SITEMAP FAQs GLOSSARY OTHER  LINKS CONTACT  US  
  ABOUT  US NOTIFICATIONS PUBLICATIONS DATABASE SPEECHES PRESS  RELEASES TENDERS FORMS EVENTS APPLICATION TRACKING SYSTEM  
Search


Home - Database
   
Note : To obtain an aligned printout please download the (922 kb) version to your machine and then use respective software to print the story.
Inclusive Growth and its Regional Dimension

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


1

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’.

2

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.

3

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.


4

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.


6

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.

2

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.

2
2

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.

5

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.


References

Aghion, P., and P. Hewitt (1998): Endogenous Growth Theory, MIT Press.

------- (2005): “Appropriate Growth Policy: A Unifying Framework”, The 2005 Joseph Schumpeter Lecture, European Economic Association, Amsterdam.

Ahluwalia, Montek S. (2000): “Economic Performance of States in Post- Reforms Period”, Economic and Political Weekly, May 6, 1637-48.

Ahluwalia, Montek S. (2002): “State Level Performance Under Economic Reforms In India”, in Anne Krueger, ed., Economic Policy Reforms and the Indian Economy, Chicago: University of Chicago Press.

Ali, Ifzai and Hyun Hwa Son (2007): “Measuring Inclusive Growth”, Asian Development Review, Vol.24, No.1, pp 11-31.

Balakrishnan, P. and Parameswaran, M. (2007): “Understanding Economic Growth in India: A Prerequisite”, Economic and Political Weekly, July 14, pp. 2915-2922.

------ (2007a): “Understanding Economic Growth in India, Further Observations”, Economic and Political Weekly, Nov.3, pp. 117-119.

Bhattacharya, B B and Arup Mitra (1990): “Excess Growth of Tertiary Sector in Indian Economy: Issues and Implications”, Economic and Political Weekly, November 3.

Bhattacharya, B.B, and S Sakthivel (2004): “Regional Growth and Disparity in India: Comparision of Pre-and Post-Reform Decades”, Economic and Political Weekly, Vol. 39, No. 10, pp. 1071-1077.

Beck, Thorsten, Ross Levine, and Narsan Loayzo (2000): “Finance and the Sources of Growth.” Journal of Financial Economics, 58 (1&2): 261-300.

Bolt, Richard (2004): “Accelerating Agriculture and Rural Development for Inclusive Growth: Policy Implications for Developing Asia”, ERD Policy Brief No 29, Asian Development Bank, Manila.

Bose, Amitava and Subhasankar Chattopadhyay (2010): “The Analytics of Changing Growth Rates”, Economic & Political Weekly, 10 July, pp 64-68.

Calderon, Cesar & Liu, Lin, (2003): “The direction of causality between financial development and economic growth,” Journal of Development Economics, Elsevier, vol. 72(1), pp. 321-334, October.

Commission on Growth and Development (2008): “Growth Report: Strategies for Sustained Growth and Inclusive Development”, World Bank, Washington, DC.

De Gregorio, Jose & Guidotti, Pablo E. (1995) : ‘‘Financial development and economic growth,’’ World Development, Elsevier, vol. 23(3), pages 433-448, March.

Demetriades, Panicos O. & Hussein, Khaled A. (1996): “Does financial development cause economic growth? Time-series evidence from 16 countries,” Journal of Development Economics, Elsevier, vol. 51(2), pages 387-411, December.

Dev, Mahendra S. (2006): “Financial Inclusion: Issues and Challenges” Economic and Political Weekly, October 14.

Dev, Mahendra S. (2007): “Inclusive Growth in India”, Oxford University Press.

Dholakia, Ravindra H. (2009): “Regional Sources of Growth Acceleration in India”, Indian Institute of Ahemdabad, W.P. No. 2009-03-06, March.

Gelb, A.H. (1989): “Financial Policies, Growth and Efficiency”, Policy, Planning and Research Working Paper (202), World Bank.

Gokarn, Subir (2010): “People, Jobs and Productivity: The ‘Simple’ Dynamics of Inclusive Growth”, Keynote address at CII Western Region’s Annual Regional Meeting and National Conference on “Achieving Double Digit Growth”, March 26.

Government of India (2006): “Towards Faster and More Inclusive Growth: An Approach to the 11th Five Year Plan”, Planning Commission, New Delhi.

------- (2009): Economic Survey, 2009-10, Ministry of Finance, New Delhi.

Granger, C. (1969): “Investigating causal relations by econometric models and cross-spectral methods”, Econometrica, 37:424–438.

Granger, C. W. J., and Newbold, P. (1977): “Forecasting Economic Time Series”, New York: Academic Press.

Gurley, J.G. and E.S. Shaw (1955): “Financial Aspects of Economic Development” The American Economic Review vol.45, September:515-538.

Jung, W.S. (1986): “Financial development and economic growth: international evidence”, Economic Development and Cultural Change, 34, 336-46.

King, Robert. G. and R. Levine (1993a): “Finance and Growth: Schumpeter Might Be Right”, The Quarterly Journal of Economics, August, 717-737.

King, R. and R. Levine (1993b): “Finance, entrepreneurship and growth: theory and evidence”, Journal of Monetary Economics, 513-542.

Levine, R and D., Renelt (1992): “A sensitivity analysis of cross-country growth regressions”, American Economic Review, 82 (4), 942-963.

Levine, R. and S. Zervous (1998): “Stock Markets, Banks and Economic Growth”, American Economic Review, Vol.88, pp 537-58.

Mishra, S (2006): “Risks, Farmers’ Suicide and Agrarian Crisis in India: Is there a way out?”, IGIDR Working Paper, No. WP-2007-014, September.

Mohan, Rakesh (2006): “Economic Growth, Financial Deepening and Financial Inclusion”, Presented at the Annual Bankers’ Conference at Hyderabad, November 2006.

Rajan, R.G. and L. Zingales (1998): “Power in a theory of the Firm”, Quarterly Journal of Economics, 113: 387-432.

------ (2003): “Saving Capitalism from Capitalists”, Crown Business, New York.

Rao, C H Hanumantha (2009): “Inclusive Growth: Recent Experience and Challenges Ahead”, Economic and Political Weekly, vol. XLIV No.13, March 28.

Ravallion, M. and S. Chen (2003): “Measuring Pro-Poor Growth”, Economics Letters, Vol.78, pp.93-99.

Reserve Bank of India (2010): First Quarter Review of Monetary Policy, 2010-11, July.

Roe Alan (1998): The Egyptian Banking System: Liberalisation, Competition, and Privatization”, in El-Erian and Mohieldin (ed.), Financial Development in Emerging Markets: The Egyptian Experience, Egyptian Centre for Economic Studies, Cairo.

Roubini and Sala-i-Martin (1992): Financial Repression and Economic Growth”, Journal of Development Economics, 39 (1), 5-30.

Schumpeter, Joseph (1912): The Theory of Economic Development, Cambridge, MA: Harvard University Press.

Singh, Nirvikar, Jake Kendall, R.K. Jain and Jai Chander (2010): “Regional Inequalities in India in the 1990s: Trends and Policy Implications”, DRG Study.

Subbarao, D(2010): “Comments at the Peterson Institute for International Economics”, Washington, DC, April 26.

United Nations Development Programme (2009): Human Development Report, 2009.

World Bank (1989): World Development Report. Oxford University Press, New York.

World Bank (2007): World DevelopmentReport. Oxford University Press, New York.


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.

5

 

 


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.

 
 Top
 
© Reserve Bank of India. All Rights Reserved.
Best viewed in 1024x768 resolution in IE 5 and above.