.89 (5) Not Normal Distribution (R = U) HHIU1 27.66 (10) Not Normal Distribution

8 TCIR1 41.05 (4) Not Normal Distribution (R ≠ U) TCIU1 19.77 (7) Not very far from Normal Distn.

9 HCRRD5 75.65 (5) Not Normal Distribution (R = U) HCRUD5 154.44 (9) Not Normal Distribution

10 PPRRD5 68.05 (6) Not Normal Distribution (R = U) PPRUD5 125.71 (6) Not Normal Distribution

11 GINIRD5 15.52 (6) Close to Normal Distribution (R ≠ U) GINIUD5 26.21 (4) Not Normal Distribution

12 LRMRD1 45.19 (8) Not Normal Distribution (R = U) LRMUD1 77.06 (6) Not Normal Distribution

13 LRFRD1 25.53 (9) Not Normal Distribution (R ≠ U) LRFUD1 7.16 (7) Normal Distribution

14 SCMWPMR1 269.48 (5) Not Normal Distribution (R = U) SCMWPMU1 243.97 (6) Not Normal Distribution

15 STMWPMR1 524.49 (6) Not Normal Distribution (R = U) STMWPMU1 400.62 (7) Not Normal Distribution

16 SCMWPFR1 77.03 (8) Not Normal Distribution (R = U) SCMWPFU1 67.83 (2) Not Normal Distribution

17 STMWPFR1 63.84 (7) Not Normal Distribution (R = U) STMWPFU1 62.21 (3) Not Normal Distribution

18 SXR06RD1 307.50 (11) Not Normal Distribution (R = U) SXR06UD1 140.82 (7) Not Normal Distribution

19 SXRAARD1 24.63 (6) Not very far from Normal Distribution (R≠U) SXRAAUD1 47.58 (3) Not Normal Distribution

47

RURAL URBAN

48

Figure 2.1: Distribution (Normal) of SDIR4 among Districts (Rural) 2001 Chi-Square test = 67.72, df = 4 (adjusted)

0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65

Category (upper limits)

0

50

100

150

200

250

No.

of

Dis

trict

s (5

75)

Figure 2.2: Distribution (Normal) of SDIR6 among Districts (Rural) 2001 & 2004-05 Chi-Square test = 9.54, df = 6 (adjusted)

0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70

Category (upper limits)

0

20

40

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80

100

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140

160

180

No.

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49

Figure 2.3: Distribution (Normal) of SDIR7 among Districts (Rural) 2001 & 2004-05 Chi-Square test = 15.37, df = 5 (adjusted)

0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70

Category (upper limits)

0

20

40

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80

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120

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160

180

200

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Figure 2.4: Distribution (Normal) of SDIR7_W among Districts (Rural) 2001 & 2004-05

Chi-Square test = 4.74, df = 3 (adjusted)

0.07 0.14 0.21 0.28 0.35 0.42 0.49 0.56 0.63 0.70 0.77

Category (upper limits)

0

20

40

60

80

100

120

140

160

180

200

220

No.

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50

Figure 2.5: Distribution (Normal) of WPIR1 among Districts (Rural) 2001 Chi-Square test = 7.76, df = 4 (adjusted)

0.110 0.165 0.220 0.275 0.330 0.385 0.440 0.495 0.550 0.605 0.660

Category (upper limits)

0

50

100

150

200

250

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Figure 2.6: Distribution (Normal) of HCIR1among Districts (Rural) 2001 Chi-Square test = 57.77, df = 5 (adjusted)

0.07 0.14 0.21 0.28 0.35 0.42 0.49 0.56 0.63 0.70 0.77

Category (upper limits)

0

20

40

60

80

100

120

140

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180

No.

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51

Figure 2.7: Distribution (Normal) of HHIR1 among Districts (Rural) 2001

Chi-Square test = 58.89, df = 5 (adjusted)

0.220 0.275 0.330 0.385 0.440 0.495 0.550 0.605 0.660 0.715 0.770

Category (upper l imits)

0

20

40

60

80

100

120

140

160

180

200N

o. o

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tions

Figure 2.8: Distribution (Normal) of TCIR1 among Districts (Rural) 2001 Chi-Square test = 41.05, df = 4 (adjusted)

-0.08 0.00 0.08 0.16 0.24 0.32 0.40 0.48 0.56 0.64 0.72

Category (upper limits)

0

20

40

60

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120

140

160

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200

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52

Figure 2.9: Distribution (Normal) of HCRR5 among Districts (Rural) 2004-05 Chi-Square test = 75.65, df = 5 (adjusted)

-11 0 11 22 33 44 55 66 77 88 99

Category (upper limits)

0

20

40

60

80

100

120

140

160

No.

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Figure 2.10: Distribution (Normal) of PPRRD5 among Districts (Rural) 2004-05 Chi-Square test = 68.05, df = 6 (adjusted)

100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600

Category (upper limits)

0

20

40

60

80

100

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140

160

180

No.

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53

Figure 2.11: Distribution (Normal) of GINIRD5 among Districts (Rural) 2004-05

Chi-Square test = 15.52, df = 6 (adjusted)

-0.04330.0000

0.04330.0867

0.13000.1733

0.21670.2600

0.30330.3467

0.39000.4333

0.47670.5200

0.56330.6067

Category (upper limits)

0

20

40

60

80

100

120

140

160

180N

o. o

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Figure 2.12: Distribution (Normal) of SCMWPMR1 among Districts (Rural) 2001 Chi-Square test = 269.48, df = 5 (adjusted)

-17.33-8.67

0.008.67

17.3326.00

34.6743.33

52.0060.67

69.3378.00

86.6795.33

104.00112.67

Category (upper l imits)

0

50

100

150

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250

300

350

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54

Figure 2.13: Distribution (Normal) of STMWPMR1 among Districts (Rural) 2001

Chi-Square test = 524.49, df = 6 (adjusted)

-17.3333-8.6667

0.00008.6667

17.333326.0000

34.666743.3333

52.000060.6667

69.333378.0000

86.666795.3333

104.0000112.6667

Category (upper limits)

0

50

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200

250

300N

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Figure 2.14: Distribution (Normal) of SCMWPFR1 among Districts (Rural) 2001 Chi-Square test = 77.03, df = 8 (adjusted)

-10 -5 0 5 10 15 20 25 30 35 40 45 50 55 60 65

Category (upper limits)

0

20

40

60

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120

140

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55

Figure 2.15: Distribution (Normal) of STMWPFR1 among Districts (Rural) 2001 Chi-Square test = 63.84, df = 7 (adjusted)

-13.33-6.67

0.006.67

13.3320.00

26.6733.33

40.0046.67

53.3360.00

66.6773.33

80.0086.67

93.33

Category (upper limits)

0

20

40

60

80

100

120N

o. o

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Figure 2.16: Distribution (Normal) of SXR06RD1 among Districts (Rural) 2001 Chi-Square test = 307.50, df = 11 (adjusted)

720738

756774

792810

828846

864882

900918

936954

972990

10081026

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1080

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Figure 2.17: Distribution (Normal) of SDIU4 among Districts (Urban) 2001

Chi-Square test = 9.59, df = 8 (adjusted)

0.24 0.27 0.29 0.32 0.35 0.37 0.40 0.43 0.45 0.48 0.51 0.53 0.56 0.59 0.61 0.64

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Figure 2.18: Distribution (Normal) of SDIU6 among Districts (Urban) 2001 & 2004-05 Chi-Square test = 22.65, df = 9 (adjusted)

0.15 0.18 0.22 0.26 0.29 0.33 0.37 0.40 0.44 0.48 0.51 0.55 0.59 0.62 0.66 0.70

Category (upper limits)

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Figure 2.19: Distribution (Normal) of SDIU7 among Districts (Urban) 2001 & 2004-05 Chi-Square test = 30.17, df = 10 (adjusted)

0.21 0.24 0.27 0.29 0.32 0.35 0.37 0.40 0.43 0.45 0.48 0.51 0.53 0.56 0.59 0.61

Category (upper limits)

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Figure 2.20: Distribution (Normal) of SDIU7_W among Districts (Urban) 2001 & 2004-05 Chi-Square test = 32.14, df = 9 (adjusted)

0.20 0.23 0.27 0.30 0.33 0.37 0.40 0.43 0.47 0.50 0.53 0.57 0.60 0.63 0.67 0.70

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Figure 2.21: Distribution (Normal) of WPIU1 among Districts (Urban) 2001 Chi-Square test = 41.94, df = 6 (adjusted)

0.18 0.20 0.23 0.25 0.28 0.30 0.33 0.35 0.38 0.41 0.43 0.46 0.48 0.51 0.53 0.56

Category (upper l imits)

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Figure 2.22: Distribution (Normal) of HCIU1 among Districts (Urban) 2001 Chi-Square test = 15.53, df = 7 (adjusted)

0.19 0.23 0.28 0.33 0.37 0.42 0.47 0.51 0.56 0.61 0.65 0.70 0.75 0.79 0.84 0.89

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Figure 2.23: Distribution (Normal) of HHIU1 among Districts (Urban) 2001 Chi-Square test = 27.66, df = 10 (adjusted)

0.24 0.28 0.32 0.36 0.40 0.44 0.48 0.52 0.56 0.60 0.64 0.68 0.72 0.76 0.80 0.84

Category (upper l imits)

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Figure 2.24: Distribution (Normal) of TCIU1 among Districts (Urban) 2001 Chi-Square test = 19.77, df = 7 (adjusted)

0.00 0.05 0.11 0.16 0.21 0.27 0.32 0.37 0.43 0.48 0.53 0.59 0.64 0.69 0.75 0.80

Category (upper l imits)

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Figure 2.25: Distribution (Normal) of HCRUD5 among Districts (Urban) 2004-05 Chi-Square test = 154.44, df = 9 (adjusted)

-8 0 8 16 24 32 40 48 56 64 72 80 88 96 104 112

Category (upper limits)

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Figure 2.26: Distribution (Normal) of PPRUD5 among Districts (Urban) 2004-05 Chi-Square test = 125.71, df = 6 (adjusted)

0.00226.67

453.33680.00

906.671133.33

1360.001586.67

1813.332040.00

2266.672493.33

2720.002946.67

3173.333400.00

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Figure 2.27: Distribution (Normal) of GINIUD5 among Districts (Urban) 2004-05

Chi-Square test = 26.21, df = 4 (adjusted)

0.00 0.05 0.11 0.16 0.21 0.27 0.32 0.37 0.43 0.48 0.53 0.59 0.64 0.69 0.75 0.80

Category (upper l imits)

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Figure 2.28: Distribution (Normal) of SXR06UD1 among Districts (Urban) 2001

Chi-Square test = 140.82, df = 7 (adjusted)

693.33720.00

746.67773.33

800.00826.67

853.33880.00

906.67933.33

960.00986.67

1013.331040.00

1066.671093.33

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Chapter 3. Inter-Temporal Transition of Districts between 1991 and 2001 Background There is rarely any disagreement among social scientist and policy makers that there is no

tendency towards convergence among the states in India in terms economic development over

last four decades. There are some evidences to let somebody see that the economic divergence as

experienced among the states is not present in some social development indicators. The soft

tendency towards convergence in social indicators is believed to be ‘natural’ by many analysts.

The socio-economic stories of the states are to a large extent known. But nobody does know for

sure what is happening across the districts over time. The reasons are not many. First of all, there

is tremendous dearth of research on the pattern of changes among the districts over the years.

Second, constitutional provision in favour of the state as the basic geographical unit analysis is

certainly responsible for this apathy to undertake district level study over all these years. Third,

nonetheless, non-availability of comparable district level information is certainly a major barrier

for the absence of experimental research on the districts, which, though, is the consequence of

the former conjecture in terms of constitutional provision. The widespread apathy towards

strengthening district level information system and independent academic research on district

might have played a decisive role for not encouraging sub-state level research across India. To

talk of districts is not enough to understand the sub-state level performance. One must have to

think about the districts with a clear rural and urban division.

The conventional wisdom has undergone tremendous changes since 1991- the year of economic

reforms as well as Census. Time is now ripe to cross the border of the states, and put the districts

on the common platform. The rising social instability and frequent failure at the sub-national

level inspired us to organize the ‘common districts’ over the last two Census years, 1991 and

2001, in terms of ‘common indicators’, and try to find out answers to five widely debated

queries:

(1) To test whether inter-state divergence is also valid for the common districts in terms of

various social development indicators between 1991 and 2001. This tested with the help of a

transition matrix between 1991 and 2001;

63

(2) To review inter-state performance on the basis of district level information in terms of the

same set of information;

(3) To understand rural urban disparity in a changing perspective between from 1991 to 2001;

(4) To identify the names of the best and worst districts over these two time spans.

Social Development Index & Its Components

There are 417 common rural and 414 common urban districts between 1991 and 2001. The list of

common districts in 1991 and 2001 is reported in Appendix 3.2. The composition of the indices

is presented in Appendix 3.1. Best efforts are given to maintain symmetry in terminology and

abbreviation. Wherever ‘9’ is used at the end of an abbreviated indicator or index, it stands for

‘1991’, and ‘1’ at similar position corresponds to the year ‘2001’. Following Appendix 3.1,

composition of the components of SDI (social development index) is restated here. WPI (work

participation index) is composed of 14 indicators for rural areas, and 10 indicators for urban

areas. HCI (human capital index) is composed of five indicators each for both rural and urban

areas separately. HHI (health and housing index) is made of six indicators each. Thus, SDI for

rural districts in 1991 is:

SDIR9 = 3

)999( HHIRHCIRWPIR ++ …..(3.1)

For urban districts, it is

SDIU9 = 3

)999( HHIUHCIUWPIU ++ …..(3.2)

The same set of indicators is used to derive the same set of rural indices for 2001 so that the

indices are of the form:

64

SDIR1 = 3

)111( HHIRHCIRWPIR ++ …..(3.3)

SDIU1 = 3

)111( HHIUHCIUWPIU ++ …..(3.4)

As before, here also we are guided by the cross correlation matrix to retain or to reject substitute

indicators, and followed the UNDP’s HDI method.

In order to make it amenable to inter-temporal comparison, the maximum and minimum values

for each indicator are set keeping in mind the highest and lowest values from the pooled data set

between 1991 and 2001.

Empirical Results on Distribution

Descriptive statistics of the indices for the rural districts in 1991 and 2001 are placed in Table

3.1a and 3.1b. The corresponding urban descriptive statistics are presented in Table 3.2a and

3.2b. It is comprehensible from the first two tables that even if CV of the indices estimated from

the lower quartile value are always higher, except female literacy rate for rural women, none is

statistically significant. Second, the max/min ratio has increased for work participation, health

and housing, and infant sex ratio between the last Census points, while it has drastically fallen

for human capital, though still remained at very high level. On the other hand, the basic features

for the urban districts have been almost the same except that the max/min ratio has increased for

human capital index.

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Chart 3.1: Nature of Distribution of the Common Districts in 1991 and 2001 Rural

Chi-square (DF) Comments S.N. Indices/

Indicators 1991 2001 1991 2001

1. WPIR 17.92(9) 3.88(7) Close to Normal Distn. Normal Distribution 2. HCIR 34.78(6) 25.25(7) Not Normal Distribution Not Normal Distribution 3. HHIR 42.74(7) 6.03(5) Not Normal Distribution Normal Distribution 4. SDIR 10.13(7) 7.49(7) Normal Distribution Normal Distribution 5. LRMR 23.42(10) 26.35(11) Not Normal Distribution Not Normal Distribution 6. LRFR 118.91(9) 19.07(10) Not Normal Distribution Not Normal Distribution 7. SXR06R 74.76(8) 222.26(10) Not Normal Distribution Not Normal Distribution

Urban S.N. Indices/

Indicators 1991 2001 1991 2001

1. WPIU 12.81(6) 15.40(7) Close to Normal Distribution Close to Normal Distribution 2. HCIU 15.98(8) 16.76(7) Close to Normal Distribution Not Normal Distribution 3. HHIU 60.77(7) 17.11(7) Not Normal Distribution Close to Normal Distribution 4. SDIU 11.27(7) 24.33(7) Normal Distribution Not Normal Distribution 5. LRMU 16.56(9) 54.80(9) Close to Normal Distribution Not Normal Distribution 6. LRFU 17.88(11) 2.80(10) Not Normal Distribution Normal Distribution 7. SXR06U 50.05(7) 104.81(9) Not Normal Distribution Not Normal Distribution

Nature of Distribution of the Indices

Before entering into inter-temporal transition of development, let us have a glance at the pattern

of distribution of the districts in terms of the indicators. We have fitted normal distribution to the

index-wise data set and obtained the value of Chi-square, which decide the strength of the Null

Hypothesis. As obvious from Chart 3.1, nature of distribution is not similar in rural and urban

areas. The recorded results are mixed for both rural and urban areas in both 1991 and 2001. Yet

one thing is clear that the rural districts have been normally distributed in SDI in both periods.

On the whole, there is fixity among the districts in both rural and urban areas. One can check

with the corresponding distribution pictures in details as captured in 12 selected diagrams from

Figure 3.1 to Figure 3.12 (six for rural and six for urban). Therefore, the relative performance

of the districts is not clear from this analysis. For more definitive results let us move to Table 3.3

and Table 3.4, which respectively report the relationship between 1991 and 2001 in each of the

indicators separately for rural and urban districts. Let us begin from Table 3.3 from which five

important pair between 1991 and 2001 are presented in five diagrams, Figure 3.13 to Figure

3.17 for rural districts. To understand these in a better way, let us pick up the corresponding

66

correlation coefficients from the same table: work participation, human capital, health and

housing, social development, and infant sex ratio. Note that in all the cases except health and

housing, Pearson’s correlation coefficients are extremely significant. We know that it measures

the strength of linear relationship between two variables. The closer the value of r is to +1 or -1,

the closer to a perfect linear relationship between the variables. Given a relatively large number

of observations (here N = 417), the value of ‘r’ may be interpreted as follows, to be on the safe

side.

If value of ‘r’ lies between -1 to -0.80 → very strong negative association;

-0.80 to -0.60, → strong negative association;

-0.60 to -0.40 → weak negative association;

-0.40 to +0.40 → little or no association;

+0.40 to +0.60 → weak positive association;

+0.60 to +0.80 → strong positive association;

+0.80 to +1.00 → very strong positive association.

This is of course a subjective rule. One has some liberty to change the cut off points for closer

explanation of the reality depending on the nature of problem. Here the cut off ranges are set on

the lower side.

(1) Under this a priori rule, there should not have any strong objection if we conclude that there

is no relationship between the rural districts in health and housing index over the two pair of

years,1991 and 2001, as the value of correlation is as low as +0.28. The exact meaning becomes

clear from the Figure 3.15, where HHIR9 is measured in X-axis and HHIR1 in Y-axis. The

bold diagonal drawn as an arrow suggests that the districts that have improved their relative

positions between 1991 and 2001 are located on the left side and those, which have failed, on the

right side of the diagonal. The visible codes of the districts make it unambiguous from the

diagram that the districts lying on right side belong mainly to the NE regions, UP, Bihar and

Tripura. Note that the rest of the six pair of values suggests quite different patterns of district

level development over the last decade.

67

(2) The correlation between SDIR9 and SDIR1 being +0.82 is presented as similar diagram in

Figure 3.16. It really motivating to note that (i) there is perfect immobility among the districts in

terms of their relative positions, (ii) all the districts have lifted their relative positions except only

one namely UP34 (Kanpur Dehat), and (iii) the relationship is absolutely linear. Thus, all the

rural districts have improved their relative positions in social development index between 1991

and 2001, but there is no change in their relative positions at all. So the question remains to be

investigated is the relative levels of improvement between the developed and backward districts

on which depends the final conclusion regarding the divergence among the districts.

(3) The relative performance of the districts in HCI is almost similar to that of SDI. And here the

relationship is perfectly linear (r = 0.88). It is also open from Figure 3.14, where all the districts

have improved their relative positions between 1991 and 2001 but they have been cemented in

the same seats for 10 years in a large moving ship.

(4) The picture with the pair WPIR9 and WPIR1 (r = 0.65) is not as statistically strong as SDI

and HCI with more than 50% districts having been left out on the right side of the diagonal ray

(Figure 3.13).

(5) Finally, the case of infant sex ratio between 1991 and 2001 (r = 0.86) is a bit different from

the above as revealed from Figure 3.17. The position of the diagonal shows that for more than

50% districts, there is no recorded improvement. But the relationship is rigidly linear and

statistically strongest, according to our prescription. This is, the female to male sex ration for age

group (0-6) years has become relatively worse in most districts of India across board except of

course a couple of states.

What about the relative changes of the urban districts between 1991 and 2001?

(1) There is very strong similarity in transition between the rural and urban districts from 1991 to

2001. But the strength of the correlations is stronger in urban areas. First, the scatter plot of

health and housing (Figure 3.20) is too dispersed with awfully low value of correlation (0.32).

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(2) In human capital with r = 0.87, out of seven districts as left to the right of the diagonal, six

belong to Uttar Pradesh alone (Figure 3.19). It is worth looking at the picture. On the whole,

there is clear cut improvement in the value of the human capital index, but relative positions of

the districts have remained absolutely fixed around a linear path.

(3) In SDI, there are almost same number of districts, which have failed to improve their relative

positions between 1991 and 2001 with very high value of r (=0.83) as revealed from Figure

3.21. It is also obvious that only a few districts belong to UP, but the rest are from Tripura,

Maharashtra, TN and Meghalaya. On the whole, the relative positions of the districts have

remained fixed in a strong linear relationship.

(4) In terms of infant sex ratio, the urban districts are more dispersed (r = 0.71) compared to

their rural counterparts, and most of the districts have failed to improve the infant FMR (female

male ratio) between 1991 and 2001. This picture is captured in Figure 3.22.

(5) Finally, in all the indices of development, the rural has almost lagged behind their urban

counterparts.

On the whole, therefore, there is a tendency of the districts to remain sticky in their relative

positions with respect to all the indices studied here between 1991 and 2001. Moreover, each of

the relationships produces a positive tendency over last 10 years. This clearly hints towards a

theory of divergence among the districts in terms of available development indicators.

Transition Matrix of District between 1991 & 2001 & Test of Chi-Square

Time is now ripe for examining whether the districts have exchanged their relative positions in

four indices (WPI, HCI, HHI, and SDI) and two indicators (LRM and LRF) between 1991

and 2001 separately for rural and urban areas. This is tested with the help of Chi-square statistic

in terms of a “transition matrix” between 1991 and 2001. More exclusively, the hypothesis that

‘there is no association between top 50% and bottom 50% of the districts’ is tested here. The

Chi-square test has rejected the hypothesis that there is no association. This means that there is

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no change in the relative positions of the districts in WPI, HCI, HHI, SDI, LRM and LRF. The

results of the Chi-square test are produced in Table 3.5, while the relevant formula is reproduced

in Table 3.5a. This is true for both rural and urban districts. Let us evoke from the earlier

discourse that this observation is fairly consistent with the correlation matrix between 1991 and

2001, and also with the corresponding scatter plots.

Given that this period falls under the process of globalization since 1991, it may not be out of

place to conclude that the better off districts have been able to mobilize the socio-economic

opportunities in their favour till 2001 thereby maintaining their relative positions compared to

the backward districts in 1991. Therefore, the better off districts have remained so in 2001 too,

and the weak districts could not change their relative positions in all the components of social

development as well as in male and female literacy. The question that immediately peeps in a

sensible mind is what went wrong with honest effort by top authorities towards balanced regional

development particularly for social sector. There is no direct answer. But given the phenomenon

of unchanging relative positions of the districts, we have also tested the typical growth regression

with respective to SDI for rural and urban districts. That is, we have regressed rate of change of

the indices against their base period values. The test result with respect to SDI itself and its

components across the 417 rural and 414 urban districts between 1991 and 2001 show that for

rural areas ‘divergence’ is a general outcome, whereas there is a tendency towards ‘convergence’

or ‘no relationship’ among the urban districts in these common variables. But the test with ‘time

dummy’ has confirmed the ‘divergence phenomenon’ over the last decade.

The most striking observation that there is an all-round improvement in relative values of the

social development index but retreat of the ranks of the districts from the backward states- is

completely consistent with the large scatter diagrams with abbreviated case names of the

districts. It is, therefore, doubtless that almost all the common districts have recorded some

improvement in SDI and its components, but districts of the backward states have failed to

improve their relative rankings compared to the districts from the developed states. In other

words, the better off districts in 1991 have been able to improve their social development at

much faster rate till 2001 compared to the backward districts in 1991. And this finding is

universal in India irrespective of rural versus urban divide. Therefore, it would not be

70

exaggerated to conclude that there is a statistically significant trend of ‘divergence’ among

Indian districts between 1991 and 2001 in terms of all types of social development indices. And

since this period is almost synonymous with globalization, one may be prompted to conclude

that the fruits of globalization have been harvested by the better off districts.

71

Appendix 3.1. Indicators used for Common Districts in 1991 and 2001, Rural & Urban Areas. I(a). WPIR9 (Work Participation Index for Rural District in 1991) consists of following indicators:

1) MWTPMRD9= Percentage of Male Main Workers to Total Male Population in Rural Part of a District in 1991.

2) MWTPFRD9 = Percentage of Female Main Workers to Total Female Population in Rural

Part of a District in 1991.

3) SCMWPMR9 = Percentage of SC Male Main Workers to Total SC Male Population in Rural Part of a District in 1991.

4) SCMWPFR9 = Percentage of SC Female Main Workers to Total SC Female Population

in Rural Part of a District in 1991.

5) STMWPMR9 = Percentage of ST Male Main Workers to Total ST Male Population in Rural Part of a District in 1991.

6) STMWPFR9 = Percentage of ST Female Main Workers to Total ST Female Population

in Rural Part of a District in 1991.

7) ALTWMRD9 = Percentage of Male Agricultural Labourer to Total Male Workers in Rural Part of a District in 1991.

8) ALTWFRD9 = Percentage of Female Agricultural Labourer to Total Female Workers in

Rural Part of a District in 1991.

9) CLTWMRD9= Percentage of Female Cultivators to Total Female Workers in Rural Part of a District in 1991.

10) CLTWFRD9 = Percentage of Female Cultivators to Total Female Workers in Rural Part

of a District in 1991.

11) HITWMRD9= Percentage of Male Household Industry Workers to Total Male Workers in Rural Part of a District in 1991.

12) HITWFRD9 = Percentage of Female Household Industry Workers to Total Female

Workers in Rural Part of a District in 1991.

13) OWTWMRD9 = Percentage of Male Other Workers to Total Male Workers in Rural Part of a District in 1991.

72

14) OWTWFRD9 = Percentage of Female Other Workers to Total Female Workers in Rural

Part of a District in 1991.

I(b).WPIU9 (Work Participation Index for Urban District in 1991) consists of following indicators:

1) MWTPMUD9 = Percentage of Male Main Workers to Total Male Population in Urban Part of a District in 1991.

2) MWTPFUD9 = Percentage of Female Main Workers to Total Female Population in Urban Part of a District in 1991.

3) SCMWPMU9 = Percentage of SC Male Main Workers to Total SC Male Population in Urban Part of a District in 1991.

4) SCMWPFU9 = Percentage of SC Female Main Workers to Total SC Female Population in Urban Part of a District in 1991.

5) STMWPMU9 = Percentage of ST Male Main Workers to Total ST Male Population in Urban Part of a District in 1991.

6) STMWPFU9 = Percentage of ST Female Main Workers to Total ST Female Population in Urban Part of a District in 1991.

7) HITWMUD9 = Percentage of Male Household Industry Workers to Total Male Workers in Urban Part of a District in 1991.

8) HITWFUD9 = Percentage of Female Household Industry Workers to Total Female Workers in Urban Part of a District in 1991.

9) OWTWMUD9 = Percentage of Male Other Workers to Total Male Workers in Urban Part of a District in 1991.

10) OWTWFUD9 = Percentage of Female Other Workers to Total Female Workers in Urban Part of a District in 1991.

II(a). HCIR9 (Human Capital Index for Rural District in 1991) consists of following indicators:

1) ASHHRD9 = Average Size of Households in Rural Part of a District in 1991.

2) LRMRD9 = Literacy Rate of Male Person in Rural Part of a District in 1991.

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3) LRFRD9 = Literacy Rate of Female Person in Rural Part of a District in 1991.

4) SXR06RD9 = Sex Ratio of 0-6 Age Group in Rural Part of a District in 1991.

5) SXRAARD9= Sex Ratio of All Age Group in Rural Part of a District in 1991.

II(b). HCIU9 (Human Capital Index for Urban District in 1991) consists of following indicators:

1. ASHHUD9 = Average Size of Households in Urban Part of a District in 1991.

2. LRMUD9 = Literacy Rate of Male Person in Urban Part of a District in 1991.

3. LRFUD9 = Literacy Rate of Female Person in Urban Part of a District in 1991.

4. SXR06UD9 = Sex Ratio of 0-6 Age Group in Urban Part of a District in 1991.

5. SXRAAUD9 = Sex Ratio of All Age Group in Urban Part of a District in 1991.

III(a). HHIR9 (Health and Housing Index for Rural District in 1991) consists of following indicators:

1) HWNERRD9 = Percentage of Households with No Exclusive Room in Rural Part of a District in 1991.

2) HW1RRD9 = Percentage of Households with One Room in Rural Part of a District in 1991.

3) HW5RRD9 = Percentage of Households with Five Rooms in Rural Part of a District in 1991.

4) HUESLRD9 = Percentage of Households using Electric as a Source of Light in Rural Part of a District in 1991.

5) HWTFRD9 = Percentage of Households with/having Toilet Facility in Rural Part of a District in 1991.

6) HULPGRD9 = Percentage of Households using Liquid Petroleum Gas in Rural Part of a District in 1991.

74

III(b). HHIU9 (Health and Housing Index for Urban District in 1991) consists of following indicators:

1) HWNERUD9 = Percentage of Households with No Exclusive Room in Urban Part of a District in 1991.

2) HW1RUD9 = Percentage of Households with One Room in Urban Part of a District in 1991.

3) HW5RUD9 = Percentage of Households with Five Rooms in Urban Part of a District in 1991.

4) HUESLUD9 = Percentage of Households using Electric as a Source of Light in Urban Part of a District in 1991.

5) HWTFUD9 = Percentage of Households with/having Toilet Facility in Urban Part of a District in 1991.

6) HULPGUD9 = Percentage of Households using Liquid Petroleum Gas in Urban Part of a District in 1991.

IV(a). SDIR9 (Social Development Index for Rural District in 1991) consists of the above

three individual rural indices.

SDIR9 = 3

)999( HHIRHCIRWPIR ++

IV(b). SDIU9 (Social Development Index for Urban District in 1991) consists of the above

three individual urban indices.

SDIU9 = 3

)999( HHIUHCIUWPIU ++

V. The same set of indicators is used to derive the same set of indices for 2001, where instead of ‘9’ used for representing 1991, ‘1’ is used for 2001. This is all about ‘common district’ analysis.

Table 3.1a. Descriptive Statistics of Common Rural Districts in 1991Lower Upper

Indicators Valid N Mean Median Minimum Maximum Quartile Quartile Std.Dev. CV1 CV2 Max/MinWPIR9 417 0.41 0.41 0.26 0.58 0.37 0.45 0.06 0.15 0.16 2.23HCIR9 417 0.26 0.25 0.06 0.59 0.20 0.31 0.09 0.35 0.45 9.83HHIR9 417 0.34 0.33 0.22 0.64 0.29 0.38 0.07 0.21 0.24 2.91SDIR9 417 0.33 0.33 0.21 0.53 0.30 0.37 0.05 0.15 0.17 5.52LRMRD9 417 47.22 46.43 15.58 86.33 38.68 54.70 12.39 0.26 0.32 5.54LRFRD9 417 25.33 21.77 3.34 83.88 13.96 33.48 15.24 0.60 1.09 25.11SXR06RD9 417 949.44 957.87 820.78 1039.17 930.79 974.10 35.83 0.04 0.04 1.27SXRAARD9 417 934.30 939.68 786.10 1229.97 891.35 969.43 61.96 0.07 0.07 1.56SCMWPMR9 417 51.41 51.38 0.00 100.00 48.78 54.61 9.91 0.19 0.20 IndefiniteSTMWPMR9 417 50.74 53.32 0.00 100.00 48.29 56.69 17.52 0.35 0.36 IndefiniteSCMWPFR9 417 21.61 19.49 0.00 58.42 9.17 31.30 14.27 0.66 1.55 IndefiniteSTMWPFR9 417 27.25 28.03 0.00 79.79 15.24 39.44 16.78 0.62 1.10 Indefinite

Table 3.1b. Descriptive Statistics of Common Rural Districts in 2001Lower Upper

Indicators Valid N Mean Median Minimum Maximum Quartile Quartile Std.Dev. CV1 CV2 Max/MinWPIR1 417 0.36 0.36 0.19 0.63 0.32 0.41 0.08 0.22 0.25 3.32HCIR1 417 0.51 0.51 0.24 0.88 0.43 0.58 0.11 0.22 0.26 3.67HHIR1 417 0.49 0.49 0.21 0.71 0.44 0.55 0.07 0.14 0.16 3.38SDIR1 417 0.46 0.46 0.32 0.65 0.41 0.49 0.06 0.13 0.15 2.03LRMRD1 417 71.37 72.30 39.20 97.30 64.40 79.00 11.27 0.16 0.18 2.48LRFRD1 417 47.33 46.10 15.40 94.30 36.50 56.60 15.02 0.33 0.41 6.12SXR06RD1 417 931.43 945.00 769.00 1027.00 916.00 964.00 47.60 0.05 0.05 1.34SXRAARD1 417 943.65 947.00 744.00 1189.00 909.00 977.00 60.31 0.06 0.07 1.60SCMWPMR1 417 42.67 42.85 0.00 100.00 38.26 46.74 11.32 0.27 0.30 IndefiniteSTMWPMR1 417 39.96 43.09 0.00 100.00 37.97 46.90 14.44 0.36 0.38 IndefiniteSCMWPFR1 417 17.50 15.11 0.00 48.89 9.36 24.20 10.90 0.62 1.16 IndefiniteSTMWPFR1 417 21.72 21.87 0.00 80.00 12.39 30.63 12.80 0.59 1.03 Indefinite

Table 3.2a. Descriptive Statistics of Common Urban Districts in 1991Lower Upper

Indicators Valid N Mean Median Minimum Maximum Quartile Quartile Std.Dev. CV1 CV2 Max/MinWPIU9 414 0.36 0.36 0.22 0.53 0.33 0.39 0.05 0.14 0.15 2.41HCIU9 414 0.44 0.44 0.23 0.72 0.38 0.50 0.09 0.20 0.24 3.13HHIU9 414 0.49 0.49 0.18 0.77 0.44 0.55 0.10 0.20 0.23 4.28SDIU9 414 0.43 0.44 0.24 0.60 0.39 0.47 0.06 0.14 0.15 2.50LRMUD9 414 66.88 67.59 39.99 86.24 61.90 72.73 7.99 0.12 0.13 2.16LRFUD9 414 50.66 50.49 25.79 83.63 41.19 58.45 11.74 0.23 0.29 3.24SXR06UD9 414 938.34 944.53 827.70 1079.71 918.18 958.67 33.97 0.04 0.04 1.31SXRAAUD9 414 884.44 886.93 563.08 1070.54 857.63 929.71 73.19 0.08 0.09 1.90SCMWPMU9 414 45.98 45.71 0.00 100.00 43.55 47.76 8.92 0.19 0.20 IndefiniteSTMWPMU9 414 42.72 46.34 0.00 100.00 40.36 50.61 16.49 0.39 0.41 IndefiniteSCMWPFU9 414 11.12 9.27 0.00 50.00 6.04 14.71 7.06 0.63 1.17 IndefiniteSTMWPFU9 414 14.26 13.89 0.00 71.43 7.05 20.10 10.42 0.73 1.48 Indefinite

Table 3.2b. Descriptive Statistics of Common Urban Districts in 2001Lower Upper

Indicators Valid N Mean Median Minimum Maximum Quartile Quartile Std.Dev. CV1 CV2 Max/MinWPIU1 414 0.35 0.35 0.20 0.60 0.31 0.40 0.06 0.17 0.19 3.00HCIU1 414 0.58 0.59 0.25 0.87 0.50 0.66 0.11 0.19 0.22 3.48HHIU1 414 0.55 0.55 0.35 0.70 0.51 0.59 0.06 0.11 0.12 2.00SDIU1 414 0.49 0.50 0.32 0.65 0.46 0.53 0.06 0.12 0.13 2.03LRMUD1 414 85.77 86.50 58.00 97.50 82.90 90.20 6.24 0.07 0.08 1.68LRFUD1 414 71.24 71.15 45.10 97.20 64.20 77.90 9.74 0.14 0.15 2.16SXR06UD1 414 910.28 920.50 751.00 1061.00 885.00 948.00 51.68 0.06 0.06 1.41SXRAAUD1 414 905.79 902.00 327.00 1112.00 869.00 946.00 71.19 0.08 0.08 3.40SCMWPMU1 414 40.78 40.62 0.00 89.04 37.15 44.36 8.72 0.21 0.23 IndefiniteSTMWPMU1 414 38.75 40.51 0.00 100.00 34.48 46.34 14.45 0.37 0.41 IndefiniteSCMWPFU1 414 10.26 9.24 0.00 39.14 6.64 12.72 5.55 0.54 0.84 IndefiniteSTMWPFU1 414 12.59 12.35 0.00 75.00 7.58 16.82 8.59 0.68 1.13 Indefinite

Table 3.3. : Pearson's Correlation Co-efficients of Relevant Indicators of Common Rural Districts between 1991 & 2001N=417

WPIR1 HCIR1 HHIR1 SDIR1 LRMRD1 LRFRD1 SXR06RD1 SXRAARD1 SCMWPMR1 STMWPMR1 SCMWPFR1 STMWPFR1WPIR9 0.65 0.47 0.09 0.60 0.17 0.16 0.48 0.41 0.17 0.45 0.48 0.58

p=0.00 p=0.00 p=.078 p=0.00 p=.001 p=.001 p=0.00 p=.000 p=.001 p=0.00 p=0.00 p=0.00HCIR9 0.33 0.88 0.15 0.74 0.71 0.85 0.09 0.62 0.05 0.15 0.26 0.20

p=.000 p=0.00 p=.003 p=0.00 p=0.00 p=0.00 p=.055 p=0.00 p=.313 p=.002 p=.000 p=.000HHIR9 0.21 0.22 0.28 0.34 0.31 0.37 -0.21 0.00 0.17 -0.05 0.08 0.07

p=.000 p=.000 p=.000 p=.000 p=.000 p=.000 p=.000 p=.997 p=.001 p=.273 p=.086 p=.137SDIR9 0.54 0.80 0.24 0.82 0.62 0.72 0.15 0.52 0.17 0.24 0.38 0.38

p=0.00 p=0.00 p=.000 p=0.00 p=0.00 p=0.00 p=.002 p=0.00 p=.001 p=.000 p=.000 p=.000LRMRD9 0.27 0.70 0.19 0.63 0.85 0.87 -0.17 0.30 -0.04 0.03 0.12 0.04

p=.000 p=0.00 p=.000 p=0.00 p=0.00 p=0.00 p=.000 p=.000 p=.406 p=.611 p=.018 p=.419LRFRD9 0.29 0.72 0.04 0.58 0.68 0.90 -0.05 0.35 0.05 0.02 0.06 0.06

p=.000 p=0.00 p=.465 p=0.00 p=0.00 p=0.00 p=.343 p=.000 p=.333 p=.676 p=.232 p=.212SXR06RD9 0.15 0.45 -0.37 0.18 -0.18 0.01 0.86 0.53 0.10 0.41 0.12 0.38

p=.003 p=0.00 p=.000 p=.000 p=.000 p=.843 p=0.00 p=0.00 p=.052 p=.000 p=.012 p=.000SXRAARD9 0.15 0.72 0.12 0.56 0.31 0.36 0.39 0.94 0.00 0.26 0.37 0.26

p=.002 p=0.00 p=.013 p=0.00 p=.000 p=.000 p=.000 p=0.00 p=1.00 p=.000 p=.000 p=.000SCMWPMR9 0.11 -0.02 0.00 0.04 -0.21 -0.14 0.11 -0.01 0.80 0.13 0.19 0.14

p=.020 p=.722 p=.981 p=.453 p=.000 p=.005 p=.023 p=.918 p=0.00 p=.006 p=.000 p=.005STMWPMR9 -0.01 0.16 -0.17 0.02 -0.12 -0.17 0.49 0.24 0.06 0.61 0.14 0.35

p=.834 p=.002 p=.001 p=.669 p=.018 p=.001 p=0.00 p=.000 p=.239 p=0.00 p=.005 p=.000SCMWPFR9 0.19 0.43 0.31 0.47 0.15 0.09 0.32 0.45 0.17 0.39 0.83 0.56

p=.000 p=0.00 p=.000 p=0.00 p=.002 p=.071 p=.000 p=0.00 p=.000 p=.000 p=0.00 p=0.00STMWPFR9 0.21 0.31 -0.01 0.28 -0.01 0.03 0.46 0.34 0.16 0.45 0.39 0.64

p=.000 p=.000 p=.807 p=.000 p=.817 p=.511 p=0.00 p=.000 p=.001 p=0.00 p=.000 p=0.00

77

Table 3.4. : Pearson's Correlation Co-efficients of Relevant Indicators of Common Urban Districts between 1991 & 2001N=414

WPIU1 HCIU1 HHIU1 SDIU1 LRMUD1 LRFUD1 SXR06UD1 SXRAAUD1 SCMWPMU1 STMWPMU1 SCMWPFU1 STMWPFU1WPIU9 0.82 0.58 0.10 0.71 0.36 0.43 0.31 0.18 0.44 0.37 0.54 0.53

p=0.00 p=0.00 p=.041 p=0.00 p=.000 p=0.00 p=.000 p=.000 p=0.00 p=.000 p=0.00 p=0.00HCIU9 0.57 0.87 -0.02 0.76 0.70 0.79 0.34 0.43 0.26 0.20 0.38 0.34

p=0.00 p=0.00 p=.750 p=0.00 p=0.00 p=0.00 p=.000 p=0.00 p=.000 p=.000 p=.000 p=.000HHIU9 0.38 0.32 0.32 0.46 0.39 0.44 -0.27 -0.09 0.24 -0.06 0.14 0.14

p=.000 p=.000 p=.000 p=0.00 p=.000 p=0.00 p=.000 p=.081 p=.000 p=.200 p=.006 p=.005SDIU9 0.72 0.78 0.20 0.83 0.67 0.76 0.10 0.22 0.39 0.17 0.42 0.40

p=0.00 p=0.00 p=.000 p=0.00 p=0.00 p=0.00 p=.034 p=.000 p=.000 p=.001 p=.000 p=.000LRMUD9 0.52 0.82 0.16 0.77 0.87 0.86 0.05 0.16 0.24 0.13 0.28 0.22

p=0.00 p=0.00 p=.002 p=0.00 p=0.00 p=0.00 p=.331 p=.001 p=.000 p=.009 p=.000 p=.000LRFUD9 0.54 0.80 0.05 0.73 0.74 0.92 0.06 0.20 0.28 0.06 0.24 0.23

p=0.00 p=0.00 p=.272 p=0.00 p=0.00 p=0.00 p=.203 p=.000 p=.000 p=.242 p=.000 p=.000SXR06UD9 0.24 0.31 -0.40 0.14 0.02 0.07 0.71 0.15 0.02 0.32 0.15 0.40

p=.000 p=.000 p=.000 p=.003 p=.637 p=.152 p=0.00 p=.003 p=.636 p=.000 p=.002 p=.000SXRAAUD9 0.16 0.30 0.06 0.27 0.12 0.11 0.22 0.79 0.11 0.10 0.42 0.13

p=.001 p=.000 p=.212 p=.000 p=.016 p=.032 p=.000 p=0.00 p=.026 p=.052 p=.000 p=.007SCMWPMU9 0.19 0.07 0.03 0.12 -0.06 -0.03 0.09 0.10 0.72 -0.01 0.15 0.05

p=.000 p=.150 p=.601 p=.013 p=.193 p=.591 p=.084 p=.054 p=0.00 p=.905 p=.002 p=.332STMWPMU9 0.28 0.22 -0.15 0.19 0.13 0.05 0.36 0.10 0.05 0.67 0.18 0.36

p=.000 p=.000 p=.002 p=.000 p=.011 p=.280 p=.000 p=.048 p=.267 p=0.00 p=.000 p=.000SCMWPFU9 0.33 0.37 0.09 0.39 0.18 0.10 0.34 0.43 0.13 0.24 0.70 0.39

p=.000 p=.000 p=.063 p=.000 p=.000 p=.041 p=.000 p=0.00 p=.010 p=.000 p=0.00 p=.000STMWPFU9 0.38 0.40 -0.06 0.38 0.23 0.20 0.43 0.25 0.13 0.33 0.42 0.64

p=.000 p=.000 p=.202 p=.000 p=.000 p=.000 p=0.00 p=.000 p=.007 p=.000 p=.000 p=0.00

Table 3.5. : Chi-square Significance Test among Districts between 1991 & 2001

Indicators Rural Urban

Work Participation Index (WPI) 352.31 235.36

Human Capital Index (HCI) 352.31 225.66

Health & Housing Index (HHI) 44.84 24.76

Social Development Index (SDI) 333.18 257.22

Literacy Rate for Male (LRM) 248.59 240.6

Literacy Rate for Female (LRF) 346.67 192.46

Note: 1) Degree of Freedom is 4. 2) If the critical value is 0.01(1% level) with degree of freedom=4, then the corresponding value of Chi-square should be 13.30, and if the critical value is 0.05(5% level) with the degree of freedom=4, the corresponding vale of Chi-square should be 9.49.

79

Table 3.5a: Chi-square Formula with Degrees of Freedom

Degrees of freedom

Value Degrees of freedom

Value

1 3.84 1 6.632 5.99 2 9.213 7.82 3 11.34 9.49 4 13.35 11.1 5 15.16 12.6 6 16.87 14.1 7 18.58 15.5 8 20.19 16.9 9 23.210 18.3 10 24.7

Alpha value = 5% Alpha value = 1%

11 19.7 11 26.212 21 12 27.713 22.4 13 29.114 23.7 14 30.615 25 15 30.616 26.3 16 3217 27.6 17 33.418 28.9 18 34.819 30.1 19 36.220 31.4 20 37.621 32.7 21 38.922 33.9 22 40.323 35.2 23 41.624 36.4 24 4325 37.7 25 44.326 38.9 26 45.627 40.1 27 4728 41.3 28 48.329 42.6 29 49.630 43.8 30 50.9

80

81

Figure 3.1: Distribution (Normal) of HCIR9 among Districts (Rural) 1991 Chi-Square test = 34.78, df = 6 (adjusted)

0.00000.0467

0.09330.1400

0.18670.2333

0.28000.3267

0.37330.4200

0.46670.5133

0.56000.6067

0.65330.7000

Category (upper limits)

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Figure 3.2: Distribution (Normal) of HCIR1 among Districts (Rural) 2001

Chi-Square test = 25.25, df = 7 (adjusted)

0.12 0.18 0.24 0.30 0.36 0.42 0.48 0.54 0.60 0.66 0.72 0.78 0.84 0.90 0.96 1.02

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Figure 3.3: Distribution (Normal) of HHIR9 among Districts (Rural) 1991 Chi-Square test = 42.74, df = 7 (adjusted)

0.14670.1833

0.22000.2567

0.29330.3300

0.36670.4033

0.44000.4767

0.51330.5500

0.58670.6233

0.66000.6967

Category (upper limits)

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Figure 3.4: Distribution (Normal) of HHIR1 among Districts (Rural) 2001

Chi-Square test = 6.03, df = 5 (adjusted)

0.13000.1733

0.21670.2600

0.30330.3467

0.39000.4333

0.47670.5200

0.56330.6067

0.65000.6933

0.73670.7800

Category (upper limits)

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Figure3.5: Distribution (Normal) of SDIR9 among Districts (Rural) 1991 Chi-Square test = 10.13, df = 7 (adjusted)

0.17730.2027

0.22800.2533

0.27870.3040

0.32930.3547

0.38000.4053

0.43070.4560

0.48130.5067

0.53200.5573

Category (upper limits)

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Figure 3.6: Distribution (Normal) of SDIR1 among Districts (Rural) 2001 Chi-Square test = 7.49, df = 7 (adjusted)

0.24 0.27 0.30 0.33 0.36 0.39 0.42 0.45 0.48 0.51 0.54 0.57 0.60 0.63 0.66 0.69

Category (upper limits)

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83

Figure 3.7: Distribution (Normal) of HCIU9 among Districts (Rural) 1991

Chi-Square test = 15.98, df = 8 (adjusted)

0.13000.1733

0.21670.2600

0.30330.3467

0.39000.4333

0.47670.5200

0.56330.6067

0.65000.6933

0.73670.7800

Category (upper limits)

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Figure 3.8: Distribution (Normal) ofHCIU1 among Districts (Rural) 2001

Chi-Square test = 16.76, df = 7 (adjusted)

0.12 0.18 0.24 0.30 0.36 0.42 0.48 0.54 0.60 0.66 0.72 0.78 0.84 0.90 0.96 1.02

Category (upper limits)

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Figure 3.9: Distribution (Normal) of HHIU9 among Districts (Rural) 1991

Chi-Square test = 60.77, df = 7 (adjusted)

0.10670.1600

0.21330.2667

0.32000.3733

0.42670.4800

0.53330.5867

0.64000.6933

0.74670.8000

0.85330.9067

Category (upper limits)

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120

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84

Figure 3.10: Distribution (Normal) of HHIU1 among Districts (Rural) 2001 Chi-Square test = 17.11, df = 7 (adjusted)

0.30 0.33 0.36 0.39 0.42 0.45 0.48 0.51 0.54 0.57 0.60 0.63 0.66 0.69 0.72 0.75

Category (upper limits)

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Figure 3.11: Distribution (Normal) of SDIU9 among Districts (Rural) 1991

Chi-Square test = 11.27, df = 7 (adjusted)

0.21 0.24 0.27 0.30 0.33 0.36 0.39 0.42 0.45 0.48 0.51 0.54 0.57 0.60 0.63 0.66

Category (upper limits)

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Figure 3.12: Distribution (Normal) of SDIU1 among Districts (Rural) 2001

Chi-Square test = 24.33, df = 7 (adjusted)

0.26670.2933

0.32000.3467

0.37330.4000

0.42670.4533

0.48000.5067

0.53330.5600

0.58670.6133

0.64000.6667

0.6933

Category (upper limits)

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Figure 3.13: Scatter Plot between WPIR9 & WPIR1 for Common Rural Districts

AP1AP2AP3

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RJ1RJ2RJ3

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SK1SK2

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0.250 0.300 0.350 0.400 0.450 0.500 0.550 0.600

WPIR9

0.15

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0.25

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0.45

0.50

0.55

0.60

0.65W

PIR

1 AP1AP2AP3

AP4

AP5

AP6

AP8

AP9

AP10

AP11AP12AP13

AP14

AP15

AP16

AP17

AP18

AP19

AP20AP21AP22

AP23

AR1AR2

AR3

AR4

AR5

AR6AR7AR8AR9

AR10AR11

AS1AS2AS3AS4

AS5

AS6

AS7

AS8

AS9AS10

AS11

AS12AS13

AS14AS15

AS16

AS17AS18

AS19

AS20AS21

AS22

AS23

BI1

BI2

BI3BI4BI5 BI6

BI7

BI8 BI9

BI10BI11BI12

BI13

BI14

BI15

BI16

BI17 BI18BI19

BI20

BI21

BI22BI23

BI24

BI25BI26

BI27

BI28

BI29

BI30

BI31

BI32

BI33

BI34

BI35

BI36

BI37

BI38BI39BI40

BI41BI42

GU1

GU2GU3

GU4

GU5

GU6 GU7

GU8GU9

GU10

GU11

GU12

GU13

GU14GU15

GU16

GU17

GU18

GU19

HA1

HA2

HA3

HA4HA5

HA6

HA7HA8

HA9HA10

HA11HA12

HA13

HA14 HA15HA16

HP1

HP2

HP3

HP4

HP5

HP6

HP7

HP8

HP9

HP10

HP11

HP12

KA1KA2

KA3KA4

KA5KA6

KA7

KA8

KA9

KA10

KA11

KA12

KA13

KA14KA15

KA16

KA17

KA18

KA19KA20

KE1

KE2 KE3

KE4

KE5

KE6KE7

KE8

KE9 KE10KE11

KE12

KE13

KE14MP1MP2

MP3

MP4

MP5 MP6

MP7

MP8

MP9

MP10

MP11

MP12MP13MP14

MP15MP16

MP17

MP18

MP19

MP20

MP21

MP22MP23

MP24

MP25MP26

MP27

MP28

MP29

MP30

MP31

MP32

MP33

MP34MP35MP36

MP37

MP38

MP39MP40

MP41

MP42

MP43

MP44

MH1

MH2MH3

MH4MH5

MH6

MH7MH8

MH9

MH10

MH11MH12

MH13

MH14MH15

MH16

MH17

MH18MH19MH20

MH21

MH22 MH23

MH24

MN1

MN2MN3

MN4

MN5

MN6MN7

MG1

MG2

MG3

MG4 MG5

MZ1

MZ2

MZ3

NG1NG2

NG3NG4NG5NG6

NG7

OR1OR2

OR3

OR4 OR5

OR6OR7

OR8

OR9OR11

OR12

OR13PU1

PU2PU3

PU4

PU5PU6PU7PU8

PU9

PU10

PU11PU12

RJ1RJ2RJ3

RJ4

RJ5

RJ6

RJ7

RJ8

RJ9RJ10

RJ11

RJ12RJ13

RJ14

RJ15

RJ16

RJ17RJ18

RJ19

RJ20

RJ21

RJ22

RJ23

RJ24

RJ25

RJ26

RJ27

SK1SK2

SK3

SK4TN1

TN2TN3

TN4

TN5TN6

TN7

TN8

TN9

TN10

TN11

TN12

TN13TR1

TR2TR3

UP1

UP2

UP3

UP4

UP5

UP6

UP7UP8

UP9UP10UP11

UP12

UP13UP14

UP15

UP16

UP17

UP18

UP19

UP20

UP21

UP22

UP23UP24

UP25UP26

UP27UP28

UP29

UP30UP31

UP32UP33

UP34 UP35

UP36UP37

UP38

UP39

UP40UP41

UP42

UP43

UP44

UP45UP46

UP47

UP48

UP49

UP50

UP51

UP52UP53

UP54

UP55

UP56

UP57

UP58

UP59

UP60

UP61

UP62WB1WB2WB3

WB5WB6

WB7 WB8

WB9WB10

WB11

WB12

86

Figure 3.14: Scatter Plot between HCIR9 & HCIR1 for Common Rural Districts

AP1AP2

AP3AP4

AP5AP6

AP8AP9

AP10

AP11

AP12

AP13

AP14

AP15

AP16AP17

AP18

AP19AP20AP21AP22

AP23

AR1AR2AR3AR4

AR5

AR6

AR7

AR8

AR9AR10AR11AS1

AS2

AS3

AS4AS5

AS6

AS7

AS8

AS9

AS10

AS11

AS12

AS13

AS14

AS15

AS16

AS17AS18

AS19

AS20

AS21

AS22AS23

BI1

BI2BI3BI4

BI5

BI6

BI7

BI8BI9

BI10BI11BI12BI13

BI14BI15

BI16

BI17BI18BI19

BI20

BI21

BI22BI23BI24BI25BI26BI27

BI28

BI29

BI30

BI31

BI32

BI33

BI34

BI35

BI36

BI37BI38

BI39

BI40

BI41

BI42

GU1GU2

GU3

GU4

GU5GU6GU7

GU8GU9

GU10GU11GU12 GU13GU14

GU15

GU16

GU17

GU18

GU19

HA1HA2

HA3HA4HA5

HA6HA7

HA8HA9

HA10

HA11

HA12

HA13

HA14HA15HA16

HP1

HP2

HP3

HP4HP5

HP6HP7HP8HP9

HP10HP11

HP12KA1KA2

KA3KA4KA5

KA6

KA7

KA8

KA9

KA10

KA11

KA12

KA13

KA14KA15KA16

KA17

KA18KA19

KA20

KE1

KE2KE3KE4

KE5

KE6KE7KE8

KE9KE10

KE11

KE12KE13

KE14

MP1

MP2

MP3

MP4MP5

MP6

MP7

MP8

MP9MP10MP11MP12

MP13

MP14

MP15

MP16

MP17MP18

MP19

MP20

MP21MP22

MP23

MP24

MP25

MP26

MP27

MP28

MP29

MP30MP31MP32

MP33MP34

MP35

MP36MP37

MP38

MP39

MP40

MP41

MP42

MP43

MP44MH1

MH2MH3

MH4MH5

MH6

MH7 MH8

MH9MH10MH11MH12MH13MH14MH15

MH16

MH17

MH18

MH19

MH20

MH21

MH22

MH23MH24

MN1MN2

MN3

MN4MN5

MN6MN7MG1

MG2

MG3MG4

MG5

MZ1

MZ2MZ3

NG1

NG2

NG3

NG4

NG5

NG6

NG7OR1OR2

OR3OR4

OR5OR6OR7

OR8

OR9

OR11OR12

OR13

PU1PU2 PU3PU4

PU5

PU6

PU7

PU8PU9

PU10

PU11

PU12

RJ1

RJ2RJ3RJ4

RJ5

RJ6

RJ7

RJ8

RJ9RJ10

RJ11

RJ12

RJ13RJ14

RJ15

RJ16

RJ17

RJ18

RJ19

RJ20

RJ21RJ22

RJ23

RJ24RJ25

RJ26

RJ27

SK1SK2SK3

SK4

TN1

TN2

TN3

TN4

TN5TN6

TN7

TN8

TN9

TN10

TN11

TN12

TN13TR1TR2TR3

UP1UP2

UP3

UP4

UP5

UP6

UP7UP8

UP9

UP10UP11

UP12

UP13

UP14

UP15UP16

UP17

UP18 UP19

UP20

UP21UP22

UP23

UP24

UP25

UP26

UP27

UP28UP29 UP30

UP31

UP32

UP33UP34 UP35

UP36UP37

UP38UP39UP40

UP41

UP42

UP43UP44

UP45

UP46

UP47

UP48

UP49

UP50UP51

UP52

UP53

UP54

UP55UP56

UP57

UP58

UP59

UP60

UP61

UP62

WB1WB2

WB3

WB5WB6WB7

WB8WB9

WB10

WB11

WB12

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

HCIR9

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

HC

IR1

AP1AP2

AP3AP4

AP5AP6

AP8AP9

AP10

AP11

AP12

AP13

AP14

AP15

AP16AP17

AP18

AP19AP20AP21AP22

AP23

AR1AR2AR3AR4

AR5

AR6

AR7

AR8

AR9AR10AR11AS1

AS2

AS3

AS4AS5

AS6

AS7

AS8

AS9

AS10

AS11

AS12

AS13

AS14

AS15

AS16

AS17AS18

AS19

AS20

AS21

AS22AS23

BI1

BI2BI3BI4

BI5

BI6

BI7

BI8BI9

BI10BI11BI12BI13

BI14BI15

BI16

BI17BI18BI19

BI20

BI21

BI22BI23BI24BI25BI26BI27

BI28

BI29

BI30

BI31

BI32

BI33

BI34

BI35

BI36

BI37BI38

BI39

BI40

BI41

BI42

GU1GU2

GU3

GU4

GU5GU6GU7

GU8GU9

GU10GU11GU12 GU13GU14

GU15

GU16

GU17

GU18

GU19

HA1HA2

HA3HA4HA5

HA6HA7

HA8HA9

HA10

HA11

HA12

HA13

HA14HA15HA16

HP1

HP2

HP3

HP4HP5

HP6HP7HP8HP9

HP10HP11

HP12KA1KA2

KA3KA4KA5

KA6

KA7

KA8

KA9

KA10

KA11

KA12

KA13

KA14KA15KA16

KA17

KA18KA19

KA20

KE1

KE2KE3KE4

KE5

KE6KE7KE8

KE9KE10

KE11

KE12KE13

KE14

MP1

MP2

MP3

MP4MP5

MP6

MP7

MP8

MP9MP10MP11MP12

MP13

MP14

MP15

MP16

MP17MP18

MP19

MP20

MP21MP22

MP23

MP24

MP25

MP26

MP27

MP28

MP29

MP30MP31MP32

MP33MP34

MP35

MP36MP37

MP38

MP39

MP40

MP41

MP42

MP43

MP44MH1

MH2MH3

MH4MH5

MH6

MH7 MH8

MH9MH10MH11MH12MH13MH14MH15

MH16

MH17

MH18

MH19

MH20

MH21

MH22

MH23MH24

MN1MN2

MN3

MN4MN5

MN6MN7MG1

MG2

MG3MG4

MG5

MZ1

MZ2MZ3

NG1

NG2

NG3

NG4

NG5

NG6

NG7OR1OR2

OR3OR4

OR5OR6OR7

OR8

OR9

OR11OR12

OR13

PU1PU2 PU3PU4

PU5

PU6

PU7

PU8PU9

PU10

PU11

PU12

RJ1

RJ2RJ3RJ4

RJ5

RJ6

RJ7

RJ8

RJ9RJ10

RJ11

RJ12

RJ13RJ14

RJ15

RJ16

RJ17

RJ18

RJ19

RJ20

RJ21RJ22

RJ23

RJ24RJ25

RJ26

RJ27

SK1SK2SK3

SK4

TN1

TN2

TN3

TN4

TN5TN6

TN7

TN8

TN9

TN10

TN11

TN12

TN13TR1TR2TR3

UP1UP2

UP3

UP4

UP5

UP6

UP7UP8

UP9

UP10UP11

UP12

UP13

UP14

UP15UP16

UP17

UP18 UP19

UP20

UP21UP22

UP23

UP24

UP25

UP26

UP27

UP28UP29 UP30

UP31

UP32

UP33UP34 UP35

UP36UP37

UP38UP39UP40

UP41

UP42

UP43UP44

UP45

UP46

UP47

UP48

UP49

UP50UP51

UP52

UP53

UP54

UP55UP56

UP57

UP58

UP59

UP60

UP61

UP62

WB1WB2

WB3

WB5WB6WB7

WB8WB9

WB10

WB11

WB12

87

Figure 3.15: Scatter Plot between HHIR9 & HHIR1 for Common Rural Districts

AP1AP2

AP3AP4AP5

AP6AP8AP9

AP10

AP11AP12AP13AP14

AP15AP16

AP17

AP18AP19AP20AP21AP22AP23

AR1AR2

AR3

AR4AR5

AR6 AR7

AR8 AR9

AR10

AR11AS1

AS2 AS3AS4AS5 AS6AS7AS8AS9

AS10

AS11AS12

AS13

AS14AS15

AS16AS17

AS18

AS19

AS20

AS21

AS22AS23

BI1 BI2 BI3BI4BI5

BI6BI7

BI8BI9

BI10BI11BI12

BI13BI14BI15 BI16BI17

BI18BI19

BI20

BI21BI22

BI23BI24

BI25BI26

BI27BI28

BI29BI30

BI31 BI32BI33

BI34

BI35BI36

BI37BI38BI39

BI40BI41 BI42

GU1

GU2GU3

GU4

GU5GU6 GU7GU8

GU9GU10GU11GU12

GU13

GU14

GU15 GU16

GU17GU18

GU19HA1HA2HA3HA4HA5HA6

HA7HA8HA9HA10 HA11

HA12HA13

HA14

HA15

HA16

HP1

HP2HP3

HP4

HP5

HP6HP7

HP8

HP9

HP10

HP11

HP12

KA1KA2

KA3

KA4

KA5 KA6KA7 KA8KA9KA10

KA11KA12

KA13

KA14KA15KA16

KA17KA18

KA19

KA20

KE1 KE2

KE3KE4KE5 KE6

KE7

KE8

KE9KE10

KE11KE12KE13KE14

MP1

MP2

MP3MP4

MP5

MP6MP7

MP8MP9MP10

MP11

MP12

MP13

MP14

MP15MP16

MP17

MP18MP19MP20

MP21MP22

MP23 MP24

MP25MP26 MP27MP28MP29

MP30

MP31MP32MP33 MP34

MP35MP36

MP37MP38

MP39

MP40MP41

MP42MP43

MP44

MH1MH2

MH3

MH4 MH5

MH6 MH7

MH8

MH9 MH10MH11MH12MH13

MH14MH15

MH16MH17MH18MH19

MH20MH21

MH22 MH23MH24

MN1

MN2

MN3MN4MN5

MN6

MN7

MG1

MG2

MG3

MG4

MG5 MZ1

MZ2

MZ3

NG1

NG2

NG3

NG4NG5NG6

NG7OR1

OR2 OR3OR4OR5OR6

OR7OR8

OR9OR11OR12 OR13

PU1

PU2

PU3PU4

PU5PU6PU7

PU8

PU9PU10

PU11

PU12RJ1RJ2RJ3

RJ4

RJ5RJ6

RJ7

RJ8RJ9

RJ10RJ11

RJ12 RJ13

RJ14

RJ15

RJ16RJ17

RJ18

RJ19

RJ20

RJ21RJ22

RJ23

RJ24RJ25

RJ26RJ27 SK1

SK2 SK3SK4

TN1

TN2

TN3

TN4

TN5

TN6TN7

TN8

TN9

TN10 TN11TN12TN13

TR1TR2TR3

UP1UP2

UP3UP4

UP5

UP6UP7UP8

UP9

UP10UP11

UP12UP13

UP14

UP15

UP16

UP17 UP18UP19

UP20 UP21UP22

UP23

UP24UP25

UP26

UP27

UP28UP29

UP30

UP31

UP32UP33

UP34UP35

UP36

UP37UP38UP39 UP40

UP41

UP42

UP43UP44

UP45

UP46

UP47

UP48

UP49

UP50UP51

UP52

UP53

UP54

UP55

UP56

UP57UP58

UP59

UP60

UP61UP62

WB1

WB2

WB3

WB5WB6WB7

WB8

WB9

WB10WB11WB12

0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70

HHIR9

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8H

HIR

1

AP1AP2

AP3AP4AP5

AP6AP8AP9

AP10

AP11AP12AP13AP14

AP15AP16

AP17

AP18AP19AP20AP21AP22AP23

AR1AR2

AR3

AR4AR5

AR6 AR7

AR8 AR9

AR10

AR11AS1

AS2 AS3AS4AS5 AS6AS7AS8AS9

AS10

AS11AS12

AS13

AS14AS15

AS16AS17

AS18

AS19

AS20

AS21

AS22AS23

BI1 BI2 BI3BI4BI5

BI6BI7

BI8BI9

BI10BI11BI12

BI13BI14BI15 BI16BI17

BI18BI19

BI20

BI21BI22

BI23BI24

BI25BI26

BI27BI28

BI29BI30

BI31 BI32BI33

BI34

BI35BI36

BI37BI38BI39

BI40BI41 BI42

GU1

GU2GU3

GU4

GU5GU6 GU7GU8

GU9GU10GU11GU12

GU13

GU14

GU15 GU16

GU17GU18

GU19HA1HA2HA3HA4HA5HA6

HA7HA8HA9HA10 HA11

HA12HA13

HA14

HA15

HA16

HP1

HP2HP3

HP4

HP5

HP6HP7

HP8

HP9

HP10

HP11

HP12

KA1KA2

KA3

KA4

KA5 KA6KA7 KA8KA9KA10

KA11KA12

KA13

KA14KA15KA16

KA17KA18

KA19

KA20

KE1 KE2

KE3KE4KE5 KE6

KE7

KE8

KE9KE10

KE11KE12KE13KE14

MP1

MP2

MP3MP4

MP5

MP6MP7

MP8MP9MP10

MP11

MP12

MP13

MP14

MP15MP16

MP17

MP18MP19MP20

MP21MP22

MP23 MP24

MP25MP26 MP27MP28MP29

MP30

MP31MP32MP33 MP34

MP35MP36

MP37MP38

MP39

MP40MP41

MP42MP43

MP44

MH1MH2

MH3

MH4 MH5

MH6 MH7

MH8

MH9 MH10MH11MH12MH13

MH14MH15

MH16MH17MH18MH19

MH20MH21

MH22 MH23MH24

MN1

MN2

MN3MN4MN5

MN6

MN7

MG1

MG2

MG3

MG4

MG5 MZ1

MZ2

MZ3

NG1

NG2

NG3

NG4NG5NG6

NG7OR1

OR2 OR3OR4OR5OR6

OR7OR8

OR9OR11OR12 OR13

PU1

PU2

PU3PU4

PU5PU6PU7

PU8

PU9PU10

PU11

PU12RJ1RJ2RJ3

RJ4

RJ5RJ6

RJ7

RJ8RJ9

RJ10RJ11

RJ12 RJ13

RJ14

RJ15

RJ16RJ17

RJ18

RJ19

RJ20

RJ21RJ22

RJ23

RJ24RJ25

RJ26RJ27 SK1

SK2 SK3SK4

TN1

TN2

TN3

TN4

TN5

TN6TN7

TN8

TN9

TN10 TN11TN12TN13

TR1TR2TR3

UP1UP2

UP3UP4

UP5

UP6UP7UP8

UP9

UP10UP11

UP12UP13

UP14

UP15

UP16

UP17 UP18UP19

UP20 UP21UP22

UP23

UP24UP25

UP26

UP27

UP28UP29

UP30

UP31

UP32UP33

UP34UP35

UP36

UP37UP38UP39 UP40

UP41

UP42

UP43UP44

UP45

UP46

UP47

UP48

UP49

UP50UP51

UP52

UP53

UP54

UP55

UP56

UP57UP58

UP59

UP60

UP61UP62

WB1

WB2

WB3

WB5WB6WB7

WB8

WB9

WB10WB11WB12

88

Figure 3.16: Scatter Plot between SDIR9 & SDIR1 for Common Rural Districts

AP1AP2

AP3AP4

AP5

AP6AP8

AP9

AP10

AP11AP12

AP13AP14

AP15AP16AP17AP18AP19

AP20AP21AP22

AP23

AR1AR2

AR3

AR4AR5

AR6AR7

AR8

AR9AR10

AR11

AS1AS2

AS3AS4

AS5

AS6

AS7

AS8

AS9

AS10

AS11

AS12

AS13

AS14

AS15

AS16

AS17AS18

AS19AS20

AS21

AS22AS23

BI1

BI2BI3BI4

BI5

BI6

BI7BI8

BI9BI10BI11BI12BI13

BI14BI15

BI16

BI17 BI18BI19

BI20

BI21BI22BI23BI24

BI25BI26

BI27

BI28

BI29

BI30

BI31

BI32

BI33

BI34

BI35

BI36

BI37

BI38BI39

BI40

BI41BI42

GU1

GU2

GU3GU4 GU5

GU6GU7GU8GU9GU10GU11

GU12

GU13

GU14

GU15

GU16

GU17

GU18

GU19

HA1HA2

HA3HA4HA5

HA6

HA7HA8

HA9

HA10

HA11

HA12HA13

HA14

HA15

HA16

HP1

HP2

HP3

HP4

HP5

HP6

HP7

HP8

HP9

HP10

HP11

HP12KA1

KA2KA3

KA4KA5KA6

KA7

KA8

KA9

KA10

KA11

KA12

KA13

KA14KA15

KA16

KA17

KA18 KA19

KA20

KE1KE2KE3

KE4KE5

KE6KE7

KE8KE9

KE10

KE11KE12KE13

KE14MP1

MP2

MP3

MP4 MP5

MP6

MP7

MP8MP9MP10

MP11

MP12

MP13

MP14

MP15

MP16

MP17

MP18MP19

MP20

MP21

MP22

MP23

MP24MP25

MP26

MP27MP28

MP29

MP30

MP31MP32

MP33

MP34

MP35

MP36

MP37

MP38MP39

MP40MP41

MP42

MP43

MP44

MH1

MH2MH3

MH4 MH5MH6

MH7MH8MH9

MH10

MH11MH12

MH13

MH14MH15

MH16MH17

MH18

MH19

MH20MH21

MH22MH23

MH24MN1

MN2

MN3

MN4

MN5 MN6MN7

MG1

MG2

MG3

MG4

MG5

MZ1

MZ2MZ3

NG1 NG2

NG3

NG4

NG5

NG6

NG7

OR1OR2

OR3

OR4OR5

OR6OR7

OR8

OR9

OR11OR12

OR13PU1

PU2PU3

PU4

PU5

PU6PU7PU8 PU9

PU10

PU11

PU12

RJ1RJ2RJ3

RJ4RJ5

RJ6

RJ7

RJ8

RJ9RJ10

RJ11RJ12

RJ13

RJ14

RJ15

RJ16RJ17

RJ18

RJ19

RJ20RJ21RJ22RJ23

RJ24

RJ25RJ26RJ27

SK1SK2SK3

SK4

TN1

TN2

TN3TN4

TN5TN6

TN7

TN8

TN9

TN10

TN11

TN12

TN13

TR1 TR2TR3

UP1UP2

UP3

UP4

UP5

UP6

UP7UP8

UP9

UP10UP11

UP12

UP13

UP14

UP15

UP16

UP17 UP18

UP19UP20UP21

UP22

UP23

UP24UP25

UP26UP27

UP28

UP29UP30UP31

UP32

UP33UP34UP35

UP36

UP37UP38

UP39

UP40UP41

UP42

UP43UP44

UP45

UP46

UP47

UP48

UP49

UP50

UP51

UP52

UP53UP54

UP55UP56UP57

UP58

UP59

UP60

UP61

UP62 WB1WB2

WB3WB5

WB6WB7

WB8

WB9

WB10

WB11WB12

0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55

SDIR9

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70S

DIR

1 AP1AP2

AP3AP4

AP5

AP6AP8

AP9

AP10

AP11AP12

AP13AP14

AP15AP16AP17AP18AP19

AP20AP21AP22

AP23

AR1AR2

AR3

AR4AR5

AR6AR7

AR8

AR9AR10

AR11

AS1AS2

AS3AS4

AS5

AS6

AS7

AS8

AS9

AS10

AS11

AS12

AS13

AS14

AS15

AS16

AS17AS18

AS19AS20

AS21

AS22AS23

BI1

BI2BI3BI4

BI5

BI6

BI7BI8

BI9BI10BI11BI12BI13

BI14BI15

BI16

BI17 BI18BI19

BI20

BI21BI22BI23BI24

BI25BI26

BI27

BI28

BI29

BI30

BI31

BI32

BI33

BI34

BI35

BI36

BI37

BI38BI39

BI40

BI41BI42

GU1

GU2

GU3GU4 GU5

GU6GU7GU8GU9GU10GU11

GU12

GU13

GU14

GU15

GU16

GU17

GU18

GU19

HA1HA2

HA3HA4HA5

HA6

HA7HA8

HA9

HA10

HA11

HA12HA13

HA14

HA15

HA16

HP1

HP2

HP3

HP4

HP5

HP6

HP7

HP8

HP9

HP10

HP11

HP12KA1

KA2KA3

KA4KA5KA6

KA7

KA8

KA9

KA10

KA11

KA12

KA13

KA14KA15

KA16

KA17

KA18 KA19

KA20

KE1KE2KE3

KE4KE5

KE6KE7

KE8KE9

KE10

KE11KE12KE13

KE14MP1

MP2

MP3

MP4 MP5

MP6

MP7

MP8MP9MP10

MP11

MP12

MP13

MP14

MP15

MP16

MP17

MP18MP19

MP20

MP21

MP22

MP23

MP24MP25

MP26

MP27MP28

MP29

MP30

MP31MP32

MP33

MP34

MP35

MP36

MP37

MP38MP39

MP40MP41

MP42

MP43

MP44

MH1

MH2MH3

MH4 MH5MH6

MH7MH8MH9

MH10

MH11MH12

MH13

MH14MH15

MH16MH17

MH18

MH19

MH20MH21

MH22MH23

MH24MN1

MN2

MN3

MN4

MN5 MN6MN7

MG1

MG2

MG3

MG4

MG5

MZ1

MZ2MZ3

NG1 NG2

NG3

NG4

NG5

NG6

NG7

OR1OR2

OR3

OR4OR5

OR6OR7

OR8

OR9

OR11OR12

OR13PU1

PU2PU3

PU4

PU5

PU6PU7PU8 PU9

PU10

PU11

PU12

RJ1RJ2RJ3

RJ4RJ5

RJ6

RJ7

RJ8

RJ9RJ10

RJ11RJ12

RJ13

RJ14

RJ15

RJ16RJ17

RJ18

RJ19

RJ20RJ21RJ22RJ23

RJ24

RJ25RJ26RJ27

SK1SK2SK3

SK4

TN1

TN2

TN3TN4

TN5TN6

TN7

TN8

TN9

TN10

TN11

TN12

TN13

TR1 TR2TR3

UP1UP2

UP3

UP4

UP5

UP6

UP7UP8

UP9

UP10UP11

UP12

UP13

UP14

UP15

UP16

UP17 UP18

UP19UP20UP21

UP22

UP23

UP24UP25

UP26UP27

UP28

UP29UP30UP31

UP32

UP33UP34UP35

UP36

UP37UP38

UP39

UP40UP41

UP42

UP43UP44

UP45

UP46

UP47

UP48

UP49

UP50

UP51

UP52

UP53UP54

UP55UP56UP57

UP58

UP59

UP60

UP61

UP62 WB1WB2

WB3WB5

WB6WB7

WB8

WB9

WB10

WB11WB12

89

Figure 3.17: Scatter Plot between SXR06RD9 & SXR06RD1 for Common Rural Districts

AP1AP2AP3

AP4

AP5

AP6AP8

AP9AP10

AP11 AP12AP13AP14AP15AP16AP17

AP18AP19

AP20AP21

AP22AP23

AR1AR2

AR3

AR4

AR5

AR6

AR7AR8

AR9

AR10AR11

AS1AS2AS3

AS4AS5AS6AS7AS8AS9

AS10

AS11AS12AS13

AS14

AS15

AS16AS17AS18

AS19AS20

AS21AS22AS23 BI1

BI2BI3

BI4

BI5

BI6

BI7BI8BI9

BI10BI11BI12

BI13BI14BI15

BI16

BI17

BI18BI19BI20

BI21BI22

BI23

BI24BI25

BI26BI27

BI28

BI29

BI30BI31

BI32BI33BI34

BI35

BI36

BI37

BI38BI39

BI40BI41BI42

GU1

GU2

GU3

GU4

GU5

GU6

GU7GU8GU9

GU10

GU11

GU12

GU13 GU14

GU15

GU16

GU17

GU18

GU19

HA1

HA2HA3

HA4

HA5HA6

HA7

HA8

HA9

HA10HA11 HA12HA13

HA14

HA15

HA16

HP1

HP2

HP3HP4

HP5HP6HP7

HP8

HP9HP10

HP11

HP12

KA1KA2

KA3

KA4KA5KA6

KA7KA8KA9KA10KA11

KA12KA13

KA14

KA15

KA16KA17KA18

KA19KA20KE1KE2

KE3KE4KE5KE6KE7KE8KE9KE10KE11KE12

KE13KE14

MP1

MP2

MP3

MP4

MP5

MP6

MP7

MP8

MP9

MP10

MP11MP12

MP13

MP14MP15

MP16

MP17 MP18MP19

MP20MP21

MP22

MP23

MP24MP25

MP26

MP27MP28

MP29

MP30

MP31MP32MP33MP34

MP35MP36

MP37

MP38

MP39

MP40

MP41

MP42MP43

MP44

MH1

MH2MH3

MH4MH5

MH6 MH7

MH8

MH9

MH10

MH11MH12MH13

MH14

MH15

MH16

MH17MH18

MH19

MH20

MH21

MH22

MH23

MH24MN1

MN2MN3 MN4

MN5

MN6MN7

MG1MG2

MG3

MG4

MG5MZ1

MZ2

MZ3NG1

NG2

NG3

NG4

NG5

NG6

NG7OR1

OR2OR3OR4

OR5

OR6

OR7

OR8

OR9

OR11

OR12OR13

PU1PU2

PU3 PU4

PU5

PU6PU7

PU8

PU9

PU10

PU11PU12

RJ1

RJ2

RJ3

RJ4

RJ5

RJ6

RJ7RJ8

RJ9

RJ10

RJ11

RJ12

RJ13

RJ14

RJ15

RJ16

RJ17

RJ18

RJ19RJ20RJ21RJ22

RJ23

RJ24

RJ25 RJ26

RJ27SK1

SK2

SK3 SK4TN1

TN2

TN3

TN4

TN5

TN6TN7

TN8

TN9TN10

TN11TN12

TN13

TR1TR2

TR3

UP1UP2

UP3UP4

UP5

UP6

UP7

UP8

UP9

UP10

UP11UP12

UP13

UP14

UP15

UP16

UP17UP18

UP19

UP20

UP21

UP22

UP23

UP24

UP25

UP26

UP27

UP28UP29

UP30

UP31

UP32

UP33UP34UP35

UP36UP37

UP38

UP39

UP40

UP41

UP42

UP43

UP44

UP45

UP46

UP47

UP48

UP49

UP50UP51

UP52

UP53

UP54

UP55

UP56

UP57

UP58UP59UP60

UP61

UP62

WB1WB2WB3WB5WB6WB7WB8WB9WB10

WB11

WB12

800 820 840 860 880 900 920 940 960 980 1000 1020 1040 1060

SXR06RD9

740

760

780

800

820

840

860

880

900

920

940

960

980

1000

1020

1040S

XR

06R

D1

AP1AP2AP3

AP4

AP5

AP6AP8

AP9AP10

AP11 AP12AP13AP14AP15AP16AP17

AP18AP19

AP20AP21

AP22AP23

AR1AR2

AR3

AR4

AR5

AR6

AR7AR8

AR9

AR10AR11

AS1AS2AS3

AS4AS5AS6AS7AS8AS9

AS10

AS11AS12AS13

AS14

AS15

AS16AS17AS18

AS19AS20

AS21AS22AS23 BI1

BI2BI3

BI4

BI5

BI6

BI7BI8BI9

BI10BI11BI12

BI13BI14BI15

BI16

BI17

BI18BI19BI20

BI21BI22

BI23

BI24BI25

BI26BI27

BI28

BI29

BI30BI31

BI32BI33BI34

BI35

BI36

BI37

BI38BI39

BI40BI41BI42

GU1

GU2

GU3

GU4

GU5

GU6

GU7GU8GU9

GU10

GU11

GU12

GU13 GU14

GU15

GU16

GU17

GU18

GU19

HA1

HA2HA3

HA4

HA5HA6

HA7

HA8

HA9

HA10HA11 HA12HA13

HA14

HA15

HA16

HP1

HP2

HP3HP4

HP5HP6HP7

HP8

HP9HP10

HP11

HP12

KA1KA2

KA3

KA4KA5KA6

KA7KA8KA9KA10KA11

KA12KA13

KA14

KA15

KA16KA17KA18

KA19KA20KE1KE2

KE3KE4KE5KE6KE7KE8KE9KE10KE11KE12

KE13KE14

MP1

MP2

MP3

MP4

MP5

MP6

MP7

MP8

MP9

MP10

MP11MP12

MP13

MP14MP15

MP16

MP17 MP18MP19

MP20MP21

MP22

MP23

MP24MP25

MP26

MP27MP28

MP29

MP30

MP31MP32MP33MP34

MP35MP36

MP37

MP38

MP39

MP40

MP41

MP42MP43

MP44

MH1

MH2MH3

MH4MH5

MH6 MH7

MH8

MH9

MH10

MH11MH12MH13

MH14

MH15

MH16

MH17MH18

MH19

MH20

MH21

MH22

MH23

MH24MN1

MN2MN3 MN4

MN5

MN6MN7

MG1MG2

MG3

MG4

MG5MZ1

MZ2

MZ3NG1

NG2

NG3

NG4

NG5

NG6

NG7OR1

OR2OR3OR4

OR5

OR6

OR7

OR8

OR9

OR11

OR12OR13

PU1PU2

PU3 PU4

PU5

PU6PU7

PU8

PU9

PU10

PU11PU12

RJ1

RJ2

RJ3

RJ4

RJ5

RJ6

RJ7RJ8

RJ9

RJ10

RJ11

RJ12

RJ13

RJ14

RJ15

RJ16

RJ17

RJ18

RJ19RJ20RJ21RJ22

RJ23

RJ24

RJ25 RJ26

RJ27SK1

SK2

SK3 SK4TN1

TN2

TN3

TN4

TN5

TN6TN7

TN8

TN9TN10

TN11TN12

TN13

TR1TR2

TR3

UP1UP2

UP3UP4

UP5

UP6

UP7

UP8

UP9

UP10

UP11UP12

UP13

UP14

UP15

UP16

UP17UP18

UP19

UP20

UP21

UP22

UP23

UP24

UP25

UP26

UP27

UP28UP29

UP30

UP31

UP32

UP33UP34UP35

UP36UP37

UP38

UP39

UP40

UP41

UP42

UP43

UP44

UP45

UP46

UP47

UP48

UP49

UP50UP51

UP52

UP53

UP54

UP55

UP56

UP57

UP58UP59UP60

UP61

UP62

WB1WB2WB3WB5WB6WB7WB8WB9WB10

WB11

WB12

90

Figure 3.18: Scatter Plot between WPIU9 & WPIU1 for Common Urban Districts

AP1

AP2 AP3AP4

AP5

AP6AP7

AP8

AP9

AP10

AP11

AP12

AP13AP14

AP15AP16AP17

AP18

AP19 AP20

AP21AP22

AP23

AR1AR2

AR3AR4

AR5

AR6

AR7

AR8

AR9

AR10

AR11AS1AS2

AS3AS4

AS5

AS6AS7AS8 AS9

AS10AS11

AS12

AS13

AS14AS15

AS16

AS17AS18

AS19

AS20

AS21 AS22AS23

BI1 BI2

BI3

BI4BI5

BI6BI7BI8

BI9 BI10BI11

BI12

BI13

BI14BI15

BI16BI17

BI18BI19 BI20BI21

BI22

BI23

BI24BI25BI26

BI27BI28

BI29BI30

BI31

BI32

BI33

BI34

BI35BI36

BI37

BI38

BI39

BI40

BI41BI42

GU1

GU2GU3

GU4GU5

GU6GU7

GU8

GU9

GU10GU11

GU12

GU13GU14

GU15

GU16

GU18GU19

HA1

HA2

HA3HA4HA5

HA6HA7

HA8HA9

HA10

HA11

HA12

HA13HA14HA15HA16

HP1HP2

HP3 HP4

HP6

HP8

HP9

HP10

HP11

HP12

KA1KA2

KA3

KA4

KA5KA6

KA7KA8

KA9

KA10

KA11

KA12

KA13

KA14KA15KA16

KA17KA18

KA19

KA20

KE1KE2

KE3KE4KE5

KE6

KE7

KE8KE9KE10

KE11

KE12KE13KE14

MP1

MP2

MP3

MP4

MP5MP6

MP7MP8

MP9

MP10

MP11

MP12MP13MP14MP15MP16MP17

MP18MP19

MP20MP21

MP22

MP23

MP24

MP25

MP26

MP27

MP28

MP29

MP30MP31

MP32

MP33

MP34

MP35

MP36MP37MP38MP39

MP40

MP41

MP42MP43

MP44MH1

MH2MH3

MH4

MH5MH6

MH7

MH8

MH9

MH10

MH11MH12

MH13

MH14MH15

MH16

MH17MH18

MH19MH20MH21

MH22 MH23

MH24

MN1

MN2

MN6

MG1

MG2

MG3

MG4

MG5

MZ1

MZ2

MZ3

NG1NG2NG3

NG4

NG5NG6

NG7

OR1

OR2

OR3

OR4

OR5OR6OR7

OR8

OR9

OR10

OR11OR12

OR13

PU1PU2PU3PU4PU5PU6

PU7PU8

PU9PU10PU11

PU12

RJ1

RJ2

RJ3

RJ4

RJ5

RJ6

RJ7RJ8

RJ9

RJ10RJ11

RJ12RJ13

RJ14RJ15

RJ16RJ17

RJ18

RJ19RJ20

RJ21RJ22

RJ23RJ24

RJ25RJ26

RJ27

SK1SK2SK3

SK4

TN1

TN2TN3

TN4

TN5TN5

TN6

TN7

TN8

TN9

TN10

TN11

TN12

TN13TR1 TR2TR3

UP1UP2UP3

UP4

UP5

UP6UP7

UP8 UP9

UP10

UP11

UP12

UP13

UP14UP15

UP16

UP17 UP18UP19UP20

UP21

UP22UP23

UP24UP25

UP26UP27

UP28

UP29

UP30

UP31

UP32 UP33

UP34

UP35

UP36

UP37

UP38

UP39UP40

UP41

UP42

UP43

UP44

UP45

UP46

UP47

UP48UP49

UP50

UP51UP52

UP53

UP54

UP55

UP56UP57

UP58

UP59

UP60

UP61UP62

WB1

WB2

WB3

WB4

WB5WB6WB7

WB8WB9WB10

WB11

WB12

0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55

WPIU9

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

WPI

U1

AP1

AP2 AP3AP4

AP5

AP6AP7

AP8

AP9

AP10

AP11

AP12

AP13AP14

AP15AP16AP17

AP18

AP19 AP20

AP21AP22

AP23

AR1AR2

AR3AR4

AR5

AR6

AR7

AR8

AR9

AR10

AR11AS1AS2

AS3AS4

AS5

AS6AS7AS8 AS9

AS10AS11

AS12

AS13

AS14AS15

AS16

AS17AS18

AS19

AS20

AS21 AS22AS23

BI1 BI2

BI3

BI4BI5

BI6BI7BI8

BI9 BI10BI11

BI12

BI13

BI14BI15

BI16BI17

BI18BI19 BI20BI21

BI22

BI23

BI24BI25BI26

BI27BI28

BI29BI30

BI31

BI32

BI33

BI34

BI35BI36

BI37

BI38

BI39

BI40

BI41BI42

GU1

GU2GU3

GU4GU5

GU6GU7

GU8

GU9

GU10GU11

GU12

GU13GU14

GU15

GU16

GU18GU19

HA1

HA2

HA3HA4HA5

HA6HA7

HA8HA9

HA10

HA11

HA12

HA13HA14HA15HA16

HP1HP2

HP3 HP4

HP6

HP8

HP9

HP10

HP11

HP12

KA1KA2

KA3

KA4

KA5KA6

KA7KA8

KA9

KA10

KA11

KA12

KA13

KA14KA15KA16

KA17KA18

KA19

KA20

KE1KE2

KE3KE4KE5

KE6

KE7

KE8KE9KE10

KE11

KE12KE13KE14

MP1

MP2

MP3

MP4

MP5MP6

MP7MP8

MP9

MP10

MP11

MP12MP13MP14MP15MP16MP17

MP18MP19

MP20MP21

MP22

MP23

MP24

MP25

MP26

MP27

MP28

MP29

MP30MP31

MP32

MP33

MP34

MP35

MP36MP37MP38MP39

MP40

MP41

MP42MP43

MP44MH1

MH2MH3

MH4

MH5MH6

MH7

MH8

MH9

MH10

MH11MH12

MH13

MH14MH15

MH16

MH17MH18

MH19MH20MH21

MH22 MH23

MH24

MN1

MN2

MN6

MG1

MG2

MG3

MG4

MG5

MZ1

MZ2

MZ3

NG1NG2NG3

NG4

NG5NG6

NG7

OR1

OR2

OR3

OR4

OR5OR6OR7

OR8

OR9

OR10

OR11OR12

OR13

PU1PU2PU3PU4PU5PU6

PU7PU8

PU9PU10PU11

PU12

RJ1

RJ2

RJ3

RJ4

RJ5

RJ6

RJ7RJ8

RJ9

RJ10RJ11

RJ12RJ13

RJ14RJ15

RJ16RJ17

RJ18

RJ19RJ20

RJ21RJ22

RJ23RJ24

RJ25RJ26

RJ27

SK1SK2SK3

SK4

TN1

TN2TN3

TN4

TN5TN5

TN6

TN7

TN8

TN9

TN10

TN11

TN12

TN13TR1 TR2TR3

UP1UP2UP3

UP4

UP5

UP6UP7

UP8 UP9

UP10

UP11

UP12

UP13

UP14UP15

UP16

UP17 UP18UP19UP20

UP21

UP22UP23

UP24UP25

UP26UP27

UP28

UP29

UP30

UP31

UP32 UP33

UP34

UP35

UP36

UP37

UP38

UP39UP40

UP41

UP42

UP43

UP44

UP45

UP46

UP47

UP48UP49

UP50

UP51UP52

UP53

UP54

UP55

UP56UP57

UP58

UP59

UP60

UP61UP62

WB1

WB2

WB3

WB4

WB5WB6WB7

WB8WB9WB10

WB11

WB12

91

Figure 3.19: Scatter Plot between HCIU9 & HCIU1 for Common Urban Districts

AP1AP2

AP3

AP4

AP5

AP6

AP7AP8

AP9 AP10

AP11

AP12

AP13

AP14AP15

AP16

AP17AP18AP19AP20

AP21AP22

AP23

AR1AR2

AR3AR4AR5 AR6

AR7

AR8

AR9

AR10

AR11

AS1

AS2 AS3AS4AS5

AS6

AS7

AS8

AS9AS10

AS11AS12

AS13

AS14

AS15AS16

AS17 AS18AS19

AS20 AS21AS22

AS23

BI1

BI2

BI3

BI4

BI5

BI6

BI7BI8

BI9

BI10

BI11BI12

BI13

BI14

BI15

BI16

BI17

BI18BI19

BI20

BI21BI22

BI23 BI24

BI25BI26

BI27

BI28

BI29

BI30

BI31

BI32

BI33

BI34

BI35BI36BI37

BI38

BI39BI40 BI41

BI42

GU1

GU2

GU3

GU4

GU5

GU6

GU7GU8GU9

GU10

GU11

GU12

GU13GU14GU15

GU16

GU18GU19

HA1

HA2

HA3 HA4

HA5

HA6HA7

HA8HA9HA10HA11

HA12HA13

HA14HA15

HA16

HP1HP2 HP3HP4HP6

HP8HP9

HP10 HP11

HP12

KA1

KA2KA3

KA4KA5

KA6

KA7

KA8

KA9

KA10

KA11

KA12

KA13

KA14KA15KA16

KA17

KA18KA19

KA20

KE1KE2KE3

KE4

KE5

KE6

KE7

KE8

KE9

KE10

KE11KE12KE13

KE14

MP1MP2MP3

MP4

MP5MP6

MP7

MP8MP9

MP10

MP11MP12

MP13

MP14MP15MP16

MP17MP18

MP19MP20

MP21

MP22

MP23

MP24

MP25

MP26

MP27

MP28

MP29

MP30

MP31

MP32MP33MP34

MP35

MP36MP37

MP38MP39

MP40

MP41

MP42MP43 MP44

MH1MH2

MH3

MH4MH5

MH6MH7MH8

MH9

MH10

MH11MH12

MH13

MH14MH15

MH16MH17

MH18

MH19MH20

MH21

MH22

MH23MH24

MN1

MN2

MN6MG1

MG2MG3

MG4

MG5

MZ1

MZ2

MZ3

NG1NG2

NG3

NG4

NG5NG6

NG7

OR1OR2

OR3OR4

OR5

OR6OR7

OR8

OR9OR10

OR11OR12

OR13

PU1

PU2PU3

PU4PU5

PU6PU7PU8PU9

PU10

PU11

PU12

RJ1

RJ2

RJ3

RJ4

RJ5

RJ6

RJ7RJ8

RJ9

RJ10

RJ11

RJ12

RJ13

RJ14

RJ15RJ16

RJ17

RJ18

RJ19

RJ20

RJ21

RJ22RJ23RJ24

RJ25

RJ26

RJ27SK1

SK2

SK3SK4

TN1

TN2

TN3

TN4

TN5TN5TN6TN7

TN8

TN9TN10TN11

TN12

TN13

TR1TR2TR3

UP1UP2

UP3

UP4

UP5

UP6

UP7

UP8UP9

UP10

UP11

UP12

UP13

UP14UP15

UP16

UP17

UP18UP19

UP20

UP21

UP22

UP23

UP24UP25

UP26

UP27

UP28

UP29

UP30

UP31UP32

UP33UP34

UP35

UP36

UP37UP38

UP39UP40

UP41

UP42

UP43UP44

UP45

UP46

UP47

UP48

UP49

UP50 UP51

UP52

UP53

UP54

UP55UP56

UP57

UP58

UP59

UP60

UP61

UP62

WB1

WB2WB3

WB4WB5WB6

WB7WB8

WB9

WB10WB11

WB12

0.2 0.3 0.4 0.5 0.6 0.7 0.8

HCIU9

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

HC

IU1 AP1

AP2

AP3

AP4

AP5

AP6

AP7AP8

AP9 AP10

AP11

AP12

AP13

AP14AP15

AP16

AP17AP18AP19AP20

AP21AP22

AP23

AR1AR2

AR3AR4AR5 AR6

AR7

AR8

AR9

AR10

AR11

AS1

AS2 AS3AS4AS5

AS6

AS7

AS8

AS9AS10

AS11AS12

AS13

AS14

AS15AS16

AS17 AS18AS19

AS20 AS21AS22

AS23

BI1

BI2

BI3

BI4

BI5

BI6

BI7BI8

BI9

BI10

BI11BI12

BI13

BI14

BI15

BI16

BI17

BI18BI19

BI20

BI21BI22

BI23 BI24

BI25BI26

BI27

BI28

BI29

BI30

BI31

BI32

BI33

BI34

BI35BI36BI37

BI38

BI39BI40 BI41

BI42

GU1

GU2

GU3

GU4

GU5

GU6

GU7GU8GU9

GU10

GU11

GU12

GU13GU14GU15

GU16

GU18GU19

HA1

HA2

HA3 HA4

HA5

HA6HA7

HA8HA9HA10HA11

HA12HA13

HA14HA15

HA16

HP1HP2 HP3HP4HP6

HP8HP9

HP10 HP11

HP12

KA1

KA2KA3

KA4KA5

KA6

KA7

KA8

KA9

KA10

KA11

KA12

KA13

KA14KA15KA16

KA17

KA18KA19

KA20

KE1KE2KE3

KE4

KE5

KE6

KE7

KE8

KE9

KE10

KE11KE12KE13

KE14

MP1MP2MP3

MP4

MP5MP6

MP7

MP8MP9

MP10

MP11MP12

MP13

MP14MP15MP16

MP17MP18

MP19MP20

MP21

MP22

MP23

MP24

MP25

MP26

MP27

MP28

MP29

MP30

MP31

MP32MP33MP34

MP35

MP36MP37

MP38MP39

MP40

MP41

MP42MP43 MP44

MH1MH2

MH3

MH4MH5

MH6MH7MH8

MH9

MH10

MH11MH12

MH13

MH14MH15

MH16MH17

MH18

MH19MH20

MH21

MH22

MH23MH24

MN1

MN2

MN6MG1

MG2MG3

MG4

MG5

MZ1

MZ2

MZ3

NG1NG2

NG3

NG4

NG5NG6

NG7

OR1OR2

OR3OR4

OR5

OR6OR7

OR8

OR9OR10

OR11OR12

OR13

PU1

PU2PU3

PU4PU5

PU6PU7PU8PU9

PU10

PU11

PU12

RJ1

RJ2

RJ3

RJ4

RJ5

RJ6

RJ7RJ8

RJ9

RJ10

RJ11

RJ12

RJ13

RJ14

RJ15RJ16

RJ17

RJ18

RJ19

RJ20

RJ21

RJ22RJ23RJ24

RJ25

RJ26

RJ27SK1

SK2

SK3SK4

TN1

TN2

TN3

TN4

TN5TN5TN6TN7

TN8

TN9TN10TN11

TN12

TN13

TR1TR2TR3

UP1UP2

UP3

UP4

UP5

UP6

UP7

UP8UP9

UP10

UP11

UP12

UP13

UP14UP15

UP16

UP17

UP18UP19

UP20

UP21

UP22

UP23

UP24UP25

UP26

UP27

UP28

UP29

UP30

UP31UP32

UP33UP34

UP35

UP36

UP37UP38

UP39UP40

UP41

UP42

UP43UP44

UP45

UP46

UP47

UP48

UP49

UP50 UP51

UP52

UP53

UP54

UP55UP56

UP57

UP58

UP59

UP60

UP61

UP62

WB1

WB2WB3

WB4WB5WB6

WB7WB8

WB9

WB10WB11

WB12

92

Figure 3.20: Scatter Plot between HHIU9 & HHIU1 for Common Urban Districts

AP1

AP2 AP3AP4AP5AP6

AP7

AP8AP9AP10

AP11AP12AP13AP14

AP15

AP16

AP17

AP18

AP19AP20AP21

AP22AP23

AR1

AR2AR3

AR4AR5

AR6

AR7

AR8

AR9

AR10

AR11

AS1

AS2AS3

AS4

AS5

AS6

AS7

AS8 AS9

AS10AS11AS12

AS13

AS14

AS15AS16AS17AS18

AS19

AS20

AS21AS22

AS23BI1 BI2

BI3BI4

BI5BI6

BI7

BI8

BI9BI10

BI11

BI12

BI13 BI14BI15BI16BI17 BI18

BI19BI20

BI21 BI22BI23BI24

BI25 BI26

BI27BI28

BI29

BI30BI31 BI32BI33 BI34

BI35BI36

BI37

BI38BI39

BI40BI41BI42

GU1GU2

GU3GU4GU5

GU6GU7

GU8GU9

GU10GU11

GU12

GU13GU14

GU15

GU16

GU18GU19HA1

HA2HA3HA4

HA5HA6HA7HA8HA9

HA10

HA11

HA12

HA13

HA14

HA15HA16

HP1HP2HP3

HP4 HP6HP8

HP9

HP10HP11

HP12

KA1

KA2

KA3

KA4KA5

KA6

KA7KA8KA9KA10

KA11

KA12

KA13

KA14

KA15

KA16

KA17

KA18

KA19

KA20

KE1

KE2KE3

KE4

KE5KE6KE7

KE8KE9

KE10

KE11KE12

KE13KE14MP1MP2MP3MP4

MP5MP6MP7

MP8

MP9

MP10MP11MP12

MP13

MP14

MP15MP16

MP17

MP18MP19MP20

MP21

MP22

MP23MP24MP25

MP26

MP27MP28MP29

MP30

MP31

MP32MP33

MP34MP35MP36

MP37MP38MP39

MP40

MP41MP42MP43MP44

MH1

MH2MH3

MH4

MH5MH6

MH7MH8

MH9

MH10

MH11

MH12

MH13

MH14

MH15

MH16MH17

MH18MH19

MH20

MH21

MH22 MH23MH24

MN1

MN2

MN6

MG1

MG2

MG3

MG4MG5

MZ1

MZ2

MZ3

NG1

NG2

NG3NG4NG5NG6

NG7

OR1OR2

OR3OR4OR5

OR6

OR7OR8OR9

OR10

OR11

OR12OR13

PU1PU2

PU3PU4PU5

PU6 PU7PU8PU9

PU10

PU11

PU12

RJ1RJ2

RJ3RJ4

RJ5RJ6

RJ7

RJ8 RJ9

RJ10

RJ11 RJ12

RJ13

RJ14

RJ15RJ16

RJ17RJ18 RJ19

RJ20

RJ21

RJ22RJ23RJ24RJ25

RJ26 RJ27

SK1SK2 SK3

SK4

TN1TN2

TN3

TN4

TN5

TN5

TN6TN7

TN8

TN9

TN10

TN11

TN12

TN13

TR1

TR2

TR3

UP1

UP2UP3

UP4

UP5

UP6

UP7

UP8UP9

UP10

UP11UP12

UP13

UP14 UP15UP16

UP17

UP18

UP19

UP20UP21

UP22

UP23

UP24UP25

UP26UP27UP28

UP29

UP30

UP31

UP32 UP33

UP34UP35

UP36

UP37 UP38

UP39

UP40UP41

UP42UP43

UP44

UP45

UP46

UP47

UP48

UP49UP50

UP51

UP52

UP53

UP54

UP55

UP56

UP57UP58

UP59

UP60

UP61UP62WB1

WB2

WB3

WB4WB5WB6

WB7WB8WB9

WB10WB11

WB12

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

HHIU9

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

HH

IU1

AP1

AP2 AP3AP4AP5AP6

AP7

AP8AP9AP10

AP11AP12AP13AP14

AP15

AP16

AP17

AP18

AP19AP20AP21

AP22AP23

AR1

AR2AR3

AR4AR5

AR6

AR7

AR8

AR9

AR10

AR11

AS1

AS2AS3

AS4

AS5

AS6

AS7

AS8 AS9

AS10AS11AS12

AS13

AS14

AS15AS16AS17AS18

AS19

AS20

AS21AS22

AS23BI1 BI2

BI3BI4

BI5BI6

BI7

BI8

BI9BI10

BI11

BI12

BI13 BI14BI15BI16BI17 BI18

BI19BI20

BI21 BI22BI23BI24

BI25 BI26

BI27BI28

BI29

BI30BI31 BI32BI33 BI34

BI35BI36

BI37

BI38BI39

BI40BI41BI42

GU1GU2

GU3GU4GU5

GU6GU7

GU8GU9

GU10GU11

GU12

GU13GU14

GU15

GU16

GU18GU19HA1

HA2HA3HA4

HA5HA6HA7HA8HA9

HA10

HA11

HA12

HA13

HA14

HA15HA16

HP1HP2HP3

HP4 HP6HP8

HP9

HP10HP11

HP12

KA1

KA2

KA3

KA4KA5

KA6

KA7KA8KA9KA10

KA11

KA12

KA13

KA14

KA15

KA16

KA17

KA18

KA19

KA20

KE1

KE2KE3

KE4

KE5KE6KE7

KE8KE9

KE10

KE11KE12

KE13KE14MP1MP2MP3MP4

MP5MP6MP7

MP8

MP9

MP10MP11MP12

MP13

MP14

MP15MP16

MP17

MP18MP19MP20

MP21

MP22

MP23MP24MP25

MP26

MP27MP28MP29

MP30

MP31

MP32MP33

MP34MP35MP36

MP37MP38MP39

MP40

MP41MP42MP43MP44

MH1

MH2MH3

MH4

MH5MH6

MH7MH8

MH9

MH10

MH11

MH12

MH13

MH14

MH15

MH16MH17

MH18MH19

MH20

MH21

MH22 MH23MH24

MN1

MN2

MN6

MG1

MG2

MG3

MG4MG5

MZ1

MZ2

MZ3

NG1

NG2

NG3NG4NG5NG6

NG7

OR1OR2

OR3OR4OR5

OR6

OR7OR8OR9

OR10

OR11

OR12OR13

PU1PU2

PU3PU4PU5

PU6 PU7PU8PU9

PU10

PU11

PU12

RJ1RJ2

RJ3RJ4

RJ5RJ6

RJ7

RJ8 RJ9

RJ10

RJ11 RJ12

RJ13

RJ14

RJ15RJ16

RJ17RJ18 RJ19

RJ20

RJ21

RJ22RJ23RJ24RJ25

RJ26 RJ27

SK1SK2 SK3

SK4

TN1TN2

TN3

TN4

TN5

TN5

TN6TN7

TN8

TN9

TN10

TN11

TN12

TN13

TR1

TR2

TR3

UP1

UP2UP3

UP4

UP5

UP6

UP7

UP8UP9

UP10

UP11UP12

UP13

UP14 UP15UP16

UP17

UP18

UP19

UP20UP21

UP22

UP23

UP24UP25

UP26UP27UP28

UP29

UP30

UP31

UP32 UP33

UP34UP35

UP36

UP37 UP38

UP39

UP40UP41

UP42UP43

UP44

UP45

UP46

UP47

UP48

UP49UP50

UP51

UP52

UP53

UP54

UP55

UP56

UP57UP58

UP59

UP60

UP61UP62WB1

WB2

WB3

WB4WB5WB6

WB7WB8WB9

WB10WB11

WB12

93

Figure 3.21: Scatter Plot between SDIU9 & SDIU1 for Common Urban Districts

AP1AP2

AP3AP4

AP5AP6

AP7

AP8AP9AP10

AP11AP12 AP13AP14

AP15

AP16

AP17AP18AP19

AP20AP21AP22AP23

AR1AR2AR3AR4

AR5

AR6AR7

AR8

AR9

AR10

AR11AS1

AS2 AS3

AS4

AS5AS6

AS7

AS8AS9

AS10AS11AS12

AS13

AS14

AS15AS16AS17 AS18

AS19

AS20

AS21AS22

AS23

BI1 BI2

BI3

BI4BI5

BI6BI7BI8BI9

BI10BI11

BI12

BI13

BI14BI15

BI16

BI17BI18BI19

BI20

BI21BI22

BI23BI24

BI25BI26

BI27

BI28

BI29

BI30

BI31

BI32

BI33

BI34

BI35BI36

BI37BI38

BI39BI40

BI41BI42

GU1GU2

GU3

GU4

GU5 GU6GU7GU8

GU9 GU10GU11GU12

GU13GU14

GU15

GU16

GU18GU19

HA1

HA2

HA3HA4HA5

HA6HA7

HA8HA9HA10

HA11HA12HA13

HA14

HA15HA16

HP1 HP2HP3HP4

HP6HP8

HP9

HP10HP11

HP12

KA1

KA2KA3

KA4

KA5

KA6

KA7KA8

KA9

KA10

KA11

KA12

KA13

KA14KA15

KA16

KA17

KA18KA19KA20

KE1 KE2KE3

KE4KE5

KE6KE7

KE8KE9

KE10KE11 KE12KE13

KE14

MP1MP2

MP3

MP4

MP5MP6

MP7

MP8

MP9

MP10 MP11MP12

MP13MP14

MP15MP16

MP17MP18MP19

MP20MP21MP22

MP23

MP24

MP25

MP26

MP27MP28

MP29

MP30MP31

MP32

MP33

MP34

MP35

MP36 MP37MP38MP39

MP40

MP41

MP42MP43

MP44

MH1MH2 MH3

MH4MH5

MH6MH7MH8

MH9

MH10

MH11MH12

MH13

MH14MH15

MH16MH17 MH18

MH19MH20

MH21

MH22

MH23

MH24

MN1

MN2

MN6

MG1

MG2

MG3

MG4

MG5

MZ1

MZ2

MZ3

NG1NG2

NG3

NG4

NG5NG6NG7

OR1OR2

OR3

OR4OR5OR6OR7

OR8OR9OR10 OR11OR12OR13

PU1PU2PU3PU4PU5

PU6PU7PU8PU9PU10

PU11

PU12

RJ1RJ2

RJ3

RJ4

RJ5

RJ6

RJ7

RJ8

RJ9

RJ10RJ11

RJ12

RJ13

RJ14

RJ15RJ16

RJ17

RJ18

RJ19

RJ20

RJ21

RJ22RJ23RJ24

RJ25

RJ26

RJ27

SK1

SK2

SK3

SK4TN1

TN2

TN3TN4

TN5TN5

TN6TN7

TN8

TN9

TN10

TN11

TN12

TN13

TR1 TR2TR3

UP1

UP2

UP3

UP4

UP5

UP6UP7 UP8

UP9

UP10

UP11

UP12

UP13

UP14 UP15

UP16UP17

UP18UP19

UP20UP21UP22

UP23

UP24UP25

UP26UP27

UP28 UP29

UP30

UP31

UP32UP33

UP34

UP35

UP36

UP37 UP38

UP39UP40 UP41

UP42

UP43

UP44

UP45

UP46

UP47

UP48

UP49

UP50

UP51

UP52

UP53

UP54

UP55 UP56

UP57

UP58

UP59

UP60

UP61

UP62

WB1

WB2

WB3 WB4WB5WB6

WB7WB8

WB9WB10WB11

WB12

0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65

SDIU9

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

SD

IU1

AP1AP2

AP3AP4

AP5AP6

AP7

AP8AP9AP10

AP11AP12 AP13AP14

AP15

AP16

AP17AP18AP19

AP20AP21AP22AP23

AR1AR2AR3AR4

AR5

AR6AR7

AR8

AR9

AR10

AR11AS1

AS2 AS3

AS4

AS5AS6

AS7

AS8AS9

AS10AS11AS12

AS13

AS14

AS15AS16AS17 AS18

AS19

AS20

AS21AS22

AS23

BI1 BI2

BI3

BI4BI5

BI6BI7BI8BI9

BI10BI11

BI12

BI13

BI14BI15

BI16

BI17BI18BI19

BI20

BI21BI22

BI23BI24

BI25BI26

BI27

BI28

BI29

BI30

BI31

BI32

BI33

BI34

BI35BI36

BI37BI38

BI39BI40

BI41BI42

GU1GU2

GU3

GU4

GU5 GU6GU7GU8

GU9 GU10GU11GU12

GU13GU14

GU15

GU16

GU18GU19

HA1

HA2

HA3HA4HA5

HA6HA7

HA8HA9HA10

HA11HA12HA13

HA14

HA15HA16

HP1 HP2HP3HP4

HP6HP8

HP9

HP10HP11

HP12

KA1

KA2KA3

KA4

KA5

KA6

KA7KA8

KA9

KA10

KA11

KA12

KA13

KA14KA15

KA16

KA17

KA18KA19KA20

KE1 KE2KE3

KE4KE5

KE6KE7

KE8KE9

KE10KE11 KE12KE13

KE14

MP1MP2

MP3

MP4

MP5MP6

MP7

MP8

MP9

MP10 MP11MP12

MP13MP14

MP15MP16

MP17MP18MP19

MP20MP21MP22

MP23

MP24

MP25

MP26

MP27MP28

MP29

MP30MP31

MP32

MP33

MP34

MP35

MP36 MP37MP38MP39

MP40

MP41

MP42MP43

MP44

MH1MH2 MH3

MH4MH5

MH6MH7MH8

MH9

MH10

MH11MH12

MH13

MH14MH15

MH16MH17 MH18

MH19MH20

MH21

MH22

MH23

MH24

MN1

MN2

MN6

MG1

MG2

MG3

MG4

MG5

MZ1

MZ2

MZ3

NG1NG2

NG3

NG4

NG5NG6NG7

OR1OR2

OR3

OR4OR5OR6OR7

OR8OR9OR10 OR11OR12OR13

PU1PU2PU3PU4PU5

PU6PU7PU8PU9PU10

PU11

PU12

RJ1RJ2

RJ3

RJ4

RJ5

RJ6

RJ7

RJ8

RJ9

RJ10RJ11

RJ12

RJ13

RJ14

RJ15RJ16

RJ17

RJ18

RJ19

RJ20

RJ21

RJ22RJ23RJ24

RJ25

RJ26

RJ27

SK1

SK2

SK3

SK4TN1

TN2

TN3TN4

TN5TN5

TN6TN7

TN8

TN9

TN10

TN11

TN12

TN13

TR1 TR2TR3

UP1

UP2

UP3

UP4

UP5

UP6UP7 UP8

UP9

UP10

UP11

UP12

UP13

UP14 UP15

UP16UP17

UP18UP19

UP20UP21UP22

UP23

UP24UP25

UP26UP27

UP28 UP29

UP30

UP31

UP32UP33

UP34

UP35

UP36

UP37 UP38

UP39UP40 UP41

UP42

UP43

UP44

UP45

UP46

UP47

UP48

UP49

UP50

UP51

UP52

UP53

UP54

UP55 UP56

UP57

UP58

UP59

UP60

UP61

UP62

WB1

WB2

WB3 WB4WB5WB6

WB7WB8

WB9WB10WB11

WB12

94

Figure 3.22: Scatter Plot between SXR06UD9 & SXR06UD1 for Common Urban Districts

AP1

AP2

AP3AP4

AP5

AP6AP7AP8

AP9AP10AP11AP12AP13AP14AP15 AP16AP17AP18AP19

AP20AP21AP22AP23

AR1

AR2

AR3

AR4

AR5AR6 AR7

AR8AR9

AR10AR11 AS1AS2

AS3AS4AS5

AS6

AS7

AS8

AS9

AS10

AS11AS12

AS13AS14

AS15AS16

AS17

AS18

AS19

AS20

AS21AS22AS23

BI1 BI2

BI3

BI4

BI5BI6BI7

BI8BI9

BI10BI11

BI12BI13

BI14

BI15

BI16

BI17BI18

BI19BI20BI21 BI22BI23

BI24

BI25BI26BI27

BI28BI29

BI30BI31 BI32

BI33BI34BI35BI36 BI37

BI38

BI39

BI40

BI41

BI42

GU1

GU2

GU3

GU4GU5

GU6

GU7GU8

GU9

GU10

GU11

GU12

GU13GU14GU15GU16 GU18

GU19

HA1HA2HA3

HA4HA5

HA6HA7

HA8

HA9

HA10HA11

HA12

HA13HA14

HA15HA16

HP1HP2

HP3HP4 HP6

HP8HP9HP10

HP11

HP12

KA1KA2KA3KA4KA5KA6

KA7KA8KA9

KA10

KA11KA12

KA13

KA14KA15 KA16KA17KA18KA19KA20KE1KE2

KE3

KE4KE5KE6

KE7KE8KE9KE10

KE11KE12KE13

KE14

MP1MP2

MP3

MP4

MP5 MP6

MP7

MP8MP9

MP10

MP11MP12

MP13MP14

MP15

MP16

MP17MP18

MP19MP20

MP21

MP22

MP23

MP24MP25

MP26

MP27MP28

MP29MP30

MP31

MP32MP33

MP34

MP35

MP36MP37

MP38MP39

MP40

MP41MP42

MP43MP44

MH1

MH2 MH3

MH4MH5MH6

MH7MH8

MH9

MH10

MH11MH12

MH13MH14

MH15MH16MH17 MH18

MH19MH20

MH21MH22MH23MH24

MN1

MN2

MN6

MG1MG2

MG3

MG4

MG5MZ1

MZ2

MZ3

NG1

NG2NG3

NG4

NG5

NG6

NG7

OR1OR2OR3

OR4OR5

OR6OR7 OR8

OR9

OR10OR11

OR12OR13

PU1

PU2

PU3PU4

PU5

PU6PU7PU8

PU9

PU10PU11PU12

RJ1

RJ2

RJ3

RJ4

RJ5

RJ6RJ7

RJ8 RJ9RJ10

RJ11

RJ12

RJ13

RJ14

RJ15

RJ16

RJ17

RJ18

RJ19RJ20RJ21RJ22RJ23RJ24

RJ25

RJ26

RJ27

SK1

SK2

SK3

SK4

TN1

TN2

TN3

TN4TN5

TN5TN6

TN7

TN8

TN9

TN10TN11TN12

TN13

TR1

TR2

TR3

UP1

UP2UP3

UP4

UP5UP6

UP7 UP8UP9UP10

UP11UP12

UP13 UP14UP15

UP16

UP17UP18

UP19

UP20

UP21

UP22

UP23UP24

UP25

UP26UP27

UP28

UP29

UP30UP31

UP32

UP33 UP34

UP35

UP36UP37 UP38

UP39

UP40UP41

UP42

UP43

UP44UP45

UP46

UP47

UP48

UP49

UP50UP51

UP52

UP53

UP54

UP55UP56

UP57UP58

UP59

UP60

UP61

UP62

WB1WB2 WB3

WB4WB5WB6

WB7

WB8WB9

WB10WB11

WB12

800 820 840 860 880 900 920 940 960 980 1000 1020 1040 1060 1080 1100

SXR06UD9

700

750

800

850

900

950

1000

1050

1100

SX

R06

UD

1

AP1

AP2

AP3AP4

AP5

AP6AP7AP8

AP9AP10AP11AP12AP13AP14AP15 AP16AP17AP18AP19

AP20AP21AP22AP23

AR1

AR2

AR3

AR4

AR5AR6 AR7

AR8AR9

AR10AR11 AS1AS2

AS3AS4AS5

AS6

AS7

AS8

AS9

AS10

AS11AS12

AS13AS14

AS15AS16

AS17

AS18

AS19

AS20

AS21AS22AS23

BI1 BI2

BI3

BI4

BI5BI6BI7

BI8BI9

BI10BI11

BI12BI13

BI14

BI15

BI16

BI17BI18

BI19BI20BI21 BI22BI23

BI24

BI25BI26BI27

BI28BI29

BI30BI31 BI32

BI33BI34BI35BI36 BI37

BI38

BI39

BI40

BI41

BI42

GU1

GU2

GU3

GU4GU5

GU6

GU7GU8

GU9

GU10

GU11

GU12

GU13GU14GU15GU16 GU18

GU19

HA1HA2HA3

HA4HA5

HA6HA7

HA8

HA9

HA10HA11

HA12

HA13HA14

HA15HA16

HP1HP2

HP3HP4 HP6

HP8HP9HP10

HP11

HP12

KA1KA2KA3KA4KA5KA6

KA7KA8KA9

KA10

KA11KA12

KA13

KA14KA15 KA16KA17KA18KA19KA20KE1KE2

KE3

KE4KE5KE6

KE7KE8KE9KE10

KE11KE12KE13

KE14

MP1MP2

MP3

MP4

MP5 MP6

MP7

MP8MP9

MP10

MP11MP12

MP13MP14

MP15

MP16

MP17MP18

MP19MP20

MP21

MP22

MP23

MP24MP25

MP26

MP27MP28

MP29MP30

MP31

MP32MP33

MP34

MP35

MP36MP37

MP38MP39

MP40

MP41MP42

MP43MP44

MH1

MH2 MH3

MH4MH5MH6

MH7MH8

MH9

MH10

MH11MH12

MH13MH14

MH15MH16MH17 MH18

MH19MH20

MH21MH22MH23MH24

MN1

MN2

MN6

MG1MG2

MG3

MG4

MG5MZ1

MZ2

MZ3

NG1

NG2NG3

NG4

NG5

NG6

NG7

OR1OR2OR3

OR4OR5

OR6OR7 OR8

OR9

OR10OR11

OR12OR13

PU1

PU2

PU3PU4

PU5

PU6PU7PU8

PU9

PU10PU11PU12

RJ1

RJ2

RJ3

RJ4

RJ5

RJ6RJ7

RJ8 RJ9RJ10

RJ11

RJ12

RJ13

RJ14

RJ15

RJ16

RJ17

RJ18

RJ19RJ20RJ21RJ22RJ23RJ24

RJ25

RJ26

RJ27

SK1

SK2

SK3

SK4

TN1

TN2

TN3

TN4TN5

TN5TN6

TN7

TN8

TN9

TN10TN11TN12

TN13

TR1

TR2

TR3

UP1

UP2UP3

UP4

UP5UP6

UP7 UP8UP9UP10

UP11UP12

UP13 UP14UP15

UP16

UP17UP18

UP19

UP20

UP21

UP22

UP23UP24

UP25

UP26UP27

UP28

UP29

UP30UP31

UP32

UP33 UP34

UP35

UP36UP37 UP38

UP39

UP40UP41

UP42

UP43

UP44UP45

UP46

UP47

UP48

UP49

UP50UP51

UP52

UP53

UP54

UP55UP56

UP57UP58

UP59

UP60

UP61

UP62

WB1WB2 WB3

WB4WB5WB6

WB7

WB8WB9

WB10WB11

WB12

Appendix 3.2: Names of Common Districts in 1991 & 2001SN Distcode Distname SN Distcode Distname1 AP1 Adilabad 54 AS20 North Cachar Hills2 AP2 Anantapur 55 AS21 Sibsagar3 AP3 Chittoor 56 AS22 Sonitpur4 AP4 Cuddapah 57 AS23 Tinsukia5 AP5 East Godavari 58 BI1 Araria6 AP6 Guntur 59 BI2 Aurangabad7 AP7 Hyderabad 60 BI3 Begusarai8 AP8 Karimnagar 61 BI4 Bhagalpur9 AP9 Khammam 62 BI5 Bhojpur10 AP10 Krishna 63 BI6 Darbhanga11 AP11 Kurnool 64 BI7 Deoghar12 AP12 Mahbubnagar 65 BI8 Dhanbad13 AP13 Medak 66 BI9 Dumka14 AP14 Nalgonda 67 BI10 Gaya15 AP15 Nellore 68 BI11 Giridih16 AP16 Nizamabad 69 BI12 Godda17 AP17 Prakasam 70 BI13 Gopalganj18 AP18 Rangareddi 71 BI14 Gumla19 AP19 Srikakulam 72 BI15 Hazaribagh20 AP20 Visakhapatnam 73 BI16 Jehanabad 21 AP21 Vizianagaram 74 BI17 Katihar22 AP22 Warangal 75 BI18 Khagaria23 AP23 West Godavari 76 BI19 Kishanganj24 AR1 Changlang 77 BI20 Lohardaga25 AR2 Dibang Valley 78 BI21 Madhepura26 AR3 East Kameng 79 BI22 Madhubani27 AR4 East Siang 80 BI23 Munger28 AR5 Lohit 81 BI24 Muzaffarpur29 AR6 Lower Subansiri 82 BI25 Nalanda30 AR7 Tawang 83 BI26 Nawada31 AR8 Tirap 84 BI27 Palamu32 AR9 Upper Subansiri 85 BI28 Pashchim Champaran33 AR10 West Kameng 86 BI29 Paschim Singhbhum34 AR11 West Siang 87 BI30 Patna35 AS1 Barpeta 88 BI31 Purba Champaran36 AS2 Bongaigaon 89 BI32 Purbi Singhbhum37 AS3 Cachar 90 BI33 Purnia38 AS4 Darrang 91 BI34 Ranchi39 AS5 Dhemaji 92 BI35 Rohtas40 AS6 Dhubri 93 BI36 Saharsa41 AS7 Dibrugarh 94 BI37 Sahibganj42 AS8 Goalpara 95 BI38 Samastipur43 AS9 Golaghat 96 BI39 Saran44 AS10 Hailakandi 97 BI40 Sitamarhi45 AS11 Jorhat 98 BI41 Siwan46 AS12 Kamrup 99 BI42 Vaishali47 AS13 Karbi Anglong 100 GU1 Ahmadabad48 AS14 Karimganj 101 GU2 Amreli49 AS15 Kokrajhar 102 GU3 Banas Kantha50 AS16 Lakhimpur 103 GU4 Bharuch51 AS17 Marigaon 104 GU5 Bhavnagar52 AS18 Nagaon 105 GU6 Gandhinagar53 AS19 Nalbari 106 GU7 Jamnagar

95

Names of Common Districts in 1991 & 2001SN Distcode Distname SN Distcode Distname107 GU8 Junagadh 160 KA14 Kolar108 GU9 Kachchh 161 KA15 Mandya109 GU10 Kheda 162 KA16 Mysore110 GU11 Mahesana 163 KA17 Raichur111 GU12 Panch Mahals 164 KA18 Shimoga112 GU13 Rajkot 165 KA19 Tumkur113 GU14 Sabar Kantha 166 KA20 Uttara Kannada114 GU15 Surat 167 KE1 Alappuzha115 GU16 Surendranagar 168 KE2 Ernakulam116 GU17 The Dangs 169 KE3 Idukki117 GU18 Vadodara 170 KE4 Kannur118 GU19 Valsad 171 KE5 Kasaragod119 HA1 Ambala 172 KE6 Kollam120 HA2 Bhiwani 173 KE7 Kottayam121 HA3 Faridabad 174 KE8 Kozhikode122 HA4 Gurgaon 175 KE9 Malappuram123 HA5 Hisar 176 KE10 Palakkad124 HA6 Jind 177 KE11 Pathanamthitta125 HA7 Kaithal 178 KE12 Thrissur126 HA8 Karnal 179 KE13 Trivundram127 HA9 Kurukshetra 180 KE14 Wayanad128 HA10 Mahendragarh 181 MP1 Balaghat129 HA11 Panipat 182 MP2 Bastar130 HA12 Rewari 183 MP3 Betul131 HA13 Rohtak 184 MP4 Bhind132 HA14 Sirsa 185 MP5 Bhopal133 HA15 Sonipat 186 MP6 Bilaspur134 HA16 Yamunanagar 187 MP7 Chhatarpur135 HP1 Bilaspur 188 MP8 Chhindwara136 HP2 Chamba 189 MP9 Damoh137 HP3 Hamirpur 190 MP10 Datia138 HP4 Kangra 191 MP11 Dewas139 HP5 Kinnaur 192 MP12 Dhar140 HP6 Kullu 193 MP13 Durg141 HP7 Lahul & Spiti 194 MP14 East Nimar142 HP8 Mandi 195 MP15 Guna143 HP9 Shimla 196 MP16 Gwalior144 HP10 Sirmaur 197 MP17 Hoshangabad145 HP11 Solan 198 MP18 Indore146 HP12 Una 199 MP19 Jabalpur147 KA1 Bangalore 200 MP20 Jhabua148 KA2 Bangalore Rural 201 MP21 Mandla149 KA3 Belgaum 202 MP22 Mandsaur150 KA4 Bellary 203 MP23 Morena151 KA5 Bidar 204 MP24 Narsimhapur152 KA6 Bijapur 205 MP25 Panna153 KA7 Chikmagalur 206 MP26 Raipur154 KA8 Chitradurga 207 MP27 Raisen155 KA9 Dakshina Kannada 208 MP28 Rajgarh156 KA10 Dharwad 209 MP29 Rajnandgaon157 KA11 Gulbarga 210 MP30 Ratlam158 KA12 Hassan 211 MP31 Rewa159 KA13 Kodagu 212 MP32 Sagar

96

Names of Common Districts in 1991 & 2001SN Distcode Distname SN Distcode Distname213 MP33 Satna 266 NG3 Mon214 MP34 Sehore 267 NG4 Phek215 MP35 Seoni 268 NG5 Tuensang216 MP36 Shahdol 269 NG6 Wokha217 MP37 Shajapur 270 NG7 Zunheboto218 MP38 Shivpuri 271 OR1 Balangir219 MP39 Sidhi 272 OR2 Baleshwar220 MP40 Surguja 273 OR3 Cuttack221 MP41 Tikamgarh 274 OR4 Dhenkanal222 MP42 Ujjain 275 OR5 Ganjam223 MP43 Vidisha 276 OR6 Kalahandi224 MP44 West Nimar 277 OR7 Kendujhar225 MH1 Ahmadnagar 278 OR8 Koraput226 MH2 Akola 279 OR9 Mayurbhanj227 MH3 Amravati 280 OR10 Phulabani228 MH4 Aurangabad 281 OR11 Puri229 MH5 Bid 282 OR12 Sambalpur230 MH6 Buldana 283 OR13 Sundargarh231 MH7 Dhule 284 PU1 Amritsar232 MH8 Jalgaon 285 PU2 Bathinda233 MH9 Jalna 286 PU3 Faridkot234 MH10 Kolhapur 287 PU4 Firozpur235 MH11 Latur 288 PU5 Gurdaspur236 MH12 Nanded 289 PU6 Hoshiarpur237 MH13 Nashik 290 PU7 Jalandhar238 MH14 Osmanabad 291 PU8 Kapurthala239 MH15 Parbhani 292 PU9 Ludhiana240 MH16 Pune 293 PU10 Patiala241 MH17 Raigarh 294 PU11 Rupnagar242 MH18 Ratnagiri 295 PU12 Sangrur243 MH19 Sangli 296 RJ1 Ajmer244 MH20 Satara 297 RJ2 Alwar245 MH21 Sindhudurg 298 RJ3 Banswara246 MH22 Solapur 299 RJ4 Barmer247 MH23 Thane 300 RJ5 Bharatpur248 MH24 Yavatmal 301 RJ6 Bhilwara249 MN1 Bishnupur 302 RJ7 Bikaner250 MN2 Chandel 303 RJ8 Bundi251 MN3 Churachandpur 304 RJ9 Chittaurgarh252 MN4 Senapati 305 RJ10 Churu253 MN5 Tamenglong 306 RJ11 Dhaulpur254 MN6 Thoubal 307 RJ12 Dungarpur255 MN7 Ukhrul 308 RJ13 Ganganagar256 MG1 East Garo Hills 309 RJ14 Jaipur257 MG2 East Khasi Hills 310 RJ15 Jaisalmer258 MG3 Jaintia Hills 311 RJ16 Jalor259 MG4 West Garo Hills 312 RJ17 Jhalawar260 MG5 West Khasi Hills 313 RJ18 Jhunjhunun261 MZ1 Aizawl 314 RJ19 Jodhpur262 MZ2 Chhimtuipui 315 RJ20 Kota263 MZ3 Lunglei 316 RJ21 Nagaur264 NG1 Kohima 317 RJ22 Pali265 NG2 Mokokchung 318 RJ23 Sawai Madhopur

97

Names of Common Districts in 1991 & 2001SN Distcode Distname SN Distcode Distname319 RJ24 Sikar 371 UP28 Hamirpur320 RJ25 Sirohi 372 UP29 Hardoi321 RJ26 Tonk 373 UP30 Hardwar322 RJ27 Udaipur 374 UP31 Jalaun323 SK1 East Sikkim 375 UP32 Jaunpur324 SK2 North Sikkim 376 UP33 Jhansi325 SK3 South Sikkim 377 UP34 Kanpur Dehat326 SK4 West Sikkim 378 UP35 Kanpur Nagar327 TN1 Coimbatore 379 UP36 Kheri328 TN2 Dharmapuri 380 UP37 Lalitpur329 TN3 Dindigul 381 UP38 Lucknow330 TN4 Kanniyakumari 382 UP39 Maharajganj331 TN5 Madras 383 UP40 Mainpuri332 TN6 Madurai 384 UP41 Mathura333 TN7 Pudukkottai 385 UP42 Mau334 TN8 Ramanathapuram 386 UP43 Meerut335 TN9 Salem 387 UP44 Mirzapur336 TN10 Thanjavur 388 UP45 Moradabad337 TN11 The Nilgiris 389 UP46 Muzaffarnagar338 TN12 Tiruchirappalli 390 UP47 Nainital339 TN13 Tirunelveli 391 UP48 Pilibhit340 TN14 Tiruvannamalai 392 UP49 Pithoragarh341 TR1 North Tripura 393 UP50 Pratapgarh342 TR2 South Tripura 394 UP51 Rae Bareli343 TR3 West Tripura 395 UP52 Rampur344 UP1 Agra 396 UP53 Saharanpur345 UP2 Aligarh 397 UP54 Shahjahanpur346 UP3 Allahabad 398 UP55 Siddharthnagar347 UP4 Almora 399 UP56 Sitapur348 UP5 Azamgarh 400 UP57 Sonbhadra349 UP6 Bahraich 401 UP58 Sultanpur350 UP7 Banda 402 UP59 Tehri Garhwal351 UP8 Barabanki 403 UP60 Unnao352 UP9 Bareilly 404 UP61 Uttarkashi353 UP10 Basti 405 UP62 Varanasi354 UP11 Bijnor 406 WB1 Bankura355 UP12 Budaun 407 WB2 Barddhaman356 UP13 Bulandshahr 408 WB3 Birbhum357 UP14 Chamoli 409 WB4 Calcutta358 UP15 Dehradun 410 WB5 Darjiling359 UP16 Deoria 411 WB6 Haora360 UP17 Etah 412 WB7 Hugli361 UP18 Etawah 413 WB8 Jalpaiguri362 UP19 Faizabad 414 WB9 Koch Bihar363 UP20 Farrukhabad 415 WB10 Maldah364 UP21 Fatehpur 416 WB11 Medinipur365 UP22 Firozabad 417 WB12 Murshidabad366 UP23 Garhwal 418 WB13 Nadia367 UP24 Ghaziabad 419 WB14 North 24 Parganas368 UP25 Ghazipur 420 WB15 Puruliya369 UP26 Gonda 421 WB16 South 24 Parganas370 UP27 Gorakhpur

98

99

Chapter 4. In Search of the Best & Worst Districts from Social and

Economic Factors

The objective of this chapter is to explore the nature of relationship among the computed

indices including the economic indicators thereby searching the best and worst districts.

Chapter 2 has established that conventional belief regarding inter-state and inter-district

disparities, distribution of the districts in various indices of development, rural versus urban

disparities, and nature of linkage between social and economic indicators is not true.

For generality, no outlying districts are ignored from the statistical exercises. Our approach

is very simple and relies solely on quantitative merits at the complete neglect of qualitative

judgment. We are aware that different districts situated in different locations even within a

state may not lie on the same steady state plane over a longer time period as per the

premises of growth economics. The results have adequately shown that there are many

districts even within the developed states and some districts within the backward states that

should not be treated as homogenous regions within the respective state. Let us to observe

the nature of the static relationship among the estimated indices across the districts.

Relationship among the Indices

Let us begin with Table 4.1, which presents the relevant cross correlation matrix among

the indices for the rural districts. The most salient features within the rural sector are noted

below.

(1) Pearson’s correlation coefficient between SDIR4 and poverty ratio (HCRRD5) is

negative (-0.41). According to our a priori notion set in earlier chapter, it is weakly

negative association, even though as per text book, it is significantly negative. Even if we

do not endorse that, the leaning it signals is of great significance: districts with higher

values of SDI computed from Census indicators represent relatively lower poverty ratio.

Consistent with this tendency, the correlation (+0.54) between SDIR4 and PPRRD5

(purchasing power real) also appears positively significant, even if it is weakly positive,

100

according to our rigidity. It is really interesting to note that information compiled by two

independent GOI sources (one census and the other survey) is of great significance for

future social policy. Finally, the relationship between social development (SDIR4) and

economic inequality (GINIRD5) at the district level gives birth to an insignificant

coefficient, r = +0.24. So there is no prima facie linkage between economic inequality and

social development.

Three scatter diagrams between poverty ratio (Figure 4.1), purchasing power (Figure 4.2)

and inequality (Figure 4.3) with social development rightly represent these relationships.

Even if the relationship between SDIR4 and HCRRD5 is lowly negative (-0.41), there is a

potent convex tendency between the two thereby suggesting increasingly lower poverty

ratio with higher social development (Figure 4.1).

(2) As revealed by Table 4.1, the correlation coefficients of SDIR4 with SDIR6, SDIR7

and SDIR7_W are respectively +0.81, +0.78 and +0.76, which are significantly positive

by any standard. But the last three variants of SDI include three economic indicators as

well thereby making the correlation coefficients high.

(3) For urban areas (Table 4.2), these associations are much weaker, because the

conditions therein are more heterogeneous across the districts. Inclusion of three economic

indicators has expectedly increased the strength of this association, but this suffers from

multi-colinearity as in rural areas.

(4) In all the cases for both rural and urban areas, there does not appear to have any

apparent relationship between the development indices and the prevalence of backward

classes. One can check this from the two tables.

(3) Table 4.3 presents the Pearson’s correlation between rural and urban indices. The

relevant pair of correlations is captured through detailed scatter diagrams from Figure 4.4

to Figure 4.10 with abbreviated code names of the districts as per the first two alphabets of

the state names followed by numerical number representing the district of the state. One

101

can check the actual names of the districts from Appendix 4.1. Note that the three pair of

correlations between social development indices in rural and urban areas of the districts

(SDI4, SDI6 and SDI7) are respectively +0.77, +0.63 and +0.64, which are not in the ideal

arrange of very strong association as stated by us. Even if social development index

computed from only social indicators (SDI4) appears to be strong (+0.77), the other two

variants, which include poverty, purchasing power (SDI6) and inequality (SDI7), are

certainly in the regular range of significance as per our arbitrary belief. Figure 4.4 (SDIR4

and SDIU4), Figure 4.5 (HCIR1 and HCIU1), Figure 4.6 (HHIR1 and HHIU1) and

Figure 4.7 (TCIR1 and TCIU1) make the following features obvious between rural and

urban areas. First, in most cases, urban parts of the districts lie on the left side of the

diagonal except a select few. Second, if social development of a district is high, it is

relatively so in both rural and urban areas and vice versa, although the rural is always lower

than urban counterpart in absolute term.

(4) As the demography of WPI is far away from common sense perception of those

accustomed to urban way of living, we are skipping its discourse here. Yet it is observed

that under Indian work ethics, rural men and women are forced to participate in whatever

work they can by ‘default choice’, whereas the richer the districts the lesser the work

participation. This will be invariably corroborated by the existing data for urban areas. On

the whole, as the value of correlation suggests (+0.47) in Table 4.3, there is no strong

relation between rural and urban work participation.

(5) Unlike the components of social development index, there is no significant association

between the rural and urban segments of the districts in the three economic indicators as

revealed by Figure 4.8 (poverty), Figure 4.9 (purchasing power) and Figure 4.10

(inequality). Quite contrary to these social components, the three economic components of

NSSO, namely poverty ratio, purchasing power and economic inequality display

completely heterogeneous linkages between the rural and urban parts of the districts. It

would, therefore, be unjustified to base social and economic policies on the

macroeconomic aggregates of the states and the nation.

102

Therefore, there is adequate evidence to the fact that in all the development indicators, the rural

districts in India are lagging far behind their urban counterparts except work participation and

infant sex ratio. In a sense, these two indicators are more suggestively linked with economic and

educational backwardness as is usual in the vast majority of rural districts. It may, therefore, be

concluded that the richer and educated we become, the more is our crime against female babies

and work ethics. Secondly, there are some vicissitudes within both rural and urban districts in

some specific zones of the country. It also points to the fact that there is huge scope for

improvement in both rural and urban sectors of the districts. Our next task is to identify the 100

best and worst districts and trace them to the states.

District-wise Share of Total Poor & Share of Consumption Endowment

So far we have discussed relative values of the indices and various proportions relating to the

economic indicators. They serve great many purposes, but they fail to represent the absolute

mass of the deprived people. In many situations, size does matter for direct intervention in terms

of government policy as well as for non-governmental operations as fund for development is not

unlimited. So for sake of prioritization of interventionist policy here is a simple attempt at

capturing the districts, which are home to 50% of India’s rural poor and urban poor. Note that

50.18% of India’s total rural poor (amounting to 200.54 Million), that is, 100.27 Million can be

found in only 107 districts out of 575 rural districts studied here.

What are these districts? Which states are home to these poor? Note that Murshidabad

district of WB accommodates 1.47511% (=30.32 lakhs) of India’s total rural poor with only

0.5305% (=Rs.920.02 Million) of India’s total rural consumption expenditure. The picture in

terms of absolute poverty distribution is posted below. As it appears, 14 rural districts of West

Bengal out of a total rural districts of 17 (Kolkata does not have any rural part) have found place

in the chart. It is just incomparable with the rest of the states including Uttar Pradesh, Bihar,

Orissa, Maharashtra, MP and Chhattisgarh, which have neither significantly less density, nor

lower number of districts. There is hardly any doubt that the rural in West Bengal is the worst in

India among the states. The conditions of UP and Bihar are quite clear from the chart.

103

Distribution of 50.18% (= 10.27 crores) of India’s Rural Poor among 13 States

States

(actual

dts)

UP

(70)

BI

(37)

WB

(18)

OR

(30)

MH

(35)

MP

(45)

CG

(16)

JH

(18)

TN

(30)

GU

(25)

RJ

(32)

AP

(23)

KA

(27)

No. of

dts.

25 24 14 9 8 8 6 4 3 2 2 1 1

What is the picture in urban areas? Quite contrary to the above chart, 80 districts supply

50.16% of India’s urban poor (60.40 Million), that is, 30.21 Million. Unlike popular belief, rural

and urban poverty distribution is not similar across the districts and states. For example, urban

poverty is worst in Maharashtra with as much as 19 districts out of 35 in the worst absolute

category. This is followed by UP, AP, Karnataka and MP. While rural poverty is alarming in UP,

Bihar, WB, Orissa, Maharashtra and MP, performance of West Bengal, Bihar and Orissa is

laudable in urban area. It is indeed difficult to understand how the rural got ignored in a state like

West Bengal. However, among the Maharashtra districts, Mumbai (Suburban) is the home to

largest urban poor in India, which keeps alive 1.9107% of India’s urban poor with a staggering

4.7263% of total urban expenditure.

Distribution of 50.16% (= 3.21 crores) of India’s Urban Poor among 13 States

States

(actual

dts)

MH

(35)

UP

(UP)

AP

(23)

KA

(27)

MP

(45)

TN

(30)

CG

(16)

KE

(14)

RJ

(32)

WB

(18)

BI

(37)

GU

(25)

OR

(30)

No. of

dts.

19 11 10 10 9 6 3 3 3 3 1 1 1

Spatial Location of Top & Bottom Districts in India Map

We are now in a position to present the best and worst districts in poverty and social

development index in Indian map, separately for rural and urban areas. The districts are

classified as follows:

104

Orange Coloured Districts: Top 25% Districts of India.

Blue Colured Districts: Upper 25% Districts of India (26% to 50%).

Grey Colured Districts: Lower 25% Districts of India (51% to 75%).

Red Colured Districts: Bottom 25% Districts of India.

An attempt is made here to select these districts on India’s district level maps by the aforesaid

colours. There are four maps as follows.

Map-1 captures these four different categories of districts in terms of rural poverty. The right

central portion of India comprising of districts from Bihar, Orissa, Jharkhand, Chhattisgarh, MP,

UP and WB is the home to India’s largest majority of the rural poor amounting to 50% (=102.70

Millions). Note that the left of the perpendicular from Delhi to Kanyakumari do not contain

much red spot.

Map-2 depicts the districts in terms of urban poverty, which covers an altogether different

geographical space. The entire central western portion of India including Orissa, Chhattisgarh

and wide regions of Maharashtra, Karnataka, MP, UP and Bihar is the home to India’s 50%

(=32.00 Millions) urban poor. It is quite transparent that WB, AP, TN and almost the entire

North East do not contain any prominent red patch.

Map-3 displays rural SDI, which is largely similar to Map-1 on rural poverty not only in terms

of red, but also in terms of orange, blue and grey coloured districts. Note that the value of

Pearson’s r between SDIR4 and HCRRD5 is -0.41 with very high t-statistic. On the whole,

there may be separate sets of factors, which decide rural poverty and rural social development in

Indian districts.

Finally, Map-4 is based on urban SDI. This map hints towards a different colour composition

from Map-2 on urban poverty. It is fairly comprehensible that urban poverty and urban social

development are not seemingly linked as per our initial assumption of strength of association,

although the relation is negative (r = -0.27). It would not be out of place, therefore, to conclude

that there are other factors which may be more decisively linked to urban poverty beyond social

development indices.

105

Map-1. Poverty Ratio in Rural Districts of India, 2004-05

106

Map-2. Poverty Ratio in Urban Districts of India, 2004-05

107

Map-3. Social Development Index in Rural Districts of India, 2001

108

Map-4. Social Development Index in Urban Districts of India, 2001

109

Best & Worst Districts in India

(1) We have presented here the 25 best and worst districts in social development, health &

housing, transport & communication, poverty, purchasing power, female literacy and Gini

coefficient separately for rural and urban areas (except Gini for worst urban districts as this is

irrelevant there). Table 4.4 and Table 4.5 respectively present the names of the 25 best and

worst rural districts in these indicators of development. Note that the names of best districts in

poverty (HCRRD5) include 37 districts in lieu of 25 as head count ratio for all these districts are

close to zero and below 1%. On the other hand, for the worst 25 rural districts in poverty ratio,

names of 84 districts are referred as head count ratio for these districts is greater than or equal to

50%.

(2) Names of the 25 best and worst urban districts are presented in Table 4.6 and Table 4.7.

Here also, for the best 25 districts in poverty ratio, names of 37 districts are enclosed as

incidentally their head count ratio is less than or equal to 1% only. On the other hand, for the

worst 25 urban districts in poverty ratio, names of 91 districts are mentioned as head count ratio

for these districts is greater than or equal to 50%.

It would be suggestive for all to glance at the names of these four types of districts in India.

Vulnerable & Rich Classes

All said and done, let us additionally venture here to signal the future course of research for

understanding spatial assimilation of the regions for balanced development, which would take

care of the provincial parameters for overall improvement of the welfare of greater participation

of the people. For example, if we slightly change the existing Poverty Line from Rs.12.00 per

person per day (PPPD) to just Rs.13.66 PPPD, 50% rural population of India as a whole come

down below the horizontal line, which is equivalent to 360.65 Million people, which is more

than the population of North America.

110

One must not forget that the idea of richness here is derived from the top 20% MPCE class for

rural India, which is equivalent to Rs.686.25 and above per person per month in 2004-05. This

becomes at least Rs. 22.88 per person per day (GOI). The corresponding urban figure is

Rs.1375.75 and above per person per month in 2004-05, which is at least Rs. 45.86 per person

per day. Note that this amount in general is not small for rural people way back in 2004-05,

given the nature of voluntary confession by the households in NSSO survey. It seems to us that if

one sets a higher percentage such as top 25% MPCE class that may not yield very bad idea of

richness in Indian standard.

Contrary to the conventional wisdom, one may try to capture the sub-state level performance by

estimating the vulnerable population, and link them with various indicators of development. As

is well known, there are some districts in many states, which may compare favourably with the

best districts of India, and the reverse is always true in India. In a sense, therefore, within the

states themselves, different levels of steady state dynamics prevail. The popular belief of

successful and failed spaces is not linearly tractable even within the states. If the purpose of

economic and social policy is to uplift the welfare of the larger magnitude of people across

board, we must know for sure the percentage as well as absolute number of people in utter

vulnerable state of life. Future research must be directed to trace in a meticulous way the

dynamics of success and failure of various communities around the same geographies. As sample

case studies, we have here attempted here to capture the extremes of success and failure in terms

of rich and vulnerable classes in some selected states and districts from the whole spectrum of

unit level consumer expenditure data of NSSO dividing the population into four expenditure

classes: rich & elite (top 20%), middle (51% to 80%), vulnerable (31% to 50%), and poor (0% to

30%). The last two classes add up to define the total vulnerable population. Let us assume this

set at all-Indian level. If we then superimpose these cut off points at the state and district level,

these moderate aggregates give birth to actually disaggregated staggering shares and magnitude

of people living at both state and district levels. In order for the readers to understand the

significance of this exercise, we have placed the standard theoretical all-India picture in Figure

4.11. A few extreme cases from the states and districts are also picked up as sample examples.

Figure 4.12, Figure 4.13, Figure 4.14, Figure 4.15 and Figure 4.16 respectively present the

distribution of the rural people as follows: Kerala, Orissa, Kurukshetra district (Haryana),

111

Dantewada district (Chhattisgarh) and Medinipur district (West Bengal) from among the best and

worst performing states and districts. Performance of Kerala, Punjab, Haryana and Himachal can

not be better captured from any other statistical tool. On the other hand, it is indeed alarming to

see that the bulk of the population in states like Orissa, Chhattisgarh, Bihar, Madhya Pradesh,

Jharkhand and Uttar Pradesh are living in real deprivation with a small minority enjoying high

affluence as elsewhere in India.

It is obvious from the following graphics that the largest majority of people in the better off

districts are living in top 20% classes, and above 90% are in all-India top 50%. Kerala and

Kurukshetra are such regions. On the other hand, in one of the worst districts like Dantewada

(Chhattisgarh), less than 4% people are fortunate enough to enjoy the highest standard of living,

while more than 95% people are living in dire helplessness. There is urgent need to visit all these

and similar other hundreds of districts to witness the availability of man made facilities there in

commensurate with the findings of this study. There is hardly any doubt that in such

neighbourhoods, where almost all the people are living in dismal economic and social

conditions, distinction between ‘relative unhappiness’ and ‘relative happiness’ become blurred.

An altogether diverse example may be found in the district of Medinipur (Figure 4.16) from the

state of West Bengal, where as much as 61.89% people amounting to as high as 5.18 Million

heads enjoy superior standard of living with a correspondingly high proportion (38.09%) of

extremely vulnerable people amounting to similarly high magnitude of 3.19 Million heads. What

is more, dependency ratio must be very high in these neighbourhoods of extreme scarcity.

112

Figure 4.11. Vertical Illusion versus Horizontal Rift: Problems of Capability &

Entitlement: Standard India Rural

Percentage of Indian

Population

Poor

Vulnerable Class

Middle Class

Rich & Elite

30%

20%

30%

20%

The bottom 50% (VC + P) may not have survival access to:

Commodity Market; Knowledge, Education & Information; Public Services & Legal System; Health Services & Drinking Water; Wealth; Decision Making.

113

Figure 4.12: Vertical Illusion versus Horizontal Rift: Problems of Capability & Entitlement in Kerala (Rural) in 2004-05

Percentage of Population (Number)

Poor

Vulnerable Class

Middle Class

Rich & Elite

6.84% (1.61 M)

9.10% (2.15 M)

27.21% (6.41 M)

56.84% (13.39 M)

The bottom 50% (15.94%) may not have survival access to:

Commodity Market; Knowledge, Education & Information; Public Services & Legal System; Health Services & Drinking Water; Wealth; Decision Making.

114

Figure 4.13: Vertical Illusion versus Horizontal Rift: Problems of Capability & Entitlement

in Orissa (Rural) in 2004-05

Percentage of Population (Number)

Poor

Vulnerable Class

Middle Class

Rich & Elite

56.98% (18.29 M)

17.45% (5.60 M)

16.68% (5.35 M)

8.88% (2.85 M)

The bottom 50% (74.43%) may not have survival access to:

Commodity Market; Knowledge, Education & Information; Public Services & Legal System; Health Services & Drinking Water; Wealth; Decision Making.

115

Figure 4.14: Vertical Illusion versus Horizontal Rift: Problems of Capability &

Entitlement in Kurukshetra District (Rural) of Haryana in 2004-05

Percentage of Population (Number)

Poor

Vulnerable Class

Middle Class

Rich & Elite

0.00%

5.81% (0.03M)

18.91% (0.11M)

75.26% (0.42M)

The bottom 50% (5.81%) may not have survival access to:

Commodity Market; Knowledge, Education & Information; Public Services & Legal System; Health Services & Drinking Water; Wealth; Decision Making.

116

Figure 4.15: Vertical Illusion versus Horizontal Rift: Problems of Capability & Entitlement in Dantewada District (Rural) in Chhattisgarh in 2004-05

Percentage of Population (Number)

Poor

Vulnerable Class

Middle Class

Rich & Elite

94.41% (0.69M)

3.10% (0.02M)

0.58% (0.04 M)

1.89% (0.01M)

The bottom 50% (97.51%) may not have survival access to:

Commodity Market; Knowledge, Education & Information; Public Services & Legal System; Health Services & Drinking Water; Wealth; Decision Making.

117

Figure 4.16: Vertical Illusion versus Horizontal Rift: Problems of Capability & Entitlement in Medinipur District (Rural) in West Bengal in 2004-05

Percentage of Population (Number)

Poor

Vulnerable Class

Middle Class

Rich & Elite

19.33% (1.62M)

18.76% (1.57M)

37.14% (3.11M)

24.75% (0.37M)

The bottom 50% (38.09% =3.19M) may not have survival access to:

Commodity Market; Knowledge, Education & Information; Public Services & Legal System; Health Services & Drinking Water; Wealth; Decision Making.

Table 4.1: District-wise Pearson's Correlation between relevant Indices for Rural Districts in 2001 and 2004-05N=575

Variables SDIR4 SDIR6 SDIR7 SDIR7_W HCRRD5 PPRRD5 GINIRD5 LRMRD1 LRFRD1 SCMWPMR1 STMWPMR1 SCMWPFR1 STMWPFR1WPIR1 0.48 0.45 0.46 0.35 -0.34 0.27 -0.06 0.23 0.27 0.36 0.27 0.27 0.43

p=.000 p=.000 p=.000 p=.000 p=.000 p=.000 p=.127 p=.000 p=.000 p=.000 p=.000 p=.000 p=.000HCIR1: 0.76 0.51 0.49 0.47 -0.17 0.29 0.22 0.74 0.79 0.06 0.23 0.33 0.31

p=.000 p=.000 p=.000 p=0.00 p=.000 p=.000 p=.000 p=0.00 p=0.00 p=.184 p=.000 p=.000 p=.000HHIR1: 0.79 0.7 0.67 0.69 -0.39 0.54 0.25 0.52 0.55 0.02 -0.21 0.22 -0.03

p=.000 p=.000 p=.000 p=0.00 p=.000 p=0.00 p=.000 p=0.00 p=0.00 p=.695 p=.000 p=.000 p=.430TCIR1: 0.72 0.58 0.56 0.59 -0.29 0.41 0.21 0.38 0.39 -0.02 -0.33 -0.05 -0.30

p=.000 p=.000 p=.000 p=0.00 p=.000 p=.000 p=.000 p=.000 p=.000 p=.606 p=.000 p=.274 p=.000SDIR4: 1 0.81 0.78 0.76 -0.41 0.54 0.24 0.69 0.73 0.12 -0.05 0.25 0.10

p=.000 p=.000 p=.000 p=0.00 p=.000 p=0.00 p=.000 p=0.00 p=0.00 p=.006 p=.249 p=.000 p=.023SDIR6: 0.81 1 0.99 0.98 -0.85 0.87 0.19 0.54 0.64 0.18 -0.13 0.14 0.09

p=.000 p=.000 p=.000 p=0.00 p=0.00 p=0.00 p=.000 p=0.00 p=0.00 p=.000 p=.002 p=.001 p=.040SDIR7: 0.78 0.99 1 0.99 -0.87 0.85 0.07 0.52 0.63 0.18 -0.13 0.12 0.08

p=.000 p=.000 p=.000 p=0.00 p=0.00 p=0.00 p=.086 p=0.00 p=0.00 p=.000 p=.002 p=.003 p=.057

Table 4.2: District-wise Pearson's Correlation between relevant Indices for Urban Districts in 2001 and 2004-05N=573

Variables SDIU4 SDIU6 SDIU7 SDIU7_W HCRUD5 PPRUD5 GINIUD5 LRMUD1 LRFUD1 SCMWPMU1 STMWPMU1 SCMWPFU1 STMWPFU1WPIU1 0.36 0.27 0.26 0.17 -0.13 0.10 -0.01 0.15 0.19 0.43 0.50 0.51 0.52

p=.000 p=.000 p=.000 p=.000 p=.002 p=.018 p=.806 p=.000 p=.000 p=.000 p=0.00 p=0.00 p=0.00HCIU1 0.57 0.45 0.4 0.38 -0.20 0.26 0.11 0.80 0.84 0.14 0.19 0.22 0.33

p=.000 p=.000 p=.000 p=.000 p=.000 p=.000 p=.011 p=0.00 p=0.00 p=.001 p=.000 p=.000 p=.000HHIU1 0.79 0.57 0.51 0.52 -0.26 0.21 0.14 0.33 0.39 0.16 -0.14 0.12 -0.09

p=.000 p=.000 p=.000 p=0.00 p=.000 p=.000 p=.001 p=.000 p=.000 p=.000 p=.001 p=.005 p=.034TCIU1 0.77 0.46 0.37 0.38 -0.12 0.13 0.26 0.21 0.24 0.16 -0.22 0.09 -0.21

p=.000 p=.000 p=.000 p=.000 p=.005 p=.002 p=.000 p=.000 p=.000 p=.000 p=.000 p=.029 p=.000SDIU4 1 0.69 0.61 0.59 -0.27 0.27 0.22 0.59 0.65 0.28 0.01 0.28 0.09

p=.000 p=.000 p=.000 p=0.00 p=.000 p=.000 p=.000 p=0.00 p=0.00 p=.000 p=.805 p=.000 p=.025SDIU6 0.69 1 0.96 0.96 -0.86 0.80 0.08 0.43 0.60 0.25 -0.10 0.09 0.08

p=.000 p=.000 p=.000 p=0.00 p=0.00 p=0.00 p=.049 p=.000 p=0.00 p=.000 p=.019 p=.027 p=.063SDIU7 0.61 0.96 1 1.00 -0.88 0.76 -0.17 0.39 0.55 0.27 -0.08 0.08 0.07

p=.000 p=.000 p=.000 p=0.00 p=0.00 p=0.00 p=.000 p=.000 p=0.00 p=.000 p=.069 p=.058 p=.082118

Table 4.3: Rural Versus Urban District-wise Correlation in 2001 and 2004-05 N=566

Variables WPIU1 HCIU1 HHIU1 TCIU1 HCRUD05I PPRUD05I GINIUD05 SDIU4 SDIU6 SDIU7WPIR1 0.47 0.41 0.18 -0.03 0.22 0.23 0.13 0.32 0.33 0.36

p=0.00 p=.000 p=.000 p=.532 p=.000 p=.000 p=.002 p=.000 p=.000 p=.000HCIR1: 0.37 0.78 0.28 0.16 0.08 0.14 -0.15 0.58 0.36 0.31

p=.000 p=0.00 p=.000 p=.000 p=.052 p=.001 p=.000 p=0.00 p=.000 p=.000HHIR1: 0.11 0.18 0.79 0.50 0.22 0.20 -0.10 0.67 0.49 0.45

p=.011 p=.000 p=0.00 p=0.00 p=.000 p=.000 p=.017 p=0.00 p=0.00 p=.000TCIR1: 0.10 0.03 0.37 0.72 0.19 0.20 -0.13 0.54 0.41 0.36

p=.015 p=.470 p=.000 p=0.00 p=.000 p=.000 p=.002 p=0.00 p=.000 p=.000HCRRD5I: 0.18 0.09 0.32 0.18 0.47 0.38 0.22 0.29 0.49 0.54

p=.000 p=.030 p=.000 p=.000 p=0.00 p=.000 p=.000 p=.000 p=0.00 p=0.00PPRRD5I: 0.13 0.16 0.39 0.27 0.43 0.43 0.07 0.38 0.53 0.54

p=.002 p=.000 p=.000 p=.000 p=.000 p=.000 p=.115 p=.000 p=0.00 p=0.00GINIRD5I: -0.01 -0.07 -0.20 -0.20 0.04 -0.02 0.36 -0.22 -0.09 0.02

p=.890 p=.085 p=.000 p=.000 p=.293 p=.561 p=.000 p=.000 p=.043 p=.704SDIR4: 0.35 0.49 0.58 0.54 0.25 0.27 -0.11 0.77 0.57 0.52

p=.000 p=0.00 p=0.00 p=0.00 p=.000 p=.000 p=.007 p=0.00 p=0.00 p=0.00SDIR6: 0.28 0.32 0.52 0.41 0.45 0.42 0.06 0.60 0.63 0.64

p=.000 p=.000 p=0.00 p=.000 p=.000 p=.000 p=.136 p=0.00 p=0.00 p=0.00SDIR7: 0.28 0.31 0.49 0.38 0.45 0.41 0.11 0.57 0.62 0.64

p=.000 p=.000 p=0.00 p=.000 p=.000 p=.000 p=.008 p=0.00 p=0.00 p=0.00SDIR7_W: 0.23 0.27 0.50 0.40 0.45 0.40 0.10 0.56 0.61 0.62

p=.000 p=.000 p=0.00 p=.000 p=.000 p=.000 p=.018 p=0.00 p=0.00 p=0.00

119

Table 4.4: Names of Best 25 Rural Districts in India in Social & Economic Indicators, 2001 & 2004-05. (To continue)(In Order of Ranks)

SN Dt Code Dt Name Dt Code Dt Name Dt Code Dt Name Dt Code Dt Name Dt Code Dt Name1 GO2 South Goa DE5 North East Delhi DE2 East Delhi GO2 South Goa KE7 Kottayam2 KE2 Ernakulam HA5 Gurgaon DE7 South Delhi DE2 East Delhi KE11 Pathanamthitta3 GO1 North Goa AR10 Upper Siang DE8 South West Delhi PU8 Jalandhar MZ1 Aizawl4 DE2 East Delhi DE8 South West Delhi PU9 Kapurthala GO1 North Goa MZ8 Serchhip5 KA26 Udupi KE12 Trivundram PU8 Jalandhar DE9 West Delhi KE1 Alappuzha6 HP7 Lahul & Spiti NG1 Dimapur HP12 Una PU4 Fatehgarh Sahib KE2 Ernakulam7 KE1 Alappuzha KA26 Udupi DE5 North East Delhi PU14 Nawanshahr KE13 Thrissur8 KE13 Thrissur KE1 Alappuzha PU4 Fatehgarh Sahib PU9 Kapurthala KE6 Kollam9 KE7 Kottayam DE7 South Delhi KE2 Ernakulam PU10 Ludhiana KE8 Kozhikode

10 HP5 Kinnaur GU9 Jamnagar PU16 Rupnagar PU7 Hoshiarpur KE4 Kannur11 KA11 Dakshina Kannada KE7 Kottayam PU7 Hoshiarpur KE2 Ernakulam MZ2 Champhai12 KE11 Pathanamthitta GO2 South Goa DE6 North West Delhi PU16 Rupnagar KE9 Malappuram13 HP3 Hamirpur NG2 Kohima PU14 Nawanshahr PU15 Patiala MZ3 Kolasib14 PU14 Nawanshahr PU4 Fatehgarh Sahib KE11 Pathanamthitta DE6 North West Delhi TN22 Thiruvallur15 PU8 Jalandhar HP7 Lahul & Spiti KE7 Kottayam DE5 North East Delhi KE3 Idukki16 PU7 Hoshiarpur KE11 Pathanamthitta DE9 West Delhi KE1 Alappuzha KE12 Trivundram17 KE6 Kollam KE3 Idukki PU10 Ludhiana DE7 South Delhi TN9 Kanniyakumari18 HP9 Shimla GO1 North Goa JK5 Jammu MN5 Imphal West KE14 Wayanad19 KE4 Kannur NG3 Mokokchung UT5 Dehradun TN7 Erode DE2 East Delhi20 KA18 Kodagu JK10 Pulwama GO2 South Goa KE13 Thrissur NG3 Mokokchung21 KE5 Kasaragod AP22 Warangal KE13 Thrissur DE8 South West Delhi KE10 Palakkad22 HP11 Solan UP23 Etawah PU6 Gurdaspur TN13 Namakkal KE5 Kasaragod23 PU16 Rupnagar HA11 Kurukshetra KE1 Alappuzha KA26 Udupi HP3 Hamirpur24 HP12 Una AP23 West Godavari GO1 North Goa PU17 Sangrur GO1 North Goa 25 HP6 Kullu PU6 Gurdaspur GU13 Mahesana HA1 Ambala PU7 Hoshiarpur

120

SDIR4 PPRRD5 HHIR1 TCIR1 LRFRD1

Table 4.4: Names of Best 25 Rural Districts in India in Social & Economic Indicators, 2001 & 2004-05. (Concld.)

Dt Code Dt Name HCR% Dt Code Dt Name HCR% Dt Code Dt Name Gini Coeff.KA26 Udupi 0 NG8 Zunheboto 0 DE7 South Delhi 0.04HP7 Lahul & Spiti 0 MN7 Tamenglong 0 MN9 Ukhrul 0.05DE7 South Delhi 0 NG6 Tuensang 0 MN8 Thoubal 0.07DE8 South West Delhi 0 NG4 Mon 0 KA20 Koppal 0.09DE5 North East Delhi 0 AS4 Darrang 0.06 AS20 North Cachar Hills 0.09DE6 North West Delhi 0 GO2 South Goa 0.16 AS4 Darrang 0.09MZ8 Serchhip 0 MP26 Neemuch 0.18 DE6 North West Delhi 0.10MZ1 Aizawl 0 MN8 Thoubal 0.23 MN6 Senapati 0.11MZ3 Kolasib 0 MN5 Imphal West 0.35 MP2 Barwani 0.11GU10 Junagadh 0 GU3 Anand 0.48 MN7 Tamenglong 0.11GU18 Porbandar 0 AP22 Warangal 0.85 MG5 South Garo Hills 0.11GU11 Kachchh 0 PU8 Jalandhar 0.94 BI33 Sheohar 0.11AR10 Upper Siang 0 AS16 Lakhimpur 0.12JK10 Pulwama 0 AS10 Hailakandi 0.12MZ6 Mamit 0 RJ18 Jaisalmer 0.12AR8 Tawang 0 MN3 Churachandpur 0.12JK1 Anantnag 0 DE5 North East Delhi 0.12NG3 Mokokchung 0 UP19 Chandauli 0.12NG1 Dimapur 0 AS13 Karbi Anglong 0.12

NG5 Phek 0 KA14 Gadag 0.12NG2 Kohima 0 MH30 Sindhudurg 0.13AS5 Dhemaji 0 MG1 East Garo Hills 0.13NG7 Wokha 0 RJ24 Kota 0.13MN9 Ukhrul 0 JH12 Lohardaga 0.13MG5 South Garo Hills 0 UP67 Sonbhadra 0.13

Note: Unlike other indicators, we have given here 37 districts as the poverty percentage for all these districts are close to zero.

121

HCRRD5 GINIRD5HCRRD5

Table4.5: Names of Worst 25 Rural Districts in India in Social & Economic Indicators, 2001 & 2004-05. (To continue)LRFRD1

Dt Code Dt Name Dt Code Dt Name PPR(Rs.) Dt Code Dt Name Dt Code Dt Name Dt Code Dt Name LRF%BI33 Sheohar CG3 Dantewada 240.79 AR3 East Kameng MZ7 Saiha BI16 Kishanganj 15.4BI34 Sitamarhi OR23 Nabarangapur 278.69 MN7 Tamenglong NG4 Mon OR20 Koraput 15.6BI27 Purnia OR28 Sambalpur 300.31 AS13 Karbi Anglong NG6 Tuensang CG3 Dantewada 17.1BI16 Kishanganj JH12 Lohardaga 301.44 OR10 Gajapati AR3 East Kameng UP64 Shrawasti 17.7BI15 Khagaria MP12 Dindori 302.38 OR27 Rayagada MH24 Parbhani OR23 Nabarangapur 18BI29 Saharsa OR20 Koraput 302.47 OR5 Baudh GU7 Dohad JH13 Pakaur 18.1BI24 Pashchim Champaran JH13 Pakaur 310.04 OR21 Malkangiri MP12 Dindori OR27 Rayagada 18.3BI1 Araria OR8 Debagarh 311.37 OR23 Nabarangapur MH20 Nanded OR21 Malkangiri 18.4BI26 Purba Champaran MP43 Umaria 313.62 AS23 Tinsukia CG3 Dantewada UP8 Bahraich 18.4BI14 Katihar JH9 Gumla 319 AS20 North Cachar Hills MZ4 Lawngtlai UP10 Balrampur 18.8BI18 Madhepura BI1 Araria 321.3 UP43 Kaushambi MP20 Jhabua BI36 Supaul 19.3BI36 Supaul OR16 Kandhamal 322.18 OR2 Balangir AR9 Tirap BI27 Purnia 19.6NG4 Mon OR5 Baudh 331.35 AS5 Dhemaji MH12 Hingoli BI14 Katihar 19.7BI19 Madhubani OR15 Kalahandi 332.2 OR17 Kendrapara OR27 Rayagada UP17 Budaun 20.3BI8 Darbhanga OR21 Malkangiri 335.82 OR20 Koraput NG8 Zunheboto BI1 Araria 20.4

OR27 Rayagada OR27 Rayagada 336.12 OR29 Sonapur OR10 Gajapati BI18 Madhepura 20.6JH6 Garhwa OR30 Sundargarh 336.69 AS9 Golaghat MN9 Ukhrul AR3 East Kameng 20.8AS6 Dhubri UP19 Chandauli 338.56 OR16 Kandhamal AS6 Dhubri MP20 Jhabua 21.1NG6 Tuensang MP22 Mandla 339.44 BI33 Sheohar MH21 Nandurbar AR9 Tirap 21.2BI3 Banka OR25 Nuapada 344.93 MN8 Thoubal AS14 Karimganj JH6 Garhwa 21.2

MP38 Sheopur MH10 Gadchiroli 346.03 MG5 South Garo Hills MN6 Senapati UP60 Rampur 21.4BI20 Munger CG1 Bastar 348.86 CG1 Bastar MG7 West Khasi Hills BI24 Pashchim Champaran 21.9BI23 Nawada UP65 Siddharthnagar 349.97 AS7 Dibrugarh MH16 Latur BI26 Purba Champaran 22BI5 Bhagalpur GU23 The Dangs 351.53 OR6 Bhadrak SK4 West Sikkim BI29 Saharsa 22.1BI21 Muzaffarpur UP60 Rampur 354.9 BI19 Madhubani MP38 Sheopur JH18 Sahibganj 22.3

122

SDIR4 PPRRD5 HHIR1 TCIR1

Table 4.5: Names of Worst 25 Rural Districts in India in Social & Economic Indicators, 2001 & 2004-05. (Concld.)HCRRD5 HCRRD5 HCRRD5

Dt Code Dt Name HCR% Dt Code Dt Name HCR% Dt Code Dt Name HCR% Dt Code Dt Name Gini Coeff.GU23 The Dangs 88.43 UT11 Tehri Garhwal 61.19 MP3 Betul 53.71 TN5 Dharmapuri 0.51CG3 Dantewada 88.18 MP35 Seoni 59.94 UP29 Ghaziabad 53.67 UP23 Etawah 0.46JH12 Lohardaga 81.58 BI4 Begusarai 59.83 MP1 Balaghat 53.49 UT8 Nainital 0.45UP19 Chandauli 81.49 BI21 Muzaffarpur 59.22 UP47 Lucknow 53.43 HA5 Gurgaon 0.45CG1 Bastar 80.59 KA23 Raichur 59.18 CG8 Kanker 53.06 TN27 Tiruvannamalai 0.44OR23 Nabarangapur 80.58 JH3 Deoghar 58.73 OR1 Anugul 53.03 UP36 Jalaun 0.43OR28 Sambalpur 79.49 OR14 Jharsuguda 58.69 UP11 Banda 52.83 MH14 Jalna 0.43BI1 Araria 76.9 CG15 Rajnandgaon 58.59 MP6 Chhatarpur 52.8 TN21 Theni 0.42

OR16 Kandhamal 76.55 JH17 Ranchi 58.41 OR22 Mayurbhanj 52.48 MH12 Hingoli 0.41MP43 Umaria 76.4 MP28 Raisen 58.09 JH1 Bokaro 52.42 KA26 Udupi 0.41JH13 Pakaur 75.55 UP60 Rampur 57.98 BI30 Samastipur 52.27 OR14 Jharsuguda 0.40OR20 Koraput 74.21 MP40 Sidhi 57.6 MH24 Parbhani 52.22 CG10 Korba 0.40MP22 Mandla 73.68 OR9 Dhenkanal 57.1 UP9 Ballia 51.52 TN12 Nagapattinam 0.39OR8 Debagarh 73.43 MP20 Jhabua 56.85 OR29 Sonapur 51.31 TN8 Kancheepuram 0.39UT2 Bageshwar 72.12 BI5 Bhagalpur 56.67 MP44 Vidisha 51.26 MH32 Thane 0.39MP12 Dindori 72 UP31 Gonda 56.46 MH5 Bhandara 51.23 KE1 Alappuzha 0.38OR5 Baudh 70.52 UP64 Shrawasti 56.13 UP4 Ambedkar Nagar 50.39 MH8 Chandrapur 0.37OR15 Kalahandi 70.45 WB13 Murshidabad 55.86 RJ3 Banswara 50.14 KE9 Malappuram 0.37OR25 Nuapada 70.06 BI31 Saran 55.85 UP46 Lalitpur 0.37OR30 Sundargarh 69.87 MP32 Sagar 55.72 Note: A 50% cut off mark in HCR CG12 Mahasamund 0.36JH9 Gumla 68.61 JH5 Dumka 55.42 produces 84 worst districts. UP42 Kanpur Nagar 0.36

OR21 Malkangiri 67.85 BI3 Banka 55.41 MH16 Latur 0.36OR27 Rayagada 67.14 JH2 Chatra 55.18 TN28 Vellore 0.36UP65 Siddharthnagar 66.34 KA17 Haveri 55.13 HA14 Panipat 0.36OR2 Balangir 66.32 MH6 Bid 54.94 UP66 Sitapur 0.36BI23 Nawada 65.25 UP44 Kheri 54.8UP57 Pilibhit 65.23 BI2 Aurangabad 54.6MH10 Gadchiroli 64.99 UP58 Pratapgarh 54.38MP36 Shahdol 64.41 JH14 Palamu 54.34JH16 Purbi Singhbhum 63.65 BI14 Katihar 54.19BI18 Madhepura 62.31 BI8 Darbhanga 54.18OR4 Bargarh 61.68 MH16 Latur 53.87OR10 Gajapati 61.35 JH18 Sahibganj 53.83 123

GINIRD5

Table 4.6: Names of Best 25 Urban Districts in India in Social & Economic Indicators, 2001 & 2004-05. (To continue)(In Order of Ranks)

SN Dt Code Dt Name Dt Code Dt Name Dt Code Dt Name Dt Code Dt Name Dt Code Dt Name1 KA26 Udupi HA11 Kurukshetra GU1 Ahmadabad PU16 Rupnagar MZ1 Aizawl2 KA11 Dakshina Kannada NG2 Kohima MH17 Mumbai PU15 Patiala MZ5 Lunglei3 KE2 Ernakulam GU8 Gandhinagar GU24 Vadodara GU19 Rajkot MZ8 Serchhip4 PU16 Rupnagar NG3 Mokokchung PU8 Jalandhar PU8 Jalandhar KE7 Kottayam5 KE11 Pathanamthitta AS7 Dibrugarh GU21 Surat HA13 Panchkula KE11 Pathanamthitta6 KE7 Kottayam AS17 Marigaon MH32 Thane PU7 Hoshiarpur MZ3 Kolasib7 GU24 Vadodara PU10 Ludhiana HP10 Sirmaur UP28 Gautam Buddha Nagar MZ6 Mamit8 MH25 Pune PU15 Patiala PU15 Patiala PU9 Kapurthala NG3 Mokokchung9 KE13 Thrissur HA10 Karnal SK3 South Sikkim PU4 Fatehgarh Sahib MZ2 Champhai10 KE1 Alappuzha NG8 Zunheboto PU4 Fatehgarh Sahib PU10 Ludhiana KE13 Thrissur11 PU15 Patiala NG1 Dimapur HP1 Bilaspur GU24 Vadodara KE2 Ernakulam12 KA18 Kodagu NG7 Wokha MH18 Mumbai (Suburban) GO1 North Goa KE4 Kannur13 KA2 Bangalore MZ1 Aizawl GU8 Gandhinagar UP3 Allahabad MZ7 Saiha14 PU8 Jalandhar NG5 Phek TN2 Chennai DE9 West Delhi KE1 Alappuzha15 KE3 Idukki CG15 Rajnandgaon JK12 Rajauri HA5 Gurgaon KE3 Idukki16 HP1 Bilaspur WB10 Kolkata MH25 Pune RJ22 Jodhpur KE8 Kozhikode17 GO1 North Goa AS12 Kamrup PU9 Kapurthala KA26 Udupi KE12 Trivundram18 HA13 Panchkula MG3 Jaintia Hills PU16 Rupnagar DE2 East Delhi NG7 Wokha19 TN2 Chennai KE12 Trivundram DE9 West Delhi PU14 Nawanshahr KE6 Kollam20 PU7 Hoshiarpur NG6 Tuensang AP7 Hyderabad HA1 Ambala HP9 Shimla21 HP8 Mandi NG4 Mon GU13 Mahesana PU12 Moga KE9 Malappuram22 HP10 Sirmaur MZ7 Saiha GU15 Navsari MH25 Pune TR2 North Tripura23 PU4 Fatehgarh Sahib PU16 Rupnagar HA13 Panchkula RJ32 Udaipur UT1 Almora24 PU9 Kapurthala HP8 Mandi DE1 Central Delhi PU2 Bathinda MG3 Jaintia Hills25 KE12 Trivundram AP20 Visakhapatnam DE2 East Delhi UP47 Lucknow TR3 South Tripura

124

LRFUD1SDIU4 PPRUD5 HHIU1 TCIU1

Table 4.6: Names of Best 25 Urban Districts in India in Social & Economic Indicators, 2001 & 2004-05. (Concld.)

Dt Code Dt Name HCR% Dt Code Dt Name HCR% Dt Code Dt Name Gini Coeff.MZ1 Aizawl 0 AR8 Tawang 0 BI3 Banka 0.1045188MZ5 Lunglei 0 JK8 Kupwara 0 JK2 Badgam 0.1117272MZ8 Serchhip 0 PU9 Kapurthala 0.22 MG1 East Garo Hills 0.113513MZ3 Kolasib 0 GU8 Gandhinagar 0.61 DE5 North East Delhi 0.1230336MZ6 Mamit 0 HA19 Yamunanagar 0.63 RJ20 Jhalawar 0.1258759MZ2 Champhai 0 AS3 Cachar 0.64 JK4 Doda 0.1331547MZ7 Saiha 0 MG6 West Garo Hills 0.67 UP63 Shahjahanpur 0.1354639NG7 Wokha 0 AS22 Sonitpur 0.7 GU9 Jamnagar 0.1410018HP9 Shimla 0 SK4 West Sikkim 0.72 AS17 Marigaon 0.1464807MG3 Jaintia Hills 0 AS19 Nalbari 0.77 UT13 Uttarkashi 0.1495949MG2 East Khasi Hills 0 HP12 Una 0.77 MN2 Chandel 0.1497212HP11 Solan 0 AS2 Bongaigaon 0.92 JK9 Leh (Ladakh) 0.1505215

NG5 Phek 0 NG7 Wokha 0.152281MG7 West Khasi Hills 0 BI15 Khagaria 0.1529862MG5 South Garo Hills 0 SK4 West Sikkim 0.1583691HA1 Ambala 0 BI32 Sheikhpura 0.1586637AS4 Darrang 0 GU18 Porbandar 0.1601952AS17 Marigaon 0 AS4 Darrang 0.1603259NG6 Tuensang 0 NG5 Phek 0.1626404DE8 South West Delhi 0 NG8 Zunheboto 0.1652494AS13 Karbi Anglong 0 MN1 Bishnupur 0.1665175AS5 Dhemaji 0 JK10 Pulwama 0.1701722SK2 North Sikkim 0 RJ18 Jaisalmer 0.1708906MG4 Ri Bhoi 0 KA1 Bagalkot 0.1720974JK4 Doda 0 BI11 Jamui 0.1745164

Note: Unlike other indicators, we have given here 37 districts as the poverty percentage for all these districts are close to zero.

125

HCRUD5HCRUD5 GINIUD5

Table 4.7: Names of Worst 25 Urban Districts in India in Social & Economic Indicators, 2001 & 2004-05. (To continue)

SN Dt Code Dt Name Dt Code Dt Name Dt Code Dt Name Dt Code Dt Name Dt Code Dt Name 1 BI33 Sheohar KA23 Raichur MN1 Bishnupur NG6 Tuensang BI33 Sheohar 2 NG6 Tuensang MH6 Bid MG4 Ri Bhoi MG4 Ri Bhoi UP17 Budaun3 MG4 Ri Bhoi CG3 Dantewada OR21 Malkangiri NG4 Mon UP39 Jyotiba Phule Nagar4 MN2 Chandel BI3 Banka MN8 Thoubal MN2 Chandel UP60 Rampur5 BI36 Supaul MP39 Shivpuri MG1 East Garo Hills MG7 West Khasi Hills JK8 Kupwara6 NG8 Zunheboto MP8 Damoh BI33 Sheohar NG5 Phek UP64 Shrawasti7 NG4 Mon MH28 Sangli AS5 Dhemaji NG8 Zunheboto RJ19 Jalor8 BI17 Lakhisarai KA5 Bellary UP43 Kaushambi MZ7 Saiha JK2 Badgam9 BI32 Sheikhpura MP6 Chhatarpur AS13 Karbi Anglong BI17 Lakhisarai BI36 Supaul 10 BI3 Banka KA1 Bagalkot MG5 South Garo Hills MZ2 Champhai BI16 Kishanganj11 BI24 Pashchim Champaran RJ16 Hanumangarh OR6 Bhadrak MZ8 Serchhip JK3 Baramula12 BI16 Kishanganj MH23 Osmanabad OR5 Baudh AR3 East Kameng MN2 Chandel13 UP17 Budaun MH20 Nanded OR29 Sonapur BI33 Sheohar UP43 Kaushambi14 BI19 Madhubani UP11 Banda NG4 Mon AS20 North Cachar Hills BI17 Lakhisarai 15 UP43 Kaushambi BI13 Kaimur (Bhabua) UP64 Shrawasti MZ6 Mamit JK1 Anantnag16 BI1 Araria OR5 Baudh BI36 Supaul BI32 Sheikhpura BI10 Gopalganj17 MP4 Bhind KA20 Koppal MN2 Chandel SK4 West Sikkim JK10 Pulwama18 UP62 Sant Kabir Nagar UP4 Ambedkar Nagar OR12 Jagatsinghapur TN20 The Nilgiris UP55 Moradabad19 UP64 Shrawasti KA17 Haveri TR1 Dhalai MZ3 Kolasib BI32 Sheikhpura 20 BI34 Sitamarhi MH10 Gadchiroli OR16 Kandhamal TN21 Theni BI3 Banka 21 MP38 Sheopur OR10 Gajapati OR18 Kendujhar NG3 Mokokchung UP63 Shahjahanpur22 UP49 Mahoba MH35 Yavatmal AR4 East Siang AS6 Dhubri BI19 Madhubani23 BI10 Gopalganj MP32 Sagar JK6 Kargil JH18 Sahibganj UP13 Bareilly24 BI15 Khagaria UP36 Jalaun AR5 Lohit MP12 Dindori UP57 Pilibhit25 UP63 Shahjahanpur KA12 Davanagere NG6 Tuensang SK2 North Sikkim BI1 Araria

126

LRFUD1SDIU4 PPRUD05 HHIU1 TCIU1

Table 4.7: Names of Worst 25 Urban Districts in India in Social & Economic Indicators, 2001 & 2004-05. (Concluded)

Dt Code Dt Name HCR% Dt Code Dt Name HCR% Dt Code Dt Name HCR% Dt Code Dt Name HCR%OR10 Gajapati 91.21 BI13 Kaimur (Bhabua) 68.08 MH2 Akola 59.21 BI2 Aurangabad 53.6KA23 Raichur 88.55 MH4 Aurangabad 67.84 KA21 Mandya 58.66 MP10 Dewas 53.36BI3 Banka 88.4 MP32 Sagar 67.53 OR18 Kendujhar 58.46 UP66 Sitapur 53.35

OR23 Nabarangapur 87.73 UP47 Lucknow 67.52 MP14 Guna 58.39 MN4 Imphal East 53.28OR5 Baudh 85.56 OR3 Baleshwar 66.99 MP41 Tikamgarh 58.35 RJ31 Tonk 53.27KA5 Bellary 84.12 KA27 Uttara Kannada 66.35 MH10 Gadchiroli 58.32 UP42 Kanpur Nagar 53.23CG3 Dantewada 84.01 MH12 Hingoli 64.72 MP25 Narsimhapur 58.07 UP53 Meerut 52.97KA17 Haveri 83.83 MH23 Osmanabad 64.42 MP2 Barwani 58.02 GU11 Kachchh 52.86MH6 Bid 80.37 UT2 Bageshwar 64.39 MN1 Bishnupur 58.01 KA8 Chamarajanagar 52.81KA1 Bagalkot 79.69 MH14 Jalna 64.13 OR14 Jharsuguda 57.48 MP22 Mandla 52.79MG1 East Garo Hills 77.59 MP9 Datia 63.97 TN14 Perambalur 57.24 UT6 Garhwal 52.57MP39 Shivpuri 77.42 OR29 Sonapur 63.78 UP44 Kheri 57.07 MP1 Balaghat 52.33MN2 Chandel 77.06 KA26 Udupi 63.22 BI14 Katihar 57.06 KA9 Chikmagalur 52.2MH35 Yavatmal 75.07 MH16 Latur 63.19 CG1 Bastar 57.05 RJ19 Jalor 52UP18 Bulandshahr 74.45 UP5 Auraiya 62.79 CG8 Kanker 56.95 MH7 Buldana 51.99

DE9 West Delhi 73.51 KA10 Chitradurga 62.41 MP21 Katni 56.87 RJ9 Bundi 51.62UP39 Jyotiba Phule Nagar 73.32 OR25 Nuapada 62.29 MP44 Vidisha 56.79 OR26 Puri 51.3KA12 Davanagere 72.08 MP6 Chhatarpur 62.21 BI23 Nawada 56.33 JH18 Sahibganj 51.29BI9 Gaya 71.7 BI28 Rohtas 62.1 HA18 Sonipat 56.26 MH1 Ahmadnagar 51.25

UP11 Banda 71.63 BI30 Samastipur 62.08 TN16 Ramanathapuram 56.14 MP28 Raisen 50.88MH28 Sangli 70.84 CG13 Raigarh 61.8 MP12 Dindori 55.83 GU12 Kheda 50.81CG4 Dhamtari 70.83 MP30 Ratlam 61.68 MH21 Nandurbar 55.51 MP16 Harda 50.58OR21 Malkangiri 70.74 UP40 Kannauj 61.51 MH33 Wardha 55.14 CG6 Janjgir - Champa 50.36UP4 Ambedkar Nagar 70.57 OR20 Koraput 60.94 MP45 West Nimar 54.9 MH24 Parbhani 50.3KA20 Koppal 70.33 MH3 Amravati 60.92 UP31 Gonda 54.83 UP69 Unnao 50.24MP8 Damoh 70.15 UP50 Mainpuri 60.91 AP13 Medak 54.52 OR19 Khordha 50.23MH20 Nanded 70.05 OR15 Kalahandi 60.32 OR9 Dhenkanal 54.5 MH22 Nashik 50.11OR17 Kendrapara 69.35 MP7 Chhindwara 60.09 UP32 Gorakhpur 54.47

UP60 Rampur 69.31 KA15 Gulbarga 60.04 BI37 Vaishali 54.34MP4 Bhind 69.12 MP35 Seoni 59.77 MP3 Betul 54.06RJ16 Hanumangarh 68.27 UP20 Chitrakoot 59.67 KA14 Gadag 53.99UP36 Jalaun 68.09 MH30 Sindhudurg 59.58 UP19 Chandauli 53.97

127

HCRUD05HCRUD05 HCRUD05 HCRUD05

128

Figure 4.1: Scatterplot between SDIR4 & HCRRD5

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129

Figure 4.4: Scatterplot between SDIR4 & SDIU4

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RJ25

RJ26RJ27

RJ28RJ29

RJ30RJ31

RJ32

SK1

SK2

SK3

SK4TN1

TN3

TN4TN5

TN6

TN7TN8

TN9

TN10TN11

TN12

TN13

TN14

TN15

TN16

TN17

TN18TN19

TN20

TN21

TN22

TN23

TN24TN25TN26

TN27TN28

TN29

TN30

TR1

TR2TR3TR4

UP1

UP2

UP3

UP4UP5

UP6

UP7UP8

UP9UP10

UP11

UP12UP13

UP14UP15

UP16

UP17

UP18UP19UP20

UP21UP22UP23

UP24

UP25UP26

UP27

UP28

UP29

UP30

UP31UP32

UP33UP34

UP35UP36

UP37UP38

UP39UP40

UP41

UP42

UP43

UP44UP45

UP46

UP47

UP48

UP49

UP50UP51

UP52UP53UP54

UP55UP56

UP57

UP58UP59

UP60

UP61

UP62UP63UP64 UP65

UP66

UP67UP68

UP69

UP70UT1

UT2UT3

UT4

UT5

UT6

UT7

UT8UT9

UT10UT11

UT12

UT13WB1

WB2WB3WB4WB5

WB6WB7

WB8

WB9WB11WB12

WB13

WB14WB15

WB16

WB17

WB18

130

Figure 4.5: Scatterplot between HCIR1 & HCIU1

AP1AP2

AP3

AP4

AP5AP6AP8

AP9AP10

AP11AP12

AP13AP14 AP15

AP16

AP17AP18

AP19AP20AP21

AP22AP23

AR1AR2AR3 AR4AR5 AR6 AR7

AR8

AR9

AR11

AR12

AR13AS1

AS2

AS3AS4 AS5

AS6

AS7

AS8

AS9AS10 AS11AS12

AS13AS14AS15

AS16AS17AS18AS19

AS20AS21

AS22

AS23

BI1

BI2

BI3

BI4

BI5 BI6BI7BI8 BI9

BI10BI11

BI12

BI13BI14

BI15BI16 BI17

BI18BI19

BI20BI21

BI22BI23

BI24

BI25

BI26BI27 BI28BI29

BI30

BI31

BI32

BI33

BI34

BI35

BI36 BI37

CG1

CG2

CG3 CG4CG5

CG6CG7

CG8

CG9

CG10CG11

CG12CG13

CG14

CG15

CG16

DE2DE4

DE5DE6

DE7DE8DE9

GO1GO2

GU1GU2

GU3

GU4

GU5

GU6

GU7

GU8 GU9GU10

GU11

GU12

GU13

GU14

GU15GU16

GU17

GU18

GU19

GU20

GU21

GU22

GU24 GU25

HA1

HA2HA3

HA4

HA5

HA6HA7

HA8HA9

HA10HA11 HA12

HA13

HA14

HA15HA16

HA17

HA18HA19

HP1HP2

HP3HP4HP6

HP8HP9

HP10HP11

HP12

JK1JK2JK3

JK4 JK5JK6

JK7

JK8

JK9

JK10

JK11JK12

JK13

JK14JH1

JH2

JH3 JH4JH5

JH6JH7

JH8

JH9JH10

JH11

JH12

JH13 JH14JH15

JH16JH17

JH18KA1

KA2

KA3

KA4

KA5KA6

KA7KA8

KA9

KA10

KA11

KA12KA13

KA14

KA15

KA16

KA17

KA18

KA19

KA20

KA21KA22

KA23

KA24KA25

KA26

KA27KE1KE2

KE3KE4

KE5 KE6

KE7

KE8KE9KE10

KE11

KE12KE13KE14

MP1

MP2

MP3

MP4

MP5

MP6

MP7

MP8

MP9MP10

MP11

MP12

MP13MP14MP15

MP16MP17

MP18MP19

MP20

MP21

MP22MP23

MP24

MP25MP26MP27MP28

MP29

MP30

MP31MP32MP33MP34

MP35MP36

MP37

MP38

MP39

MP40

MP41

MP42MP43

MP44MP45

MH1 MH2MH3

MH4

MH5

MH6MH7

MH8

MH9

MH10MH11

MH12

MH13MH14

MH15

MH16

MH19

MH20MH21

MH22MH23

MH24

MH25MH26

MH27

MH28

MH29

MH30

MH31

MH32 MH33

MH34

MH35

MN1

MN2

MN4MN5

MN8

MG1MG2MG3

MG4

MG5MG6

MG7

MZ1MZ2MZ3MZ5

MZ6

MZ7

MZ8

NG1NG2 NG3

NG4

NG5

NG6NG7

NG8

OR1

OR2OR3OR4OR5

OR6

OR7

OR8OR9

OR10OR11

OR12OR13OR14OR15

OR16

OR17OR18

OR19OR20

OR21

OR22

OR23OR24OR25 OR26

OR27OR28OR29

OR30

PU1PU2PU3

PU4

PU5

PU6

PU7

PU8PU9

PU10

PU11

PU12

PU13

PU14PU15

PU16

PU17

RJ1RJ2

RJ3

RJ4RJ5RJ6

RJ7RJ8 RJ9

RJ10

RJ11RJ12

RJ13

RJ14

RJ15RJ16

RJ17RJ18 RJ19

RJ20

RJ21RJ22

RJ23

RJ24

RJ25

RJ26

RJ27

RJ28RJ29

RJ30

RJ31

RJ32SK1

SK2

SK3

SK4

TN1

TN3TN4

TN5 TN6TN7

TN8TN9

TN10TN11TN12

TN13

TN14TN15TN16

TN17

TN18TN19TN20

TN21

TN22

TN23TN24TN25TN26

TN27TN28TN29

TN30

TR1 TR2TR3TR4

UP1UP2

UP3

UP4

UP5

UP6

UP7UP8

UP9UP10UP11

UP12UP13

UP14

UP15UP16

UP17

UP18

UP19UP20UP21

UP22

UP23UP24

UP25

UP26

UP27

UP28UP29

UP30UP31

UP32

UP33UP34

UP35UP36

UP37UP38

UP39

UP40

UP41UP42

UP43

UP44UP45UP46

UP47UP48

UP49

UP50

UP51 UP52UP53UP54

UP55

UP56UP57

UP58UP59

UP60

UP61UP62

UP63

UP64UP65UP66

UP67

UP68

UP69UP70

UT1

UT2

UT3

UT4UT5

UT6

UT7

UT8

UT9

UT10UT11

UT12

UT13

WB1

WB2WB3WB4WB5

WB6

WB7WB8

WB9

WB11WB12

WB13

WB14WB15

WB16WB17

WB18

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

HCIR1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

HC

IU1

AP1AP2

AP3

AP4

AP5AP6AP8

AP9AP10

AP11AP12

AP13AP14 AP15

AP16

AP17AP18

AP19AP20AP21

AP22AP23

AR1AR2AR3 AR4AR5 AR6 AR7

AR8

AR9

AR11

AR12

AR13AS1

AS2

AS3AS4 AS5

AS6

AS7

AS8

AS9AS10 AS11AS12

AS13AS14AS15

AS16AS17AS18AS19

AS20AS21

AS22

AS23

BI1

BI2

BI3

BI4

BI5 BI6BI7BI8 BI9

BI10BI11

BI12

BI13BI14

BI15BI16 BI17

BI18BI19

BI20BI21

BI22BI23

BI24

BI25

BI26BI27 BI28BI29

BI30

BI31

BI32

BI33

BI34

BI35

BI36 BI37

CG1

CG2

CG3 CG4CG5

CG6CG7

CG8

CG9

CG10CG11

CG12CG13

CG14

CG15

CG16

DE2DE4

DE5DE6

DE7DE8DE9

GO1GO2

GU1GU2

GU3

GU4

GU5

GU6

GU7

GU8 GU9GU10

GU11

GU12

GU13

GU14

GU15GU16

GU17

GU18

GU19

GU20

GU21

GU22

GU24 GU25

HA1

HA2HA3

HA4

HA5

HA6HA7

HA8HA9

HA10HA11 HA12

HA13

HA14

HA15HA16

HA17

HA18HA19

HP1HP2

HP3HP4HP6

HP8HP9

HP10HP11

HP12

JK1JK2JK3

JK4 JK5JK6

JK7

JK8

JK9

JK10

JK11JK12

JK13

JK14JH1

JH2

JH3 JH4JH5

JH6JH7

JH8

JH9JH10

JH11

JH12

JH13 JH14JH15

JH16JH17

JH18KA1

KA2

KA3

KA4

KA5KA6

KA7KA8

KA9

KA10

KA11

KA12KA13

KA14

KA15

KA16

KA17

KA18

KA19

KA20

KA21KA22

KA23

KA24KA25

KA26

KA27KE1KE2

KE3KE4

KE5 KE6

KE7

KE8KE9KE10

KE11

KE12KE13KE14

MP1

MP2

MP3

MP4

MP5

MP6

MP7

MP8

MP9MP10

MP11

MP12

MP13MP14MP15

MP16MP17

MP18MP19

MP20

MP21

MP22MP23

MP24

MP25MP26MP27MP28

MP29

MP30

MP31MP32MP33MP34

MP35MP36

MP37

MP38

MP39

MP40

MP41

MP42MP43

MP44MP45

MH1 MH2MH3

MH4

MH5

MH6MH7

MH8

MH9

MH10MH11

MH12

MH13MH14

MH15

MH16

MH19

MH20MH21

MH22MH23

MH24

MH25MH26

MH27

MH28

MH29

MH30

MH31

MH32 MH33

MH34

MH35

MN1

MN2

MN4MN5

MN8

MG1MG2MG3

MG4

MG5MG6

MG7

MZ1MZ2MZ3MZ5

MZ6

MZ7

MZ8

NG1NG2 NG3

NG4

NG5

NG6NG7

NG8

OR1

OR2OR3OR4OR5

OR6

OR7

OR8OR9

OR10OR11

OR12OR13OR14OR15

OR16

OR17OR18

OR19OR20

OR21

OR22

OR23OR24OR25 OR26

OR27OR28OR29

OR30

PU1PU2PU3

PU4

PU5

PU6

PU7

PU8PU9

PU10

PU11

PU12

PU13

PU14PU15

PU16

PU17

RJ1RJ2

RJ3

RJ4RJ5RJ6

RJ7RJ8 RJ9

RJ10

RJ11RJ12

RJ13

RJ14

RJ15RJ16

RJ17RJ18 RJ19

RJ20

RJ21RJ22

RJ23

RJ24

RJ25

RJ26

RJ27

RJ28RJ29

RJ30

RJ31

RJ32SK1

SK2

SK3

SK4

TN1

TN3TN4

TN5 TN6TN7

TN8TN9

TN10TN11TN12

TN13

TN14TN15TN16

TN17

TN18TN19TN20

TN21

TN22

TN23TN24TN25TN26

TN27TN28TN29

TN30

TR1 TR2TR3TR4

UP1UP2

UP3

UP4

UP5

UP6

UP7UP8

UP9UP10UP11

UP12UP13

UP14

UP15UP16

UP17

UP18

UP19UP20UP21

UP22

UP23UP24

UP25

UP26

UP27

UP28UP29

UP30UP31

UP32

UP33UP34

UP35UP36

UP37UP38

UP39

UP40

UP41UP42

UP43

UP44UP45UP46

UP47UP48

UP49

UP50

UP51 UP52UP53UP54

UP55

UP56UP57

UP58UP59

UP60

UP61UP62

UP63

UP64UP65UP66

UP67

UP68

UP69UP70

UT1

UT2

UT3

UT4UT5

UT6

UT7

UT8

UT9

UT10UT11

UT12

UT13

WB1

WB2WB3WB4WB5

WB6

WB7WB8

WB9

WB11WB12

WB13

WB14WB15

WB16WB17

WB18

131

Figure 4.6: Scatterplot between HHIR1 & HHIU1

AP1 AP2

AP3AP4

AP5

AP6

AP8

AP9 AP10

AP11AP12

AP13

AP14 AP15AP16

AP17

AP18

AP19

AP20

AP21

AP22

AP23

AR1

AR2

AR3AR4AR5

AR6

AR7

AR8

AR9

AR11

AR12

AR13AS1

AS2AS3AS4

AS5

AS6

AS7

AS8AS9

AS10AS11AS12

AS13

AS14

AS15

AS16AS17AS18

AS19

AS20

AS21AS22

AS23

BI1

BI2

BI3

BI4

BI5BI6

BI7

BI8

BI9

BI10BI11

BI12

BI13

BI14BI15

BI16

BI17BI18

BI19

BI20

BI21

BI22

BI23

BI24

BI25

BI26BI27

BI28

BI29

BI30

BI31BI32

BI33

BI34

BI35

BI36

BI37CG1

CG2CG3CG4

CG5

CG6

CG7

CG8CG9

CG10CG11CG12

CG13

CG14

CG15CG16

DE2DE4

DE5

DE6 DE7DE8

DE9

GO1GO2

GU1

GU2

GU3

GU4

GU5GU6

GU7

GU8

GU9

GU10GU11GU12GU13

GU14

GU15

GU16

GU17

GU18

GU19

GU20

GU21

GU22

GU24

GU25 HA1

HA2HA3HA4

HA5

HA6

HA7HA8HA9

HA10HA11

HA12

HA13

HA14HA15HA16

HA17HA18HA19

HP1

HP2HP3HP4

HP6HP8 HP9

HP10

HP11

HP12

JK1JK2JK3

JK4

JK5

JK6

JK7

JK8

JK9

JK10

JK11

JK12

JK13JK14

JH1

JH2

JH3JH4

JH5

JH6

JH7JH8

JH9

JH10JH11JH12

JH13JH14JH15

JH16

JH17

JH18KA1

KA2

KA3KA4KA5

KA6KA7 KA8KA9

KA10

KA11

KA12

KA13

KA14

KA15

KA16

KA17

KA18

KA19

KA20

KA21

KA22

KA23

KA24KA25

KA26

KA27 KE1

KE2

KE3 KE4KE5 KE6

KE7

KE8KE9KE10

KE11KE12 KE13

KE14

MP1

MP2MP3

MP4

MP5

MP6MP7MP8MP9

MP10 MP11

MP12

MP13

MP14

MP15

MP16

MP17

MP18

MP19

MP20MP21

MP22

MP23

MP24

MP25MP26

MP27

MP28MP29

MP30

MP31MP32

MP33

MP34

MP35MP36MP37MP38MP39

MP40MP41

MP42

MP43

MP44MP45

MH1

MH2MH3

MH4

MH5MH6

MH7MH8

MH9

MH10

MH11MH12

MH13

MH14

MH15

MH16

MH19

MH20MH21

MH22

MH23MH24

MH25

MH26MH27

MH28

MH29

MH30MH31

MH32

MH33

MH34MH35

MN1

MN2

MN4MN5

MN8MG1

MG2

MG3

MG4

MG5

MG6 MG7

MZ1

MZ2 MZ3MZ5

MZ6MZ7

MZ8NG1

NG2

NG3

NG4

NG5NG6NG7 NG8

OR1

OR2OR3

OR4

OR5 OR6

OR7

OR8

OR9

OR10

OR11

OR12

OR13 OR14

OR15OR16

OR17OR18

OR19

OR20

OR21

OR22

OR23

OR24

OR25

OR26

OR27

OR28

OR29

OR30

PU1

PU2PU3

PU4

PU5

PU6 PU7

PU8

PU9PU10

PU11 PU12

PU13

PU14

PU15PU16

PU17

RJ1RJ2RJ3

RJ4

RJ5

RJ6

RJ7RJ8

RJ9RJ10RJ11RJ12

RJ13

RJ14RJ15

RJ16

RJ17

RJ18

RJ19RJ20RJ21

RJ22

RJ23

RJ24

RJ25RJ26

RJ27

RJ28

RJ29

RJ30

RJ31

RJ32

SK1SK2

SK3

SK4

TN1

TN3

TN4

TN5

TN6TN7

TN8

TN9TN10

TN11

TN12

TN13

TN14

TN15

TN16

TN17

TN18

TN19

TN20TN21

TN22

TN23

TN24TN25TN26

TN27

TN28

TN29TN30

TR1

TR2TR3

TR4UP1

UP2UP3

UP4UP5

UP6

UP7

UP8

UP9

UP10UP11UP12

UP13UP14

UP15

UP16

UP17

UP18

UP19UP20UP21

UP22

UP23 UP24UP25UP26

UP27

UP28UP29

UP30

UP31UP32

UP33UP34

UP35UP36UP37UP38

UP39

UP40UP41

UP42

UP43

UP44

UP45UP46

UP47

UP48UP49

UP50UP51 UP52

UP53

UP54UP55

UP56

UP57UP58

UP59

UP60

UP61

UP62

UP63

UP64

UP65UP66

UP67

UP68

UP69

UP70UT1

UT2

UT3

UT4

UT5UT6

UT7 UT8UT9UT10UT11

UT12

UT13

WB1WB2

WB3WB4

WB5 WB6

WB7

WB8

WB9

WB11

WB12

WB13

WB14

WB15

WB16WB17WB18

0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75

HHIR1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

HH

IU1

AP1 AP2

AP3AP4

AP5

AP6

AP8

AP9 AP10

AP11AP12

AP13

AP14 AP15AP16

AP17

AP18

AP19

AP20

AP21

AP22

AP23

AR1

AR2

AR3AR4AR5

AR6

AR7

AR8

AR9

AR11

AR12

AR13AS1

AS2AS3AS4

AS5

AS6

AS7

AS8AS9

AS10AS11AS12

AS13

AS14

AS15

AS16AS17AS18

AS19

AS20

AS21AS22

AS23

BI1

BI2

BI3

BI4

BI5BI6

BI7

BI8

BI9

BI10BI11

BI12

BI13

BI14BI15

BI16

BI17BI18

BI19

BI20

BI21

BI22

BI23

BI24

BI25

BI26BI27

BI28

BI29

BI30

BI31BI32

BI33

BI34

BI35

BI36

BI37CG1

CG2CG3CG4

CG5

CG6

CG7

CG8CG9

CG10CG11CG12

CG13

CG14

CG15CG16

DE2DE4

DE5

DE6 DE7DE8

DE9

GO1GO2

GU1

GU2

GU3

GU4

GU5GU6

GU7

GU8

GU9

GU10GU11GU12GU13

GU14

GU15

GU16

GU17

GU18

GU19

GU20

GU21

GU22

GU24

GU25 HA1

HA2HA3HA4

HA5

HA6

HA7HA8HA9

HA10HA11

HA12

HA13

HA14HA15HA16

HA17HA18HA19

HP1

HP2HP3HP4

HP6HP8 HP9

HP10

HP11

HP12

JK1JK2JK3

JK4

JK5

JK6

JK7

JK8

JK9

JK10

JK11

JK12

JK13JK14

JH1

JH2

JH3JH4

JH5

JH6

JH7JH8

JH9

JH10JH11JH12

JH13JH14JH15

JH16

JH17

JH18KA1

KA2

KA3KA4KA5

KA6KA7 KA8KA9

KA10

KA11

KA12

KA13

KA14

KA15

KA16

KA17

KA18

KA19

KA20

KA21

KA22

KA23

KA24KA25

KA26

KA27 KE1

KE2

KE3 KE4KE5 KE6

KE7

KE8KE9KE10

KE11KE12 KE13

KE14

MP1

MP2MP3

MP4

MP5

MP6MP7MP8MP9

MP10 MP11

MP12

MP13

MP14

MP15

MP16

MP17

MP18

MP19

MP20MP21

MP22

MP23

MP24

MP25MP26

MP27

MP28MP29

MP30

MP31MP32

MP33

MP34

MP35MP36MP37MP38MP39

MP40MP41

MP42

MP43

MP44MP45

MH1

MH2MH3

MH4

MH5MH6

MH7MH8

MH9

MH10

MH11MH12

MH13

MH14

MH15

MH16

MH19

MH20MH21

MH22

MH23MH24

MH25

MH26MH27

MH28

MH29

MH30MH31

MH32

MH33

MH34MH35

MN1

MN2

MN4MN5

MN8MG1

MG2

MG3

MG4

MG5

MG6 MG7

MZ1

MZ2 MZ3MZ5

MZ6MZ7

MZ8NG1

NG2

NG3

NG4

NG5NG6NG7 NG8

OR1

OR2OR3

OR4

OR5 OR6

OR7

OR8

OR9

OR10

OR11

OR12

OR13 OR14

OR15OR16

OR17OR18

OR19

OR20

OR21

OR22

OR23

OR24

OR25

OR26

OR27

OR28

OR29

OR30

PU1

PU2PU3

PU4

PU5

PU6 PU7

PU8

PU9PU10

PU11 PU12

PU13

PU14

PU15PU16

PU17

RJ1RJ2RJ3

RJ4

RJ5

RJ6

RJ7RJ8

RJ9RJ10RJ11RJ12

RJ13

RJ14RJ15

RJ16

RJ17

RJ18

RJ19RJ20RJ21

RJ22

RJ23

RJ24

RJ25RJ26

RJ27

RJ28

RJ29

RJ30

RJ31

RJ32

SK1SK2

SK3

SK4

TN1

TN3

TN4

TN5

TN6TN7

TN8

TN9TN10

TN11

TN12

TN13

TN14

TN15

TN16

TN17

TN18

TN19

TN20TN21

TN22

TN23

TN24TN25TN26

TN27

TN28

TN29TN30

TR1

TR2TR3

TR4UP1

UP2UP3

UP4UP5

UP6

UP7

UP8

UP9

UP10UP11UP12

UP13UP14

UP15

UP16

UP17

UP18

UP19UP20UP21

UP22

UP23 UP24UP25UP26

UP27

UP28UP29

UP30

UP31UP32

UP33UP34

UP35UP36UP37UP38

UP39

UP40UP41

UP42

UP43

UP44

UP45UP46

UP47

UP48UP49

UP50UP51 UP52

UP53

UP54UP55

UP56

UP57UP58

UP59

UP60

UP61

UP62

UP63

UP64

UP65UP66

UP67

UP68

UP69

UP70UT1

UT2

UT3

UT4

UT5UT6

UT7 UT8UT9UT10UT11

UT12

UT13

WB1WB2

WB3WB4

WB5 WB6

WB7

WB8

WB9

WB11

WB12

WB13

WB14

WB15

WB16WB17WB18

132

Figure 4.7: Scatterplot between TCIR1 & TCIU1

AP1AP2AP3

AP4AP5AP6

AP8AP9 AP10

AP11AP12

AP13AP14

AP15AP16

AP17

AP18

AP19

AP20

AP21

AP22 AP23

AR1

AR2

AR3

AR4AR5

AR6

AR7

AR8

AR9

AR11AR12

AR13AS1

AS2

AS3

AS4

AS5

AS6

AS7

AS8

AS9

AS10

AS11

AS12

AS13

AS14AS15

AS16

AS17AS18

AS19

AS20

AS21

AS22AS23

BI1 BI2

BI3

BI4

BI5

BI6BI7

BI8BI9BI10

BI11BI12

BI13BI14

BI15BI16

BI17

BI18 BI19BI20

BI21

BI22BI23BI24

BI25BI26BI27

BI28

BI29BI30

BI31

BI32BI33

BI34

BI35

BI36

BI37

CG1CG2

CG3 CG4

CG5

CG6

CG7

CG8

CG9

CG10

CG11CG12CG13

CG14

CG15

CG16

DE2DE4

DE5

DE6DE7DE8

DE9GO1GO2

GU1

GU2GU3

GU4

GU5 GU6

GU7

GU8

GU9

GU10

GU11

GU12

GU13

GU14

GU15

GU16GU17 GU18

GU19

GU20

GU21GU22

GU24

GU25

HA1

HA2

HA3

HA4

HA5

HA6 HA7HA8

HA9

HA10HA11

HA12

HA13

HA14

HA15

HA16

HA17 HA18

HA19

HP1

HP2

HP3

HP4HP6

HP8

HP9

HP10HP11

HP12

JK1JK2JK3

JK4

JK5

JK6

JK7

JK8JK9

JK10JK11JK12

JK13

JK14

JH1

JH2

JH3

JH4

JH5JH6JH7

JH8

JH9

JH10JH11

JH12

JH13

JH14JH15

JH16JH17

JH18

KA1

KA2

KA3

KA4

KA5KA6

KA7

KA8

KA9

KA10

KA11

KA12KA13

KA14

KA15

KA16

KA17

KA18

KA19

KA20

KA21

KA22

KA23

KA24KA25

KA26

KA27KE1

KE2KE3

KE4KE5

KE6

KE7

KE8

KE9

KE10 KE11KE12

KE13

KE14MP1

MP2

MP3

MP4

MP5

MP6MP7

MP8MP9

MP10

MP11

MP12

MP13MP14

MP15

MP16

MP17

MP18

MP19

MP20

MP21

MP22

MP23

MP24MP25

MP26

MP27

MP28MP29

MP30

MP31

MP32

MP33MP34MP35MP36

MP37

MP38

MP39MP40

MP41

MP42

MP43MP44

MP45

MH1

MH2MH3

MH4

MH5

MH6MH7

MH8MH9

MH10

MH11

MH12

MH13MH14

MH15

MH16

MH19

MH20

MH21

MH22

MH23MH24

MH25

MH26MH27

MH28MH29

MH30

MH31

MH32MH33

MH34

MH35MN1

MN2

MN4

MN5

MN8

MG1MG2MG3

MG4

MG5MG6

MG7

MZ1

MZ2

MZ3

MZ5

MZ6

MZ7 MZ8

NG1

NG2NG3

NG4 NG5NG6

NG7

NG8

OR1

OR2OR3OR4

OR5OR6

OR7

OR8

OR9

OR10

OR11OR12

OR13

OR14OR15OR16 OR17OR18

OR19

OR20

OR21

OR22

OR23

OR24

OR25

OR26

OR27

OR28

OR29

OR30

PU1

PU2

PU3

PU4

PU5

PU6

PU7PU8

PU9PU10

PU11

PU12

PU13

PU14

PU15PU16

PU17

RJ1

RJ2

RJ3

RJ4

RJ5

RJ6

RJ7

RJ8

RJ9

RJ10

RJ11

RJ12RJ13

RJ14

RJ15

RJ16

RJ17

RJ18RJ19

RJ20RJ21

RJ22

RJ23

RJ24

RJ25

RJ26RJ27

RJ28RJ29

RJ30RJ31

RJ32

SK1

SK2

SK3

SK4

TN1

TN3TN4

TN5

TN6

TN7TN8

TN9

TN10

TN11TN12

TN13

TN14

TN15

TN16

TN17

TN18TN19

TN20TN21

TN22TN23 TN24TN25

TN26TN27TN28TN29

TN30

TR1

TR2 TR3TR4

UP1

UP2

UP3

UP4

UP5

UP6UP7

UP8UP9UP10

UP11

UP12UP13

UP14UP15

UP16UP17

UP18

UP19UP20

UP21UP22UP23

UP24

UP25UP26UP27

UP28

UP29

UP30

UP31UP32

UP33UP34UP35

UP36

UP37UP38

UP39UP40UP41

UP42

UP43

UP44UP45

UP46

UP47

UP48

UP49

UP50UP51

UP52

UP53UP54

UP55UP56

UP57

UP58UP59

UP60

UP61

UP62UP63

UP64UP65

UP66

UP67

UP68

UP69

UP70

UT1

UT2UT3

UT4

UT5

UT6

UT7

UT8

UT9

UT10

UT11

UT12

UT13

WB1

WB2WB3

WB4WB5

WB6WB7

WB8WB9WB11

WB12

WB13

WB14

WB15WB16WB17WB18

-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

TCIR1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8TC

IU1

AP1AP2AP3

AP4AP5AP6

AP8AP9 AP10

AP11AP12

AP13AP14

AP15AP16

AP17

AP18

AP19

AP20

AP21

AP22 AP23

AR1

AR2

AR3

AR4AR5

AR6

AR7

AR8

AR9

AR11AR12

AR13AS1

AS2

AS3

AS4

AS5

AS6

AS7

AS8

AS9

AS10

AS11

AS12

AS13

AS14AS15

AS16

AS17AS18

AS19

AS20

AS21

AS22AS23

BI1 BI2

BI3

BI4

BI5

BI6BI7

BI8BI9BI10

BI11BI12

BI13BI14

BI15BI16

BI17

BI18 BI19BI20

BI21

BI22BI23BI24

BI25BI26BI27

BI28

BI29BI30

BI31

BI32BI33

BI34

BI35

BI36

BI37

CG1CG2

CG3 CG4

CG5

CG6

CG7

CG8

CG9

CG10

CG11CG12CG13

CG14

CG15

CG16

DE2DE4

DE5

DE6DE7DE8

DE9GO1GO2

GU1

GU2GU3

GU4

GU5 GU6

GU7

GU8

GU9

GU10

GU11

GU12

GU13

GU14

GU15

GU16GU17 GU18

GU19

GU20

GU21GU22

GU24

GU25

HA1

HA2

HA3

HA4

HA5

HA6 HA7HA8

HA9

HA10HA11

HA12

HA13

HA14

HA15

HA16

HA17 HA18

HA19

HP1

HP2

HP3

HP4HP6

HP8

HP9

HP10HP11

HP12

JK1JK2JK3

JK4

JK5

JK6

JK7

JK8JK9

JK10JK11JK12

JK13

JK14

JH1

JH2

JH3

JH4

JH5JH6JH7

JH8

JH9

JH10JH11

JH12

JH13

JH14JH15

JH16JH17

JH18

KA1

KA2

KA3

KA4

KA5KA6

KA7

KA8

KA9

KA10

KA11

KA12KA13

KA14

KA15

KA16

KA17

KA18

KA19

KA20

KA21

KA22

KA23

KA24KA25

KA26

KA27KE1

KE2KE3

KE4KE5

KE6

KE7

KE8

KE9

KE10 KE11KE12

KE13

KE14MP1

MP2

MP3

MP4

MP5

MP6MP7

MP8MP9

MP10

MP11

MP12

MP13MP14

MP15

MP16

MP17

MP18

MP19

MP20

MP21

MP22

MP23

MP24MP25

MP26

MP27

MP28MP29

MP30

MP31

MP32

MP33MP34MP35MP36

MP37

MP38

MP39MP40

MP41

MP42

MP43MP44

MP45

MH1

MH2MH3

MH4

MH5

MH6MH7

MH8MH9

MH10

MH11

MH12

MH13MH14

MH15

MH16

MH19

MH20

MH21

MH22

MH23MH24

MH25

MH26MH27

MH28MH29

MH30

MH31

MH32MH33

MH34

MH35MN1

MN2

MN4

MN5

MN8

MG1MG2MG3

MG4

MG5MG6

MG7

MZ1

MZ2

MZ3

MZ5

MZ6

MZ7 MZ8

NG1

NG2NG3

NG4 NG5NG6

NG7

NG8

OR1

OR2OR3OR4

OR5OR6

OR7

OR8

OR9

OR10

OR11OR12

OR13

OR14OR15OR16 OR17OR18

OR19

OR20

OR21

OR22

OR23

OR24

OR25

OR26

OR27

OR28

OR29

OR30

PU1

PU2

PU3

PU4

PU5

PU6

PU7PU8

PU9PU10

PU11

PU12

PU13

PU14

PU15PU16

PU17

RJ1

RJ2

RJ3

RJ4

RJ5

RJ6

RJ7

RJ8

RJ9

RJ10

RJ11

RJ12RJ13

RJ14

RJ15

RJ16

RJ17

RJ18RJ19

RJ20RJ21

RJ22

RJ23

RJ24

RJ25

RJ26RJ27

RJ28RJ29

RJ30RJ31

RJ32

SK1

SK2

SK3

SK4

TN1

TN3TN4

TN5

TN6

TN7TN8

TN9

TN10

TN11TN12

TN13

TN14

TN15

TN16

TN17

TN18TN19

TN20TN21

TN22TN23 TN24TN25

TN26TN27TN28TN29

TN30

TR1

TR2 TR3TR4

UP1

UP2

UP3

UP4

UP5

UP6UP7

UP8UP9UP10

UP11

UP12UP13

UP14UP15

UP16UP17

UP18

UP19UP20

UP21UP22UP23

UP24

UP25UP26UP27

UP28

UP29

UP30

UP31UP32

UP33UP34UP35

UP36

UP37UP38

UP39UP40UP41

UP42

UP43

UP44UP45

UP46

UP47

UP48

UP49

UP50UP51

UP52

UP53UP54

UP55UP56

UP57

UP58UP59

UP60

UP61

UP62UP63

UP64UP65

UP66

UP67

UP68

UP69

UP70

UT1

UT2UT3

UT4

UT5

UT6

UT7

UT8

UT9

UT10

UT11

UT12

UT13

WB1

WB2WB3

WB4WB5

WB6WB7

WB8WB9WB11

WB12

WB13

WB14

WB15WB16WB17WB18

133

Figure 4.8: Scatterplot between HCRUD05 & HCRRD5

AP1AP2

AP3

AP4

AP5AP6

AP8 AP9

AP10

AP11

AP12

AP13

AP14

AP15

AP16

AP17

AP18

AP19

AP20

AP21

AP22AP23AR1

AR2

AR3

AR4

AR5

AR6AR7AR8

AR9

AR11

AR12AR13AS1

AS2AS3AS4AS5AS6AS7

AS8AS9AS10 AS11AS12

AS13

AS14

AS15AS16 AS17

AS18

AS19AS20AS21

AS22 AS23

BI1

BI2

BI3

BI4

BI5

BI6

BI7 BI8

BI9

BI10

BI11BI12

BI13

BI14

BI15

BI16

BI17

BI18

BI19BI20

BI21BI22

BI23

BI24

BI25

BI26

BI27

BI28

BI29

BI30

BI31BI32

BI33

BI34BI35BI36

BI37CG1

CG2

CG3

CG4

CG5

CG6

CG7

CG8

CG9

CG10

CG11

CG12

CG13

CG14CG15

CG16DE2DE4

DE5

DE6DE7

DE8

DE9

GO1GO2

GU1GU2

GU3

GU4

GU5GU6

GU7

GU8

GU9GU10

GU11 GU12

GU13

GU14

GU15

GU16GU17GU18

GU19

GU20

GU21

GU22

GU24GU25HA1

HA2

HA3

HA4

HA5 HA6

HA7HA8

HA9HA10

HA11

HA12

HA13 HA14

HA15HA16HA17

HA18

HA19HP1 HP2

HP3

HP4

HP6HP8HP9HP10HP11HP12JK1JK2

JK3

JK4JK5

JK6JK7 JK8

JK9JK10

JK11JK12JK13

JK14JH1

JH2

JH3

JH4

JH5

JH6

JH7

JH8

JH9

JH10

JH11 JH12

JH13

JH14

JH15

JH16

JH17

JH18

KA1

KA2

KA3

KA4

KA5

KA6KA7

KA8KA9

KA10

KA11

KA12

KA13

KA14KA15

KA16

KA17

KA18

KA19

KA20

KA21

KA22

KA23

KA24

KA25

KA26KA27

KE1 KE2KE3

KE4KE5

KE6KE7

KE8KE9

KE10

KE11KE12

KE13KE14

MP1MP2

MP3

MP4

MP5

MP6MP7

MP8MP9

MP10

MP11

MP12

MP13

MP14

MP15MP16

MP17

MP18

MP19

MP20

MP21MP22

MP23

MP24

MP25

MP26

MP27MP28

MP29

MP30

MP31

MP32

MP33MP34

MP35

MP36

MP37 MP38

MP39

MP40

MP41

MP42MP43

MP44MP45MH1

MH2 MH3

MH4

MH5

MH6

MH7

MH8

MH9

MH10

MH11

MH12

MH13

MH14

MH15

MH16

MH19

MH20

MH21MH22

MH23

MH24

MH25

MH26

MH27

MH28

MH29

MH30

MH31

MH32

MH33

MH34

MH35

MN1

MN2

MN4

MN5

MN8

MG1

MG2MG3 MG4MG5MG6MG7MZ1 MZ2MZ3MZ5MZ6 MZ7MZ8NG1NG2NG3NG4

NG5NG6NG7NG8

OR1 OR2

OR3

OR4

OR5

OR6OR7

OR8

OR9

OR10

OR11OR12

OR13

OR14OR15

OR16

OR17

OR18

OR19

OR20

OR21

OR22

OR23

OR24

OR25

OR26

OR27

OR28

OR29

OR30

PU1PU2PU3

PU4

PU5PU6PU7PU8PU9

PU10

PU11

PU12

PU13

PU14PU15PU16

PU17

RJ1

RJ2

RJ3

RJ4

RJ5

RJ6RJ7

RJ8RJ9

RJ10RJ11

RJ12

RJ13

RJ14

RJ15

RJ16

RJ17

RJ18

RJ19

RJ20

RJ21

RJ22

RJ23

RJ24

RJ25

RJ26

RJ27RJ28RJ29

RJ30

RJ31

RJ32

SK1 SK2SK3SK4

TN1TN3

TN4TN5

TN6

TN7TN8

TN9

TN10

TN11TN12TN13

TN14

TN15

TN16

TN17TN18

TN19TN20

TN21

TN22TN23

TN24

TN25TN26

TN27

TN28

TN29TN30

TR1TR2 TR3TR4

UP1UP2

UP3

UP4

UP5

UP6UP7

UP8

UP9

UP10

UP11

UP12UP13

UP14

UP15

UP16

UP17

UP18

UP19UP20

UP21

UP22

UP23UP24

UP25

UP26

UP27

UP28

UP29UP30

UP31UP32

UP33

UP34

UP35

UP36

UP37

UP38

UP39

UP40

UP41

UP42

UP43

UP44

UP45

UP46

UP47

UP48

UP49

UP50

UP51

UP52

UP53

UP54UP55

UP56

UP57

UP58UP59

UP60

UP61

UP62

UP63

UP64

UP65

UP66

UP67

UP68

UP69

UP70

UT1

UT2

UT3

UT4

UT5

UT6

UT7

UT8

UT9

UT10UT11

UT12

UT13

WB1WB2WB3

WB4WB5 WB6WB7WB8

WB9

WB11WB12

WB13

WB14

WB15

WB16

WB17

WB18

-10 0 10 20 30 40 50 60 70 80 90 100

HCRRD5

-20

0

20

40

60

80

100H

CR

UD

05

AP1AP2

AP3

AP4

AP5AP6

AP8 AP9

AP10

AP11

AP12

AP13

AP14

AP15

AP16

AP17

AP18

AP19

AP20

AP21

AP22AP23AR1

AR2

AR3

AR4

AR5

AR6AR7AR8

AR9

AR11

AR12AR13AS1

AS2AS3AS4AS5AS6AS7

AS8AS9AS10 AS11AS12

AS13

AS14

AS15AS16 AS17

AS18

AS19AS20AS21

AS22 AS23

BI1

BI2

BI3

BI4

BI5

BI6

BI7 BI8

BI9

BI10

BI11BI12

BI13

BI14

BI15

BI16

BI17

BI18

BI19BI20

BI21BI22

BI23

BI24

BI25

BI26

BI27

BI28

BI29

BI30

BI31BI32

BI33

BI34BI35BI36

BI37CG1

CG2

CG3

CG4

CG5

CG6

CG7

CG8

CG9

CG10

CG11

CG12

CG13

CG14CG15

CG16DE2DE4

DE5

DE6DE7

DE8

DE9

GO1GO2

GU1GU2

GU3

GU4

GU5GU6

GU7

GU8

GU9GU10

GU11 GU12

GU13

GU14

GU15

GU16GU17GU18

GU19

GU20

GU21

GU22

GU24GU25HA1

HA2

HA3

HA4

HA5 HA6

HA7HA8

HA9HA10

HA11

HA12

HA13 HA14

HA15HA16HA17

HA18

HA19HP1 HP2

HP3

HP4

HP6HP8HP9HP10HP11HP12JK1JK2

JK3

JK4JK5

JK6JK7 JK8

JK9JK10

JK11JK12JK13

JK14JH1

JH2

JH3

JH4

JH5

JH6

JH7

JH8

JH9

JH10

JH11 JH12

JH13

JH14

JH15

JH16

JH17

JH18

KA1

KA2

KA3

KA4

KA5

KA6KA7

KA8KA9

KA10

KA11

KA12

KA13

KA14KA15

KA16

KA17

KA18

KA19

KA20

KA21

KA22

KA23

KA24

KA25

KA26KA27

KE1 KE2KE3

KE4KE5

KE6KE7

KE8KE9

KE10

KE11KE12

KE13KE14

MP1MP2

MP3

MP4

MP5

MP6MP7

MP8MP9

MP10

MP11

MP12

MP13

MP14

MP15MP16

MP17

MP18

MP19

MP20

MP21MP22

MP23

MP24

MP25

MP26

MP27MP28

MP29

MP30

MP31

MP32

MP33MP34

MP35

MP36

MP37 MP38

MP39

MP40

MP41

MP42MP43

MP44MP45MH1

MH2 MH3

MH4

MH5

MH6

MH7

MH8

MH9

MH10

MH11

MH12

MH13

MH14

MH15

MH16

MH19

MH20

MH21MH22

MH23

MH24

MH25

MH26

MH27

MH28

MH29

MH30

MH31

MH32

MH33

MH34

MH35

MN1

MN2

MN4

MN5

MN8

MG1

MG2MG3 MG4MG5MG6MG7MZ1 MZ2MZ3MZ5MZ6 MZ7MZ8NG1NG2NG3NG4

NG5NG6NG7NG8

OR1 OR2

OR3

OR4

OR5

OR6OR7

OR8

OR9

OR10

OR11OR12

OR13

OR14OR15

OR16

OR17

OR18

OR19

OR20

OR21

OR22

OR23

OR24

OR25

OR26

OR27

OR28

OR29

OR30

PU1PU2PU3

PU4

PU5PU6PU7PU8PU9

PU10

PU11

PU12

PU13

PU14PU15PU16

PU17

RJ1

RJ2

RJ3

RJ4

RJ5

RJ6RJ7

RJ8RJ9

RJ10RJ11

RJ12

RJ13

RJ14

RJ15

RJ16

RJ17

RJ18

RJ19

RJ20

RJ21

RJ22

RJ23

RJ24

RJ25

RJ26

RJ27RJ28RJ29

RJ30

RJ31

RJ32

SK1 SK2SK3SK4

TN1TN3

TN4TN5

TN6

TN7TN8

TN9

TN10

TN11TN12TN13

TN14

TN15

TN16

TN17TN18

TN19TN20

TN21

TN22TN23

TN24

TN25TN26

TN27

TN28

TN29TN30

TR1TR2 TR3TR4

UP1UP2

UP3

UP4

UP5

UP6UP7

UP8

UP9

UP10

UP11

UP12UP13

UP14

UP15

UP16

UP17

UP18

UP19UP20

UP21

UP22

UP23UP24

UP25

UP26

UP27

UP28

UP29UP30

UP31UP32

UP33

UP34

UP35

UP36

UP37

UP38

UP39

UP40

UP41

UP42

UP43

UP44

UP45

UP46

UP47

UP48

UP49

UP50

UP51

UP52

UP53

UP54UP55

UP56

UP57

UP58UP59

UP60

UP61

UP62

UP63

UP64

UP65

UP66

UP67

UP68

UP69

UP70

UT1

UT2

UT3

UT4

UT5

UT6

UT7

UT8

UT9

UT10UT11

UT12

UT13

WB1WB2WB3

WB4WB5 WB6WB7WB8

WB9

WB11WB12

WB13

WB14

WB15

WB16

WB17

WB18

134

Figure 4.9: Scatterplot between PPRUD05 & PPRRD5

AP1AP2AP3

AP4

AP5AP6AP8

AP9

AP10

AP11AP12

AP13AP14

AP15AP16AP17

AP18AP19

AP20

AP21AP22

AP23AR1

AR2

AR3

AR4

AR5AR6

AR7AR8

AR9

AR11AR12

AR13AS1AS2

AS3

AS4

AS5AS6

AS7

AS8AS9

AS10

AS11AS12

AS13AS14

AS15 AS16

AS17

AS18AS19

AS20

AS21

AS22

AS23

BI1BI2

BI3BI4

BI5BI6BI7BI8

BI9

BI10

BI11

BI12

BI13BI14

BI15

BI16

BI17BI18

BI19BI20

BI21BI22BI23BI24BI25

BI26BI27

BI28

BI29

BI30

BI31

BI32BI33BI34BI35

BI36BI37CG1 CG2

CG3CG4

CG5

CG6

CG7

CG8 CG9

CG10CG11 CG12

CG13CG14

CG15

CG16

DE2DE4

DE5DE6DE7

DE8

DE9

GO1

GO2GU1

GU2GU3

GU4

GU5

GU6

GU7

GU8

GU9GU10

GU11

GU12

GU13

GU14 GU15GU16GU17

GU18

GU19

GU20

GU21

GU22

GU24

GU25HA1

HA2

HA3HA4

HA5

HA6HA7

HA8HA9

HA10

HA11

HA12

HA13HA14

HA15

HA16

HA17

HA18

HA19HP1HP2

HP3HP4

HP6

HP8HP9HP10HP11HP12

JK1

JK2JK3 JK4

JK5

JK6JK7JK8

JK9JK10

JK11JK12JK13JK14

JH1JH2

JH3

JH4JH5

JH6

JH7

JH8JH9

JH10

JH11

JH12JH13JH14 JH15JH16

JH17

JH18KA1

KA2

KA3KA4

KA5KA6 KA7KA8

KA9

KA10

KA11

KA12

KA13

KA14KA15

KA16

KA17

KA18KA19

KA20KA21

KA22

KA23

KA24

KA25

KA26KA27

KE1

KE2KE3

KE4KE5

KE6

KE7

KE8 KE9

KE10

KE11

KE12

KE13KE14

MP1MP2

MP3

MP4

MP5

MP6

MP7

MP8

MP9MP10MP11MP12 MP13MP14

MP15MP16

MP17

MP18

MP19MP20

MP21MP22

MP23

MP24MP25

MP26

MP27MP28

MP29

MP30

MP31

MP32MP33MP34MP35

MP36

MP37MP38

MP39

MP40

MP41

MP42

MP43MP44

MP45 MH1MH2MH3MH4

MH5

MH6

MH7MH8

MH9MH10

MH11

MH12

MH13

MH14MH15MH16

MH19

MH20

MH21MH22

MH23MH24

MH25MH26

MH27

MH28

MH29

MH30 MH31

MH32

MH33MH34

MH35

MN1MN2

MN4MN5MN8

MG1

MG2

MG3MG4

MG5MG6

MG7

MZ1

MZ2MZ3MZ5MZ6

MZ7

MZ8

NG1

NG2NG3

NG4NG5NG6

NG7NG8

OR1OR2OR3

OR4

OR5

OR6OR7

OR8OR9OR10

OR11OR12

OR13

OR14OR15OR16

OR17OR18

OR19OR20

OR21

OR22

OR23OR24

OR25OR26

OR27

OR28OR29

OR30

PU1PU2

PU3PU4PU5

PU6PU7PU8

PU9

PU10

PU11

PU12

PU13

PU14

PU15

PU16

PU17RJ1

RJ2RJ3

RJ4

RJ5

RJ6RJ7RJ8RJ9

RJ10RJ11

RJ12 RJ13

RJ14

RJ15

RJ16

RJ17

RJ18RJ19

RJ20RJ21

RJ22RJ23

RJ24

RJ25RJ26 RJ27

RJ28RJ29RJ30RJ31

RJ32SK1

SK2SK3

SK4

TN1

TN3

TN4

TN5TN6TN7

TN8

TN9TN10

TN11 TN12TN13

TN14

TN15

TN16

TN17TN18

TN19 TN20

TN21

TN22TN23

TN24

TN25

TN26

TN27

TN28TN29

TN30

TR1TR2TR3

TR4UP1

UP2UP3

UP4UP5

UP6UP7UP8

UP9UP10

UP11

UP12

UP13UP14

UP15

UP16

UP17

UP18

UP19

UP20

UP21UP22 UP23

UP24UP25UP26

UP27

UP28UP29UP30UP31UP32UP33UP34 UP35UP36

UP37

UP38

UP39UP40

UP41

UP42

UP43UP44

UP45

UP46

UP47UP48UP49UP50UP51

UP52

UP53

UP54

UP55UP56

UP57

UP58UP59UP60

UP61UP62UP63

UP64UP65 UP66UP67

UP68

UP69

UP70

UT1

UT2UT3

UT4

UT5

UT6

UT7

UT8UT9

UT10UT11

UT12

UT13WB1

WB2

WB3

WB4WB5

WB6 WB7

WB8 WB9

WB11

WB12WB13

WB14

WB15

WB16

WB17

WB18

0 200 400 600 800 1000 1200 1400 1600

PPRRD5

0

500

1000

1500

2000

2500

3000

3500PP

RU

D05

AP1AP2AP3

AP4

AP5AP6AP8

AP9

AP10

AP11AP12

AP13AP14

AP15AP16AP17

AP18AP19

AP20

AP21AP22

AP23AR1

AR2

AR3

AR4

AR5AR6

AR7AR8

AR9

AR11AR12

AR13AS1AS2

AS3

AS4

AS5AS6

AS7

AS8AS9

AS10

AS11AS12

AS13AS14

AS15 AS16

AS17

AS18AS19

AS20

AS21

AS22

AS23

BI1BI2

BI3BI4

BI5BI6BI7BI8

BI9

BI10

BI11

BI12

BI13BI14

BI15

BI16

BI17BI18

BI19BI20

BI21BI22BI23BI24BI25

BI26BI27

BI28

BI29

BI30

BI31

BI32BI33BI34BI35

BI36BI37CG1 CG2

CG3CG4

CG5

CG6

CG7

CG8 CG9

CG10CG11 CG12

CG13CG14

CG15

CG16

DE2DE4

DE5DE6DE7

DE8

DE9

GO1

GO2GU1

GU2GU3

GU4

GU5

GU6

GU7

GU8

GU9GU10

GU11

GU12

GU13

GU14 GU15GU16GU17

GU18

GU19

GU20

GU21

GU22

GU24

GU25HA1

HA2

HA3HA4

HA5

HA6HA7

HA8HA9

HA10

HA11

HA12

HA13HA14

HA15

HA16

HA17

HA18

HA19HP1HP2

HP3HP4

HP6

HP8HP9HP10HP11HP12

JK1

JK2JK3 JK4

JK5

JK6JK7JK8

JK9JK10

JK11JK12JK13JK14

JH1JH2

JH3

JH4JH5

JH6

JH7

JH8JH9

JH10

JH11

JH12JH13JH14 JH15JH16

JH17

JH18KA1

KA2

KA3KA4

KA5KA6 KA7KA8

KA9

KA10

KA11

KA12

KA13

KA14KA15

KA16

KA17

KA18KA19

KA20KA21

KA22

KA23

KA24

KA25

KA26KA27

KE1

KE2KE3

KE4KE5

KE6

KE7

KE8 KE9

KE10

KE11

KE12

KE13KE14

MP1MP2

MP3

MP4

MP5

MP6

MP7

MP8

MP9MP10MP11MP12 MP13MP14

MP15MP16

MP17

MP18

MP19MP20

MP21MP22

MP23

MP24MP25

MP26

MP27MP28

MP29

MP30

MP31

MP32MP33MP34MP35

MP36

MP37MP38

MP39

MP40

MP41

MP42

MP43MP44

MP45 MH1MH2MH3MH4

MH5

MH6

MH7MH8

MH9MH10

MH11

MH12

MH13

MH14MH15MH16

MH19

MH20

MH21MH22

MH23MH24

MH25MH26

MH27

MH28

MH29

MH30 MH31

MH32

MH33MH34

MH35

MN1MN2

MN4MN5MN8

MG1

MG2

MG3MG4

MG5MG6

MG7

MZ1

MZ2MZ3MZ5MZ6

MZ7

MZ8

NG1

NG2NG3

NG4NG5NG6

NG7NG8

OR1OR2OR3

OR4

OR5

OR6OR7

OR8OR9OR10

OR11OR12

OR13

OR14OR15OR16

OR17OR18

OR19OR20

OR21

OR22

OR23OR24

OR25OR26

OR27

OR28OR29

OR30

PU1PU2

PU3PU4PU5

PU6PU7PU8

PU9

PU10

PU11

PU12

PU13

PU14

PU15

PU16

PU17RJ1

RJ2RJ3

RJ4

RJ5

RJ6RJ7RJ8RJ9

RJ10RJ11

RJ12 RJ13

RJ14

RJ15

RJ16

RJ17

RJ18RJ19

RJ20RJ21

RJ22RJ23

RJ24

RJ25RJ26 RJ27

RJ28RJ29RJ30RJ31

RJ32SK1

SK2SK3

SK4

TN1

TN3

TN4

TN5TN6TN7

TN8

TN9TN10

TN11 TN12TN13

TN14

TN15

TN16

TN17TN18

TN19 TN20

TN21

TN22TN23

TN24

TN25

TN26

TN27

TN28TN29

TN30

TR1TR2TR3

TR4UP1

UP2UP3

UP4UP5

UP6UP7UP8

UP9UP10

UP11

UP12

UP13UP14

UP15

UP16

UP17

UP18

UP19

UP20

UP21UP22 UP23

UP24UP25UP26

UP27

UP28UP29UP30UP31UP32UP33UP34 UP35UP36

UP37

UP38

UP39UP40

UP41

UP42

UP43UP44

UP45

UP46

UP47UP48UP49UP50UP51

UP52

UP53

UP54

UP55UP56

UP57

UP58UP59UP60

UP61UP62UP63

UP64UP65 UP66UP67

UP68

UP69

UP70

UT1

UT2UT3

UT4

UT5

UT6

UT7

UT8UT9

UT10UT11

UT12

UT13WB1

WB2

WB3

WB4WB5

WB6 WB7

WB8 WB9

WB11

WB12WB13

WB14

WB15

WB16

WB17

WB18

135

Figure 4.10: Scatterplot between GINIUD05 & GINIRD5

AP1

AP2

AP3AP4

AP5AP6 AP8AP9

AP10AP11

AP12

AP13AP14

AP15

AP16

AP17

AP18AP19

AP20

AP21AP22

AP23

AR1AR2

AR3

AR4

AR5

AR6AR7AR8

AR9

AR11

AR12AR13

AS1

AS2AS3

AS4

AS5

AS6

AS7

AS8AS9

AS10

AS11

AS12

AS13

AS14AS15

AS16

AS17

AS18AS19

AS20

AS21

AS22

AS23BI1

BI2

BI3

BI4

BI5

BI6BI7

BI8

BI9

BI10

BI11BI12

BI13

BI14

BI15

BI16

BI17BI18

BI19

BI20

BI21

BI22BI23

BI24

BI25

BI26BI27

BI28BI29BI30

BI31

BI32

BI33 BI34BI35

BI36

BI37

CG1

CG2CG3

CG4

CG5

CG6CG7

CG8

CG9

CG10

CG11CG12

CG13

CG14

CG15

CG16

DE2DE4

DE5

DE6DE7DE8

DE9

GO1

GO2GU1

GU2GU3

GU4

GU5GU6 GU7

GU8

GU9

GU10

GU11

GU12GU13

GU14

GU15GU16

GU17

GU18

GU19GU20 GU21GU22

GU24

GU25HA1

HA2

HA3

HA4 HA5

HA6

HA7

HA8

HA9HA10

HA11

HA12

HA13HA14

HA15

HA16HA17

HA18

HA19HP1HP2

HP3

HP4HP6

HP8

HP9HP10

HP11

HP12

JK1

JK2

JK3

JK4

JK5

JK6

JK7JK8

JK9JK10

JK11JK12JK13

JK14

JH1

JH2

JH3

JH4

JH5

JH6

JH7

JH8

JH9JH10

JH11

JH12

JH13

JH14

JH15JH16JH17JH18

KA1

KA2KA3

KA4KA5

KA6KA7

KA8

KA9KA10

KA11

KA12

KA13

KA14

KA15KA16

KA17

KA18

KA19

KA20

KA21

KA22KA23KA24KA25

KA26KA27

KE1KE2

KE3 KE4KE5KE6

KE7KE8

KE9

KE10

KE11

KE12

KE13

KE14

MP1

MP2

MP3

MP4MP5

MP6

MP7

MP8

MP9

MP10

MP11MP12

MP13

MP14

MP15

MP16

MP17

MP18

MP19MP20

MP21MP22

MP23

MP24

MP25MP26

MP27MP28MP29MP30

MP31

MP32MP33MP34

MP35MP36

MP37

MP38

MP39MP40

MP41

MP42

MP43

MP44

MP45MH1MH2

MH3

MH4

MH5

MH6

MH7MH8

MH9

MH10MH11

MH12

MH13MH14

MH15

MH16MH19

MH20

MH21MH22

MH23

MH24 MH25 MH26

MH27

MH28

MH29

MH30

MH31MH32

MH33

MH34

MH35

MN1MN2

MN4MN5MN8

MG1

MG2

MG3

MG4MG5MG6MG7

MZ1MZ2MZ3

MZ5MZ6MZ7

MZ8 NG1NG2NG3

NG4

NG5

NG6

NG7NG8

OR1OR2

OR3

OR4

OR5OR6

OR7OR8

OR9 OR10OR11

OR12OR13

OR14

OR15

OR16

OR17

OR18

OR19

OR20

OR21OR22

OR23

OR24

OR25OR26OR27

OR28OR29OR30

PU1

PU2

PU3

PU4PU5

PU6

PU7PU8

PU9

PU10

PU11 PU12PU13

PU14

PU15PU16

PU17

RJ1RJ2

RJ3RJ4

RJ5

RJ6 RJ7 RJ8

RJ9

RJ10

RJ11RJ12

RJ13

RJ14 RJ15

RJ16

RJ17

RJ18

RJ19

RJ20

RJ21RJ22RJ23

RJ24

RJ25

RJ26

RJ27

RJ28RJ29

RJ30

RJ31

RJ32SK1

SK2

SK3

SK4

TN1

TN3

TN4

TN5

TN6TN7

TN8TN9

TN10

TN11TN12TN13TN14

TN15TN16

TN17

TN18TN19TN20

TN21

TN22

TN23

TN24TN25

TN26

TN27

TN28

TN29

TN30

TR1TR2TR3

TR4

UP1

UP2

UP3

UP4

UP5

UP6

UP7

UP8

UP9

UP10

UP11UP12

UP13 UP14

UP15

UP16

UP17

UP18

UP19

UP20

UP21

UP22

UP23

UP24

UP25

UP26

UP27 UP28

UP29

UP30UP31UP32

UP33UP34UP35

UP36

UP37UP38

UP39 UP40

UP41

UP42

UP43UP44UP45

UP46

UP47UP48

UP49

UP50

UP51

UP52

UP53

UP54

UP55UP56

UP57

UP58

UP59

UP60UP61 UP62

UP63

UP64

UP65UP66

UP67 UP68

UP69UP70

UT1 UT2UT3

UT4

UT5

UT6UT7 UT8

UT9UT10

UT11UT12

UT13

WB1

WB2

WB3 WB4

WB5WB6 WB7WB8

WB9

WB11

WB12

WB13

WB14

WB15WB16 WB17

WB18

0.0 0.1 0.2 0.3 0.4 0.5 0.6

GINIRD5

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7G

INIU

D05

AP1

AP2

AP3AP4

AP5AP6 AP8AP9

AP10AP11

AP12

AP13AP14

AP15

AP16

AP17

AP18AP19

AP20

AP21AP22

AP23

AR1AR2

AR3

AR4

AR5

AR6AR7AR8

AR9

AR11

AR12AR13

AS1

AS2AS3

AS4

AS5

AS6

AS7

AS8AS9

AS10

AS11

AS12

AS13

AS14AS15

AS16

AS17

AS18AS19

AS20

AS21

AS22

AS23BI1

BI2

BI3

BI4

BI5

BI6BI7

BI8

BI9

BI10

BI11BI12

BI13

BI14

BI15

BI16

BI17BI18

BI19

BI20

BI21

BI22BI23

BI24

BI25

BI26BI27

BI28BI29BI30

BI31

BI32

BI33 BI34BI35

BI36

BI37

CG1

CG2CG3

CG4

CG5

CG6CG7

CG8

CG9

CG10

CG11CG12

CG13

CG14

CG15

CG16

DE2DE4

DE5

DE6DE7DE8

DE9

GO1

GO2GU1

GU2GU3

GU4

GU5GU6 GU7

GU8

GU9

GU10

GU11

GU12GU13

GU14

GU15GU16

GU17

GU18

GU19GU20 GU21GU22

GU24

GU25HA1

HA2

HA3

HA4 HA5

HA6

HA7

HA8

HA9HA10

HA11

HA12

HA13HA14

HA15

HA16HA17

HA18

HA19HP1HP2

HP3

HP4HP6

HP8

HP9HP10

HP11

HP12

JK1

JK2

JK3

JK4

JK5

JK6

JK7JK8

JK9JK10

JK11JK12JK13

JK14

JH1

JH2

JH3

JH4

JH5

JH6

JH7

JH8

JH9JH10

JH11

JH12

JH13

JH14

JH15JH16JH17JH18

KA1

KA2KA3

KA4KA5

KA6KA7

KA8

KA9KA10

KA11

KA12

KA13

KA14

KA15KA16

KA17

KA18

KA19

KA20

KA21

KA22KA23KA24KA25

KA26KA27

KE1KE2

KE3 KE4KE5KE6

KE7KE8

KE9

KE10

KE11

KE12

KE13

KE14

MP1

MP2

MP3

MP4MP5

MP6

MP7

MP8

MP9

MP10

MP11MP12

MP13

MP14

MP15

MP16

MP17

MP18

MP19MP20

MP21MP22

MP23

MP24

MP25MP26

MP27MP28MP29MP30

MP31

MP32MP33MP34

MP35MP36

MP37

MP38

MP39MP40

MP41

MP42

MP43

MP44

MP45MH1MH2

MH3

MH4

MH5

MH6

MH7MH8

MH9

MH10MH11

MH12

MH13MH14

MH15

MH16MH19

MH20

MH21MH22

MH23

MH24 MH25 MH26

MH27

MH28

MH29

MH30

MH31MH32

MH33

MH34

MH35

MN1MN2

MN4MN5MN8

MG1

MG2

MG3

MG4MG5MG6MG7

MZ1MZ2MZ3

MZ5MZ6MZ7

MZ8 NG1NG2NG3

NG4

NG5

NG6

NG7NG8

OR1OR2

OR3

OR4

OR5OR6

OR7OR8

OR9 OR10OR11

OR12OR13

OR14

OR15

OR16

OR17

OR18

OR19

OR20

OR21OR22

OR23

OR24

OR25OR26OR27

OR28OR29OR30

PU1

PU2

PU3

PU4PU5

PU6

PU7PU8

PU9

PU10

PU11 PU12PU13

PU14

PU15PU16

PU17

RJ1RJ2

RJ3RJ4

RJ5

RJ6 RJ7 RJ8

RJ9

RJ10

RJ11RJ12

RJ13

RJ14 RJ15

RJ16

RJ17

RJ18

RJ19

RJ20

RJ21RJ22RJ23

RJ24

RJ25

RJ26

RJ27

RJ28RJ29

RJ30

RJ31

RJ32SK1

SK2

SK3

SK4

TN1

TN3

TN4

TN5

TN6TN7

TN8TN9

TN10

TN11TN12TN13TN14

TN15TN16

TN17

TN18TN19TN20

TN21

TN22

TN23

TN24TN25

TN26

TN27

TN28

TN29

TN30

TR1TR2TR3

TR4

UP1

UP2

UP3

UP4

UP5

UP6

UP7

UP8

UP9

UP10

UP11UP12

UP13 UP14

UP15

UP16

UP17

UP18

UP19

UP20

UP21

UP22

UP23

UP24

UP25

UP26

UP27 UP28

UP29

UP30UP31UP32

UP33UP34UP35

UP36

UP37UP38

UP39 UP40

UP41

UP42

UP43UP44UP45

UP46

UP47UP48

UP49

UP50

UP51

UP52

UP53

UP54

UP55UP56

UP57

UP58

UP59

UP60UP61 UP62

UP63

UP64

UP65UP66

UP67 UP68

UP69UP70

UT1 UT2UT3

UT4

UT5

UT6UT7 UT8

UT9UT10

UT11UT12

UT13

WB1

WB2

WB3 WB4

WB5WB6 WB7WB8

WB9

WB11

WB12

WB13

WB14

WB15WB16 WB17

WB18

Appendix 4.1: List of District Names for 2001 & 2004-05SN District Code Dt Name R SN District Code Dt Name R1 AP1 Adilabad 54 AS18 Nagaon2 AP2 Anantapur 55 AS19 Nalbari3 AP3 Chittoor 56 AS20 North Cachar Hills4 AP4 Cuddapah 57 AS21 Sibsagar5 AP5 East Godavari 58 AS22 Sonitpur6 AP6 Guntur 59 AS23 Tinsukia7 AP7 Hyderabad 60 BI1 Araria8 AP8 Karimnagar 61 BI2 Aurangabad9 AP9 Khammam 62 BI3 Banka 10 AP10 Krishna 63 BI4 Begusarai11 AP11 Kurnool 64 BI5 Bhagalpur12 AP12 Mahbubnagar 65 BI6 Bhojpur13 AP13 Medak 66 BI7 Buxar 14 AP14 Nalgonda 67 BI8 Darbhanga15 AP15 Nellore 68 BI9 Gaya16 AP16 Nizamabad 69 BI10 Gopalganj17 AP17 Prakasam 70 BI11 Jamui 18 AP18 Rangareddi 71 BI12 Jehanabad 19 AP19 Srikakulam 72 BI13 Kaimur (Bhabua) 20 AP20 Visakhapatnam 73 BI14 Katihar21 AP21 Vizianagaram 74 BI15 Khagaria22 AP22 Warangal 75 BI16 Kishanganj23 AP23 West Godavari 76 BI17 Lakhisarai 24 AR1 Changlang 77 BI18 Madhepura25 AR2 Dibang Valley 78 BI19 Madhubani26 AR3 East Kameng 79 BI20 Munger27 AR4 East Siang 80 BI21 Muzaffarpur28 AR5 Lohit 81 BI22 Nalanda29 AR6 Lower Subansiri 82 BI23 Nawada30 AR7 Papum Pare 83 BI24 Pashchim Champaran31 AR8 Tawang 84 BI25 Patna32 AR9 Tirap 85 BI26 Purba Champaran33 AR10 Upper Siang 86 BI27 Purnia34 AR11 Upper Subansiri 87 BI28 Rohtas35 AR12 West Kameng 88 BI29 Saharsa36 AR13 West Siang 89 BI30 Samastipur37 AS1 Barpeta 90 BI31 Saran38 AS2 Bongaigaon 91 BI32 Sheikhpura 39 AS3 Cachar 92 BI33 Sheohar 40 AS4 Darrang 93 BI34 Sitamarhi41 AS5 Dhemaji 94 BI35 Siwan42 AS6 Dhubri 95 BI36 Supaul 43 AS7 Dibrugarh 96 BI37 Vaishali44 AS8 Goalpara 97 CG1 Bastar45 AS9 Golaghat 98 CG2 Bilaspur46 AS10 Hailakandi 99 CG3 Dantewada47 AS11 Jorhat 100 CG4 Dhamtari 48 AS12 Kamrup 101 CG5 Durg49 AS13 Karbi Anglong 102 CG6 Janjgir - Champa50 AS14 Karimganj 103 CG7 Jashpur 51 AS15 Kokrajhar 104 CG8 Kanker 52 AS16 Lakhimpur 105 CG9 Kawardha53 AS17 Marigaon 106 CG10 Korba

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Appendix 5.1: List of District Names for 2001 & 2004-05107 CG11 Koriya 161 HA13 Panchkula 108 CG12 Mahasamund 162 HA14 Panipat109 CG13 Raigarh 163 HA15 Rewari110 CG14 Raipur 164 HA16 Rohtak111 CG15 Rajnandgaon 165 HA17 Sirsa112 CG16 Surguja 166 HA18 Sonipat113 DE1 Central Delhi 167 HA19 Yamunanagar114 DE2 East Delhi 168 HP1 Bilaspur115 DE3 New Delhi 169 HP2 Chamba116 DE4 North Delhi 170 HP3 Hamirpur117 DE5 North East Delhi 171 HP4 Kangra118 DE6 North West Delhi 172 HP5 Kinnaur119 DE7 South Delhi 173 HP6 Kullu120 DE8 South West Delhi 174 HP7 Lahul & Spiti121 DE9 West Delhi 175 HP8 Mandi122 GO1 North Goa 176 HP9 Shimla123 GO2 South Goa 177 HP10 Sirmaur124 GU1 Ahmadabad 178 HP11 Solan125 GU2 Amreli 179 HP12 Una126 GU3 Anand 180 JK1 Anantnag127 GU4 Banas Kantha 181 JK2 Badgam128 GU5 Bharuch 182 JK3 Baramula129 GU6 Bhavnagar 183 JK4 Doda130 GU7 Dohad 184 JK5 Jammu131 GU8 Gandhinagar 185 JK6 Kargil132 GU9 Jamnagar 186 JK7 Kathua133 GU10 Junagadh 187 JK8 Kupwara134 GU11 Kachchh 188 JK9 Leh (Ladakh)135 GU12 Kheda 189 JK10 Pulwama136 GU13 Mahesana 190 JK11 Punch137 GU14 Narmada 191 JK12 Rajauri138 GU15 Navsari 192 JK13 Srinagar139 GU16 Panch Mahals 193 JK14 Udhampur140 GU17 Patan 194 JH1 Bokaro 141 GU18 Porbandar 195 JH2 Chatra 142 GU19 Rajkot 196 JH3 Deoghar143 GU20 Sabar Kantha 197 JH4 Dhanbad144 GU21 Surat 198 JH5 Dumka145 GU22 Surendranagar 199 JH6 Garhwa 146 GU23 The Dangs 200 JH7 Giridih147 GU24 Vadodara 201 JH8 Godda148 GU25 Valsad 202 JH9 Gumla149 HA1 Ambala 203 JH10 Hazaribagh150 HA2 Bhiwani 204 JH11 Kodarma 151 HA3 Faridabad 205 JH12 Lohardaga152 HA4 Fatehabad 206 JH13 Pakaur 153 HA5 Gurgaon 207 JH14 Palamu154 HA6 Hisar 208 JH15 Paschim Singhbhum155 HA7 Jhajjar 209 JH16 Purbi Singhbhum156 HA8 Jind 210 JH17 Ranchi157 HA9 Kaithal 211 JH18 Sahibganj158 HA10 Karnal 212 KA1 Bagalkot 159 HA11 Kurukshetra 213 KA2 Bangalore160 HA12 Mahendragarh 214 KA3 Bangalore Rural

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Appendix 5.1: List of District Names for 2001 & 2004-05215 KA4 Belgaum 269 MP17 Hoshangabad216 KA5 Bellary 270 MP18 Indore217 KA6 Bidar 271 MP19 Jabalpur218 KA7 Bijapur 272 MP20 Jhabua219 KA8 Chamarajanagar 273 MP21 Katni220 KA9 Chikmagalur 274 MP22 Mandla221 KA10 Chitradurga 275 MP23 Mandsaur222 KA11 Dakshina Kannada 276 MP24 Morena223 KA12 Davanagere 277 MP25 Narsimhapur224 KA13 Dharwad 278 MP26 Neemuch225 KA14 Gadag 279 MP27 Panna226 KA15 Gulbarga 280 MP28 Raisen227 KA16 Hassan 281 MP29 Rajgarh228 KA17 Haveri 282 MP30 Ratlam229 KA18 Kodagu 283 MP31 Rewa230 KA19 Kolar 284 MP32 Sagar231 KA20 Koppal 285 MP33 Satna232 KA21 Mandya 286 MP34 Sehore233 KA22 Mysore 287 MP35 Seoni234 KA23 Raichur 288 MP36 Shahdol235 KA24 Shimoga 289 MP37 Shajapur236 KA25 Tumkur 290 MP38 Sheopur237 KA26 Udupi 291 MP39 Shivpuri238 KA27 Uttara Kannada 292 MP40 Sidhi239 KE1 Alappuzha 293 MP41 Tikamgarh240 KE2 Ernakulam 294 MP42 Ujjain241 KE3 Idukki 295 MP43 Umaria242 KE4 Kannur 296 MP44 Vidisha243 KE5 Kasaragod 297 MP45 West Nimar244 KE6 Kollam 298 MH1 Ahmadnagar245 KE7 Kottayam 299 MH2 Akola246 KE8 Kozhikode 300 MH3 Amravati247 KE9 Malappuram 301 MH4 Aurangabad248 KE10 Palakkad 302 MH5 Bhandara249 KE11 Pathanamthitta 303 MH6 Bid250 KE12 Trivundram 304 MH7 Buldana251 KE13 Thrissur 305 MH8 Chandrapur252 KE14 Wayanad 306 MH9 Dhule253 MP1 Balaghat 307 MH10 Gadchiroli254 MP2 Barwani 308 MH11 Gondiya255 MP3 Betul 309 MH12 Hingoli256 MP4 Bhind 310 MH13 Jalgaon257 MP5 Bhopal 311 MH14 Jalna258 MP6 Chhatarpur 312 MH15 Kolhapur259 MP7 Chhindwara 313 MH16 Latur260 MP8 Damoh 314 MH17 Mumbai261 MP9 Datia 315 MH18 Mumbai (Suburban)262 MP10 Dewas 316 MH19 Nagpur263 MP11 Dhar 317 MH20 Nanded264 MP12 Dindori 318 MH21 Nandurbar265 MP13 East Nimar 319 MH22 Nashik266 MP14 Guna 320 MH23 Osmanabad267 MP15 Gwalior 321 MH24 Parbhani268 MP16 Harda 322 MH25 Pune

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Appendix 5.1: List of District Names for 2001 & 2004-05323 MH26 Raigarh 377 OR13 Jajapur324 MH27 Ratnagiri 378 OR14 Jharsuguda325 MH28 Sangli 379 OR15 Kalahandi326 MH29 Satara 380 OR16 Kandhamal327 MH30 Sindhudurg 381 OR17 Kendrapara328 MH31 Solapur 382 OR18 Kendujhar329 MH32 Thane 383 OR19 Khordha330 MH33 Wardha 384 OR20 Koraput331 MH34 Washim 385 OR21 Malkangiri332 MH35 Yavatmal 386 OR22 Mayurbhanj333 MN1 Bishnupur 387 OR23 Nabarangapur334 MN2 Chandel 388 OR24 Nayagarh335 MN3 Churachandpur 389 OR25 Nuapada336 MN4 Imphal East 390 OR26 Puri337 MN5 Imphal West 391 OR27 Rayagada338 MN6 Senapati 392 OR28 Sambalpur339 MN7 Tamenglong 393 OR29 Sonapur340 MN8 Thoubal 394 OR30 Sundargarh341 MN9 Ukhrul 395 PU1 Amritsar342 MG1 East Garo Hills 396 PU2 Bathinda343 MG2 East Khasi Hills 397 PU3 Faridkot344 MG3 Jaintia Hills 398 PU4 Fatehgarh Sahib345 MG4 Ri Bhoi 399 PU5 Firozpur346 MG5 South Garo Hills 400 PU6 Gurdaspur347 MG6 West Garo Hills 401 PU7 Hoshiarpur348 MG7 West Khasi Hills 402 PU8 Jalandhar349 MZ1 Aizawl 403 PU9 Kapurthala350 MZ2 Champhai 404 PU10 Ludhiana351 MZ3 Kolasib 405 PU11 Mansa352 MZ4 Lawngtlai 406 PU12 Moga353 MZ5 Lunglei 407 PU13 Muktsar354 MZ6 Mamit 408 PU14 Nawanshahr355 MZ7 Saiha 409 PU15 Patiala356 MZ8 Serchhip 410 PU16 Rupnagar357 NG1 Dimapur 411 PU17 Sangrur358 NG2 Kohima 412 RJ1 Ajmer359 NG3 Mokokchung 413 RJ2 Alwar360 NG4 Mon 414 RJ3 Banswara361 NG5 Phek 415 RJ4 Baran362 NG6 Tuensang 416 RJ5 Barmer363 NG7 Wokha 417 RJ6 Bharatpur364 NG8 Zunheboto 418 RJ7 Bhilwara365 OR1 Anugul 419 RJ8 Bikaner366 OR2 Balangir 420 RJ9 Bundi367 OR3 Baleshwar 421 RJ10 Chittaurgarh368 OR4 Bargarh 422 RJ11 Churu369 OR5 Baudh 423 RJ12 Dausa370 OR6 Bhadrak 424 RJ13 Dhaulpur371 OR7 Cuttack 425 RJ14 Dungarpur372 OR8 Debagarh 426 RJ15 Ganganagar373 OR9 Dhenkanal 427 RJ16 Hanumangarh374 OR10 Gajapati 428 RJ17 Jaipur375 OR11 Ganjam 429 RJ18 Jaisalmer376 OR12 Jagatsinghapur 430 RJ19 Jalor

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Appendix 5.1: List of District Names for 2001 & 2004-05431 RJ20 Jhalawar 485 UP4 Ambedkar Nagar432 RJ21 Jhunjhunun 486 UP5 Auraiya433 RJ22 Jodhpur 487 UP6 Azamgarh434 RJ23 Karauli 488 UP7 Baghpat435 RJ24 Kota 489 UP8 Bahraich436 RJ25 Nagaur 490 UP9 Ballia437 RJ26 Pali 491 UP10 Balrampur438 RJ27 Rajsamand 492 UP11 Banda439 RJ28 Sawai Madhopur 493 UP12 Barabanki440 RJ29 Sikar 494 UP13 Bareilly441 RJ30 Sirohi 495 UP14 Basti442 RJ31 Tonk 496 UP15 Bhadohi443 RJ32 Udaipur 497 UP16 Bijnor444 SK1 East Sikkim 498 UP17 Budaun445 SK2 North Sikkim 499 UP18 Bulandshahr446 SK3 South Sikkim 500 UP19 Chandauli447 SK4 West Sikkim 501 UP20 Chitrakoot448 TN1 Ariyalur 502 UP21 Deoria449 TN2 Chennai 503 UP22 Etah450 TN3 Coimbatore 504 UP23 Etawah451 TN4 Cuddalore 505 UP24 Faizabad452 TN5 Dharmapuri 506 UP25 Farrukhabad453 TN6 Dindigul 507 UP26 Fatehpur454 TN7 Erode 508 UP27 Firozabad455 TN8 Kancheepuram 509 UP28 Gautam Buddha Nagar456 TN9 Kanniyakumari 510 UP29 Ghaziabad457 TN10 Karur 511 UP30 Ghazipur458 TN11 Madurai 512 UP31 Gonda459 TN12 Nagapattinam 513 UP32 Gorakhpur460 TN13 Namakkal 514 UP33 Hamirpur461 TN14 Perambalur 515 UP34 Hardoi462 TN15 Pudukkottai 516 UP35 Hathras463 TN16 Ramanathapuram 517 UP36 Jalaun464 TN17 Salem 518 UP37 Jaunpur465 TN18 Sivaganga 519 UP38 Jhansi466 TN19 Thanjavur 520 UP39 Jyotiba Phule Nagar467 TN20 The Nilgiris 521 UP40 Kannauj468 TN21 Theni 522 UP41 Kanpur Dehat469 TN22 Thiruvallur 523 UP42 Kanpur Nagar470 TN23 Thiruvarur 524 UP43 Kaushambi471 TN24 Thoothukkudi 525 UP44 Kheri472 TN25 Tiruchirappalli 526 UP45 Kushinagar473 TN26 Tirunelveli 527 UP46 Lalitpur474 TN27 Tiruvannamalai 528 UP47 Lucknow475 TN28 Vellore 529 UP48 Maharajganj476 TN29 Viluppuram 530 UP49 Mahoba477 TN30 Virudhunagar 531 UP50 Mainpuri478 TR1 Dhalai 532 UP51 Mathura479 TR2 North Tripura 533 UP52 Mau480 TR3 South Tripura 534 UP53 Meerut481 TR4 West Tripura 535 UP54 Mirzapur482 UP1 Agra 536 UP55 Moradabad483 UP2 Aligarh 537 UP56 Muzaffarnagar484 UP3 Allahabad 538 UP57 Pilibhit

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Appendix 5.1: List of District Names for 2001 & 2004-05539 UP58 Pratapgarh 561 UT10 Rudraprayag540 UP59 Rae Bareli 562 UT11 Tehri Garhwal541 UP60 Rampur 563 UT12 Udham Singh Nagar542 UP61 Saharanpur 564 UT13 Uttarkashi543 UP62 Sant Kabir Nagar 565 WB1 Bankura544 UP63 Shahjahanpur 566 WB2 Barddhaman545 UP64 Shrawasti 567 WB3 Birbhum546 UP65 Siddharthnagar 568 WB4 Dakshin Dinajpur547 UP66 Sitapur 569 WB5 Darjiling548 UP67 Sonbhadra 570 WB6 Haora549 UP68 Sultanpur 571 WB7 Hugli550 UP69 Unnao 572 WB8 Jalpaiguri551 UP70 Varanasi 573 WB9 Koch Bihar552 UT1 Almora 574 WB10 Kolkata553 UT2 Bageshwar 575 WB11 Maldah554 UT3 Chamoli 576 WB12 Medinipur555 UT4 Champawat 577 WB13 Murshidabad556 UT5 Dehradun 578 WB14 Nadia557 UT6 Garhwal 579 WB15 North 24 Parganas558 UT7 Hardwar 580 WB16 Puruliya559 UT8 Nainital 581 WB17 South 24 Parganas560 UT9 Pithoragarh 582 WB18 Uttar Dinajpur

141

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Chapter 5. People’s Responses: Perception Survey

Background

In addition to the foregoing analysis with the help of secondary data set from official sources, we

have organized a nationwide survey, which is called “Perception Survey”. This survey covers

selected extreme districts from 22 states. By extreme district is meant poorest and richest as well

nearest and farthest district from the state capital. The sample size is doubtless small amounting

to only 2676 in total for rural and urban areas taken together. But the strength of the approach is

that the sample audience is divided into four economically defined classes: rural poor (RP), rural

rich (RR), urban poor (UP) and urban rich (UR). The poverty line used for NSS 61st Round

Survey (2004-05) guided us as the benchmark to identify the poor and non-poor households.

Thus, limitation of sample size is partly compensated by selecting homogeneous groups on the

basis of expenditure classes. Even then, we have not used this survey based data set for any

parametric or non-parametric estimation. It better served the purpose of this study by raising

some crucial issues like BPL listing, land acquisition, voting and democracy, local governance,

corruption, criminality and religion and impact of public sector infrastructure projects. There is

no easy source of information in official statistics from which one can make an idea about public

responses to these questions. We hope that such questions should be included in future

information system of Central and State governments.

As part and parcel of this enquiry in terms of a perception survey in extreme districts of 22

states, questions were asked regarding levels of living, prices, investment climate, insurgency,

governance, leadership, trust, health, education, infrastructure, meaningfulness of voting rights,

and the like. We have documented formidable distrust among local investigators and common

people mostly towards local rulers, whose voices are valued from the Panchayat and

Municipalities to the state capitals.

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The Experience We are hereby adding some of the important information from the primary survey separately

for rural and urban areas of the states. The results are self-evident. We must mention here that

the direct field experiences gathered by the team while moving across more than 250 districts in

course of this survey in East, West, North, South and North East are of immense significance in

understanding the general attitude, economic rule of law, truthfulness, trust, security, honesty,

law and order, civil society, which are more important puling factors for future investment. This

has been strengthened by the field note books of the field investigators. For example, alienation

of the some special ethnic community in Goa villages can not be captured by either parametric or

non-parametric tests of any kind. More so is the enthrallment of a little girl (Pratima) of age 15

years, when the present author gifted them a packet of biscuit, and her brother of 5 years, who

have migrated from Bangladesh, and settled with her mother on the road side between Assam

and Manipur. Neither the sister nor the brother has any idea of what is India, what is North East,

what means by nationhood. On the other hand, in many remote regions of Kerala, Gujarat,

Himachal and Punjab, we were simply fascinated by experiencing the seer honesty, hospitality

and self esteem of the average people at midnight. More unforgettable experience was the

companion of Rizwan, who does have any extra clothing, neither any future plan, helped travel

to cover across extensive regions from Pune to Aurangabad to Ajanta and Ellora in the month of

September 2007. Or, the anger, sorrow, frustration and delight of the poor farmers in many

regions of West Bengal, Andhra Pradesh, Bihar and Jharkhand are difficult to perceive by our

formal analytical training. Such wealth of narratives is inestimable. But we present here only

selective results, which may appear unbelievable to those who are unaccustomed to the life of

vast majority of Indians.

Findings Our project report is being prepared at a time when the classic dilemma between ownership

rights versus land acquisition, agriculture versus industry, democratic right versus power of

capital came into the forefront. Prime Minister’s statement on 3rd October 2008 is perfectly time

bound and suggestive: the problems of Singur should not be viewed from the viewpoint of

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Bengal alone; it should be resolved keeping in mind India at large and the future in particular.

Organized official data are not at all adequate enough to understand these emerging crises across

vast mass of rural areas which are home and farm factory to millions of small and marginal

farmers of India. Even if the coverage of the survey is geographically very limited, it has come

out with results which are highly consistent and unique in many ways.

None of the aggregate indices intensively studied above tell us anything about what the actual

communities living in ungifted lower geographical neighbourhoods perceive as social

development. One way out may be to target the appropriate group or community, interact with

them extensively in order to understand what quality of life means to them. Typically, there will

be common factors affecting all as well as specific factors for specific communities and regions

depending on broad profession and sectoral level of development. The problem of finding an

aggregate index overlooks the examples of the specific and bare needs of millions of semi-skilled

and unskilled people striving for sheer physical survival without any social opportunity.

As foretold in the beginning, a recently electrified village will care less for a ten-hour power cut

than an urban metropolis. A community that can hardly send their children to college will never

be concerned about the problems of higher education. Average people may not be satisfied with

the law and order situation, governance, political system and judiciary (Dreze and Sen, 1995

and 2002). In general, people who live from hand to mouth will have completely different

perception about development from those who are much better placed (Ghosh and Chatterjee,

2005). The question is whether SDIs constructed for various communities will be incorporated in

a Rawlsian index of some kind or in more conservative welfarist assimilation (Marjit and

Ghosh, 2000). While an index is useful in evaluating the effectiveness of a particular

developmental programme, it may still require some foundational groundwork to be defined as a

meaningful comprehensive indicator. Here our motivation was two-fold: (a) there is no

information on these issues from official sources at the lower level, and (b) extra-economic

factors are believed to have been playing dominant role at the lower strata of the society in India.

145

The major findings may be noted here.

(1) Coefficients of variation of prices for four major commodities, namely rice, wheat, potato

and mustard oil are found to be low across both rural and urban areas, and also across four

classes of people (rural poor, rural non-poor, urban poor and urban non-poor) among the states.

(2) There appears to have serious discrepancy among the states in the list of BPL card holder

among the poor people. As revealed from Figure 5.1 for rural and Figure 5.13 for urban states,

this inconsistency pertains to both rural and urban areas of the same state. Five states are

exception: Assam, Haryana, Karnataka, Kerala and Manipur, where no discrepancy is found.

(3) State-wise discrepancy about the role of literacy in determining poverty is also captured by

Figure 5.2 (rural poor) and Figure 5.14 (urban poor). There is intense vicissitudes among the

urban poor across the states.

(4) Rural poor in most regions are of the opinion that they live in such neighbourhoods, which

are inhabited by poor only. This clearly points to the district level study with secondary data

which tells that the poor are spatially entrapped. Figure 5.3 is a perfect pointer to the fact except

three states- Kerala, Manipur and Tamil Nadu.

(5) Most rural poor across the states believe that local politicians prefer uneducated voters in

their areas except a couple of states (Figure 5.4). The picture is almost same for urban areas

(Figure 5.15). There are some exceptions from the hilly and backward rural regions. Contrary to

the rural poor, much higher proportion of rural non-poor (that is, rich and middle class) share this

view except five states, namely Chhattisgarh, Jharkhand, Orissa, TN and UT (Figure 5.10).

(6) Except Assam, Bihar, Chhattisgarh, HP, Karnataka, Manipur, Rajasthan, Tripura, UP and

Uttaranchal, most rural (Figure 5.5) and urban poor (Figure 5.16) in rest of the states do not

have any idea about ‘poverty eradication programme’.

(7) (i) The question of land acquisition was dealt in with greater detail. Responses of two

groups are relevant here- rural poor and rural non-poor. Generalization is very perilous as the

146

value of CV is as high as 89%. So let us directly mention the state-wise results. Less than 50%

rural poor support land acquisition in the states of AP, Bihar, Chhattisgarh, Goa, Gujarat,

Haryana, HP, Jharkhand, Karnataka, MP, Orissa, Punjab, TN, UP, UC and WB. Note that 100%

from Tripura and 80% from Kerala support the programme. ‘Causes of support’ and ‘causes of

no support’ are separately reported. Majority of both groups have pointed that agriculture is their

main profession and difference in prices from land acquisition may not be enjoyed by them.

Figure 5.6 nicely captures the state-wise diversity in the responses of the rural poor.

(7) (ii) Responses of rural non-poor are more consistent across the states thereby yielding a

relatively low CV of 50.30%. Here 50% or more poor people surveyed support land acquisition

from the states of AP, Assam, Chhattisgarh, Kerala, MP, Manipur, Tripura and UP. The cause of

their support is that agriculture is no more a profitable way for subsistence for the family. On the

other hand, those not supporting land acquisitions have specifically mentioned that agriculture is

their main profession. Note that 28.30% of rural poor and 30.95% of rural non-poor support land

acquisition for industrialization purposes in West Bengal. Figure 5.11 captures the state-wise

responses of rural non-poor people. We have not enquired for similar responses from urban

classes as are not direct stake holders.

(8) There is miraculous similarity of responses among the rural poor regarding poverty as a cause

of a person becoming anti-social except five states, namely Himachal Pradesh, Chhattisgarh, UP,

Punjab and MP. Figure 5.7 presents these responses.

(9) Figure 5.8 presents the percentage of rural poor reporting that justice depends on money

and/or connection. Interestingly, four states are clear exception to this hopelessness. These states

are AP, HP, TN and WB. Figure 5.17 reports the response of the urban poor in this matter:

except two states (Chhattisgarh, TN and Uttaranchal), there is widespread distrust towards the

justice system.

(10) Very large proportions of rural poor and non-poor people are in favour of educated local

politicians. Figure 5.9 captures the case for rural non-poor, which is similar to rural poor.

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(11) Most rural poor people in seven states have expressed confidence on existing police: HP,

Karnataka, MP, Orissa, Punjab, Rajasthan and UP. Responses of urban poor and both rural and

urban non-poor are quite dissimilar. Figure 5.12 presents the response of rural non-poor towards

local police.

(12) Finally, very low proportions of people have expressed satisfaction towards government

infrastructure services without any distinction between local and national schemes. Figure 5.18

captures the responses of the urban non-poor only.

Future Research Agenda

Therefore, the broader observations from such limited nation-wide survey are not incompatible

with the statistical discrepancies obtained from secondary data analysis. There are many other

peculiar findings, which could be extracted from the survey tables, which are added here. We are

confident from the existing information sources along with field visit in connection with the

‘Perception Survey’ that there are many districts in remote regions of Himachal, Uttaranchal,

Orissa, MP, Jharkhand, Chhattisgarh, Andhra Pradesh, Tamil Nadu, Karnataka, West Bengal and

the North East region, where conservative rural and urban facilities with which we are

accustomed are beyond imagination of the local inhabitants. It is recorded by the investigators

that sporadic agricultural and other primary activities (Eswaran and Kotwal, 1999) have created

limited opportunities for millions of unskilled, semi-skilled and skilled people there by public

efforts they could mobilize added by NGO activities. Incentive and productivity are not awfully

de-linked in these regions, though scarcity of governmental facilities is widely accepted by the

local people as sheer destiny. So there is urgent need to learn from these regions too as they

share and enjoy what they have. In connection with the responses to the poverty removal

measures, our team has cross verified across all four classes that local people in these extreme

regions are considerably dissatisfied with the identification and disbursement of development

funds in the name of the Prime Minister of India, which are essentially meant for the betterment

of the poor and development of the neighborhood economy. The present study stops short of

endeavouring into that domain. Time is now ripe to undertake such work in order to find out real

caveats in the governance system of the lower levels so that India can completely eradicate

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extreme poverty as per the commitments towards the Millennium Development Goals within the

time horizon of the next Five Year Plan.

Figure 5.1: Percentage of BPL Card Holder Chosen from Rural Poor by the Authority as Reported by RP, 2007-08

0102030405060708090

100110

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Note: 1) AP(1), AS(2), BI(3), CH(4), GO(5), GU(6), HA(7), HP(8), JH(9), KA(10), KE(11), MP(12), MH(13), MN(14), OR(15), PU(16), RJ(17), TN(18), TR(19), UP(20), UT(21), WB(22). 2) RP means Rural Poor

Figure 5.2: Percentage of Rural Poor Reporting Illiteracy as Cause of Poverty, 2007-08

0

10

20

30

40

50

60

70

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

149

Figure 5.3:Percentage of Rural Poor Surveyed felt Majority in the Neighbourhood are Poor, 2007-08

0102030405060708090

100110

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Note: 1) AP(1), AS(2), BI(3), CH(4), GO(5), GU(6), HA(7), HP(8), JH(9), KA(10), KE(11), MP(12), MH(13), MN(14), OR(15), PU(16), RJ(17), TN(18), TR(19), UP(20), UT(21), WB(22).

Figure 5.4: Percentage of Rural Poor Surveyed Reported Local Politicians Prefer Uneducated Voters, 2007-08

58

100

53

38

100 100

80

67 67

76

60

44

59

100

25

100

57

45

100

53

64

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

150

Figure 5.5: Percentage of Rural Poor Surveyed Reported Having no Idea of Poverty Eradication Programme, 2007-08

0

10

20

30

40

50

60

70

80

90

100

110

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Note: 1) AP(1), AS(2), BI(3), CH(4), GO(5), GU(6), HA(7), HP(8), JH(9), KA(10), KE(11), MP(12), MH(13), MN(14), OR(15), PU(16), RJ(17), TN(18), TR(19), UP(20), UT(21), WB(22).

Figure 5.6: Percentage of Rural Poor Surveyed Support Land Acquisition for Development Purposes, 2007-08

0102030405060708090

100110

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

151

Figure 5.7: Percentage of Rural Poor Surveyed Reported Poverty as a Cause of a Person Being Antisocial, 2007-08

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Note: 1) AP(1), AS(2), BI(3), CH(4), GO(5), GU(6), HA(7), HP(8), JH(9), KA(10), KE(11), MP(12), MH(13), MN(14), OR(15), PU(16), RJ(17), TN(18), TR(19), UP(20), UT(21), WB(22).

Figure 5.8: Percentage of Rural Poor Surveyed Reported that Justice Depend on Money/ Connection, 2007-08

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

152

Figure 5.9: Percentage of Rural Non-Poor Surveyed Desire Educated Politician, 2007-08

0102030405060708090

100110

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Note: 1) AP(1), AS(2), BI(3), CH(4), GO(5), GU(6), HA(7), HP(8), JH(9), KA(10), KE(11), MP(12), MH(13), MN(14), OR(15), PU(16), RJ(17), TN(18), TR(19), UP(20), UT(21), WB(22).

Figure 5.10: Percentage of Rural Non-Poor Surveyed Reported Local Politicians Prefer Uneducated Voters, 2007-08

0102030405060708090

100110

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

153

Figure 5.11: Percentage of Rural Non-Poor Surveyed Support Land Acquisition for Development Purposes, 2007-08

0

10

20

30

4050

60

70

80

90

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Note: 1) AP(1), AS(2), BI(3), CH(4), GO(5), GU(6), HA(7), HP(8), JH(9), KA(10), KE(11), MP(12), MH(13), MN(14), OR(15), PU(16), RJ(17), TN(18), TR(19), UP(20), UT(21), WB(22).

Figure 5.12: Percentage of Rural Non-Poor Surveyed Having no Trust on Police, 2007-08

0102030405060708090

100110

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

154

Figure 5.13: Percentage of BPL Card Holder Chosen from Urban Poor by Authority as Reported by UP, 2007-08

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Note: 1) AP(1), AS(2), BI(3), CH(4), GO(5), GU(6), HA(7), HP(8), JH(9), KA(10), KE(11), MP(12), MH(13), MN(14), OR(15), PU(16), RJ(17), TN(18), TR(19), UP(20), UT(21), WB(22). 2) UP means Urban Poor.

Figure 5.14: Percentage of Urban Poor Reported Illiterate as Cause of Poverty, 2007-08

0

10

20

30

40

50

60

70

80

90

100

110

AP AS BI CH GO GU HA HP JH KA KE MP MH MN OR PU RJ TN TR UP UT WB

155

Figure 5.15 : Percentage of Urban Poor Surveyed Reported Local Politicians Prefer Uneducated Voters, 2007-08

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Note: 1) AP(1), AS(2), BI(3), CH(4), GO(5), GU(6), HA(7), HP(8), JH(9), KA(10), KE(11), MP(12), MH(13), MN(14), OR(15), PU(16), RJ(17), TN(18), TR(19), UP(20), UT(21), WB(22).

Figure 5.16 : Percentage of Urban Poor Surveyed Reoprted Having no Idea of Poverty Eradication Programme, 2007-08

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

156

Figure 5.17: Percentage of Urban Poor Surveyed Reported that Justice Depends on Money/ Connection, 2007-08

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Note: 1) AP(1), AS(2), BI(3), CH(4), GO(5), GU(6), HA(7), HP(8), JH(9), KA(10), KE(11), MP(12), MH(13), MN(14), OR(15), PU(16), RJ(17), TN(18), TR(19), UP(20), UT(21), WB(22).

Figure 5.18: Percentage of Urban Non-Poor Surveyed Reported Having Satisfied with Govt. Infrastructure Projects, 2007-08

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

1Sl. No. State % of BPL % of % felt % illiterate % reported % in favour % felt that % felt that % reported % felt that % felt that

1 AP 50.00 91.70 91.70 0.00 8.30 91.70 58.30 83.30 58.30 66.70 100.00 2 Assam 100.00 100.00 100.00 5.26 68.42 100.00 100.00 100.00 42.11 94.74 100.003 Bihar 58.33 80.56 91.67 16.67 100.00 100.00 52.78 94.44 47.22 95.83 94.444 Chattisgarh 87.50 68.80 87.50 12.50 75.00 100.00 37.50 100.00 25.00 50.00 50.005 Goa 0.00 50.00 100.00 0.00 0.00 100.00 100.00 0.00 50.00 0.00 100.00 6 Gujarat 72.22 88.89 83.33 33.33 72.22 94.44 100.00 66.67 50.00 38.89 100.007 Haryana 100.00 80.00 40.00 60.00 0.00 20.00 80.00 100.00 60.00 100.00 100.008 HP 72.22 77.78 100.00 5.56 83.33 83.33 66.67 72.22 11.11 44.44 72.229 Jharkhand 44.40 74.10 77.80 18.50 37.00 66.70 66.70 66.70 77.80 55.60 0.0010 Karnataka 100.00 71.43 85.71 14.29 4.76 76.19 76.19 95.24 42.86 66.67 0.0011 Kerala 100.00 90.00 100.00 20.00 20.00 100.00 60.00 40.00 50.00 70.00 50.0012 MP 92.60 85.20 74.10 48.10 66.70 85.20 44.40 77.80 37.00 14.80 100.0013 Mahrashtra 64.71 70.59 94.12 20.59 44.12 73.53 58.82 76.47 50.00 67.65 58.8214 Manipur 100.00 100.00 100.00 0.00 55.56 100.00 100.00 44.44 44.44 22.22 100.0015 Orissa 82.50 53.75 93.75 48.75 77.50 98.75 25.00 97.50 60.00 47.50 57.8116 Punjub 66.70 100.00 100.00 0.00 0.00 100.00 100.00 66.70 33.30 66.70 100.00 17 Rajasthan 42.90 61.90 95.20 19.00 33.30 100.00 57.10 100.00 81.00 76.20 100.0018 TN 65.00 95.00 50.00 30.00 10.00 50.00 45.00 55.00 55.00 60.00 100.0019 Tripura 88.89 88.89 100.00 0.00 100.00 100.00 100.00 100.00 0.00 0.00 100.0020 UP 38.50 64.10 82.10 15.40 35.90 87.20 100.00 89.70 28.20 66.70 100.0021 UT 93.30 60.00 86.70 13.30 33.30 86.70 53.30 100.00 86.70 80.00 100.0022 WB 62.26 69.81 90.57 1.89 58.49 96.23 64.15 96.23 58.49 58.49 60.38

SD 26.04 15.09 15.84 17.28 32.98 20.01 23.86 25.77 20.75 28.00 33.24Mean 71.91 78.30 87.47 17.42 44.72 86.82 70.27 78.29 47.66 56.51 75.98CV 36.22 19.28 18.11 99.22 73.74 23.05 33.96 32.92 43.53 49.54 43.75

157

Table 5.1: People's Perception (Rural Poor)

2Sl. No. State % cast vote % having no % can meet % having no % felt that % having % felt that % prefer % having %

1 AP 91.70 8.30 50.00 83.30 75.00 66.70 16.70 75.00 16.70 25.002 Assam 100.00 0.00 31.58 0.00 100.00 94.74 100.00 0.00 0.00 NA3 Bihar 97.22 2.78 93.06 33.33 77.78 56.94 87.50 70.83 5.56 75.004 Chattisgarh 93.80 6.20 75.00 25.00 75.00 56.20 75.00 43.80 0.00 56.205 Goa 50.00 50.00 50.00 100.00 100.00 100.00 100.00 0.00 0.00 100.006 Gujarat 100.00 0.00 61.11 66.67 61.11 50.00 61.11 61.11 0.00 77.787 Haryana 60.00 40.00 80.00 100.00 60.00 100.00 80.00 100.00 40.00 100.008 HP 100.00 0.00 83.33 11.11 27.78 38.89 27.78 5.56 0.00 77.789 Jharkhand 85.20 14.80 25.90 66.70 74.10 74.10 51.90 37.00 11.10 74.1010 Karnataka 100.00 0.00 80.95 4.76 61.90 14.29 95.24 42.86 14.29 71.4311 Kerala 90.00 10.00 60.00 60.00 60.00 30.00 90.00 30.00 10.00 90.0012 MP 85.20 14.80 59.30 40.70 48.10 22.20 55.60 37.00 33.30 51.9013 Mahrashtra 88.24 11.76 100.00 44.12 61.76 64.71 50.00 38.24 0.00 52.9414 Manipur 100.00 0.00 100.00 0.00 22.22 100.00 100.00 0.00 0.00 NA 15 Orissa 96.25 3.75 86.25 52.50 43.75 22.50 66.24 60.00 7.50 86.2516 Punjub 100.00 0.00 0.00 66.70 100.00 33.30 66.70 100.00 33.30 100.0017 Rajasthan 100.00 0.00 81.00 47.60 47.60 23.80 42.90 66.70 14.30 100.0018 TN 90.00 10.00 0.00 65.00 85.00 80.00 25.00 20.00 50.00 50.0019 Tripura 88.89 11.11 100.00 0.00 88.89 100.00 100.00 0.00 0.00 NA20 UP 100.00 0.00 87.20 33.30 56.40 30.80 82.10 25.60 0.00 71.8021 UT 86.70 13.30 93.30 26.70 66.70 86.70 73.30 6.70 40.00 86.7022 WB 98.11 1.89 75.47 49.06 43.40 62.26 20.75 37.74 9.43 71.70

SD 12.94 12.94 30.10 30.47 21.79 29.48 27.46 31.14 15.89 20.46Mean 90.97 9.03 66.98 44.39 65.30 59.46 66.72 39.01 12.98 74.66CV 14.22 143.25 44.94 68.64 33.38 49.58 41.16 79.83 122.43 27.40

158

People's Perception (Rural Poor)

Table 5.2: People's Perception (Rural Non- Poor)1

Sl. No. State

% of literate family

% felt education necessary for living

% in favour of educated politician

% felt that politicians

prefer uneducated

votersDrinking

WaterHealth Centre

Primary/High School Roads Playground Other

1 AP 96.9 96.9 95.9 92.8 33 44.3 16.5 6.2 0 0 58.82 Assam 100 100 100 98.51 17.91 32.84 10.44 38.81 0 0 2.993 Bihar 100 99.04 100 88.46 5.77 11.15 30.77 19.23 15 18.08 96.154 Chattisgarh 91.3 95.7 95.7 21.7 52.2 17.4 13 13 4.4 0 69.65 Goa 95.7 100 91.3 78.3 21.7 43.5 4.3 21.7 0 8.8 52.26 Gujarat 97.37 93.42 90.79 61.84 14.47 17.11 55.26 2.63 2.32 8.21 40.797 Haryana 86.7 93.3 96.7 60 13.3 33.3 40 13.4 0 0 808 HP 97.06 88.24 94.12 52.94 67.65 0 8.82 20.59 0 2.94 509 Jharkhand 87.5 84.4 65.6 37.5 15.6 12.5 18.8 40.6 12.5 0 50

10 Karnataka 97.44 52.56 83.33 80.77 37.59 25.38 17.69 6.41 9.64 3.28 5011 Kerala 100 100 100 84.1 33.3 33.3 7.9 20.7 0 4.8 60.312 MP 97.8 78.3 91.3 45.7 15.2 23.9 15.2 32.6 2.2 10.9 58.713 Mahrashtra 95.18 98.8 87.95 60.24 34.88 24.94 21.33 14.1 3.61 1.14 50.614 Manipur 100 100 100 100 0 85.19 14.81 0 0 0 74.0715 Orissa 94.29 85.71 97.14 28.57 34.29 30 25.71 7.14 0 2.86 42.8616 Punjub 96.6 96.6 93.1 72.4 24.1 41.4 13.8 6.9 0 13.8 44.817 Rajasthan 85.1 96.8 100 54.3 46.8 7.4 21.3 20.2 3.2 1.1 78.718 TN 98.4 82.8 57.8 29.7 20.3 37.5 14.1 20.3 7.8 0 60.919 Tripura 100 100 100 100 73.33 23.33 3.34 0 0 0 56.6720 UP 79.7 96.2 94.9 94.9 35.4 10.1 12.7 35.4 0 6.4 39.221 UT 100 100 100 31.8 27.3 22.7 22.7 13.6 4.5 9.2 72.722 WB 92.86 97.62 100 78.57 11.9 39 11 21.43 5.38 11.29 64.29

SD 5.64 11.01 11.04 25.63 18.65 17.75 11.83 11.89 4.42 5.38 18.74Mean 95.00 92.56 92.53 66.05 28.91 28.01 18.16 17.04 3.21 4.67 57.01CV 5.94 11.90 11.93 38.80 64.52 63.38 65.17 69.78 137.93 115.04 32.86

159

Given sufficient funds, % will prefer to investment on

% felt that politician of

their areas are dishonest

Table 5.2: People's Perception (Rural Non- Poor)2

State povertyinjustice in

society

no punishment for criminal

actno respect for society

no future hope

% support reservation

% of persons reported

educational institute

% of persons reported better job opportunity,

industry

% of persons reported

health centres

% of persons reported roads,

electricity, irrigation etc.

% having no trust on police

AP 75.3 15.5 6.2 0 1 85.6 39.2 6.2 0 1 67 16.5Assam 34.33 10.45 1.49 4.48 49.25 47.76 NA NA NA NA 19.4 95.52Bihar 21.15 23.08 32.69 7.69 17.31 42.31 7.69 9.62 0 13.46 61.54 63.46Chattisgarh 0 34.8 21.7 13 8.7 52.2 17.4 21.7 8.7 47.8 73.9 39.1Goa 39.1 13 21.7 4.3 0 56.5 0 30.4 13 26.1 78.3 21.7Gujarat 22.37 40.79 5.26 6.58 9.21 30.26 15.79 43.42 2.63 5.26 67.11 44.74Haryana 36.7 23.3 6.7 13.3 16.7 43.3 10 60 13.3 16.7 13.3 90HP 28.21 35.9 10.26 10.26 10.26 41.03 10.26 43.59 10.26 35.9 53.85 30.77Jharkhand 46.9 25 12.5 6.2 3.1 25 12.5 25 9.4 18.8 43.8 40.6Karnataka 7.69 41.03 8.97 11.54 21.79 61.54 11.54 15.38 7.69 47.44 51.28 7.69Kerala 28.6 36.5 20.6 0 1.6 79.4 1.6 11.1 1.6 7.9 44.4 30.2MP 10.9 21.7 19.6 4.3 13 47.8 8.7 21.7 4.3 6.5 67.4 26.1Mahrashtra 53.01 49.4 14.46 6.02 16.87 43.37 37.35 48.19 25.3 36.14 57.83 42.17Manipur 18.52 22.22 0 0 59.26 62.96 NA NA NA NA 22.22 96.3Orissa 51.43 31.43 0 5.71 8.57 54.29 8.57 25.71 8.57 31.43 62.86 42.86Punjub 27.6 51.7 0 3.4 6.9 41.4 10.3 17.2 20.7 3.4 41.4 41.4Rajasthan 85.1 1.1 10.6 0 2.1 23.4 21.3 26.6 4.3 12.8 54.3 37.2TN 17.2 28.1 17.2 9.4 10.9 39.1 7.8 15.6 10.9 20.3 45.3 67.2Tripura 0 26.67 0 0 33.33 0 NA NA NA NA 93.33 80UP 49.4 8.9 34.2 0 2.5 62 5.1 22.8 5.1 0 65.8 63.3UT 63.6 13.6 0 9.1 0 50 27.3 54.5 0 13.6 77.3 22.7WB 42.86 19.05 4.76 14.29 2.38 30.95 16.67 35.71 19.05 33.33 35.71 47.62SD 22.71 13.20 10.39 4.76 15.65 18.73 10.67 15.51 7.23 15.12 20.14 25.47Mean 34.54 26.06 11.31 5.89 13.40 46.37 14.16 28.13 8.67 19.89 54.42 47.60CV 65.74 50.65 91.86 80.75 116.80 40.40 75.33 55.15 83.37 76.03 37.01 53.52

160

% Satisfied with Govt. projects

% felt that persons become antisocial because of

1 Table 5.3: People's Perception (Urban Poor)

Sl. No. State% of BPL Card

Holder% of literate

family

% felt education

necessary for living

% illiterate for poverty

% in favour of educated politician

% felt that politicians prefer uneducated voters

% felt that politicians of their area are dishonest

% felt that poverty is the cause of being a person

antisocial

% felt that skill help in getting

higher standard of living

1 AP 50 58.3 75 100 83.3 100 83.3 25 502 Assam 0 100 100 0 100 100 100 50 1003 Bihar 41.67 100 100 0 100 83.33 83.33 33.33 58.334 Chattisgarh 40 80 80 80 80 20 605 Goa 50 100 100 100 83.3 33.3 66.76 Gujarat 50 75 87.5 0 87.5 100 62.5 62.5 62.57 Haryana 0 50 0 0 100 100 100 1008 HP 44.44 100 100 0 100 33.33 100 55.56 88.899 Jharkhand 25 75 100 100 75 75 100 75 100

10 Karnataka 81.25 100 75 37.5 62.5 56.25 18.75 18.75 56.2511 Kerala 100 83.3 100 100 83.3 50 50 012 MP 45.5 100 90.9 100 90.9 45.5 81.8 36.4 87.513 Mahrashtra 35.71 92.86 92.86 7.14 85.71 75 71.43 71.43 46.4314 Manipur 100 100 100 0 100 100 0 0 10015 Orissa 75 90 100 5 100 37.5 52.5 40 8016 Punjub 50 85.49 100 0 100 100 50 5017 Rajasthan 16.7 100 100 100 50 100 91.7 10018 TN 100 100 53.3 100 33.3 53.3 66.7 40 019 Tripura 100 100 100 0 100 100 0 0 10020 UP 22.2 88.9 100 88.9 88.9 33.3 22.2 8021 UT 40 100 100 100 80 20 40 6022 WB 45.83 87.5 75 16.67 62.5 87.5 81.25 25 62.5

SD 30.58 14.35 23.32 45.34 17.19 25.30 32.93 26.23 32.15Mean 50.60 89.38 87.71 33.31 87.71 75.10 60.37 46.98 68.96CV 60.44 16.06 26.59 136.11 19.60 33.69 54.55 55.84 46.62

161

2 Table 5.3: People's Perception (Urban Poor)

Sl. No. State% cast vote

% having no trust on

vote

%can meet municipal chairman whenever they want

% having no idea of poverty

eradication programme

%not satisfied

with Govt. infra.

Projects

% having no trust on

police

% felt that Justice

depend on money/con

nection%Know

Globalisation

% felt that there has been

a wage rise since last 5-10

years

% felt that they became economically

better off since last 5-10

years

% felt that there has been a

employment oportunity rise since last 5-10

years 1 AP 83.3 0 33.3 50 41.7 75 75 66.7 41.7 41.7 252 Assam 100 0 NA 0 100 100 100 0 0 0 03 Bihar 100 0 50 33.33 58.33 58.33 91.67 33.33 8.33 0 04 Chattisgarh 100 0 80 60 20 20 20 80 0 20 05 Goa 100 0 66.7 83.3 33.3 50 100 16.7 33.3 16.7 16.76 Gujarat 87.5 0 12.5 100 50 50 75 37.5 12.5 12.5 12.57 Haryana 50 0 100 100 100 100 100 50 50 50 08 HP 100 0 55.56 77.78 44.44 88.89 66.67 0 44.44 33.33 09 Jharkhand 100 0 25 100 50 50 75 75 0 0 25

10 Karnataka 100 0 62.5 43.75 18.75 100 93.75 6.25 43.75 18.75 011 Kerala 66.7 50 50 50 50 16.7 100 83.3 0 0 012 MP 100 0 45.5 54.5 45.5 27.3 54.5 18.2 0 0 013 Mahrashtra 92.86 0 17.86 75 50 67.86 89.29 42.86 7.14 0 3.5714 Manipur 100 0 NA 0 50 100 100 100 0 0 015 Orissa 95 2.5 77.5 55 42.5 17.5 62.5 7.5 0 0 016 Punjub 100 0 50 50 100 100 100 0 50 50 017 Rajasthan 100 0 83.3 25 33.3 0 75 66.7 83.3 83.3 83.318 TN 100 0 20 46.7 66.7 66.7 40 33.3 33.3 20 2019 Tripura 100 0 NA 0 100 100 100 100 0 0 020 UP 100 0 88.9 11.1 11.1 55.6 66.7 88.9 11.1 11.1 11.121 UT 100 0 80 20 40 100 20 100 20 40 2022 WB 85.42 4.17 22.92 75 79.17 72.92 68.75 10.42 6.25 6.25 2.08

SD 12.82 10.64 26.74 32.10 26.81 32.65 25.08 36.23 23.52 22.61 18.75Mean 93.67 2.58 53.77 50.48 53.85 64.40 76.08 46.21 20.23 18.35 9.97CV 13.69 413.08 49.74 63.59 49.79 50.69 32.97 78.39 116.25 123.22 188.10

162

Table 5.4: People's Perception (Urban Non-Poor)People's Perception (Urban Non-Poor)

Sl. No. State

% of literate family

%felt edu. necessary for living

% in favour of educated politician

% felt that politicians

prefer uneducated

voters

% felt that dishonesty

is not penalized in

their area

% felt that poverty is the cause of being

a person antisocial

%satisfy with Govt.

project

%know globalisatio

n

%felt that they are getting cheaper industrial goods during last 5-10

years

% felt tht there is rise in employment opportunity during

last 5-10 years 1 AP 100 100 96.6 89.7 55.2 34.5 34.5 100 37.9 37.92 Assam 100 100 100 100 0 50 0 100 0 03 Bihar 100 100 100 83.33 75 50 16.67 100 41.67 16.674 Chattisgarh 100 100 100 50 50 33.3 66.7 100 50 33.35 Goa 100 94.7 94.7 94.7 63.2 26.3 26.3 89.5 26.3 15.86 Gujarat 97.96 100 97.96 73.47 63.27 40.82 48.98 93.88 22.45 12.247 Haryana 92.3 92.3 100 46.2 46.2 61.5 15.4 84.6 38.5 30.88 HP 100 85.71 100 71.43 50 50 14.29 92.86 21.43 09 Jharkhand 100 100 84.6 84.6 61.5 61.5 30.8 76.9 15.4 15.4

10 Karnataka 100 74.29 85.71 91.43 71.43 2.86 60 97.14 48.57 65.7111 Kerala 100 100 100 85.7 76.2 28.6 52.4 100 76.2 47.612 MP 100 100 100 31.2 37.5 0 75 43.8 0 6.213 Mahrashtra 100 100 87.27 90.91 74.55 38.18 45.45 81.82 10.91 18.1814 Manipur 100 100 100 100 0 25 8.33 100 015 Orissa 100 100 95 60 52.5 32.5 42.5 60 2.5 2.516 Punjub 100 100 100 87.5 62.5 43.8 25 100 25 31.217 Rajasthan 100 100 100 52.2 69.6 56.5 34.8 91.3 56.5 69.618 TN 100 54.9 68.6 43.1 76.5 15.7 60.8 51 25.5 5.919 Tripura 100 100 100 100 0 22.22 44.44 100 0 020 UP 100 100 100 95.7 95.7 8.7 95.7 95.7 13 21.621 UT 100 100 100 37.5 25 50 0 100 50 12.522 WB 100 94.74 91.23 82.46 19.3 40.35 33.33 98.25 29.82 15.79

SD 1.68 10.97 7.85 22.33 27.11 17.77 24.56 16.73 21.22 20.08Mean 99.56 95.30 95.53 75.05 51.14 35.11 37.79 88.94 26.89 21.85CV 1.69 11.51 8.22 29.76 53.00 50.61 64.99 18.81 78.89 91.89

163

1

Sl. No. State

displacement is

necessary

state has enough agr.

Land

agr.is no more

profitablerehabilitation must be made

agr.is the main

source

govt. & land dealers grab

land for money

job in industry is not

guaranteed1 AP 0 100 83.3 16.7 02 Assam 89.47 0 0 73.68 26.32 10.53 100 0 03 Bihar 41.67 63.33 36.67 0 0 58.33 85.71 14.29 04 Chattisgarh 37.5 33.3 66.7 0 0 62.5 70 20 105 Goa 0 100 0 100 06 Gujarat 16.67 33.33 0 33.33 33.33 83.33 66.67 20 13.337 Haryana 0 100 80 0 208 HP 0 0 0 0 0 100 33.33 55.56 11.119 Jharkhand 29.6 62.5 12.5 25 0 70.4 31.6 21.1 47.4

10 Karnataka 9.52 100 0 0 0 90.48 84.21 0 15.7911 Kerala 80 0 0 62.5 37.5 20 50 50 012 MP 29.6 0 62.5 37.5 0 70.4 15.8 5.3 78.913 Mahrashtra 55.88 68.42 5.26 5.26 15.79 44.12 66.67 33.33 014 Manipur 88.89 0 0 88.89 11.11 11.11 100 0 015 Orissa 46.25 19.44 22.22 33.33 25 53.75 86.36 20.45 2.2716 Punjub 0 100 66.7 0 33.317 Rajasthan 52.4 54.5 18.2 9.1 18.2 47.6 90 10 018 TN 5 0 0 100 0 95 78.9 15.8 5.319 Tripura 100 0 0 77.78 22.22 0 0 0 020 UP 30.8 25 16.7 50 8.3 69.2 37 44.4 18.521 UC 46.7 85.7 0 14.3 0 53.3 100 0 022 WB 28.3 20 53.33 13.33 13.33 71.7 84.21 5.26 10.53

SD 31.84 33.23 23.04 33.34 12.84 31.84 31.39 24.62 19.38Mean 35.83 31.42 16.34 34.67 11.73 64.17 64.11 19.65 12.11CV 88.87 105.76 141.04 96.18 109.50 49.62 48.96 125.33 160.07

164

Table 5.5: People's Perception about Land acquisition (Rural Poor)

% support land

acquisition

Causes of support (% reported)

% do not support land acquisition

Causes of not to support (% reported)

Sl. No. State

% support Land

Acquisition

% told Displacement is necessary

% told state has enough agrl. Land

agr.is no more

profitable

% told rehabilitation must be made

% told agr. is the main

source

% told displacement is necessary

% told state has enough

agr. land1 AP 49.5 6.2 89.6 0 4.2 50.5 83.7 16.3 02 Assam 85.07 0 0 65.67 34.33 14.93 100 0 03 Bihar 49.04 13.73 3.92 80.39 5.88 50.96 77.36 11.32 11.324 Chattisgarh 65.2 13.3 13.3 40 33.3 34.8 50 37.5 12.55 Goa 26.1 50 0 33.3 16.7 73.9 56.2 31.3 12.56 Gujarat 25 26.05 42.37 0 31.58 75 53.64 24.56 21.87 Haryana 23.3 28.6 14.3 0 57.1 76.7 73.9 13 13.18 HP 30.77 8.33 0 8.33 83.33 69.23 81.48 11.11 7.419 Jharkhand 18.8 33.3 16.7 0 50 81.2 42.3 19.2 38.5

10 Karnataka 20.51 6.25 6.25 68.75 18.75 79.49 61.59 23.58 14.8311 Kerala 69.8 31.8 4.5 22.7 40.9 30.2 36.8 31.6 31.612 MP 56.5 38.5 34.6 3.8 23.1 43.5 25 20 5513 Mahrashtra 34.94 37.93 17.24 13.8 31.03 65.06 36.33 27.96 35.7114 Manipur 77.78 0 0 77.78 22.22 22.22 100 0 015 Orissa 48.57 29.41 23.53 0 47.06 51.43 83.33 16.67 016 Punjub 31 22.2 44.4 0 33.3 69 55 30 1517 Rajasthan 42.6 37.5 42.5 0 20 57.4 55.6 5.6 38.918 TN 15.6 0 30 20 50 84.4 55.6 24.1 20.419 Tripura 73.33 0 0 33.33 66.67 41.8 100 0 020 UP 58.2 50 10.9 0 39.1 86.4 33.3 60.6 6.121 UC 13.6 66.7 33.3 0 0 86.4 84.2 5.3 10.522 WB 30.95 7.69 7.69 0 84.62 69.05 70.11 8.9 20.99

SD 21.65 19.04 21.78 28.13 22.99 21.41 22.58 14.45 15.11Mean 43.01 23.07 19.78 21.27 36.05 59.71 64.34 19.03 16.64CV 50.34 82.55 110.15 132.25 63.76 35.86 35.10 75.95 90.76

165

Table 5.6: People's Perception about Land acquisition (Rural Non-Poor)

Causes of support (% reported)% not supporting Land Acquisition

Causes of not to support (% reported)

166

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