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India's growth and poverty performance over the last three decades has been a subject of great curiosity. Unlike the East Asian countries, India's growth spurt is not associated with exceptionally high domestic savings or foreign capital inflows or manufacturing exports. So what triggered the change in the growth trajectory? Did the market liberalization policies of the 1990s help? How have the initial conditions shaped the process? And how has the "Indian model" impinged on India's central problem of mass poverty? This paper surveys the literature and offers its own assessment of the drivers of change. (JEL I32, O13, O14, O15, O21, O47)
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Economic Liberalization and Indian Economic Growth:
What's the evidence?
May 2010
Ashok Kotwal
University of British Columbia, Vancouver
Bharat Ramaswami
Indian Statistical Institute, Delhi
Wilima Wadhwa
Indian Statistical Institute, Delhi and ASER Centre, Delhi
Acknowledgments: This paper was written at the Indian Statistical Institute (ISI), New
Delhi. Kotwal's stay at ISI was made possible by the generous support of Indo-Canadian
Shastri Institute and the Indian Statistical Institute. We are grateful for fruitful
discussions with: Siwan Anderson, Pulapre Balakrishnan, Laveesh Bhandari, Satya Das,
Patrick Francois, Subhashish Gangopadhyay, Chetan Ghate, B. N. Goldar, Tim
Hazeldine, Lakshmi Iyer, Ashwini Kulkarni, Annemie Maartens, Srijit Misra, Milind
Murugkar, Dilip Nachane, Manoj Panda, Arvind Panagariya, Avinash Paranjape, K. V.
Ramaswamy, Martin Ravallion, Mark Rosenzweig, M.R. Saluja, , Kunal Sen, E.
Somanathan, Rohini Somnathan, and Rajendra Vaidya. We are grateful for comments
received from participants of seminars at University of Melbourne, University of
Adelaide, University of Auckland and Claremont McKenna College. We would also like
to thank four anonymous referees and the Editor of this journal for their very helpful
1. Introduction
By the end of the 1970’s, India had acquired a reputation as one of the most protected and
heavily regulated economies in the world. Starting in the mid-1970s and then later on in
the 1980s, a few tentative steps were taken to liberalize the regulatory regime. In 1991,
more extensive reforms followed. Since then there have been further policy changes in
diverse sectors all aimed at opening up the economy to greater private sector
entrepreneurship as well as to foreign trade and investment.
These two decades (1980-2000) have been quite special in the course of Indian economic
development. The growth rate of GDP that had stayed around 3.5 % per annum for 20
years prior to 1980, shot up to about 5% in the eighties (1980 to 1989) and increased
further in the nineties (1990 to 1999) to 6%.
Over the last few years, it has reached as
high as 9%. Moreover, the growth in the post-reform period has also been stable. In the
decade of the 1970s, the variance in GDP growth rate was 15.8. It came down to 4.6 in
the 1980s (i.e., 1981-82 to 1990-91) and further down to 1.5 in the 1990s (1992 2002)
(Panagariya (2004)
Most importantly, GDP growth has been accompanied by a poverty decline. The
proportion of the population below the poverty line (at $1.08 a day in 1993 PPP USD)
declined from about 44.5% in 1983-84 to 27.5% in 2004-05.
Consequently, India’s
growth performance has generated tremendous worldwide interest as attested by the titles
of a spate of new books on India: India’s Emerging Economy (Kaushik Basu,2004),
India: Emerging Power (Stephen Cohen, 2001), India Arriving (Rafiq Dossani, 2008),
India: The Emerging Giant (Panagariya, 2008), Propelling India (Virmani, 2006).
The fast and stable growth accompanied by a decline in poverty has also raised many
questions: What triggered growth in India? What is the Indian model? Is it replicable in
other developing countries? Is it sustainable? How does it compare with the East Asian
model in its growth as well as distributional consequences? How does the growth process
impinge on India’s central problem – its mass poverty? Our objective in this paper is to
take stock of what progress the literature has made in answering these questions and
come up with a plausible story of Indian development during the period of 1980 – 2004.
India makes a fascinating case study. On the face of it, the improved growth
performance in India seems to have been achieved by following the orthodox prescription
of removing the constraints on entrepreneurship. However, Indian economic growth,
during 1980-2004, seems to have little in common with the so-called ‘Asian Model’. Its
savings rate has improved over time but has not reached the East Asian level
. Its
GDP is measured on a rainfall corrected basis. Source: Table 1.3, Page 31, OECD Economic Survey (India), 2007.
A corroborative account can be found in (Sinha and Tejani (2004)).
According to the latest revised estimates based on new purchasing power poverty norms released by the World Bank,
at the poverty rate of $1.25 (2005 PPP) a day, the poor as a share of the total population went down from 59.8% in
1981 to 41.6% in 2005.
Gross domestic savings as a percentage of gross national income rose steadily from 15.1% in 1960’s to 32.1% in 2004
while total capital formation rose from 16.9% to 33.2%. (Bosworth, Collins and Virmani (2007)).
growth so far has not been driven by manufactured exports. Nor has it attracted massive
inflows of foreign investment. There is no industrial policy targeted toward developing
specific industries. On the contrary, it is the service sector that has led the charge in the
Indian growth experience. Another aspect of the Indian experience that makes it very
different from that of other Asian countries is that despite a fast growing non-agricultural
part of the economy, the share of agriculture in the total labor force has declined very
slowly. In fact, the agricultural labor force in absolute numbers has increased since
1980’s, dampening the process of poverty decline.
Why do we expect economic liberalization to produce growth? First, import
liberalization provides domestic firms access to capital equipment embodied with new
technologies, better intermediate inputs and expands their choice set to act. A freedom to
invest and enter the market increases the extent of competition and puts pressure on the
incumbents to upgrade their technologies often through imported machinery. With the
entry of new firms in a more competitive market, the process of creative destruction goes
to work. Efficient firms drive out inefficient firms, factors gets reallocated to more
productive use increasing the overall productivity of factors in the economy. Due to
technology transfer, productivity in industry and service sectors grows rapidly attracting
labor from agriculture. The re-allocation of labor from agriculture to more productive
sectors contributes further to growth. This process also makes the workers left behind in
agriculture better off because the real wage rises as labor markets tighten in agriculture.
Is this what has been happening in India? One might think so. But do we see this in the
data? These are the motivating questions for this paper.
In Section 2, we highlight the structural features of the economy that are relevant for
thinking about the growth process at work in India. We argue that these features justify
attention towards a disaggregated picture of the Indian economy.
In Section 3, we outline the main constraints on entrepreneurs in the pre-reform period
and the most significant of the reform measures that loosened them.
In Section 4, we present the growth performance of the Indian economy (at the aggregate
level) over the last four decades and the debate about what may have triggered the growth
acceleration in the eighties.
Sectoral growth rates of output and employment (agricultural, industry and services) are
compared across sectors and time periods in Section 5. This section also pursues the
impact of economic reforms on the manufacturing and services sector. It lays out the
pattern of growth in the Indian economy and the features that distinguish it from other
countries. The section also attempts to answer the question of why the fast growth in
GDP in India has not been accompanied by fast growth in employment.
In Section 6, we examine how the growth process in India impinges on poverty decline in
the economy.
Section 7 discusses the role of agriculture in the growth and poverty decline process.
Section 8 concludes by examining various hypotheses proposed in the literature to
explain different aspects of the pattern of Indian growth experience since 1980 with a
view to piece together a coherent story about the movement of the Indian economy from
1980 to 2004. Given the quality of the available data and the usual difficulties in
establishing causality, the story can only be suggestive. Hopefully, it will throw up
hypotheses spurring further research.
2. Why Disaggregation is Necessary: The Unorganized Sector
In a handbook chapter titled “Growth theory through the lens of development” Banerjee
and Duflo (2005) argue against the aggregate production approach of growth theory to
study development. They point out that such an approach presumes well functioning and
complete markets while underdevelopment is synonymous with underdeveloped markets.
Those with potentially high returns to capital may not have access to credit. Wage gaps
can persist for a long time among workers with the same human capital across different
occupations or different industries. For instance, even illiterate farmers growing food
staples can earn much higher incomes in horticulture or animal husbandry but do not do
so because of lack of access to credit or information. Workers with higher education may
command considerably higher salaries and yet very few from amongst the poor are able
to acquire higher education. Wage gaps can persist across different states without
generating a substantial migration perhaps because of ethnic and linguistic gaps. In such
an economy, the constraints on entrepreneurial freedom can stem not just from
government over-regulation but also from the lack of well functioning markets and other
institutions. Reforms that get rid of over-regulation will set free those who have access to
the requisite factors. They will contribute to growth in a significant way but those
lacking in access will not.
These observations of Banerjee and Duflo are relevant because a distinctive feature of the
structure of Indian economy is the predominance of small production units including
household enterprises. Under the law, factories greater than a certain size have to register
themselves with the government and are subject to the Factories Act which regulates
safety, health and work hours of employees at the workplace. This regulation does not
apply to factories that either employ less than ten workers or employ less than twenty
workers and do not use electricity in the manufacturing process. The factories not under
the purview of the Factories Act are called unregistered or unorganized manufacturing
while those subject to the law are called registered or organized sector manufacturing.
Table 1 compares the organized and unorganized sectors of manufacturing at the end of
the 1990s. In terms of enterprises and workers, most manufacturing is carried out in the
unorganized sector. On the other hand, the organized manufacturing sector accounts for
most of the output and credit.
Subsequent to registration under the Factories Act, some firms may shrink to less than ten workers. However, they
continue to be classified as part of the registered manufacturing sector.
Table 1: Organized vs Unorganized Manufacturing
Year 1999-00 2000-01
Total # of enterprises (million) 0.13 17
Total # of workers (million) 6.2 37
Average enterprise size (# of workers) 52 2.2
Annual wages per worker (Rupees) 44842 4087
Total loans outstanding (Rupees billion) 25132 868
Value of output (Rupees billion) 87391 18718
Source: Nilachal Ray (2004).
In carrying over the distinction based on enterprise size to the rest of the economy,
government statistics on employment define the organized sector as all establishments
belonging to the government (and the public sector) and all non-agricultural
establishments in the private sector employing ten or more persons. The rest constitute
the unorganized sector. The national survey on employment estimated total employment
at 457 million in 2004-05. Of this organized sector employment is about 27 million, i.e.,
about 6% of the total.
Virtually all employment in agriculture is within the unorganized
sector. But even if agriculture is excluded, unorganized sector employment is as much as
83% of all non-farm employment. In terms of value-added, the unorganized sector
contributes 58% of national domestic product and 45% of non-farm domestic product
(Kolli and Hazra, cited in National Commission for Enterprises in the Unorganized
Sector, 2008).
If liberalization led India to switch to a higher growth path, the conduit is likely to have
been technology transfers from developed countries. Firms can import capital equipment
and intermediate inputs that they did not have access to earlier. Having access to foreign
technology and equipment and the freedom to use it, would give Indian firms an
opportunity to first jump the technology gap and then grow at the rate at which TFP
grows in the developed world. However, in the context of a large part of the economy
being in the unorganized sector, the question arises whether such small firms also had
access to superior technology. If not, how could they have gained from reform
Banerjee and Duflo (2004) have shown that Indian bank managers show abnormal
amount of risk aversion in lending to even medium sized firms. Many firms do not get
adequate credit (i.e., the marginal product of capital exceeds the interest rate) and capital
does not get channeled to where it could be best used. It is possible that this is so because
of the existing incentive structure for the bank managers. Whatever the reason, the point
A different survey-based estimate pegs the organized sector employment (in 1999-00) higher at 54 million – i.e.,
about 14% of total employment (National Commission for Enterprises in the Unorganised Sector, 2008). The
proportion is about the same for 2004-05.
to note is that if this is what medium sized firms have to face, how difficult it must be for
tiny units in the unorganized sector to get credit.
A credit constrained unorganized
sector may therefore not be able to take advantage of superior technology available off
the shelf. What is likely to be the pattern of growth in an economy where the organized
sector manages to improve its technology rapidly while the unorganized sector does not?
New imported technology is likely to be skill intensive. The investment in new
technology is thus associated with an increased demand for skilled workers driving their
wages up. Through collective bargaining the wages of the unskilled workers in the
organized sectors may also rise. But how would the majority of the workforce employed
in the unorganized sector benefit from reforms? There are several possible channels.
First, the part of the unorganized sector that is able to absorb new technology benefits
directly. For example, it is possible that even small units benefit from improved
communications such as due to cell phones. Second, cheaper products from the
organized sector increase the real wage of the workers in the unorganized sector who
consume these products. Third, the increased incomes of those employed in the
organized sector may spill over into demand for goods and services produced by the
unorganized sector. The strength of this ‘trickle down’ effect would depend on the
income elasticity of the relatively better off, for the unorganized sector goods and
services. The parts of the unorganized sector for which the income elasticity is relatively
high (e.g., trade, construction and transportation) would grow relatively fast. Note,
however, that the growth in this case may not be associated with TFP growth; all inputs
could increase as demand grows. However, even such a growth process in the
unorganized sector will draw labor from the less productive sectors – especially, ‘crop
agriculture’. And moving labor to a sector with higher productivity makes a contribution
to the overall growth in the economy. In fact, for a developing country with a large share
of its labor force in agriculture, this is a major source of growth. If all the above channels
are weak and if the growth is largely confined to the organized sector, the economy can
still grow rapidly because the organized sector still accounts for 42% of the value added
but it will have little impact on employment and hence on poverty.
This is why we need to examine the disaggregated picture. What processes were
unleashed by the reform measures that would move labor to more productive activities?
What are the skill intensities in the organized and unorganized sectors? What was the
impact on unskilled employment? What is happening to the structure of the labor force?
Is the educational system transforming unskilled labor into skilled labor at a fast enough
An important caveat to our observations (and a challenge to subsequent analysis) is that
the output statistics on the unorganized sector suffer from incomplete coverage, indirect
estimation methods, frequently outdated benchmark surveys and unknown biases
(Rangarajan, 2001), Shetty (2007)).
Priority sector lending – a government initiative that required nationalized banks to lend a certain proportion of their
deposits to the rural and small scale sector was motivated by the desire to overcome this problem.
3. The License-Permit-Quota Raj and Economic Reforms
The `license-permit – quota raj’ is a short-hand description of the licenses and quotas that
characterized Indian economic policies before 1991.
There were four major elements of
the pre-reform regime that were addressed by the reforms starting in 1991.
(i) Restrictions, in the form of tariff and non-tariff barriers on imports. Import
duties were among the highest in the world and rates above 200% were
common (Ahluwalia, 1999). Table 2 displays the effective rates of protection
for the period 1980-2000. There is a clear fall in the level of protection in the
1990s. The tariff revenue relative to import values fell from over 55% in the
late 1980s to about 22% by the end of the 1990s and to close to 10% in 2005
(OECD, 2007).
Non-tariff barriers worked through import licenses which automatically
restricted the amount that could be imported. Items that could be imported
without a license were placed under the Open General License (OGL). Table
3 from Das (2007) displays the percentage of imports that were subject to
non-tariff barriers over the period 1980-2000. Like tariffs, the non-trade
barriers also began to fall in the 1990s. The import restrictions were first
removed for the capital goods and intermediate goods sector in 1992. The
quantitative restrictions on consumer goods were lifted only in 2000.
Despite the fall in both tariffs and non-tariff barriers, import penetration rates
increased substantially only in the second half of the 1990s (Das, 2007).
Although the data in Tables 2 and 3 show substantial trade liberalization only
in the 1990s, it has been pointed out that some amount of loosening occurred
in the 1980s as well. The OGL, which was introduced in 1976, contained
only 79 capital goods in 1976. By 1988, it covered 1170 capital goods and
949 intermediate inputs. By 1990, about 30% of imports happened through
the OGL route (Panagariya, 2008).
The import policy in the pre-reform regime was supported by a policy of fixed
exchange rates and administrative allocations of foreign exchange. The
reforms of 1991 led to a transition to market determined exchange rates that
came into being in 1993. The rupee became convertible on the current
account in 1994 (Virmani, 2007). Restrictions on capital transactions,
however, remain.
There is a great deal of literature that documents the License Raj and the subsequent reforms in detail. Some of these
references include Ahluwalia, (1999), Ahluwalia, (2002), Basu (2004), OECD, (2007), Joshi and Little, (1996),
Panagariya (2008), Parikh, (2006), and Virmani (2007). While this section is drawn from the literature, it is not
comprehensive but is rather meant to give the reader a basic understanding of the restrictions on Indian entrepreneurs
and the scope of the reforms.
The phrase was coined by C. Rajagopalachari, a one-time political colleague and contemporary of Nehru, to convey
his distaste for state planning mechanisms.
Table 2: Effective Rates of Protection for Manufactured Goods (%)
Industry group 1980-85 1986-90 1991-95 1996-2000
Intermediate inputs 147 149 88 40
Capital goods 63 79 54 33
Consumer goods 102 112 81 49
Source: Das (2007)
Table 3: % of Manufactured Imports subject to Non-tariff Barriers
Industry group 1980-85 1986-90 1991-95 1996-2000
Intermediate inputs 98 98 42 28
Capital goods 95 77 20 8
Consumer goods 99 88 46 33
Source: Das (2007)
(ii) Restrictions on both the domestic and foreign private sector. Restrictions on
the latter took the form of prohibition of foreign direct investment in many
sectors of the economy. Where it was allowed, foreign equity in a company
was capped at 40%. Permission was essential for higher stakes. The
threshold level of foreign equity was first lifted to 51% in 1991 and later to
100% in most sectors. In addition, sectors such as mining, banking,
insurance, telecommunications, airlines, ports, roads and highways and
defense equipment were opened up to FDI.
Restrictions on the domestic sector were implemented via investment
licensing by which Central government permission was needed for investment
by incumbents as well as by prospective entrants. In addition, industrial
groups that were designated as ‘large’ could not expand without permissions
that had to be obtained under the Monopolies and Restrictive Trade Practices
(MRTP) Act. Some industry segments were ‘reserved’ for production by
small-scale units to protect them from competition from large-scale units.
Price and distribution controls were often applied to industries such as steel,
cement, fertilizers, petroleum and pharmaceuticals.
Selective exemptions from industrial licensing were granted even before 1991.
In 1975 and then again in 1980, automatic expansion of capacity and changes
in product mix were allowed to some industries. In 1985-86, further reform
measures were undertaken under Rajiv Gandhi (Prime Minister between 1984
and 1989): broad-banding of licenses by allowing firms to switch between
similar product lines, de-licensing of 30 industries, further relaxation of
capacity constraints for larger firms, and raising of the ceiling on the asset size
in plant and machinery of small scale enterprises (Panagariya, 2008, p. 83).
And of course, in 1991, there was comprehensive de-licensing and by the end
of the 1990s, approval was only required for investment in certain sectors
such as alcohol, tobacco and defense-related industries. The 1991 reforms
also did away with special permission needed under the MRTP for large
industrial houses. On the other hand, 'de-reserving' the industries set aside for
small enterprises proceeded at a slower pace and it was only in 2002 that
industry reservations were reduced substantially. The early 1990s also saw
the abolition of price controls in several industries including iron and steel,
coal, and phospatic and potassic fertilizers.
(iii) State control of banking and insurance. 14 leading private banks were
nationalized in 1969 and six more banks were also taken over by the State in
1980. This was accompanied by a strategy of massive expansion of the
banking network especially into rural unbanked locations, targets for lending
to `under-banked’ sectors such as agriculture, and extensive regulation of
interest rates. In addition, bank deposits were substantially pre-empted by the
State in the form of stiff stipulations on investment in government securities.
Through the 1990s, reforms have sought to dilute or reverse these policies. In
addition, banking licenses were granted to several private players.
(iv) Public sector monopolies. In the pre-1991 policy regime, eighteen important
industries including iron and steel, heavy plant and machinery,
telecommunications and telecom equipment , mineral oils, mining of various
ores, air transport services, and electricity generation and distribution, were
reserved for the public sector. With reforms, sectors reserved for public sector
enterprises were reduced to atomic energy, defense aircrafts and warships, and
railway transport.
The driving principle of the License Raj regime was ‘self-reliance’. This meant anything
that could be produced at home should not be imported irrespective of the cost.
Consequently, strong incentives were given to capital intensive industrial sectors where
India had no comparative advantage. The policy also had implications for the
educational priorities. Educational expenditure was heavily biased toward post
secondary education rather than toward primary education and mass literacy. As we will
see later, this lopsided educational structure happened to play an important role in the
mid nineties in the surprising development of the software and other high tech sectors in
India. However, the undesirable consequence was perhaps the disappointing
development of India’s labor intensive manufacturing sector.
4. Growth Acceleration
Figure 1 presents five-year averages of annual GDP growth rates from 1951 to 2004.
Except for the period 1960-64 when average GDP growth is just below 5%, the period
from 1951-79 saw average growth rates of less than 4%. In the period since 1980,
however, the economy has shifted to a higher growth path. Five year average growth
rates are higher than 5% in each of the sub-periods. During the entire period, GDP
declined on three occasions – 1957, 1965 and 1979. Such contractions have not been
observed in the post-1980 period.
Figure 1: Growth in GDP 1950-2004
50-54 55-59 60-64 65-69 70-74 75-79 80-84 85-89 95-99
90-94 2000-04
Rate of Growth of GDP (%)
Source: Authors calculations using National Accounts Statistics.
The growth acceleration can also be seen from Figure 2. In this figure (inspired by De
Long (2003)) GDP per capita is plotted for the period 1951-2005 together with the trend
line in this variable from the period 1951-1980. The departure from the trend is clearly
visible in the early 1980s.
The first period is the four year period of 1951-54.
11 The growth in the period up to 1980 was itself substantially higher than that achieved during the previous half-
century (Balakrishnan, 2007, Panagariya, 2008). But it is the growth acceleration around 1980 that has received recent
attention from scholars and others and which is of interest to us.
Figure 2: Growth Acceleration in GDP since the eighties
55 60 65 70 75 80 85 90 95 00 05
Per Capit a GDP, 1980=100
Tren d
Source: Authors calculations using National Accounts Statistics
Formal econometric tests also indicate a structural break around 1980. Using an F-test,
Wallack (2003) finds the highest value of the F-statistic in 1980. Rodrik and
Subramanian (2004) use a procedure of Bai and Perron(1998, 2003) and they report a
single structural break in 1979. Balakrishnan and Parameswaran (2007) also used the Bai
and Perron procedure and they too locate a single structural break in GDP in 1978-79.
The authors also estimate structural breaks for sectoral GDP. Their principal finding is
that structural break in agricultural output occurs in the mid-1960s while it occurs in the
early to mid-1970s for various sub-sectors of services.
On the other hand, the first
positive structural break in manufacturing occurs after the GDP break in 1982-83.
Basu (2008) and Sen (2007), however, point out that GDP fell by 5.2% in 1979-80 (due
to a combination of a drought and the second oil price shock). If this outlier is
disregarded, then the trend break occurs in 1975-76. The average annual growth rate
during the period 1975-78 is 5.8% - a rate more in line with the post-1980 experience
than with the earlier period.
Is the timing of the structural break important? The discussion in the literature about the
structural break takes place in the belief that it could offer clues about what policies led to
the shift in the economy’s growth rate. Such inference is problematic because statistical
methods alone are unlikely to provide a precise timing. Judgments about outliers, the
period of analysis, and the sectors that are considered, matter. An additional
complication is that policy measures do not have instantaneous results. The delay
would be especially pronounced if the benefits flow from a structural change. It is
therefore unwise to correlate the changes in economic variables to the policy changes that
immediately preceded them. These caveats notwithstanding, the economy does seem to
They find multiple structural breaks for the service sub-sectors including in the late 1950s or early 1960s and as well
in the 1990s.
have moved to a higher growth trajectory sometime in the mid to late 1970s or early
1980s, well before the economic reforms of 1991.What could have triggered the growth
acceleration in the 1980s (or earlier) when extensive reforms such as the abolition of the
industrial licensing system and trade liberalization happened only in 1991 and later? If
liberalization leads to growth because it encourages competition and entrepreneurship,
then what about the 1980s when reforms were so minimal that the business environment
of entrepreneurs was hardly much freer than in the earlier two decades. Yet the average
annual growth rate from 1980-81 to 1990-91 was not much different from that between
1991-92 and 2004-05 (5.8% and 6.1% respectively). What was driving growth in the
1980s? This is the puzzle, and the debate on Indian economic growth has thrown up
various explanations.
Creeping Liberalization
Panagariya (2008) argues that policies in the period since 1975 were marked by a gradual
retreat from the closed economy license raj model. The rigors of the industrial licensing
system were moderated by policies in 1975, 1976, 1980 and 1984. Similarly, import
controls on capital goods and on imports by exporters were made easier. These reforms
were piecemeal and limited compared to what came later in 1991. Yet, Panagariya
contends that they lifted business activity but in a modest manner appropriate to the
piecemeal nature of reforms. In particular, he argues that the impressive growth
performance of the 1980s stems only from the three-year period from 1988-89 to 1990-91
when economic growth averaged 7.6%. If this period is excluded, the average economic
growth during 1981-88 is 4.8% - a rate which is higher than growth in earlier periods but
much lower than the rate in the post-1991 period.
Fiscal Expansion
The period from 1984-1991 saw large fiscal deficits as government debt (internal and
external) ballooned. By 1990-91, the gross fiscal deficit stood at 10% of GDP (not
including the losses of public sector enterprises). Interest payments rose from 2% of
GDP and 10% of government expenditure in 1980-81 to 4% of GDP and 20% of
government expenditure in 1990-91. Joshi and Little (1994), Srinivasan and Tendulkar
(2003), Bajpai and Sachs (1999) among others have pointed to fiscal expansion as a
cause of unsustainable growth in the 1980s. Rodrik and Subramanian (2004) accept that
the fiscal stimulus could have led to greater demand for domestic goods and services and
hence economic growth but argue that this does not explain the sustained rise in total
factor productivity that is also observed during this period (Bosworth, Collins and
Virmani, 2007).
Changing Attitudes
For Rodrik and Subramanian (2004), the minor reforms of the 1980s were important for
what they signaled – an “attitudinal change” on the part of the government in favor of
private business. They date this change to 1980 when Indira Gandhi returned to a second
stint as Prime Minister after losing power in 1977. As evidence, they show that states
where governments were allied with the Central government grew faster than other states
– a pattern not found in earlier periods. However, this finding could be consistent with
other explanations as well – for instance when some states are favored over others for
infrastructure investment.
But if attitudes were so important, why did such small changes lead to big shifts in
growth? Here Rodrik and Subramanian draw on a cross-country regression of per capita
income on its “deep” determinants – namely measures of geography, openness, economic
or political institutions (but not both). They show that India is an outlier in the sense that
India’s income was about quarter of what it should be given its economic institutions.
India is even more of an under-achiever with respect to political institutions. Its income
is only about 15% of what is predicted by the regression. By unleashing the “animal
spirits” of the private sector, and by exploiting the quality of its existing institutions, the
attitudinal change was enough to shift the economy closer to the efficiency frontier.
Savings and Investment
Table 4 displays five-year averages of savings and investment rates over the period 1950-
2004. In the 1970s, the savings rate jumped up substantially. Figure 3 shows household
savings taking off in the early 1970s. Public savings also rise in the 1970s but the overall
rise in the savings rate is driven by household savings. However, the methodology of
computing savings in India is such that household savings is estimated as a residual and
therefore contains the errors and omissions in the other components. Therefore the
composition of savings is much less certain than the overall trend in savings.
Basu and Maertens (2007) conjecture that this could have been because of nationalization
of major banks in 1969. Between 1971 and 1981, the number of bank branches nearly
tripled. The population per bank branch declined from 65,000 in 1969 to 15,000 in 1984.
Athukorala and Sen (2002) estimate that a 1% increase in bank density, resulted in a
0.03% increase in the private saving rate.
A related study is by Burgess and Pande
(2005) who argue that the branch expansion did in fact decrease rural poverty. From
1977 to 1991, the central bank (Reserve Bank of India) followed a policy that forced
banks to favor branch openings in areas that were unbanked. As a result, bank expansion
during this period followed a pattern very different from what was observed when this
regulation was not in effect. Burgess and Pande use this variation to analyze the impact
on poverty outcomes. This impact presumably works through greater access to credit for
The increase in bank branches is also associated with an increase in bank deposits as a percentage of national income
(from 15.2% to 37.9% of national income).
rural households and therefore this paper does not throw light on the mechanisms by
which rural banking could have raised household savings.
Table 4: Saving and Investment as a % of GDP – 1950-2004
Period Savings
Gross Capital
1950-54 9.63 10.01
1955-59 11.16 13.89
1960-64 12.96 15.18
1965-69 13.97 15.60
1970-74 16.89 17.50
1975-79 21.11 21.30
1980-84 19.69 21.49
1985-89 22.12 25.75
1990-94 24.63 26.23
1995-99 25.77 26.79
2000-03 30.32 29.50
Source: Authors calculations using National Accounts Statistics.
Figure 3: Sectoral Saving Rates – 1950-2004
50 55 60 65 70 75 80 85 90 95 00
Private Corporate
Saving as a proport ion of G DP
Source: Authors calculations using National Accounts Statistics.
The rise in the savings rate is closely matched by the rise in investment rates. Gross
capital formation rises from 15.6% of GDP during the period 1965-69 to 21.5% in the
period 1980-84. As Figure 4 shows, it is public investment that picks up in the mid-70s
while private corporate investment begins to shift up only in the early 1980s.
Sen (2007) shows that the increase in capital formation in the mid-1970s was due to a rise
in equipment (machinery) investment. Till the late 1970s, the investment rate in
structures was higher than in equipment. The relationship reversed in subsequent
periods. The significance of this result is the conclusion from cross-country research that
among different types of investment, it is equipment investment that matters most for
economic growth (De Long and Summers, (1991)).
By estimating an investment function, Sen explains the increase in private equipment
investment as due to (a) a fall in the relative price of capital equipment (b) financial
deepening as measured by real domestic credit to the private sector, and (c) public
investment (measured as a proportion of GDP). Sen attributes financial deepening to the
banking expansion of the 1970s and 1980s. As banks were able to access household
savings, they could also extend credit to households as well as to corporations. The fall
in the relative price of machinery is explained by the limited trade liberalization of the
1980s. Relaxation of import controls increased access to imported machinery.
Figure 4: Sectoral Investment Rates – 1950-2004
50 55 60 65 70 75 80 85 90 95 00
Private Corporate
Investment as a proportion of GDP
Source: Authors calculations using National Accounts Statistics.
Given that the informal sector forms such a large part of the Indian economy, it is
important to know something about its sources of credit. Many of these are informal
sources of credit for which micro-finance has emerged as a possible alternative. Since the
mid-eighties, NABARD (National Bank for Agriculture and Rural Development) – a
development bank set up by the Government of India and financed by the Reserve Bank
of India -- has been actively engaged in a program to link mainstream banks with SHGs
(‘Self Help Groups’). Recently the funding for this program has gone up significantly in
13 priority states that account for 70% of India’s poor. By March 2006, 2.2 million
SHGs had been linked to mainstream banks and 33 million poor households had gained
access to microfinance. NABARD is also assisting other partner organizations like NGOs
and co-operative banks in promoting SHGs. By 2006, a cumulative assistance of Rs.
334.6 million for the promotion of 250,000 groups has been granted by NABARD
Green Revolution
From about the mid-1960s, high-yielding fertilizer responsive varieties of wheat and rice
(the principal food staples in India) diffused through the agricultural economy. This
formed the basis for the Green Revolution. By 1992-93, the diffusion was complete
with about 90% of wheat area and 70% of rice area occupied by these high yielding
varieties (HYVs). In the case of wheat, much of the diffusion had happened by 1975
when diffusion exceeded 60% (see Figure 5). In the case of rice, the diffusion was
slower and similar thresholds were reached only in the early 1990s.
The productivity impact of these varieties has been much discussed in the literature
(Evenson, Pray and Rosegrant, 1999, Lipton and Longhurst, 1989). As these varieties
increased the productivity of inputs such as fertilizers and water, it was the combined
impact of HYVs together with these inputs that led to higher yields. In the period since
the mid-1960s, output growth in food crops has been powered by yield increases rather
than area.
In a closed economy (as India was during the 1970s and 1980s), where low incomes are
spent primarily on food staples (consistent with Engel's law) and where land is a
constraint to food production, an increase in food productivity necessarily reduces food
prices, increases agricultural wages and rents and increases the size of the non-farm
sector through greater demand for its products. Could this have played a role?
Figure 5: Share Under High Yielding Varieties (%)
Source: Evenson, Pray and Rosegrant (1999),
Rodrik and Subramanian (2005) dismiss this possibility because the terms of trade for
agriculture did not deteriorate in the 1980s. However, agricultural terms of trade did
decline from about the early 1970s (when the Green Revolution's impact became
apparent) to about the mid-1980s. More strikingly, relative prices of wheat and rice the
staple foods declined from the mid-1970s to 1991 (see Figure 6). The decline is
particularly pronounced for wheat which was the greater success story of the Green
Revolution. The decline was not sustained beyond 1991 partly because of exhaustion of
this source of technological change and also because of government interventions in the
immediate pre-reform period that increased these prices.
Although it is clear that GDP growth rates increased sometime in the ‘70s or early ‘80s,
the precise timing is hard to establish and depends on one’s prior. Various explanations
have been proposed and it is impossible to be sure which of these is the most important
one. The economic orthodoxy would favor one that credits trade liberalization, limited as
it was, that decreased the cost of capital equipment but it is hard to disentangle the effects
of this from more heterodox factors such as public investment and rise in savings rate
(due to bank nationalization), the diffusion of agricultural technology (entirely due to
public research and dissemination) or indeed to rule out the role of political attitudes
towards business. It is also indisputable that there was an unsustainable fiscal expansion
through 1980’s and any income growth resulting from it should be considered
qualitatively different from the much more sustainable growth that occurred in the next
Figure 6: Relative Price of Rice and Wheat – 1952-1998
55 60 65 70 75 80 85 90 95
Price relative to whole price index of all commodities
Source: Misra (2004),
5. The Impact of Reforms
The reforms that began in 1991 completely changed the direction of economic policies.
As explained in Section 3, India moved away from a state-led closed economy
framework in favor of greater integration with the world economy, lesser controls on
private business activity especially in manufacturing, and substantially lower entry
barriers to prospective entrants, whether domestic or foreign.
In principle, the removal of licensing and the barriers to trade, should allow greater
competition as well as access to cheaper factor services. TFP should rise and as
inefficient firms exit, factors should get reallocated to their most productive use further
increasing TFP. Did this happen?
It should be noted that an entrepreneur in the pre-reform period was subject to many
controls which operating together would have been more restrictive than the sum of the
effect of any one of them separately. Therefore, the success of a reform measure that lifts
a constraint depends crucially on the existence of other constraints that may still persist.
The impact of liberalization of any one of the controls (say an industrial license) would
be limited unless the other controls (such as import licenses) were relaxed as well.
Similarly, lowering of tariff on inputs to a particular industry may not pay the same
dividend if the industry is still under small scale reservation policy that disallows large
manufacturing plants. According to the theory of second best, under certain
circumstances even the coefficient of a reform measure could have a wrong sign. It is
therefore important to consider the interaction among controls and their liberalization in
analyzing the impact of reforms.
Manufacturing Sector
GDP and its components are depicted in Figure 7. It can be seen that since the 1980s, it
is the services sector that is both the dominant sector as well as the fastest growing sector
in the economy. Table 5 presents sectoral shares in value-added and employment while
similar information for growth rates is displayed in Table 6. In 2004-05, manufacturing
accounted for only 17% of value-added and 12% of employment not materially different
from the scenario in 1993-94. Panagariya (2004) argued that the main reason why Indian
growth was slower than in China was the lackluster performance of India’s
manufacturing sector. Kochhar (2006) make the same point by examining the
performance of manufacturing across two points in time – 1981 and 2002. They find
that the share of manufacturing in GDP in India was higher in 1981 (although not
strongly significant) than what would be predicted by a cross-country regression of the
sectoral share on income and country size. Repeating the regression for 2002, the authors
find the coefficient of the India dummy to be smaller than in 1981. However, in a
regression of the change in the share of manufacturing (in value-added), on initial GDP
and GDP growth rate, the India indicator is negative prompting the authors to conclude
that “a pattern of a relative slowing in manufacturing growth is suggested by the data,
ironically when reforms were removing the shackles in manufacturing”. A similar
paradox comes through in TFP estimates. TFP growth rates in manufacturing are
sensitive to a variety of measurement issues; however, estimates by different authors
agree that TFP grew slower in the 1990s compared to the 1980s (Goldar, 2006).
Figure 7: Sectoral GDP, 1960-2004
60 65 70 75 80 85 90 95 00 05
Rs. Billion in 1993-9 Prices
Source: National Accounts Statistics.
Table 5: Sectoral Shares in Value added and Employment, 1983-2004
Year Value Added as a % of GDP % of Total Employment
Manuf Services Agriculture
Manuf Services
1983-84 38.69
1993-94 30.97
1999-2000 24.99
2004-05 20.21
Table 6: Average Sectoral Rates of Growth 1973 – 2004
Period Value Added Employment
Manuf Services Agriculture
Manuf Services
1973-83 2.48 4.72 4.80
1983-93 3.02 6.11 6.48 1.61 2.01 3.85
1993-99 3.22 7.88 8.07 0.44 1.29 3.25
1999-2004 1.57 6.00 7.55 1.71 5.21 4.62
1993-2004 2.12 5.86 7.79 1.02 3.05 3.87
Notes to Tables 5 – 6:
GDP figures are at constant 1993-94 prices from National Account Statistics.
Employment figures are calculated using the usual primary plus subsidiary status from the employment
surveys of the NSSO, adjusted for population.
Agriculture: agriculture, forestry and fishing.
Manufacturing: registered + unregistered (does not include electricity, gas and water).
Services: construction; trade, hotels & restaurants; transport, storage & communication; financing,
insurance, real estate & business services; community, social & personal services.
GDP rates of growth are average trend growth over the relevant period.
Employment rates are annualized from the point to point rates of growth.
The reason for choosing these years and periods is that the employment figures are taken from NSSO (National
Sample Survey Organisation) employment surveys. These surveys are carried out every five years and they are reliable
for the years of the survey (e.g., 1983, 1987, 1993, 1999-00, and 2004-05). The data for the in-between years are based
on ‘thin rounds’ (with smaller samples) and interpolation. Output figures are taken from National Accounts Statistics
(NAS) and are more reliable for the organized sector than for the unorganized sector.. The data for the organized sector
come from the annual reports filed by the firms in the organized sector (Annual Survey of Industries (ASI) data).
However, about 44% of the value added and 88% of the employment in the non-farm sectors come from the
unorganized sector (with fewer than 10 workers in a plant with power or 20 workers in a plant without power). The
method by which the output in the unorganized sector is computed is a bit circuitous. Output per worker is taken from
‘enterprise surveys’ also conducted by NSSO once every few years. (The unorganized sector has no legal requirement
to submit a report and it is widely believed that there is under reporting of the value added. CSO adjusts these figures
upwards using its own rules of thumb and we do not know whether the available figures have an upward or downward
bias.) Labor input is available for the NSS years. With those years as benchmark years, labor input in the in-between
years is computed by interpolation. Output for those years is computed as a product of output per worker and labor
input. It is needless to say that this computation procedure makes the output figures for the unorganized sector much
less reliable than those for the organized sector. The numbers are relatively more reliable for the years of the
quinquennial surveys since the labor input values are more reliable for those years. It is therefore preferable to look at
changes across these points in time. However, often the time spans such as eighties and nineties are used in the
literature partly for convenience and partly because some policy changes took place at the beginning of those decades.
For example, the eighties began with the Industrial Policy Statement (July 1980) and of course 1991 was the year of the
IMF induced reforms.
The less than sparkling performance of the manufacturing sector has provoked a literature
seeking to explain it. Besley and Burgess (2004) examine the role of labor market
regulation to explain manufacturing performance in Indian states between 1958 and 1992.
Their basic regression is of the following form:
where s indexes the state and t indexes year, y is an outcome variable (such as output of
organized manufacturing sector),
is a state fixed effect,
is a year fixed effect, r is
the labor regulatory measure lagged by one year and x are other control variables. The
regulatory measure is constructed on the basis of coding state-level amendments to a key
central government legislation – the Industrial Disputes Act.
Each amendment is coded
as being either neutral, pro-worker or pro-employer and assigned a numerical value of
zero, one and minus one, respectively. The state level scores are cumulated to obtain a
regulatory measure that evolves over time. Besley and Burgess (BB) find that registered
manufacturing (which is the target of regulation) output is negatively affected by the
regulatory measure. On the other hand, unregistered manufacturing output is positively
affected by greater labor regulation suggesting that regulation encourages firms to remain
small and be within the unorganized sector.
The BB study does not examine the impact of economic reforms on the manufacturing
sector. However, the idea of BB to use variation in labor regulations across states to
examine their influence on manufacturing output and employment has been carried
forward in many studies. In these studies, manufacturing output or productivity (usually
disaggregated at a three digit level) is regressed against a policy variable (industrial de-
licensing dummy or trade tariffs) and other controls. Often the major point of interest is
not the direct impact of labor regulation but the impact of economic reforms conditioned
on labor market institutions. Therefore, the policy variable is interacted with a variable
that measures labor market regulation.
Aghion, Burgess, Redding and Zilibotti (2003), study the impact of industrial de-
licensing on output using data from the Annual Survey of Industries (ASI).
estimate a regression of the form
++++= ))((
where i indexes industry (at 3-digit level), s indexes state and t indexes year (during the
period 1980-97). y is log of output,
is a state-industry fixed effect,
is a industry-year
Under the constitution, both the central and state governments have the power of legislating labor laws. The
Industrial Dispute Act (IDA) is a central government legislation and it provides the machinery and procedure for the
investigation and settlement of industrial disputes. The IDA has been amended by the central government a number of
times although none have occurred after 1984. A key amendment which is often cited as causing rigidity in the labor
market was in 1976. This amendment specified that prior approval of the government was necessary in the case of
layoffs, retrenchement and closure in industrial establishments employing more than 300 workers. The threshold level
was later lowered to 100 by an amendment in 1982 (Anant, 2006).
The coverage of the ASI data is restricted to the organized manufacturing sector.
is a state-year interaction, r is the labor regulation index and d is the de-
licensing dummy. The de-licensing dummy takes the value 1 in the year that the industry
is de-licensed and retains that value for subsequent years. The labor regulation measure
is the BB index that is updated to 1997.
Note that the de-licensing dummy does not
vary across states and the regulation index does not vary across industries. Hence their
average impacts cannot be estimated in the above specification. Replacing the
by a year fixed effect and dropping the interaction terms between de-licensing
dummy and the regulation index, Aghion find that de-licensing and regulation have
opposite and almost equal effects on the number of factories. Thus, de-licensing does
have a positive effect on entry and competition but the effect is masked by labor
regulation. This result motivates estimates of the general specification above. The
coefficient on the interaction between de-licensing and labor regulation is negative. The
regulation index is larger for legislation that is more favorable to workers. Therefore, a
negative coefficient implies that industries in states with more pro-employer regulation
experienced larger increases in output relative to those located in pro-worker states. The
implication is that market reforms such as de-licensing work only with complementary
institutions. Results similar to Aghion are also reported by Bhaumik,
Gangopadhyay and Krishnan (2006) who find that while entry by firms was related to
industry level factors during the 1980s, unobserved state-level factors explain much of
the entry during 1992-97. The authors conjecture that these state-level factors relate to
`quality of governance’ which presumably also includes labor market institutions.
The shortcomings of the BB measure of labor regulation have been pointed out by some
researchers. Anant et al (2006) and Bhattacharjea (2006) point out that the application of
the law has a greater bearing on labor outcomes than the written law. How the law works
on the ground depends on how it is enforced and on judicial interpretation of its
provisions. Bhattacharjea has also disputed how BB have coded some of the
amendments and he shows that the procedure of assigning and cumulating numerical
scores leads to several anomalies. Finally, both the Central and state governments have
several other laws that matter to labor flexibility that are not captured by the index.
Hasan, Mitra and Ramaswamy (2007) propose a modified version of the BB index.
Firstly, the authors consider a binary partitioning of states into those that have flexible
markets (i.e., those are rated as anti-worker by the BB index) and those that have rigid
markets (i.e., all other states). The BB index classifies the states of Gujarat and
Maharashtra as pro-worker and the state of Kerala as pro-employer. This is at variance
with the commonly held perceptions of these states and the authors point to a World
Bank survey which highly rated the investment climate in Gujarat and Maharashtra but
awarded a poor rating to Kerala. Therefore, the second modification is to classify Kerala
as a state with rigid markets and the states of Gujarat and Maharashtra as states with
flexible markets.
The result is their FLEX dummy that is one for the states with
flexible markets.
The original index was computed for the period up to 1992.
The danger with such ex-post classifications is that the FLEX dummy could be picking up other state characteristics
that make Maharashtra and Gujarat excellent investment destinations and make Kerala a state with poor investment
Mitra and Ural (2007) use the FLEX dummy to investigate the impact of economic
reforms on labor and total factor productivity in Indian manufacturing using ASI data.
The labor productivity equation is of the following form:
+++++++= )()(
where i indexes industry (at 2-digit level), s indexes state and t indexes year (during the
period 1989-2000). y is log labor productivity, r is the time-invariant labor flexibility
dummy (FLEX),
x is the time varying tariff rate for the i’th industry and z
is the log of
real per capita development expenditures. The coefficient on the tariff rate is negative,
that on flex dummy is positive (but significant only in the base regression) and that on the
interaction of tariff rate and FLEX dummy is negative (and significant). These results
mean that lower tariffs increase productivity in all industries but the increase is larger in
industries that are located in states with flexible labor markets. An extension of the
results to de-licensing throws up a result similar to Aghion (2003).
The productivity impact of trade liberalization was also estimated by Topalova (2004).
She computes firm-level TFP for a panel data set of publicly listed firms for the period
1989-2001. The firms in the panel account for 70% of the organized manufacturing
sector. The productivity indices are regressed on lagged industry tariffs, firm
characteristics, year dummies and industry fixed effects. The results suggest that a
reduction in protection had a positive impact on TFP and this was driven not by the exit
of inefficient firms but by an improvement in TFP of existing firms. Unlike the earlier
papers, Topalova does not find any differences between states on the basis of labor
Surveys of managers in manufacturing firms show that taxation and infrastructure issues
are the ones most frequently cited as being obstacles to growth. Access to finance is also
seen as an important issue; however, labor regulations is not seen as a problem of primary
This motivates Gupta, Hasan and Kumar (2008) to widen the search for
factors that constrain Indian manufacturing. Using 3-digit industry data from ASI, they
define industry characteristics along three dimensions: dependence on infrastructure,
dependence on external finance and labor intensity. They estimate a regression of the
following form:
where i indexes industry, t indexes year, Y is log of value-added,
’s and
industry and year fixed effects, d is a dummy for de-licensed status and x’s are the set of
industry characteristics. They find that the coefficient of the de-license dummy is
A related implication of trade liberalization is that competitive pressures working through different channels will
make labor demand in manufacturing more elastic. Hasan, Mitra and Ramaswamy (2007) confirm this and show that
it is related to trade liberalization. The increase is greater in states with more flexible labor market institutions.
It is possible, of course, that the responses of prospective entrants are different from that of incumbents.
positive and significant but it is counteracted by the coefficients on the interaction terms,
all of which are negative. In other words, industries that grow slowly in the de-licensed
period are those that are either relatively more dependent on infrastructure, or more
dependent on external finance or are more labor-intensive. The paper does not identify
what factors constrain the growth of labor-intensive manufacturing firms.
The evidence from the disaggregated industry (and in some cases firm-level) data
therefore shows significant impacts of economic reforms on manufacturing: in terms of
greater firm entry, higher industry output, value added and productivity. However, it
seems that weaknesses in infrastructure, lack of adequate financing and labor market
rigidity have come in the way of faster growth of the manufacturing sector.
It should be noted, however, that these findings are based on the organized manufacturing
sector alone. What about firms in the unorganized manufacturing sector? How have they
been affected by economic reforms? These questions do not have good answers because
there is no comparable time series data set on unorganized enterprises and their level of
output and inputs, as exists for the organized sector
. There are, however, some clues
about how the dynamics of organized manufacturing affect the unorganized sector.
Table 7 reports employment in organized and unorganized manufacturing and their rates
of growth over the periods 1983 to 1993-94 and 1993-94 to 2004-05. Overall
employment growth is greater in the 1990s and this happens despite a fall in employment
in the organized segment. The 1990s are a period of robust employment growth in the
unorganized sector. What could have happened to bring this about?
Table 7: Manufacturing Sector Employment: 1983-2004-05
Employment (millions) Annualized Growth rates (%)
1983 1993-94 2004-05 1983 to 1993-94
1993-94 to 2004-05
Organized manufacturing 7.82 8.71 8.38 1.08 -0.38
Unorganized manufacturing
23.8 29.9 45.3 2.30 4.26
Total manufacturing 31.6 38.6 41.7 2.01 3.36
Source: Authors calculations using ASI data for organized manufacturing employment and NSS data for
unorganized manufacturing employment.
One plausible explanation for the low employment growth rate in the organized
manufactured sector is that due to the Small Scale Reservation policy for labor intensive
activities, a significant part of the organized sector in India has always been capital
intensive. The post-reform expansion of the organized manufacturing is likely to have
One source of information on the informal sector is the set of surveys conducted by the National Sample Survey
Organuzation in 1989-90 and 1994-95 as the follow up surveys to the Economic Censuses of 1980 and 1990
respectively. Nataraj (2009) uses these datasets to examine the impact of tariff cuts on productivity improvement in the
manufacturing sector as a whole. She finds that overall productivity has risen mostly due to the productivity
improvement in informal sector through the exit of the inefficient firms.
taken place in capital intensive production especially after the reforms made the imports
of capital equipment cheaper. If the reforms opened up expansion opportunities to the
larger firms that had access to key inputs such as credit and power that the small firms
lacked, it is possible that the Small Scale Reservation Policy was responsible for the low
rate of employment creation following the reform of 1991. It was simply that there were
too few labor intensive firms in the group that was able to take advantage of the expanded
Figure 8 reproduced from Dougherty (2008), shows clearly how employment in the
organized sector went down after 1997 while that in unorganized sector rose. One
conjecture is that competition had intensified in India’s manufacturing sector by the late
90’s as a result of easier entry and declining tariffs through the decade. Firms looked for
ways to cut costs and given the rigid labor laws, sub-contracting and use of contract labor
afforded firms lower labor costs and greater flexibility.
Figure 8: Growth of Organized and Unorganized Sector Employment
Source: Dougherty (2008).
Figure 9 shows that the period since 1997 was not one of contraction for the organized
sector. Profits, output, material and service input all increased (relative to value added).
Value added in constant prices increased by almost 6% per annum during the period 1997
to 2004-05. Yet, organized sector employment declined during this period. On the other
hand, the organized manufacturing sector did increase the use of contract labor (not
counted as part of regular workers). Figure 10 plots contract labor as a percentage of
We thank an anonymous referee for pointing this out.
person days worked for the period since the late 1970s. While this proportion has been
increasing throughout the period, the rise is sharp since the late 1990s.
Figure 9: Organized Manufacturing Ratios
1975 1980 1985 1990 1995 2000
Service Input
Material Input
Output and material Input as a Ratio of Value Added
Profit s and Se rvice I npu t as a Ra ti o o f Value Added
Source: Author’s calculations using ASI data.
Figure 10: Contract Intensity in the Manufacturing Sector
Source: Ramaswamy (2008).
Using plant level data from the ASI, Dougherty (2008) computes the job creation rate and
job destruction rate at the three-digit industry level and five-digit industry level. The
ideal measure would be at the plant level but ASI does not allow plants to be tracked over
time. Therefore, these measures displayed in Table 8 are lower bounds.
Table 8: Job Flows in the Organized Manufacturing Sector
Based on three-digit industries
Average, 1985-1988
5.3 -4.1 9.4
Average, 1999-2004
3.9 -5 8.9
Based on five-digit industries
1999-2000 18.9 -21.2 40.1
2000-01 11.4 -13.7 25.0
2001-02 8.0 -10.8 18.8
2002-03 16.5 -13.1 29.6
2003-04 15.8 -16.1 31.9
Source: Dougherty (2008)
The turnover rates in the 2000’s are surprisingly high for a labor market where
regulations are thought to restrain the ability of employers to dismiss workers. The key
to the puzzle lies in Table 9 also from Dougherty (2008). The table shows that for large
units (defined as those with more than 100 workers), the only category of workers that
has seen an increase is contract labor. For small units (less than 100 workers and
therefore exempt from the provisions of the Industrial Disputes Act), the net employment
rate is positive for all worker types. Job flows are therefore concentrated on contract
labor in large units and on all workers in small units. As the smaller units are
characterized by lower capital intensity and lower productivity, Dougherty concludes that
“Despite strong gains in employment across the economy in recent years, a dichotomy
has emerged with net increases in employment occurring almost exclusively in the least
productive, unorganized and often informal part of the economy.”
Table 9: Job Flows by Size of Plant Workforce and Type of Worker
Average for 1999-2004 (for Organized Manufacturing Sector)
Job Creation Rate
Job Destruction
Net Employment
Large Small Large Small Large Small
All employees
11.5 24.2 -17 -8.4 -5.5 15.8
Workers 13.3 26.7 -18.7 -10.4 -5.4 16.3
Contract 26.7 31.0 -22.9 -13.7 3.8 17.3
Supervisors 16.8 27.8 -27.4 -14.6 -10.6 13.2
Others 15.9 31.5 -25.2 -13.7 -9.3 17.8
Note: Large plants have more than 100 workers, and small plants have 100 or less workers.
Source: Dougherty (2008)
Sub-contracting could be the other possible explanation for the inverse correlation
between the growth in organized sector employment and unorganized sector
employment. Sub-contracting is widespread in some industries such as for instance
garments. Ramaswamy (1999) has estimates of sub-contracting in the 1980s and early
1990s but estimates for a more recent period are not available. He finds subcontracting
practices to be concentrated in labor-intensive industries. It is possible that the dramatic
improvements in telecommunications in the 1990s could have facilitated more efficient
supply chains, greater specialization and more sub-contracting. However, we lack
evidence on whether and how cell phones and better communications changed the way in
which firms conduct business.
Figure 7 and Tables 5 and 6 make it apparent that the service sector has grown faster than
other sectors and is the dominant sector in the economy.
Within the sector, business
services (which includes software and IT-enabled services), banking and communications
have grown on average at more than 10% per year in the 1990s. On the other hand, some
other services such as railways and public administration have grown more slowly
(Chanda, 2007).
The other striking feature of Figure 7 and Tables 5 and 6 is the
relatively slower growth of employment in the services sector. As a result, while the
services share of GDP is nearly 60%, its share of employment is barely 30%.
In our three-fold division of the economy into agriculture, manufacturing and services, we include the following in
services – trade, construction, transportation, communications, banking and financial services, public administration,
personal services, education and health, business services, research and scientific services, and recreation and
The data quality on service sector output has been questioned. While the estimate for the public sector component is
regarded as reliable, this is not equally so for the components relating to either the private corporate sector or the
unorganized sector (Shetty, 2007).
Some of the service sectors clearly grew on account of domestic demand: trade (i.e.,
distribution of goods and services from producers to consumers), construction,
transportation, public administration, education and health, personal services, and
recreation and entertainment. However, the most noticeable feature of service sector
growth has been the remarkable expansion of its exports which grew faster (at 17.3%
annually) than either GDP (at 7.5%) or the services GDP through the 1990s (at 9.2%).
Between 1995 and 2000, India’s services exports grew nearly six times faster than world
exports of services (Chanda, 2007). In 2001-02, software accounted for about a third of
all services exports. Until the most recent financial crisis, this sector has been growing at
35% per annum. Though as yet software sector is only a small part of the GDP and a
negligible part of the total employment, it has been the most dynamic sector in India and
has facilitated continuing growth by generating foreign exchange averting a financial
crisis. From a growth accounting exercise, Eichengreen and Gupta (2010) conclude that
domestic demand and exports are the major drivers of service sector growth (as opposed
to intermediate demand from other sectors).
The services sector has gained from reforms in two sorts of ways. The direct impact
came from the opening up of several service sectors to the private sector and foreign
direct investment. These include telecommunications, banking and insurance. The share
of services in foreign direct investment rose from 10.5% in the early 1990s to nearly 30%
in the second half of the decade (Chanda, 2007). However, FDI is still not permitted in
some sectors, the most prominent of them being retail distribution. The indirect impact
came about because of easier and cheaper access to factor services. Narayana Murthy
(2004) cites import de-licensing (that permitted immediate purchase of imported
computers), financial liberalization (that allowed firms to raise capital through public
offerings that were market determined rather than by state regulators), and current
account convertibility (that made it easier to travel, hire foreign consultants and establish
sales offices abroad). Narayana Murthy also credits FDI by software majors as reasons
why the industry adopted world-class quality processes, tools and methodologies.
Improvement in telecommunications and the use of internet facilitated the off-shoring of
IT services by US and European corporations to Indian firms. The difference in time
zones between India and the US was used by Indian companies to offer a 24-hour virtual
workday (Narayana Murthy, 2007).
During the earlier period (1983-93), there is little reason to believe that new technologies
played any role in the service sector growth. The service sector was growing mostly due
to the growing demand for it by the fast growing manufacturing sector or by other factors
that did not even depend on policy reforms. For example, as mentioned earlier, there was
a steady expansion of the banking system from 1975 through 1985 that slowed down in
the late eighties. However, aggregate deposits and credit increased very fast due to the
increased economic activity. Public administration is in a league of its own. When
wages of public servants are revised upwards, the output figures reported in the statistics
go up as long as the wage hikes exceed the cost of living index. Thus, the indicated
Chanda, (2007)
Intermediate demand from manufacturing accounts for about one-third of value added in services which is down
from about 40% in 1991.
output growth rate in ‘public administration’ can be somewhat fictitious. Trade,
construction and transportation all grew in response to an increased demand. Education
has two components: ‘public’ is autonomous while ‘private’ can move in response to a
change in demand.
The beginning of the new communications era was made in 1992 when the government
opened the sector to the private sector by relinquishing its monopoly control over the
provision of communication services. The years between 1995 and 1999 saw a lot of
churning in the telecom sector but during this period cell phones became more affordable
to common people. In a country with poor infrastructure for communications, this
development had an enormous impact
. After the arrival of cellular technology, the
service sector in India took off. The two fastest growing sectors in the period 1993-2004
were business services (24.3%), and communications (20.7%).
If we examine a slightly
later period of 2001-2007, we find further acceleration of these high-end services. Given
that the growth acceleration of these activities in this period coincides exactly with the
entry of information technology, it is very likely that the advent of IT is the main trigger
for growth acceleration in these sectors. The coincidence of educated manpower and the
presence of a huge international demand for IT services launched the Indian software
industry. Software and services exports grew at an astronomical speed from USD 754
million in 1995-96 to USD 23,600 million in 2005-06 (Gangopadhyay, Singh and Singh
An improvement in communication technologies has enormous externalities for other
sectors, especially ‘services’. ‘Trade’ includes distribution of goods and services from
the producers to consumers and it is the largest component of the service sector in India.
Its efficiency depends on the quality and timeliness of the information flows and the
advent of new communications technology facilitated both. It is not a surprise therefore
that there was a quantum jump in the growth rate of the service sector after the arrival of
cell phones and internet. It also had an impact on banking, insurance and social services
such as health and education. Interestingly, the ASI data show that the service sector
input into the organized manufacturing sector went up considerably from 1997-98 to
2001-02 and so did the value of total input (Figure 9). This is consistent with our
conjecture that improvement in the communications technology may have created
incentives for sub-contracting to smaller specialized units in the unorganized sector.
Note that the fastest growing sector during the nineties, was business services, but it
constitutes a relatively small part of GDP and therefore cannot be considered as having
contributed significantly to the overall growth of GDP during 1993-2004. However, at
the compound growth rate of 22.5% that it is growing, it is expected to rise over 7% of
GDP in 2007-08. It will then certainly start having an impact on GDP. The sector that
contributes the most to the overall non-farm growth is trade as it forms a sizeable part of
GDP in 1993 (18%). This means that almost one fifth of the economy is engaged in
trading and distributing goods and services produced in the economy. Its growth rate
See Jensen (2007) on how the use of cell phones by Kerala fishermen eliminated the price volatility in the fish
These are average annual rates of growth over 1994-2004.
though not in the fastest 12 sectors nevertheless rose from 5.4% during1983-93 to 8.5%
during the period 1993-2004. The expansion of trade also indicates increasing
specialization and expanding markets.
Gangopadhyay, Singh and Singh (2008) study the impact of IT on the organized
manufacturing sector in India
. They find that the penetration of IT in Indian
manufacturing has been less than satisfactory. Some sectors like pharmaceuticals have
adopted it much more than others. However, they also find that the use of IT has a
positive impact on productivity as well as employment. It not only increases both skilled
and unskilled employment but also increases the skill intensity of the workforce. Their
most interesting finding is that the use of IT is subject to a coordination problem due to
network externalities. A firm is more likely to use it if its suppliers and customers use it.
They cite the example of the state of Haryana where a government subsidy had a strong
impact on the spread of IT through the industries in Haryana.
Why services: The bias towards skill-intensity
Kochhar et al (2006) have argued that because of the prior emphasis on tertiary education
and a diversified skill set developed during the long import substitution phase, skill-
intensive services based on information technologies took root in India.
In addition,
Indian engineers resident in the U.S. who had played an important role in the high tech
sector there were induced to invest their human and financial capital in India by the
reforms that relaxed controls on imports and investments. Of course, the advent of new
technologies was felt by the whole world but it is possible that the reputation of Indian
engineers in the U.S. helped them create a brand name that is normally not available for a
developing country’s foray into a new high tech activity. This sequence of fortuitous
events launched India as a name to reckon with in software exports. The extra-ordinarily
high growth rate of 24.3% for business services would not have been possible if a vast
export market had not opened for custom designed software products. It also helped to
have a large pool of English speaking young population with some education to provide
other business services such as ‘call centers’. All this is of course rather special due to
India’s historical background and therefore not quite replicable in other countries.
Kochhar et al (2006) compare the sectoral shares of GDP and employment in India with
those in other comparable countries and examine whether India is an outlier in any
respect. The most noteworthy statistic they present is the change in the employment share
of agriculture from 1980 to 2000: China (68.7%), India (68.1%) and Thailand (70.8%)
had very similar figures in 1980. By 2000, the picture had changed significantly: China
(46.95%), India (59.3%) and Thailand (48.8%). As a result, the contribution in India (to
the total growth rate) from the process of reallocating labor from the agricultural sector
(characterized by low productivity) to industry and services (characterized by high
Also, Singh (2006) has pointed out many spillovers from information and communication technology to the rest of
the economy and especially to the manufacturing sector. These crucial services reduce transaction costs and speed up
Kochar et. al show that in 1981, the contribution of skill-intensive industries to total value-added in India was above
the international norm (controlling for the country’s GDP and size). This effect persists in 2000.
productivity) is extremely low compared to other Asian countries (Bosworth, Collins and
Virmani (2007)).
The main reason for this is that the fast growing non-farm sectors are all skill intensive
sectors while most of the labor in agriculture is unskilled. How little unskilled
employment growth was created by the fastest growing sectors is clear from Figures 11
and 12 that plot the contribution to the overall (GDP) growth rate and to the overall
skilled and unskilled employment growth rate for each of the 41 non-farm sectors during
the eighties and the nineties.
The computations displayed in these figures use a very
minimal definition of skill. All workers who have a middle-school education or higher
are considered to be skilled. All others are unskilled workers. In the figures, the sectors
are sorted in a descending order by their contribution to the rate of growth of GDP. We
then plot the cumulative contribution of these sectors to the rate of growth of GDP,
skilled and unskilled employment. Figure 11 shows these cumulative contributions for
the 1983-93 period and Figure 12 for the 1993-04 period.
During the eighties (Figure 11) the fastest growing 14 sectors hardly provide any
unskilled labor employment. In fact, they seem to be shedding unskilled labor. The
initial dip in Figure 11 occurs because unskilled labor employment in textile products –
the second fastest growing sector in the 1980s – dropped by 39%. Even if this sector is
excluded, the ten fastest growing sectors in the 1980s accounted for only about 4% of the
growth in unskilled employment. On the other hand, the sectoral contribution to GDP is
very similar to their contribution to skilled employment. However, as far as unskilled
employment is concerned there are just a few sectors that make abundant use of unskilled
workers and much of the unskilled employment in non-farm sectors is clustered in three
main sectors – trade, construction, and transportation. The three vertical segments of the
graph correspond to these sectors.
As is clear, from Figure 12, 1993-04 is certainly a better decade from the point of view of
employment generation for the unskilled as compared to the earlier decade. The main
reason why the picture for 1993-04 looks more favorable to unskilled labor is because the
sectors that used unskilled labor abundantly (e.g., trade and construction) grew faster in
the 1990s. In addition, the labor-shedding seen in the earlier decade does not happen in
the 1990s. As a result, the 1990s are better for the growth of overall non-farm
employment as well. Between 1983 and 1993, non-farm employment increased by 35.59
million. The increase during 1993-04 was much larger at 60.20 million. In fact, two-
thirds of the increase happened in the latter half of the decade – while the increase was
20.86 million during 1993-99, it was 39.34 million during 1999-04.
For value added and output there are two main sources of data — ASI and NAS. The level of disaggregation is much
greater in ASI than in NAS. However, the ASI covers only registered manufacturing which constitutes less than 20%
of GDP. More importantly it does not cover services which are not only dominant in GDP but also included some of
the fastest growing sectors in the nineties. Therefore, for output we have no choice but to use NAS. For employment
the only source is NSS and the recent rounds give data disaggregated up to 5 digit industry codes. However, as stated
before NSS data are available only at five year intervals. On the other hand NAS gives a time series, but with very
limited disaggregation – 41 non-farm sectors. Therefore, we are restricted to 41 non-farm sectors over the NSS time
periods – 1983-93 and 1993-04.
Figure 11: Contribution of the Fastest Growing Sectors to Employment: 1983 – 93
5 10 15 20 25 30 35 40
Sec tors sorted by their rate of growth (1983-93)
Cumulative Contribution to the Rate of Growth (%) 1983-93
Skilled Emp
Unskilled Emp
Figure 12: Contribution of the Fastest Growing Sectors to Employment: 1993 – 04
510 15 20 25 30 35 40
Sectors sorted by their rate of growth (1993-04)
Cumulative Cont ribution to the Rate of Growth (%) 1993-04
Skilled Emp
Unsk illed E mp
Source: Authors calculation using NAS and NSS data.
Why has Indian growth created much less employment in its non-farm sectors than have
China and other Asian countries that also experienced fast growth? First, as the goal of
`self-reliance’ guided Indian industrialization in the pre-reform period, the principle of
comparative advantage was deliberately sidestepped giving rise to capital and skill
intensive growth. While this favored skill-intensive exports in the later liberalization
phase, it also left a legacy of restrictive labor laws, prohibitions on large-scale units in
labor intensive sectors (through the small scale reservation policy) and inadequate
infrastructure that constrained the expansion of the corporate sector into labor intensive
The flip side is that much of India’s labor force is in the unorganized sector. Reversing
the regulatory impediments would aid the expansion of the organized sector in labor-
intensive manufacturing. However, given that the unorganized sector employs 83% of
the non-farm labor force, it is difficult to imagine that the present picture can change
rapidly on the strength of organized sector expansion alone. The under-provision of
infrastructural facilities and credit are the biggest impediments to overall entrepreneurial
In sum, the fastest growing sectors in India are capital and skilled labor-intensive sectors.
Despite the speeding up of employment growth in 1999-04, the labor share of agriculture
has fallen at a relatively slower rate than other comparable countries as the increase in
demand for unskilled labor by non-farm sectors has still not matched the increases in
labor force during this period. This has obvious implications for poverty decline as we
discuss in the next section.
6. Poverty Decline
Official poverty estimates in India are based on nationally representative consumer
expenditure surveys conducted by the National Sample Survey Organization (NSSO).
While such surveys are now undertaken every year, the so-called “thick rounds” which
take place approximately every five years are regarded as more reliable. The official
estimates of the head-count ratio of poverty are reported only for the thick rounds. These
estimates are reproduced in Table 10.
The poverty ratio in both rural and urban populations has approximately halved over
three decades from 1973-74 to 2004-05. About 61% of the decline in the rural head-
count ratio occurred in the first 14 years of this period (1973-74 to 1987-88). On the
other hand, the rate of decline in urban poverty has been more even – 46% of the
reduction happened in the first 14 years and the remainder in the next 17 years. For the
period prior to 1973-74, there is no officially released consistent series on poverty.
However, from the estimates put together by researchers (Datt and Ravallion (2002)), it
can be seen there is no trend in the poverty ratio during this period.
Table 10: Trends in Poverty, 1973 – 2004
Head count ratio (%) Number of poor (million) Total
(million) Rural Urban Combined Rural Urban Combined
1973-74 56.4 49 54.9 261.3 60.0 321.3 585.25
1977-78 53.1 45.2 51.3 264.3 64.6 328.9 641.13
1983 45.6 40.8 44.5 252.0 70.9 322.9 725.62
1987-88 39.1 38.2 38.9 231.9 75.2 307.0 789.20
1993-94 37.3 32.4 36.0 244.0 76.3 320.4 890.00
2004-05 28.3 25.7 27.5 220.9 80.8 301.7 1097.09
Source: Planning Commission, Government of India.
The poverty lines used in the official estimates have often been criticized for not
corresponding adequately to a desired caloric norm, for not capturing non-food
subsistence and for the use of incorrect price deflators across survey years.
Figure 13
displays the all India empirical cumulative distribution of per capita consumer
expenditure for the years 1983 and 2004-05. As the 2004-05 distribution dominates the
1983 distribution by first-order stochastic dominance, the choice of a poverty line would
not alter the finding of a decline in poverty. However, the choice matters in other ways.
In the figure, we draw vertical lines at a per capita expenditure level corresponding to the
poverty line and twice the poverty line. While the fall in poverty is substantial when
measured by the poverty line, the decrease in the proportion of population below twice
the poverty line is very modest. Furthermore, even in 2004, this proportion was as high
as 0.8.
Table 10 also contains numbers on the absolute number of the poor in rural and urban
populations. While the number of rural poor has dropped by about 40 million, the
number of urban poor went up by 20 million between 1973 and 2004. So the net gain is
only about 20 million. However, these changes happened at a time when the population
nearly doubled from 585 million to 1.1 billion. If the poverty ratio had not dropped
below its levels in 1973-74, India would have more than 600 million poor people.
Against this counter-factual, economic growth has lifted about 300 million out of
poverty. Clearly, however, other counterfactuals can be constructed.
Deaton and Kozel (2005) is a good reference for a recent survey of measurement issues.
Figure 13: Distribution of Consumer Expenditure
250 500 1000
750 1250
Per Capita Expenditure, Rupees, 1999 Prices
Cumulat ive Density
Source: Authors calculations using NSS data.
Like the growth story then, the decline in poverty also dates to the 1970s. The leading
candidate among rival explanations is agricultural growth. The plausibility of this is
illustrated by the Figure 14 which graphs crop yields and the head count ratio measure of
poverty for the period 1949-98.
Figure 14: Crop Yields and Poverty in India, 1949-98
Source: Palmer-Jones and Sen (2006)
In a series of papers, Datt and Ravallion (DR) used time-series data to examine the
correlates of poverty decline (Datt and Ravallion, 1998 and 2002; Ravallion and Datt,
33, 34
Their principal findings were the following:
(i) While both urban and rural poor gained from rural growth, the rural poor did not
benefit from urban growth. Rural to urban migration is not a major driver of
poverty decline in India.
(ii) Similarly, primary and tertiary sector growth mattered much more to poverty than
secondary sector growth (primarily manufacturing).
(iii) Higher farm yields increase real agricultural wages and reduce rural poverty.
(iv) Rural non-farm output also reduces rural poverty; however its impact varies
across states depending on initial conditions. The impact is lower in states with
initially low levels of farm productivity, low rural living standards relative to
urban areas, poor basic education and high infant mortality.
In a cross-sectional analysis, Palmer-Jones and Sen (2003) related rural poverty (in the
late 1980s and early 1990s) to agricultural growth and several control variables. Their
results confirm the DR finding of a strong positive correlation between agricultural
growth and poverty decline. Palmer-Jones and Sen emphasize the role of agro-ecological
conditions in determining agricultural growth suggesting that agriculture driven poverty
reduction is not available to all regions.
A contrary finding is from Foster and Rosenzweig (2003, 2004) who model a village
economy as consisting of three sectors: a traded agricultural sector, a non-traded service
sector and a traded factory sector. Capital is mobile and is used by the factory sector
alone. As capital seeks villages with low wages, a key prediction of their model is that
rural industrialization may bypass regions with high agricultural productivity (and,
therefore, high wages). The model is estimated for a panel of villages and households
over the period 1982-1999.
The important findings of their empirical application are (i)
agricultural productivity negatively affects the factory sector but positively affects the
non-traded sector (ii) both agricultural productivity and factory sector growth have had
positive impacts on rural wages but the size of the latter effect is larger. This result
emerges from the impressive growth in the rural factory sector during this period. The
percentage of villages with factories increased from 17 to 51% and the average number of
factory workers per village increased ten-fold from 5.6 to 56.7. The clear implication is
that a dynamic non-farm sector increased rural wages and rural poverty in the sample of
villages studied in the paper.
The data was drawn from Ozler (1996) who assembled poverty measures from 21 household expenditure
surveys of the NSSO for the period 1957-58 to 1990-91.
Palmer-Jones and Sen (2006) survey the older studies prior to Datt and Ravallion that also address the impact of
agricultural growth on poverty.
The panel consists of 250 village surveyed twice – in 1982 and in 1999. The survey is conducted by NCAER
(National Council of Applied Economic Research).
It should be noted, however, that the nationally representative NSS data do not show even
a modest rise in the relative share of the non-farm sector in rural employment. In the
period from 1987-88 to 1999-00, this ratio fluctuates between 26% and 29% (Kijima and
Lanjouw, 2005). It is unclear how the non-farm sector would be largely responsible for
increasing the agricultural wage without causing a substantial increase in non-farm
In a land constrained agricultural economy, a rapidly growing non-farm sector can draw
labor from land, increase labor productivity and agricultural wages and thus reduce
poverty. For the 15 major Indian states, Figure 15 (from Eswaran, 2009) plots the
average real daily wages (in 1999 rupees) in agriculture against the labor-land ratio (days
per hectare of gross cropped area) for 1983 and 2004. It can be seen that, for all but four
states (Kerala, Haryana, Punjab and Rajasthan), the labor use per hectare of land has
increased over this period. Yet, in all states, real wages have increased during this
period. At the all-India level, real daily wages increased by 74% between 1983 and 2004.
Quite clearly, if either farm TFP or agricultural inputs such as fertilizers had not
increased during this period, agricultural wages would have declined.
Figure 15: Agricultural Earnings & Labor-Land Ratios: 1983-04
0100 200 300 400 500
1983 2004
Kera la
Punjab Haryana
Raja s th a n
Biha r
Oriss a
Gu j
Punjab Haryana
Raja s th a n Gu j
Tamilnadu Bihar
Labor-Land Ratio
Source: Eswaran, Kotwal, Ramaswami and Wadhwa (2009).
It becomes interesting, therefore, to ask how much the non-farm sector growth has
contributed to the growth of agricultural wages. The extent of wage increase due to non-
farm TFP growth would depend, of course, on the amount of labor drawn away from
agriculture. Because of the limited extent to which non-farm employment has grown
(relative to the agricultural work force), Eswaran (2008) estimate that non-farm
sector TFP growth could not be responsible for more than 22% of the wage growth
during 1983-99.
The analysis of Eswaran et. al (2009) also shows that it is the younger and more educated
male cohorts that are most mobile across sectors.
Older males and females of all ages
are directly affected by a slowdown in agricultural growth. The stock of labor force
already locked into agriculture is large (relative, in particular, to new employment
opportunities in other sectors) and so non-farm employment would have to grow
substantially faster if it has to make a dent into poverty. It seems reasonable to suppose
that agricultural productivity would have to continue to increase for improving the living
standards of much of the rural poor.
For the young and mobile, access to education would determine their prospects of non-
farm jobs. Ravallion (2009) points out that educational inequalities in India are much
worse than in comparable large countries such as Brazil and China. It is only in 2005 that
India’s enrollment and literacy percentages have equalled or surpassed China’s record at
the beginning of its reform period (1981).
Recent work (Collins et al, (2009)) has revealed how important consumption smoothing
is for the poor who seldom have a steady source of income. When we are considering the
wellbeing of the poor, it is not enough to take their wages or daily earnings into account.
It is also necessary to ask if they have access to any means of consumption smoothing.
The poor may also save for this purpose or may resort to locally available informal credit.
Patron-client relationships in which workers take loans from their employers or from the
local rich survive precisely because of the informal insurance arrangements they make
possible. Micro-finance has emerged as a possible alternative. Even though it originated
as a tool to facilitate creation of self-employment for the poor, it is now well accepted
that the poor use it for various purposes including for insuring themselves against
consumption contingencies (Karlan and Morduch, 2009). Moreover, micro-finance is
much more than micro-credit. The poor also put a great deal of value on having access to
a safe way to save.
In India, some NGOs are engaged in micro-finance schemes in rural communities as well
as urban slums and often the results are encouraging. Banerjee et al (2009) performed a
randomized experiment on one such NGO engaged in group-lending in the slums of
Hyderabad. The impact on the borrowers was positive, though not necessarily through an
increase in their average consumption. There was greater investment in business
durables as well as an increase in new businesses started. By necessity, such studies are
Education and access to non-farm jobs are strongly correlated. For a recent analysis, see Kijima and Lanjouw
micro-studies of particular cases. We are not aware of any study that has tried to evaluate
the overall role of micro-finance in the Indian economy.
7. Determinants of Agricultural Growth
The change in labor productivity in agriculture is the sum of change in land productivity
(yields) and the land-labor ratio. Figure 16 summarizes the changes in all three variables
(for crop agriculture) from the mid-60s to mid-90s. This picture shows that despite
continuous decline in the land-labor ratio, labor productivity has registered positive
growth driven by land productivity. The adverse movement in the land-labor ratio
reflects the limited absorption of unskilled labor by the nonfarm sector. At the aggregate
level, this pictue is an explanation of the wage trends oberved in Figure 15.
In the period from early 60s to early 70s, the rate of growth of labor productivity was
miniscule (0.26% per annum) as land productivity increase (1.6% per annum) was almost
neutralized by the adverse change in land-labor ratio (-1.3% per annum). The land
productivity growth was slightly higher in the 1970s and much higher in the 1980s. The
land-labor ratio continues to decline through all three decades but the rate of decline is
highest in the 1980s. However, because of a substantial step-up of yields in the 1980s,
this is also the period with the highest rate of increase of labor productivity.
Figure 16: Agricultural Productivity: 1962-95
Output/hec tare
Hect are/ work er
1962-65 to1970-73 1970-73 to1980-83 1980- 83 to1992-95
Annualized Rat e of Gro wt h (%)
Source: Authors calculations using data in Bhalla and Singh (2001).
Crop output is measured in terms of 1990-93 prices and workers refer to male workers in crop agriculture.
Joshi, Birthal, and Minot (2006) decomposed the change in value of crop output into
changes in crop area, crop yields, crop prices, shifts in crop output (towards higher value
crops) and a residual term. Figure 17 present their results for the 1980s and 1990s. In
both periods, the value of crop output grew at roughly the same rate (3.5%). The figures
show that output growth owes very little to area expansion. So these figures could also
be interpreted as accounting for the change in land productivity. In the 1980s, the major
sources of higher land productivity were technology (higher crop yields) and
diversification (shift to higher value crops). In the 1990s, technology, diversification and
real price changes are all about equally responsible. Since crop output grew at the same
rate in both these decades, the figures imply that growth due to technology has slowed in
the 1990s. This is corroborated by the levelling of yields in rice, wheat, cotton and
sugarcane. The slowdown is particularly marked after 1995 (Chand, Raju and
Figure 17: Decomposition of Crop Output Growth
Dive rsifi ca ti on
Div ersifi ca ti on
10% 4%
Source: Joshi, Birthal, and Minot (2006).
Rising urban incomes and the diversification of diets towards fruits and vegetables
explains the diversification component of crop output growth in both periods. The share
of fruits and vegetables in crop output rose from 13.7% in 1982-83 to 20.5% in 1999-00.
Although the diversification is component is larger in the 1990s, this process was well
underway in the 1980s as well.
Public spending on agriculture has consisted of public investments in technology,
(especially the high yielding seed varieties of the Green Revolution), irrigation and
infrastructure (roads, markets) as well subsidies on irrigation and electricity charges and
Even crop output growth is slower in this period.
Although exports are rising, it is domestic demand that is primarily driving diversification (Kumar and
Mruthyunjaya, 2003).
fertilizer prices.
Subsidies for fertilizers, canal irrigation and electricity have grown
over time and now account for nearly 10% of agricultural GDP (Vyas, 2007). About
73% of subsidy expenditure is because of subsidies to electricity. Figure 18 plots the
movement of public and private investment in agriculture and that of input subsidies.
Compared to the economy wide rate of investment of 27% in the late 1990s, investment
in agriculture is only about 16% of agricultural GDP of which on-farm investment is only
about 6% and the remainder is in agriculture-related activities (Gulati and Landes, 2004).
Public investment has been a declining force since the 1980s while private investment
picked up in the late 1980s. Input subsidies on the other hand have moved smoothly
upwards throughout the period. This has led many to argue that it is the rising subsidies
that have led to declining allocations for public investment (Landes and Gulati, 2004,
Rao,2003). On the other hand, input subsidies have encouraged certain kinds of private
investment in agriculture.
Figure 18: Investment in Agriculture – 1980 – 2004
Input S ubs id ie s
Source: National Accounts Statistics – public and private investment in 1999-00 prices.
Mullen, Orden and Gulati (2005) – input subsidies in 2000-01 prices.
Yet, private investment cannot fully substitute for some forms of public investment.
Besides infrastructure investments such as roads and market facilities, many agricultural
technologies are themselves public goods. The best instance of this are the Green
Revolution seed varieties. Among seeds, an important distinction is between hybrids and
open-pollinated varieties. Once the seeds of open-pollinated varieties have been
The modern Green Revolution varieties achieve their high yields because they are more responsive to fertilizers than
traditional varieties. Increasing fertilizer use is therefore a cornerstone of increasing crop yields.
For instance, subsidies to electricity charges have encouraged investment in tube-wells and pumpsets to extract
distributed, they can be reproduced for several generations by farmers without serious
loss of quality. The dissemination of these seeds can therefore take place rapidly through
informal exchange of seeds between farmers. For this reason, the private sector has little
interest in developing new open-pollinated varieties. Like in the rest of the world, the
Indian private seed sector works mostly on hybrid varieties. These seeds cannot be
reproduced without a loss in yields and hence provides the seed company with some
measure of protection for its innovation. However, hybrids are unimportant for the major
food staples of rice and wheat and as well as for many other field crops.
Among other areas that must be addressed by public investments are initiatives to combat
degradation of land resources (soil erosion, salinity and water logging some of which
occurs because of negative externalities from poorly planned canal irrigation projects),
measures to harness and conserve water resources to reverse the depletion of
underground aquifers and immunization programs to control disease in the livestock
Paucity of resources is not always the constraint, however. Studies point to poor
governance as well. A case in point is the public sector research system. Although
expenditures on research have not grown as rapidly in the 1990s as in the earlier decades
(Balakrishnan, 2008; Jha and Pal, 2008), there is no precipitous decline and the
expenditure as percentage of agricultural GDP at the end of the 1990s was higher than at
any point earlier. Nonetheless, there is evidence that the productivity of the research
system has declined. For instance, according to government estimates (Planning
Commission, 2007), an index of yields of new varieties of the major field crops has
shown no change between 1996-97 and 2005-06 after growing at about 3% per year from
1980-81 to 1996-97. The review by Jha and Pal (2008) highlights poor financial
management, the proliferation of bureaucratic procedures, and the absence of
accountability in R&D projects.
The problem of low quality institutions has also been
cited in many other areas of public investment and spending including the construction
and management of canal irrigation systems and in the provision of agricultural extension
services (Iyer, Raju and Wang, 2008; Raabe, 2008; Shingi, 2004; Vaidyanathan,
The wide ranging economic reforms of the 1990s and the limited policy changes
preceeding it were principally directed at trade, industry and financial markets. It has
been argued that since it was industry that was protected during the earlier regime, the
dismantling of tariffs (and the associated exchange rate devaluation) was primarily
responsible for the improvement in the terms of trade for agriculture and hence the
sizeable price effect in the 1990s seen in Figure 17 (Ahluwalia 2002, Balakrishnan,Golait
and Kumar, 2008; Landes and Gulati, 2004). It should be noted, however, that the early
1990s also saw sharp increases in government support prices for rice and wheat and so
the movement in terms of trade in favour of agriculture cannot be entirely attributed to
trade liberalization. Indeed, the terms of trade begin to move against agriculture after the
The major hybrid seed markets are in vegetables, cotton, maize, sorghum and pearl millet.
Despite these problems, the median rate of return to agricultural research investments was 58% in the 28 studies
reviewed by Byerlee and Pal (2003). The payoff from institutional reforms is large.
mid-90s even though import tariffs on non-farm goods were falling right through the
decade. For this reason, the price effect identified in the Joshi study is unlikely to
have lasted beyond this decade.
The principal contribution of economic reforms to agricultural growth is likely to have
been the diversification effect as rising incomes have led consumers to demand more of
edible oils, milk, fruits and vegetables than of staple food cereals.
The technological
component of agricultural growth is, however, determined by the internal dynamics of the
sector – by public spending on investment and subsidies and by the capacity of public
institutions to manage investments and push technologies effectively. The economy wide
reforms have left this aspect of the agricultural economy largely untouched.
8. What is distinct about India’s experience?
It is clear from the earlier sections that the growth episode in India since the 1980’s is not
another instance of State driven growth in Asia. Instead, it is the co-incidence of the
ready availability of new technologies and having the skilled manpower that would be
necessary to take advantage of these new technologies. Technology transfers in the
1980s and early 1990s took place mostly through easier and cheaper access to imported
machinery that was made possible by trade liberalization. Improved communications
(especially cell phones) and the diffusion of internet were other technologies that played
a big role in driving growth from the mid-1990s on. It is inconceivable that without the
breakup of government monopolies and the advent of competition in the communication
sector, there would have been a revolution in communication technology in India. And,
without such a revolution, the fastest growing sectors (e.g., business services) would not
have taken off in India. The sustained growth that we have seen since the mid 1990’s
would clearly not have been possible without the liberalizing reforms of 1991. The
importance of liberalization measures can be appreciated by imagining the counterfactual
that India had stayed in its pre-reform state of constraints on entrepreneurial freedoms to
invest and import. New technologies would not have diffused at such a speed and growth
would have been much slower.
At the same time, as stressed by Kochhar et al (2006), it should be acknowledged that
some aspects of the earlier economic regime played a positive role in the pattern of
development later. For example: the creation of a diverse set of skills through import
substitution, an emphasis on tertiary education creating a pool of university graduates for
sophisticated service sector jobs and a government induced expansion of banking
network that helped in mobilizing savings. The initial conditions and their interaction
This is confirmed by an updated analysis from Birthal et. al (2008). This paper compares the period 1981-82 to
1995-96 with the period 1995-96 – 2004-05. Crop output growth decelerates from 3.8% in the first period to 2.1% in
the second period. In the first period, about 62% of output growth is due to rising yields, 20% due to diversification
and 12% due to the price effect. In the second period, these figures are 45%, 43% and 7%, respectively.
In the decade of the 2000s, several Indian states carried out reforms of their agricultural marketing sector
that allowed new marketing institutions including contract agriculture.
These reforms are particularly helpful
to the horticultural sector.
with the fortuitous arrival of new technologies created a distinctive pattern of growth that
would have been hard to predict at the time of liberalization.
Another distinctive feature of the Indian growth experience is the dominance of the
service sector. In East and Southeast Asia, it was the manufacturing sector. One could
look at this in several different ways. If we compare China with India, it is indeed the
manufacturing sector that grew the fastest in China and vice versa in India. However,
both sectors grew faster in both countries than in the rest of the world and both sectors
grew faster in China than in India. Yes, even services grew faster in China than in India.
The main distinction is in terms of what comprised their exports. Here it is services for
India and manufacturing for China. Indeed, it is the software exports to the developed
countries that spread the word that India was unique as a developing country to have
developed a comparative advantage in high-end services. In a curious way, this was the
reason for it being accepted as a development success story despite the fact that it
continues to house more of the world’s poor than any other country.
What are the implications of the fast growing component of the exports being high-end
services as opposed to manufacturing? For one thing, manufacturing uses unskilled labor
more intensively. In the Indian context, this is especially true of unorganized
manufacturing and it is conceivable that manufacturing exports would have generated a
great deal of sub-contracting to the unorganized sector. This, in turn, would have drawn
labor out of agriculture to a greater extent.
Indeed, one major feature of India’s development pattern is that the share of agriculture
in employment has not come down rapidly. In fact, the absolute amount of labor in
agriculture has risen continuously in India while it fell in all countries now developed
during their comparable development phases. An important component of growth –
moving labor from low to high productivity activities – has been conspicuous by its
absence in India. Also, as the labor to land ratio grows, it becomes that much more
difficult to increase agricultural wages and reduce poverty.
There has been much discussion in the literature as to why the manufacturing sector has
not grown faster in India. Inadequate infrastructure, restrictive labor laws and small scale
reservation policy have been identified as the main reasons (e.g., Panagariya (2008)). It
is very possible that these factors reduced the possibility of India emerging as an exporter
of labor intensive manufacturers – a possibility that would have hastened the decline in
poverty. Finding export markets in high-income countries makes the choice set of
production activities independent of domestic demand composition. The growth in
domestic demand will depend on the composition of income growth. In other words, if
the growth in incomes is skewed in favor of high skilled and therefore high-income
groups, it will be the kind of goods and services catered to by the rich that will be found
lucrative by investors. Few of them will be unskilled labor intensive. As a result, the
trickle down to the unskilled (and hence the poor) will be weak.
One possible bottleneck for the Indian pattern of growth is ‘educated workforce’. Given
that the educational premia have been rising rapidly, it does seem like a real possibility.
Most of the fast growing sectors are completely dependent on skilled manpower. If they
run into a serious bottleneck, growth may get choked. A related question is that of
quality. According to a report by the McKinsey Global Institute (2005), “India's vast
supply of graduates is smaller than it seems once their suitability for employment by
multinational companies is considered.” The report stresses that the government must
“adjust the country's educational policy to ward off the looming squeeze on talent”.
McKinsey estimates that India has 14 million young university graduates (those with
seven years or less of work experience). This pool is 1.5 times the size of China's and
almost twice that of the United States. Every year 2.5 million new graduates are added to
this pool. However, according to the report, while the numbers seem encouraging at first
glance, a closer look reveals that India is likely to face a talent crunch in the coming
The problem might get further exacerbated with the current state of primary schooling in
India. ASER (Annual Status of Education Report) (2010), a unique survey of learning in
rural India, estimates that about 47% of rural Indian children in class 5 cannot read a
simple class 2 level text. Even, in class 8, about 17% children cannot read a class 2 level
text. Many of these children may never reach university, but those who do not go to
university will join the labor force and ASER’s results are indicative of the future quality
of the labor force. The Right of Children to Free and Compulsory Education Act, which
was passed by the Parliament in April 2010, makes sure that no child will be held back
till the age of 14 (approximately class 8), regardless of how they perform. This will mean
that children could easily pass middle school (class 7/8) without being tested on any
learning indicators. Even if they drop out after class 8, they would enter the skilled labor
force (by our definition) and could be potentially unemployable. Therefore, it is quite
possible that the so called demographic dividend may disappear if the quality of the labor
force is not improved, even if the non-farm sector creates sufficient jobs to absorb the
increase in labor force.
A larger point is that India’s economic growth is not accompanied by an equally fast
improvement in the functioning of India’s institutions such as the legal system, the
governance and the educational system (Subramanian, 2007). Indeed, it is easier and
faster to transfer technology and bring about productivity improvements. But it is harder
and slower to bring about institutional improvements for sustaining and stabilizing the
growth process.
One important lesson from the Indian experience and especially from its comparison with
other Asian countries is that a country can neglect agriculture at its own peril. The
growth process in India was accompanied by a reduction in poverty at the lower level
(Rs. 356 per capita per month or approximately $1.08 per day). If we consider double the
poverty level ($2.16 per day), a staggering 80% of India’s population was poor in 1983
and the number is about the same in 2004. This is a startling fact and indicates that there
are two Indias: one of educated managers and engineers who have been able to take
advantage of the opportunities made available through globalization and the other – a
huge mass of undereducated mass of people who are making a living in low productivity
jobs in the informal sector – the largest of which is still ‘agriculture’. The most direct
impact on the second India could only come about through improvements in agricultural
productivity. But unfortunately, agriculture is dependent on well functioning rural
institutions. In general, the productivity improvements in the informal sector depend
crucially on access to credit, knowhow and skills and therefore on the quality of
institutions. India’s future will depend a great deal on how these institutional
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... Similarly, Chaudhary (1974) suggested that the degree of inequality had remained unchanged whereas Majumdar and Kapoor (1980) showed a steady increase in interstate inequalities. Prior to 1980s, GDP growth had stagnated at a dismal 3 percent per annum for almost 20 years, which shot up to 5 percent in 1980-1989, that further increased to 6 percent in the 1990-1999 period (Kotwal, Ramaswami, & Wadhwa, 2011). The proportion of poor below the poverty line has significantly declined from 44. 5 percent in 1983-1984 to 27.5 percent in 2004-2005. ...
... This makes the 1991 reforms act as a perfect ground for a natural experiment aimed to test the aforementioned hypothesis. Kotwal et al. (2011) believe that growth was kickstarted post-economic liberalization of 1991 since it provided domestic firms access to capital equipment embodied with new technology, better intermediate inputs, and expanded their choice set to act. With free markets came creative destruction increasing overall productivity, especially in the service sector. ...
... However, it is perplexing how an economy that was majorly employed in the unorganized sector was able to gain from these reforms. Kotwal et al. (2011) point out certain channels, such as direct absorption of technology (e.g., cellphones), cheaper products from organized sector leading to an increase in real wages in the unorganized sector, and demand spillovers from increased incomes of the organized sector. This further suggests how liberalization has the potential to affect economies such as India. ...
... The financial information utility and the investors' reaction to the accounting figures can also be influenced by the financial market on which the company shares are traded and for which the mentioned reports are performed (Alali & Foote, 2012). Simultaneously, in the case of emergent countries, a series of macroeconomic factors, as well as some country risk components, can influence the economic growth on the financial market, as a consequence of economic liberalization or of an increase in competitiveness and efficiency of the listed companies (Kotwal et al., 2011). ...
... Moreover, financial integration could be viewed as a decisive factor of economic growth (Bipasha, 2016), while economic liberalization can trigger economic growth by providing access to capital and new technologies (i.e. elements increasing the companies' competitiveness and efficiency, Kotwal et al., 2011). ...
... However, the share of service sector products in GDP has also increased with rise in per capita income. Within the service sector, business services (including software and information-technology (IT) related services), banking, and communications exhibited a growth, on average, at more than 10 percent per year during the 1990s; while this is also the sector showing retardation in unskilled employment (Kotwal et al. 2011). However, the most obvious feature of service sector growth has been the outstanding expansion of its exports that have been amplified nearly six times faster than world exports of services (Chanda 2007). ...
... In 2011, India has been the eighth largest exporter and seventh largest importer of services (International Trade Statistics 2012). Skill-intensive manufacturing and service industries such as communication services, financial services and business services in India experienced significant growth in exports during the liberalised regime, where software accounted for the highest share of all service exports, at least up to the recent financial crisis (Panagariya 2004;Kotwal et al. 2011). ...
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This paper, using a full-employment general equilibrium model for a developing Asian country like India with internationally non-traded goods and international fragmentation in skill-intensive production, illuminates how liberalised input trade, by enhancing demand for skills in the skill-intensive service sectors, could affect the unskilled wages prevailing in the informal sectors and employment conditions in those sectors, through the existence of finished non-tradable and the corresponding domestic demand-supply forces. The model economy is characterised by dual unskilled labour market with unionised formal and non-unionised informal sectors. Quantitative analyses have also been performed to simulate how the changes in elasticities of factor substitution in production of different sectors account for the movement in informal wage and therefore the movement in skilled–unskilled wage gap. Therefore, the relative wage inequality in a developing Asian country like India with dual labour markets has not been governed only by the increase in the skilled wages.
... Although Indian economy has been passing through a high growth trajectory during the post-liberalisation (1991) periods (Adeel-Farooq et al., 2017;Chandrasekhar & Pal, 2006;Kotwal et al., 2011), its global ranking of human development is still remaining very low. Though incidence of poverty declined massively (Chauhan et al., 2016;Planning Commission, 2012), a large sections of Indian population (rural poor and socially marginalised groups including women) still lack access to basic financial products and services (Kapoor, 2014;Lenka & Sharma, 2017;Sharma & Chatterjee, 2017). ...
This article examines the existing synergy between financial inclusion and human development in Indian states during the post-liberalisation periods (1993–2015). Using both principal component analysis and panel data regression models, first, the impact of financial inclusion on human development is measured. Second, the reverse causality from human development to financial inclusion is estimated to know whether human development should be a pre-condition for ensuring greater financial inclusiveness in Indian states. It is found that financial inclusion has a positive and statistically significant impact on human development, along with other control variables such as social sector expenditure, per capita state gross domestic product and capital receipt. However, it found that the lack of urbanisation (measured by the percentage of rural population) has a negative and significant impact on the process of human development in Indian states. On the other hand, since human development has also a significant reverse causal connection with financial inclusion, it is argued that ensuring financial inclusion through urbanisation measures would not only improve the level of human development in Indian states, but it would also sustain the process of inclusive development in itself due to the existing feedback loop with the later.
... [6]. India is the least urbanised country among the world's top ten economies [7]. Most scholars agree that migration, rural unemployment, a lack of proper infrastructure, poor medical facilities, backward transportation systems, and other push factors are the real reasons why people move to cities [8][9][10]. ...
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A socio-ecological system is a bio-geo-physical system that is inextricably linked to society and ecosystems, and in urban ecological science, a balance between the natural environment and human society and culture is sought. Migration is a common reason for population growth in urban areas because it gives people access to a better way to live and make money. Hypothetical ideas can be made about the ecological background of urban areas. For example, the growth of the population in rural or semi-rural areas creates pressure or flow of migrants to urban areas for various reasons. This helps the process of urbanisation, and urbanisation will affect the socio-ecological and socio-climate variation. Based on this background, this study explored the socio-ecological links between population growth, migration, urbanisation, and socio-climatic variation in Andhra Pradesh and Telangana. The data for this study were gathered from secondary sources such as the Census of India, the Planning Department, the State Portal, the Integrated Government Online Directory, and a few selected scientific reports. Some social sciences statistical techniques, general cartographic and GIS mapping techniques were used, and data were quantitatively and qualitatively measured. Key findings demonstrating the links and relationships between population growth, migration, and urbanisation at the district level in Andhra Pradesh and Telangana. The district's main city area also serves as a draw factor for migrants due to job opportunities and other amenities. Migration profile depicting the internal movement scenario of the study area, as well as the links to urban growth and expansion. The second set of findings discussed the socio-ecological implications of urbanisation and socio-climate variation in the study area. It is possible to conclude that the benefits of various opportunities, facilities, job scope, and income draw people away from rural areas and into cities. Finally, urbanisation causes socio-ecological variation, which can have both positive and negative consequences. This study uncovered some socio-environmental issues and made recommendations for mitigating urban socio-ecological problems and correcting haphazard urbanisation.
... The political economy of India has rapidly changed after the liberalization of its economy in 1990. Presently it has a market-based system and is the world's second fastest growing economy after China (Kotwal et al., 2011). India is recognized to be the biggest democracy on the globe. ...
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Governments around the world have begun to adopt measures in support of electric vehicles. The supply push policies have been traditionally studied leaving the demand side innovation policy without much investigation. Therefore, policy makers lack institutional knowledge and policy experience. The demand side innovation policies have always been part of the public policy decisions but not part of the innovation policy strategy. They point on increasing the uptake of the innovation and the growth in manufacture production. The aim of this study is to find good policy pull strategies to support E-cars in India with a mix of policies to build the needed capacity for E-cars in Maharashtra state, Mumbai. Maharashtra has the highest number of vehicles in India with Mumbai being one of the most polluted cities in India. India wants to increase the domestic economic activity and reduce GHGs. E-cars are known to reduce CO2 emissions, pollutants, and noise. The research question is: How can Mumbai city implement policy pull-strategies that drive the scale up of E-cars? Firstly, barriers for E-cars development in India, Maharashtra state, Mumbai are identified. Subsequently, good policy actions are pointed out in the UK, Sunderland and China, Shenzhen city and suggested as possible actions for India. Data was found through a literature review of academic and non-academic sources, been compiled using a literature synthesis. A case based oriented research is used. The TIS framework is used to arrange the data and make the analysis. Key barriers identified are missing environmental reasons in policy designs, low car ownership preference of consumers, lack of proper infrastructure and standards, high upfront costs of E-cars and lack of waivers. Some UK learnings: include environmental issues in policies; inform consumers about E-cars total cost of ownership; develop E-cars charging infrastructure; encourage E-cars producers to adopt business models. Few recommendations from China: develop the charging infrastructure for E-buses; adopt business models for E-buses; adopt uniform charger standards. These recommendations for policy makers in India may support further academic research such as to study the environmental policy issues using the environmental evaluation framework due to the missing environmental instruments in policy actions, also to make a study with all including missing functions when data is available for a more complete understanding of the TIS in India.
... The Economic Journal to literacy and primary education may bring greater economic benefits than investments in higher (i.e., secondary) education (Kotwal et al., 2011(Kotwal et al., , 1159. ...
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Does pre-colonial history – and in particular the role of interstate warfare – help explain long-run development patterns across India? To address this question, we construct a new geocoded database of historical conflicts on the Indian subcontinent. We document a robust positive relationship between pre-colonial conflict exposure and local economic development today. Drawing on archival and secondary data, we show that districts that were more exposed to pre-colonial conflict experienced greater early state-making, followed by lower political violence and higher investments in physical and human capital in the long term.
... On a more positive note, some studies show positive evidence of increased household agricultural production, even of nonfood crops such as Bt cotton, with increased income leading to increased dietary diversity and diet quantity (Qaim 2003;Qaim and Janvry 2003;Qaim and Zilberman 2003). Still other literature simply shows greater consumption of milk, meat, fruits, and vegetables (Chand, Raju, and Pandey 2007;Kotwal, Ramaswami, and Wadhwa 2011;Ramaswami, Pray, and Lalitha 2012). ...
Malnutrition, in all its forms, is a critical global public health problem. Food production and consumption patterns are also the largest challenges to planetary boundaries. Transformative change is needed in research, information and outreach, political commitment, and financial and institutional capacity to achieve sustainable and equitable food systems. Change, to date, has been incremental, not transformative; however, it is essential to significantly improve outcomes, an issue which will be a central theme of the United Nations’ Food Systems Summit 2021 and the intensive discussions leading up to it. The Sustainable Development Goals (SDGs) have powered the recent food security and nutrition discourse. And yet, SDG2 (zero hunger) is not broad enough; it does not pay enough attention to the growing incidence of obesity, as do the World Health Assembly targets for 2025. Furthermore, the interrelationships of the sub-targets of SDG2 are anything but straightforward. Growth in agricultural productivity does not necessarily increase incomes of small farmers, and productivity growth does not always assure improved nutrition. Increased income does not necessarily lead to improved nutrition. This also applies to the relationship of SDG2 to several other of the 16 SDGs. Increasingly, the concept of multidimensional poverty (MDP) has received attention in explaining food security and nutrition. MDP is substantially higher than income poverty, particularly among children. In addition, the chapter examines the relationship of gender inequality and nutrition, gender and obesity, nutrition transition, and the roles of changing lifestyles, food systems, and modern food chains.
... Since the extensive reforms followed in 1991, there have been futuristic policy changes in diverse sectors all aimed at opening up the economy to a greater private sector entrepreneurship as well as to foreign trade and investment. The growth rate of GDP, which had stayed around 3.5 per cent per annum for twenty years prior to 1980, shot up to about 5 per cent in the 1980s (1980 to 1989), and increased further in the 1990s (1990-1999) to 6 per cent (Kotwal et al. 2011). Over the last few years, India's real GDP growth clocked an average of 7.7 per cent during 2014-18 (RBI Report, 2019). ...
Conference Paper
ABSTRACT The Indian economy is facing many challenges to maintain sustainable high economic growth rates. India is one of the largest economies in the world and has created an economy with a natural growth rate of about 7% over the last two decades. In spite of the recent slowdown, the enduring development forthcoming of the Indian economy is sure because of its young populace, comparing low reliance proportion, sound funds, and speculation rates, and expanding joining into the worldwide economy. The Indian economy has the potential to turn into one of the biggest economies by mid-century. The accomplishments that India has achieved at the level of development and high growth rates formed the basis of the most important studies and future expectations for the Indian economy. The continuous increase in the level of individuals’ income and the improvements of the quality of life are important indicators, but on the other hand, the absence of studies and statistics on poverty, inequality levels, the identification of the beneficiaries of economic growth, and the reality of social indicators all constitute a basis for academic research and policymakers for the possibility of accurate forecasting of the economic growth and future expectations. Therefore, the economy still faces various problems and challenges, which threatened rapid and sustainable growth. Keywords: Economic Growth, Indian Economy, Workforce, Unemployment, Inequality
Growth theory has traditionally assumed the existence of an aggregate production function, whose existence and properties are closely tied to the assumption of optimal resource allocation within each economy. We show extensive evidence, culled from the micro-development literature, demonstrating that the assumption of optimal resource allocation fails radically. The key fact is the enormous heterogeneity of rates of return to the same factor within a single economy, a heterogeneity that dwarfs the cross-country heterogeneity in the economy-wide average return. Prima facie, we argue, this evidence poses problems for old and new growth theories alike. We then review the literature on various causes of this misallocation. We go on to calibrate a simple model which explicitly introduces the possibility of misallocation into an otherwise standard growth model. We show that, in order to match the data, it is enough to have misallocated factors: there also needs to be important fixed costs in production. We conclude by outlining the contour of a possible non-aggregate growth theory, and review the existing attempts to take such a model to the data.
Market-oriented structural reforms in India, begun in the 1980s and intensified in the 1990s, are widely believed to have put the economy on a path of higher growth. But there are concerns that outcomes in labor markets have not improved for large segments of the labor force. Many observers of India’s labor markets are bothered by the slow growth of employment in the organized sector—where the “good” jobs are. Despite growth of around 5% in GDP per capita between 1993/94 and 1999/2000, the share of the organized sector in total employment decreased from 7.3% to 7.1%.1 At the same time, jobs in the organized sector have themselves been undergoing a change, with contract labor getting a growing share of employment. More broadly, workers on daily or periodic contracts have increased their share of total wage and salary employment, in what some observers have described as the “casualization” of the Indian workforce.