term paper rohan

SETH ANANDRAM JAIPURIA COLLEGE

Department of Economics

CLASS ~ B.SC. (HONS.) ECO

YEAR ~ IIIROLL ~ 3224-61-0029

REGN. NO. ~ 224-1121-0862-10

DEPT. ~ ECONOMICS ( H )

YEAR OF SUBMISSION ~ 2013-14

PAPER ON

MENTOR: DR. NEEPA BISIDepartment 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.)

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

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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?

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.

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INTRODUCTION

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.

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http://www.answers.com/topic/economics

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

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

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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.

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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

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

7

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.

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

8

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.

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.

9

http://www.nber.org/reporter/spring03/health.html

http://www.epi.org/blog/real-hourly-wage-growth-last-generation/

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

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.

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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.

DATA ANALYSIS

INTERSTATE COMPARISON OF INEQUALITY

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Rural InequalityWhen 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

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

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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

Urban InequalityAs 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 13

1993-94 1999-2000 2003-04 2009-10


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

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 14

WB WB Orissa Orissa

Overall InequalityAssam 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

1993-94 1999-2000 2003-04 2009-10


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

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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

To illustrate this transition in overall inequality across different states the following bar diagram has

been used.

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APAssam Bih

arGujrat

Haryana

Karnataka

Maharashtra MP

Orissa

Punjab

Rajasthan TN UP WB

00.050.1

0.150.2

0.250.3

0.350.4

0.45

transition in inequality across 15 major states

1993-941999-002003-042009-10

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

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

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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

0.00 1000.00 2000.00 3000.00 4000.000

2

4

6

8

10

12

1993-94

1993-94

PCSDP

GIN

I

Figure 2: GINI-PCSDP scatter in 1993-94

0.00 1000.00 2000.00 3000.00 4000.00 5000.000

2

4

6

8

10

12

1999-00

1999-00

PCSDP

GIN

I

18


0.00 1000.00 2000.00 3000.00 4000.00 5000.00 6000.000

2

4

6

8

10

12

2003-04

2003-04

PCSDP

GIN

I


0.00 1000.00 2000.00 3000.00 4000.00 5000.00 6000.000

2

4

6

8

10

12

2009-10

2009-10

PCSDP

GIN

I


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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

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. 20

The results are reported in the following table.

Table 11: OLS regression on pooled data for 1999-00 and 2009-10

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.

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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 and Maharashtra) has been benchmarked against the average per capita net SDP of the two poorest states (Bihar and Orissa).

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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.

23

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 24

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.

25

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.

26

0

2000

4000

6000

8000

10000

12000

14000

16000

Pun

jab

Mah

aras

htra

Har

yana

Guj

arat

Tam

il N

adu

Him

acha

l Pra

desh

Ker

ala

Kar

nata

ka

And

hra

Pra

desh

Jam

mu

and

Kas

hmir

Wes

t Ben

gal

Mad

hya

Pra

desh

Raj

asth

an

Ass

am

Utta

r Pra

desh

Oris

sa

Bih

ar

0.0

1.0

2.0

3.0

4.0

5.0

6.0

Per capita Income 1993-94 Growth Rate 1993-2004

27

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

Haryana 123.1 132.3

Himachal Pradesh 212.8 174.2

Jharkhand 190.7 232.0

28

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.

29

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).

30

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

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

31

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.

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 32

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.

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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

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.

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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

Orissa 3.94 6.46 4.97

Punjab 4.64 4.94 4.76

Rajasthan 4.61 6.66 5.45

Tamil Nadu 6.11 5.65 5.92

Uttar Pradesh 2.75 7.18 4.55

West Bengal 7.01 6.97 6.99

C.V 28.93 25.03 17.76

Table 21: Annual Compound growth Rate of Agriculture and Allied Activities (at factor cost) (%)

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STATE 1991-2001 2001-08 1991-2008


Assam 2.64 0.80 1.88

Bihar -0.85 3.76 1.02

Gujarat 0.69 15.57 6.57

Haryana 1.33 3.43 2.19

Himachal Pradesh -0.14 2.88 1.09

Karnataka 6.62 -3.92 2.15

Kerala -0.90 2.07 0.31

Madhya Pradesh -1.13 5.31 1.47


Orissa 1.68 4.59 2.87

Punjab 3.17 2.52 2.90

Rajasthan -1.45 6.60 1.79

Tamil Nadu 3.85 0.02 2.25


West Bengal 5.85 3.85 5.02

C.V 134.85 109.27 61.61

Table 22: Annual Compound Growth rate of Industry (at factor cost)

STATE 1991-2001 2001-2008 1991-2008


Assam 11.57 1.55 7.33

Bihar -1.07 2.96 0.57

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Gujarat 5.40 9.55 7.09

Haryana 3.36 8.52 5.45


Karnataka 4.93 7.77 6.09

Kerala 2.64 6.34 4.15



Orissa 3.94 4.91 4.34

Punjab 2.99 4.68 3.68

Rajasthan 8.23 6.01 7.31

Tamil Nadu 5.32 2.28 4.06


West Bengal 5.83 3.46 4.85

C.V 69.17 48.94 43.84

Table 23: Annual Compound Growth rate of services (at factor cost)

STATE 1991-2001 2001-2008 1991-2008


Assam 6.71 3.20 5.25

Bihar 5.85 11.03 7.96

Gujarat 8.44 6.64 7.69

Haryana 8.86 11.42 9.91


Karnataka 9.27 10.56 9.80

Kerala 8.70 10.11 9.28



Orissa 5.64 7.86 6.55

Punjab 7.22 7.06 7.15

Rajasthan 8.20 7.03 7.72

Tamil Nadu 7.37 8.31 7.76


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West Bengal 8.05 9.11 8.49

C.V 19.52 25.43 15.87

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

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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

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.

REFERENCES

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1. P. Gottschalk & T.M. Smeeding (1997): Cross-national comparisons of earnings and

income inequality, Journal of Economic literature, Vol. 35 No. 2.

2. E. Anderson (2005): Openness and inequality in developing countries: A review of theory and recent evidence. World Development, Vol. 33 No. 7.

3. P.K. Goldberg & N. Pavenik (2007): Distributional Effects of Globalization in Developing Countries. Journal of Economic literature, Vol. 65.

4. D. Mazumdar & S. Sarkar (2008): Globalization, Labor Markets and Inequality in India. Routledge, IDRC.

5. Y. Kijima (2006): Why did inequality increase? Evidence from urban India 1983-99. Journal of Development Economics. Vol. 81 No. 1.

6. G. Dutt & M. Ravallion (1998): Why have some Indian states done better than others at reducing rural poverty?, Economica Vol. 65.

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