P. K. Nayak, Sadhan Kumar Chattopadhyay,
Arun Vishnu Kumar and V. Dhanya*
Indian economy is elevated to a high growth path triggered mainly by macroeconomic
reforms and expansion of economic activities across the sectors. However, there are some
serious concerns about a number of imbalances in the growth scenario – inter-sectoral, interregional
and inter-state. These imbalances have definitely a serious impact on the goal of
“inclusive growth” as envisaged in the Eleventh Five Year Plan. The study reveals that still
poverty ratio is very high in the economy despite high growth. There is no significant
increase in employment in the unorganised sector of the economy. The study shows that
while the contribution of the agriculture sector in the real GDP has declined fairly fast, the
share of the employment in the agriculture sector has not declined to that extent. As a result,
the average productivity in this sector has remained very low as compared to other
developing countries. Since a large section of the population continues to be dependent on
the agriculture sector, directly or indirectly, this has serious implications for ‘inclusiveness’
of the growth dynamics. The study has emphasised the role of finance in growth and
attempted to analyse the regional dimension of financial inclusion, although in a limited
sense, in terms of state-wise and sector-wise allocation of credit over the years. It was
observed that the distribution of bank credit across sectors and regions is not equitable.
Given the level of potential output, Indian economy is well poised to achieve an impressive
growth in near future. The strength and resilience of the Indian economy were well tested
while weathering the global turbulence of recent time. The paper arrives at the conclusion
that furtherance of macroeconomic reforms, harnessing synergistic links among the sectors
and availing of opportunities provided by the forces of globalization and intensive use of
technology can enable us to achieve higher level of inclusive growth. Sustainable inclusive
growth presupposes inclusive governance through empowerment, grassroot participation
and increased public accountability.
JEL Classification : C12, D92, F43, G21, O47
Keywords : Inclusive growth, inequality, financial inclusion
Introduction
Inclusive growth has become a buzzword across the globe.
Inclusiveness – a concept that encompasses equity, equality of opportunity, and protection in market and employment transitions – is
an essential ingredient of any successful growth strategy (Commission
on Growth and Development, World Bank, 2008). The Commission
of Growth and Development (2008) considers systematic inequality
of opportunity as “toxic” as it will derail the growth process through
political channels or conflict.
Indian economy has been registering a steady growth in the
recent years. However, poverty continues to be a major concern.
While some level of growth is obviously a necessary condition for
sustained poverty reduction, there is an increasingly unanimous view
that growth by itself is not a sufficient condition for eradicating
poverty (Ali and Son, 2007). Growth can marginalise the poor sections
and increase inequality. High and rising levels of inequality can hinder
poverty reduction, which in turn, can slowdown the growth process.
One important indication of inadequate inclusion in India is that
poverty reduction has been muted in the last decade even with rising
growth. The poverty rate has declined by less than 1 per cent per
annum over the past decade, markedly below trends in neighboring
countries such as Nepal and Bangladesh where both average income
levels and growth are lower (World Bank, 2007)
The importance of inclusive growth is well acknowledged among
the policy makers. The approach paper of 11th Five Year Plan adopted
in December 2006 describes the need for inclusive growth in its
discussion. The approach plan points out that the growth oriented
policies should be combined with policies ensuring broad based per
capita income growth, benefiting all sections of the population,
especially those who have thus far remained deprived.
While the need for inclusive growth is stressed, it is to be seen,
whether it is the inadequate growth of certain sectors like agriculture
or the inability of certain groups like SC/STs to form part of the growth
process or the lack of both physical and financial infrastructure that
pull back the particular regions/sections from enjoying the economic
growth. It is possible that a combination of all these factors is
preventing certain sections/areas to be out of the growth process. In that case it is necessary to know the major determinants that pull down
inclusive growth. The inter linkages between different development
indicators and growth in the context of various regions and sections
needs to be analysed to understand the nuances of India’s growth
process. In this context, a study on regional perspectives of inclusive
growth is of utmost importance.
With this backdrop, the paper is organized as follows. Section II deals with the concept of inclusive growth. Section III analyses the
inter-state growth performance. Here we look into the growth of Net
State Domestic Product and per capita income from 1980-81 onwards.
We also examine the sectoral contributions of economic growth across
different states. Section IV discusses socio-economic inclusiveness as
well as the poverty and unemployment which help in understanding
the inclusiveness of our growth processes vis-à-vis select developing
countries. Section V deals with relationship between finance and
growth. It also highlights state-wise, sector-wise allocation of credit
over the years. Section VI concludes the paper.
Section II: Concept of Inclusive Growth
Inclusive growth implies participation in the process of growth
and also sharing of benefit from growth. Thus inclusive growth is both
an outcome and a process. On the one hand, it ensures that everyone
can participate in the growth process, both in terms of decisionmaking
for organizing the growth progression as well as in participating
in the growth itself. On the other hand, it makes sure that everyone
shares equitably the benefits of growth. In fact, participation without
benefit sharing will make growth unjust and sharing benefits without
participation will make it a welfare outcome.
In view of the above, inclusive growth can be observed from
long-term perspective as the focus is on productive employment rather
than on direct income redistribution, as a means of increasing income
for excluded groups. Under the absolute definition, growth is
considered to be pro-poor as long as poor benefit in absolute terms, as
reflected in some agreed measure of poverty (Ravallion and Chen, 2003). In contrast, in the relative definition, growth is pro-poor if and
only if the incomes of poor people grow faster than those of the
population as a whole, i.e., inequality declines. However, while
absolute pro-poor growth can be the result of direct income
redistribution schemes, for growth to be inclusive, productivity must
be improved and new employment opportunities created, so that the
excluded section forms part of the growth process. In short, inclusive
growth is about raising the pace of growth and enlarging the size of
the economy, while leveling the playing field for investment and
increasing productive employment opportunities.
The concept of inclusive growth has gained wide importance in
several countries including India (Bolt, 2004). The Approach Paper of
the Eleventh Five Year Plan provides “an opportunity to restructure
policies to achieve a new vision based on faster, more broad-based
and inclusive growth. It is designed to reduce poverty and focus on
bringing the various divides that continue to fragment our society”
(GOI, 2006: 1). In fact, Indian economy has come a long way from so
called “Hindu Rate of growth” economy to high growth economy and
is compared with China in many respects. In the last five years
(2005-06 to 2009-10) the growth rate has averaged at 8.6 per cent
making India as one of the fastest growing economies in the World.
Of course, transition to high growth is an impressive achievement, but
growth is not the only measure of development. Our ultimate goal is
to achieve broad based improvement in the living standards of all our
people. Rapid growth is essential for this outcome because it provides
the basis for expanding incomes and employment and also provides
the resources needed to finance programmes for social upliftment.
However, it is not sufficient by itself. It is to ensure that its benefits, in
terms of income and employment, are percolated down to all the
sections of the society, including the poor and weaker sections. For
this to happen, the growth must be inclusive in the broadest sense. It
must be spread across all states and not just limited to some. It must
generate sufficient volumes of high quality employment to provide
the means for uplifting large numbers of our population from the low
income and low quality occupations in which too many of them have been traditionally locked. It is argued that “rapid, sustained and
inclusive growth will take place when large numbers of people move
from low-productivity jobs to high-productivity ones. The less
effective the growth process is in creating jobs, both in terms of
numbers and quality, the greater the political threat and, consequently,
the less sustainable the growth process itself” (Gokarn, 2010). Various
indicators have raised concerns that India’s growth is not inclusive or
its benefits are not widely shared. One, the agriculture sector has been
growing at a rate of 2-3 per cent per annum which has led to a fall in
its share in the total income. With the level of employment in the
agriculture sector remaining more or less constant, the slow growth in
income means that the productivity in the agriculture sector has
remained low. Regardless of the magnitude of increase and the
differential across the two sectors, the stark fact is that average labour
productivity outside agriculture is about 5 times that in agriculture
(Gokarn, 2010). Two, the poverty impact of growth has been muted:
poverty declined from 36 per cent in 1993/94 to 28 per cent in 2004/05,
a 0.8 percentage point reduction per annum compared to 1.6 per cent
poverty reduction per annum in our neighbouring countries, viz.,
Bangladesh and Nepal (World Bank, 2007). It is observed that close
to 300 million still live in deep poverty at less than a dollar a day.
Three, growth rates were generally lower in the poorer states during
the 1980s and 1990s1. Four, employment is dominated by informal
sector jobs. Five, it is observed that public services are weak in the
poorer regions. Six, female labour force participation rates have
remained low despite rising education levels among women due to
absence of opportunities. Seven, there exists significant wage
discrimination among casual laborers, women get about half the
wages of men. Less than one third of this gap can be explained by
conventional factors such as skills, location, industry, etc. Eight,
although SC groups have made progress, large sections of SC and ST
groups are agricultural workers, the poorest earners. Finally, access to
finance has been low in rural areas, 87 per cent of the poorest households surveyed (marginal farmers) do not have access to credit,
the rich pay a relatively low rate of interest (33 per cent), the poor pay
rates of 104 per cent and get only 8 per cent of the credit (World Bank,
2007). Growth has diverged across regions, leaving behind the large
populous states of North, Central and North-East India. Growth has
not been creating enough good jobs that provide stable earnings for
households to climb and stay out of poverty.
Section III : Economic Growth – Spatial and Temporal Analysis
III.1: Overall Growth
During the three decades period from the early 1950s to 1980s,
the Indian economy was witnessing so-called “Hindu” rate of growth
and the major concern was accelerated growth apart from ensuring
equity. During that time, although inequality was a major problem, it
was not as prominent as in the recent phase of accelerated growth.
With the growth in GDP, the issue of rural-urban divide, regional
divides and rich-poor divide became evident, which brought “inclusive
growth” on high priority. The Indian economy has been growing at a
faster rate in recent decades than it did earlier (Table 1 and Chart 1).
Table 1 : Average Rate of Growth of Real GDP in India |
Period |
Growth (per cent) |
1900-2008 |
3.16 |
1950-2008 |
4.79 |
1980-2008 |
6.08 |
1990-2008 |
6.39 |
2000-2008 |
7.19 |
11th Plan Period (2007-12) |
2007-08 |
9.2 |
2008-09 |
6.7 |
2009-10 |
7.2 |
Source : Bose and Chattopadhyay (2010) upto 2008 and CSO, Govt. of India for the rest of
the information |
Sector wise performance
While the growth rate of the Indian economy has been increasing
in recent times, one phenomenon which was observed was that the
growth performance of the three major sectors of the economy,
namely, agriculture, industry and services, has been diverse. The
growth in the agriculture sector has been the most volatile and also the
least among the three sectors most of the times. While the growth in
the industrial sector has remained more or less constant, growth rate
in the services sector has risen sharply (Chart 1).
The consequence of the diverse growth rate in the three sectors
has resulted in a structural change in the contribution of the sectors in
the total GDP. The share of the agriculture sector in the overall GDP
has declined more or less consistently since independence from 55.3
per cent in 1950-51 to 17.0 per cent in 2008-09. The share of the
industrial sector has increased from 10.6 per cent in 1950-51 to about
19.0 per cent in 2008-09. The share of the services sector has nearly
doubled from 34.1 per cent in 1950-51 to 64.5 per cent in 2008-09
(Chart 2).
Since a large section of the population continues to be dependent
on the agriculture sector, directly or indirectly, this has serious
implications for ‘inclusiveness’.
Potential Output2
The Indian economy grew at about 9.0 per cent during 2003-08,
which decelerated to 7.0 per cent during 2008-10. Although a part of
the gap is due to cyclical factor, different estimation methods suggest
that the potential output growth would be around 8.0 per cent during
the post-crisis period and 8.5 per cent during the pre-crisis period3. It
is argued that the loss in potential output could be due to a slowdown
of investment in various sectors, more specifically in the agriculture
sector. In fact, the public investment in agriculture in real terms has
witnessed steady decline from the Sixth Five Year Plan to the Tenth Plan. Trends in public investment in agriculture and allied sectors
reveal that it has consistently declined in real terms (at 1999-2000
prices) from ` 64,012 crore in Sixth Plan to ` 42,226 crore during the
Ninth Plan. However, during the Tenth Plan this has increased in
absolute terms to ` 67,260 crore. It can also be observed that the
public investment has gone down over the year, while private
investment remained stagnant (Table 3). The gross capital formation
(GCF) in agriculture and allied sectors as a proportion of total GDP
stood at 2.66 per cent in 2004-05 and improved to 3.34 per cent in
2008-09. Similarly, GCF in agriculture and allied sectors relative to
GDP in this sector has also shown an improvement from 14.07 per
cent in 2004-05 to 21.31 per cent in 2008-09.
Table 2 : Plan-wise investment in Agriculture |
See RBI Annual Report 2009-10 |
Investment (` crore) |
Sixth Plan (1980-85) |
64012 |
Seventh Plan (1985-90) |
52108 |
Eighth Plan (1992-97) |
45565 |
Ninth Plan (1997-2002) |
42226 |
Tenth Plan (2002-2007) |
67260 |
Eleventh Plan (2007-2012) |
- |
Source: Economic Survey, 2010, Government of India. |
Table 3 : Public and Private Investment in Agriculture & Allied Sector
at 2004-05 Prices |
|
Investment in agriculture & allied sector (` crore) |
Share in total investment (per cent) |
Total |
Public |
Private |
Public |
Private |
2004-05 |
78848 |
16183 |
62665 |
20.5 |
79.5 |
2005-06 |
93121 |
19909 |
73211 |
21.4 |
78.6 |
2006-07 |
94400 |
22978 |
71422 |
24.3 |
75.7 |
2007-08 |
110006 |
23039 |
86967 |
20.9 |
79.1 |
2008-09 |
138597 |
24452 |
114145 |
17.6 |
82.4 |
Source : Central Statistics Office, GoI. |
Declining investment in the agriculture sector had a direct
bearing on the productivity of foodgrains in the country. As can be
observed from Chart 3, although average yield/hectare (productivity)
of foodgrains in India has increased over the years, the productivity is
low compared to many other developing countries. The productivity
of foodgrains has increased from 522 kg/hectare in 1950-51 to 1854
in 2007-08. While in 1979-80 the yield per hectare was 876 kg/hectare,
it became 1380 kg/hectare in 1990. However, productivity growth
remained stagnant at a very low level throughout the period. Various
studies have been done on the agriculture sector and its associated
issues. More recent, among these, studies is done by Mishra (2007)
which states that ‘poor agriculture income and absence of non-farm avenues of income is indicative of the larger malaise in the rural
economy of India’. One of the manifestations of this has been the
increasing incidence of farmers’ suicide in various parts of the country,
especially Maharashtra, Andhra Pradesh, etc.
 |
As per the World Bank database, in respect of cereal productivity,
India remained far below even China, Indonesia, Thailand and Sri
Lanka (Table 4).
Table 4: Cereal Productivity (Kg/hectare) |
Country |
1980 |
1985 |
1990 |
1995 |
2000 |
2005 |
2006 |
2007 |
2008 |
Brazil |
1575.7 |
1827.7 |
1755 |
2513.1 |
2660.6 |
2882.5 |
3210.5 |
3553.1 |
3828.8 |
China |
2948.7 |
3827.7 |
4322.7 |
4663.7 |
4756.3 |
5225.5 |
5313.3 |
5315.3 |
5535.3 |
Egypt |
4094.4 |
4539.1 |
5702.9 |
5903.7 |
7280 |
7569.2 |
7541 |
7562.2 |
7506.4 |
Indonesia |
2865.6 |
3513.3 |
3800.2 |
3842.7 |
4026.3 |
4311.3 |
4365.8 |
4464.7 |
4694.2 |
Poland |
2336.8 |
2893.5 |
3283.7 |
3022.3 |
2534.7 |
3233 |
2598.2 |
3249.5 |
3217.2 |
Russia |
NA |
NA |
NA |
1223.5 |
1563.3 |
1860.1 |
1894.4 |
1994.9 |
2388.1 |
Sri Lanka |
2501 |
2960.5 |
2965 |
3052.6 |
3338.1 |
3467.1 |
3619.4 |
3821.6 |
3659.8 |
Thailand |
1911 |
2125.4 |
2009 |
2507.4 |
2719.1 |
3001.5 |
2963 |
3043.7 |
3013.7 |
Turkey |
1855.1 |
1931 |
2214.1 |
2037.8 |
2311 |
2624.2 |
2661.9 |
2381.4 |
2601.2 |
Vietnam |
2016.1 |
2691.7 |
3072.9 |
3569.9 |
4112.3 |
4726.1 |
4749.7 |
4833.6 |
5064.2 |
India |
1350 |
1592.2 |
1891.2 |
2111.7 |
2293.5 |
2411.5 |
2455.6 |
2618.6 |
2647.2 |
Source : World Bank website: http://data.worldbank.org/data-catalog |
In short, the analysis at the all-India level shows that agricultural
sector has lagged behind the growth process. Productivity in
agricultural sector is low not only compared with other sectors, but
also when compared to the agricultural productivity in other developing
countries. In the next section we examine the inclusiveness of growth
across the states in India.
III. 2 Inter-state Comparisons of Growth Performance
With regard to inter-state comparison of growth performance, it
can be observed from the Table 5 that there is a wide disparity in
growth performance in the three time periods (viz. 1980-81 to 1989-
90, 1990-91 to 1999-2000 and 2000-01 to 2008-09), though the
disparity has come down in the last period4. Among the three time
periods taken, nineties witnessed higher disparity as revealed from
the coefficient of variation (CV) at 32.4 per cent. The CV has increased
from 27.2 per cent during the eighties to 32.4 per cent in the nineties.
However, there was a decline in disparity during the last period where
the CV came down to 21.2 per cent.
Not only has the disparity in growth came down during 2000s, the
period (i.e., 2000-01 to 2008-09) also witnessed high growth rates
across the states. All states, with the exception of Madhya Pradesh,
recorded growth of more than 5.0 per cent and 12 states recorded growth
of above 8.0 per cent. This is further evident that the average growth
rate of 7.3 per cent was registered by all states during 2000-01 to 2008-
09 compared to 4.9 per cent and 5.3 per cent, recorded during the first
two periods respectively. Further, certain states like Kerala, Uttaranchal,
Orissa and Nagaland showed significant improvements during 2000-09.
Thus, the NSDP figures show that the years since 2000 witnessed
better inclusive growth than the previous periods. However, it is quite
premature to presume that the latter years indicate inclusive growth as
the NSDP figures hide the distributive effect of growth. To probe further into the details, we look into the per capita NSDP figures which
give a better indicator of standard of living compared to the state
average growth. Here again, it is noted that per capita income (PCI) also has limited value in examining inclusive growth as it gives little
revelation on the distribution of income across the population.
Table 5 : State-wise, Period-wise Compound Growth Rate of NSDP |
States |
1980-81 to
1989-90 |
1990-91 to
1999-00 |
2000-01 to
2008-09 |
Growth
rate |
Rank |
Growth rate |
Rank |
Growth rate |
Rank |
Gujarat |
4.8 |
12 |
8.0 |
2 |
10.6 |
1 |
Haryana |
6.3 |
3 |
4.7 |
17 |
9.5 |
2 |
Goa |
5.2 |
9 |
8.4 |
1 |
8.9 |
3 |
Uttaranchal |
-- |
-- |
2.6 |
24 |
8.9 |
4 |
Kerala |
2.6 |
22 |
5.9 |
12 |
8.5 |
5 |
Orissa |
4.8 |
14 |
4.0 |
22 |
8.4 |
6 |
Nagaland |
7.5 |
2 |
5.6 |
13 |
8.4 |
7 |
Jharkhand |
-- |
-- |
6.5 |
8 |
8.4 |
8 |
Maharashtra |
5.6 |
5 |
6.9 |
5 |
8.4 |
9 |
Tripura |
5.0 |
10 |
7.3 |
3 |
8.3 |
10 |
Sikkim |
NA |
NA |
6.3 |
10 |
8.1 |
11 |
Chattisgarh |
-- |
-- |
2.5 |
25 |
8.1 |
12 |
Tamil Nadu |
5.0 |
11 |
6.4 |
9 |
7.4 |
13 |
Andhra |
5.3 |
7 |
5.3 |
16 |
7.2 |
14 |
Bihar |
4.7 |
16 |
2.0 |
27 |
7.2 |
15 |
Karnataka |
5.3 |
8 |
7.1 |
4 |
7.2 |
16 |
Himachal Pradesh |
4.5 |
18 |
6.2 |
11 |
6.9 |
17 |
Rajashthan |
5.9 |
4 |
6.5 |
7 |
6.5 |
18 |
West Bengal |
4.6 |
17 |
6.9 |
6 |
6.3 |
19 |
Arunachal Pradesh |
8.1 |
1 |
4.6 |
20 |
5.9 |
20 |
Meghalya |
4.4 |
19 |
5.5 |
15 |
5.8 |
21 |
Manipur |
4.8 |
13 |
4.7 |
18 |
5.8 |
22 |
Uttar Pradesh |
4.8 |
15 |
3.6 |
23 |
5.4 |
23 |
Jammu &Kashmir |
2.0 |
23 |
4.7 |
19 |
5.3 |
24 |
Assam |
3.3 |
21 |
2.2 |
26 |
5.3 |
25 |
Punjab |
5.4 |
6 |
4.4 |
21 |
5.1 |
26 |
Madhya Pradesh |
3.6 |
20 |
5.6 |
14 |
4.5 |
27 |
Note : NA : Not Available; -- Not Applicable
Source: Authors’ own Calculation by using semi-logarithmic trend. |
 |
Chart 4 gives the distribution of Per capita income across states.
It shows that there is a wide disparity across States with Bihar at the
lowest and Goa at the top position. The CV is as high as 41.0 per cent.
We further examine the inequality across the States in respect of
per capita NSDP across the time periods. Annex Table 2 provides the
estimates of semi-log function. For Jammu Kashmir (1980-81 to
1989-90), Bihar and Uttaranchal for 1990-91 to 1999-00 and Nagaland
for 1990-91 to 1999-00 and 2000-01 to 2008-09, the figures came
insignificant. The growth rates for the rest of the states are given in
Table 6.
Compared to NSDP, the disparity is higher in the case of per
capita income. However, similar to NSDP, the 1990s witnessed higher
disparity which came down in 2000s. The CV increased from 48.0 per
cent during the eighties to 53.5 per cent in the nineties before coming
down to 32.9 per cent during the last period.
Though the inequality in terms of growth rates have come down
in 2000s, inequality measured by Gini coefficient of the level variables,
have shown an increase over the period. Gini coefficient has increased from 0.164 in 1980-81 to 0.245 in 2007-08 (Chart 5). Gini coefficient
has been calculated for 22 states omitting Chattisgarh, Jharkhand,
Uttaranchal, Mizoram, Sikkim and Nagaland due to non-availability
of continuous data series5.
Table 6 : State-wise, Period-wise Compound Growth Rate of Per Capita NSDP |
States |
1980-81 to
1989-90 |
1990-91 to
1999-00 |
2000-01 to
2008-09 |
Growth rate |
Rank |
Growth rate |
Rank |
Growth rate |
Rank |
Gujarat |
2.8 |
12 |
6.0 |
2 |
9.1 |
1 |
Orissa |
2.9 |
11 |
2.4 |
17 |
8.2 |
2 |
Kerala |
1.1 |
21 |
4.8 |
7 |
8.0 |
3 |
Chattisgarh |
- |
- |
0.9 |
23 |
7.8 |
4 |
Haryana |
3.7 |
2 |
2.2 |
19 |
7.3 |
5 |
Andhra Pradesh |
3.0 |
10 |
3.8 |
12 |
7.0 |
6 |
Uttaranchal |
- |
- |
0.0 |
- |
7.0 |
7 |
Goa |
3.6 |
3 |
6.8 |
1 |
6.9 |
8 |
Maharashtra |
3.2 |
8 |
4.7 |
8 |
6.8 |
9 |
Sikkim |
NA |
- |
3.4 |
14 |
6.6 |
10 |
Jharkhand |
- |
- |
4.7 |
9 |
6.6 |
11 |
Karnataka |
3.2 |
9 |
5.4 |
4 |
6.6 |
12 |
Tamil Nadu |
3.5 |
6 |
5.3 |
5 |
6.5 |
13 |
Bihar |
2.5 |
14 |
0.0 |
- |
5.9 |
14 |
Tripura |
2.0 |
18 |
5.4 |
3 |
5.8 |
15 |
Rajashthan |
3.2 |
7 |
4.0 |
11 |
5.8 |
16 |
Meghalya |
1.4 |
19 |
2.8 |
15 |
5.7 |
17 |
West Bengal |
2.3 |
16 |
5.1 |
6 |
5.4 |
18 |
Himachal Pradesh |
2.7 |
13 |
4.4 |
10 |
5.2 |
19 |
Arunachal Pradesh |
4.8 |
1 |
2.1 |
20 |
5.1 |
20 |
Manipur |
2.1 |
17 |
2.3 |
18 |
4.0 |
21 |
Jammu &Kashmir |
0.0 |
- |
2.0 |
21 |
3.7 |
22 |
Assam |
1.1 |
22 |
0.3 |
25 |
3.4 |
23 |
UP |
2.4 |
15 |
1.3 |
22 |
3.4 |
24 |
Punjab |
3.5 |
4 |
2.5 |
16 |
3.3 |
25 |
Madhya Pradesh |
1.2 |
20 |
3.4 |
13 |
2.6 |
26 |
Nagaland |
3.5 |
5 |
0.0 |
- |
0.0 |
- |
Notes: NA denotes Note Available
Source: Authors’ own Calculation by using semi-logarithmic trend. |
 |
In general, growth rates of states have improved in the last time
period with the exception of Madhya Pradesh and Punjab. Both the
states showed dismal performance in case of NSDP and Per capita
income. On the other hand, Kerala and Orissa showed significant
improvement in the last decade, with Kerala registering tremendous
improvement both in the growth and level of income. Orissa, which
ranked 17 in terms of PCI growth during the nineties, improved its
position to the second. However, in terms of the level of PCI, it is still
low at ` 15,702.
From the perspective of inclusive growth, an analysis of growth
performance of states is not enough. It calls for a more detailed
analysis of various sectors of the economy and various sections of
population. As a first step, we look into the sectoral shares and growth
in each state.
Table 7 provides the share of each sector in NSDP across the
three time periods. In all the states, the share of primary sector has
declined over the time period considered and tertiary sector showing
an increase in share and secondary sector registering marginal or no
increase6. However, Maharashtra which is often hailed as industrial capital of India, witnessed a decline in the share of secondary sector
and witnessed an increase in tertiary sector. In all the states, tertiary
sector occupies the major share of NSDP which conforms with the
earlier studies showing India’s difference in development path with
the general East Asian growth path (Bhattacharya and Mitra, 1990;
Bhattacharya and Sakthivel, 2004).
Table 7: Shares of each sector in NSDP across states (Contd.) |
|
Primary |
Secondary |
Tertiary |
Primary |
Secondary |
Tertiary |
ANDHRA Pradesh |
ARUNACHAL PRADESH |
1980-81 to 1989-90 |
48.7 |
13.3 |
38.0 |
50.9 |
16.7 |
32.4 |
1990-91 to 1999-00 |
37.1 |
18.0 |
44.9 |
42.2 |
19.7 |
38.1 |
2000-01 to 2008-09 |
30.6 |
18.4 |
51.0 |
28.6 |
28.0 |
43.4 |
|
ASSAM |
BIHAR |
1980-81 to 1989-90 |
49.0 |
15.6 |
35.4 |
50.3 |
9.4 |
40.3 |
1990-91 to 1999-00 |
44.3 |
13.7 |
42.0 |
41.6 |
9.6 |
48.8 |
2000-01 to 2008-09 |
34.4 |
14.5 |
51.1 |
32.5 |
12.6 |
54.9 |
|
CHATTISGARH |
GOA |
1980-81 to 1989-90 |
– |
– |
– |
32.9 |
22.5 |
44.6 |
1990-91 to 1999-00 |
40.5 |
21.5 |
38.0 |
20.7 |
27.9 |
51.4 |
2000-01 to 2008-09 |
35.6 |
22.5 |
41.9 |
13.3 |
36.0 |
50.6 |
|
GUJARAT |
HARYANA |
1980-81 to 1989-90 |
40.4 |
25.8 |
33.8 |
46.1 |
24.3 |
29.6 |
1990-91 to 1999-00 |
29.1 |
32.0 |
38.8 |
39.4 |
26.3 |
34.4 |
2000-01 to 2008-09 |
20.8 |
33.2 |
46.0 |
25.8 |
26.6 |
47.6 |
|
HIMACHAL |
JAMMU KASHMIR |
1980-81 to 1989-90 |
47.1 |
21.6 |
31.3 |
38.7 |
25.6 |
35.8 |
1990-91 to 1999-00 |
34.8 |
31.1 |
34.2 |
33.3 |
25.4 |
41.2 |
2000-01 to 2008-09 |
24.9 |
37.2 |
37.9 |
31.9 |
21.5 |
46.6 |
|
JHARKAND |
KARNATAKA |
1980-81 to 1989-90 |
– |
– |
– |
45.2 |
20.1 |
34.7 |
1990-91 to 1999-00 |
29.8 |
31.1 |
39.1 |
36.1 |
22.0 |
41.9 |
2000-01 to 2008-09 |
27.1 |
29.8 |
43.2 |
22.0 |
24.3 |
53.6 |
|
KERALA |
MADHYA PRADESH |
1980-81 to 1989-90 |
31.0 |
20.1 |
48.9 |
45.9 |
12.0 |
42.2 |
1990-91 to 1999-00 |
26.5 |
21.4 |
52.1 |
38.9 |
14.7 |
46.4 |
2000-01 to 2008-09 |
17.0 |
22.1 |
60.9 |
30.6 |
18.2 |
51.2 |
|
MAHARASHTRA |
MANIPUR |
1980-81 to 1989-90 |
25.7 |
30.4 |
43.9 |
39.6 |
19.9 |
40.5 |
1990-91 to 1999-00 |
20.1 |
29.1 |
50.7 |
32.4 |
19.6 |
48.0 |
2000-01 to 2008-09 |
16.3 |
23.6 |
60.1 |
28.1 |
26.8 |
45.2 |
Table 7: Shares of each sector in NSDP across states (Concld.) |
|
MEGHALAYA |
MIZORAM |
1980-81 to 1989-90 |
39.1 |
12.4 |
48.5 |
|
|
|
1990-91 to 1999-00 |
31.2 |
12.9 |
55.9 |
24.2 |
14.1 |
61.6 |
2000-01 to 2008-09 |
30.6 |
17.3 |
52.1 |
18.3 |
17.6 |
64.2 |
|
NAGALAND |
ORISSA |
1980-81 to 1989-90 |
24.6 |
6.8 |
68.5 |
52.5 |
15.6 |
32.0 |
1990-91 to 1999-00 |
23.9 |
16.1 |
60.1 |
41.9 |
16.8 |
41.4 |
2000-01 to 2008-09 |
34.5 |
14.1 |
51.4 |
32.8 |
16.9 |
50.3 |
|
PUNJAB |
RAJASTHAN |
1980-81 to 1989-90 |
46.5 |
15.7 |
37.8 |
47.6 |
16.7 |
35.7 |
1990-91 to 1999-00 |
43.6 |
19.6 |
36.8 |
41.0 |
19.2 |
39.8 |
2000-01 to 2008-09 |
36.0 |
21.5 |
42.6 |
32.8 |
22.6 |
44.6 |
|
SIKKIM |
TAMIL Nadu |
1980-81 to 1989-90 |
– |
– |
– |
26.1 |
30.3 |
43.6 |
1990-91 to 1999-00 |
31.3 |
18.5 |
50.2 |
22.2 |
28.6 |
49.2 |
2000-01 to 2008-09 |
21.0 |
26.1 |
52.9 |
14.3 |
26.5 |
59.2 |
|
TRIPURA |
UTTAR PRADESH |
1980-81 to 1989-90 |
51.2 |
9.7 |
39.1 |
44.2 |
17.8 |
38.0 |
1990-91 to 1999-00 |
37.3 |
9.6 |
53.1 |
38.3 |
19.8 |
41.9 |
2000-01 to 2008-09 |
26.4 |
20.6 |
53.0 |
33.4 |
21.4 |
45.2 |
|
UTTARAKHAND |
WEST BENGAL |
1980-81 to 1989-90 |
– |
– |
– |
40.0 |
15.6 |
44.4 |
1990-91 to 1999-00 |
30.3 |
18.8 |
51.0 |
37.6 |
14.8 |
47.6 |
2000-01 to 2008-09 |
23.5 |
28.4 |
48.2 |
28.0 |
16.1 |
55.9 |
Source: Central Statistics Office, Government of India. |
However, in terms of growth rates, secondary sector registered
highest growth rate in most of the states during the period 2000-01 to 2008-09 as is revealed from Table 8. On the other hand, tertiary sector
which registered high growth during the nineties witnessed a
slowdown or marginal growth in most of the states during the last
period.
Table 8 : Sector-wise Growth Rates of NSDP across States (Contd.) |
|
Primary |
Secondary |
Tertiary |
Primary |
Secondary |
Tertiary |
ANDHRA PRADESH |
ARUNACHAL PRADESH |
1980-81 to 1989-90 |
2.0 |
7.1 |
7.6 |
8.8 |
6.6 |
7.4 |
1990-91 to 1999-00 |
2.8 |
7.0 |
9.2 |
0.6 |
6.8 |
9.2 |
2000-01 to 2008-09 |
6.0 |
9.8 |
6.1 |
2.4 |
12.4 |
6.1 |
|
ASSAM |
BIHAR |
1980-81 to 1989-90 |
2.7 |
1.2 |
0.9 |
2.8 |
7.9 |
5.8 |
1990-91 to 1999-00 |
2.9 |
3.6 |
0.7 |
NS |
NS |
4.8 |
2000-01 to 2008-09 |
4.1 |
7.6 |
7.2 |
2.5 |
17.7 |
8.7 |
|
GOA |
GUJARAT |
1980-81 to 1989-90 |
NS |
5.6 |
7.2 |
NS |
8.1 |
7.3 |
1990-91 to 1999-00 |
2.7 |
8.7 |
10.1 |
4.0 |
9.4 |
9.2 |
2000-01 to 2008-09 |
6.3 |
7.5 |
10.5 |
10.6 |
13.6 |
9.5 |
|
HARYANA |
HIMACHAL |
1980-81 to 1989-90 |
3.9 |
9.7 |
7.2 |
1.6 |
6.7 |
5.9 |
1990-91 to 1999-00 |
1.8 |
5.2 |
7.7 |
0.8 |
11.2 |
7.6 |
2000-01 to 2008-09 |
3.6 |
10.2 |
12.1 |
1.4 |
9.7 |
7.8 |
|
JAMMU KASHMIR |
KARNATAKA |
1980-81 to 1989-90 |
NS |
5.6 |
3.4 |
2.7 |
6.7 |
7.1 |
1990-91 to 1999-00 |
4.1 |
NS |
6.2 |
4.5 |
6.8 |
9.4 |
2000-01 to 2008-09 |
3.1 |
8.6 |
5.1 |
1.7 |
10.9 |
9.3 |
|
KERALA |
MADHYA PRADESH |
1980-81 to 1989-90 |
1.3 |
2.2 |
3.6 |
1.7 |
4.6 |
5.2 |
1990-91 to 1999-00 |
2.0 |
6.7 |
8.5 |
3.6 |
8.8 |
5.9 |
2000-01 to 2008-09 |
0.8 |
12.4 |
9.9 |
5.3 |
4.1 |
4.3 |
|
MAHARASHTRA |
MANIPUR |
1980-81 to 1989-90 |
3.1 |
6.0 |
6.4 |
2.0 |
7.0 |
7.0 |
1990-91 to 1999-00 |
4.5 |
6.0 |
8.3 |
2.8 |
2.5 |
5.9 |
2000-01 to 2008-09 |
5.1 |
7.7 |
9.2 |
2.2 |
13.6 |
4.4 |
Table 8: Sector-wise Growth Rates of NSDP across States (Concld.) |
|
MEGHALAYA |
NAGALAND |
1980-81 to 1989-90 |
1.4 |
0.7 |
6.5 |
5.5 |
18.2 |
8.5 |
1990-91 to 1999-00 |
4.4 |
8.0 |
5.8 |
6.1 |
10.6 |
4.9 |
2000-01 to 2008-09 |
5.8 |
13.2 |
5.8 |
7.6 |
9.7 |
4.5 |
|
ORISSA |
PUNJAB |
1980-81 to 1989-90 |
3.0 |
7.1 |
6.3 |
5.3 |
7.2 |
4.0 |
1990-91 to 1999-00 |
2.9 |
NS |
6.3 |
2.4 |
6.7 |
5.8 |
2000-01 to 2008-09 |
5.6 |
14.7 |
10.1 |
2.5 |
8.3 |
5.8 |
|
RAJASTHAN |
TAMIL NADU |
1980-81 to 1989-90 |
3.4 |
7.1 |
8.9 |
3.5 |
2.8 |
2.3 |
1990-91 to 1999-00 |
3.9 |
9.4 |
8.0 |
4.1 |
5.3 |
7.7 |
2000-01 to 2008-09 |
6.1 |
9.1 |
8.4 |
6.2 |
9.0 |
8.6 |
|
TRIPURA |
UTTAR PRADESH |
1980-81 to 1989-90 |
2.5 |
NS |
8.5 |
2.5 |
8.3 |
5.8 |
1990-91 to 1999-00 |
3.0 |
11.2 |
8.8 |
2.6 |
3.7 |
4.3 |
2000-01 to 2008-09 |
6.0 |
7.1 |
7.7 |
2.1 |
10.1 |
5.7 |
|
WEST BENGAL |
|
1980-81 to 1989-90 |
5.8 |
3.0 |
4.4 |
|
|
|
1990-91 to 1999-00 |
5.0 |
6.2 |
8.6 |
|
|
|
2000-01 to 2008-09 |
2.3 |
8.8 |
8.1 |
|
|
|
Note: NS denotes Not Significant
Source: Central Statistics Office, Government of India |
The analysis in this section revealed that the growth process was
mostly driven by the growth in the services sector. Further, the
inequality in growth has come down in the time period since 2000,
though the inequality remains at high level. Further, there was a
change in growth performance in the last decade with many
underperformers moving up and top performers coming down which
is reflected in the declining inequality. In the next section, we look
into the socio-economic inclusiveness of the growth process.
IV : Socio-Economic Inclusiveness
While discussing inclusive growth, a major factor to be examined
is the socio-economic inclusiveness of the people. Inclusive growth
being a long term process necessarily emanates from the inclusive
nature of socio-economic development across regions and people.
But, considering the time constraint, we are limiting our analysis of
socio-economic inclusiveness to certain indicators which we feel is
able to reveal the social development of the country. We start the
analysis by looking into the poverty and unemployment figures over
the years. As far as possible, we have tried to compare India’s position
with other developing countries
The ultimate objective of planned development is to ensure
human well-being through sustained improvement in the quality of
life of the people, particularly the poor and the vulnerable segments of
population. The development of human resources contributes to
sustained growth and productive employment. Development strategy
therefore needs to continuously strive for broad-based improvement
in standards of living. High growth is essential to generate resources
for social spending. However, the benefits of growth should be shared
equitably among all sections of society. This is the main logic behind
emphasizing the concept of inclusive growth as has been pursued in
the Eleventh Five Year Plan.
As per the UNDP Human Development Report 2009 (HRD
2009), India ranked 134 out of 182 countries of the world placing it at
the same rank as in 2006 (the Human Development Index (HDI) for
India in 2007 was 0.612).7 However, the HDI value of India has
increased gradually from 0.427 in 1980 to 0.556 in 2000 and went up
to 0.612 in 2007, but it is still in the medium Human Development
category with even countries like China, Sri Lanka and Indonesia
having better ranking (Table 9). In fact, India lags behind in various social indicators of development. There is a huge gap between India
and developed world and even many developing countries in respect
of health and education, which needs to be bridged at a faster pace.
According to HDR, life expectancy at birth in India was 63.4 years in
2007 as against 80.5 years in Norway, 81.4 years in Australia, 74.0
years in Sri Lanka and 72.9 years in China. Adult literacy rate (aged
15 and above) in 1999-2007 was 66.0 per cent in India as against near
100 per cent in China and 92.0 per cent in Indonesia. In the case of
combined gross enrolment ratio in education also India was much
below the level achieved by some other comparable countries, like
China, Norway, and Thailand etc.
Table 9 : Human Development Index |
Country |
1980 |
1985 |
1990 |
1995 |
2000 |
2005 |
2006 |
2007 |
Poland |
… |
… |
0.806 |
0.823 |
0.853 |
0.871 |
0.876 |
0.880 |
Brazil |
0.685 |
0.694 |
0.710 |
0.734 |
0.790 |
0.805 |
0.808 |
0.813 |
Russia |
… |
… |
0.821 |
0.777 |
… |
0.804 |
0.811 |
0.817 |
Turkey |
0.628 |
0.674 |
0.705 |
0.73 |
0.758 |
0.796 |
0.802 |
0.806 |
Thailand |
0.658 |
0.684 |
0.706 |
0.727 |
0.753 |
0.777 |
0.78 |
0.783 |
China |
0.533 |
0.556 |
0.608 |
0.657 |
0.719 |
0.756 |
0.763 |
0.772 |
Sri Lanka |
0.649 |
0.670 |
0.683 |
0.696 |
0.729 |
0.752 |
0.755 |
0.759 |
Indonesia |
0.522 |
0.562 |
0.624 |
0.658 |
0.673 |
0.723 |
0.729 |
0.734 |
Vietnam |
… |
0.561 |
0.599 |
0.647 |
0.69 |
0.715 |
0.720 |
0.725 |
Egypt |
0.496 |
0.552 |
0.58 |
0.631 |
0.665 |
0.696 |
0.700 |
0.703 |
India |
0.427 |
0.453 |
0.489 |
0.511 |
0.556 |
0.596 |
0.604 |
0.612 |
Source: Human Development Report, 2009 |
Poverty
Poverty is a major issue in the emerging economies, though its
intensity varies across countries as reflected in the World Bank’s data
on the poverty head count ratio at $1.25 a day (PPP). South Asia
continues to have a significant amount of poor people, mainly due to
the high poverty ratios in India and Bangladesh (Table 10). It is
observed that compared to India, China has made significant progress
in reducing poverty in the last 15 years.
Table 10: Poverty headcount ratio at $1.25 a day (PPP)
(% of population) |
Country |
1990 |
2005 |
Argentina |
n.a. |
3.4 |
(2006) |
Bangladesh |
n.a. |
49.6 |
|
Brazil |
15.5 |
5.2 |
(2007) |
Chile |
4.4 |
2.0 |
(2006) |
China |
60.2 |
15.9 |
|
East Asia & Pacific |
54.7 |
16.8 |
|
India |
n.a. |
41.6 |
|
Indonesia |
n.a. |
29.4 |
(2007) |
Pakistan |
n.a. |
22.6 |
|
South Asia |
51.7 |
40.3 |
|
Source: World Bank website. |
As per the official estimates, the incidence of poverty has declined
over the years though it remains still at a very high level. The percentage
of the population below the official poverty line has come down from
36 per cent in 1993–94 to 28 per cent in 2004–05 (Table 11). However,
not only is the rate still high, but also the rate of decline in poverty has
not accelerated along with the growth in GDP, and the incidence of
poverty among certain marginalized groups, for example the poverty
rate of the STs, has hardly declined. Moreover, the absolute number of
poor people below poverty line has declined only marginally from
320 million in 1993–94 to 302 million in 2004–05. This performance
is all the more disappointing since the poverty line on which the
estimate of the poor is based is the same as it was in 1973–74 when
per capita incomes were much lower. If we take the World Bank
measurement of poverty about 41.6 per cent (as per PPP) of population
is below poverty line, which is much higher than the official national
poverty ratio of about 28 per cent.
Table 11 : Trends in Poverty in India |
Year |
Poverty
(head count index) percentage |
Number of poor (million) |
Rural |
Urban |
Total |
1973-74 |
56 |
49 |
55 |
321 |
1983 |
46 |
41 |
45 |
323 |
1993-94 |
37 |
32 |
36 |
320 |
2004-05 |
28 |
26 |
28 |
302 |
Source : Mahendra S. Dev (2007). |
It can further be stated that around 80.0 per cent of the poor are
from rural areas. Poverty is mostly concentrated in few states, viz,
Bihar, Uttar Pradesh and Madhya Pradesh, Orissa, Chattisgarh and
Jharkhand (Annex Table 3). Poverty is concentrated among agricultural
labourers, casual workers, Scheduled Castes and Scheduled Tribes.
There are concerns of inequality also in the country. During the
last four decades there is hardly any decrease in inequality in the
country. It may be observed from Table 12 that while there is a
marginal decrease in inequality in the rural area, it has increased in the urban area. A state-wise breakup of Gini coefficients, including a
division between rural and urban households, gives similar picture.
Most of the States have shown some increase in urban inequality
during the same period, but none of the states displayed any increase
in consumption inequality over the period 1973-74 to 2004-05.
Table 12 : Gini Coefficient for Per Capita Consumption Expenditure |
|
1973-74 |
1977-78 |
1983 |
1993-94 |
1999-2000 |
2004-05
(URP)* |
2004-05
(MRP)* |
Rural |
Urban |
Rural |
Urban |
Rural |
Urban |
Rural |
Urban |
Rural |
Urban |
Rural |
Urban |
Rural |
Urban |
India |
0.28 |
0.30 |
0.34 |
0.34 |
0.30 |
0.33 |
0.28 |
0.34 |
0.26 |
0.34 |
0.30 |
0.37 |
0.25 |
0.35 |
Andhra Pradesh |
0.29 |
0.29 |
0.30 |
0.32 |
0.29 |
0.31 |
0.29 |
0.32 |
0.24 |
0.31 |
0.29 |
0.37 |
0.24 |
0.34 |
Assam |
0.20 |
0.30 |
0.18 |
0.32 |
0.19 |
0.25 |
0.18 |
0.29 |
0.20 |
0.31 |
0.19 |
0.32 |
0.17 |
0.30 |
Bihar |
0.27 |
0.26 |
0.26 |
0.30 |
0.26 |
0.30 |
0.22 |
0.31 |
0.21 |
0.32 |
0.20 |
0.33 |
0.17 |
0.31 |
Jharkhand |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
0.22 |
0.35 |
0.20 |
0.33 |
Gujarat |
0.23 |
0.25 |
0.29 |
0.31 |
0.25 |
0.26 |
0.24 |
0.29 |
0.23 |
0.29 |
0.27 |
0.31 |
0.25 |
0.32 |
Haryana |
0.29 |
0.31 |
0.29 |
0.31 |
0.27 |
0.30 |
0.30 |
0.28 |
0.24 |
0.29 |
0.32 |
0.36 |
0.31 |
0.36 |
Himachal Pradesh |
0.24 |
0.27 |
0.26 |
0.30 |
0.27 |
0.31 |
0.28 |
0.43 |
0.23 |
0.30 |
0.30 |
0.32 |
0.26 |
0.26 |
Jammu & Kashmir |
0.22 |
0.22 |
0.22 |
0.33 |
0.22 |
0.24 |
0.23 |
0.28 |
0.17 |
0.22 |
0.24 |
0.24 |
0.20 |
0.24 |
Karnataka |
0.28 |
0.29 |
0.32 |
0.34 |
0.30 |
0.33 |
0.27 |
0.32 |
0.24 |
0.32 |
0.26 |
0.36 |
0.23 |
0.36 |
Kerala |
0.31 |
0.37 |
0.35 |
0.36 |
0.33 |
0.37 |
0.29 |
0.34 |
0.27 |
0.32 |
0.34 |
0.40 |
0.29 |
0.35 |
Madhya Pradesh |
0.29 |
0.27 |
0.33 |
0.38 |
0.29 |
0.29 |
0.28 |
0.33 |
0.24 |
0.32 |
0.27 |
0.39 |
0.24 |
0.37 |
Chhatisgarh |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
0.29 |
0.43 |
0.24 |
0.35 |
Maharashtra |
0.26 |
0.33 |
0.46 |
0.36 |
0.28 |
0.33 |
0.30 |
0.35 |
0.26 |
0.35 |
0.31 |
0.37 |
0.27 |
0.35 |
Orissa |
0.26 |
0.34 |
0.30 |
0.32 |
0.27 |
0.29 |
0.24 |
0.30 |
0.24 |
0.29 |
0.28 |
0.35 |
0.25 |
0.33 |
Punjab |
0.27 |
0.29 |
0.30 |
0.38 |
0.28 |
0.32 |
0.26 |
0.28 |
0.24 |
0.29 |
0.28 |
0.39 |
0.26 |
0.32 |
Rajasthan |
0.28 |
0.29 |
0.46 |
0.30 |
0.34 |
0.30 |
0.26 |
0.29 |
0.21 |
0.28 |
0.25 |
0.37 |
0.20 |
0.30 |
Tamil Nadu |
0.27 |
0.31 |
0.32 |
0.33 |
0.32 |
0.35 |
0.31 |
0.34 |
0.28 |
0.38 |
0.32 |
0.36 |
0.26 |
0.34 |
Uttar Pradesh |
0.24 |
0.29 |
0.30 |
0.33 |
0.29 |
0.31 |
0.28 |
0.32 |
0.25 |
0.33 |
0.29 |
0.37 |
0.23 |
0.34 |
Uttaranchal |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
0.28 |
0.32 |
0.22 |
0.30 |
West Bengal |
0.30 |
0.32 |
0.29 |
0.32 |
0.28 |
0.33 |
0.25 |
0.33 |
0.22 |
0.34 |
0.27 |
0.38 |
0.24 |
0.36 |
Delhi |
0.15 |
0.35 |
0.29 |
0.33 |
0.29 |
0.33 |
0.24 |
0.21 |
0.29 |
0.34 |
0.26 |
0.33 |
0.24 |
0.32 |
Note : URP - Uniform Reference Period; MRP - Mixed Reference Period. – : Not available.
Source : Planning Commission, Government of India. |
Employment and Unemployment Situation
Nature and extent of employment is crucial for poverty reduction
and inclusive growth. It can be observed from Table 13 that although
employment in the industrial and services sector has increased in 2004
in comparison to 1961, agriculture still remains the major sector
which continues to employ the largest segment of the population.
Table 13 : Sector-wise Employment (per cent) |
Sector |
1961 |
2004 |
Agriculture |
75.9 |
56.4 |
Industry |
11.7 |
18.2 |
Tertiary |
12.4 |
25.4 |
Total |
100 |
100 |
Source : Mahendra S. Dev (2007) |
Employment growth in the organized sector, both public and
private combined, has declined during the period 1994 and 2007. This
has happened due to the decline of employment in the public organized
sector. Employment in the organized sector grew at 1.20 per cent per
annum during 1983-94, but declined to (-) 0.03 per cent per annum
during 1994-2007 (Table 14). However, the decline in employment
during the later period was mainly due to a decline in employment in the public sector establishments from 1.53 per cent in the earlier
period to (-) 0.57 per cent in the later period, whereas the private
sector showed moderate growth of 1.30 per cent per annum.
Table 14: Rate of Growth of employment in organized Sector |
(per cent per annum) |
Sector |
1983-94 |
1994-2007 |
Public Sector |
1.53 |
-0.57 |
Private Sector |
0.44 |
1.30 |
Total Organized |
1.20 |
-0.03 |
Source: Economic Survey, 2009-10, Government of India. |
According to NSSO data, compared to 1999-2000, during 2004-05,
the unemployment rate in terms of the usual status remained almost
the same in rural and urban areas for males, though it has increased by
around 2 percentage points for females. As can be observed from
Table 15, overall unemployment rates are not too high. However,
urban unemployment rates are higher than the rural rates. The
unemployment rates according to current daily status (CDS) approach
are higher than the rates obtained according to ‘usual status’ approach
and ‘weekly status’ approach, thereby indicating a high degree of
intermittent unemployment. The unemployment rate, measured
through the usual status is very low in the rural areas.
Table 15 : Unemployment rates in India according to usual status, current
weekly status and current daily status during 1972-73 to 2004-05 |
Year (round) |
Male |
Female |
Usual Status |
CWS |
CDS |
Usual Status |
CWS |
CDS |
Rural |
1972-73 (27th round) |
1.2 |
3.0 |
6.8 |
0.5 |
5.5 |
11.2 |
1977-78 (32nd round) |
2.2 |
3.6 |
7.1 |
5.5 |
4.1 |
9.2 |
1983 (38th round) |
2.1 |
3.7 |
7.5 |
1.4 |
4.3 |
9.0 |
1987-88 (43rd round) |
2.8 |
4.2 |
4.6 |
3.5 |
4.4 |
6.7 |
1993-94 (50th round) |
2.0 |
3.1 |
5.6 |
1.3 |
2.9 |
5.6 |
1999-2000 (55th round) |
2.1 |
3.9 |
7.2 |
1.5 |
3.7 |
7.0 |
2004-05 (61st round) |
2.1 |
3.8 |
8.0 |
3.1 |
4.2 |
8.7 |
Urban |
1972-73 (27th round) |
4.8 |
6.0 |
8.0 |
6.0 |
9.2 |
13.7 |
1977-78 (32nd round) |
6.5 |
7.1 |
9.4 |
17.8 |
10.9 |
14.5 |
1983 (38th round) |
5.9 |
6.7 |
9.2 |
6.9 |
7.5 |
11.0 |
1987-88 (43rd round) |
6.1 |
6.6 |
8.8 |
8.5 |
9.2 |
12.0 |
1993-94 (50th round) |
5.4 |
5.2 |
6.7 |
8.3 |
7.9 |
10.4 |
1999-2000 (55th round) |
4.8 |
5.6 |
7.3 |
7.1 |
7.3 |
9.4 |
2004-05 (61st round) |
4.4 |
5.2 |
7.5 |
9.1 |
9.0 |
11.6 |
Note : CWS : Current weekly status, CDS: Current daily status.
Source : NSSO, 61st round. |
Rural Population
A significant proportion of the Indian population continues to
live in the rural areas, though the share has been declining over the
years (Table 16). The share of rural population in India is more or less
same with that in other South Asian countries. It is interesting to
observe that China’s share of rural population, which was almost
similar to that of India in early 90s, had declined much faster. With a
significant proportion of the rural population engaged in the
agricultural sector, the agricultural value added per worker continues
to be low.
Table 16 : Share of Rural Population: India and select Countries
(% of total population) |
Country |
Rural population
(% of total
population) |
Agriculture value
added per worker
(constant 2000
US$) |
1990 |
2008 |
1990 |
2008 |
Afghanistan |
81.7 |
76.0 |
- |
- |
Argentina |
13.0 |
8.0 |
6,701.7 |
11,793.1 |
Bangladesh |
80.2 |
72.9 |
250.6 |
417.6 |
Brazil |
25.2 |
14.4 |
1,625.4 |
3,857.9 |
Chile |
16.7 |
11.6 |
3,453.3 |
6,486.9 |
China |
72.6 |
56.9 |
262.8 |
504.2 |
India |
74.5 |
70.5 |
362.1 |
478.0 |
Indonesia |
69.4 |
48.5 |
511.9 |
704.9 |
Korea, Dem. Rep. |
41.6 |
37.3 |
- |
- |
Korea, Rep. |
26.2 |
18.5 |
5,338.1 |
17,703.5 |
Least developed countries: UN classification |
79.0 |
71.4 |
242.1 |
297.0 |
Low income |
77.3 |
71.3 |
242.0 |
324.1 |
Malaysia |
50.2 |
29.6 |
385.0 |
- |
Pakistan |
69.4 |
63.8 |
738.5 |
892.0 |
Philippines |
51.2 |
35.1 |
910.9 |
1,211.3 |
South Africa |
48.0 |
39.3 |
2,290.1 |
3,838.6 |
South Asia |
75.1 |
70.5 |
371.6 |
499.1 |
Sri Lanka |
82.8 |
84.9 |
678.4 |
902.7 |
World |
57.1 |
50.1 |
793.6 |
878.2 |
Source: World Bank website, World Development Indicators, 2010. |
Rural Health
India has made significant strides in terms of availability of
improved water source in the rural areas (Table 17). It is comparable
with many countries across the world. However, in terms of inclusive
growth on the provision of improved rural sanitation, our achievement
has been low.
Gender Disparity
Another important indicator of inclusive growth is the trend in
gender disparity. India has made significant strides in terms of reducing
the gender disparities as reflected in various indicators. For instance,
the female life expectancy at birth, the female literacy levels and the
share of women employed in the non-agricultural sector have improved since 1990. In comparison with select countries, it is observed that we
are still lagging behind. Even within South Asia, achievements by Sri
Lanka are much better than India (Table 18).
Table 17 : Availability of Improved Water Source and Sanitation in Rural Areas (as % of rural population with access) |
Country |
Improved Water
Source |
Improved Rural
Sanitation |
1990 |
2006 |
1995 |
2006 |
Afghanistan |
|
17.0 |
29.0 |
25.0 |
Argentina |
72.0 |
80.0 |
59.0 |
83.0 |
Bangladesh |
76.0 |
78.0 |
21.0 |
32.0 |
Brazil |
54.0 |
58.0 |
37.0 |
37.0 |
Chile |
49.0 |
72.0 |
58.0 |
74.0 |
China |
55.0 |
81.0 |
48.0 |
59.0 |
India |
65.0 |
86.0 |
8.0 |
18.0 |
Indonesia |
63.0 |
71.0 |
40.0 |
37.0 |
Korea, Dem. Rep. |
n.a. |
100.0 |
60.0 |
n.a. |
Least developed countries: UN classification |
45.3 |
55.1 |
17.8 |
27.3 |
Low income |
45.2 |
59.7 |
23.4 |
33.3 |
Malaysia |
96.0 |
96.0 |
n.a. |
93.0 |
Pakistan |
81.0 |
87.0 |
22.0 |
40.0 |
Philippines |
75.0 |
88.0 |
55.0 |
72.0 |
South Africa |
62.0 |
82.0 |
46.0 |
49.0 |
South Asia |
67.7 |
83.8 |
12.2 |
23.0 |
Sri Lanka |
62.0 |
79.0 |
74.0 |
86.0 |
World |
62.0 |
77.5 |
37.3 |
44.2 |
Source: World Bank website, World Development Indicators, 2010. |
Table 18 : Gender Disparity |
Country |
Life expectancy
at birth, female
(years) |
Literacy rate,
adult female
(% of females
ages 15 and
above) |
Share of women
employed in the
nonagricultural sector (%
of total nonagricultural
employment) |
1990 |
2008 |
2008 |
1990 |
2007 |
Afghanistan |
41.2 |
43.9 |
n.a. |
|
17.8 |
n.a. |
|
Argentina |
75.2 |
79.2 |
97.7 |
|
37.1 |
45.0 |
(2006) |
Bangladesh |
54.8 |
67.2 |
49.8 |
|
|
20.1 |
(2006) |
Brazil |
70.1 |
76.2 |
90.2 |
(2007) |
35.1 |
n.a. |
|
Chile |
76.7 |
81.7 |
98.7 |
|
34.7 |
37.4 |
|
China |
69.5 |
74.9 |
90.5 |
|
37.8 |
n.a. |
|
India |
58.5 |
65.2 |
50.8 |
(2007) |
12.7 |
18.1 |
(2005) |
Indonesia |
63.3 |
72.8 |
88.8 |
(2007) |
29.2 |
30.6 |
|
Korea, Dem. Rep. |
73.7 |
69.3 |
n.a |
|
40.7 |
n.a. |
|
Korea, Rep. |
75.5 |
83.3 |
n.a |
|
38.1 |
42.1 |
|
Least developed countries:
UN classification |
51.8 |
58.1 |
54.4 |
|
n.a. |
n.a.
|
|
Low income |
55.6 |
60.3 |
63.0 |
|
n.a. |
n.a. |
|
Malaysia |
72.3 |
76.8 |
89.8 |
|
n.a. |
39.0 |
|
Pakistan |
60.9 |
66.9 |
40.0 |
|
7.7 |
13.2 |
|
Philippines |
67.5 |
74.1 |
93.9 |
|
40.3 |
42.3 |
|
South Africa |
65.2 |
53.1 |
88.1 |
|
n.a. |
43.9 |
|
South Asia |
58.2 |
65.4 |
50.1 |
|
12.6 |
n.a. |
|
Sri Lanka |
72.9 |
78.0 |
89.1 |
|
n.a. |
31.0 |
|
World |
67.1 |
71.1 |
76.3 |
|
34.4 |
n.a. |
|
Source : World Bank website, World Development Indicators, accessed on August 23, 2010. |
Literacy
The male female literacy and literacy gap during the last two
censuses across states are given in Table 19. Though the literacy gap
across states has visibly come down over the decade, in many states and
union territories, it is more than the national average. Literacy gap is
highest among the North Indian statets with the exception of Punjab,
Himachal Pradesh and Chandigarh. However, for Punjab, the low
literacy gap is more to do with the low literacy rates which itself is a worrisome phenomenon considering that Punjab ranks fifth in terms of
per capita NSDP.
Table 19 : Male-female Literacy Gap in India |
States /UT |
Literacy Rate 1991
census |
Literacy
Gap |
Literacy Rate 2001
census |
Literacy
Gap |
Male |
Female |
Male |
Female |
Rajasthan |
55.0 |
20.4 |
34.6 |
75.7 |
43.9 |
31.9 |
D &N Haveli |
53.6 |
27.0 |
26.6 |
71.2 |
40.2 |
31.0 |
Jharkhand |
55.8 |
25.5 |
30.3 |
67.3 |
38.9 |
28.4 |
Uttar Pradesh |
54.8 |
24.4 |
30.5 |
68.8 |
42.2 |
26.6 |
Bihar |
51.4 |
22.0 |
29.4 |
59.7 |
33.1 |
26.6 |
Madhya Pradesh |
58.5 |
29.4 |
29.2 |
76.1 |
50.3 |
25.8 |
Chhattisgarh |
58.1 |
27.5 |
30.6 |
77.4 |
51.9 |
25.5 |
Orissa |
63.1 |
34.7 |
28.4 |
75.4 |
50.5 |
24.8 |
Uttarakhand |
72.8 |
41.6 |
31.2 |
83.3 |
59.6 |
23.7 |
Jammu & Kashmir |
N.A |
N.A |
N.A |
66.6 |
43.0 |
23.6 |
Haryana |
69.1 |
40.5 |
28.6 |
78.5 |
55.7 |
22.8 |
Gujarat |
73.4 |
48.9 |
24.5 |
79.7 |
57.8 |
21.9 |
Daman & Diu |
82.7 |
59.4 |
23.3 |
86.8 |
65.6 |
21.2 |
Arunachal Pradesh |
51.5 |
29.7 |
21.8 |
63.8 |
43.5 |
20.3 |
Andhra Pradesh |
55.1 |
32.7 |
22.4 |
70.3 |
50.4 |
19.9 |
Manipur |
71.6 |
47.6 |
24.0 |
80.3 |
60.5 |
19.8 |
Karnataka |
67.3 |
44.3 |
22.9 |
76.1 |
56.9 |
19.2 |
Maharashtra |
76.6 |
52.3 |
24.2 |
86.0 |
67.0 |
18.9 |
Tamil Nadu |
73.8 |
51.3 |
22.4 |
82.4 |
64.4 |
18.0 |
Himachal Pradesh |
75.4 |
52.3 |
23.2 |
85.4 |
67.4 |
17.9 |
West Bengal |
67.8 |
46.6 |
21.3 |
77.0 |
59.6 |
17.4 |
Assam |
61.9 |
43.0 |
18.8 |
71.3 |
54.6 |
16.7 |
Tripura |
70.6 |
49.7 |
20.9 |
81.0 |
64.9 |
16.1 |
Sikkim |
65.7 |
46.8 |
18.9 |
76.0 |
60.4 |
15.6 |
Puducherry |
83.7 |
65.6 |
18.1 |
88.6 |
73.9 |
14.7 |
Goa |
83.6 |
67.1 |
16.6 |
88.4 |
75.4 |
13.1 |
Delhi |
82.0 |
67.0 |
15.0 |
87.3 |
74.7 |
12.6 |
Lakshadweep |
90.2 |
72.9 |
17.3 |
92.5 |
80.5 |
12.1 |
Punjab |
65.7 |
50.4 |
15.3 |
75.2 |
63.4 |
11.9 |
A&N Islands |
79.0 |
65.5 |
13.5 |
86.3 |
75.2 |
11.1 |
Nagaland |
67.6 |
54.8 |
12.9 |
71.2 |
61.5 |
9.7 |
Chandigargh |
82.0 |
72.3 |
9.7 |
86.1 |
76.5 |
9.7 |
Kerala |
93.6 |
86.2 |
7.5 |
94.2 |
87.7 |
6.5 |
Meghalaya |
53.1 |
44.9 |
8.3 |
65.4 |
59.6 |
5.8 |
Mizoram |
85.6 |
78.6 |
7.0 |
90.7 |
86.8 |
4.0 |
INDIA |
64.1 |
39.3 |
24.9 |
75.3 |
53.7 |
21.6 |
Source : Selected Socio Economic Statistics, India, CSO |
In the case of infant mortality rates, the disparity is very high
(Table 20). It ranges from 10 in Goa to 70 in Madhya Pradesh.
Table 20 : State-wise Infant Mortality Rates (per 1000) |
States/Union
Territories |
1961 |
2007 |
2008 |
Male |
Female |
Person |
Male |
Female |
Person |
Male |
Female |
Person |
Goa |
60 |
56 |
57 |
11 |
13 |
13 |
10 |
11 |
10 |
Kerala |
55 |
48 |
52 |
14 |
10 |
13 |
10 |
13 |
12 |
Manipur |
31 |
33 |
32 |
13 |
9 |
12 |
13 |
15 |
14 |
Puducherry |
77 |
68 |
73 |
31 |
22 |
25 |
22 |
27 |
25 |
Nagaland |
76 |
58 |
68 |
18 |
29 |
21 |
23 |
29 |
26 |
Chandigarh |
53 |
53 |
53 |
25 |
28 |
27 |
27 |
29 |
28 |
Andaman |
78 |
66 |
77 |
38 |
23 |
34 |
29 |
32 |
31 |
Lakshadweep |
124 |
88 |
118 |
25 |
23 |
24 |
29 |
34 |
31 |
Tamil |
89 |
82 |
86 |
38 |
31 |
35 |
30 |
33 |
31 |
Daman & Diu |
60 |
56 |
57 |
29 |
23 |
27 |
26 |
37 |
31 |
Arunachal Pradesh |
141 |
111 |
126 |
41 |
15 |
37 |
30 |
34 |
32 |
Maharashtra |
96 |
89 |
92 |
41 |
24 |
34 |
33 |
33 |
33 |
Sikkim |
105 |
87 |
96 |
36 |
20 |
34 |
34 |
32 |
33 |
Tripura |
106 |
116 |
111 |
40 |
32 |
39 |
34 |
35 |
34 |
Dadra |
102 |
93 |
98 |
38 |
18 |
34 |
33 |
35 |
34 |
Delhi |
66 |
70 |
67 |
41 |
35 |
36 |
34 |
37 |
35 |
West |
103 |
57 |
95 |
39 |
29 |
37 |
34 |
37 |
35 |
Mizoram |
73 |
65 |
69 |
27 |
16 |
23 |
37 |
38 |
37 |
Punjab |
74 |
79 |
77 |
47 |
35 |
43 |
39 |
43 |
41 |
Himachal Pradesh |
101 |
89 |
92 |
49 |
25 |
47 |
43 |
45 |
44 |
Uttarakhand |
– |
– |
– |
52 |
25 |
48 |
44 |
45 |
44 |
Karnataka |
87 |
74 |
81 |
52 |
35 |
47 |
44 |
46 |
45 |
Jharkhand |
– |
– |
– |
51 |
31 |
48 |
45 |
48 |
46 |
Jammu Kashmir |
78 |
78 |
78 |
53 |
38 |
51 |
48 |
51 |
49 |
Gujarat |
81 |
84 |
84 |
60 |
36 |
52 |
49 |
51 |
50 |
Andhra Pradesh |
100 |
82 |
91 |
60 |
37 |
54 |
51 |
54 |
52 |
Haryana |
87 |
119 |
94 |
60 |
44 |
55 |
51 |
57 |
54 |
Bihar |
95 |
94 |
94 |
59 |
44 |
58 |
53 |
58 |
56 |
Chhatisgarh |
– |
– |
– |
61 |
49 |
59 |
57 |
58 |
57 |
Meghalaya |
81 |
76 |
79 |
57 |
46 |
56 |
58 |
58 |
58 |
Rajasthan |
114 |
114 |
114 |
72 |
40 |
65 |
60 |
65 |
63 |
Assam |
na |
na |
na |
68 |
41 |
66 |
62 |
65 |
64 |
Uttar Pradesh |
131 |
128 |
130 |
72 |
51 |
69 |
64 |
70 |
67 |
Orissa |
119 |
111 |
115 |
73 |
52 |
71 |
68 |
70 |
69 |
Madhya Pradesh |
158 |
140 |
150 |
77 |
50 |
72 |
68 |
72 |
70 |
India |
122 |
108 |
115 |
61 |
37 |
55 |
52 |
55 |
53 |
Source: Economic Survey 2009-10. |
Another aspect of looking into the development of the region is
the provision of basic facilities. Table 21 provides the data on the
percentage of population with housing amenities. While there is
significant improvement in the availability of electricity, there is huge
difference in rural urban. While only 8 per cent of urban population is
not having electricity, the share is 44 per cent in the case of rural
areas.
Table 21 : Percentage of population living with Housing Amenities (Lighting) |
|
1999-2000 |
2005-06 |
R |
U |
R |
U |
No lighting |
0.5 |
0.3 |
0.5 |
0.2 |
Kerosene |
50.6 |
10.3 |
42.2 |
7.2 |
Other oil |
0.2 |
0.1 |
0.2 |
0.1 |
Gas |
0.1 |
0.1 |
0.1 |
0.1 |
Candle |
0.1 |
0.0 |
0.2 |
0.3 |
Electricity |
48.4 |
89.1 |
56.3 |
92 |
Other |
0.1 |
0.1 |
0.5 |
0.1 |
Not recorded |
0.0 |
0.0 |
0.0 |
0.0 |
All |
100 |
100 |
100 |
100 |
R Rural; U: Urban
Source: Selected Socio Economic Statistics, India, CSO |
The above indicators provided significant facts on differences in
the socio-economic conditions across regions. However, it is possible
that within regions, certain groups are marginalized. This was evident
when we looked into the poverty ratio across different class of
population. In the following Tables we looked into the entitlement to
different population groups (Tables 22 and 23).
In rural India, among the social groups, the proportion of
households possessing land less than 0.001 hectares, during 2004-05,
was the highest for ST households (nearly 4 per cent). The
corresponding proportion for SC households was about 3 per cent and
for OBC and others category of households around 2 per cent each.
The survey results also show that the proportion of households
possessing land of size 4.01 hectares or more was maximum for other
category of households (6 per cent), followed by the OBC (4 per cent),
ST (about 3 per cent) and SC households (1 per cent).
Table 22 : Per 1000 distribution of households of different social groups by size of land possessed (Rural India) |
size class of land possessed (hectares) |
ST |
SC |
OBC |
Other |
all |
ST |
SC |
OBC |
Other |
ST |
SC |
Other |
all |
61st round (2004-05) |
55th round (1999-00) |
50th round (1993-94) |
0 |
36 |
27 |
16 |
20 |
22 |
72 |
100 |
65 |
58 |
133 |
181 |
112 |
129 |
|
(3.6) |
(2.7) |
(1.6) |
(2.0) |
(2.2) |
(7.2) |
(10.0) |
(6.5) |
(5.8) |
(13.3) |
(18.1) |
(11.2) |
(12.9) |
0.001-0.40 |
428 |
722 |
544 |
505 |
561 |
391 |
650 |
500 |
463 |
299 |
536 |
379 |
404 |
|
(42.8) |
(72.2) |
(54.4) |
(50.5) |
(56.1) |
(39.1) |
(65.0) |
(50.0) |
(46.3) |
(29.9) |
(53.6) |
(37.9) |
(40.4) |
0.41-1.00 |
239 |
147 |
195 |
185 |
187 |
243 |
147 |
202 |
191 |
214 |
149 |
195 |
187 |
|
(23.9) |
(14.7) |
(19.5) |
(18.5) |
(18.7) |
(24.3) |
(14.7) |
(20.2) |
(19.1) |
(21.4) |
(14.9) |
(19.5) |
(18.7) |
1.01-2.00 |
163 |
67 |
128 |
134 |
120 |
165 |
65 |
120 |
128 |
187 |
80 |
151 |
140 |
|
(16.3) |
(6.7) |
(12.8) |
(13.4) |
(12.0) |
(16.5) |
(6.5) |
(12.0) |
(12.8) |
(18.7) |
(8.0) |
(15.1) |
(14.0) |
2.01-4.00 |
106 |
27 |
76 |
99 |
75 |
99 |
28 |
75 |
93 |
119 |
39 |
99 |
88 |
|
(10.6) |
(2.7) |
(7.6) |
(9.9) |
(7.5) |
(9.9) |
(2.8) |
(7.5) |
(9.3) |
(11.9) |
(3.9) |
(9.9) |
(8.8) |
4.01 & above |
29 |
10 |
40 |
57 |
36 |
30 |
11 |
38 |
67 |
48 |
15 |
64 |
52 |
|
(2.9) |
(1.0) |
(4.0) |
(5.7) |
(3.6) |
(3.0) |
(1.1) |
(3.8) |
(6.7) |
(4.8) |
(1.5) |
(6.4) |
(5.2) |
All |
1000 |
1000 |
1000 |
1000 |
1000 |
1000 |
1000 |
1000 |
1000 |
1000 |
1000 |
1000 |
1000 |
|
(100.0) |
(100.0) |
(100.0) |
(100.0) |
(100.0) |
(100.0) |
(100.0) |
(100.0) |
(100.0) |
(100.0) |
(100.0) |
(100.0) |
(100.0) |
Note: 1. The households with size class of land possessed ‘0.000’ hectares comprise households which possessed land less than 0.001 hectares as well as households which reported no information on land possessed.
2. Figures in parenthesis refer to percentage share to total.
3. All includes not reported also.
Source: Employment and Unemployment Situation Among Social Groups in India, 50, 55 and 61st Round. |
In the case of Monthly Per capita Consumption Expenditure
(MPCE) also, the SC/ST communities are marginalized (Table 23).
In rural India, proportion of households in each of the five lower
MPCE classes (i.e., less than ` 410) was higher among the STs (49
per cent), SCs (40 per cent) and OBCs (30 per cent) than among the
others (20 per cent) social group. Between STs and SCs, proportions
of households in the lowest two MPCE classes were higher among
STs (15 per cent) than among the SCs (8 per cent), and these
households spent only ` 270 or less per month. The proportion of
households in the highest MPCE class (i.e. those who spent ` 1155 or
more per month) was higher among others category of households
(12 per cent) than among the OBCs (5 per cent), SCs (3 per cent) or
STs (2 per cent).
Table 23 : Per 1000 distribution of households by household monthly per capita consumer expenditure for each social group |
Monthly per-
capita consumer expenditure (`) |
Rural |
Monthly per-
capita consumer expenditure (`) |
Urban |
ST |
SC |
OBC |
Others |
all |
ST |
SC |
OBC |
Others |
all |
less than 235 |
91 |
35 |
20 |
12 |
29 |
less than 335 |
81 |
70 |
34 |
16 |
33 |
|
(9.1) |
(3.5) |
(2.0) |
(1.2) |
(2.9) |
|
(8.1) |
(7.0) |
(3.4) |
(1.6) |
(3.3) |
235-270 |
62 |
43 |
24 |
16 |
30 |
335-395 |
54 |
58 |
42 |
15 |
32 |
|
(6.2) |
(4.3) |
(2.4) |
(1.6) |
(3.0) |
|
(5.4) |
(5.8) |
(4.2) |
(1.5) |
(3.2) |
270-320 |
113 |
94 |
70 |
37 |
71 |
395-485 |
84 |
120 |
88 |
46 |
73 |
|
(11.3) |
(9.4) |
(7.0) |
(3.7) |
(7.1) |
|
(8.4) |
(12.0) |
(8.8) |
(4.6) |
(7.3) |
320-365 |
117 |
115 |
89 |
60 |
90 |
485-580 |
122 |
131 |
116 |
63 |
93 |
|
(11.7) |
(11.5) |
(8.9) |
(6.0) |
(9.0) |
|
(12.2) |
(13.1) |
(11.6) |
(6.3) |
(9.3) |
365-410 |
108 |
113 |
95 |
71 |
94 |
580-675 |
84 |
131 |
120 |
69 |
97 |
|
(10.8) |
(11.3) |
(9.5) |
(7.1) |
(9.4) |
|
(8.4) |
(13.1) |
(12.0) |
(6.9) |
(9.7) |
410-455 |
92 |
108 |
95 |
73 |
92 |
675-790 |
75 |
110 |
107 |
78 |
93 |
|
(9.2) |
(10.8) |
(9.5) |
(7.3) |
(9.2) |
|
(7.5) |
(11.0) |
(10.7) |
(7.8) |
(9.3) |
455-510 |
94 |
112 |
113 |
92 |
106 |
790-930 |
85 |
109 |
109 |
90 |
99 |
|
(9.4) |
(11.2) |
(11.3) |
(9.2) |
(10.6) |
|
(8.5) |
(10.9) |
(10.9) |
(9.0) |
(9.9) |
510-580 |
93 |
114 |
121 |
122 |
117 |
930-1100 |
113 |
78 |
98 |
102 |
97 |
|
(9.3) |
(11.4) |
(12.1) |
(12.2) |
(11.7) |
|
(11.3) |
(7.8) |
(9.8) |
(10.2) |
(9.7) |
580-690 |
97 |
107 |
135 |
142 |
127 |
1100-1380 |
135 |
82 |
104 |
127 |
113 |
|
(9.7) |
(10.7) |
(13.5) |
(14.2) |
(12.7) |
|
(13.5) |
(8.2) |
(10.4) |
(12.7) |
(11.3) |
690-890 |
82 |
93 |
122 |
153 |
119 |
1380-1880 |
92 |
70 |
96 |
157 |
121 |
|
(8.2) |
(9.3) |
(12.2) |
(15.3) |
(11.9) |
|
(9.2) |
(7.0) |
(9.6) |
(15.7) |
(12.1) |
890-1155 |
30 |
37 |
63 |
108 |
65 |
1880-2540 |
43 |
28 |
49 |
112 |
75 |
|
(3.0) |
(3.7) |
(6.3) |
(10.8) |
(6.5) |
|
(4.3) |
(2.8) |
(4.9) |
(11.2) |
(7.5) |
1155 & above |
19 |
27 |
53 |
115 |
60 |
2540 & above |
33 |
14 |
34 |
126 |
74 |
|
(1.9) |
(2.7) |
(5.3) |
(11.5) |
(6.0) |
|
(3.3) |
(1.4) |
(3.4) |
(12.6) |
(7.4) |
all classes |
1000 |
1000 |
1000 |
1000 |
1000 |
all classes |
1000 |
1000 |
1000 |
1000 |
1000 |
Note: Figures in parenthesis refers to percentage share to total.
Source: Employment and Unemployment Situation among Social Groups in India, NSSO 61st Round. |
In urban India too, proportion of households in each of the five
lower MPCE classes (i.e. less than ` 675) was higher among SCs, STs
and OBCs than among the other categories of households. About 51
per cent of the SCs of urban India spent less than ` 675 per month
during 2004-05; the corresponding percentages being 43, 40 and 21
for the STs, OBCs and the others, respectively. The proportion of
households in the lowest MPCE class (i.e. those spending less than ` 335 per month) was higher among the STs (8 per cent) than that
among SCs (7 per cent). The proportion of urban households spending
` 2540 or more per month was higher among other (13 per cent)
categories of households than among the OBCs or STs (3 per cent
each) or SCs (1 per cent).
The analysis in this section has shown that India’s achievement
in terms of various social indicators are not that commendable
compared to that of the growth in GDP. India lags behind many
developing countries in terms of povery and other social indicators.
There are sections of population that remains marginalized irrespective
of the high growth. Urban Inequality in terms of consumption
expenditure have increased in almost all states, while rural inequality
has come down in most of the states.
So far, we have examined the various facets of inclusive growth
by looking into the various indicators of economic and social
development. A major pre-requisite of economic development is
finance. Access to finance and awareness on the availability of finance
can play a major role in promoting economic growth. In the next
section we look into the interplay between institutional finance and
economic growth.
Section V : Institutional Finance and Growth
There is a general consensus among economists that financial
development spurs economic growth. Theoretically, financial
development creates enabling conditions for growth through either
a supply-leading (financial development spurs growth) or a demand following
(growth generates demand for financial products) channel.
A large body of empirical research supports the view that development
of the financial system contributes to economic growth (Rajan and
Zingales, 2003). Empirical evidence consistently emphasizes the nexus
between finance and growth, though direction of causality is debatable.
At the cross-country level, evidence indicates that various measures of
financial development (including assets of the financial intermediaries,
liquid liabilities of financial institutions, domestic credit to private
sector, stock and bond market capitalization) are robustly and positively related to economic growth (King and Levine, 1993; Levine and Zervos,
1998). Other studies establish a positive relationship between financial
development and industrial growth (Rajan and Zingales, 1998). Even
the recent endogenous growth literature, building on ‘learning by doing’
processes, assigns a special role to finance (Aghion and Hewitt, 1998
and 2005).
For any productive activity, capital investment is vital and capital
investment is possible only when finance is available. The endogenous
growth literature stresses the importance of financial development
for economic growth as many important services are provided by a
country’s financial system. Thus, as part of our inclusive growth study
it is useful to examine if there is finance-growth nexus in our economy.
Before the nationalization of banks in 1969, most of the needy sectors,
viz, agriculture, small scale sector and other productive sectors were
deprived of the institutional finance. Major sections of the population
under these sectors were under the clutches of the money lenders.
So in a way they were mostly excluded from the growth process of
the economy because of their indebtedness. Now, after 60 years of
Independence of our country, although banking sector has developed
to a great extent, it is worth examining whether formal finance did play
any role in our growth process. At this stage, it is important to examine
the relationship between finance and growth at the aggregated level8.
The Model
Empirical work on causality between financial development and
economic growth is sparse, owing to a lack of sufficiently long time series
data for developing countries. Jung (1986) was among the first to test
for causality by applying a Granger-causality procedure. He used annual
data on per capital GNP and two measures of financial development: the
ratio of currency to M1 and the ratio of M2 to GDP, for 56 developed and
developing countries. However, his results were inconclusive because they varied according to the financial development indicator used and
the development level of the various countries. For example, using
the currency ratio as a measure for financial development, Granger
causality from financial development to economic growth in LDCs was
more frequently observed than the reverse and an opposite conclusion
was obtained for the developed countries. However, when the M2/
GDP ratio was used, causality from financial development to economic
growth was as frequently observed as causality from economic growth
to financial development both in LDCs and developed countries. Jung’s
test was conducted in a levels vector autoregression (VAR) framework
without testing for stationarity of the data. As data are very likely to
be nonstationary, Jung’s findings are debatable (Granger and Newbold,
1974). In a frequently-cited paper, Demetriades and Hussein (1996)
tested for cointegration among variables and used an error correction
model for 16 countries to test for a possible long-run causal relationship
between financial development and economic growth. Their findings
showed little evidence to support the view that finance leads economic
growth.
In the present paper, we examine the causal relationship between
financial and economic development from a time-series perspective
for India. For this, we apply the most current econometric techniques,
in particular testing causality applying cointegration tests and error
correction models after pre-testing for unit roots in all variables and
choosing the optimal lag order in our VAR system. These tests are
essential for attaining the proper inferences. We use three different
measures of financial development and relatively long annual time
series data. We also include a third variable, namely the share of fixed
investment in GDP, in the system. This allows us to test channels
through which financial development and investment are explaining
changes in the growth rate of per capita GDP beyond the sample period.
Measurement and Data Sources
Financial Development Indicators
Financial development is usually defined as a process that marks
improvements in quantity, and efficiency of financial intermediary services. This process involves the interaction of many activities and
institutions. Consequently, it cannot be captured by a single measure.
In this study we employ three commonly used measures of financial
development for the sake of testing the robustness of our findings.
The first, M3Y, represents the ratio of money stock, M3, to nominal
GDP. M3Y has been used as a standard measure of financial development
in numerous studies (Gelb, 1989, world Bank, 1989; King and Levine,
1993a, b; Calderon and Liu 2003). According to Demetriades and
Hussein (1996), this indicator accords well with McKinnon’s outside
money model where the accumulation of lumpy money balances is
necessary before self-financed investment can take place. However, it
conflicts somewhat with the debt-intermediation approach developed
by Gurley and Shaw (1995) and the endogenous growth literature,
because a large part of the broad money stock in developing countries
is currency held outside banks. As such, an increase in the M3/GDP
ratio may reflect an extensive use of currency rather than an increase
in bank deposits, and for this reason this measure is less indicative of
the degree of financial intermediation by banking institutions. Financial
intermediaries serve two main functions: to provide liquidity services
and saving opportunities, the latter being relevant for promoting
investment and consequently growth. For this reason, Demetriades
and Hussein (1996) proposed to subtract currency outside banks from
M3 and to take the ratio of M3 minus currency to GDP as a proxy for
financial development. On this basis, we chose QMY, the ratio of M3 minus currency to GDP, to serve as our second measure of financial
development.
Our third measure of financial development is PRIVY, the ratio
of bank credit to the private sector to nominal GDP. This indicator
is frequently used to provide direct information about the allocation
of financial assets. A ratio of M3 (including or excluding currency)
to GDP may increase as a result of an increase in private financial
saving. On the other hand, with high reserve requirements, credit to
the private sector which eventually is responsible for the quantity
and quality of investment and therefore to economic growth, may
not increase. Therefore, an increase in this ratio does not necessarily mean an increase in productive investments. Rather, the private credit
GDP ratio can be a better estimate of the proportion of domestic assets
allocated to productive activity in the private sector. Figure 6 shows
that M3Y had increased tremendously starting 1979 to reach around
90 per cent in 2008. However, the high M3Y rate does not necessarily
imply a larger pool of resources for the private sector and therefore
is not a good indicator of financial development, in contradiction, to
PRIVY. Two explanations for this behavior were given by Roe (1998).
The first is the possibility that the dominating state-owned banks did
not have a profit maximizing goal. The second is that banks preferred
to serve the interest of their non-private clients, and offered loans to
public enterprises even at the expense of their profitability. The latter
is most evidently related to the quantity and efficiency of investment
and hence to economic growth (Gregorio and Guidotti, 1995). PRIVY
has been used extensively in numerous works (King and Levine,
1993a, b, Gregorio and Guidotti, 1995, Levine and Zeroves, 1993,
Demetriades and Hussein, 1996, Beck et al, 2000 among others), with
different definitions of the stock of private credit depending on the
institutions supplying the credit.
Other Variables
Following standard practice, we use real GDP per capita, GDPPC,
as our measure for economic development (see Gelb, 1989, Roubini
and Sala-i-Martin, 1992, King and Levine, 1993a,b Demetriades
and Hussein, 1996). In addition to the per capita real GDP and the
financial development indicator, we introduced a third variable in
our VAR system, the share of investment in GDP, IY. This variable
is considered to be one of the few economic variables with a robust
correlation of economic growth regardless of the information set
(Levine and Renelt, 1992). Including the investment variable in our
regressions enables us to identify the channels through which financial
development causes economic growth. If financial development
causes economic development, given the investment variable,
then this causality supports the endogenous growth theories that
finance affects economic growth mainly through the enhancement
of investment efficiency. Furthermore, we can then test if financial development causes economic growth through an increase of
investment resources. We can examine this supposition indirectly by
testing the causality between financial development indicators and
investment on the one hand and between investment and economic
growth on the other. All the variables in our data set are expressed in
natural logarithms.
 |
Data Sources
We used the following data resources: All data have been obtained
from the Handbook of Statistics on Indian Economy published by the
Reserve Bank of India. Our sample covers the period 1950-2008; the
choice of this period is governed by data availability.
The Econometric methodology
Standard Granger Causality (SGC)
According to Granger’s (1969) approach, a variable Y is caused by
a variable X if Y can be predicted better from past values of both Y
and X than from past values of Y alone. For a simple bivariate model,
we can test if X is Granger-causing Y by estimating Equation (1) and
then test the null hypothesis in equation (2) by using the standard
Wald test.
 |
 |
Granger causality from variable j to variable i in the presence of
cointegration is evaluated by testing the null hypothesis that βij,k = αi
= 0 for all k in the equation where i is the dependent variable, using
the standard F test. By rejecting the null, we conclude that variable j
Granger-causes variable i. These tests differ from standard causality
tests in that they include error correction terms (ECTt-1) that account
for the existence of cointegration among the variables. At least one
variable in Equations (4) to (6) should move to bring the relation back
into equilibrium if there is a true economic relation, and therefore at
least one of the coefficients of the error correction terms has to be
significantly different from zero (Granger, 1988).
Empirical Results
Granger Causality Results
The first of our empirical work was to determine the degree of
integration of each variable. The ADF test results for the levels and
first differences are reported in Table 24. The results show that all the variables are nonstationary i.e. I(1) in their levels, but stationary in their
first differences.9
Table 24 : ADF Unit Root Test Results |
Variable |
ADF with trend and intercept |
Levels |
First differences |
ADF |
k* |
ADF |
k* |
LGDPPC |
-3.403 |
0 |
-6.827*** |
0 |
LPRIVATE |
-1.183 |
0 |
-6.922*** |
0 |
LM3Y |
-2.643 |
0 |
-7.976*** |
0 |
LQMY |
-1.969 |
1 |
-19.601*** |
0 |
LIY |
-3.836 |
0 |
-7.884*** |
0 |
LGDPPC, LPRIVATE, LM3Y, LQMY and LIY are the natural logarithms of real per capita GDP, share of credit to private sector in GDP, share of M3 in GDP, share of M3 minus currency outside of banking in GDP, and the share of gross fixed capital formation in GDP, respectively.
K* the optimal lag lengths chosen by Schwarz selection criterion with maximum of 9 lags.
*, **, and *** indicate significance at the 10% , 5% and 1% levels, respectively. |
The second step was to test for a cointegration relationship among
the relevant variables. The results of Johansen’s maximum eigenvalue
test (λmax) support the existence of a unique long run relation between per
capita GDP, the investment ratio and financial development under the
various measures of the latter. In all cases, we reject the null hypothesis
of a no-cointegration relationship at least at the 5% level (Table 25).
It is also observed from Granger causality test that the null hypothesis of finance does not lead to economic growth is rejected at 1% level of
significance. It also confirms that financial development leads to capital
formation.
Table 25 : Johansen Cointegration Test Results |
Variables |
λmax |
P* |
r* |
r = 0 |
r = 1 |
r = 2 |
LGDPPC, LIY, LPRIVATE |
29.809*** |
11.979 |
3.041 |
1 |
1 |
LGDPPC, LIY, LM3Y |
37.175*** |
10.289 |
2.333 |
1 |
1 |
LGDPPC, LIY, LQMY |
37.860*** |
10.606 |
1.927 |
1 |
1 |
*; **; *** indicate significance at the 10%, 5% and 1% levels, respectively.
λmax is the maximum eigen value statistic.
p* represents the optimal lag length based on AIC from the unrestricted VAR model.
r* is the number of co-integration vectors based on Johansen’s method. |
Table 26 : Results of Granger Causality Tests (Direct) |
|
Null Hypothesis |
Financial Development
Indicator |
Financial Development does not Granger cause income growth |
F-Statistic |
Prob |
LPRIVATE |
6.63*** |
0.003 |
LM3 |
9.69*** |
.0003 |
LMQ |
7.89*** |
0.001 |
Panel B |
|
Null Hypothesis |
Financial Development Indicator |
Income growth does not Granger cause financial development |
F-Statistic |
Prob |
LPRIVATE |
1.49 |
0.235 |
LM3 |
3.90** |
0.0264 |
LMQ |
1.49 |
0.236 |
***: significant at 1% level of significance;
**: significant at 5% level of significance |
Table 27 : Results of Granger Causality Tests (Indirect) |
|
Null Hypothesis |
Financial Development Indicator |
Financial Development does not Granger cause fixed capital formation share in GDP |
F-Statistic |
Prob |
LPRIVATE |
6.63*** |
0.003 |
LM3 |
9.69*** |
.0003 |
LMQ |
7.89*** |
0.001 |
Panel B |
|
Null Hypothesis |
Financial Development Indicator |
Fixed capital formation share in GDP does not Granger cause income growth given the financial indicator below |
F-Statistic |
Prob |
LPRIVATE |
1.49 |
0.235 |
LM3 |
3.90** |
0.0264 |
LMQ |
1.49 |
0.236 |
*, **, *** indicate significance at the 10%, 5% and 1% levels, respectively. |
In a nutshell, we have examined the causal relationship between
measures of financial development and real GDP per capita in India over
the past five decades. It is found that the null hypothesis of no causality
from financial development to economic growth was significantly
rejected in all the cases. The causality is mostly unidirectional since
the other direction of causality from economic growth to financial
development was not observed. Thus our results support our hypothesis
that institutional finance leads to economic growth in our economy. One
of the leading proponents of this theory is Joseph Schumpeter (1912)
who stated that well-functioning banks spur technological innovation
by identifying and funding those entrepreneurs with the best chances
of successfully implementing innovative products and production
processes.
Thus, the causality tests provide some preliminary evidence
that financial development leads to growth. But how strong are these relationships? What is the pattern of the response from one year to
the next? These questions can be answered within the framework of
impulse response analysis and analysis of variance decomposition of
the forecast errors, which we have also dealt with in this section.
We first report the results which demonstrate how the forecast error
variance of our focus variables can be broken down into components
that can be attributed to each of the variables in the VAR. It can be
observed from Table 28 that credit (LPRIVY) explains 63.5 per cent
of the forecast error variance of GDP (LGPDPC) and it becomes the
most important variable affecting economic growth whereas gross fixed
capital formation (LIY) as the second one explaining 18.4 per cent of
forecast error variance of GDP. It is also observed that LGDPPC explains
13.2 per cent of its forecast error variance. The fact that GDP growth
is explained by its past values suggests that current period economic
growth influences future growth trends or that the phenomenon is due
to a “lag effect” in the business cycle.
Table 28 also shows that both credit to private sector and fixed
capital formation appear to have strong lagged effects and are, to a
larger extent, explained by their own past values (around 67 per cent in
case of credit and 60 per cent in case of fixed capital formation). It is
interesting to note that economic growth explains more than 46 per cent
of the forecast error variance of M3 which appears to be quite logical.
However, the fact that credit to private sector contributes more than
gross fixed capital formation to GDP growth in India implies that its
primary source of growth is extensive use credit in the private sector.
Table 28 : Variance Decomposition Percentage of 20-year Error Variance |
Variance decomposition of |
After 20 years, % of Decomposition due to |
|
LGDPPC |
LIY |
LM3Y |
LPRIVY |
LQMY |
LGDPPC |
13.2 |
18.4 |
0.9 |
63.5 |
4.1 |
LIY |
9.1 |
60.0 |
1.3 |
27.4 |
2.2 |
LM3Y |
46.2 |
8.2 |
15.5 |
24.8 |
5.3 |
LPRIVY |
18.6 |
5.5 |
3.9 |
67.1 |
5.0 |
LQMY |
13.7 |
3.0 |
16.7 |
53.9 |
12.8 |
To investigate further the impact of credit on GDP growth as compared
to other variables, we then have used impulse response function to trace
the time paths of GDP in response to one-unit shock to the variables
such as three different financial indicators and gross fixed capital
formation. A graphical illustration of an impulse response function can
provide an intuitive insight into dynamic relationships because it shows
the response of a variable to a “shock” in itself or another variable over
time. For example, it allows us to examine how GDP growth responds
over time to a “shock” in credit and compare it with the effects on other
variables.
Chart 7 depicts the time paths of the responses of GDP growth
to “shocks” in financial indicators and gross fixed capital formation. It
can be observed that all the financial indicators have a positive impact
on economic growth. However, the response of GDP to a shock in
credit has a longer and stronger effect than other variables and series
is not convergent even after 20 years. On the other hand, impacts of
other financial indicators (viz., LM3 and LQMY) on growth are smaller
and “die out” quickly from the 3rd year. However, in this case also it is
found that gross fixed capital formation has second largest impact on
economic growth and the effect is longer as well.
Therefore, we can argue that financial development does promote
economic growth in India. It can also be argued that the innovations in
bank credit were the most important source of the variance of forecast
errors for economic growth. Similarly, economic growth was not found
to have greater impacts on investments (LIY) (LGDPPC explaining 9.1
per cent of forecast error variance on LIY) than bank credit, LPRIVY
(LPRIVY explains 27.4 per cent of LIY). This suggests that economic
growth have a greater influence on availability funds than investment
behavior.
It may also be observed that GDPPC also affects financial
development indicators. Table 28 shows that LGDPPC explains about
18.6 per cent of forecast error variance of bank credit, 46.2 of forecast
error variance of LM3 and 13.7 per cent of forecast error variance of
LQMY. Therefore, the above findings suggest that there is a bi-directional causality between GDP growth and financial development. In other
words, the empirical evidence provided in this study has supported the
view in the literature that financial development and economic growth
exhibit a two-way causality and hence is against the so-called “financeled”
growth hypothesis. However, it is also clear that the impact of
credit on GDP is stronger than the reverse situation as suggested by the
above impulse response function analysis.
Distribution of Credit across Sectors and Regions
In this section, we have discussed the distribution of formal credit
across sectors and regions, given the importance of formal credit. It is
necessary to examine now whether allocation of finance is equitable
across regions and sectors. This is because nature of distribution of
credit has a direct bearing on economic growth, which in turn can
impact on poverty and inequality.
 |
Thus, in this section we have examined the distribution of credit
across various sectors and regions of the country. It can be noticed
from Table 29 that there has been a gradual decrease in share of
agriculture credit over the years. The share had gone down from 14.8
per cent 1980 to 9.9 per cent in 2005, though it had gone up to 11.3 per
cent in 2008. The share of industrial credit had also gone down from
48.0 per cent in 1980 to 38.4 per cent in 2008. However, the share of
credit to services has gone up substantially from 37.2 per cent in 1980
to 50.4 per cent in 2005, although it has gone down marginally to 50.2
per cent in 2008.
Sector-wise region-wise allocation of credit as provided in Table
30, indicates that there has been a decline in credit to agriculture and
industrial sectors across all the regions over the period 1980-2008.
However, the share of credit to services sector has increased
substantially in all the regions during the period 1980-2008. This
change in the distribution of credit is in alignment with the growth
pattern of the economy showing significant contributions from the
services sector. But, if we consider from the point of sector-wise
dependency of population, it means agriculture getting marginalized.
The shares of agriculture in the northern region decreased marginally
while in the southern region it decreased substantially by around 10
percentage points. The share of agriculture credit in the Western
Region has remained below 10 per cent throughout the three-decade
period. Further, the share of agriculture credit to the Eastern Region
went down by around 3.0 percentage points.
Table 29 : Sector-wise allocation of credit (Percentage to total) |
Sector |
1980 |
1985 |
1990 |
1995 |
2000 |
2005 |
2008 |
Agriculture |
14.8 |
16.9 |
15.0 |
11.3 |
9.9 |
10.8 |
11.3 |
Industry |
48.0 |
42.0 |
47.6 |
48.0 |
46.5 |
38.8 |
38.4 |
Service Sector |
37.2 |
41.1 |
37.5 |
40.7 |
43.6 |
50.4 |
50.2 |
Total Bank Credit |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
Source : Basic Statistical Returns, Reserve Bank of India (various issues). |
Table 30 : Sector-wise Region-wise allocation of credit |
|
Northern
Region |
Southern
Region |
Western
Region |
Eastern
Region |
1980 |
Agriculture |
24.8 |
22.2 |
8.7 |
13.5 |
Industry |
39.3 |
44.0 |
62.7 |
59.4 |
Service Sector |
35.9 |
33.8 |
28.7 |
27.1 |
Total Bank Credit |
100.0 |
100.0 |
100.0 |
100.0 |
1985 |
Agriculture |
26.6 |
55.1 |
7.8 |
14.4 |
Industry |
37.0 |
21.2 |
43.1 |
51.7 |
Service Sector |
36.3 |
23.8 |
49.0 |
33.9 |
Total Bank Credit |
100.0 |
100.0 |
100.0 |
100.0 |
1990 |
Agriculture |
23.8 |
19.6 |
7.6 |
13.4 |
Industry |
40.7 |
43.3 |
55.7 |
50.2 |
Service Sector |
35.5 |
37.0 |
36.8 |
36.4 |
Total Bank Credit |
100.0 |
100.0 |
100.0 |
100.0 |
1995 |
Agriculture |
19.3 |
15.7 |
5.3 |
12.0 |
Industry |
42.3 |
42.2 |
55.2 |
47.6 |
Service Sector |
38.4 |
42.1 |
39.4 |
40.3 |
Total Bank Credit |
100.0 |
100.0 |
100.0 |
100.0 |
2000 |
Agriculture |
19.9 |
13.9 |
4.7 |
8.5 |
Industry |
39.9 |
39.9 |
54.5 |
45.4 |
Service Sector |
40.3 |
46.2 |
40.8 |
46.0 |
Total Bank Credit |
100.0 |
100.0 |
100.0 |
100.0 |
2005 |
Agriculture |
21.6 |
13.6 |
8.6 |
8.6 |
Industry |
30.4 |
33.9 |
74.0 |
36.0 |
Service Sector |
48.0 |
52.5 |
17.3 |
55.3 |
Total Bank Credit |
100.0 |
100.0 |
100.0 |
100.0 |
2008 |
Agriculture |
22.3 |
12.5 |
6.2 |
10.6 |
Industry |
32.1 |
32.9 |
45.0 |
37.1 |
Service Sector |
45.6 |
54.6 |
48.8 |
52.2 |
Total Bank Credit |
100.0 |
100.0 |
100.0 |
100.0 |
Source : Basic Statistical Returns, Reserve Bank of India (various issues) |
Access to bank finances can also be gauged from the creditdeposit
ratio which has been provided in Table 31. It can be observed
from the Table that there is wide disparity in CD ratio across the regions and states. The ratio remains above 80 per cent in the Southern
and Western Regions during 2009, which has increased from above
75 per cent in 1985. On the other hand, the situation is not encouraging
in the Northern Region where the CD ratio has become 68.5 per cent
in 2009, gone down from 76.1 per cent in 1980. The condition is much
worse in the Eastern, North-Eastern and Central Regions. While the
CD ratio in the N-E Region and Central Region remained constant at
36 per cent and 45 per cent, respectively during the three decade
period, it has gone down significantly from 56.1 per cent in 1980 to
48.9 per cent in 2009 in the Eastern Region.
Table 31 : Region-wise, State-wise Credit-Deposit Ratio |
Region/State/ |
1980-
(JUNE) |
1985-
(JUNE) |
1990 |
1995 |
2000 |
2005 |
2009 |
Northern Region |
76.1 |
63.7 |
54.8 |
48.6 |
51.1 |
59.5 |
68.5 |
Haryana |
66.1 |
67.6 |
61.2 |
45.5 |
42.4 |
51.4 |
61.5 |
Himachal Pradesh |
28.6 |
44.0 |
38.6 |
26.0 |
23.8 |
36.3 |
38.5 |
Jammu & bKashmir |
31.5 |
42.8 |
31.8 |
28.6 |
33.5 |
46.7 |
46.3 |
Punjab |
38.6 |
44.6 |
45.5 |
41.4 |
39.4 |
50.1 |
65.5 |
Rajasthan |
65.9 |
70.5 |
62.2 |
47.7 |
46.7 |
68.7 |
80.2 |
North-Eastern |
35.6 |
48.9 |
51.7 |
35.6 |
28.1 |
35.0 |
35.8 |
Region |
|
|
|
|
|
|
|
Arunachal Pradesh |
6.2 |
21.5 |
20.1 |
12.4 |
15.7 |
22.0 |
24.8 |
Assam |
40.6 |
53.3 |
55.5 |
38.7 |
32.0 |
35.3 |
38.3 |
Manipur |
25.1 |
70.5 |
69.9 |
58.2 |
37.4 |
42.4 |
38.7 |
Meghalaya |
14.1 |
26.5 |
24.6 |
17.0 |
16.3 |
43.6 |
27.6 |
Mizoram |
6.0 |
22.8 |
34.2 |
16.5 |
23.3 |
47.8 |
58.7 |
Nagaland |
23.7 |
39.6 |
42.6 |
37.8 |
15.3 |
22.9 |
30.7 |
Tripura |
51.3 |
72.9 |
72.2 |
47.5 |
25.7 |
28.6 |
29.8 |
Eastern Region |
56.1 |
52.0 |
52.6 |
47.1 |
37.0 |
45.5 |
48.9 |
Bihar |
41.8 |
41.7 |
40.0 |
32.5 |
22.5 |
27.7 |
27.3 |
Jharkhand |
- |
- |
- |
- |
- |
29.6 |
32.0 |
Orissa |
61.1 |
92.8 |
81.3 |
54.5 |
41.5 |
61.8 |
50.8 |
West Bengal |
60.9 |
51.9 |
54.9 |
53.9 |
45.5 |
52.3 |
60.8 |
Central Region |
45.7 |
52.7 |
52.8 |
39.0 |
33.9 |
40.8 |
44.8 |
Chhattisgarh |
- |
- |
- |
- |
- |
43.6 |
53.3 |
Madhya Pradesh |
52.0 |
62.6 |
68.6 |
49.6 |
49.1 |
43.6 |
57.4 |
Uttar Pradesh |
43.5 |
49.2 |
47.0 |
35.1 |
28.2 |
37.9 |
42.1 |
Uttarakhand |
- |
- |
- |
- |
- |
24.3 |
25.6 |
Western Region |
70.5 |
79.9 |
74.0 |
63.2 |
75.4 |
83.5 |
85.2 |
Gujrat |
51.8 |
54.7 |
61.3 |
46.6 |
49.0 |
46.5 |
63.2 |
Maharashtra |
79.2 |
90.8 |
79.7 |
69.5 |
86.4 |
94.9 |
90.8 |
Southern Region |
77.6 |
84.4 |
87.4 |
69.4 |
66.2 |
78.1 |
88.4 |
Andhra Pradesh |
71.5 |
78.3 |
87.1 |
73.0 |
64.2 |
74.8 |
97.6 |
Karnataka |
77.8 |
86.7 |
91.0 |
65.8 |
63.3 |
73.8 |
76.6 |
Kerala |
67.5 |
68.5 |
64.0 |
44.8 |
41.5 |
54.6 |
60.4 |
Tamil Nadu |
88.0 |
98.6 |
99.4 |
86.7 |
88.6 |
101.2 |
108.9 |
Source : Statistical Tables Relating to Banks in India, Reserve Bank of India (various issues) |
Financial Inclusion
A developed financial system broadens access to funds;
conversely, in an underdeveloped financial system, access to funds is
limited and people are constrained by the availability of their funds
and have to resort to high cost informal sources such as money lenders.
Lower the availability of funds and higher their cost, fewer would be
the economic activities that can be financed and hence lower the
resulting economic growth (Rakesh Mohan, 2006)10.
Financial inclusion can be defined as delivery of banking services
at an affordable cost to the vast sections of disadvantaged and lowincome
groups. In the case of credit, the proper definition of the
financially excluded would include households who are denied credit
in spite of their demand. Although credit is the major component,
financial inclusion covers various other services such as savings,
insurance, payments and remittance facilities by the formal financial
system to those who tend to be excluded.
Credit to farmer households is one of the important elements of
financial inclusion. As per the results of the All-India Debt and
Investment Survey (AIDIS), the share of non-institutional sources of
credit in total credit for cultivator households had declined sharply from about 93 per cent in 1951 to about 31 per cent in 1991, with the
share of money lenders having declined from 69.7 per cent to 17.5 per
cent. In 2002, however, the share of money lenders had again increased
to 27 per cent, while that of non-institutional sources rose to 39 per
cent (Table 32).
Coincidentally, it is also a fact that there has been a slowdown in
the rate of agricultural growth during the last decade and it is
particularly striking in respect of foodgrain production. Banks have
been mainly focusing on crop loans since the period of green
revolution. There is, therefore, reason to believe that financial
exclusion may have actually increased in the rural areas.
It can be observed from Table 33 that the share of direct accounts
with a credit limit of less than ` 25000 in total direct accounts declined
from 93.4 per cent in 1980 to 35.8 per cent in 2008. The decline in
share is observed across all the sectors.
It can also be observed from Table 34 that there is an inverse
proportional relation between size class distribution of land and nonindebtedness.
In other words, minimum size class had the lowest
inclusion.
Table 32 : Relative Share of Borrowing of Cultivator Households@ |
(per cent) |
Sources of Credit |
1951 |
1961 |
1971 |
1981 |
1991 |
2002$ |
Non-institutional |
92.7 |
81.3 |
68.3 |
36.8 |
30.6 |
38.9 |
of which : |
|
|
|
|
|
|
Money Lenders |
69.7 |
49.2 |
36.1 |
16.1 |
17.5 |
26.8 |
Institutional |
7.3 |
18.7 |
31.7 |
63.2 |
66.3 |
61.1 |
of which: |
|
|
|
|
|
|
Co-operative societies, etc. |
3.3 |
2.6 |
22 |
29.8 |
30 |
30.2 |
Commercial banks |
0.9 |
0.6 |
2.4 |
28.8 |
35.2 |
26.3 |
Unspecified |
- |
- |
- |
- |
- |
- |
Total |
100 |
100 |
100 |
100 |
100 |
100 |
@: Borrowing refers to outstanding cash dues.
$: AIDIS, NSSO, 59th Round, 2003.
Source : All India Debt and Investment Surveys. |
Table 33 : Percentage of Small Borrowal Account (` 25,000 and less) to
Total No. of Accounts |
Occupation |
1980 |
1990 |
2000 |
2008 |
Agriculture |
96.7 |
97.3 |
85.4 |
51.4 |
Industry |
56.9 |
83.4 |
69.4 |
34.4 |
Transport Operators |
68.6 |
81.4 |
48.2 |
14.6 |
Professional & other services |
96.5 |
91.6 |
76.5 |
22.0 |
Personal loans |
- |
- |
59.6 |
22.7 |
Trade |
86.3 |
96.0 |
77.1 |
44.0 |
Finance |
94.4 |
91.6 |
60.0 |
29.7 |
All others |
97.7 |
99.5 |
50.3 |
40.0 |
Total |
93.4 |
95.0 |
72.2 |
35.8 |
Note : For 1980, the small borrowal account is defined as ` 10, 000 and less.
Source : Basic Statistical Returns, Reserve Bank of India (various issues) |
Table 34 : Distribution of size-class wise indebtedness of Farmers Households - 2002 |
Size Class |
Number |
Per cent |
Included |
Excluded |
Total |
Included |
Excluded |
Total |
Upto 0.40 ha |
135820 |
169641 |
305471 |
44.5 |
55.5 |
100.0 |
0.41 to 1.00 ha |
129211 |
154399 |
283610 |
45.6 |
54.4 |
100.0 |
1.01 to 2.00 ha |
81920 |
78680 |
160600 |
51.0 |
49.0 |
100.0 |
Upto 2.00 |
346951 |
402720 |
749671 |
46.3 |
53.7 |
100.0 |
2.01 to 4.00 ha |
54409 |
39095 |
93504 |
58.2 |
41.8 |
100.0 |
4.01 and above |
32882 |
17447 |
50329 |
65.3 |
34.7 |
100.0 |
Above 2.00 ha |
37291 |
56542 |
143833 |
60.7 |
39.3 |
100.0 |
All sizes |
434242 |
459262 |
893504 |
48.6 |
51.4 |
100.0 |
Source : Situational Assessment Survey of Farmers (59th Round NSSO). |
Role of Self-Help Groups
The RBI recognized the problem of financial exclusion in the
Annual Policy Statement in 2005 and since then several initiatives
have been initiated in order to promote financial inclusion especially
in the groups of pensioners, self-employed and those employed in the
unorganized sector. Some of these include “no frills” account, a
simplified general purpose credit card (GCC), introduction of pilot
project for 100 per cent financial inclusion, etc. On the other hand,
NABARD has also taken several steps in this direction. The self-help
group (SHG) – bank linkage programme of NABARD is an innovative
programme. It started as a pilot programme in 1992. At present India has around 22 lakh SHGs under this programme (Dev, 2006),
comprising more than three crore poor households who are accessing
credit through commercial and cooperative banks. Every year six lakh
SHGs are added. The programme is now spread across the country.
Following the success of SHG-linkage programme as also the
Bangladesh Gramin Bank model, many of the NGOs have taken to
financial intermediation by adopting innovative delivery approaches.
Following the RBI guideline in 2000, commercial banks including the
RRBs are providing funds through micro-finance institutions for
lending to poor clients. In fact, MFIs have been playing an important
role in substituting moneylenders and reducing burden on formal
financial institutions.
With the objective of ensuring greater financial inclusion and
increasing the outreach of the banking sector, banks have been allowed
to use the services of NGOs, self-help groups, MFIs and other civil
society organizations as intermediaries in providing financial and
banking services through the use of business facilitator and
correspondent models. Provisions for this kind of financial
intermediation have opened up new and diverse avenues to address
the issue of financial inclusion by banks. SHG linkage programme has
already been successful in South India, viz., Kudumbasree programme
in Kerala and Velugu in Andhra Pradesh. This has not only been
successful in India, it is also popular and successful in countries like
Bangladesh, Thailand, Mexico and Brazil.
Postal Savings and Remittance
Apart from the banking system, the post offices in India also
provide the services of maintaining deposits and remittances. The
Indian Postal Service with 155,516 post offices at end-March 2005 is
the most widely spread post office system in the world. The numbers
of post offices were more than twice the number of bank branches in
the country with a large presence in remote areas. A post office in
India, on an average, served 7,046 persons at end-March 2005. Indian
post offices offer various types of small savings schemes and also
provide other banking and financial services. Small savings schemes include deposits of various maturities and public provident funds.
Other financial services include money order, international remittance,
mutual fund and postal life insurance. The number of savings bank
accounts with the post offices, which provide cheque facility, was
60.3 million, i.e., about 19 per cent of the savings accounts with banks
(about 320 million). The amount of savings deposits per account in
post offices was around ` 2,500 at end-March 2005 as compared with
around ` 15,000 with banks. This was because post offices largely
cater to the banking needs of the low income groups. Apart from the
savings bank accounts, post offices also offer several other financial
products.
Insurance Services
In most countries, a large segment of the population does not
have access to formal insurance services. Micro-insurance services in
a number of countries have begun to expand only in recent years. The
Insurance Regulatory and Development Authority (IRDA) has been
actively encouraging insurance services for low-income households.
In 2002, the IRDA established rural and social sector targets for
insurance companies. All insurers entering the business after the start
of the IRDA Act, 1999 are required to comply with the obligations
towards the rural and social sectors in a phased manner. In India, the
total number of life insurance policies (individual single premium)
was about 3.41 million in November 2007 (IRDA, 2008). This implies
that there are only around 3.1 policies per thousand persons. The
insurance penetration (insurance premium as percentage of GDP) in
India was relatively higher as compared with several emerging market
economies, but significantly lower than that in advanced economies.
Section VI : Conclusion and Policy Prescriptions
The study found that bank finance has been playing a major role
in our growth process. The empirical findings of the study suggest that
there is a bi-directional causality between GDP growth and financial
development. In other words, the study has supported the view in the
literature that financial development and economic growth exhibit a two-way causality and hence is against the so-called “finance-led”
growth hypothesis. However, the impulse response function analysis
undertaken by the study suggests that the impact of credit on GDP is
stronger than the reverse situation.
In the post-reform period, the Indian economy is elevated to high
growth path triggered mainly by the expansion of economic activities
across the sectors. However, there are some serious concerns about a
number of imbalances in the growth scenario – inter-sectoral, interregional
and inter-state. These imbalances have definitely a serious
impact on the goal of “inclusive growth” as envisaged in the Eleventh
Five Year Plan. The study reveals that still poverty ratio is very high
in the economy. There is no significant increase in employment in the
unorganised sector of the economy. The study also shows that while
the contribution of the agriculture sector in the real GDP has declined
fairly fast, the share of the employment in the agriculture sector has
not declined to that extent. As a result, the average productivity in this
sector has remained very low. Since a large section of the population
continues to be dependent on the agriculture sector, directly or
indirectly, this has serious implications for ‘inclusiveness’.
Inclusive growth implies delivering social justice to all,
particularly the disadvantaged groups. One aspect of social justice
is that all programmes that provide generalised access to essential
services such as health, education, clean drinking water, sanitation
etc. should be implemented in a way that ensures that disadvantaged
groups get full access to these services. Further, designing and
implementing schemes specifically targeted to these groups will go a
long way in achieving inclusive growth. This may need an innovative
approach of Public Private Partnership in providing basic needs to
these groups.
In this context, innovations are needed in products and services
which reduce costs, economise on energy and serve the needs of the
common man in an affordable manner. Innovations are also needed
in processes and delivery mechanisms, especially in government
delivery mechanisms which need to be redesigned so that they can deliver outcomes commensurate with the considerable resources they
now absorb.
In India, there is dominance of unorganized sectors such as,
agriculture, small and micro enterprises, weavers, artisans, craftsmen,
etc., which provide bulk of employment. This has been highlighted
by the National Commission for Enterprises in the Unorganised
Sector. In view of the predominance of informal-sector workers in
the workforce, there is an urgent need for expansion in the scope and
coverage of social security schemes for these unorganized workers so
that they are assured of a minimum level of social protection and ensure
their contribution for growth. Further, rapid growth can promote the
inclusiveness agenda if the growth is associated with faster growth
in agriculture, rural infrastructure and greater absorption of labour
in manufacturing. The latter requires a special thrust in the MSME
area. Inclusiveness will also be promoted by various ongoing social
sector oriented programmes aimed specifically at the weaker section
of the society. However, a much greater effort is needed to improve
the implementation of social sector programmes in the field. These
programmes receive assistance from the Central Government but
they are implemented by State agencies. Much greater devolution of
power to Panchayati Raj Institutions (PRIs) and Urban Local Bodies
(ULBs), together with effective participation by the local community
is needed to achieve better oversight and accountability. Progress in
governance agenda is critical to achieve the goal of inclusiveness and
should be given high priority by State Governments.
Financial institutions are to play crucial role in the overall
scheme of inclusive growth. The nexus between finance and growth
is well established and thus financial inclusion has taken a central
stage in the recent times. Innovation of different financial products
and process that increases the accessibility of common man to the
financial institution can be considered as sine qua non of inclusive
growth. Further, the financial institutions can play an important role in
the inclusive growth strategy in promoting innovations by providing
capital through various stages of product development. Infrastructure
constraints have been considered as a binding constraint on growth.
The promotion of infrastructure especially, in rural areas can be a
catalyst of inclusive growth through better delivery of social services
to the common man. Financial institution should play a crucial role in
infrastructure financing specially in rural areas.
In the globalised world order there has been interplay among
macroeconomic reforms, globalization and technology, which can
propel growth towards high trajectory. However, the synergetic
links among the sectors can be reaped for achieving inclusive
growth. Inclusive governance and inclusive growth go hand in hand.
Empowerment and participation of people through further activation
of Panchayati Raj Institutions and Urban Local Bodies can enable to
achieve inclusive governance and public accountability, thus ensuring
demand driven inclusive growth.
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Annexure : 1
Inter-state Comparisons of Growth Performance
Data Source and Methodology
NSDP and Per capita NSDP figures are taken from Central Statistics
Office web site and RBI Handbook of Statistics on Indian Economy. For
Maharashtra and Gujarat, latest figures were taken from the respective
Government sites.
Data pertains to the period 1980-81 to 2008-09. Since the series
follows three base year; 1980-81, 1993-94 and 1999-00, the first two
series is spliced to arrive at a general data series with base year 1999-00.
A simple splicing method is used to arrive at combined series.
For comparing economic performance across states, we have
looked into the decadal annual compounded growth rates of NSDP
and Percapita NSDP. The annual compounded rate of growth has been
worked out by applying the semi-log model with respect to time (t).
Accordingly, the following regression is run:
Log (NSDP) = a + bt, where b represents instantaneous rate of
growth. The compounded growth rate (r) is further arrived by applying
the following equation
r = ((antilog of b)-1)*100
Annex Tables
Annex Table 1 : Estimated Semi-log equation for NSDP of Indian States at different time period (Contd.) |
States |
1980-81 to 1989-90 |
1990-91 to 1999-00 |
2000-01 to 2008-09 |
Intercept |
Slope |
R2 |
Intercept |
Slope |
R2 |
Intercept |
Slope |
R2 |
Andhra pradesh |
10.5732 |
0.051527 |
0.842526 |
10.6341 |
0.051378 |
0.964858 |
10.2498 |
0.069682 |
0.98067 |
|
(216.00) |
(6.54) |
|
(195.00) |
(14.80) |
|
(104.00) |
(17.40) |
|
Arunachal Pradesh |
5.95267 |
0.078142 |
0.987864 |
6.45698 |
0.044635 |
0.858208 |
6.2195 |
0.056907 |
0.910578 |
|
(313.0) |
(25.5) |
|
(63.9) |
(7.0) |
|
(34.7) |
(7.8) |
|
Assam |
9.80228 |
0.032034 |
0.900289 |
9.94893 |
0.021723 |
0.961399 |
9.30635 |
0.051242 |
0.994261 |
|
(419.0) |
(8.5) |
|
(410.0) |
(14.1) |
|
(252.0) |
(34.8) |
|
Bihar |
10.0908 |
0.046374 |
0.9355 |
10.2884 |
0.019716 |
0.397433 |
9.31499 |
0.069664 |
0.856186 |
|
(378.0) |
(10.8) |
|
(76.0) |
(2.3) |
|
(34.3) |
(6.5) |
|
Chattisgarh |
– |
– |
– |
9.59694 |
0.025136 |
0.922899 |
8.39508 |
0.077911 |
0.982403 |
|
|
|
|
(173.0) |
(7.7) |
|
(84.8) |
(19.8) |
|
Goa |
7.21694 |
0.051007 |
0.808614 |
6.99611 |
0.080526 |
0.959159 |
6.72813 |
0.085471 |
0.978276 |
|
(133.0) |
(5.8) |
|
(75.5) |
(13.7) |
|
(52.6) |
(16.4) |
|
Gujarat |
10.3199 |
0.047291 |
0.750582 |
9.97502 |
0.076888 |
0.919983 |
9.22783 |
0.101023 |
0.992622 |
|
(173.0) |
(4.9) |
|
(78.9) |
(9.6) |
|
(112.0) |
(30.7) |
|
Haryana |
9.64176 |
0.060772 |
0.936346 |
9.82092 |
0.04606 |
0.962882 |
8.89974 |
0.091099 |
0.992915 |
|
(277.0) |
(10.8) |
|
(195.0) |
(14.4) |
|
(122.0) |
(31.3) |
|
Himachal |
8.34033 |
0.044418 |
0.834337 |
8.18222 |
0.060239 |
0.976722 |
8.0766 |
0.066539 |
0.99351 |
|
(192.00) |
(6.35) |
|
(158.00) |
(18.30) |
|
(149.00) |
(30.30) |
|
Jammu &Kashmir |
8.86246 |
0.019422 |
0.671891 |
8.6028 |
0.045464 |
0.996446 |
8.4406 |
0.051243 |
0.989902 |
|
298 |
4.05 |
|
569 |
47.4 |
|
162 |
24.3 |
|
Jharkhand |
– |
– |
– |
9.08807 |
0.062992 |
0.761319 |
8.474 |
0.080331 |
0.983812 |
|
|
|
|
(33.7) |
(4.0) |
|
(86.6) |
(20.6) |
|
Karnataka |
10.2283 |
0.05135 |
0.967014 |
10.0295 |
0.068452 |
0.985817 |
9.91112 |
0.0692 |
0.96744 |
|
(492.00) |
(15.30) |
|
(219.00) |
(23.60) |
|
(82.20) |
(14.40) |
|
Kerala |
10.1217 |
0.025487 |
0.758699 |
9.88953 |
0.056972 |
0.978393 |
9.29938 |
0.081749 |
0.986144 |
|
(321.00) |
(5.02) |
|
(210.00) |
(19.00) |
|
(95.50) |
(20.70) |
|
Maharashtra |
11.0159 |
0.054251 |
0.928298 |
10.9624 |
0.066653 |
0.975856 |
10.5308 |
0.080261 |
0.987564 |
|
(333.0) |
(10.2) |
|
(188.0) |
(18.0) |
|
(123.0) |
(23.6) |
|
Manipur |
7.01064 |
0.047103 |
0.989028 |
7.01452 |
0.045519 |
0.908185 |
6.73351 |
0.056317 |
0.971041 |
|
(644.0) |
(26.9) |
|
(87.0) |
(8.9) |
|
(68.9) |
(14.2) |
|
Meghalya |
7.00259 |
0.042617 |
0.944976 |
6.93646 |
0.054009 |
0.91647 |
6.94765 |
0.056485 |
0.995793 |
|
(310.0) |
(11.7) |
|
(76.3) |
(9.4) |
|
(188.0) |
(37.7) |
|
Madhya Pradesh |
10.2291 |
0.035133 |
0.875074 |
10.0585 |
0.054592 |
0.949548 |
10.1629 |
0.044431 |
0.937805 |
|
(351.0) |
(7.5) |
|
(143.0) |
(12.3) |
|
(88.4) |
(9.5) |
|
Nagaland |
6.59308 |
0.07199 |
0.957531 |
6.82925 |
0.054845 |
0.927216 |
6.34915 |
0.080624 |
0.989596 |
|
(198.0) |
(13.4) |
|
(79.7) |
(10.1) |
|
(65.2) |
(19.5) |
|
Orissa |
9.79262 |
0.046831 |
0.84239 |
9.73912 |
0.039493 |
0.911492 |
8.80232 |
0.080665 |
0.97913 |
|
(220.0) |
(6.5) |
|
(142.0) |
(9.1) |
|
(78.7) |
(18.1) |
|
Annex Table 1 : Estimated Semi-log equation for NSDP of Indian States at different time period (Concld.) |
States |
1980-81 to 1989-90 |
1990-91 to 1999-00 |
2000-01 to 2008-09 |
Intercept |
Slope |
R2 |
Intercept |
Slope |
R2 |
Intercept |
Slope |
R2 |
Punjab |
10.0537 |
0.052986 |
0.988873 |
10.1411 |
0.043407 |
0.993927 |
9.97336 |
0.049419 |
0.967785 |
|
(815.0) |
(26.7) |
|
(536.0) |
(36.2) |
|
(116.0) |
(14.5) |
|
Rajashthan |
9.99689 |
0.057704 |
0.804137 |
9.97146 |
0.06322 |
0.907635 |
9.85198 |
0.062781 |
0.89451 |
|
(160.0) |
(5.7) |
|
(88.7) |
(8.9) |
|
(48.1) |
(7.7) |
|
Sikkim |
– |
– |
– |
5.42528 |
0.061551 |
0.980684 |
5.05634 |
0.078219 |
0.999562 |
|
|
|
|
(82.0) |
(15.9) |
|
(307.0) |
(117.0) |
|
Tamil Nadu |
10.5695 |
0.048758 |
0.945066 |
10.463 |
0.062159 |
0.986675 |
10.158 |
0.071245 |
0.931188 |
|
(410.00) |
(11.70) |
|
260 |
24.3 |
|
(55.20) |
(9.73) |
|
Tripura |
7.15459 |
0.049262 |
0.856056 |
6.96475 |
0.070253 |
0.964515 |
6.82108 |
0.079646 |
0.991025 |
|
(161.0) |
(6.9) |
|
(92.7) |
(14.7) |
|
(83.6) |
(23.5) |
|
Uttar Pradesh |
11.1236 |
0.046774 |
0.966067 |
11.2542 |
0.034893 |
0.939536 |
10.8457 |
0.052339 |
0.977068 |
|
(578.0) |
(15.1) |
|
(228.0) |
(11.1) |
|
(142.0) |
(17.3) |
|
Uttaranchal |
– |
– |
– |
8.84264 |
0.025213 |
0.838858 |
7.63292 |
0.084926 |
0.991294 |
|
|
|
|
(105.00) |
(5.10) |
|
89 |
23.9 |
|
West Bengal |
10.6529 |
0.044855 |
0.969848 |
10.4036 |
0.066381 |
0.99631 |
10.4801 |
0.061152 |
0.990478 |
|
(614.0) |
(16.0) |
|
(462.0) |
(46.5) |
|
(174.0) |
(25.0) |
|
Annex Table 2 : Estimated Semi-log equation for PCNSDP of Indian States at different time period (Contd.) |
States |
1980-81 to 1989-90 |
1990-91 to 1999-00 |
2000-01 to 2008-09 |
Intercept |
Slope |
R2 |
Intercept |
Slope |
R2 |
Intercept |
Slope |
R2 |
TN |
9.00002 |
0.034036 |
0.88376 |
8.85386 |
0.051634 |
0.981065 |
8.50118 |
0.063054 |
0.911973 |
|
(332.00) |
(7.80) |
|
(221.00) |
(20.40) |
|
(45.70) |
(8.52) |
|
Kerala |
9.21375 |
0.011366 |
0.384004 |
8.93477 |
0.047132 |
0.972616 |
8.24323 |
0.076731 |
0.987155 |
|
(292.00) |
(2.23) |
|
(203.00) |
(16.90) |
|
(99.10) |
(23.20) |
|
Andhra |
8.91582 |
0.029837 |
0.641826 |
8.88983 |
0.036896 |
0.92826 |
8.24781 |
0.06757 |
0.97113 |
|
(182.00) |
(3.79) |
|
(156.00) |
(10.20) |
|
(74.50) |
(15.30) |
|
Karnataka |
8.93651 |
0.031308 |
0.907094 |
8.70271 |
0.052415 |
0.972982 |
8.35978 |
0.063469 |
0.94099 |
|
(407.00) |
(8.84) |
|
(179.00) |
(17.00) |
|
(55.40) |
(10.60) |
|
J&K |
9.39111 |
-0.005986 |
0.162299 |
9.1328 |
0.020043 |
0.97962 |
8.74666 |
0.036251 |
0.973206 |
|
(315.00) |
-1.24 |
|
(567.00) |
(19.60) |
|
(145.00) |
(14.80) |
|
Himachal |
9.21781 |
0.026372 |
0.626634 |
9.04662 |
0.042588 |
0.951929 |
8.9132 |
0.050299 |
0.990956 |
|
(206.00) |
(3.66) |
|
(170.00) |
(12.60) |
|
(195.00) |
(27.70) |
|
Uttaranchal |
– |
– |
– |
9.39742 |
0.006909 |
0.285687 |
8.17035 |
0.067477 |
0.99337 |
|
|
|
|
(112.00) |
(1.41) |
|
156 |
32.4 |
|
Punjab |
9.56424 |
0.034333 |
0.975848 |
9.64995 |
0.024495 |
0.981121 |
9.45257 |
0.032041 |
0.930127 |
|
(807.0) |
(18.0) |
|
(510.0) |
(20.4) |
|
(113.0) |
(9.7) |
|
Haryana |
9.41124 |
0.036497 |
0.840523 |
9.59058 |
0.022061 |
0.866616 |
8.59649 |
0.070312 |
0.989486 |
|
(270.0) |
(6.5) |
|
(199.0) |
(7.2) |
|
(125.0) |
(25.7) |
|
UP |
8.78877 |
0.023727 |
0.874486 |
8.91188 |
0.013273 |
0.716346 |
8.44767 |
0.033036 |
0.939464 |
|
(446.0) |
(7.5) |
|
(191.0) |
(4.5) |
|
(106.0) |
(10.4) |
|
Rajashthan |
8.79248 |
0.031674 |
0.54617 |
8.7571 |
0.039338 |
0.799405 |
8.24669 |
0.056017 |
0.853686 |
|
(139.0) |
(3.1) |
|
(79.7) |
(5.7) |
|
(37.4) |
(6.4) |
|
MP |
8.91503 |
0.011613 |
0.439717 |
8.71951 |
0.033045 |
0.871289 |
8.76638 |
0.025645 |
0.827397 |
|
(310.0) |
(2.5) |
|
(123.0) |
(7.4) |
|
(74.5) |
(5.4) |
|
Chattisgarh |
– |
– |
– |
9.20242 |
0.008878 |
0.603248 |
7.71054 |
0.074686 |
0.963087 |
|
|
|
|
(167.0) |
(2.8) |
|
(55.5) |
(13.5) |
|
Gujarat |
9.11397 |
0.0273 |
0.498463 |
8.76067 |
0.058569 |
0.865608 |
7.89998 |
0.087235 |
0.993791 |
|
(152.0) |
(2.8) |
|
(68.1) |
(7.2) |
|
(121.0) |
(33.0) |
|
Goa |
9.54376 |
0.035442 |
0.657214 |
9.32495 |
0.065341 |
0.936154 |
9.09767 |
0.067127 |
0.935977 |
|
(170.0) |
(3.9) |
|
(98.1) |
(10.8) |
|
(51.6) |
(9.4) |
|
Maharashtra |
9.20526 |
0.031594 |
0.821982 |
9.12126 |
0.04639 |
0.951133 |
8.57375 |
0.06556 |
0.97871 |
|
(285.0) |
(6.1) |
|
(156.0) |
(12.5) |
|
(93.3) |
(17.9) |
|
Bihar |
8.46617 |
0.025009 |
0.8122 |
8.7004 |
-0.004 |
0.033325 |
7.44796 |
0.057535 |
0.75299 |
|
(321.0) |
(5.9) |
|
(65.8) |
(-0.5) |
|
(23.8) |
(4.6) |
|
Jharkhand |
– |
– |
– |
8.47029 |
0.045862 |
0.632175 |
7.82624 |
0.064193 |
0.972903 |
|
|
|
|
(31.6) |
(2.9) |
|
(76.9) |
(15.9) |
|
Orissa |
8.85749 |
0.028816 |
0.670428 |
8.77366 |
0.023596 |
0.785458 |
7.51945 |
0.078879 |
0.967961 |
|
(200.0) |
(4.0) |
|
(128.0) |
(5.4) |
|
(55.2) |
(14.5) |
|
Annex Table 2 : Estimated Semi-log equation for PCNSDP of Indian States at different time period (Concld.) |
States |
1980-81 to 1989-90 |
1990-91 to 1999-00 |
2000-01 to 2008-09 |
Intercept |
Slope |
R2 |
Intercept |
Slope |
R2 |
Intercept |
Slope |
R2 |
West Bengal |
8.98146 |
0.023097 |
0.894323 |
8.67193 |
0.049493 |
0.991886 |
8.5843 |
0.052152 |
0.982582 |
|
(516.0) |
(8.2) |
|
(348.0) |
(31.3) |
|
(130.0) |
(19.9) |
|
Arunachal Pradesh |
8.7475 |
0.046875 |
0.965959 |
9.16177 |
0.020874 |
0.588061 |
8.56625 |
0.050063 |
0.920527 |
|
(453.0) |
(15.1) |
|
(94.1) |
(3.4) |
|
(61.3) |
(9.0) |
|
Assam |
9.24938 |
0.010899 |
0.49811 |
9.35591 |
0.003058 |
0.480132 |
8.7036 |
0.03382 |
0.988998 |
|
(385.0) |
(2.8) |
|
(527.0) |
(2.7) |
|
(257.0) |
(25.1) |
|
Meghalya |
9.05597 |
0.014292 |
0.706725 |
8.96752 |
0.027257 |
0.728313 |
8.40909 |
0.055695 |
0.98117 |
|
(448.0) |
(4.4) |
|
(96.7) |
(4.6) |
|
(115.0) |
(19.1) |
|
Tripura |
8.77992 |
0.020009 |
0.501878 |
8.44203 |
0.052895 |
0.915865 |
8.4698 |
0.056538 |
0.960433 |
|
(201.0) |
(2.8) |
|
(94.5) |
(9.3) |
|
(73.5) |
(12.1) |
|
Manipur |
8.99699 |
0.021184 |
0.951964 |
8.96394 |
0.023028 |
0.708844 |
8.56957 |
0.039074 |
0.959795 |
|
(862.0) |
(12.6) |
|
(109.0) |
(4.4) |
|
(113.0) |
(12.9) |
|
Nagaland |
9.18135 |
0.034124 |
0.821393 |
9.45135 |
0.010186 |
0.206276 |
9.4809 |
0.010627 |
0.403184 |
|
(263.0) |
(6.1) |
|
(84.9) |
(1.4) |
|
(68.1) |
(1.8) |
|
Sikkim |
– |
– |
– |
8.96817 |
0.032978 |
0.935847 |
8.2704 |
0.064281 |
0.998177 |
|
|
|
|
(136.0) |
(8.5) |
|
(317.0) |
(61.9) |
|
Annex Table 3 : Poverty Rates Across States (%) |
State/UT |
1973-74 |
1977-78 |
1983-84 |
1987-88 |
1993-94 |
1999-
2000 |
2004-05 |
Andhra Pradesh |
48.9 |
39.3 |
28.9 |
25.9 |
22.2 |
15.8 |
15.8 |
Arunachal Pradesh |
51.9 |
58.3 |
40.9 |
36.2 |
39.4 |
33.5 |
17.6 |
Assam |
51.2 |
57.2 |
40.8 |
36.2 |
40.9 |
36.1 |
19.7 |
Bihar |
61.9 |
61.6 |
62.2 |
52.1 |
55.0 |
42.6 |
41.4 |
Goa |
44.3 |
37.2 |
18.9 |
24.5 |
14.9 |
14.4 |
13.8 |
Gujarat |
48.2 |
41.2 |
32.8 |
31.5 |
24.2 |
14.1 |
16.8 |
Haryana |
35.4 |
29.6 |
21.4 |
16.6 |
25.1 |
8.7 |
14.0 |
Himachal |
26.4 |
32.5 |
16.4 |
15.5 |
28.4 |
7.6 |
10.0 |
Jammu |
40.8 |
39.0 |
24.2 |
23.8 |
25.2 |
3.5 |
5.4 |
Karnataka |
54.5 |
48.8 |
38.2 |
37.5 |
33.2 |
20.0 |
25.0 |
Kerala |
58.8 |
52.2 |
40.4 |
31.8 |
25.4 |
12.7 |
15.0 |
Madhya Pradesh |
61.8 |
61.8 |
49.8 |
43.1 |
42.5 |
37.4 |
38.3 |
Maharashtra |
53.2 |
55.9 |
43.4 |
40.4 |
36.9 |
25.0 |
30.7 |
Manipur |
50.0 |
53.7 |
37.0 |
31.4 |
33.8 |
28.5 |
17.3 |
Meghalaya |
50.2 |
55.2 |
38.8 |
33.9 |
37.9 |
33.9 |
18.5 |
Mizoram |
50.3 |
54.4 |
36.0 |
27.5 |
25.7 |
19.5 |
12.6 |
Nagaland |
50.8 |
56.0 |
39.3 |
34.4 |
37.9 |
32.7 |
19.0 |
Orissa |
66.2 |
70.1 |
65.3 |
55.6 |
48.6 |
47.2 |
46.4 |
Punjab |
28.2 |
19.3 |
16.2 |
13.2 |
11.8 |
6.2 |
8.4 |
Rajasthan |
46.1 |
37.4 |
34.5 |
35.2 |
27.4 |
15.3 |
22.1 |
Sikkim |
50.9 |
55.9 |
39.7 |
36.1 |
41.4 |
36.6 |
20.1 |
Tamil Nadu |
54.9 |
54.8 |
51.7 |
43.4 |
35.0 |
21.1 |
22.5 |
Tripura |
51.0 |
56.9 |
40.0 |
35.2 |
39.0 |
34.4 |
18.9 |
Uttar Pradesh |
57.1 |
49.1 |
47.1 |
41.5 |
40.9 |
31.2 |
32.8 |
West Bengal |
63.4 |
60.5 |
54.9 |
44.7 |
35.7 |
27.0 |
24.7 |
A&N Islands |
55.6 |
55.4 |
52.1 |
43.9 |
34.5 |
21.0 |
22.6 |
Chandigarh |
28.0 |
27.3 |
23.8 |
14.7 |
11.4 |
5.8 |
7.1 |
D&N Haveli |
46.6 |
37.2 |
15.7 |
67.1 |
50.8 |
17.1 |
33.2 |
Delhi |
49.6 |
33.2 |
26.2 |
12.4 |
14.7 |
8.2 |
– |
Daman & Diu |
– |
– |
– |
– |
15.8 |
4.4 |
10.5 |
Lakshdweep |
59.7 |
52.8 |
42.4 |
35.0 |
25.0 |
15.6 |
16.0 |
Pondicherry |
53.8 |
53.3 |
50.1 |
41.5 |
37.4 |
21.7 |
22.4 |
All India |
54.9 |
51.3 |
44.5 |
38.9 |
36.0 |
26.1 |
27.5 |
* Dr. P. K. Nayak is Assistant Adviser, REMD, DEPR Central Office and Shri Sadhan
Kumar Chattopadhyay, Shri Arun Vishnu Kumar and Smt. V. Dhanya are Research
Officers attached to DEPR, RO of Kolkata, Bangalore and Kochi, respectively. The
views expressed in this paper are of the authors and not of the organization they are
working with.
1 However, in the current period (2000 to 2009), some of these States, viz.,
Uttaranchal, Orissa, Nagaland, Jharkhand, Tripura, Sikim, and Chattisgarh have
performed better with more than 8 per cent growth rates.
2 Potential Output is defined as the maximum level of output that an economy can
sustain without creating macroeconomic imbalances.
3 See RBI Annual Report, 2009-10
4 The estimates of semi-log model for the three time periods taken are significant
for all states (Annex Table 1). Accordingly, the compound growth rates are calculated
for the states which are given in Annex Table 2.
6 We have followed the CSO allocation of sectors and accordingly construction
is included in the secondary sector.
7 HDI is based on three indicators, viz., GDP per capita (PPP US $), life expectancy
at birth, and education as measured by adult literacy rate and gross enrolment ratio
(combined for primary, secondary and tertiary education)
8 Ideally it should have been an analysis at the disaggregated level using panel data
framework, but due to time constraint and non-availability of data at the disaggregated
level we have done the exercise at the aggregated level.
9 Using Phillips-Perron test we obtained similar results.
10 Mohan, Rakesh (2006): ‘‘Economic Growth, Financial Deepening and Financial
Inclusion”, Presented at the Annual Bankers’ Conference at Hyderabad, November 2006.
|