1
Poster Session: #1
Time: Monday, August 6, 2012 PM
Paper Prepared for the 32nd General Conference of
The International Association for Research in Income and Wealth
Boston, USA, August 5-11, 2012
A Modified Index of Economic and Social Well-being
Using Multivariate Factor Analysis: An Indian case
Somesh Mathur and Shweta Sharma
For additional information please contact:
Name: Somesh Mathur
Affiliation: IITK India
Email Address: skmathur@iitk.ac.in
This paper is posted on the following website: http://www.iariw.org
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A Modified Index of Economic and Social Well-being Using Multivariate Factor Analysis:
An Indian case
Somesh Mathur
IITK, India
Shweta Sharma
IITK, India
Introduction
------------------------------------------------------------------------------------------For a long time, the economic progress of a country was measured by growth in Gross
National Product (GNP) per capita. It was assumed that growth would trickle down to the
poorer sections so as to increase the well being of a country. Also, it was realized that an
increase in the availability of goods and services to the masses, while other aspects of life like
health, education, etc were ignored. But over the years, perspectives on development and its
rationale and measures have changed. Today, development extends far beyond economic
performance. It focuses on people and their well being, with the rationale that improving
people’s lives is the ultimate objective. Economic growth cannot be an end in itself; rather
benefits must be translated into people’s daily realities. In fact, the process of development in
any society should ideally be viewed and assessed in terms of what it does for the average
individual. The decade of 1990s saw a visible shift in the focus of development planning
from the mere expansion of the production of goods and services and the consequent growth
of per capita income to planning to enhancement of well being. It is now realized that human
development is about much more than the rise or fall of national income. It is about the
quality of life, the level of human well being and the access to basic social services.
Recent development scenario of various countries of the world is a powerful reminder that
the expansion of output and wealth is only a means. The end of the development is an integral
part of the development process for any country. Without human development the
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development process for any country is incomplete. The reason is that the human
development is a process to enlarge people’s choices. In principle, these choices can be
infinite and change over time. But at all the level of development, there are three essential
ones are for people to lead a long and healthy life, to acquire knowledge and to have access to
resources needed for decent standard of living.
1.1Conceptual framework
1.1.1Why measuring well being?
Measures of economic and social well-being are necessary for at least five purposes. First,
there is need for an aggregate index of economic activity, which would help one to
summarize an economy. Income has been useful in this role. Secondly, one may wish to
compare the states of affairs in different places (e.g., countries), or between different groups
of people (e.g., the poor versus the rich, or men versus women), at a given point in time. The
third reason quality-of-life indices are needed is that one might wish to make welfare
comparisons over time of people in the same place (e.g., the same country) or members of a
particular group (e.g., the poor or rich, or women). the fourth reason stems from a desire to
estimate the economic component of the standard of living an economy is capable of
sustaining along alternative programs Finally, the fifth reason a quality-of-life index is
necessary is that there should exist ways to evaluate alternative economic policies.
1.1.2 Shift from GDP TO Human Development: From the 1980s there has been a shift in
approach towards human development in terms of the real wealth of a nation in its people.
The ultimate objective of development planning is human development or increased social
welfare and well-being of the people. Increased social welfare of the people requires a more
equitable distribution of development benefits along with better living environment.
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Development process therefore needs to continuously strive for broad-based improvement in
the standard of living and quality of life of the people through an inclusive development
strategy that focuses on both income and non income dimensions.
The purpose of human development is to create an enabling environment for people to enjoy
long, healthy, creative, and secure lives. Human development is a concern with expanding
capabilities, widening choices, assuring human rights, and promoting securities in the lives of
people. Progress in the human development framework is judged not by the expanding
affluence to the rich, but by how well the poor and socially disadvantaged are faring in
society. Human development is a multidimensional concept. This is because the quality of
life of an individual human being has more than one aspect or dimension, and the extent of
human development of a population of individuals should epitomize the set of qualities of
Life of its members.
1.1.3 Alternative measurement to human development:
In the 1950s and 1960s, scholars raised doubts regarding the desirability of using GNP/ GDP
as a measure of development and presented alternative measures. It was observed during
these decades that human welfare was declining in spite of increased incomes in some
industrialized countries. Crime was increasing, congestion and slums were increasing, and
social development was suffering. Since then, scholars have presented several alternative
measures of welfare. Broadly described, these are:
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1.1.3.1Social statistics, social accounting and social reporting: These data referred to the
social aspects of development and were presented in the form of social accounts, social
reports, or social indicators (Henderson 1974; Land 1971; OECD 1976). Social accounts
presented the data in input-output tables. Social reports and social indicators were a sub-set of
the universe of social statistics (Land 1974). According to a recent survey of social indicators
by Ken Land, a sociologist at Duke University (Land, 1999), three types of social indicators
can be identified: normative welfare indicators, life satisfaction and/or happiness indicators,
and descriptive indicators.
1.1.13.1.1Normative welfare indicators
The first types of social indicators relate directly to social policy-making considerations, and
have been termed criterion indicators, normative welfare indicators, and policy indicators.
Mancur Olson, principle author of one of the key social indicator volumes of the 1960s,
characterized a social indicator as a “statistic of direct normative interest which facilitates
concise, comprehensive and balanced judgments about the condition of major aspects of
society.” Such a measure is a direct measure of welfare and changes in the “right” direction
means everything else being equal, people are better off. In the language of policy analysis,
this type of social indicator is a target or outcome variable which public policy tries to
influence. Land points out that use of social indicators in this sense requires that society agree
about what needs to be improved, that agreement exist on what “getting better” means, and
that it is meaningful to aggregate the indicators to the level of aggregation at which policy
can be defined.
1.1.3.1.2Life satisfaction indicators
A second type of social indicators, called life satisfaction, subjective well-being, or happiness
indicators, attempt to measure psychological satisfaction, happiness, and life fulfillment
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through survey research instruments that ascertain the subjective reality in which people live.
The approach is based on the belief that direct monitoring of key social-psychological states
is necessary for an understanding of social change and the quality of life. It is argued that the
link between objective conditions and subjective wellbeing can be paradoxical and, therefore,
subjective as well as objective states of wellbeing should be monitored.
1.1.3.1.3 Descriptive social indicators
A third type of social indicator focuses on social measurement and analyses designed to
improve our understanding of society. This type of social indicators may be related to public
policy objectives, but is not restricted to this use. Descriptive social indicators come in many
forms, and can vary greatly in the level of abstraction and aggregation, from a diverse set of
statistical social indicators to an aggregated index of the state of society.
1.1.3.1.4 Level of living, living standards, and state of welfare index: Level of living and
living standards indicated the level of satisfaction of the needs of a population as a result of
goods and services enjoyed by them (Drewonoski 1974). The state of welfare index measured
the level of welfare (using output indicators) of a population (Oscar and Juan 1980).
1.1.3.2. Selected Indexes on Economic and Social Well-being
The five indexes that provide historically consistent estimates of trends in wellbeing are:
1.1.3.2.1 Measure of Economic Welfare (MEW)
The Measure of Economic Welfare was developed in the early 1970s by William Nordhaus
and James Tobin. Like the GPI, the MEW uses personal consumption expenditures as a
starting point. Various additions, subtractions, and imputations are made to derive a measure
of total consumption deemed to generate economic welfare. All aggregation is done in terms
of prices.
Genuine Progress Indicator (GPI)
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The Genuine Progress Indicator (GPI) developed by the Redefining Progress Institute and
estimated for Canada by Statistics Canada in 1995. The GPI bears much similarity to the
MEW, as both start with a measure of consumption from the national accounts and then
proceed to make a large number of adjustments. The GPI has been falling in the United States
since the early 1970s, largely because of the negative effect of resource depletion. The GPI
can be broadly split into two blocks: a measure of current economic welfare and a measure of
sustainable economic development. Elements of current economic welfare consist of
consumer spending, government spending, non-market production and leisure, and external
factors. Sustainable economic development includes depletion of natural resources
(nonrenewable energy and farmland); net investment in produced business fixed assets; net
foreign lending/borrowing; long-term environmental damage (“greenhouse effect” and ozone
depletion); and, long-term ecological damage resulting from the loss of wetlands and the
harvesting of old growth forests.
1.1.3.2.3. Index of Economic Well-being (IEWB)
Lars Osberg and Andrew Sharpe (1998) of the Centre for the Study of Living Standards have
developed an index of economic well-being for Canada, where well-being depends on the
level of average consumption flows, aggregate accumulation of productive stocks, and
inequality in the distribution of individual incomes and insecurity in the anticipation of future
incomes.
1.1.3.2.4 Index of Social Health (ISH)
The Index of Social Health (ISH) developed by Marc Miringoff at Fordham University. A set
of socio-economic indicators covering 16 social issues dealing with health, mortality,
inequality and access to services were selected to cover all stages of life, with separate
indicators for each age group
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1.1.3.2.5 Index of Living Standards (ILS).
Christopher Sarlo (1998) developed an exploratory index of living standards for the Fraser
Institute based on eight components. He has estimated it for the 1973-94 periods for Canada.
The eight components, each equally weighted, are real household consumption per capita;
real household income per capita; index of household facilities, percentage of the population
with a post-secondary degree or diploma; one minus the unemployment rate; life expectancy,
indicator of household wealth (net worth per capita). Because of strong increases in the index
for post-secondary education, household facilities, and to a lesser degree wealth, this index
has outpaced both GDP per capita and the Index of Economic Well-being in the 1980s and
1990s.
1.1.3.3 Quality of life: Quality of life has always referred to the life people enjoy in the
context of environmental pollution, deteriorating safety and security, and declining living
standards
(Szalai 1980). It has referred to subjective perception of people regarding their life’s
objective conditions (Henderson 1974; Datta and Agarwal 1980) or subjective assessment of
needs in the context of Maslowian hierarchy of needs (Sethi 1992).
1.1.3.4 Physical quality of life index (PQLI) -This index was presented by Morris D. Morris
to measure the conditions of the world’s poor in terms of three indicators, namely LEB (Life
Expectancy at Birth), IMR (Infant Mortality Rate), and basic literacy (percentage of literates
in society) (Morris and McAlpin 1982).
1.1.3.5 Social progress index and others: A few more indices have been presented by
scholars in recent years to focus on the positive aspects of human development while
overcoming the lacunae of GDP. The Social Progress Index (SPI) is one of these and is
defined at the individual level in three units: longevity or potential life time, consumption of
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private goods, and access to public goods such as clean water, sanitation, safety, transport etc.
(Desai 1994).
1.1.3.6 Human Development Index:
The concept of human development is much broader than any single composite index can
measure; the HDI offers a powerful alternative to GDP per capita as summary measure of
human well being. In the Human Development Index (HDI), prepared by the United Nations
Development Programme (UNDP) and published in its annual Human Development Reports,
attention is confined to its three basic dimensions of quality of life (I) longevity, (ii)
educational attainment, and (iii) standard of living. To compute the HDI, first a component
index is determined for each of these dimensions; this is a sort of average measure on a
suitably standardized scale representing the general level of the dimensional character in the
population. The overall index is then obtained as a simple average of the three component
indices. Thus for longevity, the expectation of life at birth, scaled so that its putative
minimum value (25 years) and maximum value (85 years) become, respectively, 0 and 1, is
taken as the component index. For educational attainment, a weighted average of the adult
literacy rate and the gross enrolment ratio (each expressed as a fraction) is taken as the
component index. In the case of standard of living, the adjusted per-capita income (logarithm
of the real Gross Domestic Product [GDP] per capita in PPP$), scaled so that its putative
minimum value (log 100) and maximum value (log 40,000) become, respectively, 0 and 1, is
taken for the component index (see the Technical Notes in the UNDP HDRs, 1999 and 2003).
A drawback of the index described above is that, while it more or less takes account of the
general level of Quality of Life in the population, it ignores the extent of inequality in Quality
of Life over the members of the population, and a population cannot be regarded as having a
high degree of human development if the general level of Quality of Life in it is high but
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there is too much inequality among its members. This point was recognized quite early at
least in respect of the dimension standard of living as represented by income.
1.2Developments of HDI Reports:
Human development Reports have been focused to certain dimensions of development.
Main themes and agendas of different reports as given:
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1.2.1 Agendas of HDR reports from 1991 to 2004: The 1993 HDR discussed participatory
and employment intensive development.
The 1994 HDR discussed sustainable human
development based on development cooperation at the global level. The 1995 HDR argued
for engendered development path. The 1996 HDR analysed the relationship between
economic growth and human development. The 1997 HDR explored the link between human
development and poverty. The 1998 HDR discussed issues related to consumption and
increasing inequality in consumption. The 1999 HDR presented the challenges to human
development in the context of globalization. The 2000 HDR was on human rights and the
links to human development. The 2001 HDR discussed the relationship between new
technologies and human development The 2002 HDR presented the role of democracy and
governance in human development. The HDR 2003 gave the global level and individual
country level achievements and efforts. The HDR 2004 focused on the cultural liberty as a
vital part of human development because being able to choose one’s identity is important in
leading a full life.
1.2.2 The 2005 Human Development Report: It takes stock of human development,
including progress towards the Millennium Development Goals. It highlights the human costs
of missed targets and broken promises. Extreme inequality between countries and within
countries is identified as one of the main barriers to human development. It gives a
comprehensive overview of international development assistance, looking at both its quality
and quantity.
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1.2.3 Human Development Report 2006: It investigates the underlying causes and
consequences of a crisis that leaves 1.2 billion people without access to safe water and 2.6
billion without access to sanitation. It argues for a concerted drive to achieve water and
sanitation for all through national strategies and a global plan of action. This report examines
the social and economic forces that are driving water shortages and marginalizing the poor in
agriculture It looks at the scope for international cooperation to resolve cross-border tensions
in water management.
1.2.4 The Human Development Report 2007/8: It shows the climate change as a future
scenario.
Increased exposure to droughts, floods and storms is already destroying opportunity and
reinforcing inequality.
HDR 2009: The report investigates migration in the context of demographic changes and
trends in both growth and inequality. It also presents more detailed and nuanced individual,
family and village experiences, and explores less visible movements typically pursued by
disadvantaged groups such as short term and seasonal migration.
The 2010 Report: It continues the tradition of pushing the frontiers of development thinking.
For the first time since 1990, the Report looks back rigorously at the past several decades and
identifies often surprising trends and patterns with important lessons for the future. These
varied pathways to human development show that there is no single formula for sustainable
progress and that impressive long-term gains can and have been achieved even without
consistent economic growth. Looking beyond 2010, this Report surveys critical aspects of
human development, from political freedoms and empowerment to sustainability and human
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security, and outlines a broader agenda for research and policies to respond to these
challenges.
The 2011 Human Development Report: It argues that the urgent global challenges of
sustainability and equity must be addressed together and identifies policies on the national
and global level that could spur mutually reinforcing progress towards these interlinked
goals. Past Reports have shown that living standards in most countries have been rising - and
converging - for several decades now. Yet the 2011 Report projects a disturbing reversal of
those trends if environmental deterioration and social inequalities continue to intensify, with
the least developed countries diverging downwards from global patterns of progress by 2050.
The Report shows further how the world’s most disadvantaged people suffer the most from
environmental degradation, including in their immediate personal environment, and
disproportionately lack political power, making it all the harder for the world community to
reach agreement on needed global policy changes. The Report also outlines great potential for
positive synergies in the quest for greater equality and sustainability, especially at the
national level. The Report further emphasizes the human right to a healthy environment, the
importance of integrating social equity into environmental policies, and the critical
importance of public participation and official accountability. The 2011 Report concludes
with a call for bold new approaches to global development financing and environmental
controls, arguing that these measures are both essential and feasible.
Recent trends in Human Development Index in Indian context: The HDI is an important
tool for monitoring long term trends in human development. There has been steady progress
on HDI over the past 20 years and India’s HDI is above the average for countries in South
Asia. The index value of India improved from 0.320 in 1980 to 0.519 in 2010 and the global
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ranking was 128 in 2005 and 132 in 2007 and to 119 in 2010 according to HDR 2010.
Table (1.1) Trends in the HDI 1980-2011
HDI
COUNTRY
1980
1990
1995
2000
2005
2009
2010
2011
1
Norway
0.788
.838
.869
.906
.932
.937
.938
.943
2
Australia
.791
.819
.887
.914
.925
.935
.937
.929
39
Poland
.683
.710
.753
.775
.791
.795
.813
61
Malaysia
.616
.659
.691
.726
.739
.744
.761
65
Russia
.692
.644
.662
.693
.714
.719
.755
84
Brazil
-
-
-
.649
.678
.693
.699
.718
97
Sri Lanka
.513
.558
.584
-
.635
.653
.658
.691
101
China
.368
.460
.518
.567
.616
.655
.663
.687
134
India
.320
.389
.415
.440
.482
.512
.519
.547
145
Pakistan
.311
.359
.389
.416
.468
.487
.490
.504
146
Bangladesh
.259
.313
.350
.390
.432
.463
.469
.500
world
.455
.526
.554
.570
.598
.619
.624
.682
.541
Very high, high, medium, low, Source: HDR 2010
The HDI trend calculated at five year intervals over a period of 30 years is shown in Figure 1.
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Economic Review 2010
Trends in Human Development Index in Selected States in India (1981-2010) Human
development is highest in the state of Kerala with an index value of 0.92 and was lowest in
the state of Bihar with an index value of 0.44. If we classify the states as below and above
national average then the states are:
High human development: Kerala, Chandigarh
Medium human development (States above national average): Lakshadweep, Mizoram,
Delhi, Goa, Nagaland, Andaman and Nicobar Islands, Daman and Diu, Pondicherry,
Manipur, Maharashtra, Sikkim, Himachal Pradesh, Punjab, Tamil Nadu,
Medium human development (States below national average): Haryana, Uttarakhand,
West Bengal, Gujarat, Dadra Nagar Haveli, Arunachal Pradesh, Tripura, , Karnataka, Jammu
Kashmir, Karnataka, Meghalaya, Andhra Pradesh, Rajasthan, Assam, Chhattisgarh,
Jharkhand.
Low Human development: Uttar Pradesh, Madhya Pradesh, Orissa, Bihar
Table (1.2) Selected indicators of human development for major states
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State
Life expectancy at birth
(2007)IMR (per 1000 live
(2008)
(2002-06)
birth)
Birth
(2008)
rate
Death rate per
M
F
T
M
F
T
per 1000
1000
62.9
65.5
64.4
54
56
54
18.4
7.5
Bihar
62.2
60.4
61.6
57
58
58
28.9
7.3
Gujarat
62.9
65.2
64.1
50
54
52
22.6
6.9
Karnataka
63.6
67.1
66.3
46
47
47
19.8
7.4
Kerala
71.4
76.3
74
12
13
13
14.6
6.6
Madhya
58.1
57.9
58
72
72
72
28.0
8.6
Maharashtra
66.0
68.4
67.2
33
35
34
17.9
6.6
Punjab
68.4
70.4
69.4
42
45
43
17.3
7.2
Tamil Nadu
65.0
67.4
66.2
34
36
36
16.0
7.4
Uttar Pradesh
60.3
59.5
60
67
70
69
29.1
8.4
West Bengal
64.1
65.8
64.9
36
37
37
17.5
6.2
India
62.6
64.2
63.5
55
56
55
22.8
7.4
Andhra
Pradesh
Pradesh
Source: Office of the Registrar General of India; Ministry of Home Affairs; Economic Survey, 2010-11
This study is confined to two major states Uttar Pradesh and Gujarat of India. One comes
under medium developed state and other comes in very low category.
India Human Development Report, 2011: It is prepared by Institute of Applied Manpower
Research. India's HDI from 1999 to 2008 has increased by 21 per cent from 0.387 to 0.467.
Kerala is placed on top of the index for achieving highest literacy rate, quality health services
and consumption expenditure of people. Delhi, Himachal Pradesh and Goa were placed at
second, third and fourth position respectively.
It clarifies as on today, two-thirds of the
households in the country reside in pucca (cemented) houses and three-fourth of families has
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access to electricity for domestic use. According to the report, India's HDI has registered an
impressive gain in the last decade as the index increased by 21 per cent to 0.467 in 2007-08,
from 0.387 in 1999-2000. It noted that Chhattisgarh, Orissa, Madhya Pradesh, Uttar Pradesh,
Jharkhand, Rajasthan and Assam are those states which continue to lag behind in HDI and
remain below the national average of 0.467. At the same time, the quantum of improvement
in HDI in some of the poor states was higher than the national average, the report said, citing
the cases of Bihar, Andhra Pradesh, Chhattisgarh, Madhya Pradesh, Orissa and Assam. The
overall improvement in the index was largely attributed to the 28.5 per cent increase in
education index across the country. It ranges from 0.92 for Kerala to 0.41 in the case of
Bihar. The improvement in the education index was the "greatest" in states like Uttar
Pradesh, Rajasthan and Madhya Pradesh. The analysis also indicates that improvement in the
health index, as compared to education, has been lower. It ranges from 0.82 in Kerala to.41 in
Assam. It observed that despite the Right to Education Act, school education faces challenges
of quality and employability.
The report also said that despite improvements, health,
nutrition and sanitation challenges are most serious problems in India.
Socio- Economic Development Indicators: Inter State comparison
According to economic survey 2010-11, the socio-economic performance of States has been
varied. While developed states like Gujarat, Maharashtra, Karnataka, Haryana, Kerala, and
Tamil Nadu have performed well in terms of many indicators, many hitherto backward states
like Bihar, Orissa, and Uttarakhand are showing good growth performance and many of the
backward states like Rajasthan, Uttar Pradesh, Madhya Pradesh, and Bihar are benefiting
from poverty-alleviation employment schemes like the Mahatma Gandhi National Rural
Employment Guarantee (MGNREG) Schemes and National Rural Health Mission (NRHM).
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Growth related Facts: The best performer in terms of growth during 2002-03 to 2008-09
was Gujarat, followed by Bihar, Orissa, Haryana, and Uttarakhand. States like Madhya
Pradesh, Assam, Punjab, and Uttar Pradesh registered a relatively lower growth rate.
Interestingly, the best performer in 2008-09 was Bihar with a growth rate of 16.59 per cent.
While the good growth performance of some of the hitherto backward states like Bihar and
Orissa is a welcome sign, this may also be partially due to the low base effect because of the
growth deficit in earlier years. In fact, many states like Bihar, Chhattisgarh, Orissa, and
Uttarakhand that showed high growth in 2002-03 to 2008-09, had witnessed low growth in
1994-95 to 2001-02.
Poverty Related Facts: The percentage of people below the poverty line is very high in
states like Orissa, Bihar, Chhattisgarh, Jharkhand, Uttarakhand, and Madhya Pradesh, both in
terms of URP and MRP. Punjab is the best performing state in terms of this indicator. Income
inequality measured by the Gini coefficient (in rural areas) is highest in Haryana followed by
Kerala, Maharashtra, Punjab, Tamil Nadu, and West Bengal. Though inequality is lowest in
rural areas of Bihar and Assam, this may mean greater equality at low levels of income. In
urban areas, income inequality is highest in Madhya Pradesh followed by West Bengal,
Haryana, Karnataka, Kerala, Maharashtra, and Chhattisgarh.
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Data Source: Ministry of Statistics and Program Implementation, Government of India. Central Statistical Organization
Health Related Facts Infant mortality rates (IMR) i.e. the number of infant deaths (one year
of age or younger) per 1000 live births, for which relatively recent data are available, were
highest in Madhya Pradesh, Orissa, Uttar Pradesh, Assam, Rajasthan, Chhattisgarh, and
Bihar. Kerala was by far the best performing State, way above Tamil Nadu and Maharashtra.
Birth rates in 2008 were lowest in Kerala, while UP had the highest rates, followed by Bihar,
Madhya Pradesh, and Rajasthan. While death rates do not show large variation across States,
the worst performer in this regard was Orissa, followed by Madhya Pradesh, Assam, and
Uttar Pradesh.
Education Related Facts: Interestingly, the best performer in terms of gross enrolment ratio
(GER) for elementary education was Jharkhand, followed by Madhya Pradesh, Chhattisgarh,
and Gujarat and the worst performers were Haryana, Kerala, and Punjab which were the best
performers in many other areas. This may be due to overage children studying in primary
schools in backwards states and double entry of data in some states. GER for secondary
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education was highest in Himachal Pradesh, Tamil Nadu, Kerala, and Madhya Pradesh while
Bihar was the worst performing State
Economic issues: One-third of India's population (roughly equivalent to the entire population
of the United States) lives below the poverty line and India is home to one-third of the
world's poor people. Though the middle class has gained from recent positive economic
developments, India suffers from substantial poverty. According to the new World Bank's
estimates on poverty based on 2005 data, India has 456 million people, 41.6% of its
population, living below the new international poverty line of $1.25 (PPP) per day. The
World Bank further estimates that 33% of the global poor now reside in India. Moreover,
India also has 828 million people, or 75.6% of the population living below $2 a day,
compared to 72.2% for Sub-Saharan Africa. Wealth distribution in India is fairly uneven,
with the top 10% of income groups earning 33% of the income. Despite significant economic
progress, 1/4 of the nation's population earns less than the government-specified poverty
threshold of $0.40/day. Official figures estimate that 27.5% of Indians lived below the
national poverty line in 2004–2005. A the first factor explains the largest variance as it is
19.283 and 6.748 Eigen value 2007 report by the state-run National Commission for
Enterprises in the Unorganized Sector (NCEUS) found that 25% of Indians, or 236 million
people, lived on less than 20 rupees per day with most working in "informal labour sector
with no job or social security, living in abject poverty.
Human development profile of Gujarat: Gujarat, a high economic growth model recently
applauded in a US Congressional report, figures the worst in terms of overall hunger and
malnutrition in the country. Among the industrial states, Gujarat has very high incidence of
malnutrition among Scheduled Caste and Scheduled Tribe women, says 'India Human
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Development Report 2011' brought out by Institute of Applied Manpower Research of the
Planning Commission. Gujarat also comes one notch down in the Human Development Index
(HDI), at the 11th position among various states in the country in 2007-08, against the 10th
spot in 1999-2000. It has now been replaced by Jammu and Kashmir, which was earlier
holding the 11th spot on the HDI in 1999-2000. The score of Gujarat on the Human
Development Index has, however, improved to 0.527 in 2007-08, from 0.466 in 1999-2000.
Gujarat HDI, however, is above the national average of 0.467 as per the report of 2007-08.
The performance of Gujarat on the HDI is lower as compared to many smaller states like
Kerala, Himachal Pradesh, Goa, Punjab, Jammu and Kashmir and the North-East (excluding
Assam). .
Human development Profile of Uttar Pradesh: Uttar Pradesh is often described as the
“Hindi speaking heartland” of India. The status of human development in U.P. continues to
be far from satisfactory even after more than five decades of development planning aimed at
social and economic upliftment of the people. It ranked at 13th position in terms of Human
Development Index (HDI) prepared by the Planning Commission in 2001. This shows a
marginal improvement from the 14th position that U.P. occupied in 1991. In 20011 India
human development report HDI value for Uttar Pradesh is below from national level. Health
status is a serious issue in UP. Uttar Pradesh is lagging behind most of the States of the
country in terms of the major indicators of social development. Literacy rate in U.P. (56.3
percent) is very low when compared with States like Kerala (90.9 percent), Goa (82.0
percent), Himachal Pradesh (76.5 percent) and Tamil Nadu (73.5 percent). The ranking of
Uttar Pradesh in terms of literacy is 31 in 2001 among total of 35 States and UTs.
Study Objectives
22
There have been many efforts made on measuring and improving human development at
national and sub national level. There are many issues related with the dimensions and
methodology. HDI is limited to very few dimensions. But there is no comprehensive attempt
to compute an index at micro level. The present study makes a modest contribution by
providing a comprehensive analysis of the various dimensions of the EASWBI of the
Individuals of different cities. This study identifies the indicators responsible for the social
and economic well being at individual level instead of studying at national level. The
objectives of the study are as follows:

What are the determinants of Economic and Social Well being at individual level of
different cities?

The main objective of the study is to explore the new core dimensions of well being
and to construct a multidimensional index of Economic and Social Well Being Index
(EASWBI).

To introduce a better statistical measure of multivariate factor analysis to find the
dimensions of well being.

Is there any method to check the input oriented efficiency of the individuals in terms
of index values of different cities?

How are the dimensions of economic and social well being correlating to each other?

Does EASWBI affect the economic growths of Indian states? How the states of India
are performing in terms of EASWBI?

How EASWBI is an improvement over other development indices (particularly HDI)
in international framework?
. Hypotheses
23

Individual well being is not only function of income but also a function of various
factors such as economic, social and institutional
such as income, assets,
consumption, health ,knowledge, security, satisfaction from living, awareness for the
current issues, utilities, distance to basic facilities, environment and dwelling.
•
There is high positive correlation between the different dimensional indicators of
health, knowledge, income, assets, awareness, satisfaction, consumption, utilities,
dwelling, infrastructure, environment and suggested Economic and Social Well being
Index for the sampled individuals of different cities.
•
There is high positive correlation between Economic and Social Well being Index
(EASWBI) worked out on the basis of exploratory and confirmatory analysis.
Organization of the work
The most relevant development and economic welfare theories and empirical studies on
human development are analysed in section II, ranging from welfare economics to human
development reports. section III contains the dimensions, variables, and methodology and
survey methods of the research. The city specific exploratory and confirmatory factors
driving to EASWBI find its place in the subsequent section IV. The impact of EASWBI on
Indian states has been analyzed through panel data in section IV. The relative importance of
EASWBI among several alternative indices is examined in the section V. Last but not the
least, the section VI presents an over-all summary with the key findings and salient
conclusions.
24
Theoretical Foundations and Empirical Studies
This section presents a review of the different theoretical models and empirical studies. It
starts from economic welfare theories, capability theory, basic need approach.
Theories of Economic welfare
A proper conceptualization of Economic well being is an important part of welfare
economics. Welfare economics is a branch of economics that uses microeconomic techniques
to evaluate economic well-being. It analyzes social welfare, however measured, in terms of
economic activities of the individuals. Accordingly, individuals, with associated economic
activities, are the basic units for aggregating to social welfare, whether of a group, a
community, or a society, and there is no "social welfare" apart from the "welfare" associated
with its individual units. This chapter starts from the seventeen centaury’s approach of
economic well being focuses on the utility approach (Bentham, 1789) such as happiness and
pleasure.
Classical welfare theories
The Utility Approach
Traditional welfare economics give attention to utility approach it is reflected by happiness,
pleasure and desire fulfilment. The doctrine of utilitarianism saw the maximization of utility
as a moral criterion for the organization of society. According to utilitarian, such as Jeremy
Bentham (1748–1832) and John Stuart Mill (1806–1873), society should aim to maximize the
total utility of individuals, aiming for "the greatest happiness for the greatest number of
people". Another theory forwarded by John Rawls (1921–2002) would have society
maximize the utility of those with the lowest utility, raising them up to create a more
25
equitable distribution across society. The theory suggests that welfare should be based on the
requirements of the poorest individuals in a population, and that the aim of welfare should be
to make them better off. Social inequality is only acceptable in that it encourages the less
well-off to work harder to improve their position. However economic well being in terms of
utility is less exclusive approach. Happiness or desire fulfilment represents only one aspect of
human existence. It does not reflect freedom to achieve happiness. It sees a person as just a
location of pleasure. It does not have any scale to measure happiness.
Opulence approach:
Another focuses on opulence approach of economic well being (Adam Smith, 1776) which is
reflected by incomes, commodity possessions etc. The opulence approach had a more
practical origin, and is based on the idea that having well being is closely related with “being
well off”, and is thus a matter of having goods and services. The focus on the growth of GNP
per head is, of course, a simple version of this approach. But while the possession of
commodities is valuable, it is not valuable in itself. Its value must depend on what
commodities can do for people, or rather, what people can do with these goods and services.
New Welfare Economics theories
New welfare approach is developed by the Pareto, John Hicks and Nicholas Caldor. This
approach puts more stress upon the efficiency of the distributed utilities. Pareto has talked
about efficiency to maximise welfare. Pareto efficiency is a necessary, but not a sufficient
condition for social welfare. Each Pareto optimum corresponds to a different income
distribution in the economy. Some may involve great inequalities of income. So how do we
decide which Pareto optimum is most desirable?
26
We specify the social welfare function to solve the decision problem. This function embodies
value judgments about interpersonal utility. The social welfare function shows the relative
importance of the individuals that comprise society. A utilitarian welfare function (also called
a Benthamite welfare function) sums the utility of each individual in order to obtain society's
overall welfare. All people are treated the same, regardless of their initial level of utility. One
extra unit of utility for a starving person is not seen to be of any greater value than an extra
unit of utility for a millionaire. At the other extreme is the Max-Min, or Rawlsian utility
function (Stiglitz, 2000). According to the Max-Min criterion, welfare is maximized when the
utility of those society members that have the least is the greatest. No economic activity will
increase social welfare unless it improves the position of the society member that is the worst
off. Most economists specify social welfare functions that are intermediate between these two
extremes. The social welfare function is typically translated into social indifference curves so
that they can be used in the same graphic space as the other functions that they interact with.
A utilitarian social indifference curve is linear and downward sloping to the right. The MaxMin social indifference curve takes the shape of two straight lines joined so as they form a 90
degree angle. A social indifference curve drawn from an intermediate social welfare function
is a curve that slopes downward to the right. The intermediate form of social indifference
curve can be interpreted as showing that as inequality increases, a larger improvement in the
utility of relatively rich individuals is needed to compensate for the loss in utility of relatively
poor individuals.
Capability theory
The capability approach is a broad normative framework for the evaluation and assessment of
individual well-being and social arrangements, the design of policies, and proposals about
social change in society. It is used in a wide range of fields, most prominently in development
27
studies, welfare economics, social policy and political philosophy. It can be used to evaluate
several aspects of people’s well-being, such as inequality, poverty, the well-being of an
individual or the average well-being of the members of a group. It can also be used as an
alternative evaluative tool for social cost– benefit analysis, or as a framework within which to
design and evaluate policies, ranging from welfare state design in affluent societies, to
development policies by governments and non-governmental organizations in developing
countries. The capability approach is not a theory that can explain poverty, inequality or wellbeing; instead, it rather provides a tool and a framework within which to conceptualize and
evaluate these phenomena. Applying the capability approach to issues of policy and social
change will therefore often require the addition of explanatory theories. The core
characteristic of the capability approach is its focus on what people are effectively able to do
and to be; that is, on their capabilities. This contrasts with philosophical approaches that
concentrate on people’s happiness or desire-fulfillment, or on income, expenditures, or
consumption.
Some aspects of the capability approach can be traced back to, Aristotle, Adam Smith, and
Karl Marx (Nussbaum, 1988, 2003b; Sen, 1993, 1999a). The approach in its present form has
been pioneered, by the economist and philosopher Amartya Sen (1980, 1984, 1985a, 1985b,
1987, 1990b, 1992, 1993, 1995, 1999a) and has more recently been significantly further
developed by the philosopher Martha Nussbaum (1988, 1992, 1995, 1998, 2000, 2003a,
2004, forthcoming), and a growing number of other scholars.
Sen argues that our evaluations and policies should focus on what people are able to do and
be, on the quality of their life, and on removing obstacles in their lives so that they have more
freedom to live the kind of life that, upon reflection, they have reason to value.
Sen (1985; 1993) makes the following conceptions:
28
Functioning - A functioning is an achievement of a person: what she or he manages to do or
be. It reflects, as it were, a part of the .state of that person. (Sen, 1985, p.10). Achieving a
functioning (e.g. being adequately nourished) with a given bundle of commodities (e.g. bread
or rice) depends on a range of personal and social factors (e.g. metabolic rates, body size,
age, gender, activity levels, health, access to medical services, nutritional knowledge and
education, climatic conditions, etc). A functioning therefore refers to the use a person makes
of the commodities at his or her command.
Capability - A capability reflects a person’s ability to achieve a given functioning (.doing. or
.being.) For example, a person may have the ability to avoid hunger, but may choose to fast
or go on hunger strike instead.
Functioning n-tuple - A functioning n-tuple (or vector) describes the combination of ‘doings
and beings’ that constitute the state of a person’s life. The functioning n-tuple is given by the
utilization (through a personal utilization function) of the available commodity bundle. Each
functioning n tuple represents a possible life-style.
Capability Set - The capability set describes the set of attainable functioning n-tuples or
vectors a person can achieve. It is likely that a person will be able to choose between different
commodity bundles and utilizations. The capability set is obtained by applying all feasible
utilizations to all attainable commodity bundles (Sen, 1985). Sen (1999) emphasizes that
capabilities reflect a person’s real op or positive freedom of choice between possible lifestyles.
The capability approach has been advanced in somewhat different directions by Martha
Nussbaum, who has used the capability approach as the foundation for a partial theory of
justice.
Theories of basic needs:
29
Rawls identifies primary goods through ‘deliberative rationality’. According to The Theory
of Justice, primary goods ‘‘are in general necessary for the framing and execution of a
rational plan of life’’ (Rawls, 1999, pp. 359, 380). They are derived from ‘some general facts
about human wants and abilities’ and the necessities of social interdependence.
Finnis’ approach is derived from practical reasoning (Finnis, 1980; Finnis et al., 1987),
which has a lot in common with ‘deliberative rationality’, as it is derived from critical
reflection about the planning of one’s life or the internal reflection of each person upon her
own thoughts, reading, imagination and experiences.
Doyal and Gough’s definition of basic needs is based on the principle of ‘the avoidance of
serious harm’, where harm is defined as preventing people realizing activities that are
essential to their plan of life (Doyal and Gough, 1991). Nussbaum’s list is developed on the
basis of what is termed ‘overlapping consensus’ plus intuition as to what is needed to be
‘truly human’ (Nussbaum, 2000).3 An overlapping consensus is an informed view of what
people agree about, even with different overall philosophies or religions. The Voices of the
Poor analyses (Narayan-Parker, 2000) represent what the poor identify as their needs, based
on focus groups of poor people carried out around the developing world. A similar exercise is
being conducted by the ESRC Research Group of Well-being in Developing Countries
(Camfield, 2005), in which people are consulted as to what makes for a good quality of life in
four countries.
Broadly, these efforts reflect two approaches: some aim to identify the constitutive elements
of a good or flourishing life (e.g. Aristotle and Finnis), while others are concerned primarily
with the necessary requirements of such a life (e.g. Doyal and Gough, and Rawls in relation
to primary goods). This is one reason why the six sets of requirements for human flourishing
are not in total agreement those looking at the constitutive requirements of a good life place
30
less emphasis on material aspects, while those exploring the necessary requirements for such
a life tend to emphasize material aspects more. For example, Finnis et al. and Nussbaum are
quite thin on material aspects, but emphasize non-material aspects such as friendship and
emotions, which are given little or no emphasis by others. Environmental issues appear
explicitly in Nussbaum, while they are neglected or discussed briefly by others. Nussbaum is
the only author to record ‘respect for other species’ as a significant dimension.
Table (2.1) Requirements for human flourishing
Rawls
Defining
concepts
Primary
goods
Bodily well
being
Material
well being
Income
and
wealth
Mental
developme
nt
Work
Security
Finnis,B
oyle and
Gough(
1987)
Basic
human
values
Bodily
life
health,
vigor
and
safety
Freedo
m
of
occupati
on
Knowle
dge
practical
reasona
bleness
Skilful
perform
ance in
work
and play
Doyal and Gough
(1987)
Nussbaum(2000)
Narayan
parker(2000)
Camfield(2005)
Basic needs and
intermediate needs
Central human
functional
capabilities
Life
Bodily health
Bodily integrity
Dimensions
of well being
Quality of life
Physical health
a. Nutrition:
food and
water
b. Health care
c. Safe birth
control and
d. Safe
physical
environme
nt
Protective housing
economic security
Basic education
Bodily well
being
Access
to
health
services
Good
physical
environment
Material well
being
Food assets
Senses
Food shelter
Education
Work
work
Physical security
Civil peace
Physically
safe
environment
Lawfulness(a
ccess
to
justice)
Personal
31
Social
relations
Social
bases of
self
respect
Spiritual
well being
Empowerm
ent
and
political
freedom
Friends
hip
Significant primary
relationship
Affiliation social
bases for self
respect
physical
security
Security in
old age
Social well
being
Family
Self respect
and dignity
Community
relations
Self
integrati
on
Harmon
y with
ultimate
source
of
reality
Rights
Libertie
s
Opportu
nities
Powers
and
prerogat
ives of
office
and
position
s
of
responsi
bility
Freedo
m
of
movem
ent
Religion
(important
in
Bangladesh and
Thailand)
Autonomy
of
agencies
Civil and political
rights
Control
over
one’s
environment
Freedom of
choice and
actions
Literature on some existing indices of human development
Mahbub ul Haq (1996) proposed the concept of Human development in his book Reflections
on Human Development. The human development paradigm is concerned both with human
capabilities (through investment in people) and with using those human capabilities fully
(through an enabling framework for growth and employment).He described the four essential
components of human development such as Equity, Sustainability, Productivity and
Empowerment to enlarge the people’s choices. Human development regards economic
32
growth as essential but emphasizes the need to pay attention to its quality and distribution. He
explored the linkages between economic growth and human development. Accordingly,
there are four ways to create desirable links between economic growth and human
development. First, emphasis on investment in the education, health, skills of the people can
enable them to participate in the growth process as well as to share its benefits in terms of
employment.
Second, more equitable distribution in income and assets is critical for creating a close link
between economic growth and human development. Third, some countries have managed to
make significant improvements in human development even in the absence of good growth.
Fourth, empowerment of women is a desirable link of growth and development.
There are some indexes have been developed to measure human well being. In an influential
study, Morris D. Morris (1979) proposed the Physical Quality of Life Index (PQLI) to focus
on development as achieved well-being. The social indicators used in the construction of
PQLI are: life expectancy at age one (LE), (ii) infant mortality rate (IMR) and (iii) literacy
rate (LIT). In particular, he took a simple average of the indices for each of the three factors.
For each indictor, the performance of individual countries is placed on a scale of 0 to 100,
where 0 represents worst performance and 100 represents best performance.
Dasgupta and Weale (1992) constructed a measure of social well-being to focus on the
quality of life on the basis of the following indicators: (i) per capita income (1980 ppp US $)
(ii) life expectancy at birth (years), (ii) infant mortality rate (per 1000), (iv) adult literacy rate
(percent), (V) an index of political rights (1979) and (vi) an index of civil rights, 1979. He
proposed using Borda method to rank 48 countries. He extended measures of general well
being by including ordinal indices of political and civil liberties and provide a ranking of the
33
world’s poorest countries on the basis of BORDA rule. Then he compared the improvements
in socio economic performance with the availability of political and civil liberties during the
decades of the 1970’s and observed the improvements in per capita national income, life
expectancy at birth, infant mortality rate are positively correlated with the extent of political
and civil liberties enjoyed by citizens, while improvements in literacy are negatively
correlated with these liberties.
The UNDP’s Human Development Index (1990-2011) revolutionized the concept of human
development. The HDI differs from the conventional approach in economic growth models as
described earlier in bringing together the production and distribution of commodities and the
expansion and use of human capabilities. HDI also focuses on choices .on what people should
have, be and do to be able to ensure their own livelihood. HDI is based on three indicators:
life expectancy, educational attainment, and per capita income.
While the HDI measures overall progress in achieving human development, the Human
Poverty Index (HPI) measures the distribution of progress through the backlog of deprivation.
The broad dimension in which deprivation is measured is the same as in the HDI – health
status, knowledge and standard of living. In HDR 2010 inequality in each dimension of the
HDI addresses an objective first stated in the 1990 HDI .This Report introduces the
Inequality adjusted HDI , a measure of the level of human development of people in a society
that accounts for inequality. Under perfect equality the HDI and the IHDI are equal. When
there is inequality in the distribution of health, education and income, the HDI of an average
person in a society is less than the aggregate HDI; the lower the IHDI (and the greater the
difference between it and the HDI), the greater the inequality.
34
Another index to measure global competitiveness has been developed by world economic
forum in global competitiveness report 2011.It includes a weighted average of many different
components, each measuring different aspects of competitiveness. These are called economic
pillars of economy. These pillars comprises of institution, infrastructure, health and primary
education, higher education and training. These are the basic requirements of the economy
for sustainable development.
Economic well being Index was initially constructed for Canada by centre for the study of
living standard in 1998. Lars Osberg and Andrew Sharpe (2001) measured different
dimensions of Consumption, Wealth, Equality and Security to develop EWBI in Canada. The
basic framework of their index was that a society’s well being depends on societal
consumption and accumulation and on the individual inequality and insecurity that surrounds
the distribution of macroeconomic aggregates.
OECD (2001) report on The Well-being of Nations addressed the interlinkage between the
investment in human and social capital with positive outcome of the sustainable economic
and social development. The role of social capital has been stressed for enlarging the human
well-being of the countries.
World Bank’s, Quality of Growth (2000) publication also stressed that there are four factors
especially relevant for poverty outcomes: distribution, sustainability, variability, and
governance surrounding the growth process.
Gustavo rains, France stewarty and Alejandra Ramirez (2005) studied Economic growth and
human development. In their study they found strong connection between economic growth
and human development on the one hand EG provides the resources to permit sustainable
35
improvement in HD on the other hand improvements in Quality of the labor force are
important contributors to EG.
Steve Chan &Cal Clark(1998)They found in their study that although few countries in the
developing world have achieved rapid economic growth but the achievement has often failed
to produce social equity or mass well being, while average income of these countries has
increased sharply, their median income has not risen nearly as fast. These income gains from
economic growth have trickled down very slowly to masses. They have gone largely to small
elite class.
H.W.Ardent (1983) worked about the mechanism of trickle down which implies a vertical
flow from rich to poor. It is assumed that with rise in per capita income the benefit of the
growth would trickle down to the poorest section in the economy so poverty and inequality
get automatically reduced at the later stage of development.
There are some studies which are looking into the relationship between human development
and growth. K.M. Naidu, L.K.Mohan Rao and K. Mahesh Naidu (2001)supported that both
EG and HD should be promoted simultaneously but at the sometime they suggested that HD
should be given priorities when choice is necessary due to the resources or any other
constraint
Existing literature on Methodological issues to measure human development
Human development has been criticized as a redundant measure that adds little to the value of
the individual measures composing it; as a means to provide legitimacy to arbitrary
weightings of a few aspects of social development; and as a number producing a relative
ranking which is useless for inter-temporal comparisons, and difficult to interpret because the
36
HDI for a country in a given year depends on the levels of, say, life expectancy or GDP per
capita of other countries in that year.
Basudeb Biswas and Frank Calindo (1998) did a multivariate analysis of the Human
Development Index. In this study they have criticized the method of calculating HDI.
According to them the simple averaging of the component indexes causes concern. The
weighting procedure of the components is arbitrary. To the extent one component index has a
different variance than another so equal weights seems unsatisfactory. Arbitrary weighting
procedures are unable to adequately capture the inherent interdependence of these
components .multivariate approaches are able to incorporate interdependence among the
components. They have used PC analysis, which combines various measures of human
development in an optimal manner to create a development index. The simple average of the
component indexes yields rankings roughly equivalent to a more complex multivariate
technique that selects the weights optimally.
Jaya Krishanakumar and A.L.Nagar (2007) examines the different statistical properties of
multidimensional indices. For this purpose they have applied PC analysis, FA model, MIMIC
model and SEM.
Wagle (2005) used SEM model for driving multidimensional poverty measures using
household data from a survey conducted in Kathmandu, Nepal in 2002 and 2003.five major
dimensions of well being were considered: subjective economic well being, objective well
being, economic well being, economic inclusion and civil/cultural inclusion. Each of these
dimensions is measured by a series of indicators and they influence one another through a
system of simultaneous equations.
Krishanakumar (2007) proposes a general SEM model with exogenous variables in structural
and measurement parts for operationalising Sen’s capability approach, including three
37
dimensions namely knowledge, health and political freedom and demonstrates the utility of
such framework for driving a multidimensional index of human development using
worldwide country level data.
Rati Ram proposed multivariate method of principal components for construction of various
composite indices of economic development that capture per capita income, basic needs
fulfillment and other possible indicators of well being. He computed basic needs fulfillment
index by five components as adult literacy, life expectancy, safe water access, and physician
supply and calorie intake. the weights implicated in the principal component are
0.27,0.10,0.29,0.25 and 0.09.thus it captures 86% of the variation in the five indicators which
shows that composite index is quite good.
Nagar and S.R. Basu (2001) have proposed principal component method of estimating the
human development index. They have criticized HDI on the basis of arbitrary weights (two
thirds to ALR and one-third to CGER in measuring educational attainment) .complex
phenomena like human development and quality of life are determined by a much larger set
of social indicators than only the few considered thus far. An estimator of the human
development index is proposed as the weighted average of the principal components, where
weights are equal to variance of successive principal components. This method helps in
estimating the weights to be attached to different social indicators. They have used the data of
173 countries on the same variables as were used in HDI 1999.human development is an
abstract conceptual variable which cannot be directly measured but is supposed to be
determined by the interaction of a larger number of socio economic variables. So latent
variable H is linearly determined by causal variables x1...........xk as
H    1 x1  ...........k xk  u
38
So the total variation in H is composed of two orthogonal parts (a) variation due to casual
variables, and (b) variation due to error. if the model is well specified, including an adequate
number of causal variables so that the mean of the probability distribution of u is zero (Eu=0)
and the error variance is small relative to the total variance of the latent variable H it can be
assumed that the total variation in H is largely explained by the variation in causal variables.
They have replaced the set of the causal variables by an equal number of their principal
components, so that 100% of variation in causal variables is accounted for by their principal
components.
For this firstly they transformed the causal variables into their standardized form to solve it
for determinantal equation, then find the characteristic vector and obtain the first principal
component as
. p1  11  X1..............1 X1k
Where, the coefficients are the elements of successive characteristic vectors of R.
Then the estimator of the human development index is obtained as the weighted average of
the principal pal component: thus
H
1P1  .................K PK
1  ..........K
Where, the weights are the characteristic roots of R.
By using this methodology they proposed to construct the HDI by using the same casual
variables as were used by the HDI report 1999 of the UNDP. They have found that GDP
(measured as loge Y) is the most dominant factor. If only four variables (loge Y, LE, CGER
and ALR) are used (as in HDR 1999), loge Y has the largest weight, followed by LE and
CGER. ALR has the least weight. In another results they took eleven social indicators as
determinants of human developments. these variables are real GDP per capita(Y),life
39
expectancy (LE),IMR, hospital beds (HB),access to sanitation(ASA), access to safe water
(ASW), health expenditure (HE), adult literacy rate (ALR),CGER, commercial energy use
per capita (CEU) and average annual deforestation (ADE ) and they found that after the
highest weight for loge Y, health services (in terms of hospital beds available) and an
environmental variable (measured as average annual deforestation) with the second and third
largest weights. LE ranks fourth in order of importance, ASW is the fifth, and CGER has
higher weight than ALR. These results the order of importance of different social indicators
in determining human development.
Seema Vyas and Lilani Kumar Ranayake (2005) proposed the process to derive a Socio
Economic Status index in the absence of income or consumption data by performing PCA on
durable asset ownership, access to utilities and infrastructure, and housing characteristic
variables. The main advantage of this method over the more traditional methods based on
income and consumption expenditure is that it avoids many of the measurement problems
associated with income- and consumption-based methods, such as recall bias, seasonality and
data collection time, compared with other statistical alternatives.
Rajesh shukla and preeti kakar (2006) worked on the Role of science and technology, Higher
education and research in regional socio-economic development this study’s conceptual
framework provides some basic information on regional disparities in terms of economic,
scientific and technological and human development. They have made an attempt to focus on
the role of science and technology on regional development of India by considering 21 Indian
states. They have proposed multivariate technique of principal component analysis to define a
synthetic measure that is able to capture interactions and interdependence between the
selected set of indicators making up the three indices. They have proposed a composite index
of S $ T which comprises three sub indices: scientific manpower, Health and infrastructure.
40
Their Welfare Index evaluates the society’s overall well-being or standard of living. It
comprises two sub-indices, namely, Asset and Wellbeing.
(a) Components of Asset: Asset comprises six indicators – TV set, computer, telephone,
mobile phone, internet and cable. The proportion of each state’s population that owns these
durables are evaluated and the indicators reveal the overall affluence of the society
(b) Components of well-being: As the name suggests, this Index evaluates the overall
wellbeing of society, which in turn reflects the wellbeing of the state as a whole. To measure
the Wellbeing, they have included nine indicators: People below poverty line, literacy rate,
per capita consumption expenditure (in Rs.), per capita expenditure on education, per capita
expenditure on health per capita expenditure on telephone, per capita expenditure on mobile ,
per capita expenditure on internet and per capita expenditure on cable., Higher the value of
the Index, the better the level of wellbeing of the region. Thus, Welfare Index is quite a
comprehensive composite measurement to capture the quality of life of the people.
In results they found that Delhi, Goa, Tamil Nadu, Kerala and Andhra Pradesh have emerged
as the “most advanced”, and are at the top-most rung with a score of >0.70. The “more
advanced” states are a step below with scores ranging between 0.42 and 0.70. These include
Maharashtra, Karnataka, Gujarat, Uttaranchal and Punjab. The third tier of “less advanced”
states are those with S&T Index scores of between 0.16 and 0.42 including West Bengal,
Assam, Haryana, Himachal Pradesh, Orissa and Uttar Pradesh. Finally, at the bottom of the
S&T ladder are the “least advanced” states with scores of <0.16 – Chhattisgarh, Bihar,
Jharkhand, Madhya Pradesh and Rajasthan..
Mark McGillivray and Farhad Noorbakhsh (2004) surveyed the various composite well-being
indices that have been inter-country assessments over the last 40 or so years, including the
41
well known Human Development Index (HDI). A number of issues are considered, including
the choice of components, component weights, scale equivalence, non-linearity, correlations
among components and the policy relevance of such measures. A number of these issues are
examined in the context of a critical review of the many criticisms of the HDI and the United
Nations Development Programme’s responses to these criticisms (some involving changes to
the design of the index). A basic premise of the paper is that indices used for international
well-being comparisons should be relevant to the policies and individual priorities of
countries. Possible directions for the future design and application of composite well being
indicators are identified, including adoption of country-specific variables, participatory,
country and time variant component weighting schemes and the inclusion of human security
measures.
Bernhard Mahlberg &Michael Obersteiner (2001) used models from Data Envelopment
Analysis (DEA) literature to compare performance in a multiple output setting. The models
were evaluated by empirically re-estimating the human development index (HDI).
DK Despotis (2004) assessed the HDI in the light of data envelopment analysis
(DEA).Instead of a simple rank of the countries, human development is benchmarked on the
basis of empirical observations of best practice countries. First, on the same line as HDI, he
developed a DEA-like model to assess the relative performance of the countries in human
development. They extended results with a post-DEA model to derive global estimates of
new development index by using common weights for the socioeconomic indicators.
Relevant literature on state level human development in India
There are some studies which are looking into the Human Development and Regional
Disparities in India. Farhaad Noorbaksh (1998) this paper discusses a modified index for
measuring human development. The Suggested index is based on the components of the
42
Human Development Index (HDI) developed by the United Nations Development
Programme (UNDP) since 1990. It discusses two categories of technical issues related to the
HDI for 1995: those related to the components and those relevant to the structure of the
index. The data from the Human Development Report 1995 for 174 countries are used to test
the robustness of the suggested index and the results are compared to those of the HDI. The
new index is then used to delineate, with some justification, different groups of countries at
various levels of human development. He studied Human Development and Regional
Disparities in India. His research analyses regional disparities amongst major states in India
to find out if they are on a convergence or further divergence course. It compares human
development and poverty indices for various states in India and investigates if there has been
any reduction in disparities over a decade. The analysis is extended to the evolution of
disparities amongst the states with respect to a larger set of socio-economic indicators.
Another study (1990) on the post Trade labour linkages in India through a set of case studies
from West Bengal, Maharashtra and Gujarat. it was undertaken to measure the effects of
international trade on the quality of life of the people in the developing countries. This study
examines the case studies in sufficient detail and explores the connection between openness
to international trade in goods and services and financial flows and conditions of workers
engaged in related occupation. This study, based on surveys of a number of traditional
villages, sub-urban and urban industries, explores the implications of trade on the labour
market. This study is measuring the welfare impact of trade liberalization on low income
groups in a country. This study shows that liberalization of trade in India has had far reaching
impact at the grassroots level at least in research areas, thereby confirming the role positive of
trade in uplifting the economic conditions of the poor. This study proves that reformatory
measures have improved the conditions of the people. It was found in West Bengal that the
43
export sector has upgraded the quality of life of a section of the poor. On the other hand, the
traditional sector has not changed much.
R.H Dolakia (2004) examines the trends in regional disparity in India's economic and human
development over the past two decades, and the direction of their causality. The Indian
regional data suggest a two-way causality between human and economic development. The
paper argues that the Planning Commission and the finance commissions need not be unduly
concerned about regional imbalance in human or economic development. Emphasis on
economic growth is likely to address the issue of disparities in income and human
development speedily
Ghosal R.K. (2005)
examined the nature of disparities in economic growth and human
development across the states of India during the period of liberlisation.the result confirms
that the social sector expenditure made by the government and private household together
explain significant proportion of interstate disparity in human development and these two are
found to be statistically significant explanatory factors.
Madhusudan Ghosh (2006) studied the two way relationship focusing on the performance of
Indian states. The ranks of the states were arrived on the basis of their HDI, Literacy rate and
expansion of the life at birth.
Farhaad Noorbaksh (1998) studied Human Development and Regional Disparities in India. is
research analyses regional disparities amongst major states in India to find out if they are on a
convergence or further divergence course. It compares human development and poverty
indices for various states in India and investigates if there has been any reduction in
disparities over a decade. The analysis is extended to the evolution of disparities amongst the
states with respect to a larger set of socio-economic indicators.
44
Dreze and Sen (1995) find the diversities in economic and social development amongst the
Indian states remarkable.
Ravallion and Datt (2002) in a cross-state study of poverty in 15 major states in India
conclude that various states have different capacities for poverty reduction for a variety of
reasons. They argue that a substantial difference of the elasticity of poverty index to non-farm
output between the state with the lowest elasticity, Bihar, and the state of Kerala is due to the
difference in literacy rates between these states.
In a previous study Datt and Ravallion (1998) referring to major states in India - after
controlling for a number of socio-economic conditions, which in turn explains why some
states in India did better in reducing poverty than others – conclude that “Starting endowment
of physical infrastructure and human resources appear to have played a major role in
explaining the trend in poverty reduction;”
The same authors (1993) observe “Disparities in living standards among regions and
between urban and rural sectors have long raised concern in India.”
The National Human Development Report 2001 for India (2002) reveals vast differences in
human development and poverty between the States of India in 1981. The report notes that
“At the state level, there are wide disparities in the level of human development.” (NHDR
2002, page 4). The report also notes that disparities amongst the States with respect to human
poverty are quite striking.
A Report by the commission on the measurement of economic performance and social
progress by Sen, Fittousi and Stiglits published in 2009. This report distinguishes between an
assessment of current well being and assessment of sustainability, whether this can last over
time. Current well being has to do with both economic resources, such as income and with
45
non economic aspects of people’s life. Whether these level of well being can be sustained
over time. It depends on whether stocks of capital that matters for our lives (natural, human,
social and physical) are passed to future generations. This report has focused on three
dimensions such as classical GDP issues, quality of life and sustainability. The aim of the
report is to identify the limits of GDP as an indicator of economic performance and social
progress including the problem of its measurement for a better measurement of economic
performance in a complex economy, capturing quality change and underestimating quality
improvements is equivalent to overestimating the rate of inflation, underestimating real
income, the focus of the report is to prepare a satisfactory measure of economic performance
and living standard, it is important to come to grips with measuring government output and to
solve methodological issues for adjustments in quality change.
Menon, Sudha Venu (2008) examines growth experience in various sectors of the state and
analyzes the medium and long term growth potential of the economy. Sector wise
performance of Gujarat economy is analyzed with a focus on key engines of growth and the
effective role of these growth engines in macroeconomic acceleration of Gujarat Economy
.this study attempts to identify principal drivers of the economy in the state and their
contribution to economic growth. The sectors are- Energy, Oil & Gas, Agro &food
processing, Textiles, Diamonds, Petrochemicals, and SEZ etc. Concluding sect ion highlights
policy recommendations for sustained economic growth including land reform, investment in
education and infrastructure, ports, more FDI, transparency and efficiency in administration,
attaining social cohesion, macroeconomic management etc
Concluding remarks: this section contains the most of the existing literature and work on
well being. It explains the theories from economic welfare to human development, the most
important the shift of economic growth to overall wellbeing. This section includes various
46
type of indices, developed to measure well being, the main focus was on the dimensions and
methodology. We have also accounted for the criticism of human development index with
respect to limited dimensions and simple methodology. Some of the literature indicates the
justification for applying multivariate model to develop an index. We also focus on regional
disparity among Indian states on economic and social well being. On the basis of existing
literature we establish a well-built background in the field of economic and social well being.
The work of economic and social well being index is inspired from Nagar and Basu (2001).
We wish to extend his work to other parts of society in context to India.
47
Section III
Constructing an Index of Economic and Social Wellbeing at Micro level
In recent years, interest in aggregate or composite indicators of economic and social wellbeing at the community, national and international levels has grown significantly. Many
communities have attempted to develop social indicators to monitor trends in the welfare of
their citizens. We follows this path in some different way, as to capture all the responsible
factors for economic and social well being for individuals of urban area of three cities.The
objective of this chapter is to explore the of major indicators of economic and social wellbeing through exploratory analysis and justify the role of existing indicators that have been
developed from various studies by using confirmatory factor analysis.. The section provides
an overview of the available economic and social indicators, their role in well being, and
method for development of summary indexes, the focus of this section, is to generate the
EASWBI at micro level.
Conceptualizing Economic and Social well-being Index: Being multidimensional
phenomena, all the dimensions of well being should be considered simultaneously. We
propose to formulate a composite Index of economic and social well being, comprising of
different aspects of development. It considers the foremost dimensions of well being such as
Standard of living (in terms of income, consumption and wealth), Health, Educational
attainment, Environmental conditions, security measures, Living satisfaction, Access to basic
48
facilities and housing conditions etc. The composite indicator should ideally measure
multidimensional concepts which cannot be captured by a single indicator, e.g. health,
knowledge, security, etc. Economic and Social well being index is a composite index
measuring well being for different dimensions through multivariate factor analysis. A brief
summary of these dimensions is as follows:
Dimensions and indicators of Economic and social well being index
The dimensions of economic and social well being have been categorized into two major
categories. One is measuring subjective well being in terms of life satisfaction and awareness
etc. Another is objective measurements such as health, knowledge, environment, security,
services, housing conditions and access to basic facilities to measure individual capabilities
as it is a combination of ‘doings & beings”. The justification for the selection of these
dimensions, including variables is described in following manner:
Ability to lead a long and healthy life: Good health is both the means and end of the
development. The health of human capital generates both higher income and individual well
being. Improved health generates economic growth and poverty reduction in the long run.
World Development Report, 1993 stated, “Improved health contributes to economic growth
in four ways: It reduces production losses caused by worker illness, it permits the use of
natural resources that had been totally inaccessible because of disease, it increases the
enrolment of children in schools and makes them better able to learn, and it frees for
alternative uses resources that would otherwise have to be spent on treating illness.
Health and poverty is closely related. If poverty is eliminated, health can
easily be elevated. The role of health in influencing economic outcomes has been
acknowledged at the micro level (Strauss and Thomas, 1998) Health is a basic feature
49
shaping length and quality of people’s lives. In a study, by Adriane white, found that
happiness is closely associated with health, followed by wealth and education. There are
seven indicators1 have been used to measures the health status of the individuals, these
indicators are: nutrition level, proper vaccination, and exercise habits, sport habits and health
awareness. Information on these indicators have been captured by variables such as family
planning, health awareness, quality of food, routine checkups, exercise and sports, meals,
children and women immunization, health insurance and nutrition level etc.
Access to knowledge or educational attainment: Educations build character and ensure
effective productive work and contribute to efficient human development. The World Bank
(1993) began to recognize the strong linkage of education and human well being. Education
is basic input required to improve the quality of human resources. Education is more
important factor required to make labour, a productive factor. Labour without education and
skill cannot be graded as human resources. It becomes a hindrance to development.
Therefore, one of the necessary conditions for development is the improvement in the quality
of human resources through education. Higher level of literacy lead to greater economic
output, higher employment levels, better health, better social structures, and higher ranks in
other indicators. More specifically, the impact of educating girls and women has been shown
to result in rapid improvements in family planning, nutrition, health and income and is seen
as one of the best tools for promoting social and economic development. There are two scales
have been formed to confine this dimensions.
1
These indicators have been selected from various issues of Human Development reports, A CGAP Report on
microfinance poverty assessment tools and living standard measurement surveys and district household
surveys.
50
Access to Knowledge: It has been measured in terms of years of schooling, professional
education, computer education, political awareness etc. these indicators are based in thirteen
significant variables2.
General awareness: general awareness is the subjective measure of quality of life over the
time. To measure awareness we have considered people participation in determining
economic, political and development policies (Table2.1).
Standard of living
Standard of living is a key component of overall well being is economic and social well
being. Over fifty years ago, the united nations’ universal declaration of human rights stated
“everyone has the right to a standard of living adequate for the health and well being of
himself and of his family, including food, clothing, housing and medical care and necessary
social services”.3
The proposed Economic and Social Well being index (EASWBI) is constructed on the basis
of these dimensions. Income, consumption and wealth (assets) have been considered together.
Income and consumption are crucial for assessing living standard, but they can be estimated
in conjunction with information on wealth (report by the Commission on the Measurement of
Economic Performance and Social Progress2008). Income indicator has been recognized as
the single most important yardstick for welfare, until economists start constructing the
composite measure of quality of life. To measure income we take income per month, pension
and other sources of income such as rental income (Table2.1).
2
3
Asset brings the time
Variables have been described in methodology section.
Basic idea is inspired from “the guide to the construction and methodology of the index of economic well
being” by Jeremy Smith (2003).
51
dimension. It is measured in terms of home and electric appliances, vehicles, availability of
house (Table2.1).
Income based measures: Income data are needed to understand the causal processes
generating living standards. If people are getting richer or poorer, we need to understand the
reasons. These causal questions are difficult to address without an income measure. Income
measures can help us to understand causes of inequality
Consumption-based measures:
Measuring incomes becomes far more complex when a
large proportion of the economically active population is self-employed. Households,
especially poor households, typically conflate household and business expenditures so the
concept of income as the net result of gross receipts minus business expenditures may be
difficult for respondents to report. Capturing all the sources of income in poor economies
Consumption has long been the conventional measure of economic standing in developing
country surveys. The leading review of developing country surveys recommends that
“consumption ... is the best measure of the economic component of living standards” (Deaton
& Grosh, 2000). consumption based measures often have to be constructed from surveys
designed for purposes other than measuring inequality Consumption measures also better
capture an intuitive sense of what most people mean by a “standard of living”. Income and
wealth may provide the resources to enable that standard of living, but consumption more
directly measures the concept itself.
There is also a widespread belief that households will deliberately under-report their
incomes, at least more so than expenditures, since incomes are a basis for household taxation.
Given all these problems, The use of consumption expenditure data is better for measuring
standard of living as (a) It allows for smoothening of income fluctuations; (b) it allows
inclusion of non monetized transactions which may be significant in developing countries; (c)
52
It allows for the implications an individual’s intake or command over commodities, and (d)
given the large-scale under-reporting of income data in developing countries, it may capture
individual’s command over resources more accurately. Consumption is estimated on monthly
expenditure on food, cloth, house; health etc .it would measure the overall economic status
(Table2.1).
Asset based measures: A low income household with above average wealth is better off than
a low income household without wealth. The existence of wealth is also one reason why
income and consumption are not necessarily equal. The time dimension brings in wealth4.
This indicator reveals the overall affluence of the society. Asset comprises number of assets,
quality and resale value of assets. Assets are divided into basic assets, transportation assets
and luxurious assets.
Housing and related facilities: Housing has its intrinsic value as a goal of human
development as it ensures safety and security as well as privacy to human beings. It also
ensures better health and higher productivity. For a large number of people in rural and urban
areas, a house is also a work place. Housing is a capability that expands opportunities for
people, apart from the availability of a shelter, basic amenities, such as electricity, water
supply, and sanitation availability. The indicators for housing could be: (a) availability of a
durable (pucca) house, and (b) availability of three basic facilities, namely, water, electricity
and sanitation.
Environmental status: Environmental status of a society tends to affect the macro level
opportunities available to people. No amount of individual capabilities would help much if
the macro environment is degraded, depleted and highly polluted. Further, macro
environment degradation may adversely affect possibilities of developing capabilities as
4
(Report by the Commission on the Measurement of Economic Performance and Social Progress2008)
53
people might be too engaged in the struggle for survival to pay attention to development of
their capabilities for better life. Environmental conditions are necessary to estimate because
of their immediate impact on the quality of people’s lives. They affect human health both
directly (through air, noise and water pollution) and indirectly (climate change). It is believed
to be an essential element for development. This dimension is captured by pollution,
pollution measures, and availability of park near home (table).
Availability of basic utilities: It refers the availability of connections of basic requirements
in terms of water, electricity, fuel, internet etc.
Access to Basic services: Availability of basic services at the community level is a primary
enabling condition at the macro level. These services include not only the infrastructure for
health or education, but also community services such as approach roads, street lights, post
and telecommunication facilities, fair price shops, etc. Such services create and promote
opportunities for people for using their capabilities. Distances from main basic facilities
measure people’s right to have basic infrastructure for development. Access to health facility,
bank, post office, and schools has been used to construct access to basic facilities.
Security measures:
Quality of life depends on people’s objective conditions and
capabilities. Social connections provide services to people (e.g. insurance, security). It
consists of personal and economic security such as crime, accidents, insurance, Job security,
and mobility, insecurity in travelling and outside.
Personal insecurity
Personal insecurity includes external factors that put at risk the physical integrity of each
person: crime, accidents, natural disasters, and climate changes are some of the most obvious
factors. Less extreme manifestations of personal insecurity such as crime affect quality of life
54
for a significantly larger number of people, with even larger numbers reporting fear of being
victim of a physical aggression. One of the most remarkable feature of reports on subjective
fear of crime is how little they are related to experienced victimization:
Economic insecurity: The realization of these risks has negative consequences for the
quality of life, depending on the severity of the shock, its duration, the stigma associated with
it, the risk aversion of each person, and the financial implications. Job loss can lead to
economic insecurity when unemployment is recurrent or persistent, when unemployment
benefits are low relative to previous earnings, or when workers have to accept major cuts in
pay, hours or both to find a new job. The consequences of job insecurity are both immediate
(as replacement income is typically lower than the earnings on the previous job) and longer
term (due to potential losses in wages when the person does find another job). While
indicators of these consequences are available, cross-country comparisons are difficult,
requiring special investments in this direction. Job insecurity can also be measured by asking
workers either to evaluate the security of their present job or to rate their expectation of
losing their job in the near future. The fear of job loss can have negative consequences for the
quality of life of the workers (e.g. physical and mental illness, tensions in family life) as well
as for firms (e.g. adverse impacts on workers’ motivation and productivity, lower
identification with corporate objectives) and society as a whole. Illness can cause economic
insecurity both directly and indirectly. For people with no (or only partial) health insurance,
medical costs can be devastating, forcing them into debt, to sell their home and assets, or to
forego treatment at the cost of worse health outcomes in the future. One indicator of illnessrelated economic insecurity is provided by the share of people without health insurance.
However, health insurance can cover different contingencies, and even insured people may
incur high out-of-pocket health expenses in the event of illness. To these out-of-pocket health
55
expenses should be added the loss of income that occurs if the person has to stop working and
the health (or other) insurance does not provide replacement income. Old age is not a risk per
se, but it can still imply economic insecurity due to uncertainty about needs and resources
after withdrawal from the labor market. Two types of risk, in particular, are important. The
first is the risk of inadequate resources during retirement, due to insufficient future pension
payments or to greater needs associated with illness or disability. The second is the risk of
volatility in pension payments: while all retirement-income systems are exposed to some
types of risk, the greater role of the private sector in financing old-age pensions (in the form
of both occupational pensions and personal savings) has made it possible to extend the
coverage of pension systems in many countries but at the cost of shifting risk from
governments and firms towards individuals, thereby increasing their insecurity.
Life Satisfaction: Measuring feelings can be very subjective, but is nonetheless a useful
complement to more objective data when comparing quality of life across countries. The data
can provide a personal evaluation of an individual’s health, education, income, personal
fulfilment and social conditions. Bernice Neugarten, Robert Havighurst, and Sheldon Tobin
developed the original forms of the Life Satisfaction Index (1961)5. It is an attempt to show
life satisfaction in different nations. In this calculation, subjective well being correlates most
strongly with health wealth, and access to basic education. This is an example of directly
measuring happiness asking people how satisfied they are with their education, health,
income, employment society and nation.
These dimensions are measured by different indicators as given in table 1.These dimensions
are supposed to evaluate the society’s overall welfare and standard of living. Our measure of
5
Life Satisfaction Index for the Third Age (LSITA): A Measurement of Successful Aging Andrew J. Barrett
and Peter J. Murk (2006)
56
well being is a composite measurement to capture the quality of life of people. The higher
value of the index indicates better level of well being
Table (2.1): Indicators of Economic and Social well being Index (EASWBI)
Dimensions
Indicator1
Indicator2
Indicator3
Indicator4
Indicator 5
Indicator 6
Indicator 7
Health
Nutrition
level
Children
vaccination
Exercise
sports
Years of
schooling
at age 25
Technical
education
Computer
literacy
Education
pattern
Health
consciousne
ss
Political
awareness
Health
insurance
Knowledge
Women
vaccinatio
n
Profession
al
education
Income
Per capita
income
Pension
Asset
Area and
value of
land
owned
Consumpti
on
Monthly
expenditu
re on food
Monthly
Expenditu
re
on
health
Monthly
Expenditu
re on cloth
Monthly
Expenditur
e on travel
Monthly
Other
Expenditure
Utility
Internet
connectio
n
Ownership
and value of
transportati
on related
asset
Monthly
expenditure
mobile,
internet and
cable
Cable
connection
Per capita
electricity
Telephone
connectio
ns
Per capita
energy
consumpti
on
Awareness
General
awareness
News
Awarenes
s
for
governme
nt policies
Security
Insurance
policies
Awareness
for political
and
economic
issues.
Savings
Peace in
society
Job
security
Security in
travelling
Migration
Life
Satisfaction
Personal
satisfaction
Emotional
satisfactio
n
Source of
Drinking
water
Access
to
Basic
facilities
Health
satisfactio
n
Housing
and
dwelling
conditions
Distance
to
hospitals
Security
against
Crime
Job
satisfactio
n
Distance to
primary
school
Distance
to college
Distance
to
post
office
Distance to
commercia
l bank
Distance to
medical
store
Distance to
computer
education
centre
Environme
nt
Disposal
of
Recreationa
l facilities
Source &
intensity
Pollution
control
Dwelling
Sanitation
facility
Governme
nt policies
of
developme
nt
Other
sources of
income
Ownership
and value
of electric
appliances
57
garbage
near home
of
pollution
measures
Methodology
Indexes compress the information content of a large number of indicators into summary
measures. The essence of the index construction process is to extract the relevant information
content from an array of different indicators and weight them appropriately in constructing a
single series. There are several methods to derive such indexes and factor analysis has
emerged widely accepted scientific method to perform such tasks, mostly for its statistical
foundations.
It is a multivariate statistical technique to reduce the large quantity of variables
in few factors. Here we discuss the steps of statistical methods used for constructing factor
analysis.
1. Scaling the variables: Cronbach Coefficient Alpha
The Cronbach Coefficient Alpha (henceforth c-alpha) (Cronbach, 1951) is the most common
estimate of internal consistency of items in a model or survey – Reliability/Item Analysis
(e.g. Boscarino et al., 2004; Raykov, 1998; Cortina, 1993; Feldt et al., 1987; Green et al.,
1977; Hattie, 1985; Miller, 1995). It assesses how well a set of items (in our terminology
individual indicators) measures a single uni-dimensional object.
Cronbach’s Coefficient Alpha can be defined as:
Where M indicates the number of observations considered, Q the number of individual
indicators available, and
is the sum of all individual indicators. C-alpha
measures the portion of total variability of the sample of individual indicators due to the
correlation of indicators. It increases with the number of individual indicators and with the
58
covariance of each pair. If no correlation exists and individual indicators are independent,
then C-alpha is equal to zero, while if individual indicators are perfectly correlated, C-alpha
is equal to one.
C-alpha is not a statistical test, but a coefficient of reliability based on the correlation between
individual indicators. That is, if the correlation is high, then there is evidence that the
individual indicators are measuring the same underlying construct. Therefore a high c-alpha,
or equivalently a high “reliability”, indicates that the individual indicators measure the latent
phenomenon well.. A set of individual indicators can have a high alpha and still be multidimensional. This happens when there are separate clusters of individual indicators (separate
dimensions) which inter correlate highly, even though the clusters themselves are not highly
correlated. Nunnally (1978) suggests 0.7 as an acceptable reliability threshold.
Factor analysis model
Factor analysis is used to define the underlying structure in a data matrix. It addresses the
problem of analyzing the structure of the interrelationships (correlations) among a large
number of variables (questionnaire responses) by defining a set of common underlying
dimensions, known as factors. These factors have small correlation with each other, and each
factor captures a particular underlying aspect of original variables (Jhonson & wichern 2002).
Confirmatory and Exploratory Factor Analysis
There are basically two types of factor analysis: exploratory and confirmatory. Exploratory
factor analysis (EFA) attempts to discover the nature of the constructs influencing set of
responses. Confirmatory factor analysis (CFA) tests whether a specified set of constructs is
influencing responses in a predicted way. Both types of factor analyses are based on the
Common Factor Model.
59
Exploratory factor analysis: Exploratory factor analysis (EFA) attempts to discover the
nature of the constructs influencing a set of responses.an exploratory factor analysis is used in
the early investigations of a set of multivariate data to determine whether the factor analysis
model is useful in finding a parsimonious way of describing the relationships between the
observed variables. Which observed variables are most highly correlated with the common
factors and how many common factors are needed? In an exploratory factor analysis, no
constraints are placed on which variables load on which factors.
Confirmatory factor model: The primary objective of a CFA is to determine the ability of a
predetermined factor model to observed set of data. Confirmatory factor analysis (CFA) is a
statistical technique used to verify the factor structure of a set of observed variables. CFA
allows testing the hypothesis that a relationship between observed variables and their
underlying latent constructs exists. The researcher uses knowledge of the theory, empirical
research, or both, postulates the relationship pattern a priori and then tests the hypothesis
statistically
Assumptions in factor analysis: It doesn't make sense to use factor analysis if the different
variables are unrelated. The correlations should be grater then 0.3 (Rule of thumb). Another
mode of determining the appropriateness of factor analysis is Bartlett test of Sphercity, a
statistical test for the presence of correlations among the variables. It provides the statistical
probability that the correlation matrix has the significant correlations among the variables.
The increasing sample size causes the Bartlett test to become more sensitive to detecting
correlations among variables. Another measure to quantify the degree of inter-correlations
among the variables and the appropriateness of factor analysis is the measure of sampling
adequacy (MSA). This index ranges from 0 to 1, reaching 1 when each variable is perfectly
60
predicted without error by the other variable.6 The MSA increases as the sample size
increases.
Sample Size to determine factor analysis: A general rule of thumb is that we should have at
least 50 observations and at least 5 times as many observations as variables. Stevens (2002,
pg. 395) summarizes some results that are a bit more specific and backed by simulations. The
number of observations required for factors to be reliable depends on the data. In particular
on how well the variables load on the different factors. A factor is reliable if it has: 3 or more
variables with loadings of 0.8 and any n 4 or more variables with loadings of 0.6 and any n10
or more variables with loadings of 0.4 and n≥150 Factors with only a few loadings require
n≥300
Model Estimation: The unobserved factors can be inferred from observed indicators. The
basis of factor analysis is a regression model, linking the manifest variables to a set of
unobserved latent variables. The structure of the model to obtain EASWBI is expressed as
following:
The original observed indicators are standardized (by subtracting from means and dividing by
their variance respectively), the basic FA model is the correlation matrix of R.
A linear FA model yields:
6
X1 =
λ11 .f 1 + λ12.f2+
X2 =
λ21 .f 1 + λ22.f2+
………..
………..
λ1q.fq+e1
λ2q.fq+e2
(1)
The measure can be interpreted with the following guidelines: .80 or above, .70 or above, middling; .60 or above, mediocre; .50 or above,
miserable; and below .50, unacceptable.
61
Xp
The
= λp1 .f 1 + λp2.f2+
………..
λpq.fq+ep
values are essentially regression coefficients showing how each
depends the on k
common factors; in this context, they are known as factor loadings. When estimated from a
sample correlation matrix, the factor loadings are the estimated correlations between factors
and manifest variables. the factor loadings are used in the interpretation of factors, that is
large values relate a factor to the corresponding observed variables, and from looking at
which variables load highly on a factor, we can try to come up with a meaningful description
or label for each factor. The
values are analogous to the residual terms in the usual multiple
linear regression model, but as they are specific to each
in factor analysis context, they are
more commonly known as specific variates. The series of regression equation can be written
more concisely as
X= Λ f+ e........ (2)
Where
Λ
λ
λ
λ
λ
λ
λ
We assume that residual terms
the common factors
are uncorrelated with each other and with
the two assumptions imply that given the value of factors, the
manifest variables are independent, that is, the correlations of the observed variables arise
from their relationships with the factors. Since the factors are unobserved, we can fix their
location and scale arbitrarily. We assume that they occur in standardized form with mean 0
and standard deviation 1. We will also assume, initially at least, that the factors are
uncorrelated with one another, in which case for factor loadings are the correlations of the
manifest variables and the factors. With these assumptions about the factors, the factor
analysis model implies that the variance of the variable
, is given by
62
Where
is the variance of
. So the factor analysis model implies that the variance of each
observed variable can be split into two parts. The first part,
, given by
, is
known as communality of the variable and represents the variance shared with the other
variables via common factors. The second part
and relates to the variability in
is called the specific or unique variance
not shared with the other variables. In addition, the factor
model leads to the following expression for the covariance of variables
Where
denotes
:
column ofΛ.
This equation implies the squared factor loadings,
for j=1,...,q.
The total contributions of all the common factors to the total variance among all the
indicators is the total communality, i.e,
The variance among all the variables that is accounted for by a factor
that accounted for by all the factors is given by
So the total variance is written as
So the base model can be rewritten as:
as a percentage of
63
This set of equation is called a factor pattern. The p x q matrix of factor loadings with factor
designations as columns is referred to as the pattern matrix. The correlation between the
observed indicators and the common factors is called a factor structure for a complete
solution. Λ is p×q matrix of unknown constants called factor loadings. There are p unique
factors and it is assumed that the unique part of each indicator is uncorrelated with their
common part. The total number of parameters, need of estimation is the number of factor
loadings, namely pq. The relationship within a set of p observed indicators reflects the
correlation of each observed indicators with q mutually uncorrelated underlying factors, with
the above assumption that number of factors to be extracted should be less than the number of
indicators,
.
Criteria for the numbers of the factors to extract:
The objective of the model framework is to determine the minimum of common factors that
would satisfactory produce the correlations among the observed indicators. We discuss
principal axis factoring method to find out initial solutions. This method extracts factors such
that each factor accounts for the maximum possible amount of the variance contained in the
set of indicators being factored. Here, the method generates the coefficients
for the factor
in such a manner that the contribution of
maximized, subject to
to the total communality V is
correlations and the p specified communalities. This
solution is equivalent to finding the Eigen values and eigenvectors of the reduced correlation
matrix R. there are several methods for extracting the factor,
Latent Root Criteria: the most commonly used technique is the latent root criterion. The
rationale for this technique is that any individual factor should account for the variance of at
least a single variable if it is to be retained for the interpretation. Each value contributes a
value of 1 to the total eigenvalues. Thus only the factors with latent roots less than 1 are
64
considered insignificant. It is known as Kaiser's Criterion. It takes as many factors as there
are eigenvalues > 1 for the correlation matrix.
A Priori Criterion: – this method works according to the hypothesis about the number of
factors that should underlie the data, then that is probably a good (at least minimum) number
to use. In practice there is no single best rule to use and a combination of them is often used,
Scree Plot – it takes the number of factors corresponding to the last eigen value before they
start to level off. Hair, et.al. (1998) reports that it tends to keep one or more factors more than
Kaiser's criterion. Stevens (2002) reports, that both Kaiser and Scree are accurate if n>250
and communalities ≥ 0.6.
Fixed % of Variance Explained – This method keeps as many factors as are required to
explain 60%, 70%, 80-85%, or 95% variance. There is no general consensus and one should
check what is common in your field. It seems reasonable that any decent model should have
at least 50% of the variance in the variables explained by the common factors.
Rotation of factors: Then factor analysis finds simpler and more easily interpretable factors
through rotation, while keeping the number of factors and communalities of each indicator
fixed. The term rotation means exactly what it implies. Specifically, the reference axes of the
factors are turned about the origin until some other positions has been reached. Unrotated
factor solutions extract factors in the order of their importance. The first factor tends to be a
general factor with almost every variable loading significantly, and it accounts for the largest
amount of variance. The second and subsequent factors are then based on the residual amount
of variance. Each accounts for successively smaller portions of variance. The ultimate effect
of rotating the factor matrix is to redistribute the variance from earlier factors to later ones to
achieve a simpler, theoretically more meaningful factor pattern. This rotation is done to see
how the observed indicators are clustered into sub-groups, one sub-group lying close to one
65
rotated factor and the other sub-groups lying close to the other rotated factor and so on. There
are two types of rotation method: a) orthogonal rotation, and b) oblique rotation, first method
restrict the rotated factors to being uncorrelated and the other method allow correlated
factors. We apply oblimin rotation method of oblique rotation. This method, invented by
Jennrich and Sampson (1966), attempts to find simple structure with regard to the factor
pattern matrix through a parameter that is used to control the degree of correlation between
the factors. Fixing a value for this parameter is not straightforward, but Lackey and Sullivan
(2003) suggest that values between about -0.5 and 0.5 are sensible for use.
Interpreting the factor loading matrix, we have used a certain procedure to interpret the
matrix of factor loading. In the oblique rotation we get the factor pattern matrix, which has
loadings that represent the unique contribution to each variable to the factor. Then we
identify the highest loadings for each variable. The lowest factor loading to be considered
significantly would be in most instances be  .30. Each variable has only one loading on
one factor that is considered significant. Once all the variables have been underlined on their
respective factors, communalities of each variable should be assessed. At least one half of the
variance of each variable must be taken into account. We need to identify all variables with
communalities less than .50 as not having sufficient explanation. When a factor solution has
been obtained in which all variables have a significant loading on a factor, we assign some
meaning to the pattern of factor loadings. Variables with higher loadings are considered more
important and have greater influence on the name or label selected to represent a factor. the
signs are interpreted just as with any correlation coefficients.
Factor scores: Then we estimate factor scores in the FA model as belowfor a given factor f j
the
extracted factor score, denoted by
, is given by
66
Fij =β1 Xit +……….βp Xip
Where β1, β2…….. βp
……... (4)
are referred to as regression coefficients and Xi1, Xi2, ...Xip are p
observed indicators, for the ith observations.
Finally we define the Economic Well being Index as a weighted average of the factor scores,
where the weights are the Eigen values of the correlation matrix R.
λ
EWBIK
λ
….where k= cities ………….. (5)
Then we normalize well being for each household in the following form
XK 
X K  MinimumX K
.........(6)
MaximumX K  MinimumX k
Where, maximum Xk and minimum Xk are the values of EASWBI for individuals.
Data Envelopment Analysis (DEA): An Index-Maximizing Model: In constructing the best
practice
frontier
and
identifying
benchmark
individuals,
we
assume
that data includes a number of observations; here we assume that there are K observations.
Each observation would include data on all inputs and outputs, i.e., k
Where, for example
is the value of the n the dimension, and
is the value of the first index
that is calculated for k individual.
We begin with the Input Requirement Set. An Input Requirement Set L( y) that shows all the
combinations of inputs that can be used to produce the output vector y , We construct input
67
requirement sets from the observations of individual indicators.
L(y | C, S) = {(x1, x2):
},
Where the zk, k = 1, K are what are referred to as the intensity variables.
In order to construct the best practice life index from the data we use these explicit equations.
L(y | C, S) = {(x1, x2):
z11+z2 1+z31≥ y,
(a)
z11+z2 2+z3 2≤ x1, (b)
z11+z2 1+z3 1≤ x2 ,
(c)
},
Data Sources: – the work in this chapter is based on individuals’ perception so required data
has been collected through primary survey of households in Kanpur, Agra and Surat city.
These cities have been selected for their special features. The main objective to select these
cities is to make interregional and intraregional comparisons.
The city of Surat in Gujarat is known for its textile trade, diamond cutting and polishing
industries and, since 1994, for the Plague, is today known for its strength to convert adversity
into advantage. Subsequent to the Plague of 1994, the city authorities undertook one of the
most massive clean-up operations in recent times and revamped the entire administration of
68
the city. Within two years, Surat had been transformed from the one of the filthiest cities to
the second cleanest city in the country. A systematic process to upgrade infrastructure, both
quantitatively and qualitatively, has been made by the local government. The city governance
has come to be recognized as an example of a good governance system.
Kanpur has traditionally been an industrial city and on economic center. At one point in time
it was the second most industrialized city in India being second only to Calcutta. Due to large
number of cotton textile units and a vibrant trade center for cotton it was also called the
‘Manchester of India’. Over a period of time, the industrial profile of Kanpur has undergone a
drastic change.
Agra is ranked amongst the most outstanding historic cities in the world and certainly best
known tourist destinations in India. The city boasts three World Heritage Sites namely the
Taj Mahal, Fatehpur Sikri & Agra Fort and innumerable other monuments of national and
indeed international importance. Development has failed to keep pace with population
growth. The city of Agra has several such deficiencies and there is a need to make substantial
improvement in basic infrastructure prevailing in the city to raise the standard of health,
sanitation, urban environment keeping pace with rapid urbanization and growing population.
The importance of Agra city as a leading tourist destination has to be kept in view while
designing the system to make the city beautiful, attractive to the tourists visiting the city.
A questionnaire based survey was conducted to get information
about different dimension during 2009-2010. Questionnaire7 has been designed to assess
information about economic well being of households to measure the impact of economic
reforms on their health, education, infrastructure, living standard and other aspects of human
7
Questionnaire has been designed with the help of LSMS survey questionnaire and CGAP microfinance survey
questionnaire and attached in appendix.
69
development. Every item of questionnaire has given specific codes. It is based on likert scale.
And statistical software has been used to sort out the basic issues.
Sample Design: The sample design adopted for the survey is a systematic stratified random
sampling of households.
Sample size and calculation: Sample size8 has been decided to be 300 at confidence
interval9 of 95% with confidence level10 of 5.66 by using formula given
SS 
Z 2   p   1  p 
C2
Where Z= z value (1.96 for 95% confidence interval)
P = percentage picking a choice, expresses as decimal (0.5 used for sample size)
C = confidence interval, expressed as decimal (0.4=  .0.4)
Then we made some corrections for finite population by given formula:
NewSS 
ss
ss  1
1
pop
Where pop = population.
8
Sample size is determined by using sample size calculator by creative research system (1984).
9
It is expressed as a percentage and represents how often the true percentage of the population who would pick
an answer lies within the confidence interval. The 95% confidence level means you can be 95% certain.
10
The confidence interval is also called margin of error. It takes into account both sampling error and non-
sampling error
70
The sampling fraction is the same for all areas, thus the sample is completely self-weighted.
The overall sampling fraction computed as follows:
f 
n *1.10
N
n = number of households to be selected in city;
N =projected households in city in 2001.
Surat Sample Design: The total number of households in Surat11 is 585532 with an average
family size is 4.90.the sample size selected for Surat is 300 households with 95% confidence
level and 5.65 margin of error. The city has been divided into seven zones viz. Varachcha
(East), Rander (west), Katargam (North), Udhna (South), Vesu (South-East), Athwa Lines
(South- West) and Nanpura(Central). The overall sampling fraction (probability of selecting a
household from Surat) is 0.000564. The probability of selecting a zone was computed as
fi 
a  si
 si
a = number of blocks selected from the city
si =the population size of the selected city
 si = total number of households in city
Table (2.2) probability in Surat Stratum
Zone
East zone
West zone
North zone
South zone
Central zone
South west zone
South east zone
1111
Households
143574
57687
82775
94582
74679
50236
81999
Brief introduction of Surat is in appendix.
Probability
1.72
0.69
0.98
1.13
0.89
0.60
0.98
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The probability of selecting a block from the selecting zone is 0.09, computed as f 2 
b
,
p
where b = number of wards
p = total number of wards
The probability of selecting a household from the selected ward ( f 3 )12 is computed for each
ward.
Sample size determination in Kanpur: The total number o households are 439619
Kanpur13. as per the census of 2001. Out of the total households, 45 percent belong to BPL
and EWS categories; 21 percent and 18 percent belongs to LIG and MIG households
respectively and HIG households were 16 percentages (Census 2001). The sample was a
representative sample in terms of representing the different Income Groups (BPL, EWS, LIG,
MIG and HIG)14.
The sample size is 300 at 95% confidence level with confidence level of 5. The probability, f,
for selecting a household from Kanpur) is 0.000875, for stratification Whole city has been
divided into six categories such as city core area, intermediate northwest area, intermediate
southwest area, intermediate Eastern area, eastern housing area and western housing area.
The frame provided by the city deployment plans served as the sampling frame
The city is subdivided into six categories according to their zones as per the Environment
Management plan (2000), existing housing localities has been broadly categorized into six
zones.
12
13
14
f3 
f
f1  f 2
Brief summary on the development of Kanpur is in appendix1(A).
These categories defined in a study (2004) have been conducted by RITES.
72
City core area - It comprises old interior areas in 24 densely populated wards. It has
maximum housing density and many dilapidated houses. It is characterized by limited civic
amenities, which are already exploited beyond their capacity.
Intermediate North-West Areas- It comprises primarily of houses which were constructed
for accommodating various government officers. It includes posh Civil Lines, Tilak Nagar,
Arya Nagar and Swaroop Nagar. It has comparatively better housing quality than other areas.
Intermediate South-West Areas- It comprises areas which are dominated by middle-income
group. These areas are still developing in terms of population as well as housing construction.
There is sharp contrast in quality of housing blocks in these areas as most of them are
individual private dwellings and owned by households with uneven economic status.
Intermediate Eastern Areas - These areas are characterized by abundance of slum, which
affects the overall scenario of housing quality. These areas are quite similar to city core area
as it also has high housing density, dilapidated houses and limited civic amenities.
Eastern Housing Areas - These areas are close to defense establishments and situated far
away from main city. Housing development along GT Road after Chakeri is very slow
Western Housing Areas- This area comprises of housing schemes i.e. Panki, Kalyanpur, and
Indra Nagar which falls in western part of the city. This area has high growth rate of housing
development but is lagging behind in terms of provision of corresponding civic amenities.
We have stratified our sample according to these zones. Kanpur there are
110 wards in these zones with an average ward population range from 19000 to 26000.we
have randomly selected 35 wards of these zones. Main survey area is Phool Bagh, Aazad
nagar, Arya nagar, Ashok Nagar, Baradevi, Barra, Birhanaroad, Civil lines, Coolie Bazar,
Gandhi Nagar, Govind nagar, gujjeni, guptar ghat, harsh nagar, jawahar nagar, juhi, kidwai
nagar, Krishna nagar, kurswan, lajpat nagar, lakhanpur, mall road, maswan pur, nawabganj,
73
nobasta, sivaji nagar, swarrop nagar and saket nagar. Households has been selected from the
electronic voter list15
Sample design for Agra City: As per the census, 2001, the number of households in the
Agra16 city was 209997 with an average family size of 6.The sample size, selected for Agra
city, is 300 households with 95% confidence level and 5.65 margin of error. The probability
of selecting a household is 0.0015 in Agra. Stratification is based on zones described in Agra
development plan.17 The whole city is divided into eight zones. Tajganj zone covering
Tajganj, Northern zone covering Dayalbagh and parts of Sikandara, Eastern zone covering
Transyamuna, · Western Sewerage District covering Bodla, Shahganj and Lohamandi,
Southern-I covering Bundukatra · Southern-II covering part of Shahganj, Central zone is
covering Ghatwasan-I, Kotwali, Maithan, Hariparbat, Chhata, Rakabganj and parts old
Khandari, Ghatwasan-II and Lohamandi and last zone is Cantonment covering Sadar. In this
study we have captured all these zones. Households have been selected randomly from the
electronic voter list of Agra.
Results and interpretation
This section provides the core of our overall analysis, as we attempt to explore new
dimensions of well being. Given methodology has been used in two different ways as one is
confirmatory factor analysis where dimensions are well defined and another approach is
exploratory where new dimensions are found.
Results of Confirmatory and Exploratory Factor Analysis for Agra
15
16
17
Electronic voter list is available on Uttar Pradesh state election commission’s website.
Brief summary of Agra city is presented in Appendix1(B)
City Development Plan for Agra City in the state of Uttar Pradesh under JNNURM (2006)
74
There are basically two types of factor analysis: exploratory and confirmatory. Exploratory
factor analysis (EFA) attempts to discover the nature of the constructs influencing set of
responses. Confirmatory factor analysis (CFA) tests whether a specified set of constructs is
influencing responses in a predicted way. Both types of factor analyses are based on the
Common Factor Model.
Results of Confirmatory Factor Analysis (CFA)
Initially we have 207 variables to measure eleven dimensions of Economic and social well
being index (EASWBI). To perform confirmatory factor analysis we have gone through a
certain procedure of reliability tests, sampling adequacy tests, correlation matrix, and
principal axis factor extraction model and oblimin rotation method for all dimensions. Here
we describe the procedure of analysis.
Health: Health is major dimension to measure EASWBI. To measure health initially we
have considered 20 items for scale measurement. These variables measure number of meals,
daily Calorie intake, child immunization, women immunization, weakly intake of rich food,
regular disease to any family member, day loss due to any disease within last four weeks,
health expenditure, disability, choice of hospital, exercise habits and type, physical fitness
health insurance, family planning, nutrition awareness, government health programmes,
health awareness, quality of food and regular checkups. When we test for the reliability of the
scale, we get only 14 items significant in terms of cronbach alpha analysis (appendix1). So
we use only these variables for index construction. We have also accounted for inter item
correlation. Inter-item correlation is greater than .15 for all the scaled variables.These
fourteen variables have been used to find heath index. We have followed certain procedure to
obtain the dimensional index. Here we have KMO test is greater than .7. We retain four
factors with Eigen values greater than 1 (Kaiser Criterion). The cumulative percentage of
75
variance (58%) explained by the factors is satisfactory. On the basis of extracted factors we
have aggregated the twelve variables within four factors. Two variables have been suppressed
due to low factor loadings. These factors can be named as health consciousness as it
combines health awareness, government health programme and quality of food. Second
factor represents Physical health in terms of exercise and type of exercise. Third factor infer
about food and immunization as it clubs meals per day, weekly intake of rich food and
women immunization. Fourth factor combines health insurance, child immunization, nutrition
and family planning. It can be labelled as health security. In order to formulate single
yardstick for health, we have used second order factor analysis using four factors sum of
twelve variables. The cronbach alpha is .752 for these twelve variables. We find the single
factor to gauge health dimension. This explains 44.528 % of total variance. It mostly depends
on first and third factors health consciousness and food and immunization. The mean value is
.54 for health index for the individuals of Agra city for the index. It follows the normal
distribution.
Knowledge: Economic research has recognized the skills and talents embodied in the
population as a critical input into economic production human capital is created by investing
in education and training. Here we have developed knowledge scale based on 18 items. These
variables are basic education, years of schooling, mode of education, degree, importance of
education board, computer literacy, internet user, favourites browser, internet hours, need for
primary and higher education, need of women education, government education programmes,
education cess, loan facilities for higher education, carrier opportunities, abroad education
opportunities, need for political and adult education. Initially we use 20 variables to check
reliability.
Cronbach alpha is .764 for eighteen variables and inter item correlation is
significant for these variables used in study (appendix2). We have removed two variables
76
from the analysis. Then we pursue factor analysis for index generation. The value for KMO
measure is .770. It indicates a good measure of sampling adequacy. The Bartlett’s test of
sphericity also justifies the model appropriateness (appendix2). Communalities are initially
worked out for factor extraction. Variables with high variance can be combined with other
variables to create factors (appendix2).by using principal axis factoring; four factors have
been extracted on the basis of Kaiser Criteria of Eigen value, scree plot and variance. The
total explained variance is 61% for these four factors and fifteen variables (appendix2). Scree
plot also justifies the factor selection model.
We follow the pattern matrix for the contribution of the variables in given
factors. The factor loading for basic education, years of schooling, mode of education,
degree, computer literacy, and internet user are highly correlated with first factor. So this
factor referred as fundamental education and computer literacy. Second factor is highly
correlated with need for primary and higher education and need of women education and
computer knowledge. So we entitle it development and changes in educational requirements.
The factor loadings for third factor are highly loaded with government education
programmes, education cess, loan facilities for higher education. It is labelled as government
educational financing schemes. The fourth factor, entitled as influence of adult education and
board on present scenario, is explained by adult education and importance of education
board. Once we find the key factors for knowledge, we aggregated fifteen variables into four
factors. Cronbach alpha is significant (.714) for these variables. Second order factor analysis
constructs the Knowledge index for Agra. The Eigen value is 1.596 for the given factor and
followed by 40% of total variance. It is correlated with all variables. Then we have
normalized the index scores by max-min method. Index mean value is .526 for the Agra
people.
77
Security: Initially seventeen variables are analyzed to capture the impact of financial and
personal and social security on well being; these variables are measuring financial security in
terms of burden of debt and taxation, importance of saving and investment, pension.
Employment security is measured in terms of type of employment, changes in wages and
nature of job, remuneration, mobility and degree of insecurity in job. Personal Social security
is reflected as protection against crime and bomb blast, travelling in train, safety
arrangements in city and security in night and outside. Fourteen variables have been taken
into account after testing the reliability. Cronbach alpha is .703 and inter-item correlation is
positive and greater than .15 for these variables. KMO measure (.743) justifies the sampling
adequacy for the index. The Bartlett test of sphericity (appendix3) also approves the model
for factor analysis. Communalities (appendix 3) shows the high variance for insurance,
wages, benefits security in night and outside and low variance for loan repayment and
savings. As Eigen value is greater than one for four factors, we are considering these four
factors extracted by principal axis factoring. These factors are explaining 58% of total
variance (appendix3). Scree plot shows the decreasing slops of Eigen values for factors. On
the basis of oblimin rotation (pattern matrix) we recognize the significant factor loadings
(appendix3). The pattern matrix gives the understandable scenario for the factor loadings .the
first factor employment security is highly correlated with type of employment, changes in
wages and remuneration. Safety in outside and night is highly correlated with second factor.
It shows peace in city. Social security include against bomb blast, train accidents and crime.
Fourth factor is about financial security associated with insurance and tax payment. Thus
these four factors consider ten variables. For developing the security scale we are considering
ten variables aggregated into four factors extracted in earlier stages. The reliability of scale is
around .7. And index explains 57 % of total variance (appendix3). Normalization (Max-Min)
78
gives a high-quality scale for measuring security. Mean value for the scale is .45 for Agra. It
follows the normal distribution.
Life Satisfaction: This dimension brings happiness as an indicator of EASWBI index. It
captures mental satisfaction, job satisfaction and satisfaction from national development. It is
measured by ten variables such as satisfaction from health, life, society, income,
employment, education, living standard, dwelling, national development and policies. All
variables used in analysis are statistically reliable as cronbach alpha is .849 for these
variables. It shows high reliability of scale variables. Inter item correlation is also very high
among the variables. KMO measure of sampling adequacy is quite high (.830) for the items.
Bartlett test also shows appropriateness of model selection. Communalities show high
variance shared by health and development variables (appendix 4). Three factors are
extracted as Eigen value is 4, 1.2 and 1.0 for first three factors. These factors explain 65% of
total variance. We can also use scree plot for factor determination. The first factor is most
explaining factor. First factor is highly depended on satisfaction from income, education,
employment and living standard. We name it as personal satisfaction. Second factor is
reflected by satisfaction from national development. Third factor infers about satisfaction
from society and health. This index is measured by seven variables aggregated into three
factors. Reliability of scale is .814 for these variables. Second order factor analysis gives the
single measure of satisfaction. It explains 59% variance. Mean value of the scale is .54. We
define individuals into five groups.
Analysis of General Awareness index: We calculate general awareness index with the help
of variables such as news reading habits, watching television, awareness about the hot issues,
interest for about life insurance, non- life insurance, pension schemes, investment and saving
schemes. Cronbach alpha is .701 for five variables so we use only these variables for further
79
analysis.KMO sampling adequacy test (.738) and Bartlett’s test of sphericity approves the
model justification. Single factor is extracted as Eigen value is 2.233 for the factor and 47%
variance is explained by the factor. We can see the scree plot for the factor. It gives the index
for general awareness. We get the index of general awareness by using first order factor
analysis. The scale value is .57 for the individual of Agra. Results show that people are much
aware now days.
Wealth index: It is the most important dimension of the EASWBI. We have considered
necessary availability of assets, comfort assets and luxuries. We have also analyzed the assets
with respect to the brand and resale value. Cronbach alpha is .955 for 34 items. Some items
have been deleted due to low inter-item correlations. KMO measure of sampling adequacy
(.910) and Bartlett’s test of sphericity (12883) is very high for this dimensional index. Eigen
values are greater than one for nine factors. These factors explain 78% variance extracted
from 26 variables. Factors have been determined on the basis of pattern matrix of oblimin
rotation method. First factor refers about electrical appliances in terms of freeze, television
and cooler. Second factor is highly correlated with luxuries assets such as availability of car
and air conditioner. Third factor is correlated with energy appliances such as inverter. Next
factor is related to washing appliances followed by kitchen appliances, transportation assets
and computer.We aggregate variables within factors for second order factor analysis. The
reliability (.941) is very high for the index. Two factors are extracted in process, but we
consider only first factor as it explains around 57% variance. It is pretty high for constructing
the index. Mean value is .51 for the scale.
Consumption: As we know that income does not explain the whole picture. So we have
taken monthly expenditure as proxy of well economic wellbeing. Here we measure monthly
expenditure on food items, health, cloths, travel, house, mobile, landline, cable, electricity,
80
fuel, water and other expenses. It gives the real picture of living standard. Cronbach alpha is
.779 and inter-item correlation is significant for these items. So we consider all variables for
factor analysis.KMO test of Sampling Adequacy (.900) and Bartlett’s test of sphericity justify
the sample size and model selection. Communalities are high for food expenditure, cloth
expenses and mobile expenditure. Three factors are extracted on the basis of Eigen values.
Eigen value for the first factor is very high as it is 5.588 followed by 1.527 and 1.003 for
other two factors. This is shown in scree plot. These three factors explain 68% variance. First
factor, essential expenses, is associated with expenditure on food, water and fuel. Second
factor is correlated with basic expenses on electricity, health, clothing, traveling and house.
So it is referred as. Third factor is related with expenses on phone and entertainment as it is
aggregating expenses on mobile, landline and cable tv. Second order factor analysis gives the
index value. This Index captures 74% of variance. Cronbach alpha is .757 for these variables.
The mean value is .245 for the index. In Agra 57 % people spends less on their basic
necessities.
Living conditions in Agra: we measure dwelling conditions with the help of thirteen
variables. these variables capture the information related to dwelling, structure, floor type,
number of rooms, drinking
water and purification facility and , toilet type, electricity
connection and hours, road, cleanliness and drainage in society. The cronbach alpha is .757
for these items. KMO (.773) and Bartlett’s test (835) results justify the model for factor
analysis. Communalities are high for sanitation and structure. Eigen values are high of four
factors. It explains 57% variance. Scree plot shows the decreasing slope of Eigen values. On
the basis of pattern matrix we detect the structure of the factors. First factor is highly loaded
with quality of roads and fuel. Second is explained by dwelling conditions such as structure,
floor type and water facility. Third factor is about sanitation facilities as it comprises of
81
drainage and cleanliness. Fourth factor is about basis requirements of drinking water and
electricity supply hours. in order to develop a single measure we analyze the factors of priori
analysis again through factor analysis. It gives the index of living conditions. It explains 44%
variance. The scale value is .52. The reliability of scale is .712 for the index. Around 50 % of
total population does not have suitable conditions for living.
Index for access to basic facilities: It defines the general facilities such as availability of
school, bank, post office, health services, and transport. Cronbach alpha is .770 for the
measuring variables. Initial solution gives the appropriate results of KMO (.697) and Bartlett
test (621). Further stages extract three factors on the basis of Eigen values, variance and scree
plot. These factors explain 65% of variance. First factor is highly loaded with access to
medical facilities such as hospital, dispensary and maternity hospital. A second factor,
general services, is correlated with public transportation and school. Third factor is associated
with doctor and medical store. Second order factor analysis uses these factors for further
analysis. It explains 50% variance. These facilities are easily accessible to the Agra. Mean
value is .522 for this scale.
Environment index for Agra: Environment measures the pollution, type of pollution,
pollution measures, availability of park and recreation and garbage disposal method.
Reliability of the variables is .775, measured by Cronbach alpha. KMO measure (.727) and
Bartlett’s test is good. Only one factor is extracted by principal axis factoring. That is called
the index of physical environment. It explains 60% variance.Mean value is .235 for Agra
citizens. Environmental conditions are very poor in Agra as 76% people report for bad
environment.
82
Index of utilities: It is measured in terms of availability of basic utilities such as electricity,
LPG, phone connection, cable connection, internet connections etc. Cronbach alpha is .76 for
these variables. First order factor analysis gives two factors, which explain 54% variance.
KMO and Bartlett’s test give the significant results for model selection. Two factors have
been extracted by principal axis factoring. First factor is associated with modern utilities such
as mobile, internet, cable and second factor is related with necessary facilities in terms of
water and electricity. To get the index we follow second order factor analysis. Reliability of
the scale is .76%. It explores 74% variance. Mean value of the scale is .41 for Agra. We
define the groups.
Estimation of Economic and Social Well-being Index using Dimensional indices: We
define Economic and Social Well Being Index as a weighted average of the factor scores,
where the weights are the Eigen values of the R matrix. Here we have computed factor scores
and Eigen values for each dimension to construct the EASWBI. This index is the function of
eleven dimensions. The scale value is .44 for the individuals of Agra. Reliability of the
dimensions is .876. It shows the efficiency of the index. We have defined the groups on the
basis of mean and standard deviation.
83
Table (2.3) Percentage of people for EASWBI and different dimensions
categor
y
Easw
bi
healt
h
kno
wle
dge
secur
ity
aware
ness
basi
c
facil
ity
living
condit
ion
satisfac
tion
utili
ty
environ
ment
expendi
ture
ass
et
very
low
14.0
2.7
30.
0
11.
7
7.3
.7
4.7
19.7
9.
0
24.3
29.7
15
.0
low
31.7
21.
3
23.
7
22.
7
24.0
32.
0
45.0
13.7
23
.0
52.3
28.0
17
.3
mode
rate
30.0
26.
0
36.
0
28.
0
16.3
41.
0
15.0
42.7
31
.3
10.0
16.0
10
.0
high
18.3
42.
0
9.0
20.
3
31.0
6.0
25.0
11.3
21
.0
6.3
16.7
47
.0
very
high
6.0
8.0
1.3
17.
3
21.3
20.
3
10.3
12.7
15
.7
7.0
9.7
10
.7
Exploring Correlation between EASWBI and dimensional indices of confirmatory
analysis:
84
We have hypothesized that Economic and social well being index is positively correlated
with its dimensional indices. Correlation is found to be statistically significant at .01 levels
for index and dimensions (table 5). This index is very high and positively correlated with
most of the dimensions such as asset, consumption, utility and life satisfaction index. Positive
and high correlation is found with health, knowledge and environment conditions. . It is on
average correlated with awareness, security, living conditions and less correlated with access
to basic facilities dimension. It indicates that people are less interest in government policies
and schemes. This analysis shows that policy should be focused on all the dimensions to
achieve higher index of well being.
Table (2.4): correlations between EASWBI and dimensions
DIMENSI
ONS
Heal
th
Knowle
dge
Ass
et i
Consump
tion
Utili
ty
Awaren
ess
Secur
ity
Life
Sati
Dwell
ing i
Acc
ess
Environ
ment
EASWBI
.697
648*
.84
7**
.852**
.850
.614**
.591**
.76
8**
.543**
.292
.652**
**
**
**
**. Correlation is significant at the 0.01 level (2-tailed)
Correlation among the dimensional indices of confirmatory analysis: On the basis of
correlation matrix (table 6) positive and high correlation is found among the major
dimensions of the index. Health, knowledge, asset, security; satisfaction and dwelling are
highly and positively correlated with one another. Few dimensions such as living conditions,
Access to basic facilities are less correlated with other dimensions. There is need for more
developed infrastructure to facilities access and living conditions. We can justify the strong
and significant relationship among the indices for a better index of EASWBI.
Table (2.5): Correlations among the dimensions of EASWBI for Agra
85
Dims
Easw
health
know
securi
aware
basic
living
satisf
utility
envir
expe
asset
easw
1
.697*
.648*
.591*
.614*
.292*
.543*
.768*
.850*
.652*
.852*
.847*
health
.697**
.455*
.397*
.460*
.115*
.281*
.489*
.502*
.423*
.489*
.564*
know
.648**
.455*
.272*
.358*
.184*
.363*
.477*
.501*
.350*
.503*
.500*
securi
.591**
.397*
.272*
.369*
.105
.410*
.446*
.532*
.306*
.519*
.462*
aware
.614**
.460*
.358*
.369*
.203*
.247*
.411*
.376*
.220*
.425*
.437*
basic
.292**
.115*
.184*
.105
.203*
1
.106
.176*
.147*
.085
.128*
.151*
living
.543**
.281*
.363*
.410*
.247*
.106
1
.490*
.506*
.209*
.467*
.311*
satisf
.768**
.489*
.477*
.446*
.411*
.176*
.490*
.683*
.434*
.607*
.573*
utlity
.850**
.502*
.501*
.532*
.376*
.147*
.506*
.683*
.586*
.824*
.686*
envir
.652**
.423*
.350*
.306*
.220*
.085
.209*
.434*
.586*
.562*
.464*
expe
.852**
.489*
.503
.519
.425*
.128*
.467*
.607*
.824*
.562*
*
*
.437*
.151*
.311*
.573*
.686*
.464*
asset
.847**
1
.564*
1
.500*
1
.462*
1
1
1
1
1
.705*
.705*
1
Results of Exploratory Factor Analysis (EFA)
Factor analysis attempts to bring inter correlated variables together under more general,
underlying variables. More specifically, the goal of exploratory factor analysis is to reduce
the dimensionality of the original space and to give an interpretation to the new space,
spanned by a reduced number of new dimensions which are supposed to underlie the old ones
or to explain the variance in the observed variables in terms of underlying latent factors.
Initially we have considered 123 variables for factor analysis. To check the internal
consistency of the model, we have performed reliability test. Cronbach alpha of these
variables is .7. Inter-item correlation is also found significant. So we process to factor
analysis. To perform exploratory factor analysis we have used 123 reliable variables to find
new dimensions. The results of KMO measure (.871) and Bartlett test of sphericity (29138)
86
justified the appropriateness of the model for factor analysis (Appendix). The continue
process of factor extraction by oblimin rotation explores 30 factors. Eigen value for these
thirty factors is greater than one. These factors explain 75% of variance. Here we can see the
scree plot for the factors. First factor has highest Eigen value (27.914) and variance (22%).
On the basis of pattern matrix, we aggregate the variables with respective factors. Now we
consider these factors as variables for further analysis. We again repeat the whole process of
KMO and Bartlett’s test, communalities and oblimin rotation method. Eight factors are
extracted from the factor analysis, but here we are considering only seven factors as there is
no significant factor loading with last factor. These seven factors explain 56 % of total
variance.
Interpretation of factors: finally we have discovered seven important latent variables as
result of exploratory factor analysis. These factors have aggregated 65 variables. Here we
present dimension wise analysis.
Asset and Personal education: first factor is referred as Asset and Personal education as it
is largely explained by asset and education variables. Eigen value for this factor is 7.386. It
87
explains 24.629% variance. It is a combination of twenty variables. Mean value of the scale is
.558. We define five categories from very low to very high.
Access to basic facility: Second factor is highly dominated with the variables of access to
basic facilities. So it is referred as access to basic facility. It has used nine variables. Eigen
value for this factor is 2.261 and variance is 7.537%. Mean value is .428 for the index.
Majority of people falls in high category.
Living conditions and expenses: This dimension tells about, living conditions and
expenditures on fundamental requirements and happiness. It is a combination of nine
variables. Eigen value is 1.973 for this dimension and it explains 6.577 % variance. Cronbach
alpha is .716 for these variables. Mean value is .461 for the scale.
Health and security: This dimension comprises of health variables and financial security. It
takes seven variables into account. Eigen value is 1.543 for this factor and it explains 5.144%
variance. Cronbach alpha is .7 for these variables. Mean value for the scale is .513 for Agra
Wealth and Environment: This factor explains different dimensions of luxurious assets and
environment. It considers fifteen variables. Eigen value for this factor is 1.355 and explained
variance is 4.518. Mean value for the scale is .234.
Modernization of Education: Here we get a new dimension of modern education as it talks
about adult education and medium of education. Eigen value is 1.233 and variance is 4.110
for this dimension. Mean value of the scale is .412 for this dimension.
Safety measures in city: This dimension captures the information on safety measures in
city. Eigen value is 1.084 and variance is 3.614 for this factor. Mean value is .506 for the
Agra.
88
Composition of EASWBI Using Exploratory Dimensions: We use these seven factors to
get the Index of Economic and Social Well Being for Agra. This index is weighted average of
the factor scores, where weights are corresponding Eigen values of the factors. Mean value of
the scale is .457 for Agra city. We find that 33% people come under low category 38% and
29% falls under medium and high category.
Table (2.6) EASWBI categories in Agra
Category
Education
Wealth
& env
Health
&
security
Living
con
Basic
fac
Asset
&pe
4.0
7.3
11.7
6.7
12.7
3.7
7.3
23.0
28.3
25.0
25.0
20.7
23.3
29.3
25.7
Moderate
38.3
36.0
41.7
44.3
41.7
30.0
18.7
18.7
High
24.0
21.3
20.3
14.7
22.0
24.0
44.7
35.3
Very
high
5.3
10.3
5.7
4.3
9.0
10.0
3.7
13.0
EASWBI
Safety
measures
Very low
9.3
Low
Correlation between EASWBI and Dimensions: we have explored the correlation between
Index and dimensions. Table shows that EASBI is highly correlated with health & financial
security and wealth & environment. It is moderately related to living conditions & expenses
and modernization of education. We observe that it is low correlated with asset & personal
education and access to basic facility. Here we found very low correlation with safety
measures.
Table (2.7): Exploring Correlations between EASWBI and Latent variables in Agra
DIMENSO
NS
EASWBI
EASW
BI
.300**
1
FACTRO1
.300
FACTOR2
FACTOR
1
**
FACTOR
2
.300**
**
FACTOR
3
.457**
-.184
**
FACTOR
4
.655**
.178
**
FACTO
R5
.641**
FACTOR
6
.519**
FACTOR
7
.034
.046
.164
**
.146*
1
1.000
.300**
1.000**
1
-.184**
.178**
.046
.164**
.146*
FACTOR3
.457**
-.184**
-.184**
1
.346**
.289**
.283**
-.140*
FACTOR4
.655**
.178**
.178**
.346**
1
.269**
.311**
-.064
89
FACTOR5
.641**
.046
.046
.289**
.269**
1
FACTOR6
.519**
.164**
.164**
.283**
.311**
.318**
FACTOR7
.034
.146
*
.146
*
-.140
*
-.064
.127
*
.318**
.127*
1
.036
.036
1
EASWBI and Income level: We also test the Index relationship with Income. Here we find
that income is not highly related with the index. Income does not explain the overall well
being, as classical theory say.
Table (2.8) Correlation between EASWBI and Income Level in Agra
Indices
Exploratory Index
Confirmatory Index
**
Income
.393**
Exploratory Index
1
.946
Confirmatory Index
.946**
1
.458**
Income
.393**
.458**
1
**. Correlation is significant at the 0.01 level (2-tailed).
Results of Data Envelopment Analysis (DEA)
We develop a DEA model to assess the relative efficiency of the individuals in terms of
EASWBI. We use a DEA model to estimate capabilities. The efficiency of an individual is
defined as the weighted sum of its outputs divided by a weighted sum of its inputs and it is
measured on a bounded ratio scale. The weights for inputs and outputs are estimated by linear
program in the best advantage for each unit into efficient and inefficient ones by assuming
other constant returns to scale. Here we have used input oriented efficiency model for
analysis. We have used dimensions as inputs and index itself as output (index of both
confirmatory and exploratory factor analysis). For confirmatory analysis the average of input
oriented efficiency is .0930267and for exploratory analysis it is 0.8121. In confirmatory
analysis 180 individuals are found inefficient and in exploratory index 245 individuals are
inefficient (Appendix 6).
90
Results of Confirmatory Factor Analysis (CFA) for Kanpur
Initially we have 207 variables to measure eleven dimensions of Economic and social well
being index (EASWBI). To perform confirmatory factor analysis we have gone through a
certain procedure of reliability tests, sampling adequacy tests, correlation matrix, and
principal axis factor extraction model and oblimin rotation method for all dimensions. Here
we describe the results of analysis.
Health: To measure health initially we have considered 20 items scale for measurement.
When we test for the reliability of the scale, we get only 17 items significant in terms of
cronbach alpha analysis (appendix1); it is .818 for seventeen items. So we use only these
variables for index construction. We have also accounted for inter item correlation. Inter-item
correlation is greater than .15 for all the scaled variables. These seventeen variables have
been used to find heath index. We have followed certain procedure to obtain the dimensional
index. KMO test is greater than .7. We retain six factors with Eigen values greater than 1
(Kaiser Criterion). The cumulative percentage of variance (58%) explained by the factors is
satisfactory. On the basis of extracted factors we have aggregated the fifteen variables within
six factors. Two variables have been suppressed due to low factor loadings. These factors can
be named as health security as it combines consciousness as it combines health insurance,
child immunization and nutrition. Second factor is about health consciousness as it combines
women immunization, family planning quality of food and routine checkups. Third factor
infer about food and health as it clubs weekly intake of rich food and child health care. Fourth
factor represents Physical health in terms of exercise and type of exercise. Fifth factor
combines daily calorie intake and meals in a day. It can be labelled as nutrition level. Sixth
factor is related to health programmes as it clubs government health programmes and
awareness. In order to formulate single yardstick for health, we have used second order factor
91
analysis using six factors sum of seventeen variables. The cronbach alpha is .806 for these
variables. We find the single factor to gauge health dimension. Eigen value for the factor is
2.234.This explains 39 % of total variance (appendix1). It mostly depends on first, second,
third and sixth factors. We have normalized the index with max-min method. The mean value
is .59for health index for the individuals of Kanpur city for the index. It follows the normal
distribution.
Knowledge: we have developed knowledge scale based on 19 items. Initially we use 22
variables to check reliability. Cronbach alpha is .810 for nineteen variables and inter item
correlation is significant for these variables used in study (appendix2). We have removed
three variables from the analysis. Then we pursue factor analysis for index generation. The
value for KMO measure is .836. It indicates a good measure of sampling adequacy. The
Bartlett’s test of sphericity also justifies the model appropriateness (appendix2).
Communalities are initially worked out for factor extraction. five factors have been extracted
on the basis of Kaiser Criteria of Eigen value, scree plot and variance. The total explained
variance is 61% for these four factors and fifteen variables (appendix2). Scree plot also
justifies the factor selection model. we follow the pattern matrix for the contribution of the
variables in given factors. The factor loading for degree, internet user, browser and internet
hours are highly correlated with first factor. So this factor referred as Computer literacy. The
second factor, entitled as Influence of adult education and board on present scenario, is
explained by adult education and importance of education board. Third factor is highly
correlated with need for primary and higher education and computer knowledge. So we
entitle it Development and changes in educational requirements. The factor loadings for
fourth factor are highly loaded with schooling, write and years of school. So we call it
Educational attainment. Fifth factor is related to opportunities of abroad education and loan
92
facilities for higher education. It is labelled as foreign education and finance. We find the
key factors for knowledge; we aggregated fourteen variables into five factors. Cronbach
alpha is significant (.785) for these variables. Second order factor analysis constructs the
Knowledge index for Kanpur. The Eigen value is 2.337 for the given factor and followed by
47% of total variance. It is correlated with all variables. Then we have normalized the index
scores by max-min method. Index mean value is .456 for the Kanpur people. Here we show
the normal distributed index value.
Security: Initially seventeen variables are analyzed to capture the impact of financial and
personal and social security on well being. Twelve variables have been taken into account
after testing the reliability. Cronbach alpha is .7 and inter-item correlation is positive and
greater than .15 for these variables. KMO measure (.755) justifies the sampling adequacy for
the index. The Bartlett test of sphericity (appendix3) also approves the model for factor
analysis. Communalities (appendix 3) shows the high variance for insurance, wages, benefits
security in night and outside and low variance for loan repayment and savings. As Eigen
value is greater than one for three factors, we are considering these factors extracted by
principal axis factoring. These factors are explaining 54% of total variance (appendix3).
Scree plot shows the decreasing slops of Eigen values for factors. On the basis of oblimin
rotation (pattern matrix) we recognize the significant factor loadings (appendix3). The pattern
matrix gives the understandable scenario for the factor loadings .the first factor employment
security is highly correlated with changes in wages and remuneration. Safety in outside, train
and night is highly correlated with second factor. It shows peace in city. Third factor is about
financial security associated with insurance. Thus these factors consider six variables. For
developing the security scale we are considering six variables aggregated into three factors
extracted in earlier stages. The reliability of scale is around .7. Eigen value is 1.453 for the
93
factor and it explains 48 % of total variance (appendix3). Normalization (Max-Min) gives a
high-quality scale for measuring security. Mean value for the scale is .69 for Kanpur. It
follows the normal distribution.
Life Satisfaction: This dimension brings happiness as an indicator of EASWBI index. It
captures mental satisfaction, job satisfaction and satisfaction from national development. All
variables used in analysis are statistically reliable as cronbach alpha is .833 for these
variables. It shows high reliability of scale variables. Inter item correlation is also very high
among the variables. KMO measure of sampling adequacy is quite high (.855) for the items.
Bartlett test also shows appropriateness of model selection. Communalities show high
variance shared by health and development variables (appendix 4). Three factors are
extracted as Eigen value is 4.414, 1.3 and 1.1for first three factors. These factors explain 69%
of total variance. We can also use scree plot for factor determination. The first factor is most
explaining factor. First factor is highly depended on satisfaction from housing, income,
education, employment and living standard. Second factor infers about satisfaction from
society and health and life. We name it as personal satisfaction. Third factor is reflected by
satisfaction from national development. This index is measured by nine variables aggregated
into three factors. Reliability of scale is .856 for these variables. Second order factor analysis
gives the single measure of satisfaction. It explains 52% variance. Mean value of the scale is
.65. We define individuals into five groups.
Analysis of General Awareness index: We calculate general awareness index with the help
of variables such as news reading habits, watching television, awareness about the hot issues,
interest for about life insurance, non- life insurance, pension schemes, investment and saving
schemes. Cronbach alpha is .710 for five variables so we use only these variables for further
analysis.KMO sampling adequacy test (.738) and Bartlett’s test of sphericity approves the
94
model justification. Single factor is extracted as Eigen value is 2.431 for the factor and 49%
variance is explained by the factor. We can see the scree plot for the factor. It gives the index
for general awareness. We get the index of general awareness by using first order factor
analysis. The scale value is .71 for the individual of Kanpur. Results show that people are
much aware now days.
Wealth index: It is the most important dimension of the EASWBI. We have considered
necessary availability of assets, comfort assets and luxuries. We have also analyzed the assets
with respect to the brand and resale value. Cronbach alpha is .943 for 36 items. Some items
have been deleted due to low inter-item correlations.KMO measure of sampling adequacy
(.907) and Bartlett’s test of sphericity (11901) is very high for this dimensional index. Eigen
values are greater than one for seven factors. These factors explain 76% variance extracted
from 33 variables. Factors have been determined on the basis of pattern matrix of oblimin
rotation method. . First factor refers about electrical appliances in terms of freeze, television
and fan. Second factor is highly correlated with luxuries assets such as availability of car.
Third factor is related to use of cycle. Fourth factor is correlated with computer availability.
Fifth sixth and seventh factor is related to kitchen appliances followed by washing, inverter
and cooling appliances. We aggregate variables within factors for second order factor
analysis. The reliability is very high for the index. One factor is extracted in process; it
explains around 57% variance. Eigen value is 4.010 for the factor. It is pretty high for
constructing the index. Mean value is .35 for the scale.
Consumption: Here we measure monthly expenditure on food items, health, cloths, travel,
house, mobile, landline, cable, electricity, fuel, water and other expenses. It gives the real
picture of living standard. Cronbach alpha is .776 and inter-item correlation is significant for
these items. So we consider all variables for factor analysis. KMO test of Sampling Adequacy
95
(.706) and Bartlett’s test of sphericity justify the sample size and model selection.
Communalities are high for food expenditure, cloth expenses and mobile expenditure. Four
factors are extracted on the basis of Eigen values. Eigen value for the first factor is very high
as it is 6.404 followed by 1.335, 1.164 and 1.001 for other factors. This is shown in scree
plot. These three factors explain 68% variance. First factor is related with basic expenses on
cable, internet, health, clothing, travelling and other expenditures. Second factor is related
with phone expenses. Third factor is mainly house expenses as it is made of housing and
mobile expenditures. Fourth factor, essential expenses, is associated with expenditure on
food, electricity and fuel. Second order factor analysis gives the index value. This Index
captures 56% of variance. Cronbach alpha is .778 for these variables. The mean value is .111
for the index.
Living conditions in Kanpur: we measure living conditions with the help of fourteen
variables. these variables capture the information related to dwelling, structure, floor type,
number of rooms, drinking
water and purification facility and , toilet type, electricity
connection and hours, road, cleanliness and drainage in society. The cronbach alpha is .701
for these items. KMO (.736) and Bartlett’s test (898) results justify the model for factor
analysis. Communalities are high for sanitation and structure. Eigen values are high of four
factors. It explains 55% variance. Scree plot shows the decreasing slope of Eigen values. On
the basis of pattern matrix we detect the structure of the factors. First factor is highly loaded
with quality of roads and sanitation facilities. Second is explained by dwelling conditions
such as rooms and water facility. Third factor is about basic requirements as it comprises of
water, fuel and electricity. Fourth factor is about dwelling.In order to develop a single
measure we analyze the factors of priori analysis again through factor analysis. It gives the
index of living conditions. It explains 46% variance. The scale value is .29. The reliability of
96
scale is .7 for the index. Around 28 % of total population does not have suitable conditions
for living.
Access to basic facilities: It defines the general facilities such as availability of school, bank,
post office, health services, and transport. Cronbach alpha is .762 for the measuring variables.
Initial solution gives the appropriate results of KMO (.619) and Bartlett test is justified.
Further stages extract three factors on the basis of Eigen values, variance and scree plot.
These factors explain 63% of variance. First factor is highly loaded with access to medical
facilities such as hospital, dispensary and maternity hospital. Second factor is associated with
doctor and medical store. Third factor, general services, is correlated with bank and post
office. Second order factor analysis uses these factors for further analysis. Cronbach alpha is
.746 for the scale. It explains 53% variance. Mean value is .598 for this scale.
Environment index for Kanpur: Environment measures the pollution, type of pollution,
pollution measures, availability of park and recreation and garbage disposal method.
Reliability of the variables is .746, measured by Cronbach alpha. KMO measure (.783) and
Bartlett’s test is good. Only one factor is extracted by principal axis factoring. Eigen value is
2.501 for the factor. That is called the index of physical environment. It explains 51%
variance. Mean value is .67 for the index.
Index of utilities: It is measured in terms of availability of basic utilities such as electricity,
LPG, phone connection, cable connection, internet connections etc. Cronbach alpha is .776for
these variables. First order factor analysis gives one factor, which explains 71% variance.
KMO and Bartlett’s test give the significant results for model selection. Mean value of the
scale is .90 for Kanpur.
97
Estimation of Economic and Social Well-being Index using Dimensional indices: We
define Economic and Social Well Being Index as a weighted average of the factor scores,
where the weights are the Eigen values of the R matrix. Here we have computed factor scores
and Eigen values for each dimension to construct the EASWBI. This index is the function of
eleven dimensions. The scale value is .545 for the individuals of Kanpur. Reliability of the
dimensions is .729. It shows the efficiency of the index. We have defined the groups on the
basis of mean and standard deviation. Here we also show the normal distribution of the index.
Table (2.9) Economic and Social Well being Index for Kanpur
Categ
ory
Ver
y
low
Low
Mo
dera
te
Hig
h
Ver
y
high
Basi
c
facil
ity
5.0
Livi
ng
con
ditio
ns
6.3
cons
ump
tion
3.3
EA
SW
BI
9.7
Utili
ty
9.0
envi
ron
men
t
5.0
Wea
lth
19.3
awa
rene
s
6.0
Life
satis
facti
on
2.0
Sec
urit
y
9.7
edu
cati
on
2.7
Hea
lth
3.7
37.0
9.7
11.3
25.0
21.3
28.7
26.0
18.3
27.7
15.0
31.0
31.7
28.0
0
25.0
34.3
50.7
38.0
25.7
18.0
19.7
12.0
27.3
40.3
15.3
81.3
32.7
25.3
21.3
13.7
23.7
32.7
36.0
47.0
34.7
11.7
10.0
0
26.0
10.3
.3
16.3
5.3
25.0
14.7
16.3
4.3
12.7
98
Exploring Correlation between EASWBI and dimensional indices of confirmatory
analysis:
Correlation is found to be statistically significant at .01 levels for index and dimensions
(table 5). This index is high and positively correlated with most of the dimensions such as
Health, knowledge, assets, security, consumption and satisfaction. Positive correlation is
found with living conditions, utilities, awareness and environment. This index is low
correlated with access to basic facilities .This analysis shows that policy should be focused on
all the dimensions to achieve higher index of well being.
Table (2.10): correlations between EASWBI and dimensions
DIME
NSIO
NS
Heal
th
inde
x
Knowl
edge
index
Asset
index
Cons
umpt
ion
index
Utilit
y
index
Aw
aren
ess
inde
x
Secu
rity
inde
x
Life
Satisfa
ction
index
Livin
g
condi
tions
index
Acce
ss to
basic
facilit
ies
index
Enviro
nment
index
income
EAS
WBI
.728
.739**
.892*
.710*
.562**
.747**
.601*
.120*
.348**
.523**
*
.67
9**
.707
*
**
**. Correlation is significant at the 0.01 level (2-tailed).
**
*
99
Correlation among the dimensional indices of confirmatory analysis
On the basis of correlation matrix (table 6) positive and high correlation is found among the
major dimensions of the index. Health, knowledge, asset, security; satisfaction and income,
awareness are highly and positively correlated with one another. Few dimensions such as
living conditions, Access to basic facilities are less correlated with other dimensions. There is
need for more developed infrastructure to facilities access and living conditions. We can
justify the strong and significant relationship among the indices for a better index of
EASWBI.
Table (2.11): correlations among the dimensions
DIM
ENSI
ONS
acce
ss
Acces
s
1
health
wealt
h
aware
ness
Livin
g
condi
tions
envir
onme
nt
satisf
action
utility
educa
tion
securi
ty
Cons
umpti
on
Inco
me
.147
.139
.139
.095
.088
.087
.051
*
.038
.082
*
.173
.041
*
.090
.511
.382
.537
.519
.407
.289
**
**
**
**
**
**
**
heal
th
.147
1
wea
lth
.139
.581
*
**
awa
rene
ss
.139
.531
.513
*
**
**
Livi
ng
con
ditio
ns
.173
.353
.459
.292
**
**
**
envi
ron
men
t
.041
.090
.240
.136
**
*
satis
facti
on
.038
.511
.669
.438
.527
.138
**
**
**
**
*
utili
ty
.095
.382
.347
.313
.497
.179
.394
**
**
**
**
**
**
*
.581
.531
.353
**
**
**
1
.513
.459
.240
.669
.347
.683
.575
.680
.516
**
**
**
**
**
**
**
**
**
1
.292
.136
.438
.313
.412
.488
.398
.287
**
*
**
**
**
**
**
**
1
.024
.527
.497
.405
.483
.477
.264
**
**
**
**
**
**
.138
.179
.154
.107
.089
.100
*
**
**
1
.394
.577
.526
.472
.390
**
**
**
**
**
1
.278
.452
.226
.163
**
**
**
**
**
.024
1
100
Edu
cati
on
.088
.537
.683
.412
.405
.154
.577
.278
**
**
**
**
**
**
**
Sec
urit
y
.082
.519
.575
.488
.483
.107
.526
.452
.441
**
**
**
**
**
**
**
cons
ump
tion
.087
.407
.680
.398
.477
.472
.226
.498
.450
**
**
**
**
**
**
**
**
Inco
me
.051
.289
.516
.287
.264
.390
.163
.413
.349
.668
**
**
**
**
**
**
**
**
**
.089
.100
1
.441
.498
.413
**
**
**
1
.450
.349
**
**
1
.668
**
1
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
Results of Exploratory Factor Analysis (EFA) for Kanpur
Factor analysis attempts to bring inter correlated variables together under more general,
underlying variables. More specifically, the goal of exploratory factor analysis is to reduce
the dimensionality of the original space and to give an interpretation to the new space,
spanned by a reduced number of new dimensions which are supposed to underlie the old ones
or to explain the variance in the observed variables in terms of underlying latent factors.
Initially we have considered 142 variables for factor analysis. To check the internal
consistency of the model, we have performed reliability test. Cronbach alpha of these
variables is .729. Inter-item correlation is also found significant. So we process to factor
analysis. To perform exploratory factor analysis we have used 142 reliable variables to find
new dimensions. The results of sampling adequacy (.836) and Bartlett test of sphericity
(1383) justified the appropriateness of the model for factor analysis (Appendix). Common
shared variance among the variables is explained by communalities. The continue process of
factor extraction by oblimin rotation explores 30 factors. Eigen value for these thirty factors
is greater than one. These factors explain 74% of variance (appendix). Here we can see the
scree plot for the factors. First factor has highest Eigen value (31.309) and variance (24%).
101
On the basis of pattern matrix, we aggregate the variables with respective factors. Now we
consider these factors as variables for further analysis. We again repeat the whole process of
KMO and Bartlett’s test, communalities and oblimin rotation method. Nine factors are
extracted from the factor analysis, but here we are considering only eight factors as there is
no significant factor loading with last factor. These seven factors explain 58% of total
variance. Finally we have discovered eight important latent variables as result of exploratory
factor analysis. These factors have aggregated 58 variables. Here we present factor wise
analysis. We use pattern matrix to detect the factor structure.
Index of luxury assets: First factor is highly explained by assets variables. So it is referred as
index of luxury assets. It comprises sixteen variables. It explains 23 % of total variance.
Mean value for the index is .256. Mostly people fall in moderate category.
102
Index of Physical Environment: This factor is explained by eight variables of environment.
Eigen value is 1.835 and variance is 6.326 for this factor. The scale value is .599 for the
individuals of Kanpur city. 43% people report in bad environmental conditions in Kanpur.
Index of Access to basic facility: This factor is dominated by variables of access to basic
facilities. Eigen value is 1.675and explained variance is 5.776%. The scale value is .524 for
the Kanpur city. Results show the good infrastructure for the basic facilities in Kanpur.
Index of Health & Educational awareness: This factor explains the variables of health,
education and awareness. Eigen value is 1.411 and variance is 4.885 for this factor. The scale
value for Kanpur is .612.
Index of local transport: This index is mostly explained by means of local transport. Eigen
value is 1.341 and variance is 4.624 for this factor. Mean value of the index is .50 for Kanpur.
Index of consumption: This index is explained by monthly consumption of basic
requirements. Eigen value of the factor is 1.210 and variance is 4.172. Mean value for this
index is .123 for the Kanpur, which is quite low. Range varies zero to .8 for this factor.
Index of overall satisfaction: This index is highly correlated with variables of satisfaction.
Eigen value is 1.139 and explained variance is 3.928 for this factor. Mean value is .431 for
Kanpur city. Around 60% people are not satisfied with development in Kanpur.
Living conditions in Kanpur: This index explains the living conditions in Kanpur city. It
mostly explains the sanitation, quality of road, quality of drinking water, drainage etc. Eigen
value is1.057 and explained variance is3.644 for this index. Scale value is .462 for Kanpur.
Composition of EASWBI Using Exploratory Dimensions: We use these eight factors to
get the Index of Economic and Social Well Being for Kanpur. This index is weighted average
of the factor scores, where weights are corresponding Eigen values of the factors. Mean value
103
of the scale is .356 for Kanpur city. We find that 34% people come under low category 38%
and 29% falls under medium and high category.
Table (2.13): EASWBI categories in Kanpur
Category
Very
low
Low
Moderat
e
High
Very
high
EASWB
I
2.7
Living
conditio
n
5.3
satisfactio
n
2.7
consumptio
n
1.7
Transpor
t
6.0
Healt
h edu
2.7
acces
s
9.0
environmen
t
11.7
Luxur
y asset
18.3
34.3
25.7
29.0
23.3
21.7
25.7
17.3
32.0
19.0
23.3
25.3
28.0
38.3
12.3
23.0
23.3
40.7
33.3
16.7
16.0
10.7
22.3
32.7
21.3
26.0
11.7
9.0
23.0
27.7
29.7
14.3
27.3
27.3
24.3
4.0
20.3
Correlations among the factors and Index
On the basis of correlation matrix (table 30) we have found high and positive correlation
between EASWBI and other dimensions. This index is very highly correlated with luxury
assets, physical environment, health and educational awareness.. And positive correlated
104
living conditions, access to facilities, transport, satisfaction and consumption. This index is
highly correlated with first factor as it explains the maximum variance (table)
Table (2.14) Correlation explored within different dimensions
Factors
f1inde
x
f2inde
x
f3inde
x
f4inde
x
f5inde
x
f6inde
x
f7inde
x
F8inde
x
EASW
BI
f1index
1
.288**
.112
.495**
.250**
.379**
.142*
.315**
.919**
f2index
.288**
1
-.030
.529**
.432**
.186**
-.022
.216**
.519**
f3index
.112
-.030
1
.032
.064
-.079
.119*
-.279**
.204**
f4index
.495**
.529**
.032
1
.402**
.260**
.241**
.287**
.686**
f5index
.250**
.432**
.064
.402**
1
.187**
.083
.243**
.471**
f6index
.379**
.186**
-.079
.260**
.187**
1
.140*
.384**
.487**
f7index
.142*
-.022
.119*
.241**
.083
.140*
1
.185**
.271**
F8index
.315**
.216**
-.279**
.287**
.243**
.384**
.185**
1
.421**
EASWB
I
.919**
.519**
.204**
.686**
.471**
.487**
.271**
.421**
1
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
(ᴠII.7) Results of Data Envelopment Analysis (DEA)
We have used dimensions as inputs and index itself as output (index of both confirmatory
and exploratory factor analysis). For confirmatory analysis the average of input oriented
efficiency is .0973933 and for exploratory analysis it is 0.92. In confirmatory analysis 100
individuals are found inefficient and in exploratory index 134 individuals are inefficient
(Appendix 6).
Results and interpretation of confirmatory factor analysis for Surat:
To construct the EASWBI for Surat households, we have applied the same method of
confirmatory model, explained before; here we describe the some results and inferences of
dimensional indicators as follows;
105
Health: ability to lead healthy life is measured by this index. The reliability of 16 variables
(out of 18) is found .807 and inter-item correlation is also significant, which is good enough
for further analysis. Then we go through first order factor analysis, the value of KMO and
Bartlett ‘test is .749 and 2771.869. Kaiser criterion extracts six factors that explain 72.433%
of total variance. These factors can be labelled as Health awareness (six variables), Exercise
habits (three variables), Sport habits (two variables), Vaccination (2 variables), Nutrition and
government programme (two variables). To get the single dimension we have gone through
for second order factor analysis. It gives two factors, explaining 52% variance. To construct
the index we consider only first factor consisting health awareness and government health
programmes (consisting of seven variables). The health indicator of Surat is explained by
health insurance, child immunization, nutrition, and quality of food, concern for routine
checkups, government health programmes and attitude for health awareness. The scale value
is, 0.60, quite good with comparison to other cities.
Educational Attainment: According to the predefined structure to capture all areas of
knowledge, 22 variables have been analyzed to develop a scale for assessing knowledge.
Only 19 variables have found reliable with 0.743 cronbach alpha. The values for KMO
measure and Bartlett’s test are .557 and 75.942 that is moderately significant for factor
analysis. Five factors have been extracted, having Eigen value greater than one. All factors
explain around 61% variance. The first factor (λ4.835) infers about the individual educational
qualities and combines eight variables. Second factor (λ 2.525) emphasizes on government
role and policies for improving education. Third factor (λ 1.907) explains the importance of
higher education and women education. The fourth and fifth factors (λ 1.231, 1.110) signify
the foreign education. All variables are positively related with the factors.
106
These five indicators have been analyzed to construct one dimensional index of knowledge.
Second order factor analysis clubs these factors into two main components. The first factor
explains the highest variance (31.187). So the factor scores of first factor give the Index for
assessing knowledge. The scale value for Surat is 0.566, which shows the average education
standard for Surat. The index is constructed of level of education, writing skills, computer
literacy, and type of degree, years of schooling, usage of internet and browser. Basically it
focuses on quality of personal education.
Index of General Awareness: this is the supplement index to measure access to knowledge.
It is measured separately from educational attainment because it measures the people’s
awareness for different issues. We have taken seven variables to measure this dimension. The
reliability is .711 for these variables. The values for KMO and Bartlett’s test (.726 and
486.54) are appropriate for further analysis. The first order factor analysis gives two factors,
consisting of 53% variance for measuring general awareness. The first factor (λ 2.708) infers
about awareness for government welfare schemes, saving etc. The second factor (λ 1.042)
consciousness for life insurance. The second order factor analysis gives the proper
measurement for general awareness. The variance explained by the factor is 65%. The scale
value is .745, which is quite high for the Surat.
Security measures in Surat city: we want to capture all forms of securities, such as financial
security, personal security, mental security etc, to formulate this dimension. Initially we had
sixteen variables of different measurement, but only nine variables are found reliable
(Cronbach’s alpha .710). KMO and Bartlett’s test value are significant (0.705, 573.01) for
priori analysis. First order factor analysis gives three factors with 65% variance. The first
factor (λ 2.860) infers about financial security, second (λ 1.322) and third factor (λ 1.058)
explain job and income security. Second order factor analysis gives the final factor for the
107
index formation. This index explains 57% variance and captures only financial and job
security. It doesn’t explain anything about social security. The scale value is 0.63 for Surat
city. It indicates the financial security is on above average for Surat.
Index for Life satisfaction: To measure the subjective well being we have included few
measurements of life satisfaction, job satisfaction, income satisfaction, mental satisfaction
and social satisfaction. Cronbach’s alpha is (.869) is high for these ten variables. KMO and
Bartlett’s test value (0.811 &1454.85) justify the data set for further analysis. first order
factor analysis extracts two factors with eigen value 4.669 and 1.484. These two factors
jointly explain 61% of variance. The first factor (47% variance) infers about personal
satisfaction in terms of job, income and educational satisfaction and the second factor
explains (15%) social and health satisfaction. second order factor analysis gives factor to
measure life satisfaction index. It consists of 75% variance and positively related to the all
variables measured in Surat. The scale value is 0.56 for households in Surat.
Wealth index: this is the one of the most important dimension of the EASWBI, as it explains
the prosperity of the households in terms of asset availability. We have also captured the
quality and the present value of the assets in terms of resale value. The reliability is
significant (.932) for 26 items. The KMO and Bartlett’s test is highly significant (.882 &
8531) for the data set. The first order factor analysis gives six factors with 72% variance.th e
first factor (λ 12.618)for the Surat is highly loaded with luxurious assets such as computer
and comfortable assets. Second factor (λ 2.812) explains the vehicles. Third fourth and sixth
factor (λ 1.870, 1.466 & 1.331) infers about basic requirement assets. The fifth factor (λ
1.596) explains the luxurious assets. The second order factor analysis extracts one factor (λ
3.061and 51% variance). This factor is highly loaded from luxurious, comfortable to basic
assets. The scale value is .48 for Surat.
108
Consumption index: we have collected data on monthly expenditure of household in order
to formulate measurement for standard of living. The all fourteen items are reliable
(Cronbach alpha .767) but we drop two of them due to low inter-item correlation. The KMO
and Bartlett’s test (.857, 1007) prove the appropriateness of the data set. The initial factor
analysis gives three factors, explaining 55% variance. The first factor (λ 4.423) is highly
loaded with basic expenditure on food, health, clothes and house. The second factor (λ 1.163)
is loaded with expenditure on phone and electricity. Third factor (λ 1.021) explains the
entertainment expenditure. The second order factor analysis gives single index (λ 1.775,
59.174variance) for consumption including all the expenditures. The scale value for the Surat
is .30.
Housing and related facility: in order to capture basic housing requirements, we have
measured this index by using fifteen variables. All these variables are reliable in terms of
alpha analysis (.772). KMO and Bartlett’s test is also significant (.791 & 1447). Moving
towards the factor analysis, we get five factors with 67% variance. The first factor (λ 4.326)
is highly explained by dwelling structure; the second factor (λ 1.840) and Fifth factor (λ
1.078) infers about the sanitation conditions of the society, third and fourth factor (λ 1.674,
1.183) measures the qualities of road, electricity and drinking water. To summarize these
factors, second order factor analysis is performed. The first factor is linear combination of
first, second and fifth factor. So this index focuses on housing and sanitation conditions using
nine variables. The scale value for Surat is 0.66 that is higher for Surat city.
Access to basic services: this dimension is measured with nine variables. All variables are
found reliable with .812 cronbach alpha. Both tests of sampling adequacy and
appropriateness are significant (.776, 1008). Three factors have been extracted with 69%
variance. The first factor and second factor (λ 3.699, 1.350) largly explained the access to
109
medical facilities, explaining 56% variance. The third factor (λ 1.161) infers about social
infrastructure in terms of access to school, banks and public transportation. All variables are
positively related with the related factors. Second order factor analysis provides the single
indicator to measure this dimension. It contains λ 1.170 and variance 56%. This index covers
all the sub indicators of the measurement. The scale value is .536 for Surat. We can say that
people in Surat have the average access to the basic services.
Environmental status in Surat: Environment status is measured in terms of pollution
intensity, type of pollution, pollution measures, availability of park and recreation and
garbage disposal method. Reliability of the variables is .713, measured by Cronbach alpha for
these variables.. KMO measure (.661) and Bartlett’s test (173) is appropriate for the analysis.
Only one factor is extracted by principal axis factoring. Eigen value is 2.029 for the factor.
That is called the index of physical environment. It explains 50% variance. This indicator
measures the intensity of pollution and social environment. Mean value is .170 for the index.
Availability of utilities: It is measured in terms of availability of basic utilities such as
electricity, LPG, phone connection, cable connection, internet connections etc. Cronbach
alpha is .764 for these variables. First order factor analysis gives one factor, which explains
56% variance. KMO and Bartlett’s test give the significant results for model selection. Mean
value of the scale is .28 for Surat.
Estimation of Economic and Social Well-being Index using Dimensional indices: we
have already defined the economic and social Well Being Index (EASWBI) as a weighted
average of the factor scores, where the weights are the Eigen values of the R matrix. Here we
have computed factor scores and Eigen values for each dimension to construct the EASWBI.
This index is the function of eleven dimensions. The reliability of the index is .720 for all the
110
used variables in the analysis. The mean value is .5207 for Surat people. But the percentage
of highest groups is dominating.
Table (2.15)Percentage of different groups for EASWBI and
Categ
Inde
x
Exp
Hea
Kno
Sati
Utili
Sec
envi
hou
awa
Acc
asset
end
lth
wl
sfac
ty
urit
ro
se
ren
ess
Ver
y
low
6.0
2.3
9.0
8.3
11.7
6.7
30.3
5.3
20.3
4.0
4.0
3.7
Low
21.0
33.3
20.7
17.0
13.3
24.0
10.3
19.7
52.3
14.7
21.0
28.0
Mo
dera
te
27.0
19.0
28.7
15.3
13.7
16.0
24.7
6.7
1.7
26.3
9.3
16.3
Hig
h
16.3
18.0
14.3
30.0
23.7
21.0
10.3
46.3
12.3
22.3
23.0
20.7
Very
high
29.7
27.3
27.3
29.3
37.7
32.3
24.3
22.0
13.3
32.7
42.7
31.3
111
Exploring Correlation between EASWBI and dimensional indices of confirmatory
analysis Correlation is found to be statistically significant at .01 levels for index and
dimensions (table). This index is very highly and positively correlated with most of the
dimensions such as consumption and wealth. It is highly correlated by satisfaction, security
measures and housing facilities. Health, knowledge is moderately correlated. But access to
basic services and awareness has low correlation with the dimension. But in Surat there is no
environmental participation on economic and social well being.
Table (2.16) correlation between EASWBI and dimensional indicators
dime
nsion
s
HEA
LTH
EDUC
ATION
EAS
WBI
.40
2**
.433**
SATISF
ACTION
.723**
UTI
LIT
Y
.82
0**
EXPEN
DITURE
.985**
SECU
RITY
.610
ENVIRO
NMENT
HO
US
E
AWAR
ENESS
AS
SE
T
ACC
ESS
.6
36
.372**
.8
69
.26
9**
.044
**
**
**
Correlation among dimensional indices: as we have hypothesized that all dimensions are
highly correlated with one another. Most of the dimensions are significantly correlated.
Environment has negative significant correlation with other dimension. It shows that there is
a need for improving the environmental conditions.
Table (2.17) correlation among dimensional indicators
dimensio
ns
health
healt
h
Educa
ted
satisfact
ion
utilit
y
expendi
ture
secur
ity
environ
ment
hous
e
awaren
ess
asset
acces
s
1
.231*
.295**
.254
.419**
.212
-.212**
.111
.090
.246
.074
*
educatio
.231
n
**
satisfact
.342**
**
.196
.474**
**
.23
1
.342**
**
ion
1
utility
.25
4
1
**
**
.196*
*
.504**
1
.360
-.314**
**
.19
6
*
.474**
**
.711**
-.314**
**
.38
8
.333
.201**
**
.36
0
**
**
.33
3
.160**
**
.38
3
**
.201*
.349
.00
**
5
.34
*
9
.109*
.75
9
**
**
.00
5
.06
4
112
expendit
.41
**
ure
9
security
.21
2
environ
ment
-
.11
1
awarene
**
*
.360*
-.093
*
.333
.618
**
*
asset
.24
.349*
.299
.007
.566**
.044
.667
**
7
-.321**
1
.424
**
.06
.47
4
**
.47
-.181
1
-.155
**
**
.48
7
-.027
**
.11
.33
2
-.007
*
**
.31
**
1**
.48
.11
3
.294*
*
0
.155
*
*
*
**
0*
-
-
.02
.00
7
7
.46
2
1
.22
.26
**
5**
4
.224*
**
.02
*
**
.46
.84
*
.337
**
.29
0
1
**
.424*
-
.18
0
.311**
**
**
4
.843**
1
**
.32
8
**
*
.75
9
.66
**
-
**
.10
9
*
.005
**
.007
8
**
.38
3
*
0
4
.635**
1
*
ss
.07
5
*
*
access
.63
**
.16
0
1
**
.38
8
.201
6
.455**
*
.314
.71
1
.09
**
.739**
*
-
.21
2
house
**
.474*
1
.14
*
.02
.265*
2
*
2
0**
.14
0
1
**
Results of exploratory analysis: The main focus of this work is to explore new composite
index for measuring economic and social well being. We have employed the exploratory
factor analysis to explore the new dimension responsible for the development in Surat city.
This is a different way to look the well being in one dimension. The original date set contains
139 variables for analysis. We checked the reliability of all variables, which is .720 for all the
variables, followed by the significant sample adequacy measure and Bartlett’s test. We get
the clear picture after first order factor analysis. 36 factors with 75.18% variance18 are
extracted initially to start the procedure. These factors are those that have eigenvalues greater
than one. We proceed by lowering the number of factors by subsequent analysis and
recomputing the factor loadings until we arrive at a set of that have significant loadings. This
gives us ten important factors. These factors are considered as sub indices of the overall
index. Here we present a brief discussion on that:
18
Results of Eigen values and variance for all factors are shown in appendix1.
113
Indicator of wealth: the indicators that load significantly on Factor 1, which explains the
largest share of the variation of other variables, are mostly related to assets and wealth. It
shows the importance of assets and wealth in economic and social well being; all variables
are positively related with the factor. The first factor explains the largest variance as it is
19.283 and 6.748 Eigen value. The mean value for this factor is .3193 for Surat. It justifies
the role of standard of living in well being.
Indicator for access to basic facilities: the second factor gives a comprehensive indicator of
access to basic facility. It clubs the eight variables to measure this dimension. Distance to
medical facilities, school, and banks, public transportation facility has been measured in this
dimension. This factor extracts 6% variance and λ 2.150. The scale value is .5301 for the
people in Surat.
Indicator for educational attainment: this factor is largely explained by eight education
factors and two variables of food and house. So it is dominated by educational attainment. It
focuses on
personal educational qualification and computer literacy. It also infers about
tendency to dyne out and type of house. This factor contains 2.085% of variance and λ
2.085.the scale value is .5952 for the Surat city.
Indicator of quality of basic services: This indicator is measuring the role of quality of
basic services such as electricity, drinking water and road etc in economic and social well
being index. Its variance and latent roots are 4.959% and 1.746.the scale value for this
measurement is .5903, which indicates the good quality of services.
Indicator of health and life satisfaction: This indicator shows the importance of health and
life satisfaction in well being index. It is a combination of nine variables of health and
114
satisfaction scale. The Eigen value is 1.601 and explained variance is 4.573 for this indicator.
The mean value is .58 for Surat city.
Indicator of exercise habits: it measures the exercise habits of people of Surat. The mean
values for this indicator is quite low (.32) for the Surat city. It shows the people negligence
for exercise habits. The explained variance of this factor is 4.315% with λ 1.51.
Indicator of basic assets: this indicator is a supplement indictor of wealth index, as it
measures the availability of basis assets. The Eigen value for this indicator is 1.257 and
variance is 3.590 the mean value is .33 for the indicator.
Indicator for preference to global education: This indicator measures the role of abroad
education in increasing well being. This is the new factor and represents the Guajarati culture
for abroad education as they prefer to move abroad for higher education. The variance is 3.48
and λ is 1.221 for this indicator. The mean value is .32 for the people in Surat.
Indicator for financial security and expenditure: This factor (varaince3.713 and λ 1.11)
infers about financial securities and some parts of expenditure, but highly loaded by security
aspects. The mean value (.70) for this indicator is very high.
Indicator to measure sanitation: This factor explains the sanitation conditions in Surat city.
This measurement extracts 3.1% variance and 1.08 Eigen value. The scale value is .43 for the
Surat city.
Composition of EASWBI by using exploratory indicators: The extracted ten factors
describe and measure different aspects of development. However every one of them is useful
in constructing a development index. We compute the EASWBI using the scores for these
factors and apply the weighted average (Where weighs are eigenvalues) method. This index
115
explains overall 58% variance. The scale value is .5 for the Surat city, which indicates the
level of well being for the people in Surat.
Table (2.18) percentage of people in different categories
dimensions
Very low
Low
Average
High
very high
In1
2
3
4
5
6
7
8
9
10
Easwbi
19.7
3.3
10.0
5.7
7.3
19.0
.7
3.0
6.3
16.0
5.3
19.0
26.0
18.0
26.0
17.0
29.0
29.0
25.7
12.0
17.7
25.7
17.7
21.3
14.3
15.3
19.7
6.0
28.0
24.0
16.0
22.3
18.7
17.7
14.3
32.7
17.7
22.3
15.3
27.3
24.3
32.0
15.7
22.3
26.0
35.0
25.0
35.3
33.7
30.7
15.0
23.0
33.7
28.3
28.0
Correlation among the indicators and EASWBI
We check the degree of correlation between the economic and social well being index. On the
basis of correlation we prove that this index is very highly and positively correlated with
wealth for Surat city. Second highest correlation is with the educational attainment. The
index is significantly correlated with the all dimensions. But it is negatively correlated with
the security and sanitation.
Table (2.19) correlation among the factors of EASWBI
116
dimensions
Easwbi
Easwbi
1
1
.898
In1
2
3
4
5
6
7
8
9
.898**
.395**
.612**
.434**
.374**
.463**
.326**
.109
-.241**
**
**
**
**
**
.095
**
-.088
.038
**
1
.234
.386
.306
.159
.285
**
.278
2
.395**
3
.612**
.386**
.169**
1
.298**
.285**
.281**
.232**
4
.434**
.306**
-.007
.298**
1
.173**
.281**
.112
.234**
.169**
1
.127*
-.007
.112
10
-.387
.026
-.114*
-.044
-
-.253**
-.071
-.057
-.229**
-.145*
-.248**
.165**
5
6
7
8
9
.374**
.159**
.127*
.285**
.173**
**
1
.085
.011
**
.112
**
**
.085
1
.110
.326**
.278**
-.088
.112
.011
.110
1
.019
-.154**
-.169**
.109
.095
.038
-.165**
.075
-.056
.019
1
-.196**
.034
-.241**
-.387**
.026
-.253**
-.002
-.079
-.154**
1
.151**
.151**
1
.463
.285
.281
.232**
-.114*
-.044
.281
.075
-.056
-
-.002
.003
-.079
-.055
.196**
10
-.071
-.057
-.229**
-.145*
-.248**
.003
-.055
-.169**
.034
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Intercity comparisons:
On the basis of previous analysis we make comparisons by using factor extracted by
exploratory factor analysis. There are seven main factors explored for Agra to construct the
EASWBI. These factors are related to wealth assessment, educational attainment, Access to
basic facility, Living conditions and expenses, Health and security, Environment
Modernization of Education and safety measures. Main factors extracted for Kanpur are
Luxury assets, physical environment, access to basic facility, health & educational awareness,
local transport, consumption and satisfaction. And ten factors have been accounted for Surat,
such as wealth, access to basic facility, educational attainment, quality of services, etc. There
are some common factors shared by these cities responsible for the economic and social well
being. Wealth and asset get the highest weights for economic and social well being index for
three cities. But the second important factor differs in Kanpur. Access to basic facilities plays
a important role in Agra and Surat. But environment gets the second highest weight for
Kanpur. Some factors are common for constructing EASWBI for all cities such as health and
117
security. These factors are the crucial pillars for Economic and social well being index. There
are some additional factors responsible for well being in Kanpur are consumption and
satisfaction and mode of local transport which are different from Agra’s EASWBI such as
living conditions, modernization of education and security and For Surat, quality of basic
services, sanitation facility and crucial factors. Security for Agra is a very important
indicator; it also captures the safety measures in city. But in Surat, financial security is a
leading factor for the index construction. So we can say that city characteristics also have an
effect on the individual well being. We present the important factors extracted for these cities.
TABLE (2.20) Factors responsible for Economic and social well being for Agra, Kanpur
and Surat
Agra
Kanpur
Surat
EASWBI (0.45)
EASWBI (0.35)
EASWBI(0.50)
Indicator of luxurious assets (0.25)
Indicator to wealth (0.31)
Access to basic facility (0.42)
Environment (0.59)
access to basic facilities (0.53)
Living conditions and expenses
Access to basic facility (0.52)
Educational attainment (0.59)
Health & educational awareness
Quality of basic services (0.59)
Asset
and
personal
education
(0.55)
(0.46)
Health & security (0.51)
(0.61)
Wealth and environment (0.23)
Transport facility (0.50)
Health and life satisfaction (0.57)
Modernization of education (0.41)
Consumption (0.12)
Exercise habits (0.31)
Safety measures in city (0.50)
Overall satisfaction (0.43)
Basic necessity (0.32)
Living conditions in Kanpur (0.46)
Globalization of education (0.40)
118
Indicator for financial security and
expenditure (0.70)
Indicator to measure sanitation
(0.43)
Surat has performed well in most of the indicators. The overall index value is also high for
the Surat as it is 0.44, followed by Agra and Kanpur. Most of the indicators have higher value
for Surat than other cities. Agra performs better in terms of Asset and personal education,
health & security and safety measures in city as scale value is greater than .5 for these
indicators. While index shows comparative high scale for environment, access to basic
facility and health & educational awareness in Kanpur. Kanpur is deprived in terms of
Luxury asset and consumption (very low scale value) and Agra is deprived in terms of wealth
and environment.
Cities are compared on the basis of dimensions scales of confirmatory factor analysis.
Health, life satisfaction, general awareness and access to basic facility are the dimensions
perform better in all cities with high scale value. Knowledge, wealth and living conditions are
better in Agra in comparison to Kanpur, while dimensions of security, environment and
utilities are superior in Kanpur.
Table (2.21) Comparison on the basis of scale values of EASWBI & Dimensional
indexes
Dimensions
Agra
Kanpur
Health
0.54
0.59
Educational
0.52
attainment
0.45
Surat
0.60
0.56
119
Security
0.45
0.60
0.63
Life satisfaction
0.54
0.52
0.56
General awareness
0.57
0.71
0.74
Wealth
0.51
0.35
0.48
Consumption
0.24
0.11
0.30
Housing conditions
0.52
0.29
0.66
Access
0.52
0.59
0.66
Environment
0.23
0.66
0.17
EASWBI
0.44
0.54
0.52
to
basic
services
Robustness checks
Robustness is the ability of an economic model to remain valid under different assumptions,
parameters and initial conditions. To check the robustness and validity if the Index we have
correlated values of both confirmatory and exploratory approaches as the weights are
different in both the analysis. Here we found that value of both indexes is highly and
positively correlated to each other. Therefore this index shows its validity with different
dimensions.
Table (2.21) Correlation between Confirmatory and Exploratory Index Value of
Kanpur and Agra and Surat
Confirmatory EASWBI
Exploratory EASWBI for
Exploratory
EASWBI
Exploratory
Kanpur
for Agra
for Surat
0.79
0.95
0.90
EASWBI
120
Concluding remarks: In this chapter, the main focus is on exploring the new dimensions for
measuring economic and social well being index for three different cities. It is found that
some basic factors play a crucial role in development but there are some city specific
characteristics also responsible for well being. Another thing is that income has not taken into
account as a dimension of the index but as a determinant of the economic well being. Income
is found significant variable for the economic and social well being index. Surat has the
highest index value due to its strong economic foundations.
121
Section : IV
Economic and Social well being Index for Indian States
The present section is the extension of previous work done for individual level to state level.
National performance depends on the necessary inputs from the different regions, and hence
the focus is exclusively at the regional and/or sub-national level variations to see how the
regions differ in terms of their economic performance. First the main focus is on the quality
of life, as that includes the overall social progress of a country, then development of regions
is measured in terms of their growth performance. Hereby, the main attempt is to investigate
the regional level performance in terms of economic and social well-being for Indian states.
Further the growth and well-being inter linkages is being discussed in this section. It is
revealed that higher level of income is correlated to higher level of well being. Moreover
negative relationship between well being and poverty is to be explored in this chapter. Higher
poverty ratio would decrease the well being for the society. Income inequality is also a
responsible factor of variation of well being among the states. The impact of economic
inequality19 is also gauged on economic and social well being index. A comparison is also
provided to show the superiority of economic and social well being index on human
development index developed by Planning Commission of India (National Human
Development Report 2001).
In this section an index of economic and social well being has been developed for seventeen
Indian states. The basic development indicators are extended to some additional parameters
to generate a composite index of economic and social well being for seventeen Indian states
for three time periods (2001, 2007, and 2009). A different methodology is also proposed for
19
We have considered Gini coefficient as a measure of inequality. It is a measure of the inequality of a
distribution, a value of 0 expressing perfect equality and a value of 1 maximal inequality.
122
weighting and aggregation of the indicators (by using principal component method of factor
analysis). Panel data estimations have been used to extract the significant impact of growth,
inequality and poverty on well being level for Indian states.
Hypotheses:

There is negative and significant relationship between EASWBI and poverty ratio.

Income inequality has positive effect on well being. Higher inequality leads to higher
well being.

Income growth has positive and significant relationship with well being.
Regional disparity among Indian states:
Differential level of economic growth has been a major responsible factor for regional
disparity among Indian states. According to Dreze and Sen (1995), enormous variations in
regional experiences and achievements coupled with the even sharper contrasts in some fields
of social development have resulted in remarkable internal diversities in India. Furthermore,
the long-term progress in raising rural living standards has been diverse across Indian states
(Datt and Ravallion, 1998). Such disparities are responsible for various states having different
capacities for poverty reduction (Datt and Ravallion, 2002). Similarly, Rajarshi Majumdar
(2004) has observed that states like Kerala, Maharashtra and Himanchal Pradesh put up
consistently good performance regarding social and human development indicators, however,
Kerala has not been able to convert its social development into economic progress. On the
other hand, Gujarat, in spite of its having low Human Development (HD) ranks, have
consistently good ranking in per capita Net State Domestic Product (PCNSDP).
The National Human Development Report-2001 for India reveals considerable differences in
human development among Indian states during 1981-2001. The report notes that in the early
eighties, states like Bihar, U.P., M.P., Rajasthan and Orissa had HDI close to just half that of
123
Kerala’s. The inter-state differences in human poverty are quite striking and report notes that
while there have been improvements in the human development index and human poverty
index during the 1980s, the inter-state disparities and the relative position of the states has
practically remained the same. Facts show that inter-state disparity as measured in terms of
standard deviation in human development index stood 0.083 for 1981 which further increased
and stood at 0.100 in 1991 [Tenth Five Year Plan (2002-2007), Vol. III]. The World Bank
(2006) in its report entitled, “India-Inclusive Growth and Service delivery: Building on
India’s Success” has observed sharp differentiation across states since the early 1990s reflects
acceleration of growth in some states but deceleration in others. The report further adds that
growth failed to pick up in states such as Bihar, Orissa and Uttar Pradesh that were initially
poor to start with, with the result that the gap in performance between India’s rich and poor
th
states widened dramatically during the 1990s. An approach to the 11 Five Year Plan
(Planning Commission, Government of India, 2006) has also acknowledged regional
backwardness as an issue of concern. The differences across states have long been a cause of
concern and therefore, we cannot let large parts of the country be trapped in a prison of
discontent, injustice and frustration that will only breed extremism.
Growth performance:
Economic growth is the most powerful instrument for reducing poverty and improving the
quality of life in developing countries. The goal of development is to improve human wellbeing in a sustainable way, with particular emphasis on less well off. Rapid and sustained
economic growth, though not sufficient for eliminating poverty, is certainly a necessary
condition for improving living standards. Economic growth is quantity centric. It indicates a
rise in GDP or GNP of a nation which focuses on the quantity of goods and services
produced. Positive economic growth implies expansion of economic activities in terms of
124
increased production etc, while negative growth suggests it’s down turn; recession
culminating into depression. The growth performance of the seventeen Indian states is presented
for the analysis periods.
Table (4.1) Gross state domestic product growth rates (at constant prices)
States
2001-02
2007-08
2009-10
Andhra Pradesh
4.22
12.02
5.79
2.6
4.82
6.82
-4.73
7.61
9.3
3.87
11.19
10.28
8.41
11
10.23
7.73
9.8
9.95
5.21
8.55
8.12
2.8
8.77
9.73
5.17
4.69
8.49
4.05
10.78
8.08
7.12
8.61
10.29
6.25
10.91
10.57
1.92
8.87
7.57
10.87
5.14
4.3
-1.56
6.13
9.43
2.17
7.33
6.99
3.84
7.76
8.44
Assam
Bihar
Delhi
Gujarat
Haryana
Himachal Pradesh
Karnataka
Kerala
Maharashtra
Madhya Pradesh
Orissa
Punjab
Rajasthan
Tamilnadu
Uttar Pradesh
West Bengal
Source: Directorate of Economics Statistics of respective State Governments, and Central Statistical Organization
It is clear from the table that Delhi, Gujarat, Haryana and Maharashtra are the states with
consistent gdp growth rates. Bihar has converted its growth rates from negative into positive
and high growth rates. The figure shows that in 2009- 10 most of the estates have registered
higher growth rates except Rajasthan and Andhra Pradesh.
125
Economic inequality:
Income equality is the distribution of total income amongst the representative population. In a
nation with perfect income equality, each and every individual has an equal share of the total
income. This is contrasted with perfect income inequality, where one individual has all of the
total income. The common value for representing income equality is the Gini coefficient20..
Perfect income equality is equal to zero, perfect income inequality is equal to one, and all
other values are somewhere between. The World Bank (2008) in its recent release “The
Growth Report Strategies for Sustained Growth and Inclusive Development” has mentioned
that disparity in income distribution in India has risen during 1993-2005. The report further
adds that Gini-coefficient in this connection stood at 0.3152 during 1993-94 which increased
later on and was recorded at 0.3676 in the year 2004-05.
Table shows the gini coefficients for different states. Income inequality has increased for
Haryana, Punjab and Gujarat over the years.
Table (4.2) Gini coefficient for Indian states
States
2001-02
2007-08
Andhra Pradesh
0.31
20
0.34
Gini coefficient (or Lorentz ratio) is a measure of equality of distribution and is a ratio (with value between 0
and 1) - the numerator in the ratio is the area between the curve of the distribution (known as Lorenz Curve) and
the uniform (45 degree) distribution line; the denominator is the area under the uniform distribution line. Lorenz
curve is often used to represents income distribution, where it indicates the percentage of the total income (y
percent) that the bottom (x percent) of the household have. The percentage of households is plotted on the xaxis, the percentage of income on the y-axis. Gini Index is the Gini Coefficient expressed in percentage (by
multiplying the Gini Coefficient with 100). Here, 0 corresponds to perfect income equality (i.e. everyone has the
same income) and 100 corresponds to perfect income inequality (i.e. one person has all the income, while
everyone else has zero income).
126
Assam
0.31
0.3
0.32
0.31
0.34
0.32
0.29
0.32
0.29
0.36
0.3
0.26
0.36
0.36
0.32
0.35
0.31
0.37
0.32
0.35
0.29
0.33
0.29
0.32
0.28
0.3
0.38
0.34
0.33
0.34
0.34
0.36
Bihar
Delhi
Gujarat
Haryana
Himachal Pradesh
Karnataka
Kerala
Maharashtra
Madhya Pradesh
Orissa
Punjab
Rajasthan
Tamilnadu
Uttar Pradesh
West Bengal
Source: Planning commission of India
Poverty head-count ratio:
Poverty is one of the main problems. It indicates a condition in which a person fails to
maintain a living standard adequate for his physical and mental efficiency. It is a situation
people want to escape. It gives rise to a feeling of a discrepancy between what one has and
what one should have. The term poverty is a relative concept. It is very difficult to draw a
demarcation line between affluence and poverty. According to Adam Smith - Man is rich or
poor according to the degree in which he can afford to enjoy the necessaries, the
conveniences and the amusements of human life. Poverty and well being has negative
relationship. Even after more than 50 years of Independence India still has the world's largest
127
number of poor people in a single country. According to a recent Indian government
committee constituted to estimate poverty, nearly 38% of India’s population (380 million) is
poor. This report is based on new methodology and the figure is 10% higher than the present
poverty estimate of 28.5%. The committee was headed by SD Tendulkar has used a different
methodology
to reach at the current figure. It has taken into consideration indicators for
heath, education, sanitation, nutrition and income as per National Sample Survey
Organization survey of 2004-05. This new methodology is a complex scientific basis aimed
at addressing the concern raised over the current poverty estimation. Poverty level is not
uniform across India. The poverty level is below 10% in states like Delhi, and Punjab etc
whereas it is below 50% in Bihar (43 & 54) and Orissa (47 & 57). It is between 30-40% in
Northeastern states of Assam, and in Southern states of TamilNadu and Uttar Pradesh.
Table (4.3) Percent of population below poverty line (Tendulakar committee)
States
2001
2004-05
Andhra Pradesh
15.77
29.9
36.09
34.4
42.6
54.4
8.23
13.1
14.07
31.8
8.74
24.1
7.63
22.9
20.04
33.4
12.72
19.7
25.02
38.1
37.43
48.6
47.15
57.2
6.16
20.9
Assam
Bihar
Delhi
Gujarat
Haryana
Himachal Pradesh
Karnataka
Kerala
Maharashtra
Madhya Pradesh
Orissa
Punjab
128
Rajasthan
15.28
34.4
21.12
28.9
31.15
27.02
40.9
34.3
Tamilnadu
Uttar Pradesh
West Bengal
Source: planning commission of India
National human development report: Planning commission of India (2001) developed its
own State-wise human development index. India’s human development index is slightly
different from UNDP’s human development index. India included few more variables to
arrive human development index. Like UNDP, India also assessed the same three dimensions
of human development, they are, longevity- the ability to live long and healthy life,
education- the ability to read, write and acquire knowledge and command over resources-the
ability to enjoy a decent standard of living and have a socially meaningful life but to measure
the human development it look few more indices for assessment. While choosing the
indicators, UNDP consider only life expectancy at birth for the measurement of longevity,
whereas, India has chosen life expectancy at birth at age one and the reciprocal of the infant
mortality rate and 0.65weightage has given for the life expectancy at birth at age one and
0.35weightage has given for the reciprocal of the infant mortality rate. For the calculation of
education index (knowledge) UNDP is being used adult literacy rate (one-third weightage)
and adjusted intensity of formal education (0.65weightage). But India has utilized literacy
rate for the age group 7 years (0.65 weightage) and adjusted intensity of formal education
(0.35) weightage. The standard of living is measured by real GDP per capita (PPP US $) by
the UNDP. India, on the other hand, has used inflation and inequality adjusted per capita
consumption expenditure for the measurement of economic attainment. By the inclusion of
these sensitive indicators, India has broadened the HDI measurement. According to UNDP
HDI measurement, India’s HDI value was 0.309 in 1990 and 0.590 in 2001 but according to
129
the National Human Development Report of Planning Commission, India, the HDI value of
India was 0.381 in 1991 and 0.472 in 2001. It seems that according to India’s HDI calculation
method, India is very much lagged in HDI in 2001 than the UNDP’s calculation, but this is
mainly because of the inclusion of more sensitive indicators like infant mortality and
inequality adjusted per capita consumption expenditure.
India’s HDI formula:
n
HDI J  1/ 3x i  xi 
1
Where j is the concern State taken for the assessment i refer to three indicators. They are
longevity, educational and economic attainment.
xi
x

x
ij
2
i
 xi1 
 xi1 
Where, X IJ refers to attainment of the state on the ith indicator,
X i1 = the minimum scaling norms and
xi2 = the maximum scaling norms.
X 1 = inflation and inequality adjusted per capita consumption expenditure,
X 2 = (e1  0.35)  (e2  0.65)
Where, e1 is literacy rate for the age group 7 years, and e2 is adjusted intensity of formal
education.
X 3  (h1  0.65)  (h2  0.35)
Where, h1 is life expectancy at age one and, h2 is the reciprocal of the infant mortality rate.
130
The scaling norms used for the assessment are, for per capita consumption expenditure per
month, the minimum norms Rs.65 and the maximum was Rs.325 for literacy rate 7+ years,
the minimum norms was 0 and the maximum was 100, for adjusted intensity for formal
education, the minimum norms was 0 and the maximum was 7, for life expectance at age one,
the minimum norms was 50 years and the maximum was 80 years and for infant mortality
rate only minimum norms was used i.e. 20 per 1000. Based on this HDI formula, Human
Development Index has been estimated for the States and Union Territories for the periods of
1981, 1991 and 2001.
Table (4.4) HDI of India for 1981, 1991 and 2001 for Major States
Si no.
states
HDI 1981
RANK1981
HDI 1991
RANK1991
HDI 2001
RANK2001
1
Kerala
.500
1
.591
1
.638
1
2
Punjab
.411
2
.475
2
.537
2
3
Tamilnadu
.343
7
.466
3
.531
3
4
Maharashtra
.363
3
.452
4
.523
4
5
Haryana
.36
5
.443
5
.509
5
6
Gujarat
.36
4
.431
6
.479
6
7
Karnataka
.346
6
.412
7
.478
7
8
W. Bengal
.305
8
.404
8
.472
8
9
Rajasthan
.256
12
.347
11
.424
9
10
Andhra
.298
9
.377
9
.416
10
Pradesh
11
Orissa
.261
11
.345
12
.404
11
12
MP
.245
14
.328
14
.394
12
13
UP
.255
13
.314
14
.388
13
131
14
Assam
.272
10
.348
10
.386
14
15
Bihar
.237
15
.308
15
.367
15
Source: NHDR 2001, Govt of India.
Economic and social well being index at sub national level
In this framework, the measurement of economic and social well being depends on different
dimensions of the socio-economic characteristics that foster favourable environment for the
growth and development. In this chapter Economic and social well being is computed by
taking into account a broad perspective of different dimensions. The proposed economic and
social well being index is constituted on the basis of six different socio economic dimensions,
namely, health, knowledge, consumption income, technological progress, and infrastructure.
These dimensions are supposed to evaluate the society’s overall welfare and standard of
living. There are four indicators to measure health status of the people in the state: infant
mortality rate (converted to positive values), life expectancy at birth, per capita health
expenditure, and total number of children with immunization. Three indicators have been
included for knowledge: literacy rate, total enrolment number and schools for general
education. For income, per capita net state domestic product is taken as a measure economic
progress. Monthly per capita consumption expenditure for different states is also taken into
consideration to gauge the impact of consumption in development. Research projects are
considered as the measure of technological progress. Finally, infrastructural dimension is
believed to be an essential element of growth and development. In this analysis, thirteen
different indicators have been accounted to capture this dimension. They include total
number of hospitals, population per hospital bed, per capita electricity consumption, per
capita LPG consumption, per capita access to safe drinking water, per capita availability of
vehicles, road and train route, post offices, bank branches, telephone lines, and village
132
electrification. This indicator focuses on availability of health, financial, transport,
communication and rural infrastructure. The better infrastructure facilities help allocate
resources quickly to every place and reduce cost of production, hence induce economic
growth and development process. The higher value of index indicates better level of well
being for the region. Thus measure of economic and social well being is a comprehensive
composite measure to capture quality of life of people.
Table (4.5) Indicators of Economic and Social well being index (EASWBI)
Health
Knowledge
Income
Technological
Infrastructure
Consumption
Telephones line
Per capita monthly
progress
Infant mortality
Literacy rate
rate
Per
capita
Number
state
gross
Research
consumption
projects
expenditure
domestic
of
product
Life expectancy
Total
Post offices
Enrolments
Per capita health
Schools
expenditure
general
for
Bank branches
education
Child
LPG consumers
immunization
Road length
Railway route
Villages
electrified
133
Households
with Electricity
Access to safe
drinking water
Availability
of
vehicles
Government
hospitals
Hospital beds
Estimation methodology and data sources:
In this chapter Principal Component Analysis (PCA), a multivariate statistical approach, is
employed.
This method transforms a set of correlated variables into a set of uncorrelated
variables called components. These components are linear combinations of the original
variables. PCA is used to reduce the dimensionality problems and to transform
interdependent coordinates into significant and independent ones. Nagar and Basu (2002)
presented more comprehensive presentation of this approach for development of social
indicators. This technique is slightly different from factor analysis.
Principal Components (PC) are used as linear combinations of the variables selected to
compose the economic social indicators. They have special statistical properties in terms of
variances. The first PC is the linear combination, which accounts for the maximum variance
of the original variables. The second PC accounts for the maximum variation of the
remaining variations, and so on. Maximizing variances helps maximize information involved
among the set of variables, and, hence, it is most appropriate for weighting these variables for
134
the development of the Index. The main reason for employing PCA is that it makes it
possible to define a synthetic measure that is able to capture interactions and interdependence
between the selected set of indicators making up the three indices. These indicators are called
Causal Variables, while the corresponding Index is the explained variable. While standard
regression techniques require the explained/dependent variable to be observed, PCA treats the
latter as a latent variable. Principal Component constitutes a canonical form and helps to
understand both the individual contribution of each of the indicators to the Index and their
aggregate contribution. An attractive feature of this methodology is that it permits calculation
of statistical weights of the various components of the Index for the sample that thereby
identifies what drive the results. A brief technical description of the methodology is presented
below: A Indicator is an Abstract Conceptual Variable and is supposed to be linearly
dependent on a set of observable components plus a disturbance term.
Let indicator is
I    1 X1  .............  n X n  e...........(1)
Where, X1 , X 2 ,.... X n is a set of components of the Index. The total variation in the Indicator
is composed of two orthogonal parts: (a) variation due to set of proposed components, and (b)
variation due to error. Subtracting the minimum value of the particular component from its
actual value and dividing it by the range, which is the difference between the maximum and
minimum value of the selected components by following equation, individually normalize all
components.
X i  X min
X max  X min
When necessary, raw data have been transformed such that normalized values equal to unity
corresponds to the best situation in the sample.
135
Correlation Matrix R is computed from standardized variables, followed by solving the
determinant equation  R   I   0 for where R is an n x n matrix. This provides an nth
degree polynomial equation in λ and hence K roots. These roots are called Eigen Values of
Correlation Matrix R. The λ is arranged in descending order of magnitude, as
1  2  ..........  n . corresponding to each value of λ , the matrix equation (R −λI )α = 0 is
solved for the n x1 Eigen Vectors α subject to the condition that α α' = 1 (normalization
condition.). The last step deals with the construction of the weights from the matrix of factor
loadings after rotation, given that the square of factor loadings represents the proportion of
the total unit variance of the indicator which is explained by the factor. The approach used by
Nicoletti et al., (2000) is that of grouping the individual indicators with the highest factors
loadings into intermediate composite indicators. The intermediate composites are aggregated
by assigning a weight to each one of them equal to the proportion of the explained variance in
the data set. The Index is estimated as weighted average of n principal components (P’s),
I  1P1  2 P2  ................n Pn
We normalize the index for each state in the same manner explained in previous chapter.
The Econometric approach
The panel data is used for econometric analysis. The panel dataset is a pooled cross-section
time series data (I, denotes cross-section units/states, and t time points). The economic and
social well being index is considered as a dependent variable, and the framework is modeled
it to explain in terms of economic growth, income of previous year gini coefficient and
poverty ratio. The model contains N units of observations (cross-section units), over the T
time points. The purpose is to estimate a standard regression model of the form:
Yitpo     ' X it  eit ...............1
136
Where i= 1, 2,…..N and t=1,2,…T
, by assumption that eit are iid over I and t, i.e.,
E (eit )  0 and var (eit )   e2 .
The vector contains K regressors (exogenous/independent variables), not including constant
term. It is assumed that there is a presence of cross sectional heteroscedasticity in the model,
therefore we use Generalized Least Square (GLS) to obtain unbiased estimator.
Next model specification is to estimate the fixed effect model, which allows us to take into
consideration the unobservable differences in the dependent variable specific to individual
states. In this model the fixed effect estimation is done by least square dummy variable
method. fixed effects model allows us to take into consideration the unobservable differences
in the dependent variable specific to individual states. As, in this specification all the
intercepts differ across cross section units (17states), i.e., the differences across cross
sectional units are captured in differences in the constant term reflecting parametric shifts of
the model for these different units.
In this Fixed Effect estimation model, specification for the individual state specific effects is
given by:
yitFE   i   X it  ei .................2
i  1, 2,...N , t  1, 2,..., T
Where is β’ is 1 k vector of constants and  i is a 11 scalar constant representing effect of
that variable peculiar to the i th individual. The error term eit represents the effect of omitted
variable that are peculiar to both the individual periods and time periods. It is assumed that eit
can be characterised by iid random variable with mean zero and variance  e2
137
The estimation results on the basis of the two different model specifications have noted
above, as shown below:
Y PO     gdpt1 ln gdpit 1   g git   gini gini   pov povit  eit ..............(3)
Where PO implies the pooled least squares regressions, and =1,…17 (states), t=2001, 2007,
and 2009 (three time point), the Y (dependent variable) takes the economic and social well
being index (EASWBI) and the independent variables are gdpt-1 is state gross domestic
product for previous period, g represents the economic growth rates for the given periods,
gini (gini) coefficient is also taken to measure the impact of income inequality on the
economic and social well being index and poverty headcount ratio (pov) is another variable to
assess the impact.
From the equation (2), the specification of the model is rewritten, as follows
YitFE     gdpt1 ln gdpit 1   g git   gini gini   pov povit  eit ..............(4)
Where FE denotes fixed effects regression model. Least square dummy variable method is
used to estimate fixed effects. This method accounts for the time effect over the t years with
dummy variables on the right-hand side of the equation. In Equation 3, the dummy variables
are named according to the year they represent.
yitFE  0  1 2001  2 2007  3 2009   gdpt1 ln gdpit 1   g git   gini gini   pov povit  eit ..............(5)
In general panel effects can be either taken to be fixed effects or random. In this case, since
the individuals are the states and all of them are included in the data, it is appropriate to
consider them to be fixed.
Data sources:
Economic and social well being index is to be computed for seventeen Indian states for three
years 2001, 2007 and 2009. These states have been considered because of availability of data
138
on various dimensions.To compute the index the data has been obtained from different data
sources, namely, economic surveys of India, India stat, various versions of CMIE and
planning commission of India. The detailed description of the data and their respective
sources are shown in the appendix. Per capita monthly consumption expenditure is taken
from various report of NSSO.
For panel data analysis four independent variables have been taken. Data for these variables
(Previous year state gross domestic product (log), economic growth rates, gini coefficient and
poverty ratio of the analysis period) has been extracted from India stat and planning
commission of India.
Empirical results of principal component analysis:
This section attempts to explore the how at the sub national both countries are performing
and to extent higher growth related to well being. The economic and social well being index
is computed for seventeen Indian states. It is proposed that the level of well being increases
with the higher value of index. It is clearly shown from table (4.3) that states like Delhi,
Kerala, Punjab, Himachal Pradesh Tamilnadu, Maharashtra are the best performing states in
terms of economic and social well being index, and on the other hand, Bihar, Madhya
Pradesh ,Assam, Uttar Pradesh are in the lower end of well being. The trend has not changed
from 2001 to 2007 and 2009.
Table (4.6) State wise Economic and social well being index
States
EASWBI
RANK
2001
1
Andhra
EASWBI
RANK
2007
EASWBI
RANK
2009
.283199
10
.40304
10
.394098
9
Pradesh
2
Assam
.105972
15
.053091
16
.173562
14
3
Bihar
0
17
0
17
0
17
139
4
Delhi
1
1
.89239
2
.9999
1
5
Gujarat
.5082
5
.340083
11
.383723
10
6
Haryana
.4202
9
.579792
6
.474932
7
7
Himachal
.6567
2
.622493
5
.818153
3
Pradesh
8
Karnataka
.5035
6
.485522
7
.437274
8
9
Kerala
.5299
3
.0637003
4
.925315
2
10
Maharashtra
.4872
8
.464976
9
.539827
6
11
Madhya
.2169
12
.10789
14
.133996
15
Pradesh
12
Orissa
.0473
16
.06839
15
.189274
13
13
Punjab
.5104
4
.642877
3
.646825
4
14
Rajasthan
.1743
13
.225849
12
..233146
12
15
Tamilnadu
.5019
7
1.001278
1
.541619
5
16
Uttar
.1707
14
.123797
13
.065914
16
.2521
11
.478038
8
.317723
11
Pradesh
17
West Bengal
The table (4.4) shows that there is a rise in the mean value of the EASEBI index for all the
states. The standard deviation has increased during the period. This shows that there has been
over all increase in the welfare level across the Indian states, but still the level of welfare is
divergent in nature. Pearson correlation coefficient between the periods well being level is
pretty high. This also indicates that the relative position of the states well being has not
changed much in this duration. This is very clear indication of slow catching up of the states
with poor well being level to good ones.
Table (4.7) Descriptive Statistics
140
Statistics
2001
2007
2009
Average
.3747
.4192
.4279
Standard deviation
.2536452
.29561150
.29347135
Standard Error
.0615180
.07169632
Variance
.064
.087
Pearson Correlation
.949**,.846**
.848**,.846**
.07117726
.086
.848 **,.909**
Four categories21 (table 4.5) for Economic and social well being index are also defined. The
categories have not been changed much during the analysis period. Delhi has shown
consistency of very high well being level over the years. Kerala and Himachal Pradesh have
improved lot since 2007. There is a decline in Economic and social well being for Gujarat.
The index value for West Bengal has shifted from low category to average economic and
social well being. Maharashtra, Karnataka, Punjab have been following the same category.
Bihar, Assam, UP, Rajasthan, Orissa is still grabbed in low well being trap.
Table (4.8) Categories for EASWBI
2001
2007
21
Very high EASWBI
High EASWBI
Average EASWBI
Low EASWBI
Delhi
Haryana, Karnataka,
Andhra Pradesh
Bihar , Assam,
Tamilnadu, Delhi
Maharashtra, Punjab
Uttar Pradesh,
, Tamilnadu, Gujarat
Madhya Pradesh ,
, Kerala, Himachal
Orissa, Rajasthan ,
Pradesh
West Bengal
Haryana, Karnataka,
Andhra Pradesh ,
Bihar , Assam,
Maharashtra, Punjab
Gujarat,
Uttar Pradesh,
, West Bengal ,
Madhya Pradesh ,
Kerala Himachal
Orissa, Rajasthan
Lower and upper limits have been decided on the basis of mean  0.5 standard deviation
141
Pradesh
2009
Delhi , Kerala,
Haryana, Karnataka,
Andhra Pradesh ,
Bihar , Assam,
Himachal Pradesh
Maharashtra, Punjab
Gujarat, west Bengal
Uttar Pradesh,
, Tamilnadu
Madhya Pradesh ,
Orissa, Rajasthan
Comparison of EASWBI and HDI:
Human Development Index and Index of economic and social well being can be compared
on the basis of previous analysis. Highest well being states do not vary in EASWBI and HDI.
States
EASWBI 2001
RANK
Hdi
Rank
Andhra Pradesh
.283199
10
.416
10
Assam
.105972
15
.386
14
Bihar
0
17
.367
15
Delhi
1
1
Gujarat
.5082
5
.479
6
Haryana
.4202
9
.509
5
Himachal Pradesh
.6567
2
Karnataka
.5035
6
.478
7
Kerala
.5299
3
.638
1
Maharashtra
.4872
8
.523
4
Madhya Pradesh
.2169
12
.394
12
Orissa
.0473
16
.404
11
Punjab
.5104
4
.537
2
Rajasthan
.1743
13
.424
9
Tamilnadu
.5019
7
.531
3
142
Uttar Pradesh
.1707
14
.388
13
West Bengal
.2521
11
.472
8
Results of Panel data:
Panel data estimates of the explained model are described here. Table (4.6) presents results
for the simple pooled estimation. First it reports results of ordinary least square method. The
coefficients of previous year income and economic growth are positive and significant. It
implies that well being is dependent on economic growth. The negative and significant
coefficient of poverty ratio indicates that at higher the well being, poverty decreases.
TABLE (4.9) model 1 &2: Panel estimation regression results of economic and social well being index
(dependent variable EASWBI)
Model 1
independent
Model 2
Coefficient
Standard error
Coefficient
Standard error
Income level
.0662755*(.016)
.0263762
.0662755*(0.035)
.0287263
Economic growth
.0274951*(.019)
.01127728
.0274951*(0.038)
.012147
Gini coefficient
1.164709
.6560321
1.164709
1.485392
Poverty ratio
-.0164223***(.000)
.0020724
-0.164223***(0.00)
.0023952
No of states
16
No of observation
3
variable
for each state
Total panel
51
observation
R2
.6205
Adjusted R2
.05875
F statistics
18.81
* P <0.05;** P <0.01; ***P <0.001
12.24
143
Model 2 presents the result with heteroscedasticity consistent standard errors. The
coefficients of lngdp and growth are positive and significant. Gini coefficient is positive but
not significant. Poverty ratio shows the negative and significant impact on economic and
social well being level. The result implies that economic growth foster the well being. These
results could be actually corroborated to the fact that states like Maharashtra, Delhi are the
one which are doing better in both terms of economic growth and well being.
Now, another model is estimated with different specification of the error structure of the
covariance matrix in the panel data. The results are based on cross sectional heteroskedastic
model, with common slopes and intercept, assuming that V (u) varies across the cross
sections. Here GLS is applied to get the coefficients for explanatory variables.
Table (4.10) Model 3 panel results of GLS regression
Model 3
independent
Coefficient
Model 4
Standard error
Coefficient
variable
Robust Standard
error
Income level
.620472***(.006)
.0226804
.0620472*(.023)
.0273563
Economic growth
.0118583
.0102746
.0118583
.0092357
Gini coefficient
1.304572
1.024398
1.304572
1.459098
Poverty ratio
-.0116322***(.000)
.0024017
-.0116322***(.000)
.0028183
Constant
-.4121711
.3543364
-.4121711
.4476171
No. of states
17
No. of observation
3
for each state
Total panel
51
observation
R2
.6083
between R2
.7953
within R2
.0086
144
CHI2
28.76
* P <0.05;** P <0.01; ***P <0.001
19.39
It is found that income level has positive and statistically significant, implying that initial
level of income plays an important role in determining high level of well being. Poverty ratio
negatively and significantly affects the well being. Model 4 presents the results with robust
standard errors. The R2 is also high.
Next to it, the estimated results for the fixed effect model is shown. This is estimated with
least square dummy variable method. It is observed that income level, economic growth and
Gini coefficient is not significant but positive in sign. Coefficient of poverty ratio is negative
and statistically significant. It is interpreted this result that over the time poverty is a great
hurdle in well being. The goodness of fit doesn’t vary much from OLS to fixed effect.
(4.11)PANEL DATA REGRESSION RESULTS OF FIXED EFFECT (LSDV)
independent
Coefficient
Standard error
t statistics
P value
Income level
.0530126
.0443042
1.20
.238
Economic growth
.0197161
.0117337
1.68
.100
Gini coefficient
1.120538
.9479139
1.18
0.244
Poverty ratio
-0.0177017
.0023924
-7.40
.000***
Constant
-.1194086
.5678328
-.21
.834
No of states
17
No of observation
3
variable
for each state
Total panel
51
observation
R2
.6515
adjusted R2
.6039
F
13.71
* P <0.05;** P <0.01; ***P <0.001
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Between effect model is also estimated and it proves the coefficient of economic growth is
significant and positive for EASWBI. And poverty ratio is highly significant and negative for
the well being.
Table (4.12) Panel data regression estimates of between effects model
independent
Coefficient
Standard error
t statistics
P value
Income level
-.010321
.103824
-0.10
.922
Economic growth
.0673677**
.02606452
3.26
.007
Gini coefficient
.3795123
1.458097
0.26
.799
Poverty ratio
-.0225675***
.0037949
-5.95
.000
Constant
.5654508
1.033452
0.55
.594
No of states
17
No of observation
3
variable
for each state
Total
panel
51
observation
Overall R2
.5349
Between R2
.8719
Within
.0305
F
20.41
* P <0.05;** P <0.01; ***P <0.001
CONCLUSION
This paper attempts to develop an index of economic and social well being of individuals
(EASWBI) based on survey data collected from the stratified sampled individuals of Kanpur
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city, Uttar Pradesh, India in 2009-10 using factor analysis. This study investigates new
dimensions of well being and works out to correlation among different dimensions of the
index and the index itself to check the validity of EASWBI of Kanpur city. Results from
confirmatory and exploratory factor analysis is also worked out for understanding validity of
different dimensions of economic and social well being index of individuals. The EASWBI
dimensions considered in confirmatory analysis are health, knowledge, income, asset,
consumption, utility, awareness, security, life satisfaction, dwellings, environment and access
to basic facilities. On the basis of correlations we can deduce that EASWBI (worked out by
exploratory factor analysis) is significantly and positively correlated with assets and
expenditure, knowledge and peace, health and security and fuel consumption of individuals.
Also, it is positively correlated with social and physical infrastructure and access to facilities
in the city of Kanpur (UP) India.DEA is also worked out for the individuals to measure the
efficiency on which the various dimensions of EASWBI are constructed the best practice
quality of life index.. The index can be applied to other cities and countries to work out the
well being of individuals. This work on EASWBI index is an extension of the work done by
Basu and Nagar (2004).
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