1. SETH ANANDRAM JAIPURIA COLLEGE
Department of Economics
CLASS ~ B.SC. (HONS.) ECO
YEAR ~ III
ROLL ~ 3224-61-0029
REGN. NO. ~ 224-1121-0862-10
DEPT. ~ ECONOMICS ( H )
2. 2
YEAR OF SUBMISSION ~ 2013-14
PAPER ON
MENTOR: DR. NEEPA BISI
Department of Economics,
S.A. Jaipuria College
This paper is submitted for the partial completion of my B.SC
Degree.
I am a student of Part 3 Economics (Hons.)
3. 3
I declare that this term paper has been completed by me to the
best of my knowledge under the supervision of Dr. Neepa Bisi,
Dept. of Economics, S.A. Jaipuria College.
ABSTRACT
The present paper analyses the trends and patterns of economic growth and inequality across Indian
states since the early 1990s.
The present study would attempt to address the following research questions:
The million dollars question, or, if one wills, the Rs. 32 question: How does one define the
poverty line in India, in which old yardsticks may not hold good, either in terms of the
food that money can buy or in terms of defining who the poor are?
Do these statistics accurately measure what poverty is?
What is the next step in poverty reduction for middle-income countries like India?
Should a uniform line, at whatever level, be at all used, in an indiscriminate manner, across
programmes, as has been done for decades now?
Do these most recent estimates stand up to economic scrutiny?
Is the behaviour of the incidence of poverty compatible with the policy evolution followed
post the reforms?
Does the conventional hypothesis that “growth is a necessary but not sufficient condition
for the reduction of poverty across the states” hold?
Have economic reforms caused regional inequality?
Why estimate poverty?
4. 4
This paper is a modest attempt to examine the nature and causes of the patterns of cross state
behaviour of the growth and inequality and also to examine the relations between them. Since the
economic liberalization in the early 1990s, the evidence suggests increasing inequality as well as
persistent poverty. No support has been found for sweeping claims that the nineties have been a
period of ‘unprecedented improvement’ or ‘widespread impoverishment’.
Key words: India, inequality, poverty, growth and distribution, macroeconomic policies.
INTRODUCTION
5. 5
In Economics, growth typically refers to the increase in the amount of the goods and services
produced by an economy over time. Economic inequality between individuals or populations is
described as the gap between rich and poor in the distribution of their assets, wealth, income,
employment opportunities and concentration of economic power. The issue of economic inequality
involves equity, equality of outcome, equality of opportunity, and life expectancy.
Defining the poverty line is itself a subjective matter and many feel that it should be raised further.
Indian journalist Ravi S Jha suggests measuring poverty by segregating India's poor in different
groups; those living in abject poverty, those who are vulnerable to poverty and those who are lifted
out of poverty through government welfare. Since 1991, India has undergone a great deal of
liberalization internally and externally, but its benefits have mostly gone to the middle and upper
classes. The Planning Commission’s new official poverty line — remarkably low at Rs. 32 — could
have moved millions out of poverty: on paper.
For decades it has followed a limited definition of poverty. The official poverty line in India is based
only on calories and accounts for little else but the satiation of hunger. It would have been more
accurate to call it the "starvation line".
At present the poverty line stands at Rs 368 and Rs 559 per person per month for rural and urban
areas, just about enough to buy 650 grams of food grains every day. A nutritious meal itself would
cost around Rs 573 per capita per month, let alone the cost of securing other basic needs. When such
an inclusive measure of poverty is used, as many as 68-84% of Indians would qualify as poor. The
average cost of 1 kilogram of rice sold through the government’s public distribution system at
subsidized rates for instance is currently around 18-20 rupees.
For decades the Planning Commission of India has followed a limited definition of poverty. The
latest definition puts the poverty line slightly below the lowest levels set by the World Bank; levels
at which the bank says people are living at the edge of subsistence.
While the fast economic growth under the neo-liberal policy regime helps reduce poverty, it
increases inequality in income distribution in a way that retards the progress in poverty-reduction.
The empirical validity of this proposition is examined by tracing trends in per capita income (NSDP)
growth and GINI coefficients, estimated from the data on household consumer expenditure of NSS
surveys, in India across the major states during post reform periods.
Undeniably, there is some connection between growth and inequality in a country. One cannot
directly jump to a conclusion as to whether growth is inequality enhancing or suppressing. For
6. 6
growth to reduce the incidence of inequality, it is very important for growth to be ‘inclusive’. Before
it is decided if growth is inclusive, inclusive growth must be defined. Growth is said to be inclusive
if it allows each and every individual to contribute to and benefit from economic growth; i.e., when
the benefit of growth is reaped by each and every sector of the society, we can say that growth is
‘inclusive’.
The Indian economy has been growing at a fast rate over the last twenty years, particularly in the
new millennium, is well known. But there is growing criticism about the pattern of growth that has
been taking place in India. A significant number of academicians and social-scientists believe that
the type of growth India has been experiencing over the years is not ‘inclusive’. In view of these
scholars, a very large section of population is not getting the benefit of the growth process at all. This
potentially may lead to sharply worsening economic inequality which can destabilize the economy in
the long run. That even the government is worried about this phenomenon is evident from the fact
that all major recent policy documents call for ‘inclusive growth’. Growth in the Indian economy has
been diverging across regions and sectors, leaving behind large sections of population. Growth in
agricultural sector which employs more than half of India’s labour force has been around 2%.
Growth has not been creating enough jobs and the achievements of India have not been distributed
equally, thus aggravating the problem of inequality.
The Indian economy continues to grow as a global economic powerhouse. India’s development is
particularly impressive given the considerable obstacles in fostering economic growth. These
obstacles are truly epic with widespread poverty, limited natural resources, and one of the largest
populations. While this growth is impressive, India continues to have hundreds of millions in abject
poverty and much of the economic prosperity has been fairly localized to specific regions and
sectors. The booming software and technology sector receives daily world attention. However those
languishing in poverty remain largely ignored. Thus, it is important to understand whether the
nascent economic prosperity has also caused an increase in income inequality. Economic theories
vary on both the causes and implications of income equality; however empirical evidence indicates
that India has been able to maintain low income inequality during periods of significant economic
growth.
8. 8
OBJECTIVEs
The basic objective here is to understand the dynamics of growth in the country which is
resulting in regional imbalances.
The other objectives of this project are:
To analyze the trends of growth and inequality in India across states, with focus on the post-reform
period.
To analyze the role of the primary, secondary and tertiary sectors on poverty in India across states.
To analyze the trends in consumption inequality in India since 1991.
To explore the causes and factors behind differentials of growth and inequality levels in India across
15 major states.
Survey of LITERATURE
9. 9
The literature on the analysis of poverty in India is indeed very rich. This brief review of the
literature clearly indicates there is a storm of controversy regarding the magnitude of the incidence of
poverty, its rate of decline and methodologies of estimation. But there is as such no study on the
estimation of the impact of the growth, social sector expenditure, literacy, inequality as well as the
sectoral growth on the incidence of poverty across the states of India .So instead of entering into the
controversy we have actually tried to find out the principal correlates of cross-state and cross-time
variations in the magnitude of poverty in India. Under this backdrop our study concentrates on the
detection of the proximate explanatory factors behind the persistence of poverty by using a panel
data econometric technique.
“India’s Economic Development since 1947” by Uma Kapila (2008-09) mentioned that the last 2
decades has seen a substantial increase in the amount of research that has been done.
In his book “Growth and Development”, Thirlwall has studied in details the benefits and possibilities
of internalizing externalities. Thus he has discussed the relation between the environment and the
economy and the ways in which a market approach can be used to save the environment. He has
concluded that only if private firms, which because the most amount of destruction of the
environment, are included in the mitigation of destruction can the environment, be saved.
In their book “Economic Development”, Todaro and Smith have discussed how both developed and
developing countries can ensure their participation in the eradication of environmental degradation.
They have further talked about how the developed world can help the under developed world to
ensure this. Such collaboration and cooperation can help the required expansion of the carbon market
to different parts of the world. Not only can the first world nations reduce their own emission levels
and use clean technology but also provide assurance of fair trade policies, relief and assistance to
nations of the so-called third world.
A large number of studies have examined regional economic growth and disparity in India. We make
a brief review of the findings of the earlier studies to compare them with those offered by the present
one.
10. 10
The major findings of the earlier studies are summarized in Table 1 to make the comparison
across studies easier. It can be seen that there are variations in the sample period, number of states
covered and findings across studies.
Despite voluminous literature that exist on regional growth and disparities in India, the review of
literature is focused on growth and convergence to identify the factors that explain, determine and
affect the differences in growth rates and predict convergence or divergence in income across states
of Indian federation. Attempt is made to explore lapses and find research issues in these studies to
pursue the present study.
Thus a review of the theoretical literature on growth and convergence is carried out in general while
a brief review of empirical studies is provided in particular on inter-regional growth and convergence
in Indian federal context. The review of literature excludes the conventional pure empirical analysis
to explain the wide disparities in per capita income growth across states (Ahluwalia, 2001).
In his book ‘Economics: Principles and Applications’, N. Gregory Mankiw discusses that rising
inequality has obvious economic costs: stagnant wages despite rising productivity, rising debt that
makes us more vulnerable to financial crisis. It also has big social and human costs. There is, for
example, strong evidence that high inequality leads to worse health, a higher mortality and
inequality by discussing the role of the state in economic development.
METHODOLOGY
The research project is analytical in nature. It is mainly based on secondary data sources available
from the various rounds of NSSO; Reports of Planning Commission ; Economic and Political weekly
(EPW) Research Foundation Data base,2003,2008; Reserve Bank of India on-line data base;
National Accounts Statistics: Census reports ;India Development Report,2008 and also from the
existing literature.
11. 11
We have examined the cross state and cross time behaviour of growth and inequality in
India and tried to find out the proximate factors for the cross-state and cross time variations in the
incidence of income poverty for the period from 1991-92 to 2009-10 by using panel data technique.
While analyzing the incidence of poverty both at the national and at the cross-state level we have
used the head-count ratio of poverty as is estimated by the planning commission. For the year 2009-
10 we have also used the head count ratio of income poverty estimated by the Planning commission.
Squared poverty gap (SPG): It is a normalized weighted sum of the squares of the poverty gaps of
the population and reflects the intensity of poverty. For a given value of the PG, a regressive transfer
among the poor would indicate a higher SPG value. HCR, PG and SPG are special cases of a
measure suggested by Foster, Greer and Thorbecke (1984).
Lorenz curve: It is a curve that represents the relationship between the cumulative proportion of
income and cumulative proportion of the population in income distribution, beginning with the
lowest income group. If there were perfect income equality, the Lorenz curve would be a 45-degree
line.
Gini coefficient: It is the area between the Lorenz curve and the 45-degree line, expressed as a
percentage of the area under the 45-degree line. It is a commonly used measure of inequality. With
perfect income equality, the Gini coefficient would be equal to zero; with perfect inequality, it would
equal one. Gini coefficient normally ranges from 0.3 to 0.7 in cross-country data.
Some Concepts in Measurement of Poverty:
Poverty line: It is the income or consumption expenditure level that is considered to represent the
minimum desirable level of living in a society for all its citizens. This minimum level may be defined
12. 12
in absolute or relative terms. The absolute poverty line is often defined as the threshold income that
just meets food expenditure corresponding to minimum energy (calorie) need of an average person
and makes a small allowance for nonfood expenditure.
Head count ratio (HCR): It is the proportion (or percentage) of persons in a society whose income
or expenditure falls below the poverty line. It is the most commonly used measure of poverty.
Poverty gap (PG): It refers to the proportionate shortfall of income of all the poor from the poverty
line and expressed in per capita terms of the entire population. It tells us whether the poor are more
or less poor and thus reflects the average depth of poverty. If the numbers of poor and total
population are the same in two societies but the poor have less income in the second society than the
first, PG index would be higher for the second society even though HCR is the same for the two.
$1 a-day poverty line: It is used by several international organizations for comparison of poverty
across countries and actually refers to an income or consumption level of $1.08 per person per day
based on 1993 dollars adjusted for purchasing power parity (PPP). The Millennium Development
Goal sets its poverty target in terms of this poverty line.
To examine how income growth affects inequality, a multiple regression analysis is performed. Gini
Index has been used as the explained variable and per capita state domestic product (PCSDP) and
share of agriculture SDP (AGSHARE) as the two explanatory variables. A null hypothesis has been
assumed that an increase in share of agriculture is inequality suppressing while an increase in
PCSDP in inequality enhancing.
13. 13
DATA ANALYSIS
INTERSTATE COMPARISON OF INEQUALITY
Rural Inequality
When we look at the rural Gini of the different states across India, we see that Assam has got a low
Gini value in respect to the other states. This implies that as far as the rural sector is concerned
Assam has consistently maintained low level of inequality. Similarly, we can also see states like
West Bengal, Bihar, Gujarat and Rajasthan have also maintained low levels of inequality. Then again
Punjab and Haryana have shown frequent changes in their relative inequality ranking. Karnataka,
Tamil Nadu and Maharashtra have shown some improvements in the sense that the incidence of
inequality has reduced compared to the earlier years. So these states have shown some considerable
amount improvements over other states. Kerala is one such state which has shown a recent increase
in the level of inequality. No rural growth has affected Madhya Pradesh as it has maintained a high
level of inequality.
Table 1: States with low rural inequality
1993-94 1999-2000 2003-04 2009-10
Assam Assam Assam Assam
Bihar Haryana Bihar Bihar
Gujarat Gujarat Gujarat Karnataka
Rajasthan Rajasthan Haryana Rajasthan
WB Punjab WB WB
14. 14
Table 2: States with medium rural inequality
1993-94 1999-2000 2003-04 2009-10
Karnataka AP AP Gujarat
Kerala Bihar Karnataka Maharashtra
Orissa Karnataka Tamil Nadu Orissa
Punjab UP Punjab Tamil Nadu
UP WB Rajasthan UP
Table 3: States with high rural inequality
1993-94 1999-2000 2003-04 2009-10
AP Kerala Kerala Kerala
Maharashtra Maharashtra Maharashtra AP
MP MP MP MP
Haryana Orissa UP Haryana
Tamil Nadu Tamil Nadu Orissa Punjab
15. 15
Urban Inequality
As far as urban inequality is concerned states like Assam and Gujarat have shown consistently low
levels of inequality. Haryana and Punjab have moved from low to medium level of inequality.
Rajasthan over the years have shown a low level of inequality. West Bengal, Uttar Pradesh and
Tamil Nadu have shown improvements as far as urban inequality is concerned. Maharashtra has
constantly maintained a high level of inequality.
Table 4: States with low urban inequality
1993-94 1999-2000 2003-04 2009-10
Assam Assam Assam Assam
Gujarat Gujarat Gujarat Gujarat
Haryana Haryana Tamil Nadu Tamil Nadu
Punjab Punjab Punjab Bihar
Rajasthan Rajasthan Rajasthan Karnataka
Table 5: States with medium urban inequality
1993-94 1999-2000 2003-04 2009-10
AP AP Bihar Punjab
Bihar Kerala Haryana Haryana
Karnataka Karnataka Karnataka MP
MP MP UP UP
Orissa Orissa WB WB
16. 16
Table 6: States with high urban inequality
1993-94 1999-2000 2003-04 2009-10
Kerala Bihar AP AP
Maharashtra Maharashtra Maharashtra Maharashtra
Tamil Nadu Tamil Nadu Kerala Kerala
UP UP MP Rajasthan
WB WB Orissa Orissa
Overall Inequality
Assam and Rajasthan have constantly maintained low levels of inequality. This shows that despite
growth in different sectors the entire population has benefited from growth. So growth has been
‘inclusive’ in nature. Bihar has moved from a low level of inequality to a medium level and recently
the incidence of inequality has reduced. Punjab has shown similar result but the inequality has
increased more drastically in comparison to Gujarat. West Bengal has had a Gini close to the all
India level all through and thus the level of inequality has been consistent in the medium category.
Uttar Pradesh and Karnataka have shown similar results except the fact that Karnataka recently has
shifted from a medium level of inequality to low category. Kerala has maintained a more or less high
level of inequality. Orissa has shown quite a drastic fluctuation from medium level to high level of
inequality and finally a low level of inequality. Madhya Pradesh and Maharashtra despite growth
have shown high levels of inequality which gives a reason to conclude that growth in this state must
have been ‘exclusive’ in nature.
Table 7: States with low overall inequality
17. 17
1993-94 1999-2000 2003-04 2009-10
Assam Assam Assam Assam
Bihar Haryana Haryana Bihar
Gujarat Gujarat Gujarat Karnataka
Punjab Punjab Punjab Orissa
Rajasthan Rajasthan Rajasthan Rajasthan
Table 8: States with medium overall inequality
1993-94 1999-2000 2003-04 2009-10
Haryana AP Tamil Nadu AP
Karnataka Karnataka Karnataka Gujarat
Orissa Bihar Bihar Tamil Nadu
UP Kerala UP UP
WB WB WB WB
Table 9: States with high overall inequality
1993-94 1999-2000 2003-04 2009-10
AP Orissa Orissa Haryana
Kerala UP Kerala Kerala
MP MP MP MP
Maharashtra Maharashtra Maharashtra Maharashtra
Tamil Nadu Tamil Nadu AP Punjab
18. 18
To illustrate this transition in overall inequality across different states the following bar diagram has
been used.
Figure 1: Transition of Inequality across major states
INEQUALITY AND INCOME GROWTH
A significant number of research scholars and academicians are of the view that in India growth is
not ‘inclusive’, it is rather urban centric, if the view that an increase in per capita income should
increase inequality is followed. Again if we consider the share of agriculture in total SDP then the
states which have the higher share of agriculture should also record low levels of inequality. It is
intended to perform this regression analysis to test how far this hypothesis holds. However before
performing any regression analysis, the data needs to be filtered. The data has been collected from
different sources. The data is also crude in nature. Moreover, the Gini is a measure of relative and is
always a fraction between 0 and 1, while the explanatory variables are different in nature. PCSDP is
measured in terms of money while AGSHARE is a relative measure. To make the data for different
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
transitionininequalityacross15majorstates
1993-94
1999-00
2003-04
2009-10
19. 19
variables comparable and unit-free, an indexation exercise is performed where the index for variable
X is given by
X^ = [
𝑋−min{ 𝑋𝑖}
max{ 𝑋}−min{ 𝑋}
] 𝑋 100
Our analysis is performed in terms of these indices. Before getting into formal regression analysis,
first the scatters between Gini and PCSDP of the states for each year separately to see if any
association between these two variables is observed.
graphs
Figure 2: GINI-PCSDP scatter in 1993-94
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.00 500.00 1000.00 1500.00 2000.00 2500.00
GINI
PCSDP
1993-94
1993-94
21. 21
Figure 5: GINI-PCSDP scatter in 2009-10
However the scatters do not show any close association. So a formal regression analysis is run.
The GINI index (G) is first regressed on the indices for the two explanatory variables, namely
PCSDP and AGSHARE based on the pooled data with 60 observations. The regression equation is of
the following form
Gjt = α + β1 PCSDPjt + β2AGSHAREjt + ɣ1D1t + ɣ2D2t + ɣ3D3t + µjt
The results are reported in the following table.
Table 10: Year-wise regression results
1993-94 1999-2000 2003-04 2009-10
0
0.05
0.1
0.15
0.2
0.25
0.3
0.00 500.00 1000.00 1500.00 2000.00 2500.00 3000.00
GINI
PCSDP
2009-10
2009-10
22. 22
Coeff t-
statistic
Coeff t-
statistic
Coeff t-
statistic
Coeff t-
statistic
PCSDP 0.1389 0.2526 -0.2020 0.2746 -0.2294 0.2476 0.3359 0.2190
AGSHARE -0.2246 0.2171 -0.4828 0.2321 -0.0952 0.2163 -0.0598 0.2242
R-square 0.106773 0.265089 0.066817 0.266211
Adjusted R-
square
-0.04209 0.1426 -0.8887 0.1439
Observations 15 15 15 15
From our analysis of the year wise regression it is seen that the results for 1993-94 and 2003-04 are
not satisfactory. The R2 values are small for these two years.
This phenomenon that there exists an inverse relationship between growth and inequality gained
prominence mainly in the post reform period. Since the period 93-94 is the earliest stage of reform it
is quite justified that the regression results are not very satisfactory. When one moves to the period
99-00 the results are much stronger. R2 is much higher. 2003-04 regression results are unexpectedly
bad. However this can be due to the fact that this round being a short round of NSSO, the data may
not be comparable to other rounds. So a pooled regression is performed using 1999-00 & 2009-10.
The results are reported in the following table.
Table 11: OLS regression on pooled data for 1999-00 and 2009-10
23. 23
Coefficients t-statistic
PCSDP 0.231895 1.371978
AGSHARE -0.25113 -1.35496
D1 -30.7333 -3.24309
D2 -11.6903 -1.6260
D3 -28.6064 -3.1333
R-square 0.30387
Adjusted R-square 0.223547
Observations 60
This result shows that the coefficients are not very significant though they have their expected signs.
Even then from this result it may be concluded that the criticism is somewhat valid and the results
lead to justify this criticism about the inverse relationship between growth and inequality. Given
a better and a larger data set this matter can be explored further and in a much better manner.
TABLES
The disparity between the richest and poorest state shot up remarkably during the 1990s
(Figure 4). The average per capita net SDP of the three richest states (Punjab, Haryana
24. 24
and Maharashtra) has been benchmarked against the average per capita net SDP of the
two poorest states (Bihar and Orissa).
Figure 6: Disparity between the richest and the poorest states
The trend of the Gini coefficient indicating inter-state inequality is shown in Figure 7, which
confirms that inter-state inequality grew steadily in India with liberalization.
26. 26
It is important to note that the inequality indices are much higher when these are worked
out by weighing the state figures by their population, compared to when each state figure is
given equal weight (Figure 9). This can be attributed to the fact that the states with low
levels of per capita income have high shares in the population. Furthermore, the weighted
indices report a slightly sharper increase during the 1990s than the unweighted indices and
this trend has continued till 2004-05. One would infer that the states with low population
share have done relatively better than those having large shares in the population.
The Gini Index too has maintained a rising trend, as exhibited in the 1990s, as presented in
Figure 3, along with the CVs.
27. 27
The Human Development Index (HDI) achievements of states in India both at the aggregate
and disaggregate levels are shown in Figure 1. India has a HDI value of 0.504 (Table 3).
The HDI is the highest for Kerala (0.625) followed by Punjab (0.569) and the lowest for
Orissa (0.442), Bihar (0.447) and Chhattisgarh (0.449). As the graph reveals, while the HDI
scores across states show little variation and range between 0.442 (Orissa) and 0.625
(Kerala), the variation in the sub-indices for education and health show a greater degree of
variation. The income index shows the least degree of variation.
29. 29
Table 12: Poverty Head Count Ratio: Major Indian States
Table 13: Urban-Rural Differences in Mean Consumption Expenditure
States Urban MPCE as % of Rural MPCE
1993-94 2004-05
Andhra Pradesh 141.5 173.9
Assam 177.9 194.8
Bihar 142.9 166.9
Chhattisgarh 180.6 232.9
Gujarat 149.8 187.1
30. 30
Haryana 123.1 132.3
Himachal Pradesh 212.8 174.2
Jharkhand 190.7 232.0
Karnataka 157.2 203.3
Kerala 126.7 127.4
Madhya Pradesh 155.7 205.9
Maharashtra 194.1 202.1
Orissa 183.2 189.7
Punjab 118.0 156.6
Rajasthan 132.0 163.1
Tamil Nadu 149.0 179.4
Uttar Pradesh 141.2 151.2
Uttaranchal 166.7 158.5
West Bengal 169.9 200.0
All India 163.0 188.2
An attempt has, therefore, been made to compute three yearly averages for SDP for 20 large states
including the newly formed states, providing the basis for the computation of per capita income as
also the growth rates, as presented in Table 14. It may be noted that eight of the backward states such
as Bihar, Uttar Pradesh, Rajasthan, Assam, Orissa, Madhya Pradesh, Chhattisgarh, and Jharkhand
occupy the bottom positions in terms of per capita SDP during the latest triennium, 2007–9.
Uttarakhand is the only state, identified as backward as a part of the state of Uttar Pradesh, wherein
the average SDP is about the national average. Considering the growth scenario in SDP, the less
developed states reported low figures in the late 1990s, especially during 1998–2000. The situation,
however, seems to be changing rapidly. Three of the states, viz., Madhya Pradesh, Rajasthan, and
Orissa, showed high income growth during 2004–6. The distinct change in the spatial thrust in
growth in favour of backward states has further increased in the subsequent period, as almost all
these nine states record high growth rates.
31. 31
The inequality in per capita SDP has gone up consistently including the recent periods, by
both weighted and unweighted CV, as presented in Table 14.
The growth rate of less developed states was less than 4 per cent, much below the average
of the developed states during the Eighth and the Ninth Plans (Table 15).
32. 32
Table 16 gives variations across states in life expectancy and infant mortality. As is well known,
Kerala’s score in human development is close to that of developed countries.
Life expectancy at birth in Kerala is 72 years for males and 75 years for females. Among the rest, the
states of Punjab, Tamil Nadu and Maharashtra have achieved better life expectancy for both male
33. 33
and females. Bihar, one of the poorest states has larger life expectancy for male than Indian average,
but not for females. On the other hand, a rich state like Gujarat has lower record on life expectancy
than many other states. Turning to infant mortality, Kerala again stands out way above other Indian
states with a rate of 9 and 12 for boys and girls respectively. Punjab again has the second lowest
infant mortality rate of 38 for boys. But, it has a very large difference in mortality rate for boys and
girls, the latter being as high as 66. Indeed, Punjab exhibits the highest difference by gender among
all the major states, followed by Haryana. It is worth noting that infant mortality rate for girls is
lower than boys in several states such as Andhra Pradesh, Karnataka, Maharashtra, Orissa, Tamil
Nadu and West Bengal.
34. 34
Table 18 gives growth rates in GSDP for two periods: 1980-81 to 1992-93 and 1993-94 to 2003-04.
Some states like Bihar, Gujarat, Karnataka, Kerala, Madhya Pradesh and
West Bengal have improved their growth performance in per capita terms while Punjab, Rajasthan
and Uttar Pradesh are among the major losers. Andhra Pradesh, Haryana, Karnataka, and Rajasthan
have achieved more than 5 per cent growth in both the periods. The case of Rajasthan is particularly
noteworthy because it was among the poorest states in India till 1970s. In per capita terms, however,
Rajasthan’s growth performance has been moderate owing to disadvantage of higher population
growth. The Southern states, on the other hand, do better in per capita terms because of demographic
advantage.
35. 35
All regions in India are not equally poor. Table 19 shows head count ratio of poverty for 15 major
states that account for more than 90 per cent of the country’s population. The estimates refer to three
36. 36
thick NSSO rounds used for official poverty estimates and average of four thin rounds carried out
during 2000-2003 as an indicator of more recent developments. Incidence of poverty varies largely
across states. On the one end of the spectrum lie the developed states like Punjab and Haryana where
poverty ratio lies within a single digit, while Orissa and Bihar lie at the other end with above 40
percent of the population remaining below the poverty line in recent years.
Table 20: Annual Compound Growth Rate of NSDP
STATE 1991-2001 2001-2008 1991-2008
Andhra Pradesh 3.80 7.65 5.37
Assam 5.89 2.22 4.36
Bihar 2.39 8.18 4.74
Gujarat 5.80 9.25 7.20
Haryana 4.75 8.71 6.36
Himachal Pradesh 5.14 6.92 5.87
Karnataka 7.62 6.67 7.23
Kerala 5.07 8.27 6.38
Madhya Pradesh 3.65 5.36 4.35
Maharashtra 5.64 7.75 6.50
40. 40
Once again we find a paradoxical relation between growth performance and regional concentration
of poverty.
CONCLUSION
The objective of the research has been mainly to explore if at all there is an element of truth in the
arguments made by critics as far as the inclusiveness of growth in India is considered. Unless growth
is ‘inclusive’ in nature it cannot have a positive impact in reducing inequality. Though no convincing
evidence has been found, it seems that the critics’ arguments are not absolutely baseless. On the basis
of the descriptive analysis it can be said that states like Maharashtra and Tamil Nadu which have
shown consistent high levels of inequality have also recorded high levels of per capita SDP. The
benefits of growth have not been shared by all. Then again states like Punjab and Haryana which
record a strong share of agriculture in SDP show medium levels of inequality though inequality
levels should be low in these states. There could be many reasons behind this – one reason being the
large land holding patterns in these states. In short, there are too many variables other than the ones
41. 41
that have been explored, which do affect inequality. Within the limited scope of our research it has
not been possible to capture all these effects. Nevertheless, the issue of growing inequality associated
with high growth is a problem in our country and needs to be addressed much more seriously in the
near future.
The spending line has been drawn too low. The lower the poverty line, the fewer people
who qualify as existing beneath it. Economic realities, such as the high and rising cost of
food, rent and commodities, in India, mean it is impossible to make even bare minimum
purchases of food with such small amounts of money. The estimates don’t present an
accurate picture of the number of those who live in very poor conditions. The estimates of
numbers living in poverty are meaningless in the current economic climate. The poverty line
has been historically set at “very very low levels” in India.
It is important to note that, India’s economic miracle is a recent phenomenon and that future
prospects are far from certain. How well the Indian people and government will be able to
channel current growth into long-term prosperity remains to be seen.
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