Three Empirical Essays on Trade and Development in India
by
Petia Topalova
B.A., Brandeis University (1999)
Submitted to the Department of Economics
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
at the
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Three Empirical Essays on Trade and Development in India
by
Petia Topalova
Submitted to the Department of Economics
on 15 May 2005, in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
Abstract
This thesis is a collection of three empirical essays on economic development and trade in India.
Chapter 1 uses the sharp trade liberalization in India in the early 1990s, spurred to a large
extent by external factors, to measure the causal impact of trade liberalization on poverty and
inequality in districts in India. Variation in pre-liberalization industrial composition across
districts in India and the variation in the degree of liberalization across industries allow for
a difference-in-difference approach, establishing whether certain areas benefited more from, or
bore a disproportionate share of the burden of liberalization. In rural districts where industries
more exposed to liberalization were concentrated, poverty incidence and depth decreased by
less as a result of trade liberalization, a setback of about 15 percent of India's progress in
poverty reduction over the 1990s. The findings are related to the extremely limited mobility
of factors across regions and industries in India. Indeed, in Indian states where inflexible labor
laws impeded factor reallocation, the adverse impact of liberalization on poverty was more
pronounced. The findings, consistent with a specific factors model of trade, suggest that to
minimize the social costs of inequality, additional policies may be needed to redistribute some
of the gains of liberalization from winners to those who do not benefit as much. Creating a
flexible institutional environment will likely minimize the need for additional interventions.
Using a panel of firm-level data, Chapter 2 examines the effects of India's trade reforms
on firm productivity in the manufacturing sector, focusing on the interaction between this
policy shock and firm and environment characteristics. The rapid and comprehensive tariff
reductions-part
of an IMF-supported adjustment program with India in 1991-allow us to
establish a causal link between variations in inter-industry and inter-temporal tariffs and consistently estimated firm productivity. Specifically, I find that reductions in trade protectionism
led to higher levels and growth of firm productivity, with this effect strongest for private companies. Interestingly, state-level characteristics, such as labor regulations, investment climate,
and financial development, do not appear to influence the effect of trade liberalization on firm
productivity.
Chapter 3, coauthored with my advisor Esther Duflo, studies the impact of reservation for
women on the performance of policy makers and on voters' perceptions of this performance.
Since the mid 1990's, one third of Village Council head positions in India have been randomly
reserved for a woman: In these councils only women could be elected to the position of chief.
Village Councils are responsible for the provision of many local public goods in rural areas.
Using a data set which combines individual level data on satisfaction with public services
3
with independent assessments of the quality of public facilities, we compare objective measures
of the quantity and quality of public goods, and information about how villagers evaluate the
performance of male and female leaders. Overall, villages reserved for women leaders have more
public goods, and the measured quality of these goods is at least as high as in non-reserved
villages. Moreover, villagers are less likely to pay bribes in villages reserved for women. Yet,
residents of villages headed by women are less satisfied with the public goods, including goods
that are beyond the jurisdiction of the Panchayat. This may help explain why women rarely win
elections even though they appear to be at least as effective leaders along observable dimensions,
and are less corrupt.
Thesis Supervisor: Esther Duflo
Title: Professor of Economics
Thesis Supervisor: Abhijit Banerjee
Title: Ford Foundation International Professor of Economics
4
Acknowledgements
I am grateful to my parents, Rumiana and Boris Topalovi, and my sister, Jana TopalovaGura, for their love, support and belief in me.
This thesis would not have been possible without continual help from my advisors, Esther
Duflo and Abhijit Banerjee. Their teaching inspired my interest in development
economics and the topics explored in this thesis. I am enormously appreciative of their
invaluable academic advice, infinite encouragement and patience.
The faculty and students at MIT have served as inspiration and motivating force during
the past five years. In particular, this work benefited greatly from comments and
suggestions by Pol Antras, David Autor, Shawn Cole, Emmanuel Fahri, Ivan FernandezVal, Rema Hanna, Ashley Lester, Andrei Levchenko, Sendhil Mullainathan and Tal
Regev. Nina Pavcnik and Robin Burgess provided very valuable advice. Kalpana
Kochhar and David Cowen provided data for the second chapter of this thesis, while Sita
Sekhar provided data for the third. I thank Joanna Lahey and especially Shawn Cole for
extensive editing.
I am indebted to Adam Jaffe, my undergraduate advisor, as well as Amy Candell,
Cresenta Fernando, Rajiv Mallick and Elena Ranguelova for steering me towards doing a
Ph.D. in economics.
I am grateful for the financial support provided by the MIT Department of Economics,
the McArthur Foundation and the Finch Fellowship.
I want to thank all my friends for keeping me sane during the past five years, especially
Ariele who never tired of my complaints and made me laugh when I was down, and
Kavita, Diana, Lisa, Rema and Dafne who have been wonderful roommates and
confidantes.
Finally, graduate school would not have been the same had I not met my classmate and
partner Shawn Cole. Shawn held my hand through every success and misfortune with
endless advice and encouragement. His integrity, concern for others and openmindedness have taught me some of the most valuable lessons in graduate school.
In memoryof my dearfriend CresentaFernando
Contents
1 Factor Immobility and Regional Impacts of Trade Liberalization: Evidence
on Poverty and Inequality from India
11
1.1 Introduction ..............
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The Indian Trade Liberalization
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Empirical Strategy, Measurement of C)utcomes s and Trade Expos ure
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1.10 Bibliography ..............................
2 Trade Liberalization and Firm Productivity: The Case of India
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3 Unappreciated Service: Performance, Perceptions, and Women Leaders in
India
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Institutions: The Panchayat system and Reservations
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Chapter 1
Factor Immobility and Regional
Impacts of Trade Liberalization:
Evidence on Poverty and Inequality
from India
Summary
Although it is commonly believedthat trade liberalizationresults in higher GDP,
little is known about its effects on poverty and inequality. If economic integration leads to
further growth in income inequality and a rise in the number of people in poverty, especially
in developing countries with large vulnerable populations, the benefits of liberalization may be
realized at a substantial social cost unless additional policies are devised to redistribute some
of the gains from the winners to the losers. This paper uses the sharp trade liberalizationin
India in 1991, spurred to a large extent by external factors, to measure the causal impact of
trade liberalizationon poverty and inequalityin districts in India. Variationin pre-liberalization
industrial compositionacross districts in India and the variation in the degreeof liberalization
across industries allow for a difference-in-difference approach, establishing whether certain areas
benefitedmore from, or bore a disproportionateshare of the burden of liberalization.In rural
districts where industries more exposed to liberalization were concentrated, poverty incidence
and depth decreased by less as a result of trade liberalization, a setback of about 15 percent of
11
India's progress in poverty reduction over the 1990s. The findings are related to the extremely
limited mobility of factors acrossregions and industries in India. Indeed, in Indian states where
inflexible labor laws impeded factor reallocation, the adverse impact of liberalization on poverty
was more pronounced. The findings, consistent with a specific factors model of trade, suggest
that to minimize the social costs of inequality, additional policies may be needed to redistribute
some of the gains of liberalization from winners to those who do not benefit as much. Creating
a flexible institutional environment will likely minimize the need for additional interventions.
1.1
Introduction
After the Second World War, India, along with other developing countries, chose a strategy
of import substitution to promote industrialization. In the past two decades, however, many
countries have begun to favor global economic integration, and in particular trade liberalization,
as a development strategy. Although it is commonly believed that trade liberalization results in
a higher Gross Domestic Product, little is known about its effects on income distribution. The
distributional impacts of trade are particularly important in developing countries, where income
inequality is typically pronounced and there are large vulnerable populations.
If economic
integration leads to further growth in income inequality and a rise in the number of people
in poverty, the benefits of liberalization may be realized at a substantial social cost unless
additional policies are devised to redistribute some of the gains from the winners to the losers.
Standard economic theory (Heckscher-Ohlin model) predicts that gains to trade should flow
to abundant factors, which suggests that in developing countries, unskilled labor would benefit most from globalization. The rising skill-premium in the U.S. is often cited in support
2
of standard trade theory.1 However, recently these sharp predictions have been challenged.
Trade liberalization could reduce the wages of unskilled labor even in a labor abundant country,
thereby widening the gap between the rich and the poor. Even if global economic integration
induces faster economic growth in the long run and substantial reductions in poverty, if reallocation of factors across sectors is impeded, the adjustment might be costly, with the burden
1
See Freeman and Katz (1991), Gaston and Trefler (1993) among others.
See Stiglitz (1970), Davis (1996), Feenstra and Hanson (1997), Cunat and Maffezzoli (2001), Kremer and
Maskin (2003), Banerjee and Newman (2004).
2
12
falling disproportionately on the poor (Banerjee and Newman, 2004). 3 In developing countries,
where rigidities in the labor market and credit market imperfections are more pronounced, these
theories are particularly relevant. Due to the ambiguity of the theory, the question of how trade
liberalization affects poverty and inequality remains largely an empirical one.
Recent empirical work has addressed this question, focusing mostly on the effect of trade liberalization on within country income inequality. Studies using cross-country variation typically
find little relationship between trade liberalization and levels or rates of change of inequality. 4
However, these studies face significant limitations: cross-country data may not be comparable,
sample sizes are small, and changes in liberalization may be highly correlated with other variables important to income processes. A promising alternative is to use micro evidence from
household and industry surveys. Several studies examine the relationship between trade reforms and skill-premia, returns to education, industry-premia, and the size of informal labor
markets. 5 However, the findings of these studies are typically based on correlations and may
not always be given a causal interpretation.
And while there is some evidence on the effect of
liberalization on industrial performance and wage inequality, the literature has fallen short of
measuring the impact of these performance changes on poverty.
This paper investigates the impact of trade reforms on poverty and inequality in Indian
dlistricts. Does trade liberalization affect everyone equally or does it help those who are already
relatively well off while leaving the poor behind? How does it affect income distributions within
rural and urban areas? Is the effect of liberalization felt equally across regions in India? What
are the mechanisms through which trade affects income: do institutional characteristics that
ease the reallocation of factors across sectors, such as labor laws, play a role in the propagation
of liberalization shocks?
India presents a particularly relevant setting to seek the answers to these questions. First,
India is the home of one third of the world's poor (World Bank, 2001). Second, the nature of
India's trade liberalization-sudden,
3
See also Mayer (1974), Davidson
comprehensive and largely externally imposed-facilitates a
et al. (1999).
"See Edwards (1998), Dollar and Kraay (2002), Milanovic (2002), Lundberg and Squire (2003) and Rama
(2003).
5
See Cragg and Epelbaum (1996), Revenga (1997), Hanson and Harrison (1999), Feliciano(2001), Goldberg
and Pavcnik (2001), Wei and Wu (2001), Attanasio et al. (2004), Porto (2004), Verhoogen (2004), Hanson (2004),
Golberg and Pavcnik (2004b) among others.
13
causal interpretation of the findings. India liberalized its international trade as part of a major
set of reforms in response to a severe balance of payments crisis in 1991. Extremely restrictive
policies were abandoned: the average duty rate declined by more than half and the percentage
of goods importable without license or quantitative restriction rose sharply. The lower average
tariffs, combined with changes in the tariff structure across industries, provide ample variation
to identify the causal effects of trade policy on income processes.
Coincident with these tariff reductions were significant changes in the incidence of poverty
and income inequality. To determine whether there is a causal link between liberalization and
changes in poverty and inequality, this paper exploits the variation in the timing and degree
of liberalization across industries, and the variation in the location of industries in districts
throughout India. The interaction between the share of a district's population employed by
various industries on the eve of the economic reforms and the reduction in trade barriers in these
industries provides a measure of the district's exposure to foreign trade. This paper establishes
whether district-level poverty and inequality are related to the district-specific trade policy
shocks. Because industrial composition is predetermined and trade liberalization was sudden
and externally imposed, it is appropriate to causally interpret the correlation between the levels
of poverty and inequality and trade exposure. Of course if there were migration across districts
in response to changes in factor prices, an analysis comparing districts over time may not give
the full extent of the impact of globalization on inequality and poverty in India. However,
the analysis still gives a well defined answer to the question of whether inequality and poverty
increased more (or less) in districts that were affected more by trade liberalization (and in any
case there is very limited migration as will be shown below).
It is important to note that this empirical strategy does not measure the level effect of
liberalization on poverty in India, but rather the relative impact on areas more or less exposed
to liberalization. Therefore, while opening to trade may have had an overall effect of increasing
or lowering the poverty rate and poverty gap, this paper captures the fact that these effects
were not equal throughout the country, and certain areas and certain segments of the society
benefited less (or suffered more) from liberalization.
The study finds that rural districts where industries more exposed to liberalization were
concentrated experienced a slower progress in poverty reduction, both in terms of poverty
14
incidence and poverty depth. The effect is quite substantial. According to the most conservative
estimates, compared to a rural district experiencing no change in tariffs, a district experiencing
the mean level of tariff changes saw a 2 percentage points increase in poverty incidence and a
0.6 percentage points increase in poverty depth. This set back represents about 15 percent of
India's progress in poverty reduction over the 1990s.
Finding any effect of trade liberalization on regional outcomes runs counter to standard
trade theory, where factors are mobile both across geographical regions within a country and
across industries.
Factor reallocation would equate incidence of poverty across regions and
factor returns across industries. However, standard theory assumptions do not apply in rural
India: migration in rural India is remarkably low, with no signs of an upward trend after the
1991 reforms.
This paper demonstrates the validity of theories of trade liberalization that do not assume
free movement of factors across sectors, by uncovering the importance of factor mobility, and
institutions that may affect it, in mitigating the unequal effects of trade liberalization. Indian
states with inflexible labor laws, where I find no measurable effect of liberalization on the
allocation of labor across sectors, are precisely the areas where the adverse impact of trade
opening on poverty was felt the most. In contrast, in states with flexible labor laws, movements
of capital and labor across sectors and the overall faster growth of manufacturing, eased the
shock of the relative price change. While there was no effect on inequality in India as a whole,
in these states, trade liberalization seems to have led to a rise in inequality.
Finally, this paper examines the evolution of industry wages and wage premia over time, and
how these vary with the extent of liberalization. Wages and wage premia seem to have absorbed
the effect of the relative price change. The findings are thus consistent with a specific-factor
model of trade.
The remainder of the paper is organized as follows. Section 1.2 presents the conceptual
framework for this study and Section 1.3 describes the Indian reforms of 1991 focusing on trade
liberalization. Section 1.4 presents the data used in the analysis. In Section 1.5 the empirical
strategy is explained, and the results follow in Section 1.6. Section 1.7 considers the mechanisms
that drive the evolution of poverty and inequality. Section 1.8 concludes the paper.
15
1.2
1.2.1
Background
Conceptual Framework
International trade theory can deliver contradictory predictions regarding the effect of international trade on income distribution within a country. To provide a framework for my empirical
strategy and results, I describe the two basic trade models that demonstrate the link between
factor prices and product prices.
In the Heckscher-Ohlin (H-O) model with its companion Stolper-Samuelson theorem, countries will export goods that use intensively the factors of production that are relatively abundant, and import goods that use intensively the relatively scarce factor of the country. Trade
liberalization raises the real returns to the relatively abundant factor (unskilled labor in the
case of India) as the relative price of the unskilled labor intensive good increases, thus reducing
inequality, and possibly poverty. In the H-O model, the factors of production are assumed to
be perfectly mobile, and their returns are equalized across sectors. Thus, price changes only
affect economy-wide, and not sector-specific returns. Movements of labor and capital across
sectors are precisely what allow countries to reap the benefits of trade openness in this classical
trade model.
However, these stark predictions can be easily reversed. If labor employed by a given
industry is temporarily immobile and can reallocate only gradually over an extended period
of time, the short-run response of factor returns to exogenous price changes will differ from
the long-run equilibria with the bulk of the adjustment stemming from adjustments in factor
returns, as opposed to employment and output. This immobility may arise from capital market
imperfections (Banerjee and Newman, 2004), or frictions in the labor market (Davidson et al.
(1999) develop the case when there are search costs in the labor market).
The institutional
environment as reflected in labor regulations (for example legislation on dismissals, imposition
of severance payments etc.) can be another important source of relationship specific rents and
can induce sectoral specific attachment. In a cross-country setting, Caballero et al. (2004) find
that job security regulation clearly hampers the creative-destruction process and the annual
speed of adjustment of employment to shocks.
To illustrate the simplest case, when labor immobility is assumed to be exogenous, consider
16
each district in India to be a two-by-two economy with two factors, K and L, and two goods, X
and Y. The goods are produced according to functions Fx(Kx, LX) and Fy(Ky, Ly), assumed
to be homogeneous of degree 1, twice differentiable, strictly quasi-concave and increasing in both
factors of production (the Y good is more capital intensive). Kx, LX, Ky, Ly are the capital and
labor allocated to the production of goods X and Y, respectively. The total endowment of these
factors in the district is L and K. NormalizingPx = 1, py = p, the long-run equilibrium, when
both K and L are mobile across industries, is characterized by the following set of equations:
1) Lx ± Ly = L, 2) Kx + Ky = K, 3) W = FLXX = FLYY, 4) r = FKXX = FKYY.Factor
markets clear and the returns to factors are equalized across industries.
In the short run, however, only capital is perfectly mobile between industries within the
district. The equilibriumwilltake the followingform: 1) Lx =
3) Wx
Fxx(Kx,Lx),
wy =pFFjy(Ky,Ly),
4) r
Lx, = Ly
L, 2) Kx+Ky = K,
FKXX(KX,LX) = PFKYY(KY, Ly),
where LX and Ly are the optimal amounts of labor allocated to the production of X and Y
in the long-run. Note that the returns to labor are not equalized across industries. There are
industry-specific rents (which in this empirical work are referred to as industry wage premia).
Trade liberalization can be seen in this framework as a reduction in the relative price of
the capital intensive good, p. It is obvious from the set of equations describing the short run
equilibrium that the effect of this price change on labor returns depends crucially on the sector
in which labor is employed.6 The fall in p will lead to a less than proportionate rise in the
earnings of workers in industry X and an improvement in their welfare. The mobile factor K,
however, will experience a less than proportionate drop in its returns, and the specific factor in
the Y industry a more than proportionate fall in its earnings. Unlike the standard H-O model,
both factors employed in the industry with tariff reduction experience a drop in earnings. The
workers in industry Y are unambiguously worse off as their income has decreased both in terms
of good Y and good X. If these workers are close to or below the poverty line, one will see an
increase in aggregate poverty rates and poverty depth.
The juxtaposition of these two basic models of trade demonstrates that the effect of trade
6The elasticity of factor returns with respect to output prices can be derived by totally differentiating the
dr
=- FKx Kxxx(KxLx)
0
2 -- FKXKXX
Xr
equations characterizing the short run equilibrium:
dwy ap
PFKK
yLy]
< 0 where A
dp -
A_X
= -[FKXKXX(Kx,Lx)
17
dp
+pFKyKyY(KY,
-
A
Ly)] > O.
Lxwx
=
liberalization on poverty is largely dependent upon what extent factors are able to relocate in
response to the change in relative prices. If labor were fully mobile, in this example all workers
would have been unambiguously better off, and capital unambiguously worse off.
1.2.2
Related Literature
This study is related to several strands of literature. First, it fits into the empirical literature
on the effects of trade reforms on labor outcomes. This literature has largely dealt with the
experience of Latin American countries: Cragg and Epelbaum (1996), Revenga (1997), Hanson
and Harrison (1999), Feliciano (2001), Goldberg and Pavcnik (2001), Attanasio et al. (2004),
Verhoogen (2004), Hanson (2004). Currie and Harrison (1997) study the effect of trade liberalization in Morocco. These papers typically use variation in trade policy over time and across
manufacturing industries in urban areas to identify the relationship between trade policy and
labor market outcomes, focusing mostly on the effect on wages or labor income.7 ' 8 In general,
previous studies found small effects of trade on wage inequality of workers in the manufacturing
sector. This paper extends this type of analysis, by focusing not only on the effect of trade reforms on relative wages in manufacturing, but by looking at regional outcomes in general, thus
capturing how trade effects seeped from the directly affected workers to the their dependents,
as well as people involved in the non-traded goods sectors.
This is also one of the first studies to examine the link between trade liberalization and
poverty. So far, Porto (2004) and Goldberg and Pavcnik (2004b) have analyzed the relationship between trade and poverty in the case of Argentina and Colombia respectively. Porto's
approach has several advantages. It provides a general equilibrium analysis of the relationship
between trade liberalization and poverty, by simultaneously considering the labor market and
consumption effects of trade liberalization.
His results, however, rely on simulations based
on cross-sectional data. Goldberg and Pavcnik (2004b) exploit cross-sectional and time-series
variation of trade protection at the industry level and find little evidence of a link between the
Colombian trade reforms and poverty. Yet, as the study focuses on urban areas, and people involved in manufacturing, it may miss the effects on the poorest segments of society. This paper
7
Verhoogen (2004) uses the peso crisis of late 1994 to test for the relationship between trade induced quality
upgrading and wage inequality in Mexico.
8
Wei and Wu (2001) study the impact of trade on urban-rural inequality in China.
18
relates plausibly exogenous changes in trade policy to poverty and inequality, studying both
manufacturing and agricultural workers in both urban and rural areas. Moreover, by defining
the district as the unit of observation, it overcomes important selection and composition effects
that studies at the industry level may face. Finally, the paper contributes to the literature on
industry wage premia and their relation to trade protection.
1.3
The Indian Trade Liberalization
India's post-independence development strategy was one of national self-sufficiency, and stressed
the importance of government regulation of the economy. Cerra et al. (2000) characterized it
as "both inward looking and highly interventionist, consisting of import protection, complex
industrial licensing requirements, pervasive government intervention in financial intermediation and substantial public ownership of heavy industry." In particular, India's trade regime
was amongst the most restrictive in Asia, with high nominal tariffs and non-tariff barriers,
including a complex import licensing system, an "actual user" policy that restricted imports
by intermediaries, restrictions of certain exports and imports to the public sector ("canaliza-
tion"), phased manufacturing programs that mandated progressive import substitution, and
government purchase preferences for domestic producers.
It was only during the second half of the 1980s, when the focus of India's development
strategy gradually shifted toward export-led growth, that the process of liberalization began.
Import and industrial licensing were eased, and tariffs replaced some quantitative restrictions,
although even as late as 1989/90 a mere 12 percent of manufactured products could be imported
under an open general license; the average tariff was still one of the highest, greater than 90
percent (Cerra et al., 2000).
However, the gradual liberalization of the late 1980s was accompanied by a rise in macroeconomic imbalances-namely
fiscal and balance of payments deficits- which increased India's
vulnerability to shocks. The sudden increase in oil prices due to the Gulf War in 1990, the
drop in remittances from Indian workers in the Middle East, and the slackened demand of
important trading partners exacerbated the situation.
Political uncertainty, which peaked in
1990 and 1991 after the poor performance and subsequent fall of a coalition government led
19
by the second largest party (Janata Dal) and the assassination of Rajiv Gandhi, the leader of
the Congress Party, undermined investor confidence. With India's downgraded credit-rating,
commercial bank loans were hard to obtain, credit lines were not renewed and capital outflows
began to take place.
To deal with its external payments problems, the government of India requested a stand-by
arrangement from the International Monetary Fund (IMF) in August 1991. The IMF support
was conditional on an adjustment program featuring macroeconomic stabilization and structural
reforms. The latter focused on the industrial and import licenses, the financial sector, the tax
system, and trade policy. On trade policy, benchmarks for the first review of the Stand-By
Arrangement included a reduction in the level and dispersion of tariffs and a removal of a
large number of quantitative restrictions (Chopra et al., 1995). Specific policy actions in a
number of areas - notably industrial deregulation, trade policy and public enterprise reforms,
and some aspects of financial sector reform - also formed the basis for a World Bank Structural
Adjustment Loan, as well as sector loans.
The government's export-import policy plan (1992-97) ushered in radical changes to the
trade regime by sharply reducing the role of the import and export control system. The share of
products subject to quantitative restrictions decreased from 87 percent in 1987/88 to 45 percent
in 1994/95. The actual user condition on imports was discontinued. All 26 import licensing
lists were eliminated and a "negative" list was established (Hasan et al., 2003). Thus, apart
from goods in the negative list, all goods could be freely imported (subject to import tariffs)
(Goldar, 2002). In addition to easing import and export restrictions, tariffs were drastically
reduced (Figure 1.1, Panel A and B). Average tariffs fell from more than 80 percent in 1990
to 37 percent in 1996, and the standard deviation of tariffs dropped by 50 percent during the
same period. The structure of protection across industries changed (Figure 1.1 Panel G). Figure
1.1 Panel H shows the strikingly linear relationship between the pre-reform tariff levels and the
decline in tariffs the industry experienced. This graph reflects the guidelines according to which
tariff reform took place,9 namely reduction in the general level of tariffs, reduction of the spread
or dispersion of tariff rates, simplification of the tariff system and rationalization of tariff rates,
along with the abolition of numerous exemptions and concessions. Agricultural products, with
9
The guidelines were outlined in the Chelliah report of The Tax Reform Commission constituted in 1991.
20
the exception of cereals and oil seeds, faced an equally sharp drop in tariffs, though the non-tariff
barriers of these products were lifted only in the late 1990s (Figure 1.1, Panels C-F). There were
some differences in the magnitude of tariff changes (and especially NTBs) according to industry
use type: i.e. Consumer Durables, Consumer Nondurables, Capital goods, Intermediate and
Basic goods (Figure 1.1, Panel D and F). Indian authorities first liberalized Capital goods,
Basic and Intermediates, while Consumer Nondurables and agricultural products were slowly
moved from the "negative" list to the list of freely importable goods only in the second half
of the 1990s. The Indian Rupee was devalued 20 percent against the dollar in July 1991 and
further devalued in February 1992. By 1993, India had adopted a flexible exchange rate regime
(Ahluwalia, 1999).
Following the reduction in trade distortions, the ratio of total trade in manufactures to GDP
rose from an average of 13 percent in the 1980s to nearly 19 percent of GDP in 1999/00 (Figure
1.2). Export and import volumes also increased sharply from the early 1990s, outpacing growth
in real output (Figure 1.2). India's imports were significantly more skilled-labor intensive than
India's exports and remained so throughout the 1990s, as demonstrated in Figure 1.3 which
plots cumulative export and import shares by skill intensity in 1987, 1991, 1994 and 1997.
India remained committed to further trade liberalization, and since 1997 there have been
further adjustments to import tariffs. However, at the time the government announced the
export-import policy in the Ninth Plan (1997-2002), the sweeping reforms outlined in the previous plan had been undertaken and pressure for further reforms from external sources had
abated.
1.4 Data
The data for this analysis were drawn from three main sources. Household survey data are available from the 1983-84, 1987-88, 1993-94 and 1999-2000 ("thick") rounds of the Indian National
Sample Survey (NSS). The NSS provide household level information on expenditure patterns,
occupation, industrial affiliation (at the 3 digit NIC level) and various other household and individual characteristics. In general, the surveys cover all Indian states and collect information
21
on about 75,000 rural and 45,000 urban households.1 0 Using this data, I construct district level
measures of poverty (measured as headcount ratio and poverty gap)1l and inequality (measured
as the standard deviation of the log of per capita expenditure and the logarithmic deviation
of per capita expenditure). Following Deaton (2003a, 2003b), I adjust these estimates in two
ways. First, I use the poverty lines proposed by Deaton as opposed to the ones used by the
Indian Planning Commission, which are based on defective price indices over time, across states
and between the urban and rural sector. The poverty lines are available for the 16 bigger states
in India and Delhi to which I restrict the analysis.12 In addition, the 1999-2000 round is not
directly comparable to the 1993-1994 round. The 1999-2000 round introduced a new recall
period (7 days) along with the usual 30-day recall questions for the household expenditures on
food, pan and tobacco. Due to the way the questionnaire was administered, there are reasons
to believe that this methodology led to an overestimate of the expenditures based on the 30-day
recall period, which in turn affects the poverty and inequality estimates. To achieve comparability with earlier rounds, I follow Deaton and impute the distribution of total per capita
expenditure for each district from the households' expenditures on a subset of goods for which
the new recall period questions were not introduced. The poverty and inequality measures were
derived from this "corrected" distribution.13
Throughout the 1990s, there were substantial changes in the administrative division of India,
with districts' boundaries changing as new districts were carved out of existing ones. I construct
consistent time-series of district identifiers using Census Atlases and other maps of India. These
were also used to match the NSS and Census district definitions.
For industrial data, I use the Indian Census of 1991, which reports the industry of em10 The NSS follows the Indian Census definition of urban and rural areas. To be classified urban, an area needs
to meet several criteria regarding size and density of the population, and the share of male working population
engaged in non-agricultural pursuits.
llThese measures are explained in detail in Section 5.3. The headcount ratio represents the proportion of the
population below the poverty line, while the poverty gap index is the normalized aggregate shortfall of poor
people's consumption from the poverty line.
12Poverty lines were not available for some of the smaller states and union territories, namely: Arunachal
Pradesh, Goa, Daman and Diu, Jammu and Kashmir, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim, Tripura,
Andaman and Nicobar Islands, Chandigarh, Pondicherry, Lakshwadweep, Dadra Nagar and Haveli. The results
are not sensitive to the inclusion of these states, with poverty lines assumed to be the same as those of the
neighboring states.
l3Using the uncorrected distribution does not change qualitatively the results at the district level, though for
some of the robustness checks specifications presented in Section 6.3, it renders the point estimates insignificant.
All the results at the region level are insensitive to whether the "corrected" or "uncorrected" distribution is used.
22
ployment at the 3-digit National Industrial Classification (NIC) code for each district in India.
Because the Census does not distinguish among crops produced by agricultural workers, I use
the 43rd round of the NSS to compute agricultural employment district weights. There are
about 450 industry codes of which about 190 are traded agricultural, mining or manufacturing
industries.
Finally, I use tariffs to measure changes in Indian trade policy. While non-tariff barriers
(NTB) have historically played a large role in Indian trade policy, data are not available at a
level disaggregated enough to allow the construction of a time-series of NTBs across sectors. 14
Instead, I construct a database of annual tariff data for 1987-2001 at the six-digit level of
the Indian Trade Classification Harmonized System (HS) Code based on data from various
publications of the Ministry of Finance. I then match 5,000 product lines to the NIC Codes,
using the concordance of Debroy and Santhanam (1993), to calculate average industry-level
tariffs. The data on NTBs available come from various publications of the Directorate General
of Foreign Trade, as well as the 1992 study of the Indian Trade Regime by Aksoy (1992).
In order to identify the mechanism through which trade liberalization affects regional poverty
and inequality, I turn to an additional source of industrial data: the Annual Survey of Industries
(ASI). The ASI reports information on production activity in the registered manufacturing
sector by state for more than 100 3-digit industries during 1982-97.
1.5
Empirical Strategy, Measurement of Outcomes and Trade
Exposure
1.5.1
Empirical Strategy
The Indian liberalization was externally imposed, comprehensive, and the Indian government
had to meet strict compliance deadlines. The period immediately before the reform, and the
five-year plan immediately following, give rise to a natural experiment. India's size and diversity (India was divided into approximately 450 districts across 27 states at the time of the 1991
'4In addition, the experience of other developing countries shows that NTB coverage ratios are usually highly
correlated with tariffs, thus estimates based on tariffs may capture the combined effect of trade policy changes
(Goldberg and Pavcnik, 2004a). This relationship seems to hold in the case of India as well, based on the patchy
data available.
23
Census) allow for a cross-region research design. The identification strategy is straightforward:
districts whose industries faced larger liberalization shocks are compared to those whose industries remained protected. That is, depending on the industrial composition within a district and
the timing of liberalization across industries, districts across India experienced to a different
extent and at a different time the shock of trade liberalization.
The identification strategy exploits variation in the industrial composition across Indian
districts, prior to liberalization. I construct a measure of district trade exposure as the average
of industry-level tariffs weighted by the number of workers employed in that industry in 1991 as
a share of all registered workers. The variation in industrial composition generates a differential
response of the district level trade exposure to the exogenous changes in tariffs. In a regression
framework, the baseline specification takes the following form:
Ydt
=a
/ . Tariffdt+ t + 6 d + -dt
(1.1)
Where Ydt is district level outcome such as poverty and inequality, and Tariffdt is the district
exposure to international trade.
The coefficient of interest, A, captures the average effect
of trade protection on regional outcomes. The inclusion of district fixed effects (d)
absorbs
unobserved district-specific heterogeneity in the determinants of poverty and inequality, while
the year dummies (t) control for macroeconomic shocks that affect equally all of India.
The above methodology estimates the short to medium-run effect of trade liberalization in a
specific district compared to other districts. Note that in the presence of perfect factor mobility
across regions, one would expect no effect of liberalization on regional outcomes. If workers
can easily migrate in response to adverse price changes, the effect of liberalization captured
in
would be zero. A further advantage of this identification strategy is that it includes the
general equilibrium effect of trade liberalization within a geographical unit. Previous studies
have focused on the effect of trade opening on manufacturing workers, who, in developing
countries, typically represent a small fraction of the population, though often a large share of
income. This strategy captures not only the effect of trade liberalization on manufacturing and
agricultural workers, but also on their dependents, and individuals in allied sectors.
Trade liberalization affects individuals as consumers, and as wage earners. Porto (2004)
24
outlines a methodology to evaluate the distributional impact of trade, by considering the effect
of liberalization on both final goods' prices and workers' incomes.
The empirical strategy
employed in this paper focuses primarily on the effect of trade on the income earner, without
explicitly modeling the effect of changes in prices of final goods. Yet, because the poverty
line is adjusted over time using state-level price deflators, my analysis implicitly accounts for
the impact trade liberalization had on consumers through goods' prices. This is a nontrivial
advantage of the comprehensiveness of the Indian data.
It is important to emphasize that this empirical strategy does not reveal the first order effect
of trade on poverty. Trade liberalization is likely to have effects common across India, through
prices, availability of new goods, faster growth etc. However, it would be very difficult to draw
a causal lesson using only time variation in trade liberalization and poverty levels, since the
Indian economy was subject to numerous other influences over the period studied. This study,
based on regional variation, does not seek to answer questions about overall levels. Instead, it
answers the question of whether all regions in India derived similar benefits (or suffered similar
costs) from liberalization, or whether some suffered disproportionately.
This is an important
question for policy makers who might need to devise additional policies to redistribute some of
the gains from the winners to the losers in order to minimize potential social cost.
The balance of this section addresses two potential complications. First, the process of trade
liberalization is explored in detail, including the possibility that liberalization was correlated
with other factors that affect regional poverty and inequality.
Second, the measures used to
quantify poverty and inequality are described, including careful attention to possible problems
with the data, and their solution. And finally, I introduce the district-level measure of trade
exposure.
1.5.2
Endogeneity of Trade Policy
There are strong theoretical reasons (Grossman and Helpman, 2002) to believe that in the
absence of external pressure, trade policy is an endogenous outcome to political and economic
processes. As the empirical strategy of this paper exploits the interaction of regional industrial
composition and differential degree of liberalization across industries to identify the effect of
trade liberalization on poverty and inequality, understanding the source of variation in the tariff
25
levels is important. In particular, there are two aspects to the potential endogeneity of trade
policy. First, the initial decrease in tariffs might have been just a continuation of a secular trend.
The timing of trade reform might have reflected Indian authorities' perception of domestic industries as mature enough to face foreign competition, and labor and credit markets as flexible
enough to ease the intersectoral reallocation that would ensue. Second, the cross-sectional variation in changes of protection might be related to economic and political factors. The relatively
less efficient industries might have enjoyed higher degree of protection; the political strength of
labor as well as business is also often cited as a determinant of trade protection. If authorities
did not liberalize as intensively the least productive industries, and if these industries were concentrated in slower growing districts, one might observe small decline in tariffs associated with
small declines in poverty and erroneously conclude that trade liberalization boosted poverty
reduction. These two concerns are addressed in sequence below.
As discussed in Section 1.3, the external crisis of 1991 opened the way for market-oriented
reforms in India, such as trade liberalization. The Indian government required IMF support
to meet external payments obligations, and was thus compelled to accept the conditions that
accompanied the support. "Given several earlier attempts to avoid IMF loans and the associated
conditionalities, the large number of members of the new cabinet who had been cabinet members
in past government with inward-looking trade policies and the heavy reliance on tariffs as a
source of revenues, these reforms came as a surprise." (Hasan et al., 2003). According to a study
on the political economy of economic policy in India, "the new policy package was delivered
swiftly in order to complete the process of changeover so as not to permit consolidation of
any likely opposition to implementation of the new policies. The strategy was to administer a
'shock therapy' to the economy. . . There was no debate among officials or economists prior to
the official adoption... The new economic policy did not originate out of an analysis of the data
and information or a well thought out development perspective," (Goyal, 1996).15
Varshney (1999) describes the political environment in which the trade reforms were passed.
Mass political attention at the time was focused on internal politics (ethnic conflict in partic15
This view is confirmed in a recent interview with Dr. Chelliah, one of the masterminds of the reforms
"We didn't have the time to sit down and think exactly what kind of a development model we needed...there
was no systematic attempt to see two things; one, how have the benefits of reforms distributed, and two,
ultimately what kind of society we want to have, what model of development should we have?," July 5, 2004
http://in.rediff.com/money/2004/jul/05inter.htm
26
ular), and trade reforms pushed through by a weak coalition government apparently escaped
general attention, in contrast to the failed reform attempts of the much stronger Congress
Party in 1985. As late as 1996, less than 20% of the electorate had any knowledge of the trade
reform, while 80% had opinions on whether India should implement caste-based affirmative
action. While some liberalization efforts (for example privatization) were diluted or delayed
due to popular opposition, trade liberalization was generally successful. As Bhagwati wrote:
"Reform by storm has supplanted the reform by stealth of Mrs. Gandhi's time and the reform
with reluctance under Rajiv Gandhi." (Bhagwati, 1993).
Even if the timing of the sharp drop in average tariffs (Figure 1.1) appears exogenous, there
is significant variation in the tariff changes across industries, which could confound inference.
More precisely, it is important to understand whether the changes in tariffs reflected authorities' perceptions on industry's ability to compete internationally, or the lobbying power of the
industry. Ideally, the concern of potentially endogenous changes in trade protection could be
alleviated by knowledge of the "true" intentions of Indian policymakers or, failing that, through
a detailed study of the political economy behind tariff changes in India over the period. In the
absence of objective and detailed analyses of such policy changes, the data may be examined
for possible confounding relationships.
First, I investigate to what extent tariffs moved together. An analysis of the tariff changes
of the 5,000 items in the dataset for 1992-96, the Eighth Plan, and for 1997-2001, the Ninth
Plan, reveals that movements in tariffs were strikingly uniform until 1997 (Figure 1.4). During
the first 5-year plan that incorporated the economic reforms of 1991, India had to meet certain
externally imposed benchmarks, and the majority of tariff changes across products exhibited
similar behavior (either increased, decreased, or remained constant each year). After 1997,
tariff movements were not as uniform. Policymakers may have been more selective in setting
product tariffs during 1997-2001, and the problem of potential cross-sectional endogenous trade
protection is more pronounced.
Second, there is no evidence that policymakers adjusted tariffs according to industry's perceived productivity during the Eighth Plan, i.e. until 1997. In a related study (Topalova,
2004), I test whether current productivity levels and productivity growth predict future tariffs
- a relationship one would expect if policymakers were trying to protect less efficient industries.
27
I find that the correlation between future tariffs and current productivity, and future tariffs and
current productivity growth is indistinguishable from zero for the 1989-96 period. For the period after 1997 however, future tariff levels are negatively correlated with current productivity.
This evidence and the evidence on uniformity in tariff movements until 1997 suggest it may not
be appropriate to use trade policy variation after 1997. As a result, this study focuses only on
the 1987-1997 period.
A third check uses data from the ASI to test for "political protection." Even if the change in
industry tariffs appears uncorrelated with the initial productivity of the industry, tariff changes
may be correlated with politically important characteristics of the industry. Using data from
the ASI, (which covers the manufacturing and mining sectors), and following the literature on
political protection, I regress the change in tariffs between 1987 and 1997 on various industrial
characteristics in 1987.16 These characteristics include employment size (a larger labor force
may lead to more electoral power and more protection), output size, average wage (policy
makers may protect industries where relatively low skilled/vulnerable workers are employed),
concentration (measured by the average factory size, which captures the ability of producers to
organize political pressure groups to lobby for more protection), and share of skilled workers.
The results are presented in Table 1.1, Panel A. Tariff changes are not correlated with any of
the industry characteristics.
Because agricultural workers are not included in the ASI data, but comprise a large share
of India's population, I conduct a similar exercise using data from the 1987 NSS. I estimate for
all industries the average per capita expenditure, wage, poverty rate and poverty depth, and I
check whether there is a correlation between these industry characteristics and tariff declines.
Results, presented in Table 1.1, Panel B, show no significant relationship between tariff changes
and these measures of workers' wellbeing, once controls for industry use type are included.
A possible explanation for these results can be found in Gang and Pandey (1996). They
conducted a careful study of the determinants of protection across manufacturing sectors across
three plans, 1979-80, 1984-85 and 1991-92, showing that none of the economic and political factors are important in explaining industry tariff levels in India.17 They explain this phenomenon
16I use 1987 as the pre-reform year since the data on pre-reform poverty and inequality come from the 43rd
round of the NSS which was collected in 1987. The results are robust to using 1988 or 1990 as the "pre" year.
17
In other developing countries, protection tends to be highest for unskilled, labor-intensive sectors. See Gold-
28
with the hysteresis of policy: trade policy was determined in the Second Five Year Plan and
never changed, even as the circumstances and natures of the industries evolved.
The evidence presented here suggests that the differential tariff changes across industries
between 1991 and 1997 were as unrelated to the state of the industries as can be reasonably
hoped for in a real-world setting.
One big exception to the seemingly random pattern of tariff reductions are two major
agricultural crops: cereals and oilseeds. Throughout the period of study, the imports of cereals
and oilseeds remained canalized (only government agencies were allowed to import these items)
and no change in their tariff rates was observed (the tariff rate for cereals was set at 0). Thus,
they were de facto non-traded goods. The delay in the liberalization of these major agricultural
crops was due to reasons of food security. However, the cultivators of these crops were also
among the poorest in India (Figure 1.7). This brings some additional complications in the
analysis, which are discussed at length in the following sections.
1.5.3
Measurement of Poverty and Inequality
For poverty, I use two standard measures: the "headcount ratio" (HCR) and the poverty gap.
The former, which I refer to as the poverty rate, represents the proportion of the population
below the poverty line. While the HCR is widely used, it does not capture the extent to which
different households fall short of the poverty line, and is highly sensitive to the number of poor
households near the poverty line. Thus, I also analyze the poverty gap index, defined as the
normalized aggregate shortfall of poor people's consumption from the poverty line.18",
9
Figure
1.5 plots the evolution of poverty in India, and indicates a substantial decline over the past two
decades.
I chose two measures of inequality, the standard deviation of log consumption and the
berg and Pavcnik (2001), Hanson and Harrison (1999), Currie and Harrison (1997) for evidence from Colombia,
Mexico and Morocco respectively.
18Both the headcount ratio and the poverty gap are members of the Foster-Greer-Thorbecke class of poverty
measures, defined as Pa, =
(T-)
f(y)dy, where z is the poverty line and incomes are distributed according to
the density function f(y). The headcount ratio is calculated by setting ac to be 0, and the poverty gap by setting
c to be 1.
19 Since the survey design changed for the 1999-2000 round of the NSS, in order to obtain internally consistent
measures of poverty and inequality, the per capita expenditure data were adjusted at the district level, following
Deaton (2003).
29
mean logarithmic deviation of consumption, 20 both because they are standard measures, and
because similar values are obtained when they are estimated from either the micro data or
the estimated distributions. In contrast to poverty's steady decline, inequality follows a more
complicated pattern.
While it registered a substantial decline between 1987 and 1993, both
measures record a break in that trend and a slight increase in inequality after 1993 in rural
India. In urban India, after a period of decline, inequality rose between 1993 and 1999.
1.5.4
Measurement of Regional Exposure to Trade Liberalization
As mentioned above, the measure of trade policy is the tariff that a district faces, calculated as
the 1991 employment-weighted average nominal ad-valorem tariff at time t.2 1 Table 1.2 provides
summary statistics of the variables included in the analysis at the district level, including a
breakdown of the workers across broad industrial categories. In the average rural district about
80 percent of main2 2 workers are involved in agriculture, of whom 87 percent are involved in
cultivation of cereals and oilseeds. Mining and manufacturing account for about 6 percent of
the workers and the remaining 12 percent are involved in services, trade, transportation,
and
construction. In urban India agricultural workers represent only 19 percent.2 3 Manufacturing
and mining workers account for another fifth of the urban population and the remaining threefifths comprise workers in services etc.
The district tariffs are computed as follows:
Tariffd = i
Workerd,i,1991 Tariffi,t
Total Workerd,19 9l
Tariffdt is a "scaled" version of district tariffs. In this measure, workers in non-traded industries
20
The mean deviation of consumption is part of the family of Generalized Entropy coefficients. It is calculated
according to the following formula, I(0) = f Y log(f)f(y)dy, where p is mean income.
2
'As described in Section 4, the 1991 population and housing census is used to compute employment by
industry for each district. The employment data are available for the urban and rural sector separately by
industry at the 3 digit (NIC) level for all workers except agricultural workers. To match agricultural workers to
the tariff data, I compute district employment weights from the 43rd round of the National Sample Survey (July
1987-June 1988).
22The 1991 Indian Census divides workers into two categories: "main" and "marginal" workers. Main workers
include people who worked for 6 months or more during the year, while marginal workers include those who
worked for a shorter period. Unpaid farm and family enterprise workers are supposed to be included in either
the main worker or marginal worker category, as appropriate.
23
0f these, 73 percent are cultivators of cereals and oilseeds.
30
are assigned zero tariffs for all years. These are workers in services, trade, transportation,
construction as well as all workers involved in growing of cereals and oilseeds. The latter
assumption is justified by the fact that all product lines of these two industries were canalized
(imports were allowed only to the state trading monopoly) as late as 2000.24 Furthermore, the
tariffs of all product lines under the growing of cereals industry are zero throughout the entire
period of interest.
One concern with the use of Tariffdt is that it is very sensitive to the share of people
involved in non-traded industries, the majority of whom are the cereal and oilseed growers. Since
agricultural workers are usually at the bottom of the income distribution, Tariffdt is correlated
with initial poverty levels. The interpretation of results based on this measure may be unclear
if there were (for other reasons) convergence or divergence across districts. In particular, poorer
districts, which have a large fraction of agricultural workers may experience faster reduction
in poverty due to mean-reversion or convergence. These districts may also record a lower drop
in tariffs, since initially the Tariffdt measure is low. Thus, one might find a spurious negative
relationship between changes in tariffs and changes in poverty and erroneously conclude that
trade liberalization led to a relative increase in poverty at the district level. Alternatively, if
workers in non-traded activities are on a different growth path than those in traded industries,
Tariffdt might capture this differential growth, rather than the effect of trade policies. To
overcome this shortcoming, I instrument Tariffdt with TrTariffdt, defined as:
TrTariffdt =
t
i Workerd,i,19 91 Tariffit
i Workerd,i,1991gl
TrTariffdt, "non-scaled" tariffs, ignores the workers in non-traded industries. It weighs industry
tariffs with employment weights that sum to one for the share of people in traded goods in each
district. Thus, a district which has 1 percent workers in traded industries and another district
where 100 percent of workers are in traded industries will have the same value of TrTariffdt
if, within the traded industries, the industrial composition is the same. Since the variation in
TrTariffdt does not reflect the size of the traded sector within a district, the "non-scaled" tariff
does not reflect the magnitude of the effect trade policy might have. Yet, TrTariffdt forms a
2'These
products also have minimum support prices fixed by the Government of India.
31
good instrument for Tariffdt, as it is strongly correlated with the "scaled" tariffs and overcomes
the correlation with district initial poverty that is there by assumption in Tariffdt. Table 1.3
presents the results from the first stage. Following equation (1.1), I estimate the following
specification:
Tariffdt =
with
at
+
TrTariffdt + 'Yt+ ad + Edt
(1.2)
and d defined as above. Panel A gives the results for India as a whole, while in Panels
B and C, I provide the results for two subsamples of states (to which I will return shortly).
Columns (1) and (3) present the correlation between the scaled and nonscaled tariffs. There
is a very strong relationship between the non-scaled and scaled tariffs in both urban and rural
India.
Another instrument is suggested by Figure 1.1, Panel G: tariff changes are linearly related
to initial tariffs. One important principle in the tariff changes was to standardize tariffs (reduce
the standard deviation). A natural consequence of this is that the higher the tariff initially, the
greater the reduction. One could take advantage of this relationship by using the initial level
of the "scaled" tariff interacted with a post dummy as an instrument. However, as previously
argued, the "scaled" tariff measure is correlated with the pre-reform levels of district income
and poverty and may thus not form a valid instrument.
Instead, I use pre-reform unscaled
tariffs times a post dummy, in addition to the unscaled tariffs, as instruments for tariff:
Tariffdt= c +
. TrTariffdt+
0. Postt TrTariffd,1987 + Yt + ad + Edt
(1.3)
Table 1.3 columns (2) and (4) include the interaction of the initial unscaled tariff and a postliberalization dummy. The interaction of the non-scaled tariffs times a post dummy is strongly
correlated with the scaled tariffs and adds explanatory power in all rural subsamples. In the
urban sector, the relationship is not as strong.2 5 ' 26
Data on outcome variables are available for 3 years: 1987, 1993 and 1999, while tariff
25
Since the TrTariff and Post*TrTariff measures are highly colinear, it is hard to interpret the coefficients in
the first stage regression.
26
An alternative way to think of the above specification is as inclusion of a non-linear function of the instrument
TrTariff. As interpretion of the first stage is not needed, it might be useful to include additional functions of the
instruments in order to increase the power in the first stage.
32
data are available annually. It is not known how soon national policy changes affect regional
outcomes, though there is probably some lag. If the 1993 outcomes were matched to the 1991
tariffs, 1993 would count as a "pre" year, while if they were matched to the 1992 tariffs, it would
be a post year. To avoid this problem, 1993 is omitted from the analysis. I use the earliest
available data, 1987, for the "pre" tariff measure, and the 1997 data as the "post" measure.
1.6 Results and Robustness
1.6.1
Basic Results
I estimate four versions of equation (1.1): the OLS relationship using Tariffdt; a reduced form
using TrTariffdt; instrumenting for Tariffdt using TrTariffdt; and finally instrumenting for Tariffdt
with both TrTariffdt and with TrTariffd,1987 *Postt, where Postt is a dummy equal to 1 in year
1999. Since the dependent variable is an estimate, I weight the observations by the square root
of the average number of households in a district across rounds. 2 7 A post-liberalization dummy
is included to account for macroeconomic shocks and time trends that affect outcomes equally
across India, while district fixed effects absorb district-specific time-invariant heterogeneity.
Outcomes of districts within a state might be correlated (since industrial composition may be
correlated within a state), therefore I cluster the standard errors at the state-year level. The
results for the four outcomes of interest are presented in Table 1.4a Section I for rural India
and Section II for urban India. Each panel gives the results for a different dependent variable.
Columns (1) and (5) show the OLS relationship, columns (2) and (6) the reduced form, and
columns (3), (4), (7) and (8), the IV results. In column (4) and (8), I use both the unscaled
tariffs and the pre-reform unscaled tariffs times a post reform dummy as an instrument.
In rural India, for both measures of poverty, there is a strong statistically significant negative
relationship between district tariffs and poverty. The decline in tariffs as a result of the sharp
trade liberalization appears to have led to a relative increase in the poverty rate and poverty gap
in districts whose exposure to liberalization was more intense. The average district experienced
27
The results presented in this section are insensitive to whether the district-level observations are weighted
or not. However, weighting does increase the precision of the estimates at the district level in some of the
specification checks discussed in Section 6.3. Across all specifications at the region level, weighting does not have
an effect.
33
5.5 percentage point reduction in the "scaled" district tariffs.
The point estimates of the
various specifications are similar, and suggest that this 5.5 percentage point drop would lead
to an increase in the poverty rate of 3.2 to 4.6 percentage points, and a 1.1 to 1.8 percentage
point increase in the poverty gap. Given that poverty rate in the average district decreased
by 12.7 percentage points and that poverty gap decreased by 4 percentage points during the
entire decade, the effects of exposure to liberalization are rather large. Surprisingly, there is
no statistically significant relationship between trade exposure and poverty in urban India.
Though the point estimates are still negative, the magnitude of the coefficients is much smaller
than in rural India.
In Panel E of Table 1 4a, I present the effect of trade liberalization on log average per capita
expenditures in the district. Though there is no statistically significant relationship in this set
of specifications, the estimated coefficient on the tariff measure from the OLS, reduced form
and the IV clearly demonstrate the biases that the OLS (and the "scaled" measure of tariff
exposure) may introduce: while the OLS relationship between changes in tariff measure and
log consumption is negative, the sign is reversed in the reduced form and IV specifications.
The negative relationship between changes in tariffs and changes in per capita expenditures
in the OLS (column (1)) implies that trade liberalization was associated with faster growth at
the district level: larger drops in "scaled" tariffs corresponded to larger increases in the mean
consumption.
However, the greater the share of workers involved in traded goods industries
is (i.e. the more industrialized and richer is the district), the larger is the drop in "scaled"
tariffs. If there is divergence across districts, so that initially richer districts grow faster, then
the OLS relationship between changes in "scaled" tariffs and changes in consumption will be
negative, even in the absence of any effect of trade liberalization, as the change in "scaled"
tariff reflects the effect of being in an initially richer district on subsequent growth. This is why
the OLS estimates may be downward biased, as is the case for both measures of poverty and
log consumption
(columns (1) vs. columns (3) and (4)).
As a robustness check, I perform the analysis discussed above at the region level. NSS regions
typically consist of several districts within a state with similar agroclimatic conditions and
socioeconomic features. India is divided into 77 such regions, of which I use 62 for the analysis
after dropping the states with missing poverty lines or other relevant data. I calculated all
34
the outcomes measures and constructed the two tariff measures at the region level. Estimating
equation (1.1) at the region level, I find almost identical results, though somewhat larger in
magnitude (Table 1.4b). At the region level, the same pattern as in rural regions emerges for
the urban sample. Rural and urban areas where more affected industries were concentrated
experienced a slower reduction in poverty rate and poverty depth.
There is no statistically significant relationship between trade liberalization and either measure of inequality for the average district (or average region) in either rural or urban India.
1.6.2
Why rural
The empirical literature on trade liberalization so far has focused predominantly on the manufacturing sector and urban areas because these were the areas most commonly affected by
trade liberalization (Goldberg and Pavcnik, 2004a). Therefore, it is surprising that the effect
of trade liberalization on districts is more pronounced in rural rather than in urban India.2 8 A
close look at the evolution of tariff and non-tariff barriers in Figure 1.1 suggests an explanation.
Agriculture was not omitted from the 1991 reforms in India. Tariffs of agricultural products
fell in line with tariffs of manufacturing and other goods. While quantitative restrictions and
licensing requirements on both the import and export of agricultural products (out of a concern
for food security) were removed later than on other goods, the share of agricultural products
that could be freely imported jumped from 7 percent in 1989 to 40 percent in 1998. By 2001,
more than 80 percent of agricultural products could be imported without any license.
In addition, the agricultural tariffs and non-tariff barriers are strongly correlated.
The
post-liberalization data (the 55th round of the NSS) was collected from mid 1999 to mid 2000,
right when the bulk of the removal of NTB was taking place. Thus, the tariff measure may be
capturing the effect of both tariff and non-tariff barriers and may reflect the short term effect
of the change in relative price of agricultural products on the extensive rural population. I
construct separate measures of agricultural tariffs and mining and manufacturing tariffs that
a district faces and regress district poverty and inequality on these measures of trade policy.
28
0n the other hand, rural areas are where the poor people in India are concentrated. In 1987, both poverty
rates and poverty depth were almost double in rural areas (40 versus 22.8 percent poverty rate and 9 versus 4.7
percent poverty depth).
35
Table 1.A1 in the Appendix reveals that the results are driven by agricultural tariffs.2 9 There
is little relationship between mining and manufacturing tariffs and district outcomes, though,
due to the large standard errors of the point estimates, I can not reject that the effect of mining
and manufacturing tariffs and of agricultural tariffs is the same for any of the outcomes and for
any of the subsamples. The finding is not that surprising; manufacturing and mining workers
represent only 6 percent of workers in the typical rural district - therefore, it is plausible that
even if trade liberalization had a sizeable effect on their wellbeing or relative earnings, it would
not be reflected in district-level outcomes.
Furthermore, people involved in agriculture are the most vulnerable, often with little access
to insurance devices. There is no shortage of press accounts on farmers committing suicide in
the face of adverse shocks in India.3 0 Manufacturing workers, on the other hand, tend to be
relatively richer than agricultural workers as presented in Figure 1.7: a significant decline in
income may not be enough to push them below the poverty line.
1.6.3
Robustness
The effects of liberalization identified in this paper could be incorrect if measures of trade liberalization were correlated with omitted district-level time-varying variables that affect poverty
and inequality. In this section, I first examine whether districts with different initial industrial
I then determine whether pre-existing condi-
compositions were on different growth paths.
tions within districts are correlated with subsequent tariff changes. I measure whether "initial"
(1987) conditions other than industrial composition in districts are correlated with subsequent
changes in poverty, and if so, whether they are driving the results. Finally, I test whether the
findings are confounded by other reforms, concurrent to trade liberalization.
To address the concern that districts with different industrial composition may be experiencing different time trends in poverty and inequality that are (spuriously) correlated with
tariff changes, I perform a falsification test. In particular, I test whether changes in poverty
and inequality in the two periods prior to the reform (from 1983 to 1987) are correlated with
measures of trade liberalization from 1987 to 1997. This analysis can be performed only at the
29
Note that the magnitudes of the coefficients in table Al are not interpretable as the measures of agricultural
and mining and manufacturing tariffs are not scaled by the share of population employed in the particular sector.
30
See, for example,
http://news.bbc.co.uk/2/hi/southasia/3769981.stm.
36
region level as district identifiers are not available in the 38th round of the NSS. I use the four
specifications (OLS, reduced form, and both IV specifications), but now using 1983 and 1987
outcomes as pre and post, rather than the 1987 and 1999 outcomes. The results are presented
in Table 1.5. The coefficients on the tariff measure are rather imprecisely estimated, though
they are smaller in magnitude than those in Table 1.4b (which presents the main regressions
at the region level) and of opposite sign. In both urban and rural areas, there seems to be no
correlation between tariff changes and the pre-reform trend in any of the outcomes.
The regressor of interest in equation (1.1) is Tariffdt. If it is correlated with initial conditions,
and initial conditions determine subsequent changes in poverty rates, one may find a spurious
effect. For example, if convergence across districts became stronger after liberalization, so that
poverty reduction is larger, while intensity of the treatment is smaller in poorer regions, then
one would see large declines in poverty levels, small declines in tariffs and may conclude that
the reduction in tariffs led to a relative increase in poverty even if such a relationship does not
exist. Since, by assumption, Tariffdt is correlated with the initial poverty, this is a valid concern.
However, if the instruments non-scaled tariff TrTariffdt and initial tariff level, are uncorrelated
with initial levels of district outcomes, then the IV estimates should be free of bias.
Table 1.6 examines the relationship between tariff changes and initial conditions. Instead
of looking at the pre-reform trends of outcomes, I regress district outcomes in 1987 (both rural
and urban sectors) on changes in tariffs, controlling for the initial industrial composition in the
district (namely percentage of workers in agriculture, manufacturing, mining, trade, transport,
services - workers in construction are the omitted category), percentage literate and the share
of scheduled caste and scheduled tribes population, Xd,1987.
Yd,1987=
a + 3 (Tariffd,1987 - Tariffd,19 9 9) + 0- Xd,1987 + Edt
(1.4)
The scaled measure of trade exposure, Tariffdt, is statistically significantly correlated with prereform poverty measures. Even the decline in TrTariffdt has a statistically significant correlation
with initial level of poverty depth (Table 1.6 columns (3) and (7)). However, if I use both the
decline in TrTariffdt and its initial level as instruments for the decline in scaled tariffs, the
relationship disappears (columns (4) and (8)). In the rural sector, the coefficients not only
37
become statistically insignificant but notably smaller in magnitude.
This suggests that the
most appropriate instrument for the "scaled" tariffs is the combination of the "non-scaled"
tariffs and the initial value of these tariffs interacted with a post dummy.
In Tables 1.7, I further investigate the possibility that the results might be driven by convergence or omitted variables.3 1 I control for time-varying effects of various pre-reform district
characteristics as well as initial levels of outcomes, by including the interaction of these initial
characteristics and a post liberalization dummy, estimating:
Ydt
= - + /3 Tariffdt + 0 Postt
Xd,1987 + 'Yt + d + 6 dt
(1.5)
In all specifications I include in Xd,198s7initial industrial composition (namely percentage of
workers in agriculture, manufacturing, mining, trade, transport,
services - workers in con-
struction are the omitted category), percentage literate and the share of scheduled caste and
scheduled tribes population. I sequentially add as controls the initial level of the log of mean per
capita expenditure in the district, the pre-reform trend in the outcome variable (the difference
between its 1983 and 1987 value), and finally the initial value of the dependent variable itself. I
also allow for differential time trends in district outcomes across states with pro-employer, proworker and neutral labor laws by including post times labor law fixed effects.3 2' 3 3 In columns
(1)-(5), I use only TrTariffdt as an instrument for Tariffdt, while in columns (6)-(10), I instrument
the scaled tariff with both TrTariffdt and the initial level interacted with a post-liberalization
dummy. Columns (5) and (10) include the instrumented value of the lagged dependent variable,
where the 1983 level is used as an instrument
for the 1987 level.
The inclusion of district initial characteristics reduces slightly the point estimates of the
effect of trade liberalization at the district level, from 0.69-0.83 to 0.41-0.58 for poverty rate and
from 0.21-0.32 to 0.12-0.22 for poverty depth. Controlling for initial per-capita expenditure or
31I present the analysis only for the rural sample from now on as the effect of trade liberalization in the urban
sector can not be precisely estimated.
32
Indian states are classified as having pro-worker, neutral, or pro-employer labor laws by Besley and Burgess
(2004).
33
As I argue in Section 2, trade liberalization (and the ensuing change in relative prices) should lead to
reallocation of resources from industries where the relative price fell to those where it rose in a H-O world with
perfect factor mobility. Yet, the institutional environment as reflected in labor laws may affect the ease to hire
and fire workers, change wages etc., in other words the speed of factor reallocation.
38
pre-reform outcome reduces further the size of the coefficients when only the scaled tariff is used
as an instrument. Thus, it may be that some of the variation in poverty depth and incidence that
equation (1.1) attributed to trade liberalization was in fact due to certain omitted time-varying
district specific characteristics. The inclusion of the actual value of the pre-reform dependent
variable (column (4) and (8)) lowers slightly the coefficient on the tariff measure and in some
cases renders it statistically insignificant. This specification, however, is equivalent to regressing
changes on levels: if there is mean reversion and measurement error, the estimated coefficients
would be biased. In fact, the size of the coefficient on the initial level of the outcomes suggests
implausibly strong convergence. Instrumenting the 1987 level of the dependent variable, with
its 1983 level (columns (5) and (10)) solves to a certain extent that problem. In Table 1.A2
in the Appendix, I reestimate all of the specifications discussed above at the region level. The
findings are somewhat larger in magnitudes and more statistically significant.
After controlling for various district initial characteristics, the estimated effect of trade liberalization on the poverty measures remains statistically and economically significant. According
to these estimates, the decline in tariffs increased relative poverty incidence by about 2 and
poverty gap by 0.6 percentage points in the average district. The inclusion of the additional
controls also helped estimate more precisely the effect of trade policy on the average per capita
expenditures (the estimated coefficient is again larger in magnitude and more robust in the
region-level regressions in Table 1.A2). Districts more exposed to trade liberalization experienced slightly slower growth in consumption per capita. One percentage point decrease in the
tariff a district faces translates into 0.6 percent lower average per capita expenditures. 34
Next, I address the concern that some other reforms concurrent with trade liberalization
may be driving the results. In particular, in 1991 the government of India increased the number
of de-licensed industries and specified a list of industries for automatic approval for foreign direct
investment.3 5 Substantial reforms were initiated in the financial and banking sector as well.
Following the same methodology as in the construction of district tariffs, I construct district
34
The estimated effect of liberalization across specifications tends to be larger in magnitude and more robust
at the region level. This could be due to lower measurement error in region-level variables. Migration across
region boundaries is also likely to be smaller than migration across district boundaries.
35
Foreign investment was tightly regulated prior to 1991. Foreign companies needed to obtain specific prior
approval from the Indian government and foreign investment was limited to 40 percent. In 1991, the government
created a list of high technology and high investment priority industries with automatic permission for foreign
equity share up to 51 percent. Over the 1990s this list was gradually expanded.
39
employment-weighted share of license-industries and district employment-weighted share of
industries that are open to foreign direct investment.3 6
The number of bank branches per
capita in a district captures the potentially confounding effect of banking reforms.3 7
In Table 1.8, I replicate the specifications presented in Table 1.7 including these time-varying
district level measures of reforms (the results on per capita consumption are not reported for
brevity). The effect of trade liberalization on poverty is completely insensitive to the additional
controls. There is no correlation between change in poverty and change in the number of bank
branches per capita or share of industries under a license.3 8 A larger share of industries open
to FDI, however, is associated with faster reduction in poverty. As globalization is typically
defined not only as trade liberalization but also opening to foreign investment, it is important
to emphasize this finding. It also reconciles Hanson's (2004) conclusion that more globalized
areas in Mexico3 9 experienced a larger increase in labor income with the finding that trade
liberalization slowed poverty reduction in more exposed districts in India. In Table 1.A3 in the
Appendix, I investigate the role of imports versus exports in addition to FDI, by including the
district employment-weighted industry imports and exports. I use 1987 import/export
data
for the pre-reform period, and the 1993-1997 annual average for the post-reform period. Since
imports and exports are the endogenous response to trade policy, exchange rate shocks, foreign
demand etc., these regressions do not warrant a causal interpretation, yet they illustrate that
imports (or more precisely potential exposure to imports) are associated with higher, while
exports with lower incidence of poverty.
For the whole sample, for urban as well as rural areas, trade liberalization seems to have
had no effect on inequality.
36Data on policies regarding industrial delicensing and opening to foreign direct investment were compiled
from various publications of the Handbook of Industrial Statistics.
37
The Indian government heavily regulates private and public banks, as it considers the banking system an
integral tool in its efforts to meet a number of social goals, such as poverty reduction. Indeed, Burgess and Pande
(2004) have shown that rural bank branch expansion over the 1980s led to reduction in poverty.
38The absence of an effect of the number of bank branches per capita on rural poverty is not a contradiction to
Burgess and Pande (2004) findings. Rural bank branch expansion levelled off after 1987; very few new branches
opened throughout the 1990s. The results in Table 8 are robust to including the number of bank branches per
capita in 1987 interacted with a post dummy.
39Hanson's definition of exposure to globalization takes into account the share of maquiladora value added,
the share of FDI, and the share of imports in state GDP.
40
1.7
Mechanisms
So far this paper has established that, whatever the India-wide effects of trade liberalization
were, rural areas with high concentration of industries that were disproportionately affected by
trade liberalization, experienced slower progress in poverty reduction. There was no measurable
impact of liberalization on inequality. In the remainder of the paper, I interpret these results
within the framework of the two basic trade theories presented in Section 1.2, highlighting
the underlying mechanisms that link trade policy, poverty and inequality. Understanding these
mechanisms is crucially important to policymakers seeking to mitigate the unequal impact of
trade liberalization on regions within a country.
I explore why there is an effect of trade liberalization on regional outcomes by looking
at two types of factor mobility: geographical and intersectoral.
First, I look at migration
patterns in India over time. Finding very little migration, I then examine whether, as the H-O
model predicts, there is intersectoral reallocation of labor and capital. There is no evidence of
significant reallocation. To determine whether features of the institutional environment may
affect trade liberalization, I examine whether the effects of trade liberalization varied with the
flexibility of labor laws. I then investigate whether the adjustment came through returns to
factors, by looking at the effect of tariff changes on wages and wage premia. I find substantial
adjustment in wages and industry premia.
1.7.1
Reallocation Across Regions
The regionally disparate effects of liberalization are not consistent with standard trade theory.
In a standard trade model with perfect factor mobility across regions, labor would migrate in
response to wage and price shocks, equalizing the incidence of poverty across regions. Estimating
equation (1.1) would yield an estimate of 3 equal to zero, indicating that the local intensity of
liberalization has no effect on local poverty.
The interpretation of the estimates of
in equation (1.1) as effects of liberalization on
regional outcomes is correct if labor is immobile across geographical districts within India in the
short to medium-run, that is, if each district constitutes a separate labor market. This represents
an immediate departure from standard trade theory. However, actual levels of migration in India
41
contrast sharply with the assumptions of the standard trade model. The absence of mobility is
striking. The pattern of migration has also remained remarkably constant through time, with
no visible increase after the economic reforms of 1991.
Table 1.9 presents some estimates of migration for urban and rural India based on three
rounds of the NSS (1983, 1987 and 1999).
Overall migration
is not low - 20-23 percent
of
rural and 31-33 percent of urban residents have changed location of residence at least once in
their lifetime. However, most migrants are women relocating at marriage: around 40 percent of
females in rural and urban India report a change in location, versus 7 percent of men in rural and
26 percent of men in urban locations. The migration most relevant for this study is short-run
movement (within the past 10 years) of people across district boundaries or within district across
different sectors (i.e. from an urban area to a rural one, or vice versa). Short-run migration
figures are low: only 3-4 percent of people living in rural areas reported changing either district
or sector within the past 10 years. Once again, the percentage of women relocating is double
the share of men. For people living in urban areas, the percentage of migrants is substantially
higher. Yet, less than 0.5 percent of the population in rural and 4 percent of the population in
urban areas moved for reasons related to economic considerations (or employment).
Even the 8 percent level of urban residents who migrated from rural areas reported in Table
1.9 does not indicate substantial rural to urban migration.
Since the median urban sector
of a district has only one fifth of the population of the median rural sector of a district, the
7.6 percent rural migrants in the median urban district in the 1990s would translate to only
1.6 percent of the median rural district migrating to the city. Thus, rural-urban migration
is unlikely to have a significant impact on outcomes in rural districts, though it may have
some impact on urban areas. This may be a reason why it is difficult to detect an effect of
liberalization in the urban sector.
These low migration figures are combined with a second characteristic of India's economy,
namely the large and growing disparities in income across Indian states.
Ahluwalia (2002),
Datt and Ravallion (2002), Sachs et al. (2002), Bandyopadhyay (2003) and others document
significant differences in the level of state GDP per capita and growth rate of state output.
42
1.7.2 Reallocation Across Industries and the Effect of Trade Liberalization
Reallocation Across Industries - Overall Patterns
Even if there is little migration across districts, there could be high levels of reallocation within
districts, across industries. In the H-O world, where factors are assumed to be fully mobile
across industries, trade liberalization in a labor-abundant country will lead to expansion of the
labor-intensive industry, thus benefiting labor and reducing inequality and possibly poverty.
Yet, in contrast to the predictions of the H-O model, many developing countries experienced
an increase in skill premium and overall inequality in the aftermath of trade liberalization.
Moreover, intersectoral reallocation has been very limited (see Attanasio et al. (2004), Wacziarg
et al. (2003), Hanson and Harrison (1999)). I therefore investigate whether the evidence from
India supports the mechanism of adjustment suggested by the H-O: a contraction of the sectors
that experienced a decline in their output price (those that experienced a tariff reduction), and
an expansion of those that experienced a relative price increase.
Several dependent variables are created for this analysis, using data from the Annual Survey
of Industries (ASI). I also conduct robustness checks with the NSS data, because the NSS include
agricultural workers and workers in non-traded industries. Following Wacziarg et al. (2003),
I define a measure of structural change that accounts for the movement of workers directly
from sector to sector as well as sectorally unequal changes in aggregate employment (resulting
from population growth and uneven entry into the labor force). Structural change in sector
s is measured as the absolute value of the change in a sector's employment share, St, over a
certain time period (in this case, two years).
CH8 t = St-
2|
Excess job reallocation, first defined by Davis et al. (1996), focuses on the movement of labor
across sectors, independently of overall employment gains or losses. Denoting employment in
sector s at time t as E t :
SHt=18Es-
2
Es
-EEs
1 XZ JEt
43
Est-21
I
The term Es
Et
-
Et-2
measures the total number of employment changes within a 2-year
period, from which I subtract the number of job losses or gains that are not offset by a gain or
loss in other sectors IFs E t
-
ZsE t - 2
And finally the third dependent variable isolates the net change in aggregate employment:
E >t
EMt
=A
Et
-l
t- 2
EE1
Figure 1.6 presents the evolution of the three variables over time.4 0 There is no evidence of
an increase in job reallocation post 1991. In fact, the measures of excess reallocation and
structural change decline until 1996. Consistent with the findings of low structural reallocation,
employment shares remained remarkably constant.
Table 1.10 presents the correlations of
employment shares between 1981 and 1997, based on data from the ASI (Panel A) and NSS
(Panel B). The latter includes agricultural workers and workers in non-traded industries, in
addition to mining and manufacturing. The correlations between employment shares pre-reform
and post-reform exceed 90 percent. In rural India, the correlations are extremely high, but that
is largely due to the fact that about 65 percent of workers are involved in the growing of cereals.
Regressing industry employment shares from the ASI (at the 3-digit NIC) on industry lagged
tariffs, industry and year indicators, and clustering the standard errors at the industry level
in order to correct for serial correlation over the 1988-1997 period confirms this conclusion
(Table 1.11). The coefficient on lagged tariff is small in magnitude (-0.001) and statistically
insignificant. Neither industry output, employment, fixed capital, nor the share of fixed capital
are correlated with lagged industry tariffs. A similar exercise with employment shares from the
NSS (Table 1.12) for all traded industries as well as separately for agriculture and mining and
manufacturing finds no correlation between employment shares and tariffs.4 1
There is thus little evidence H-O style reallocation is occurring in India as a whole. As
mentioned previously, the very stable employment pattern in India is consistent with the ex40
It is worth noting that India's average structural change, 0.04-0.1 percentage points, is much lower than
Wacziarg et al. (2003)'s estimate of the average structural change across 20 developing countries, which is about
0.35 percentage points.
41
In order to perform the above exercise, I match the 3 versions of industry codes used in the NSS43, 50 and
55th round - respectively, NIC 1970, NIC 1987 and NIC 1998 version. I match these classifications at a more
disaggregate level than the official concordance, which bundles almost all agricultural production in one industry.
This matching is not immediately obvious in many cases and inevitably introduces additional measurement error.
44
perience of other developing countries. Papageorgiou et al. (1991) study 19 episodes of trade
liberalization in less developed countries, finding very little relationship between trade liberalization and shifts in employment. Roberts and Tybout (1996) show that industry exit and
entry (one indicator of intersectoral reallocation of labor) do not increase with import competition in their case studies of developing countries. Micro studies, focusing on a specific country,
such as Attanasio et al., 2004 (Colombia), Currie and Harrison, 1997 (Morocco), also find little
relationship between trade liberalization and intersectoral reallocation. Indeed, these studies
show that adjustment occurred through changes in relative wages. In contrast, in the US and
Canada, employment exhibits greater sensitivity than wages to trade shocks (Grossman (1986),
Freeman and Katz (1991), Revenga (1997), Gaston and Trefler (1993)).
Reallocation Across Industries and Labor Laws
The 'sluggish' labor market response in developing countries may be institutionally driven
through rigidities in the labor market. In a cross-country setting, Blanchard and Wolfers (2000)
argue that the interaction of labor market institutions and macroeconomic shocks can explain
the rise of equilibrium unemployment in Europe. Caballero et al. (2004) find that job security
regulation clearly hampers the creative-destruction process and the annual speed of adjustment
to shocks. In a micro study of trade liberalization in Morocco, Currie and Harrison (1997) point
out that many firms responded by reducing profit margins and raising productivity rather than
laying off workers. Similarly, in India, firms that should have expanded might not have done
so for fear of getting stuck with too much labor (Indian growth in manufacturing employment
was almost nil during this period except for a sharp rise in 1996). From the point of view of
the agricultural workers, the poorest in India, the inflexibility of the labor market is directly
related to their outside option. If industries are not expanding, agricultural workers may be
unable to switch occupation even in the face of an unfavorable price shock, thus slowing down
the exit out of poverty.
In India, hiring and firing laws were quite rigid until the amendment of the Industrial
Disputes Act in 2001. Since this study focuses on the period before 2000, it is worth briefly
outlining the specifics of the labor laws prior to the amendment. Datta Chaudhuri (1996) argues
that the primary concern of the worker in the organized sector in India is job security. (This
45
is consistent with an idea developed by Grossman (1984) that unions may extract rents in the
form of employment guarantees rather than wages, see also Attanasio et al., 2004). The Industrial Disputes Act, 1947, required firms employing more than 100 workers to seek government
permission for any retrenchment, and required giving notice to workers three months prior to
any action.4 2 Retrenchment authorizations, however, were almost impossible to get. In theory,
employers with 50-99 workers needed only to notify the government, while those with fewer than
50 employees did not need to do even that to shut down. However in practice workers in such
firms could appeal to other laws, such as the Indian Contracts Act, 1972, to resist dismissal.
To close a plant, a company employing more than 100 workers needed to receive government
permission; the government could deny permission for closure even if the company were losing
money on the operation (Basu et al., 2000). It was virtually impossible to close an unprofitable
factory if the owner was able to pay workers. Instead, the unit was declared sick, and continued to function on the basis of government subsidies (Datta Chaudhari, 1996). Businesses
could potentially resort to contract workers, yet the Contract Labour Act put some restrictions
on that practice as well. According to the Contract Labour Act, state governments may ban
contract labor in any industry in any part of the state (Dollar et al., 2002). Though firms
probably found alternative ways to gain some control over the allocation of manpower (such
as subcontracting, etc.), in an interview of managers throughout India, Dollar et al. (2002)
found that managers would lay-off 16-17 percent of their work force if given the chance. (This
estimate is nearly identical to an estimate of the share of redundant labor in manufacturing
calculated by Agarwala et al., 2001).
Even though the Industrial Disputes Act was passed at the central level, state governments
could amend it under the Indian Constitution. Besley and Burgess (2004) examine all the 113
amendments made by state governments between 1958 and 1992 and code them as pro-worker,
pro-employer or neutral.
Hasan et al. (2003) combine these categories with the ranking of
the investment climate in Indian states from a survey of managers conducted by the World
Bank (Goswami et al., 2002), in order to classify states as having flexible or inflexible labor
laws (Table 1.A3). Using industry-level disaggregated data by states, Hasan et al. (2003) find
42
In fact the only country other than India which has enacted similar laws requiring prior permission of the
government before lay-offs and retrenchment is Zimbabwe.
46
that lower protection led to higher elasticity of labor demand, and more importantly that the
elasticities are not only higher for states with more flexible labor regulations, but were also
significantly affected by trade reforms. If employment is more sensitive to exogenous shocks in
output demand conditions in these states, then one is also likely to see more labor reallocation
and a higher correlation between the exogenous change in price due to tariffs and employment
shares.
I turn to the NSS in order to complement Hasan et al. (2003)'s findings on this issue. I
calculate two measures of employment shares for each state from the NSS, focusing only on
the manufacturing and mining industries (those likely influenced by labor laws). The first
measure is the number of workers employed in industry i in state j at time t as a share of total
agricultural, mining, manufacturing and service workers in state j at time t, while the second
measure is the number of workers employed in industry i in state j at time t as a share of total
workers in mining and manufacturing in state j at time t. Unfortunately, once I break down the
data by state and by industry there are only a few observations per state-industry (the median
state-industry
has 3 observations).4 3 I regress these shares on industry tariffs, state-industry
fixed effects and time dummies (the unit of observation is industry in a state in a particular
year). I cluster the errors at the industry-year level to account for correlations of outcomes
of the same industries across different states in India. Confirming the results in Table 1.12,
there is no significant correlation between tariffs and employment shares in the sample of all
Indian states (Table 1.13, column (1)). I then split the states according to their labor laws, as
classified by Besley and Burgess (2004) and modified by Hasan et al. (2003).44 The results are
not nearly as stark as the findings of Hasan et al. (2003), but there is some suggestive evidence
that reallocation of labor across industries was correlated with industry tariffs in states with
flexible labor laws. However, the estimates are not precise enough to estimate the extent of this
43The matching of industry classification across rounds imposes an additional constraint: some agricultural and
manufacturing industries (namely in the cotton cleaning, and spinning/weaving industries) are not separately
defined in the 55th round. I have not included these industries in the analysis of the manufacturing and mining
industries. Their inclusion substantially increases the already large standard errors.
44
Besley and Burgess (2004) classify each state as pro-worker, pro-employer or neutral according to the amendments to the Industrial Disputes Act that the states passed. Hasan et al. (2003) modify this classification noting
that certain states, like Maharashtra and Gujarat, though recorded as having pro-worker labor laws, have been
pointed as the states with the best investment climate according to a recent survey by Goswami et al. (2002)
while Kerala, with pro-employer labor laws, is one of the states with the worst investment climate.
47
difference.
Trade Liberalization and Institutional Characteristics
As suggested in Section 1.2, the impact of liberalization may vary across states with different
legal, institutional, or economic environments.
Since Hasan et al. (2003)' study (and the
evidence presented above) indicate that there was more reallocation in states with more flexible
labor laws, it makes sense to examine whether these differences in the institutional environment
affected the impact of liberalization.
I thus estimate equation (1.5) for the subsamples of states as in Table 1.13.4
Panel A,
B and E of Table 1.14 present the results for poverty rate, poverty gap and mean per capita
consumption, while Panel C and D give the results for inequality.
This table presents the
specification as in Table 1.7 (columns (3) and (8)), which includes pre-reform district literacy,
share of SC/ST population, industrial structure, log of per capita expenditures, and trend in
the outcome variable interacted with a post dummy in the set of controls. For brevity, I report
only the coefficient and standard error of the tariff measure. In column (1) and (3) I use the
non-scaled tariffs as an instrument, while in column (2) and (4), I instrument with both the
non-scaled tariffs and their pre-reform level.
An interesting pattern emerges. Trade liberalization had an effect on poverty and per capita
expenditures predominantly in states with less flexible labor laws. Though the standard errors
are too large to statistically reject that the effect is equal across subsamples, the point estimates
are generally much smaller in magnitude and in some cases even of the opposite sign in the
sample of states with flexible labor laws. Though there was no effect of trade liberalization on
inequality for India as a whole, trade liberalization seems to have significantly increased relative
inequality as measured by the standard deviation of log consumption in states with flexible labor
laws. There is a similar pattern, though less clear-cut, when logarithmic deviation is used as a
measure of inequality.
India's inflexible labor laws have been criticized for limiting the efficacy of policy reforms in
other areas, including, for example, export growth. (Sachs, Varshney and Bajpai, 1999). Rajan
45I estimate the effect of trade liberalization on outcomes for subsamples of states only at the district level due
to the small number of observations at the region level.
48
(2002) goes as far as writing: "the reforms in India per se are not ex-ante biased towards the
capital and skill-intensive sectors and thus 'anti-poor.'
Rather, they have become so ex-post
mainly because of draconian labour laws and resulting labour market distortions and rigidities."
The apparent lack of intersectoral mobility in the short run, fostered by the institutional environment, indicates a departure from the framework of the H-O model when seeking to explain
the observed effects on poverty and inequality. The inability of labor to relocate in the short run
creates sector-specific rents for employed workers, which suggests the framework of the specific
factor model, with labor as the specific factor as presented in Section 1.2. If wages contain a
rent component, workers may be willing to trade off wages to preserve jobs. Thus, if workers
absorb the bulk of the pressure of the trade policy induced change in relative output price
(by giving away rents), it may be possible to maintain employment. This explanation would
be consistent with Revenga (1997), who suggests that Mexican workers in manufacturing, who
were very unionized and enjoyed sector-specific rents, adjusted primarily through sector-specific
wage declines, rather than through employment reallocation in response to trade liberalization.
1.7.3 Industry Premia
The final empirical section of this paper explores whether factor returns adjusted in response
to liberalization.
Using data from the ASI, I construct several measures of industry wage:
average payments per worker (including wages and all other payments to workers), the average
wage per worker, and the average wage per non-production employee. I regress the log of these
wage measures on lagged industry tariffs, and industry and year dummies for the period 19881997. Results are presented in Table 1.11, columns (6)-(8). The average industry wage, and
more specifically the wage to workers is positively and statistically significantly correlated with
industry tariffs. A 10 percent drop in tariffs leads to a 0.5 percent decrease in industry wages.
The result is driven primarily by the wages of workers, rather than non-production employees.
Thus, instead of inducing factors to relocate, the change in relative prices stemming from the
tariff reductions led to changes in industry specific factor returns.
Though the above findings are indicative of the effect of lowering tariffs on industry specific
returns, they omit important factors, such as the composition of the industry labor force, which
could be driving this correlation. When faced with lower output prices, producers might choose
49
to substitute unskilled for skilled labor, without any change in relative wages, which would lead
to a correlation between industrial wages and tariffs similar to what I find in the data. Though
the ASI does distinguish between workers and employees, the distinction is relatively crude,
and may disguise more subtle changes in the composition of the labor force. Gaston and Trefler
(1994) point out that looking at the correlation between average industry (plant level) wage
and trade protection may overstate the effect of trade policy on wages precisely for the reasons
given above. Even if there were no compositional changes in the labor force, if the returns to
education changed concurrently with tariffs, as happened in several Latin American countries,
one might falsely conclude that tariff cuts in sectors with large proportion of skilled workers
led to an increase in the wage premia (Goldberg and Pavcnik, 2001). In addition the ASI only
captures the effects in registered manufacturing, which employs a small fraction of the Indian
labor force. Individual-level data from the NSS Employment/Unemployment
surveys can help
overcome these concerns.
Industry premia in 1982, 1987, 1993 and 1999 are calculated using standard techniques in
the literature (see Krueger and Summers, 1988). I then regress industry premia Pjt on lagged
industry tariffs, industry and time dummies:
Pjt =/'
LagTariffjt + Ij + -yt + cjt
(1.6)
The inclusion of industry dummies controls for time invariant political economy factors that
might affect both the cross sectional variation across industry tariffs and wage premia, thus
inducing spurious correlation between the two variables (though, as pointed out in Section 1.5.2,
there is little evidence that, during the time period studied, changes in tariffs were correlated
with any economic or political factors).
Since the dependent variable in the second stage is estimated, equation (1.6) is estimated
using weighted least squares, with the weight equal to the inverse of the standard deviation
of the estimated Pjt. This puts higher weight on industries whose premia are estimated more
precisely. Errors are clustered by industry to allow for serial correlation.
The results are presented in Table 1.15. I run the regression for the industry premia estimated from the urban and rural sample of the NSS separately (panel 1 and 2). Since a very
50
low percentage of people report a non zero wage in rural sample of the 43rd (1987) round (7
percent versus 30 percent in the other rounds), I use the wage premia for the 38th (1983) round
instead, to which I assign the earliest available 1987 tariffs. Columns (1), (3) and (5) presents
results for all three rounds (38, 50, 55), while in columns (2), (4) and (6) only data from the
38th and 55th rounds were used. The estimates indicate that there is a positive statistically
significant relationship between industry wage premia and tariffs in the urban sample. While
this result is not statistically significantin the rural sample, the point estimates are virtually
identical to the ones in the urban sample. It appears that the measurement error resulting
from the unreliable wage data in the rural 43rd round biases the rural sample toward finding no
relationship. Workers in more protected industries receive higher wages than observationally
identical workers in less protected industries. The point estimate of 0.14-0.16 is in line with the
previous findings on the relationship between average industry wage and tariffs, based on the
ASI database. The magnitude of the effect is substantial.
The average industry experienced
a tariff decline of about 65 percentage points between 1991 and 1997, which would translate
in about 9 percentage point decrease in the real wage premium. (0.14*0.65). For industries
which experienced the largest decline in tariffs (180 percentage points), the effect would be a
25 percentage points decrease in wage premium. This effect is not driven by mining and manufacturing, but also holds for wage-earners in agricultural industries. If only traded agricultural
industries are included in the sample (columns (3) and (4)), the point estimate in fact increases
to 0.28 suggesting that workers in the average agricultural industries saw a 17 percentage points
reduction in their wage premia.
1.8
Discussion and Conclusion
The available evidence indicates that trade liberalization did not lead to significant reallocation
of factors across industries. Rather, adjustment to changing tariffs occurred through the price
system: relative returns to specific labor absorbed the change in product prices. How far does
the decrease in industry wage premia go towards explaining the relative increase in and deepening of poverty, and the rise in inequality, in certain areas? Figure 1.7 plots the cumulative
density of log per capita expenditure by broad industry affiliation (agriculture traded, agricul-
51
ture nontraded, mining and manufacturing and other nontraded) for the urban and rural sample
and according to the 43rd and 55th round of the NSS.4 6 While workers in manufacturing and
traded agricultural industries are poorer than the relatively few and well-off workers in nontraded non-agricultural industries (services, transport, construction, etc.), by far the poorest
are cultivators and agricultural workers involved in non-traded agricultural industries. Thus,
while the fall in the wages of workers in certain traded industries (those who felt the direct effect of trade liberalization) may have pushed them below the poverty line, and a lower demand
for products and services from other agents in the economy may have amplified the shock, it
is unlikely that the fall of wages in the affected industries can fully explain the slowdown in
poverty reduction in areas with inflexible labor laws.
The most likely explanation for the relative increase in poverty as a result of trade liberalization is the slower rate of growth of the relatively richer formal non-agricultural industries in
areas with inflexible labor laws. Besley and Burgess (2004) argue that pro-labor legislation, as
measured by amendments to the Industrial Disputes Act, caused slower growth in registered
(formal) manufacturing sectors. Aghion et al. (2004) find that the negative effect of pro-worker
labor regulations was strengthened after the 1991 liberalization. Menon and Sanyal (2004) show
that investment projects initiated in the second half of the 1990s are less likely to be located
in states with pro-worker labor laws. This suggests that flexible labor laws eased the shock of
liberalization by facilitating reallocation of factors, and enhancing overall faster growth, while
the slower growth in areas with inflexible labor laws was less able to pull people out of poverty.
The findings on the role of labor laws are of course indicative rather than definitive as states
that adopted flexible labor laws may be different in various other dimensions, such as attitude
towards business, preferences for faster economic growth, urbanization etc.
The mechanisms discussed above are consistent with a specific factor model of trade in which
labor is the specific factor in the short run. Rigid labor markets prevented the reallocation of
factors in the face of trade liberalization in certain areas. Changes in relative output prices led
to changes in relative sector-returns to the specific factors. The reduction in income resulting
from trade liberalization may have led manufacturing workers (particularly those not at the top
46Due
to the incomparability of consumption data across the 43rd and 55th round in the Consumption Schedule
of the NSS, data on consumption from the Employment-Unemployment Schedule of the NSS were used for this
exercise.
52
of the wage distribution prior to reforms) to fall below the poverty line. However, this effect
was likely dwarfed by the slower overall growth in registered manufacturing employment in
areas with pro-worker labor laws. In contrast, areas where reallocation was easier, and growth
was faster, (because of labor laws or other environmental characteristics) were shielded from
the effect of trade liberalization. In those areas, the changes in the income distribution seem
to have taken place in the high end, as some workers tapped into the benefits of liberalization,
thus increasing the consumption inequality.
The findings in this study are important from a policy perspective: an increasing number
of developing countries are pursuing trade liberalization to achieve faster economic growth,
increased living standards, and poverty reduction. Though this paper does not measure whether
the overall effect of trade liberalization on income growth and poverty alleviation was positive
or negative, it establishes that different areas and segments of the population experienced
differential effects of trade liberalization.
Those areas that were more exposed to potential
foreign competition did not reap as much of the benefits (or bore a disproportionate share of the
burden) of liberalization in terms of poverty reduction. Institutional characteristics mattered.
Laws that hindered the movement of factors across sectors of the economy exacerbated this
adverse effect. The implementation of additional policies to redistribute some of the gains of
liberalization from winners to losers may both mitigate the effects on inequality, and increase
the political feasibility of liberalization. Creating a flexible institutional environment will likely
minimize the need for additional interventions.
1.9
Appendix
Industry Premia
An industry wage premium represents the portion of an industry wage
that cannot be explained through worker or firm characteristics.
It can be interpreted as
industry rents, or returns to industry specific skills that are not transferable in the short run,
and is particularly relevant in the presence of imperfect competition and/or in cases in which
labor mobility is constrained (Attanasio et al, 2004). In the case of India, there is substantial
evidence that wages for observationally equivalent tasks differ across industries, consistent with
the previously presented evidence on limited intersectoral mobility.
53
To investigate the effect of trade liberalization on industry wage premia, I follow the labor
literature and employ a two-stage estimation framework. In the first stage, individual level data
from 4 different NSS rounds (1983, 1987, 1993, and 1999), are used to estimate separate cross
section wage equations.
in wijt
Iijt Pjt + Xijt ·yt + Eijt
where wijt is log wage for individual i in industry j in year t, Iijt is a dummy indicating
industry of occupation, and Xijt is a vector of human capital and demographic controls such as:
education, age, gender, marital status, religion, caste, nine occupation dummies, and geographic
location expressed as state dummies. The coefficients Pjt on industry dummies ijt reflect the
"value" of a person's industrial affiliation (industries are reported at the 3-digit NIC level in
the NSS).47 The industry dummies are jointly significant and generally individually significant
as well (results are available from the author).
Following Krueger and Summers (1988), the omitted industry is assumed to have a zero
premium. The measure of industry wage premium used is the difference between the industry
premium and the employment-weighted average wage differentials across all industries. This
premium is the proportionate difference in wages between an employee in a given industry and
the average employee in all industries with the same observable characteristics.
1.10
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Table 1.1 Tariff Declines and Pre-Reform Industrial Characteristics
DepVar: Tariffl987-Tariffl997
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Panel A. Evidence from the ASI
Log Real Wage
0.037
(0.062)
0.312
(0.399)
Share of Non-production Workers
0.013
Capital Labor ratio
(0.025)
0.019
(0.020)
Log Output
0.000
(0.000)
Factory size
Log Employment
-0.002
(0.016)
Growth Log Output 82-87
-0.038
(0.061)
Growth Log Employment 82-87
0.024
(0.083)
R2
Obs
Log Per Capita Expenditure
0.093
135
0.096
0.091
135
135
0.096
0.094
0.090
0.092
0.091
135
134
135
135
135
Panel B. Evidence from the NSS, Rural and UrbanPooled
-0.040
(0.051)
-0.002
Log Wage
(0.033)
Poverty Rate
0.019
(0.113)
-0.205
(0.339)
Poverty Depth
R2
0.06
0.07
0.06
0.06
Obs
315
274
315
315
Note: Robust standard errors in parentheses. Significance at the 10 percent level of confidence is represented by a *, at the 5 percent
level by **, and at the 1 percent level by ***. All regressions
include
indicators for industry use type: i.e. Capital goods, Consumer
Durables, Consumer Non Durables, and Intermediate. In Panel A, regressions are weighted by the square root of the number of factories.
Data are from the 1987 ASI and cover mining and manufacturing industries. In Panel B, regressions are weighted by the square root of
the number of workers in each industry in the 1987 NSS. Urban and Rural sample are pooled and an indicator for urban is included.
Separate regressions for the urban and rural sample exhibit similar patterns. Note that cereals and oilseeds cultivation has been treated as
a non-traded industry, because imports of these agricultural products were canalized (restricted only to state trading monopolies) until
2000.
Table 1.2 Summary Statistics
RURAL 38TH ROUND 1983
Variable
Poverty Rate
Poverty Gap
Std Dev Log Consumption
Logarithmic Deviation
Log Per Capita Consumption
Obs
364
364
364
364
364
Mean
0.435
0.120
0.501
0.139
4.736
Std. Dev.
0.173
0.067
0.058
0.036
0.195
URBAN 38TH ROUND 1983
Variable
Poverty Rate
Poverty Gap
Std Dev Log Consumption
Logarithmic Deviation
Log Per Capita Consumption
RURAL 43rd ROUND 1987
Variable
Poverty Rate
Poverty Gap
Std Dev Log Consumption
Logarithmic Deviation
Log Per Capita Consumption
Poverty Rate Change in the 80s
Poverty Gap Change in the 80s
Std Dev Change in the 80s
Logarithmic Deviation Change in the 80s
Log Per Capita Consumption Change in the 80s
Tariff
TrTariff
Agricultural Tariff
Mining and Manufacturing Tariff
FDI
Licensed Industries
Number of Banks per 10,000 people
Percent Literate
Percent SC/ST
Percent Farmers
Percent Manufacturing
Percent Mining
Percent Service
Percent Trade
Percent Transport
Obs
364
364
364
364
364
364
364
364
364
364
364
364
364
364
364
364
364
364
364
364
364
364
364
364
364
Mean
0.375
0.090
0.459
0.121
5.060
-0.060
-0.029
-0.041
-0.018
0.324
0.081
0.883
0.822
0.909
0.000
0.323
0.644
0.368
0.293
0.816
0.056
0.005
0.065
0.032
0.013
Std. Dev.
0.195
0.064
0.080
0.046
0.253
0.163
0.062
0.075
0.047
0.181
0.080
0.096
0.142
0.042
0.000
0.158
0.252
0.137
0.161
0.103
0.045
0.014
0.037
0.020
0.011
RURAL 55th ROUND 1999
Variable
Poverty Rate
Poverty Gap
Std Dev Log Consumption
Logarithmic Deviation
Log Per Capita Consumption
Tariff
TrTariff
Agricultural Tariff
Mining and Manufacturing Tariff
FDI
Licensed Industries
Number of Banks per 10,000 people
Obs
361
361
361
361
361
361
361
361
361
361
361
361
Mean
0.242
0.048
0.462
0.116
5.758
0.026
0.306
0.236
0.341
0.223
0.071
0.773
Std. Dev.
0.138
0.035
0.102
0.042
0.262
0.022
0.061
0.077
0.022
0.114
0.136
0.300
Obs
359
359
359
359
359
Mean
0.449
0.125
0.542
0.165
5.034
Std. Dev.
0.140
0.049
0.063
0.041
0.182
URBAN 43rd ROUND 1987
Variable
Poverty Rate
Poverty Gap
Std Dev Log Consumption
Logarithmic Deviation
Log Per Capita Consumption
Poverty Rate Change in the 80s
Poverty Gap Change in the 80s
Std Dev Change in the 80s
Logarithmic Deviation Change in the 80s
Log Per Capita Consumption Change in the 80s
Tariff
TrTariff
Agricultural Tariff
Mining and Manufacturing Tariff
FDI
Licensed Industries
Number of Banks per 0,000 people
Percent Literate
Percent SC/ST
Percent Farmers
Percent Manufacturing
Percent Mining
Percent Service
Percent Trade
Percent Transport
Obs
353
353
353
353
353
351
351
351
351
351
362
362
362
362
362
362
362
362
362
362
362
362
362
362
362
Mean
0.254
0.058
0.503
0.150
5.384
-0.195
-0.066
-0.038
-0.014
0.351
0.172
0.891
0.782
0.915
0.000
0.359
0.647
0.591
0.154
0.194
0.191
0.013
0.264
0.217
0.073
Std. Dev.
0.167
0.051
0.110
0.074
0.274
0.144
0.049
0.114
0.079
0.227
0.085
0.083
0.090
0.057
0.000
0.161
0.256
0.094
0.064
0.101
0.088
0.041
0.073
0.045
0.025
URBAN 55th ROUND 1999
Variable
Poverty Rate
Poverty Gap
Std Dev Log Consumption
Logarithmic Deviation
Log Per Capita Consumption
Tariff
TrTariff
Agricultural Tariff
Mining and Manufacturing Tariff
FDI
Licensed Industries
Number of Banks per 10,000 people
Obs
350
350
350
350
350
350
350
350
350
350
350
350
Mean
0.148
0.030
0.533
0.159
6.161
0.061
0.318
0.212
0.336
0.255
0.082
0.777
Std. Dev.
0.107
0.027
0.089
0.054
0.271
0.030
0.044
0.052
0.030
0.136
0.125
0.307
Table 1.3 First Stage. Relationship Between Scaled and Non-Scaled Tariffs
DepVar: Tariff
I. RURAL
(1)
II. URBAN
(2)
(3)
(4)
Panel A. Whole Sample
TrTariff
0.356 ***
(0.090)
TrTariff*Post
0.633
***
(0.089)
0.288
0.407 ***
(0.091)
TrTariff
0.84
728
(0.118)
0.91
724
0.86
728
Panel B. States with Flexible Labor Laws
0.688 ***
0.539
0.276 *
(0.151)
(0.156)
TrTariff*Post
0.320
0.91
724
***
0.82
266
(0.156)
0.041
(0.157)
0.94
270
0.94
270
***
0.86
266
0.586
(0.082)
(0.084)
R2
Obs
***
0.214 *
***
(0.051)
R2
Obs
0.687
(0.150)
***
Panel C. States with Inflexible Labor Laws
TrTariff
0.487
(0.100)
***
0.606
***
(0.101)
0.255
0.89
428
0.90
428
(0.100)
R2
Obs
0.86
432
0.87
432
0.641
(0.240)
0.265
(0.181)
0.169 *
TrTariff*Post
***
(0.088)
***
Note: All regressions include year and district dummies. Standard errors (in parentheses) are
corrected for clustering at the state year level. Regressions are weighted by the square root of
the number of people in a district. Significance at the 10 percent level of confidence is
represented by a *, at the 5 percent level by **, and at the 1 percent level by ***.
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Table 1.6 Correlation Between Pre-Reform Level of Outcomes and Tariff Change
I. RURAL
Tariff Change
OLS
RF
IVTrTariff
IV-TrTariff,
Init TrTariff
OLS
RF
(1)
(2)
(3)
(4)
(5)
(6)
Panel A. Dependent variable: Poverty Rate
-0.457
0.101
-0.716 *
-0.416 **
(0.384)
(0.165)
TrTariff Change
R2
Obs
Tariff Change
II. URBAN
(0.270)
(0.417)
-0.129
0.299
364
0.306
364
(0.042)
TrTariff Change
0.291
364
0.354
353
(0.101)
-0.074
(0.083)
(0.109)
0.262
364
0.260
364
(7)
(8)
-1.134
(0.754)
(0.637)
0.351
353
0.353
353
-0.290
(0.218)
-0.223
(0.198)
0.296
353
0.297
353
-0.101
(0.358)
0.072
(0.312)
0.096
353
0.098
353
-0.033
(0.221)
0.004
(0.206)
0.063
353
0.063
353
-0.983 **
-0.077
(0.066)
***
(0.026)
R2
Obs
0.354
353
Panel B. Dependent variable: Poverty Gap
-0.225 **
-0.007
-0.210 *
***
IV-TrTariff,
Init TrTariff
-0.303
(0.230)
-0.150
(0.112)
0.306
364
IVTrTariff
0.257
364
0.255
364
0.297
353
0.295
353
Panel C. Dependent variable: StdLog Consumption
Tariff Change
TrTariff Change
R2
Obs
-0.365
(0.273)
-0.039
(0.154)
-0.245
(0.248)
0.114
(0.218)
-0.027
-0.119
(0.102)
0.057
364
0.067
364
(I0.093)
0.023
364
0.043
364
0.098
353
0.098
353
Panel D. Dependent variable: Log Deviation of Consumption
Tariff Change
-0.021
(0.070)
TrTariff Change
R2
Obs
-0.106
(0.159)
-0.060
(0.135)
0.064
(0.123)
-0.035
(0.056)
0.051
364
0.053
364
-0.009
(;0.058)
0.044
364
0.049
364
0.063
353
0.063
353
Panel E. Dependent variable: Log Mean Per Capita Expenditures
Tariff Change
0.506 **
0.400
(0.470)
(0.254)
TrTariff Change
R2
Obs
-0.051
(0.322)
0.131
(0.134)
0.410
364
0.402
364
3.391 **I
(1.161)
1.757 ***
(0.637)
0.905
2.951
**=
(1.005)
***
(0.349)
0.409
364
0.399
364
0.427
353
0.435
353
0.414
353
0.420
353
Note: Standard errors (in parentheses) are corrected for clustering at the state year level. Regressions are weighted by the square root of
the number of people in a district. Tariff change is define as Tariffl 987-Tariffl 997 (generally a positive number). In all regressions I
control for industrial composition in the district in 1987: namely, percent manufacturing workers, percent farmers, percent workers in
trade, transportation and services, as well as percent literate and share of scheduled caste and scheduled tribe population. Significance at
the 10 percent level of confidence is represented by a *, at the 5 percent level by **, and at the 1 percent level by ***.
Table 1.7 Effect of Trade Liberalization on Poverty and Inequality at the District Level in Rural India Controlling for Initial Characteristics
(1)
I. IV-TrTariff
(3)
(2)
(4)
(5)
(6)
II. IV-TrTariff, Init TrTariff
(7)
(8)
(9)
(10)
Panel A. Dependent variable: Poverty Rate. District Level (Obs=725)
Tariff Measure
Logmean
-0.607 *** -0.434 **
(0.232)
(0.217)
0.469 ***
(0.035)
Trend
-0.441
(0.281)
0.340 ***
(0.044)
-0.322 ***
(0.067)
Lagged 43
Tariff Measure
Logmean
-0.235 *** -0.175 ***
(0.075)
(0.066)
0.161 ***
(0.015)
Trend
-0.346
(0.242)
-0.444 **
(0.208)
-0.670 ***
(0.040)
-0.419 ***
(0.123)
-0.418 ***
(0.141)
-0.426 ***
(0.163)
0.469 ***
(0.034)
(0.066)
(0.062)
(0.063)
0.162 ***
(0.015)
-0.782 ***
(0.045)
Lagged 43
(0.069)
-0.481 *** -0.456 ***
(0.164)
(0.134)
-0.665 *** -0.417 ***
(0.039)
(0.120)
Panel B. Dependent variable:Poverty Gap. District Level (Obs= 725)
-0.196 **
-0.124 **
-0.077
-0.118 *
-0.121 **
(0.090)
0.126 ***
(0.013)
-0.319 ***
(0.064)
-0.522 **
(0.206)
0.338 ***
(0.041)
-0.322 ***
(0.067)
-0.177 **
(0.080)
0.126 ***
(0.013)
-0.318 ***
(0.064)
-0.576 ***
(0.144)
-0.118 *** -0.118 ***
(0.042)
(0.041)
-0.778 *** -0.576 ***
(0.044)
(0.131)
Panel C. Dependent variable.:StdLog Consumption.District Level (Obs= 725)
Tariff Measure
-0.192
(0.258)
Logmean
Trend
-0.244
(0.260)
-0.258
(0.249)
-0.140 * ** -0.047
(0.035)
(0.040)
-0.635 ***
(0.063)
Lagged 43
0.075
(0.313)
-0.057
(0.232)
-0.752 ***
(0.069)
-0.382
(0.278)
-0.083
(0.197)
-0.078
(0.203)
-0.136 ***
(0.035)
-0.175
((0.187)
-0.045
(0.041)
(
-0.635 ***
I(0.063)
0.081
(0.287)
0.006
(0.202)
-0.752 *** -0.410
(0.068)
(0.261)
Panel D. Dependent variable: Log Deviation of Consumption.District Level (Obs=725)
Tariff Measure
-0.009
(0.131)
Logmean
Trend
-0.037
(0.120)
-0.078 ***
(0.018)
-0.095
(0.098)
-0.031 *
(0.018)
-0.584 ***
(0.100)
Lagged 43
0.066
(0.128)
0.044
(0.108)
-0.803 ***
(0.043)
-0.570 *
(0.309)
-0.005
(0.081)
-0.004
(0.082)
-0.077 ***
(0.019)
-0.079
(0.074)
-0.030
(0.019)
-0.584 ***
(0.100)
0.032
(0.124)
0.020
(0.097)
-0.801 *** -0.547 *
(0.043)
(0.309)
Panel E. Dependent variable: Log Mean Per Capita Expenditures (Obs=725)
Tariff Measure
0.668 *
((.374)
0.647
(0.431)
0.446
(0.378)
Logmean
Trend
Lagged 43
0.664 *
(0.359)
-0.010
(0.186)
-0.713 ***
(0.059)
0.491 **
(0.243)
0.605
(0.374)
0.503 *
0.490 **
(0.287)
(0.243)
-0.022
(0.187)
-0.714 ***
(0.057)
-0.602 ***
(0.074)
-0.010
(0.186)
-0.601 *** -0.022
(0.072)
(0.187)
I
r
,'1
r'
Note: All regressions include year, district dummies, state labor laws-year dummies and pre-reform literacy, share of SC/ST population and industrial structure
interacted with a post dummy. Regressions are weighted by the square root of the number of people in a district. The data are from the 43rd and 55th rounds of
the NSS. Standard errors (in parentheses) are corrected for clustering at the state year level. Significance at the 10 percent level of confidence is represented by a
*, at the 5 percent level by **, and at the 1 percent level by ***. In columns (1)-(5), the district tariff is instrumented by the non-scaled tariff. In columns (6)( 10), the district tariff is instrumented by the non-scaled tariff and the interaction of pre-reform non-scaled tariff and a post dummy. In column (5) and (10) the
level of the lagged dependent variable is instrumented with the value of the dependent variables in 1983.
Table 1.8 Effect of Trade Liberalizationon Poverty and Inequality in Rural India Controllingfor Initial Characteristicsand Other Reforms
(1)
Tariff Measure
(2)
-0.573 *** -0.446 **
(0.222)
(0.201)
0.485 ***
Logmean
(0.034)
Trend
I. IV-TrTariff
(3)
(4)
(5)
(6)
II. IV-TrTariff,Init TrTariff
(7)
(8)
(9)
Panel A. Dependent variable:Poverty Rate. District Level (Obs=725)
-0.375
-0.447 **
-0.428
-0.413 ***
-0.402 ***
(0.274)
(0.236)
(0.202)
(0.149)
(0.152)
0.353 ***
0.486 ***
(0.043)
(0.033)
-0.310 ***
FDI opened industries
License industries
Bank branchesper capita
Tariff Measure
-0.051
(0.059)
0.008
(0.059)
-0.215 ***
(0.057)
0.050
(0.077)
3802 ***
1013
(789)
(766)
-0.224 ***
(0.073)
Logmean
-0.134*
(0.073)
0.069
(0.074)
1285
(861)
-0.691 ***
-0.441 ***
(0.040)
(0.135)
-0.210 ***
(0.049)
0.028
(0.090)
-131
(924)
-0.152 ***
(0.055)
0.020
(0.074)
1293
(1125)
-0.688 ***
-0.055
(0.059)
0.012
(0.059)
3787 ***
(771)
-0.216 ***
(0.054)
0.051
(0.075)
1001
(770)
-0.313 ***
-0.797 ***
License industries
Bank branches per capita
Tariff Measure
-0.064 ***
(0.020)
0.012
(0.021)
260
1213 ***
(232)
(224)
-0.175
(0.255)
Logmean
-0.028
(0.024)
0.021
(0.021)
330
(267)
-0.049 ***
(0.018)
0.007
(0.023)
-235
(262)
-0.011
(0.019)
0.000
(0.017)
1204 ***
(224)
-0.066 ***
(0.021)
0.014
(0.021)
242
(219)
-0.622 ***
License industries
Bank branches per capita
Tariff Measure
(0.074)
-0.006
(0.048)
-0.005
(0.050)
1050
(1059)
-0.316
(0.324)
-0.054
(0.053)
0.037
(0.052)
1090
(1032)
-0.040
(0.049)
0.054
(0.045)
1964 *
(1091)
-0.002
(0.119)
Panel D. Dependent variable: Log Deviation of Consumption.District Level (Obs= 725)
-0.022
-0.089
0.066
0.040
0.008
0.007
(0.116)
(0.097)
(0.132)
(0.104)
(0.081)
(0.083)
-0.078 *** -0.029 *
-0.077 ***
(0.018)
(0.017)
(0.018)
Trend
-0.092*
(0.049)
0.070
(0.044)
1109
(1075)
-0.045
(0.050)
0.059
(0.046)
1922 *
(1109)
-0.579 ***
FDI opened industries
License industries
Bank branches per capita
-0.029
(0.022)
0.037 **
(0.018)
704
(518)
-0.039
(0.026)
0.024
(0.023)
423
(436)
-0.028
(0.024)
0.022
(0.022)
324
(268)
-0.162
(0.193)
-0.048
(0.039)
-0.049 ***
-0.607 ***
(0.147)
-0.040 ***
(0.017)
0.006
(0.022)
-227
(268)
(0.015)
0.005
(0.019)
110
(342)
0.080
(0.295)
0.004
(0.204)
-0.754 ***
(0.074)
-0.006
(0.048)
-0.005
(0.050)
1050
(1056)
-0.356
(0.295)
-0.051
(0.052)
0.035
(0.051)
1081
(1042)
-0.057
(0.051)
0.035
(0.052)
1226
(962)
-0.070
(0.076)
-0.028 *
(0.017)
0.031
(0.128)
0.021
(0.095)
-0.805 ***
-0.463
(0.388)
-0.025
(0.032)
0.015
(0.026)
253
(455)
(0.102)
-0.806 ***
-0.055 **
(0.023)
0.044 **
(0.017)
258
(510)
0.129 ***
(0.014)
-0.579 ***
(0.102)
Lagged 43
-0.115 ***
(0.042)
-0.622 ***
-0.089*
(0.049)
0.067
(0.042)
1119
(1057)
Logmean
-0.152 ***
(0.052)
0.021
(0.073)
1291
(1117)
(0.068)
-0.754 ***
-0.054
(0.052)
0.033
(0.051)
1249
(964)
-0.113 ***
-0.794 ***
(0.069)
FDI opened industries
(0.043)
-0.207 ***
(0.046)
Panel C. Dependent variable: StdLog Consumption.District Level (Obs=725)
-0.244
0.084
-0.066
-0.061
-0.063
(0.251)
(0.318)
(0.228)
(0.201)
(0.208)
-0.147 *** -0.050
-0.142 ***
(0.036)
(0.038)
(0.036)
Lagged 43
-0.169 **
(0.082)
-0.604 ***
(0.160)
-0.039 **
(0.016)
0.005
(0.019)
115
(366)
-0.213
(0.260)
Trend
(0.047)
0.025
(0.088)
-103
(948)
(0.063)
(0.047)
-0.008
(0.018)
-0.002
(0.017)
-0.132 *
(0.069)
0.067
(0.074)
1304
(894)
-0.441 ***
(0.133)
-0.312 ***
(0.063)
FDI openedindustries
0.350 ***
(0.040)
(0.039)
Panel B. Dependent variable:Poverty Gap.District Level (Obs=725)
-0.190 **
-0.085
-0.118
-0.122 *
-0.117 *
(0.093)
(0.068)
(0.073)
(0.066)
(0.069)
(0.063)
0.128 ***
0.166 ***
0.168 ***
(0.017)
(0.015)
(0.017)
Lagged 43
-0.445 ***
(0.129)
(0.068)
-0.181 ***
Trend
-0.464 ***
(0.156)
-0.310 ***
(0.068)
Lagged 43
-0.495 **
(0.203)
(10)
(0.046)
-0.003
(0.024)
-0.006
(0.022)
246
(452)
-0.492
(0.404)
-0.023
(0.033)
0.013
(0.027)
251
(458)
-0.056 **
(0.023)
0.044 **
(0.018)
257
(509)
-0.030
(0.023)
0.038 **
(0.019)
696
(519)
-0.039
(0.026)
0.025
(0.024)
418
(436)
(0.047)
-0.002
(0.024)
-0.007
(0.021)
250
(443)
.
.
Note: All regressionsincludeyear, district dummies, state labor laws-yeardummies and pre-reformliteracy, shareof SC/STpopulation and industrialstructureinteractedwith a post
dummy. Regressionsare weighted by the squareroot of the number of people in a district/region.The data are from the 43rd and 55th roundsof theNSS. Standard errors (in parentheses)
are corrected for clustering at the state year level. Significanceat the 10percent level of confidenceis representedby a *, at the 5 percent level by **, and at the percent level by ***. In
columns (l)-(5), thedistrict tariff is instrumentedby thenon-scaled tariff. In columns (6)-(10),the districttariff is instrumentedby the non-scaled tariff and the interactionof pre-reform
non-scaled tariff and a post dummy. In column (5) and (10) the level of the lagged dependentvariable is instrumentedwith the value of the dependentvariablesin 1983.
II
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Table 1.10 Industry Shares in Overall Employment. Correlations Over Time.
Panel A. Manufacturing sector. Annual Survey of Industries
Correlations Based on 2-digit NIC
1981
1981
1.00
1987
1991
1997
0.96
0.95
0.91
1987
1.00
0.99
0.96
1991
1.00
0.98
Correlations Based on 3-digit NIC
1997
1.00
1981
1987
1991
1997
1981
1.00
0.96
0.95
0.90
1987
1991
1997
1.00
0.98
0.96
1.00
0.98
1.00
1987
1993
1999
1.00
0.98
0.92
1.00
0.95
1.00
Panel B. All sectors. National Sample Surveys, 38th, 43rd, 50th and 55th round
RURAL
1983
1987
1993
1999
1983
1.00
1.00
1.00
1.00
URBAN
1987
1.00
1.00
1.00
1993
1.00
1.00
1999
1.00
1983
1987
1993
1999
RURAL: Mining and Manufacturing
1983
1983
1.00
1987
1993
1999
0.99
0.94
0.90
1987
1.00
0.95
0.91
1993
1.00
0.91
RURAL: Sevices, Trade, Transport,Construction
1983
1987
1993
1999
1983
1.00
0.96
0.95
0.84
1987
1.00
0.95
0.88
1993
1.00
0.96
1983
1.00
0.99
0.97
0.88
URBAN: Mining and Manufacturing
1999
1.00
etc.
1983
1987
1993
1999
1987
1993
1999
1.00
0.96
0.91
1.00
0.93
1.00
URBAN: Sevices, Trade, Transport,Construction
1999
1.00
1983
1.00
0.98
0.94
0.90
1983
1987
1993
1999
1983
1.00
0.99
0.98
0.92
etc.
1987
1993
1999
1.00
0.98
0.93
1.00
0.97
1.00
Note: This table presents the correlation of the share of employees in each industry across time. Panel A presents estimates
based on the Annual Survey of Industries, which covers the manufacturing and mining sectors (approximately 190 3-digit
NIC codes). In panel B, the shares are estimated based on principal industry affiliation from the individual employment and
unemployment files in the 38th, 43rd, 50th and 55th rounds of the NSS. About 330 industries in rural India and 340 in
urban India are represented in the NSS sample. All estimates are significant at the percent confidence level.
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Table 1.12 Effect of Tariffs on Industry Employment Shares. NSS Evidence.
All Industries
Agriculture
(1)
Mining and
Manufacturing
(2)
(3)
-0.002
(0.004)
-0.006
(0.010)
0.001
(0.000)
486
67
419
-0.002
(0.003)
0.000
(0.001)
Panel A. Rural Sample
Tariff
Obs
Panel B. Urban Sample
Tariff
0.000
(0.001)
Obs
494
62
432
Note: All regressions include industry dummies and year dummies. Regressions are
weighted by the square root of the number of observations per industry. The data are
from the 43rd, 50th and 55th rounds of the NSS. Standard errors (in parentheses) are
corrected for clustering at the industry level. Significance at the 10 percent level of
confidence is represented by a *, at the 5 percent level by **, and at the 1 percent level
by ***
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Table 1.14 Effect of Trade Liberalization on Poverty and Inequality in Rural India at the District
Level by State Characteristics
Flexible Labor Laws
(1)
Inflexible Labor Laws
(2)
(3)
(4)
Panel A. Dependent Variable: Povery Rate
Tariff Measure
Obs
0.091
(1.302)
-0.121
(0.189)
265
265
-0.608 **
( ).301)
431
-0.672 **
(0.298)
431
Panel B. Dependent Variable: Poverty Gap
Tariff Measure
Obs
-0.143
(0.372)
-0.045
(0.102)
265
265
- 0.209 **
(C0.084)
431
-0.229
***
(0.087)
431
Panel C. Dependent Variable: StdLog Consumption
Tariff Measure
Obs
-1.384 **
(0.559)
-0.590 **
(0.241)
265
265
0.101
(0.196)
0.093
(0.188)
431
431
Panel D. Dependent Variable.:Log Deviation of Consumption
Tariff Measure
Obs
-0.487 ***
(0.172)
265
-0.210 *
(0.124)
265
0.005
(0.078)
-0.003
(0.074)
431
431
Panel E. Dependent variable: Log Mean Per Capita Expenditures
Tariff Measure
Obs
-1.181
(1.508)
-0.049
(0.191)
265
265
0.831 *
(0.451)
431
0.923 **
(0.458)
431
Note: All regressions include year, district dummies, state labor laws-year dummies and pre-reform
literacy, share of SC/ST population, industrial structure, log of per capita expenditures, and trend in
the outcome variable interacted with a post dummy. Regressions are weighted by the square root of
the number of households in a district. The data are from the 43rd and 55th rounds of the NSS.
Standard errors (in parentheses) are corrected for clustering at the state year level. Significance at the
10 percent level of confidence is represented by a *, at the 5 percent level by **, and at the percent
level by ***. In columns (1) and (3), the district tariff is instrumented by the non-scaled tariff. In
columns (2) and (4), the district tariff is instrumented by the non-scaled tariff and the interaction of
pre-reform non-scaled tariff and a post dummy.
Table 1.15 Effect of Tariffs on Industry Premia
Dep: Wage Premium
All
Agiculture
(1)
(2)
0.121
(0.107)
0.132
(0.145)
0.74
336
0.81
221
(3)
Mining and Manufacture
(4)
(5)
(6)
0.103
(0.121)
0.140
(0.172)
0.165
(0.136)
0.156
(0.199)
0.94
23
0.96
15
0.72
313
0.80
206
Panel A. Rural Sample
Tariff
R2
Obs
Panel B. Urban Sample
Tariff
R2
Obs
0.139 ***
(0.049)
0.69
347
0.161 ***
(0.052)
0.84
230
0.286 *
(0.152)
0.64
23
0.248
(0.162)
0.84
15
0.137 ***
(0.053)
0.70
324
0.150 ***
(0.058)
0.84
215
Note: All regressions include industry dummies and year dummies. Robust standard errors are in parentheses.
Regressions are weighted by the inverse of the standard error of the industry premium estimate. Industry level tariffs for
1983 are assumed to be equal to those is 1987. In columns (1), (3), (5) data from the 38, 50 and 55th rounds were used,
while columns (2), (4) and (6) only data from the 38th and 55th rounds were used.
Figure 1.1 Evolution of Tariffs in India
PanelD: Tariffs by Industrial Use Based
Category
Panel C: Tariffs by Broad Industrial Category
120
100
-
80
Cerealsand
1zu
Oilseeds
100
-4--
Basic
80
----
Capital
-Agriculture
60
Mining& Mfg-K
40
Mining& Mfg-C
20
60
Consumer
Durables
ConsumerNon
Durables
,, Intermediate
+
40
20
0
0
_qA, _9!
_ON, _05
_C;
_.,
_Ckb _C,
I
Panel F: Share of Free HS Lines by Industrial
Use Based Category
Panel E: Share of Free HS Lines by Broad
Industrial Category
1.2
1.2
MBasic
1
* Capital
0.8
. i.';i;"" ~'~~~~~'~'
~~ ~
El;·Agriculture 0.6
03Mining& Mfg-K
0 Mining& Mfg-C
0.4
O ConsumerDurables
0.4
O ConsumerNon
Durables
* Intermediate
0.2
0.2
0
1989
1
1996
0
1998
1989
2001
1996
1998
2001
Panel H: Tariff Decline and Industry Tariffs in 1987
Panel G: Correlation of Industry Tariffs in 1997 and 1987
8a-
.®
~® ee
8
co.
B
I.
*.
i%:I
To
I
0
* :1. I
50
10I5
1
987 tariff10
tarifff
1987
.
a
9 ,
0
5
200
2o
0
50
150
100
1987tariff
200
250
Figure 1.2 Evolution of India's Trade
800
Export,Import,and OutputIndices
(1978/79= 100)
700
-
600
500
eS=4_-~~~~~~~~~/I,
400
|~~~~~~~~~~~~~~
/!]§.
300
200
100
0
1980/81 1982/83 1984/85 1986/87 1988/89 1990/91 1992/93 1994/95 1996/97 1998/99 2000/01
Ilmport
25
volume---
Exportvolume_iReal
GDP
Merchandise Trade
(In percentof GDP)
20
00
-- ww!
t
15
10
5
0
1980/81
1983/84
1986/87
1_Exports
1989/90
1992/93
1998/99
1995/96
2001/02
-U-Imports -*Total trade
Figure 1.3 Pattern of India's Trade
XM Shares on Skilled/Unskilled Ratio 90
I
1987
i
,,-r
1991
e~~~~~~~~.
l
'-
X0'
t ,f.
.E
0
a-,
3
.5
L
6
1.5
skilusktlO
|
cumsharnexp1sSU90
*
E
12
u
o
ii
~...P1ns5
il u ski
1.5
pSU990
*' cum.~,.nxpotss
U0 * cum
harr.,porsS
U90
cumshaimpatsSU90
i5
9
rn_
1994
~2
1997
InX
w
E ,.
i
IC.
.*
r
(
.,,
I,.,'
4~
i
'
I31
.
E
J._.
Est
0
1.5
skLunskM90
cumshaexprs5SU90
*
cursharmprtsSU90
.5
0
.5
ski-_uns
.
k9
-cumhshateexpsSU90
*
1
CUmsharrn prsSU90
1.5
Figure 1.4 Direction of Tariff Changes
100 -
90I
1
80
7060
50
40302010
0
1992
1993
I
I
I
I
1994
1995
1996 1997
I
j
1998
1999 2000
2001
|U % Items with tariff reductions E % Items with tariff increases
Figure 1.5 Trends in Rural and Urban Poverty and Inequality
Evolution of the Poverty Rate
Evolution of the Poverty Gap
0.140
0.500 0.450 -
0.4000.3500.300-
0.120 -
t
0.100-\
X
-_
0.250-
0.200-_->
0.080
-
0.060
0.150
0.100 0.050
0.000
0.040
0.020
0.000
1983
1988
1993
1999
,
,
1983
1988
,
1993
1999
[+,Rural -- Urban
-4-Rural -- Urban
Evolution of the Std Dev of Log Consumption
Evolution of the Log Deviation of Consumption
0.220
0.650
.
0.200
,,--a
0.600
0.180
0.550
0.160
t--7-77
0.500
0
0.140
0.450
0.120
0.100
0.400
,
1983
1988
--
1993
l
I
1983
1999
Rural
Urban
1988
I--$--Rural
1993
--iW-Urbani
--
1999
Figure 1.6 Intersectoral Reallocation in India
Structural Change
_ _ __ _
.__
,8
In.
u. 1L
.
0.1 '
0.08 N l
0.06 -
l
l
l
l
l
l
l
l
0.04 0.02
0
1
91 ,)
1
% % % % F
NO -,C 110)
%NO 1
.
.
.
.
Nc -lop -ICF -le
1
.
0
11
1
F
Excess Reallocation
18
1614 1210
8
6
2
0
'b'o~~ ~ 4
Growth in Manufacturing Employment
al
I\
15
10
5
0
-10
.
Note: Data from the Annual Survey of Industries which covers the registered manufacturing and mining sectors.
Structural change in sector is measured as the absolute value of the change in a sector's employment share over
two years. Excess job reallocation is defined as in Davis et al. ( 996).
Figure 1.7 Comparison of the Cumulative Distribution of Log Per Capita Expenditure in 1987 and 1999 by Industrial Affiliation
RURAL 1987
RURAL 1999
"I
q.-
1
IQ -
/
i.,
:/?,;
cN
-
0
4
__
4.5
I
i_--_
5
5.5
Log Per CapitaExpenditure
Non-traded
Traded-|.----Agriculture
_-
6
,
-
6.5
IMvning/lvMg
--------- Cereals and Oilseeds
Non-traded
--------- TradedAgriculture
URBAN 1987
Mvning/Ig
-----
.
Cereals
.
and Olseeds
URBAN 1999
0
5
6
Log Per CapitaExpenditure
Non-traded
--------- Traded Agriculture
-----
M-ning/Mg
Cereals and Oilseeds
7
5.5
Log Per CapitaExpenditure
Non-traded
--------- TradedAgriculture
----- -
-
Mning/Mg
Cereals and Oilseeds
Note: Cumulativedistributionof log per capitaexpenditurebased on NSS43rdand NSS55thEmployment-Unemployment
Surveys.The verticallinesrepresentthe
poverty linesfor the particularyear and sector.
-
Table
.A1 Sectoral Tariffs and Poverty and Inequality in Rural and Urban India
I. RURAL
(1)
II. URBAN
(2)
(3)
(4)
(5)
(6)
Panel A. Dependent Variable: Poverty Rate
Agricultural Tariff
-0.219
***
-0.213
Mining and Manufacturing Tariff
Obs
***
-0.242 **
(0.097)
0.277
(0.318)
(0.070)
0.221
(0.297)
725
725
703
(0.071)
725
-0.240 **
(0.102)
-0.154
-0.148
(0.163)
(0.154)
703
703
-0.072
(0.049)
(0.029)
-0.071
(0.047)
703
703
0.000
(0.131)
0.060
(0.092)
-0.001
(0.129)
703
703
0.024
(0.076)
0.053
(0.066)
0.022
(0.074)
703
703
Panel B. Dependent variable: Poverty Gap
Agricultural Tariff
-0.081
(0.021)
Mining and Manufacturing Tariff
Obs
Agricultural Tariff
725
~
***
***
0.062
(0.123)
725
725
703
Panel C. Dependent variable.:StdLog Consumption
-0.110 *
-0.110 *
Mining and Manufacturing Tariff
725
-0.066 **
(0.027)
(0.064)
Obs
-0.080
(0.020)
0.041
(0.113)
0.030
(0.220)
(0.062)
0.002
(0.208)
725
725
-0.065 **
0.060
(0.091)
703
anel D. Dependent variable: Log Deviation of Consumption
Agricultural Tariff
-0.037
(0.025)
Mining and Manufacturing Tariff
Obs
725
0.053
(0.066)
0.073
(0.109)
-0.035
(0.025)
0.064
(0.111)
725
725
703
Note: All regressions include year and district dummies. Standard errors (in parentheses) are corrected for clustering at the
state year level. Regressions are weighted by the square root of the number of people in a district. Significance at the 10
percent level of confidence is represented by a *, at the 5 percent level by **, and at the 1 percent level by ***
Table 1.A2 Effect of Trade Liberalization on Poverty and Inequality at the Region Level in Rural India Controlling for Initial Characteristics
(1)
I. IV-TrTariff
(3)
(2)
(4)
(5)
(6)
II. IV--TrTariff, Init TrTariff
(7)
(8)
(9)
.
.
.
(10)
.
.
PanelA. Dependentvariable:Poverty Rate.Region Level (N=124)
Tariff Measure
Logmean
Trend
Lagged 43
-1.161 *** -1.063 **
-1.035 *** -0.802 ***
(0.428)
(0.421)
(0.361)
(0.297)
0.343 ***
0.328 ***
(0.065)
(0.073)
-0.108
(0.103)
-0.460 ***
(0.078)
-0.821 ***
(0.284)
-1.049 *** -0.978 *** -0.992 ***
(0.242)
(0.285)
(0.299)
0.329 ***
0.345 ***
(0.065)
(0.074)
-0.107
(0.106)
-0.436 ***
(0.107)
-0.874 *** -0.886 ***
(0.218)
(0.213)
-0.457 *** -0.426 ***
(0.076)
(0.109)
Panel B. Dependentvariable:Poverty Gap. Region Level (N=124)
Tariff Measure
Logmean
Trend
Lagged 43
-0.438 *** -0.398 *** -0.407 *** -0.175 **
(0.144)
(0.131)
(0.107)
(0.087)
0.139 ***
0.136 ***
(0.026)
(0.026)
-0.152
(0.117)
-0.645 ***
(0.107)
-0.197 **
(0.084)
-0.326 *** -0.297 *** -0.322 ***
(0.085)
(0.093)
(0.076)
0.141 ***
0.138 ***
(0.027)
(0.027)
-0.148
(0.123)
-0.591 ***
(0.133)
-0.181 *** -0.192 ***
(0.049)
(0.052)
-0.644 *** -0.594 ***
(0.100)
(0.123)
Panel C. Dependentvariable:StdLog Consumption.Region Level (N=124)
Tariff Measure
-0.301
(0.388)
Logmean
-0.338
(0.380)
-0.131 **
(0.053)
Trend
-0.357
(0.339)
-0.111 **
(0.049)
-0.236 *
(0.125)
Lagged 43
0.259
(0.279)
0.344
(0.287)
-0.650 ***
(0.128)
-0.749 **
(0.301)
-0.151
(0.350)
-0.177
(0.336)
-0.128 **
(0.052)
-0.221
(0.308)
-0.109 **
(0.048)
-0.229 *
(0.130)
0.210
(0.270)
0.237
(0.248)
-0.642 *** -0.690 ***
(0.126)
(0.248)
PanelD. Dependentvariable:Log Deviation of Consumption.Region Level (N=124)
Tariff Measure
-0.109
(0.178)
Logmean
Trend
-0.129
(0.172)
-0.070 **
(0.028)
-0.148
(0.161)
-0.059 **
(0.025)
-0.195
(0.141)
Lagged 43
0.137
(0.120)
0.208
(0.162)
-0.661 ***
(0.116)
-0.853 ***
(0.313)
-0.032
(0.164)
-0.046
(0.156)
-0.068 **
(0.027)
-0.074
(0.153)
-0.059 **
(0.024)
-0.185
(0.145)
0.101
(0.115)
0.115
(0.118)
-0.654 *** -0.732 ***
(0.115)
(0.231)
Panel E. Dependentvariable:Log Mean Per CapitaExpenditures(bs=124)
Tariff Measure
1.090 **
(0.474)
1.122 **
(0.488)
0.980 **
(0.444)
1.012 **
(0.431)
1.082 ***
(0.357)
1.099 ***
(0.374)
1.004 *** 1.026 ***
(0.327)
(0.322)
Logmean
Trend
Lagged 43
-0.380 **
(0.173)
-0.380 **
(0.175)
-0.382 ***
(0.132)
-0.272
(0.182)
-0.381 *** -0.272
(0.132)
(0.183)
Note: All regressions include year, region dummies, state labor laws-year dummies and pre-reform literacy, share of SC/ST population and industrial structure
interacted with a post dummy. Regressions are weighted by the square root of the number of people in a region The data are from the 43rd and 55th rounds of
the NSS. Standard errors (in parentheses) are corrected for clustering at the state year level. Significance at the 10 percent level of confidence is represented by
a *, at the 5 percent level by **, and at the 1 percent level by ***. In columns (1)-(5), the region tariff is instrumented by the non-scaled tariff. In columns (6)(10), the region tariff is instrumented by the non-scaled tariff and the interaction of pre-reform non-scaled tariff and a post dummy. In column (5) and (10) the
level of the lagged dependent variable is instrumented with the value of the dependent variables in 1983.
*
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Table 1.A4 Classification of States
Flexible Labor Laws
Inflexible Labor Laws
Andhra Pradhesh
Gujarat
Assam
Bihar
Karnataka
Madhya Pradesh
Maharashtra
Rajasthan
Tamil Nadu
Haryana
Kerala
Orissa
Punjab
Uttar Pradesh
West Bengal
Note: Classification is identical to Hasan (2003). It is a combination of the
Besley and Burgess (2004) classification of state labor laws as pro-employer,
pro-worker and neutral, and Goswami et al. (2002) survey of states' investment
climate.
Chapter 2
Trade Liberalization and Firm
Productivity: The Case of India
Summary 2 Using a panel of firm-level data, this paper examines the effects of India's trade
reforms in the early 1990s on firm productivity in the manufacturing sector, focusing on the
interaction between this policy shock and firm and environment characteristics. The rapid and
comprehensive tariff reductions-part of an IMF-supported adjustment program with India in
1991-allow us to establisha causal link betweenvariations in inter-industry and inter-temporal
tariffs and consistently estimatedfirm productivity. Specifically,reductionsin trade protectionisrn lead to higher levels and growth of firm productivity, with this effect strongest for private
companies. Interestingly, state-levelcharacteristics,
such as labor regulations, investment cli-
mate, and financial development, do not appearto influence the effect of trade liberalizationon
firm productivity.
2.1
Introduction
Over the past two decades, trade liberalization has become an important part of many countries'
development strategies. 1 Advocates of liberalization argue that opening up local markets to
In a recent lecture, Anne Krueger, the First Deputy Managing Director of the International Monetary Fund,
argued that liberalization is essential to growth and poverty reduction: "First, no country has achieved rapid and
sustained growth in living standards without using the international economy and integrating with it. Second,
countries wanting to achieve lasting reductions in poverty will be more successful the sounder are their own
87
foreign competition and foreign direct investment can lead to improvements in the productivity
of domestic industries, resulting in a more efficient allocation of resources and greater overall
output. Critics warn that domestic firms may not be able to realize efficiency gains, because they
are unable to successfully adapt foreign technologies to local methods of production or because
domestic firms face binding credit constraints that prevent expansion of efficient industries as
well as investments in new technology. Which of these two views is closer to the truth has
important implications for trade policy: if the latter holds, benefits of liberalization may not
be realized unless additional policies are devised to facilitate technology transfer or ease credit
constraints.
The evidence on whether trade liberalization increases firm-level efficiency is mixed. Tybout
et al. (1991) find no evidence of increased productivity following liberalization in Chile, while
Harrison (1994), Tybout and Westbrook (1995), Pavcnik (2002), and Fernandes (2003) do
observe productivity increases following liberalization in, respectively, C6te d'Ivoire, Mexico,
Chile, and Colombia.
This paper examines the effects of recent trade liberalization in India using a panel of firmlevel data. In particular, I try to answer several questions: did India's sweeping trade reforms
in the early 1990s lead to higher economy-wide and firm-level productivity, and what was the
interaction between this policy shock and various firm and environment characteristics? Were
the effects of liberalization influenced by the type of firm ownership or by the initial level
of productivity?
And did institutional characteristics of the Indian states, such as financial
development, investment climate, or labor laws play a role in the propagation of the trade
liberalization shock?
India is a particularly relevant setting to seek the answers to these questions: in 1991, in
response to a severe balance of payments crisis, India turned to the International Monetary Fund
for assistance in solving its external payments problem. Financial assistance was received from
the IMF to support India's adjustment program, which included major structural reforms-a
key one being trade liberalization. A massive overall reduction in tariffs and non-tariff barriers,
as well as a reduction in the standard deviation of protection, followed. Coinciding with these
economic policies and the more rapid their economic growth. And third, for countries with a sound domestic
policy framework, poverty reduction and growth will be more rapid the more open is the international economy
and the more rapid the growth of trade in goods and services" (Heinz Arndt Memorial Lecture, August 13, 2003).
88
tariff reductions were significant changes in firm-level productivity, as documented by Unel
(2003).
There are strong theoretical reasons to think that, in the absence of external pressure,
trade policies are endogenously determined, with increases in firm productivity preceding trade
liberalization.
However, because trade liberalization in the early 1990s can be characterized
as rapid and relatively comprehensive, it is reasonable to assume that the changes in level of
protectionism were exogenous. Thus, the reforms initiated in 1991 and completed in the context
of the export-import policy announced in the government's Eighth Plan (1992-96) comprise an
excellent setup to test whether changes in firm productivity can be attributed to the exogenous
variation in tariff changes across industries. 2
More specifically, in order to estimate the effect of trade liberalization on productivity, I
employ methodology similar to that used in Pavcnik (2002) and Fernandes (2003); this methodology overcomes some weaknesses of earlier studies. First, I obtain consistent estimates of the
parameters of the industry-level production functions in constructing firm level productivity
measures, using the methodology of Levinsohn and Petrin (2003). Next, I examine the correlation between trade policies and manufacturing productivity in a regression framework. To
limit the confounding effect of possible selective protection of industries, I focus on the pre- and
immediately post-reform period, using plausibly exogenous intertemporal variation in nominal
tariff levels across industries.
I find that reductions in trade protection led to higher levels of productivity. While this
effect is robust and highly statistically significant for private companies, there is no evidence
that trade liberalization leads to any productivity improvements for government-owned or foreign companies. State-level characteristics, such as labor regulations, investment climate, and
financial development, do not seem to influence the effect of trade liberalization on productivity.
2.2
The Case of India: the 1991 Reforms
India's post-independence development strategy was one of national self-sufficiency, and stressed
the importance of government regulation of the economy. Cerra et al. (2000, p. 3) charac2
India's trade policy is developed according to five-year plans. While these plans may be modified during the
implementation phase, they are by and large carried out.
89
terized it as "both inward looking and highly interventionist, consisting of import protection,
complex industrial licensing requirements, pervasive government intervention in financial intermediation and substantial public ownership of heavy industry." In particular, India's trade
regime was amongst the most restrictive in Asia, characterized by high nominal tariffs and
pervasive non-tariff barriers, including a complex import licensing system, an "actual user"
policy that restricted imports by intermediaries, restrictions of certain exports and imports to
the public sector ("canalization"), phased manufacturing programs that mandated progressive
import substitution, and government purchase preferences for domestic producers.
It was only during the second half of the 1980s, when the focus of India's development
strategy gradually shifted toward export-led growth that the process of liberalization began.
Import and industrial licensing were eased, and tariffs replaced some quantitative restrictions,
although even as late as 1990 a mere 12 percent of manufactured products could be imported
under an open general license; still, the average tariff was more than 90 percent. (Cerra et al.,
2000). 3
However, concurrent with the gradual liberalization in the mid to late 1980s was a rise in
macroeconomic imbalances-namely
fiscal and balance of payments deficits-which
increased
India's vulnerability to shocks. The sudden increase in oil prices, resulting from the conflict in
the Middle East in 1990, the drop in remittances from Indian workers in the same region, and the
slackened demand of important trading partners, as well as political uncertainty, undermined
investor confidence and resulted in large capital outflows. To deal with its external payments
problems, the government of India requested a Stand-By Arrangement from the IMF in August
1991. The IMP support was conditional on an adjustment program featuring macroeconomic
stabilization and structural reforms. The latter focused on the industrial and import licenses,
the financial sector, the tax system, and trade policy. On trade policy, benchmarks for the
first review of the Stand-By Arrangement included a reduction in the level and dispersion of
tariffs, a removal of quantitative restrictions on imported inputs and capital goods for export
production, and elimination of public sector monopoly on imports of all items except petroleum,
3
Hasan et. al (2003, p. 5) notes that "given several earlier attempts to avoid IMF loans and the associated
conditionalities, the large number of members of the new cabinet who had been cabinet members in past government with inward-looking trade policies and the heavy reliance on tariffs as a source of revenues, these reforms
came as a surprise."
90
edible oils, and fertilizer and certain items canalized for health and security reasons (Chopra et
al. (1995)).
The government's export-import policy plan (1992-97) ushered in radical changes to the
trade regime by sharply reducing the role of the import and export control system (Table 2.1).
The share of products subject to quantitative restrictions decreased from 87 percent in 1987/88
to 45 percent in 1994/95. All 26 import licensing lists were eliminated and a "negative" list was
established (Hasan et al., 2003)). Restrictions on exports were also relaxed, with the number
of restricted items falling from 439 in March 1990 to 210 in March 1994.
In addition to easing import and export restrictions, tariffs were drastically reduced (Figure
2.1). Average tariffs fell from more than 80 percent in 1990 to 37 percent in 1996, and the
standard deviation of tariffs dropped by 50 percent during the same period (Figure 2.1). Figure
2.2 presents the evolution of tariffs in selected industries. Most industries faced a sharp drop in
tariffs from 1991 to 1992, although the magnitude of the shock varied widely by industry. The
Indian rupee was also devalued by 20 percent against the U.S. dollar in July 1991, and further
devalued in February 1992.4
transactions.
Subsequently, it became fully convertible for current account
The economy reacted positively to the reduction in trade distortions and, as a
result, the ratio of total trade to GDP rose from an average of 13 percent in the 1980s to nearly
1.9percent of GDP in 1999/00 (Figure 2.3). Export and import volumes also increased sharply
from the early 1990s, outpacing growth in real output (Figure 2.3).
India remained committed to further trade liberalization, and since 1997, there have been
further adjustments to import tariffs. However,at the time, the government announced the
export-import policy in the Ninth Plan (1997-2002), sweeping reforms outlined in the previous
plan had been undertaken and pressure for further reforms from external sources had abated. In
this context, the problem of potential endogeneity of trade policy becomes more pronounced. In
particular, if policy decisions on tariff changes across industries were indeed based on expected
future productivity or on industry lobbying, the empirical strategy would not be valid. Simply
comparing productivity in liberalized industries to productivity in non liberalized industries
would possibly give a spurious correlation between total factor productivity (TFP) growth and
4
Although exchange rate devaluation may also inhibit imports, Chopra et al. (1995) argue that the magnitude
of the exchange rate devaluations can hardly be compared to the impact of pervasive trade reforms in reshaping
industry.
91
trade policies. As a simple check of the validity of the empirical strategy, I look for evidence
that tariffs were correlated with past industry-level performance during two periods: the period
before and immediately after the crisis (1989-1996), when India's trade policy was significantly
affected by externally imposed benchmarks, and the period 1997-2002, when external pressure
was virtually absent.5 In the latter period, there is some evidence to suggest that tariffs may
have been used selectively to protect less efficient industries (Table 2.2), but this is more fully
discussed in the next section.
2.3
Empirical Strategy, Data, and Related Literature
The theoretical literature on trade and productivity does not provide an unambiguous prediction of the impact of trade liberalization on firm level productivity.
Some argue that trade
liberalization in poor economies may have a detrimental effect on growth by preventing a country's involvement in certain industries, thus potentially denying it knowledge accumulation and
productivity growth (Young, 1991 and Stiglitz, 2002). Others argue the opposite: trade liberalization can actually increase overall domestic productivity through several channels. In the
presence of imperfectly competitive domestic markets, trade liberalization and concurrently
foreign competition can improve allocative efficiency by forcing firms to lower cost-price markups (i.e., the pro-competitive effects of trade) and thus to move them down their average cost
curves, thereby effectively raising firm size and scale efficiency (i.e., scale efficiency gain of
trade) (Epifani, 2003). With firm heterogeneity, trade opening may also induce a reallocation
of market shares towards more efficient firms and generate an aggregate productivity gain, without any productivity change at the firm level (Melitz, 2005). Going beyond this reallocation
effect of trade liberalization, Aghion et al. (2003) suggest another mechanism through which
liberalization might affect productivity: the increased threat of foreign competition raises the
innovation incentives by domestic producers as they seek to deter entry by foreign competitors.
The higher level of innovation leads to productivity growth at the firm level. Finally, the access
to superior inputs and technology might also increase technical efficiency. However, whether
5
ideally, we would like to estimate the production function for the periods before 1991, 1991-1996, and after
1996, but due to the small number of observations before 1991, we combine the pre-reform and immediately
post-reform period.
92
domestic producers can take advantage of increased access to knowledge remains questionable.
Due to the ambiguity of the theory, the question of whether trade liberalization leads to
higher productivity remains largely an empirical one. I employ the natural experiment of the
trade liberalization of India in 1991 to answer this question in the Indian context, contributing to
a growing body of empirical literature on the topic.6 In particular, I extend Krishna and Mitra's
(1998) attempt to rigorously estimate the effects of trade liberalization on firm performance in
Indian manufacturing for the 1986-1993 period and the recent study by Aghion et al. (2003),
which models the growth in performance inequality that might occur from trade liberalization as
a result of increases in the gap between the best and worst performers and between pro-employer
and pro-worker biased regions. While Aghion et al. (2003) test the predictions of their model
using state-industry
level data from 1980 to 1997 and a post-reform dummy to capture the
effect of liberalization (the same approach adopted by Krishna and Mitra (1998)), I use firmlevel panel data, employing intertemporal and across-industry variations in trade protection
to identify the effect of trade policies. The methodology in this paper follows closely Pavcnik
(2002) and Fernandes (2003), who estimate the impact of tariffs on levels of productivity for
Chile and Colombia, respectively.
2.3.1
Productivity Measure
To begin the analysis, I construct consistent measures of firm-level TFP. Previous studies estimated productivity by ordinary least squares (OLS), taking as TFP the difference between
actual and predicted output. This technique is subject to omitted variables bias, as the firm's
choice of inputs is likely to be correlated with any unobserved firm-specific productivity shocks.
If productivity is assumed time-invariant, the simultaneity problem may be solved by including
firm fixed effects (Harrison (1994) and Balakrishnan et al. (2000)); however, this strategy may
not be appropriate when I am interested in changes in firm-level productivity.
I construct a consistent firm-level measure of TFP following the methodology of Levinsohn
and Petrin (2003). Building on Olley and Pakes (1996),7 Levinsohn and Petrin (2003) use firm's
;Tybout et al. (1991) find no evidence of increased productivity following liberalization in Chile, while
Harrison (1994), Tybout and Westbrook (1995), Pavcnik (2002), and Fernandes (2003) do observe productivity
increases following liberalization in, respectively, C6te d'Ivoire, Mexico, Chile and Colombia.
7
Olley and Pakes (1996) develop a methodology in which an investment proxy controls for correlation between
93
raw material inputs to correct for the simultaneity in the firm's production function. 8 The inclusion in the estimation equation of a proxy that controls for the part of the error correlated
with inputs ensures that the variation in inputs related to the productivity term will be eliminated. Levinsohn and Petrin (2003) show that if the demand function for intermediate inputs
is monotonic in the firm's productivity for all relevant levels of capital, then raw materials can
serve as a valid proxy. Assuming a Cobb-Douglas production function, the equation estimated
for company i in industry j at time t in the first step can be written as follows:
Y~~~~~~t~
~
4
it -- + 1-'llt
+-pit+ raTnit
+ kki't+Wt+
where y denotes output,
(2.1)
(2.1)
denotes labor, p denotes power and electricity expenditures, m denotes
raw material expenditures, and k denotes capital used; all variables are expressed in natural
logarithm.
The simultaneity problem arises from the wi3itt term, a firm-specific, time varying
productivity shock that cannot be observed by the econometrician but is correlated with the
firm's choice of variable inputs, p, m, and 1. Using a process described in Levinsohn and Petrin
(2001), I derive consistent estimates of the parameters of the production functions for each
industry j.9 In doing this, I allow for the input demand function as well as the production
function to differ across two periods: before 1996 (a period of very high economic growth) and
after 1996 (a relative slowdown). In this way, I partially address the concern that the changing
economic environment may have affected the relative input and output prices, which are not
included in the raw materials demand estimation. The correctly estimated production function
coefficients differ substantially from the (biased) OLS estimates, confirming the importance of
the Levinsohn and Petrin methodology. Discussion of the OLS result is omitted for the sake of
brevity.1 0 Using the input coefficients obtained with the Levinsohn and Petrin methodology, I
input levels and unobserved productivity shocks, allowing for the consistent estimation of the firm's production
function. However, this methodology can only be applied to plants reporting non-zero investment, usually leading
to a sizable truncation of the available data. The Levinsohn and Petrin (2003) method avoids this problem.
8
For a detailed description of the production function estimation methodology, see Levinsohn and Petrin
(2003).
9Due to the small number of companies in some of the 4-digit level industries, the production function
parameters were estimated at the 2-digit National Industrial Classification codes.
0
l°Because
firm exit rates are so low (see Table 1 in Appendix II), we do not correct for potentially endogenous
exit decisions by firms. In contrast to Chile, where exit rates were high, exit does not appear to have been an
important feature of adjustment in India. However, Pavcnik (2002) does develop a methodology that would allow
for this correction if necessary.
94
obtain estimates of a firm's Hicks-neutral TFP by subtracting firm i's predicted output from its
actual output at time t. In order to make the estimated TFP comparable across industries, I
create a productivity index
l
following the standard methodology in the literature (Aw, Chen,
and Roberts, 2001, Pavcnik, 2002, and Fernandes, 2003).
2.3.2
Empirical Strategy
The standard approach to estimating the effects of trade liberalization is to estimate the coefficient of an indicator variable for post-reform period (Tybout et al., 1991, Krishna and Mitra,
1998, Balakrishnan et al., 2000, and Aghion et al., 2003). This estimate captures the cumulative
effect of all changes in the economic environment in which firms operate after trade reforms.
Since trade liberalization often occurs as a package of a host of other economic reforms, simply
looking at "post" effects may not accurately capture the impact of trade liberalization. This is
an especially important concern in the case of India, as trade liberalization was just one part
of a major package of reforms in the early 1990s, as noted earlier.
The empirical strategy exploits the specific timing as well as the differential degree of liberalization across industries to identify the effect of trade policy on firm-level productivity.
Although I build on the methodology of Pavcnik (2002) and Fernandes (2003), compared to
these studies, I benefit here from both a rather clean natural experiment of trade liberalization
coming from external factors and the availability of data before and after trade reforms. In this
context, the baseline specification takes the following form:
Pr = a +
Trade
+ X -y + t +
+v
where prat is the productivity index of company i in industry j at time t; Tradetl
(2.2)
is a measure
of lagged trade protection at the 4-digit National Industrial Classification (NIC) level; and X
is a set of company characteristics, including age, age squared, ownership categories (private
stand alone, private group, government-owned, and foreign firms), and size categories (large, if
average sales over the entire period are in the top 1 percent of the distribution; medium, if sales
ll The productivity index is calculated as the logarithmic deviation of a firm from a reference firm's productivity
in the particular industry in a base year. In other words, we subtract the productivity of a firm with the mean
log output and mean log input level in 1989/90 from the estimated firm-level TFP.
95
are greater than the median, excluding the top 1 percentile; and small if average sales over the
period are less than the median). Yt is a set of year dummies and I j are industry fixed effects.
The inclusion of industry fixed effects absorbs unobserved heterogeneity in the determinants
of productivity that are industry-specific, while the year dummies control for macroeconomic
shocks common to all firms. I am interested in the magnitude and sign of the coefficient on
lagged trade protection, 3, which captures the percentage change in firm-level productivity
associated with industry level differences in trade protection.
2.3.3
Data Description
A firm-level dataset is compiled from the Prowess database, which contains information primarily from the income statements and balance sheets of listed companies comprising more than
70 percent of the economic activity in the organized industrial sector of India. 12 The size of
the dataset, which covers the period 1989-2001, varies by year, as demonstrated in Table 2.A1
in the Appendix. Since overall exit rates are very low, I use an unbalanced panel of companies
for estimation purposes. 13 For this reason, I verify the robustness of the results by conducting
the analysis using only the subset of companies whose information is available for all years.
The dataset contains information on about 4,100 individual manufacturing companies. Firms
are categorized by industry according to the 4-digit 1998 NIC code, and span the industrial
composition of the Indian economy. There are 116 industries represented in the sample. The
largest sectors, measured by the number of companies, are chemicals and basic metals, and
manufactures of food products, beverages, and textiles. Tables 2.A2-2.A4 in the Appendix
provide other summary statistics on the dataset, including a breakdown of companies by industry classification, ownership, and year of incorporation. For the estimation of the production
function and TFP, all relevant variables were deflated using appropriate price deflators from
India's national accounts statistics.
The data on firm economic activity are complemented with annual tariff data at the six
digit level of the Indian Trade Classification Harmonized System (HS) code. More than 5,000
' 2 The Prowess database comprises firm-level data collected by the Centre for Monitoring the Indian Economy,
a private company in India. It is used to derive firm-level productivity, as described in Appendix I.
13
Because firms are under no legal obligation to report balance sheet data, a small percentage of firms exit
and re-enter the database. These firms are excluded from the analysis.
96
product lines have been matched to the 116 NIC codes to calculate average industry-level tariffs.
These industry-level tariffs are used as a measure of trade protection, as they reflect the tariffs
faced by the industry, as well as potential exposure to foreign competition. However, this is an
incomplete measure of protection, as non-tariff barriers have been used as an important tool of
trade policy, especially in India. I plan to include these additional measures in the future.
Previous studies have also used volume measures, such as import penetration, in order to
capture the importance of actual exposure to foreign competition. However, this type of variable
is endogenously determined; while increased competition is expected to cause firms to become
more productive, the Ricardian model of trade predicts that certain goods may be imported
precisely because domestic productivity in that industry is low (Fernandes, 2003). Thus, the
preferred measure of trade protection is lagged nominal tariffs.
2.4
2.4.1
Results
Endogeneity of Trade Policy
Before proceeding with estimating equation (2.2), I address the concern of the possible endogeneity of trade policy, which could potentially invalidate the empirical strategy. Specifically,
the major trade liberalization in 1991/92 was driven largely by external pressure, as discussed
in Section 2.2, and was completed by the time of the drafting of the export-import policy in
the Ninth Plan. Thereby, the concern arises in the post-1996 period that policy decisions on
tariff changes may have been based on expected future productivity. Thus, the coefficient on
lagged trade protection might be capturing a reverse relationship-tariffs
are lowered in certain
industries precisely because these industries are more productive.
Ideally, I could alleviate this concern by learning the "true" intentions of Indian policymakers, or, failing this, through a detailed study of the political economy behind tariff changes
in India over the period. However, objective and detailed analyses of such policy changes are
not available. Instead, I use the available data to conduct a simple test of the validity of the
empirical strategy. First, I examine to what extent tariffs moved together. An analysis of the
tariff changes of the 5,000 items in the dataset for 1989 96 and for 1997 2001 suggests striking
differences: in the first period, the majority of tariff changes across products exhibited simi-
97
lar behavior (either increased, decreased, or remained constant); thereafter, tariff movements
were not as uniform. In particular, Figure 2.4 demonstrates that, conditional on tariffs being
changed, the probability of the changes being uniform across items is significantly higher before
1997. This suggests that policymakers were more selective in setting product tariffs during
1997-2001.
If indeed policymakers adjusted tariffs according to an industry's perceived productivity, one
should expect current productivity levels to predict future tariffs. Therefore, I calculate the
average industry-level productivity as the average firm-level TFP, weighted by companies' sales.
I then regress industry-level tariffs in period t+1 on industry-level productivity, controlling for
industry and year fixed effects and weighting each industry by the number of companies in
the industry for the particular year. The results are presented in Table 2.2. As expected, the
correlation between future tariffs and current productivity is indistinguishable from zero for
the 1989-96 period. The 1997-2001 period, however, paints a different picture. The coefficient
on current productivity is negative and significant at the 1 percent level, suggesting that trade
policy may have been adjusted to reflect industries' relative performance.
This test implies that to correctly identify the effect of trade policies on productivity, one
should restrict attention to the period immediately before and after the major trade reforms
(1989-96). While there is no evidence that variation in tariffs may have been used to selectively
protect the less productive industries during this period, I nevertheless follow Fernandes (2003)
and estimate the effect of lagged rather than contemporaneous tariffs.14 Including industry fixed
effects may also absorb time-invariant political economy factors underlying trade protection
across industries (Goldberg and Pavcnik, 2001).
2.4.2
Average Impact of Trade Policy and Robustness Checks
The results from estimating equation (2.2) for the period 1989-96 are presented in Table 2.3
(Panel A). I correct for heteroskedasticity and adjust standard errors for clustering at the
industry-year level. The regression in column (2) includes industry fixed effects at the 4 digit
level; column (3) includes industry dummies at the 2 digit level, and column (4) repeats the
14
Lagged tariffs are also more appropriate
if we expect that productivity
instantaneously.
98
adjustments
do not occur
analysis on the balanced panel of companies. To further test the robustness of the findings,
I include firm-level fixed effects in column (5) and account for the Markov process assumed
to be followed by firm's productivity in column (6) by including lagged TFP as a regressor.
Finally, in column (7), I use the Arellano Bond panel estimator to correct for the bias introduced
through the inclusion of lagged dependent variables. The coefficient of interest : is negative and
statistically significant at the 1 percent level across all specifications. Since the productivity
measure is in log terms, the estimated coefficient implies that a 10 percent reduction in tariffs
(at the 4 digit industry level) will lead to about 0.5 percent increase in TFP. The results are
highly statistically significant, robust across specifications, and very similar to the estimates of
Fernandes (2003) in her study on Colombia's trade policy. Decreasing trade protection in the
form of lower tariffs raises productivity at the firm level.
Referring again to Table 2.3 (Panel B), I estimate equation (2.2) but look at the effect of
liberalization on productivity growth (rather than on levels). The coefficient of lagged tariffs
is again negative and statistically significant, implying that lowering tariffs not only generates
productivity gains, but also leads to faster productivity growth. The estimates on some firm
characteristics are also of interest. Most notably, the coefficient on the indicator for whether
a company is public is negative and significant. Government owned companies are on average
10 percent less productive than private companies not associated with a business group. The
growth rate of their TFP is also significantly smaller.
If the high level of protection before trade reforms allowed companies with different levels of
productivity to co exist, the higher average productivity associated with tariff reductions could
be due to the exit of the least efficient producers, as shown by Melitz (2005). Although exit
rates of firms in the sample are low, I investigate whether the productivity gains arise through
that channel by reestimating equation (2.2) only for the set of companies in operation in 1996.
The positive impact of tariff reductions on productivity levels is virtually unchanged. While the
exit of less efficient companies might contribute to productivity improvements, it does not drive
the results. In addition, the probability of exit is not significantly related to trade protection at
the industry level, and there is no evidence that exiting companies were less productive. Trade
liberalization seems to have induced productivity improvements within the firm.
As an additional robustness check, I aggregate the productivity data at the 4 digit industry
99
level, in order to see whether the significance of the results is driven by the disaggregated nature
of the data. I regress industry-level productivity on lagged tariffs, with industry and year fixed
effects weighted by the number of companies in each industry. The effect of lagged tariffs on
productivity remains negative and statistically significant at the 1 percent level (Table 2.4).
The effect of tariff reductions on productivity growth is similar in magnitude but statistically
significant only when the Arellano Bond dynamic panel estimator is used.
2.4.3
Average Impact of Trade Policy and Company Characteristics
Trade liberalization allows us to test whether certain company characteristics interact with
trade liberalization shocks in determining post-reform firm-level performance. For example,
it is argued that private firms more quickly adapt to changing circumstances such as policy
shocks (Shleifer, 1998 and World Bank, 1995). Because there are both public and private firms
operating in most industries in India, one can compare how the productivity of public and
private companies changes with increased competition from imported goods. Similarly, within
the private sector, I investigate whether the effect of lower tariffs on productivity is different
for stand-alone companies, companies that are part of a business group, and foreign companies.
Although firm size is arguably endogenous (as a firm's size in the years before the reform may
be directly related to firm's productivity), I examine whether it is correlated with firm's ability
to adapt to a new environment. I also test whether trade liberalization favored firms that were
closer to the industry technological frontier at the eve of the reform. The results are presented in
Table 2.5. For each subgroup, I estimate equation (2.2), allowing industry and year fixed effects
and the coefficients on firm characteristics to differ across the various groups. The specification
in row (1) in Table 2.5 is equivalent to column (2) in Table 2.3, i.e., I include industry fixed
effects at the NIC 4 digit level. In row (2) of Table 2.5, I control for firm fixed effects. In row
(3), I introduce the lagged firm productivity as a regressor, while in row (4), the Arellano Bond
panel estimator is presented.
Columns (1) and (2) of Table 2.5 show evidence that while trade liberalization raises productivity in private companies, the same increase in efficiency may not be experienced by public
companies. Although the coefficient on lagged tariffs is imprecisely estimated, the point estimates for the effect of trade policy on productivity of the public enterprises are much smaller
100
in magnitude than almost all those for private enterprises. Thus, I find support for the view
that public sector firms are less productive than privately held firms by providing evidence that
public firms react differently to shocks. Unlike private companies, the productivity of public
sector firms does not respond to trade liberalization.
On the other hand, no difference is found in the way private stand-alone companies and
companies belonging to a business group responded to decreased trade protection (see columns
(3) and (4) of Table 2.5). One might have expected, say, that the easier access to financing to
members of a business group to result in a more favorable response to the new environment,
however this is not borne out in the data. In addition, foreign companies operating in India
experienced no change in their productivity as a result of the tariff reductions (column (5)),
which is not surprising given that many were already exposed to foreign competition.
A somewhat unexpected result is the comparatively higher effect of trade liberalization on
dispersed companies versus companies with concentrated ownership (columns (6) and (7) of
Table 2.5).
5
Again, the results are not significantly different, but the point estimate is much
lower for companies with concentrated ownership. Corporate finance theory does not provide
a clear-cut answer on which set of companies one should expect to adjust faster to the new
economic environment. On the one hand, one might expect concentrated ownership to be more
conducive to a faster response to foreign competition since the coordination problem among
owners is smaller. On the other hand, one might expect companies with dispersed ownership to
more quickly undertake productivity improvements, as absent this, these companies as opposed
to ones dominated by block shareholders might be more subject to takeovers or mergers. Using
a slightly more refined distinction, under which the top third most concentrated companies
(column (8)) are separated from the bottom most concentrated companies (column (9)), I still
find that the effect of trade liberalization on dispersed companies is much larger in magnitude,
while the effect on companies with concentrated ownership is statistically insignificant in all
specifications.
Unlike Aghion et al. (2003), I do not find that trade liberalization leads to a divergence in
productivity, in the sense that it fosters productivity growth most among firms already close
15
Dispersed companies are considered those in which the share of promoters is less than half, while companies
with concentrated ownership are otherwise.
101
to the technological frontier. I split the sample of firms into two groups: those whose average
TFP from 1989-1991 was above and below the industry median. Estimating equation (2.3)
for each of the two subsamples, I find no compelling evidence that trade liberalization had
a differential impact (columns (10) and (11) of Table 2.5). Similar results are obtained if I
modify the classification of high TFP and low TFP firms to be the top and bottom third of
the distribution of the average pre-reform TFP by industry (columns (12) and (13)). While
the Arellano Bond estimator (row (4)) does suggest that firms with higher initial productivity
might have experienced a higher rise in productivity as a result of the lower trade protection,
the result is not robust across specifications. To further investigate this possibility, I estimate
the coefficient on the interaction of lagged tariffs and pre-reform productivity relative to the
industry's best performer. Table 2.6 shows no consistent pattern across specifications. The
positive coefficient on the interaction term in some of the specifications suggests that if anything,
trade liberalization lead to convergence in firm performance. This finding is in sharp contrast
with the post-1991 divergence in state-industry performance documented and attributed to
trade liberalization by Aghion et al. (2003). Lastly, referring back to Table 2.5, the size of the
firm does not seem to affect the ability of firms to respond to the trade liberalization shock
(columns (14), (15) and (16)).
2.4.4
Average Impact of Trade Policy and Environment Characteristics
So far, I have established that there is some evidence at least to suggest that certain firm
characteristics such as type and concentration of ownership might matter for the transmission
of the trade liberalization shock. However, other characteristics like institutions, geography,
and level of development of the state in which firms operate do not seem to affect the way firms
respond to lower trade protection. 1 6
First, I look at whether firms that are located in coastal states were more affected by the
reform. In a country where product markets might not be fully integrated across regions due
to the sheer size of the country or poor infrastructure, firms in the heart of the country or in
less accessible regions might not experience the threat of increased foreign competition as much
as firms in regions in more immediate contact with internationally traded goods. However,
16See Table 4 in Appendix II for the classification of states by various categories.
102
columns (1) and (2) of Table 2.7 do not confirm this hypothesis. The estimated effect of trade
does not significantly differ across firms in coastal and non-coastal states. 1 7 In fact, the point
estimate is slightly higher for companies operating in non-coastal states.
The investment climate in the state also does not seem to matter. Using the Goswami et al.
(2002) classification of the states' investment climates, one can see in columns (3) and (4) that
there is little difference in the estimated impact of lagged tariffs, and if anything, the firms in
states with poor investment climate seem to benefit more from the trade reforms.
Surprisingly, the level of financial development of the state (measured as credit per capita
in 1992, with states above the median classified as having "high financial development") also
makes no difference (columns (8) and (9)). Although the estimated effect of trade liberalization
on productivity is almost never statistically significant for the subset of firms operating in
less financially developed states, the point estimates are virtually identical.
This finding is
unexpected since a major concern regarding trade liberalization has been the ability of domestic
firms to access sufficient credit to invest in more efficient technologies and survive in the face
of foreign competition.
I also do not find evidence that the rise in firm-level productivity as a result of trade liberalization contributed to the disparity between state performances in the 1990s. For example,
firms in states that experienced rapid growth in the 1980s, did not react much differently than
firms in low growth states (columns (10) and (11)). And similarly firms in states with above
median per capita income in 1991 did not reap any more benefit from trade liberalization than
firms in states with below median income in 1991 (columns (12) and (13)).
Finally, I examine the role of institutions. Besley and Burgess (2003) classify Indian states as
having pro-worker, neutral, or pro-employer labor laws. I find no difference in firms' response to
trade liberalization by the quality of the state's institutions as they relate to workers (columns
(5), (6) and (7)).
17
Since the Prowess data are at the company rather than plant level, a particular company may report data
from business activity in more than one state. In addition, data on the location of a company's headquarters are
not available at this time. Thus, assuming a company has an equal number of plants in all states, we classify it
as operating in a coastal state if more than 50 percent of the company's plants are in coastal states. A similar
methodology is used to classify companies in the other state categories.
103
2.5
Conclusion
This paper makes several important contributions to the empirical literature on trade liberalization using the case of India to examine the link and causality between tariff reform and
firm productivity. As India's economy still remains highly protected compared to other large
developing economies, establishing this link may have important implications for future trade
reforms and growth prospects.
Using a consistent empirical methodology to estimate productivity, I first find that trade
liberalization in India causes increased efficiency among firms. Specifically, a decrease in tariffs
by 10 percent leads to about 0.5 percent increase in TFP. The results are derived from a period
when trade liberalization, specifically tariff reductions, can be viewed as largely exogenous.
This is important because there are strong theoretical reasons to think that, in the absence of
external pressure, trade policies are endogenously determined (Grossman and Helpman, 2002).
I also show that this result is not driven by the demise of unproductive companies-exit
are in fact low for the period under study-but
rates
rather by the increasing efficiency of existing
manufacturers.
Second, this paper pays careful attention to industry-specific tariff reduction, using both
inter-temporal and inter-industry variations from over 5,000 tariff line items, during an eight
year period (1989-96), to increase the power and precision of the estimates. Unlike other pre
reform and post reform comparisons, which rely on the assumption of no common secular trend,
I include year fixed effects, ensuring that the results are not driven by a common year trend.
I also include industry fixed effects to control for unobserved time-invariant political economy
factors underlying trade protectionism across industries.
Third, I use trade liberalization in India to shed light on how the effect of this comprehensive
reform differed across companies with different economic characteristics.
While it has been
established that public sector firms are less productive than privately held firms, there has been
less evidence on how these firms react to shocks. I find that the productivity of public sector
firms does not change with trade liberalization. This may be because public sector firms do not
face hard budget constraints, and the government of India continues to run many firms at a
loss in the face of competition. There is also some evidence that less concentrated firms might
have been forced to adapt faster to foreign competition.
104
Other firm characteristics such as
initial level of productivity and firm size do not seem to have influenced the way in which firms
reacted to the lower trade protection.
Surprisingly, the environment in which firms operate
(i.e., geography, institutions, financial development, investment climate) does not appear to
have any effect on the transmission of the trade liberalization shock.
While I find that liberalization leads to higher firm-level productivity, this result cannot
necessarily be linked to welfare improvement without including the cost of productivity gains
(Tybout, 2001). Still, two lessons can be drawn from India's experience with trade liberalization. First, trade policy, in the absence of external pressure, may be strongly related to the
productivity of firms. Therefore, simply looking at the effects of tariffs on productivity could
give misleading results. Second, trade liberalization can increase productivity, but the effect
might be limited to privately held industries. Consequently, liberalization may lead to greater
productivity gains if combined with more intensive privatization efforts.
2.6
Appendix: Estimating the Production Function
For the estimation of the production function, the following variables were used: value of total
output, gross fixed assets, salaries and wages, raw materials expenses, power and fuel expenses,
and depreciation. The data provided in the Centre for Monitoring the Indian Economy (CMIE)
database are drawn from companies' balance sheets and income statements.
The values of
output and power and fuel expenses were converted in real terms by industry-specific wholesale
price indices. As for the salaries and wages and raw materials expenses, the wholesale price
index was used.
The difficult task of measuring capital employed by the firm in its production process was
done by closely following the methodology of Balakrishnan et al. (2000). It applies the Perpetual
Inventory Model, while correcting for the fact that the value of capital is recorded at historic
an(d not replacement cost. In order to arrive at a measure of the capital stock at its replacement
cost for a base year (in the case assumed to be 1997), I follow Balakrishnan et al. and construct
a revaluation factor assuming a constant rate of change of the price of capital and a constant
rate of growth of investment throughout the 20 year lifetime assumed for capital stock. This
revaluation factor converts the capital in the base year into capital at replacement cost at
105
current prices, which is then deflated using a deflator constructed from the series on gross capital
formation. To get at the capital stock for every time period, I take the sum of investment in
subsequent years.
2.7
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106
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108
Table 2.1 India: Non-Tariff Barriers on Imports
Open
1987/88
InpercentofH.S.
codes
In percent of imports
1992/93
InpercentofH.S.
codes
Inpercentofimports
1994/95
In percent of H.S. codes
In percent of imports
1997/98
In percent of H.S. codes
In percent of imports
Source: Nouroz, 2001.
Banned
Limited
Permissible
General
License
33
16
18
23
13
16
Banned
Restricted
Free
1
56
40
2
1
100
0
21
46
33
0
100
Banned
Restricted
Free
0
0
43
20
55
55
Banned
Restricted
Free
0
0
41
15
57
64
NotCanalized Identified
7
27
29
18
NotCanalized Specified
NotCanalized Specified
2
25
0
0
NotCanalized Specified
2
21
0
0
Total
100
100
Total
Total
100
100
Total
100
100
Table 2.2 Trade Policy Endogeneity: Effect of Current Productivity on Future Tariffs
Period
1989-2001
1989-96
1997-2001
Panel A
Total factor productivity 1/2/
-0.130 **
-0.028
-0.180 ***
(0.056)
(0.070)
(0.065)
Y
Y
Y
Y
Y
Y
R2
0.88
0.89
0.93
Obs
1,317
817
500
-0.120
(0.125)
0.030
(0.142)
-0.274
(0.196)
Y
Y
Y
Y
Y
Y
0.90
0.92
0.93
1,185
691
494
Year fixed effects
NIC4
Panel B
Total factor productivity growth 1/ 2/
Year fixed effects
NIC4
R2
Obs
Source: Author's estimates.
1/ Robust standard errors are in parentheses. Errors are adjusted for clustering at the industry
level. Regressions are weighted by the number of companies in a given industry and year.
2/ Significance at the 10, 5, and 1 percent level is denoted by *, **, and ***, respectively.
Table 2.3 Effect of Trade Protection on Total Factor Productivity 1/ 2/
(1)
(2)
(3)
(4)
(5)
(6)
(7)
-0.056 ***
(0.016)
-0.050 ***
(0.015)
-0.050 ***
(0.016)
Y
Y
Y
Y
Y
0.80
13,884
0.85
10,754
7,765
-0.043
(0.030)
-0.056
(0.041)
-0.086 ***
(0.021)
Y
Y
Y
Y
PanelA: TotalFactorProductivity
Lagged Tariffs
Private Group Company
Government Owned
Foreign
Medium
Small
Age
Age2
Year fixed effects
Industry fixed effects (NIC4)
Industry fixed effects (NIC2)
Company fixed effects
Balanced panel of companies
Lagged total factor productivity
Arellano Bond estimator
-0.070
(0.022)
-0.024
(0.01 1)
-0.103
(0.031)
0.002
(0.018)
-0.030
(0.012)
-0.060
(0.015)
-0.001
(0.001)
***
-0.085
(0.018)
**
-0.023
(0.010)
*** -0.101
(0.031)
-0.003
(0.018)
-0.034
**
(0.013)
*** -0.082
(0.016)
*** -0.001
(0.000)
-0.089
(0.021)
**
-0.027
(0.010)
*** -0.106
(0.029)
0.006
(0.018)
*** -0.027
(0.012)
*** -0.072
(0.015)
**
-0.001
(0.000)
***
0.001
(0.001)
0.001
(0.001)
0.001
(0.001)
Y
Y
Y
Y
***
***
***
**
***
**
-0.065
(0.016)
0.014
(0.014)
-0.073
(0.037)
0.076
(0.021)
-0.025
(0.015)
-0.081
(0.021)
0.000
(0.001)
***
**
***
***
0.008 ***
(0.002)
Y
Y
Y
Y
Y
Y .
Obs
0.05
13,884
0.20
13,884
0.13
13,884
0.22
7,238
Panel B: Total Factor Productivity Growth
Lagged Tariffs
Private Group Company
Government Owned
Foreign
Medium
Small
Age
Age2
Year fixed effects
Industry fixed effects (NIC4)
Industry fixed effects (NIC2)
Company fixed effects
Balanced panel of companies
Lagged total factor productivity
Arellano Bond estimator
R2
Obs
-0.021 **
(0.009)
0.005
(0.003)
-0.015
(0.005)
0.006
(0.005)
0.007
(0.003)
-0.001
(0.005)
0.000
(0.000)
-0.047*
(0.027)
0.005
(0.003)
-0.014
(0.005)
0.005
(0.005)
0.007
(0.003)
-0.001
(0.005)
0.000
(0.000)
-0.015*
(0.008)
0.003
(0.003)
-0.017
(0.005)
0.002
(0.005)
0.005
(0.003)
-0.005
(0.004)
0.000
(0.000)
-0.044 **
(0.021)
0.000
(0.002)
-0.014
(0.006)
0.001
(0.004)
0.000
(0.003)
-0.007
(0.005)
0.000
(0.000)
0.001
(0.000)
0.001
(0.000)
0.001
(0.000)
-0.001
(0.000)
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
0.04
10,754
0.06
10,754
0.05
10,754
0.08
6,186
0.36
10,755
0.37
7,940
5,553
5,553
Source: Author's estimates.
1/ Robust standard errors are in parenthesis. Errors are adjusted for clustering at the industry-year level in columns (1)-(6).
2/ Significance at the 10, 5, and percent level is denoted by *, **, and ***, respectively.
Table 2.4 Effect of Trade Protection on Total Factor Productivity at the Industry Level 1/ 2/
(3)
(2)
(1)
PanelA: TotalFactorProductivity
-0.066 *
Lagged tariffs
(0.040)
Year fixed effects
Industry fixed effects (NIC4)
Lagged total factor productivity
Arellano Bond estimator
R2
Y
Y
-0.054 **
(0.025)
Y
Y
Y
Y
Y
0.84
817
Obs
-0.043 **
(0.022)
0.90
693
.568
568
Panel B: Total Factor Productivity Growth
Lagged tariffs
Year fixed effects
Industry fixed effects (NIC4)
Lagged total factor productivity growth
Arellano Bond estimator
-0.045
(0.028)
-0.050
(0.033)
Y
Y
Y
Y
Y
-0.082 **
(0.037)
Y
Y
R2
0.31
0.37
Obs
691
567
1/ Robust standard errors are in parentheses. Errors are clustered at the industry level in
columns (1) and (2).
2/ Significance at the 10, 5, and 1 percent level is denoted by *, **, and ***, respectively.
.446
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Panel A: Average Nominal Tariffs
120
100
80
-
60
40
20
19S1 9
19S9 990 991 992 993 994
995 996
991
99S 999
000 200X
Panel B: Standard Deviation of Nominal Tariffs
60
50
40
30
20
10
X9s1 9% 9
\990 99\ 99' 993
Source: Author's estimates.
994
995 996
991
99% 999 20°0 200x
Figure 2.2 Evolution of Tariffs by Industry, 1987-2001
F-
Agculturl
Prducts
Wood
Weed
lO -
100
90
ao
-
=
-
70
80
=
-
,
70
60
A
50
so-__X\~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~j
40
30 -
20 10 -
I
:L-
I . .
Extraction
100
90
-- _
--_
.
70
60-
"
01
1987
1989
1991
1993
1995
1997
1999
2001
Uranium
100
.....
901
_
80
70
60 90
40
30 _20-
_-
-.
50 - _
30
. ;
of Crude Petroleum
__
-
80
. I I . I
___
__
_
_
101987
1987
99
'.989
191
1991
1993
93
1995
19
1997
1997
1999
1999
2001
20
1987
1989
1991
1993
1995
1997
1999
2001
ri
-
.
Food Products
120
100
80
60
40
20
1987
1989
1991
100
_
-
. .- __
1999
2001
Tanning
_
-
100 --
-
-
-
100
90
80
70
60
50
40
30
20
10
0
-
___\
60-
_
_
_
40-
_
-____
60-
____
20 -
40___
8 .
1987
.1.9.99
198'1 1991
.
99.
1993
. .
1995
.0.
1997
1999
20
2001
1987
1989
Wood
_____
90
90
80
70
60
50
40
30
20
1997
120 1
_
_
80-
1995
Wearing Apparel
Textiles
120 -
1993
...
_
.
.
_____
1989
1991
1993
1995
1997
1999
2001
I . . I I
1987
1995
100
90 1
~
1989
1991
I . . .
1993
1995
1997
1997
1999
2001
1999
2001
Publishing, Printing and Recording Media
'
80
70
60
50
40
30
20
10
1\
1987
1993
Paper and Paper Products
........
I
.
1991
and Dressing of Leathers
1
1987
1989
1987
1989
1991
1993
1999
1997
1999
2001
1991
1993
1995
1997
1999
2001
1987
1997
1989
1989
1991
1991
1993
1993
1995
1995
1997
1997
1999 2001
1999 2001~~~~~~~~~~~~~~~~~~~~~~
l
Figure 2.2 Evolution of Tariffs by Industry, 1987-2001 (continued)
Rubber and Plastic Products
Coke, Refined Petroleum Products etc.
140 -
100
90 -
120
-
80
-
70-
80 5040 -
80
60
-_
30 -
-
40
__
20 10 -
20
o- 7 19
1987
1989
19.....
.. .
1991
1995
1993
1
1997
1999
1987
2001
1989
1991
1993
1995
1997
1999
2001
l
Non-Metallic Mineral Products
Fabricated Metal Products
Basic Metals
140
100
90
120
100
80
100
70
80
\1
60
60
50
40
30
20
80
20 -
... I.... I.-
10
0
,
.............
1987
1989
1
1991
1993
1995
Macinery
NEC
1997
1999
1987
2001
\
40 40
40
i
._
-
-
1989
1991
1993
1995
1997
20
1999
1987
2001
1989
1991
1993
1995
1997
1999
2001
Electrical Machinery etc.
Office, Accounting and Computing Equipment
140
100
90-
120
100
70
80
60
30
20
10-
40
'I
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
50
t
~
)~
I
40
~~
30
20
10/
. I
..
,..
1989
0
.....
1991
1993
1995
1997
1999
1
. . . . . . . . . . .
1987
2001
100-
11
100
90
80
70
\
80
1991
1993
1995
1997
1999
1987
2001
Medical, Precision and Optical Instruments
Radio, Television etc. Equipment
120
1989
__
_
8_-
20
1987
j
A
60\
50
40
.
80
70 -
80
1989
1991
1993
1995
1997
1999
2001
Motor Vehicles, Trailers etc.
120
-
100-
*
1
\4
60
60
80
90 1987
30
40
20
10-
40
20
40
.,
19
11
200
I
,..............
1987
1989
1991
1993
1995
1997
1999
2001
1987
1
1989
1991
1993
1995
1997
1999
1987
1987
2001
1
1989
9
11993
1991
1-'~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
O0hr. Transpct
Equipnent
100
I
i
90-_
_
70000
Furniture
12o]
100
-
-
Manufacure NEC
-
80
60
!
1
40 -40
30!
201o
01987
20
0
1989
1991
Note: Averagetariffs usingthe NIC2 classification.
1993
1995
1997
1999
2001
1987
1989
1991
1993
1995
1997
1999
2001
5
1995
.
11999
1997
2001
Figure 2.3 Evolution of India's Trade
25
Merchandise Trade
(Inpercent of GDP)
.. ^_
20
15
0~~~~~~~~~~~
.
10
5
,_
,5
1980/81
1983/84
1989/90
1986/87
--
U
Exports
1992/93
Imports A
1995/96
1998/99
2001/02
Total trade
8()o
700
6)0
500
400
300
2(00
1 O0
1980/81
1982/83 1984/85
|
1986/87
1988/89
1990/91
Import volume
1992/93 1994/95
Export volume
IReal
1996/97 1998/99 2000/01
GDP
Figure 2.4 Direction of Tariff Changes
1.2 -
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I1
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*
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.....
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0.2
0
i
1992
1993
I
1994
1995
I
1996
1997
1998
I
1999
1 % Items with tariff reductions E % Items with tariff increases
2000
2001
C9
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Table 2.A4 State Classifications
Geographical
classification
CoastalState
AndhraPradhesh
DamanandDiu
DadraandNagarHaveli
Goa
Gujarat
LandLockedStates
Assam
Bihar
Chandigarh
Chattisgarh
Delhi
Haryana
Kamrnataka
Kerala
HimachalPradesh
Maharashtra
JammuandKashmir
Jharkhand
Orissa
Pondicherry
MadhyaPradesh
Nagaland
TamilNadu
WestBengal
Punjab
Rajasthan
Uttar Pradesh
Investmentclimate(WorldBank,2002)
Best
Good
Gujarat
TamilNadu
Maharashtra
AndhraPradesh
Medium
Delhi
Punjab
Kamrnataka
Laborlaws(BesleyandBurgess,2002)
EmployerFriendly
WorkerFriendly
AndhraPradhesh
Gujarat
Karnataka
Maharashtra
Kerala
Orissa
MadhyaPradesh
WestBengal
Rajasthan
TamilNadu
FinancialDevelopment
(creditpercapita,ReserveBankof India)
Abovemedian
Belowmedian
AndhraPradesh
Andaman& NicobarIslands
Chandigarh
ArunachalPradesh
Daman& Diu
Assam
Delhi
Bihar
Goa
Dadra& NagarHaveli
Gujarat
Lakshadweep
Haryana
MadhyaPradesh
HimachalPradesh
Manipur
Jammu& Kashmir
Meghalaya
Karnataka
Mizoram
Kerala
Nagaland
Maharashtra
Orissa
Pondicherry
Rajasthan
Punjab
Sikkim
TamilNadu
Tripura
WestBengal
UttarPradesh
Pre-ReformGrowth(1980s)
Highgrowth
Lowgrowth
ArunachalPradesh
Andaman& NicobarIslands
Delhi
AndhraPradesh
Goa, Daman and Diu
Assam
Gujarat
Bihar
Haryana
Jammu&Kashmir
HimachalPradesh
Kerala
Karnataka
MadhyaPradesh
Maharashtra
Manipur
Nagaland
Meghalaya
Punjab
Orissa
Rajasthan
Pondicherry
Sikkim
Tripura
Tamil Nadu
UttarPradesh
WestBengal
Pre-ReformPerCapitaGDP (1990/91)
High Per Capita GDP
Andaman& NicobarIslands
ArunachalPradesh
Delhi
Goa,DamanandDiu
Gujarat
Haryana
HimachalPradesh
Maharashtra
Pondicherry
Punjab
Sikkim
TamilNadu
WestBengal
Neutral
Assam
Bihar
Haryana
Punjab
UttarPradesh
JammuandKashmir
Low Per Capita GDP
AndhraPradesh
Assam
Bihar
Jammu&Kashmir
Karnataka
Kerala
MadhyaPradesh
Manipur
Meghalaya
Nagaland
Orissa
Rajasthan
Tripura
UttarPradesh
Sources:NationalAccountsStatistics,unlessotherwiseindicated;andauthor'sestimates.
Poor
Kerala
WestBengal
UttarPradesh
Chapter 3
Unappreciated Service:
Performance, Perceptions, and
Women Leaders in India
Summary 3 This paper studies the impact of reservation for women on the performance of
policy makers and on voters' perceptions of this performance. Since the mid 1990's, one third
of Village Council head positions in India have been randomly reservedfor a woman: In these
councils only women could be elected to the position of chief. Village Councils are responsible
for the provision of many local public goods in rural areas. Using a data set which combines
individual level data on satisfaction with public services with independent assessments of the
quality of public facilities, we compareobjectivemeasures of the quantity and quality of public
goods, and information about how villagers evaluate the performance of male and female leaders.
Overall, villages reserved for women leaders have more public goods, and the measured quality
of these goods is at least as high as in non-reserved villages. Moreover, villagers are less likely
to pay bribes in villages reserved for women. Yet, residents of villages headed by women are less
satisfied with the public goods, including goods that are beyond the jurisdiction of the Panchayat.
This may help explain why women rarely win elections even though they appear to be at least
as effective leaders along observable dimensions, and are less corrupt.
127
3.1
Introduction
Relative to their share in the population, women are under-represented in all political positions.
In June 2000, women comprised 13.8% of all parliament members in the world, up from 9% in
1987. Relative to economic opportunities, education and legal rights, political representation is
the area in which the gap between men and women has narrowed the least between 1995 and
2000 (Norris and Inglehart, 2000).. Many governments are taking active steps to encourage
the participation of women in policy making, notably by establishing quotas for women in
parliaments or in local governments.
Previous research has shown that mandated representation of women leads to a dramatic
increase in women's access to political decision making (Jones, 1997, Chattopadhyay and Duflo,
2004). There is also evidence that reservations for women do affect policy (Chattopadhyay and
Duflo (2004) present evidence for India, which is also the setting of this study). In particular,
women leaders increase the provision of public goods that benefit women.
The potential role of political reservation is related to the reasons women find it difficult
to become politicians in the first place. One possibility is that it is hard for women to win
elections, because voters believe women would be less effective once in office. In this case,
political reservation may allow voters to learn about the efficacy of women as policy makers.
On the other hand, women may indeed be less effective (at least initially) or voters may require
time to adjust their priors. This would mean that reservations would have to remain in place
for a long time before equality in political representation was achieved.
Laboratory experiments suggest that women leaders are often evaluated more negatively
than male leaders, holding performance constant. These studies (see Eagly and Karau (2002)
for a survey) normally either provide written description of leadership situations, varying the
sex of the leaders, or use trained actors to lead, allowing the experimenter to control the degree
of success the leader achieves. Women are typically judged to have less leadership abilities
than men with similar characteristics, and the same actions performed by men and women in
leadership situations are evaluated more negatively when women are the leaders. The survey
concludes that the bias against women is most pronounced when the leadership role is typically
a male role.
This evidence stands in contrast with survey data, which suggest that women leaders are
128
seen as more effective and less likely to be corrupt. For example, a survey of 800 people in 8
countries in East Africa by the British council (British Council, 2002) found that more than
70% of people thought women performed better than or as well as men, and more than half of
the people interviewed thought that women politicians were less corrupt and cared more about
basic needs of the community than men.
This discrepancy may arise naturally because so few women are elected as politicians. Those
who manage to win elections may be extremely effective leaders, and perceived as such. The
same fact may bias the cross-sectional relationship that has been observed between women's
representation and the quality of governance (see World Bank (2001) for a survey of these
studies). Studies documenting a relationship across countries between women's representation
and the quality of governance (e.g. Dollar, Fisman and Gatti (2001), Swamy, Knack, Lee and
Azfar (2001)) are also possibly biased, since women are more likely to be elected in countries
that are more liberal and progressive, and these may also be countries in which corruption is
less prevalent.
In summary, little is known about the relative performance of women as policy makers, or
about how their performance is evaluated by voters.
This paper presents evidence on three aspects of women's performance in office (as measured
by the quality and quantity of various public goods provided and the likelihood of taking bribes)
and on the perception of this performance by the voters in India's village councils. In 1993,
an amendment to the constitution of India required Indian states both to devolve more power
over expenditures to local village councils (Gram Panchayats, henceforth GPs) and to reserve
one-third of all positions of chief (Pradhan) to women. Since then, most Indian states have had
two Panchayat elections (Bihar and Punjab had only one, in 2001 and 1998 respectively), and
at ]least one-third of village representatives are women in all major states except Uttar Pradesh,
where only 25% of the village representatives are women (Chaudhuri, 2003).
A particularly attractive feature of the reservation policy, from our point of view, is that
the states randomly selected which GPs would be reserved for women. When we compare
measures of performance and satisfaction in reserved and unreserved GPs, the difference can
be confidently attributed to the reservation policy. These comparisons will thus not suffer from
the bias of previous studies.
129
We use data collected by the Public Affairs Centre (PAC) in Bangalore, an NGO concerned
with the dissemination of user-satisfaction reports for public services. In 2000, PAC conducted
a survey of households and facilities in 2,304 randomly selected villages in 24 states. The survey
combines individual survey data on satisfaction with public goods (for a range of public goods)
and information on whether villagers had to pay bribes for different services with detailed direct
observation of the public goods themselves. We thus have objective measures of the performance
of women vs. men (in ex-ante identical villages), and information about how villagers evaluate
the performance of male and female leaders.
The results are striking.
Overall, villages reserved for women leaders have more public
goods, and the measured quality of these goods is at least as high as in non-reserved villages.
Moreover, villagers are less likely to pay bribes in villages reserved for women. Yet, villagers are
less satisfied with the public goods they receive in villages headed by women. Consistent with
previous studies, we find that the results on the quantity and quality of public goods are driven
by larger investment in drinking water infrastructure; yet, villagers (both men and women) in
reserved villages are also more dissatisfied with the quality of the water infrastructure. While
satisfaction with water infrastructure is positively correlated with the number of public taps
and handpumps in villages headed by men, this is not the case in villages headed by women. In
addition to suffering a lower overall satisfaction rating, women Pradhans are not given credit
for improvement in the infrastructure. Moreover, in villages reserved for women, the villagers'
satisfaction rating is lower even for goods over which the Panchayat has no control.
Prima facie, women do not appear to be ineffective leaders for their communities. As a
large experimental literature suggests should be the case,1 they are also less likely to be corrupt. However, they receive less favorable evaluation from villagers than men. This apparent
contradiction could occur either because women perform worse along important but unobservable dimensions, or because women are less favorably evaluated than men for reasons unrelated
to performance.
The remainder of the paper is organized as follows. Section 3.1 describes the institutional
context of the Gram Panchayats while Section 3.2 presents the data used in the analysis. In
Section 3.3 the empirical strategy is explained, and the results are presented.
'Eagly and Crowley (1986), Eckel and Grossman (1998).
130
Section 3.4
concludes the paper.
3.2
Institutions: The Panchayat system and Reservations
The Panchayat is a system of village level (Gram Panchayat), block level (Panchayat Samiti),
and district level (Zilla Parishad) councils, responsible for the administration of local public
goods. Members are elected by the people. The size of the Gram Panchayat (GP) in terms
of number of people and villages varies across states. In West Bengal for example, each GP
encompasses 10,000 people in several villages (between 5 and 15). The GPs do not have jurisdiction over urban areas, which are administered by separate municipalities. Voters elect
a council and in most states directly vote for a Pradhan or council chief.2' 3 Candidates are
generally nominated by political parties, but must be residents of the villages they represent.
The council makes decisions by majority voting (the Pradhan does not have veto power). The
Pradhan, however, is the only member of the council with a full-time appointment.
The Panchayat system has formally existed in most of the major states in India since the
early 1950s. However, prior to the 1990s, it was not generally effective: elections were not
held, and the Panchayats did not assume any active role (Ghatak and Ghatak, 2002). In an
effort to correct this, the 73rd amendment to the Constitution of India in 1992 established the
framework of a three-tiered Panchayat system, with regular elections, throughout India. It gave
the GP primary responsibility in implementing development programs, as well as in identifying
the needs of the villages under its jurisdiction.
Between 1993 and 2003, all major states but
two (Bihar and Punjab) have had at least two elections.
Although states have devolved powers to the GP to differing extents, the core responsibilities
of the village panchayats include administering local infrastructure (public buildings, water,
roads) and identifying recipients of targeted welfare. The main source of financing is still the
state, but most; of the money which was previously earmarked for specific uses is now allocated
through four broad schemes: the Jawhar Rozgar Yojana (JRY) for infrastructure (irrigation,
drinking water, roads, repairs of community buildings, etc.); a small additional drinking water
2
In Karnataka, Kerala, Maharashtra and West Bengal, voters elect the council, which then elects the Panchayat chief from its members.
3
'In some states, the chief is called a Sarpanch. In this paper, we will use the terminology "Pradhan."
131
scheme; funds for welfare programs (widow's, old age, and maternity pensions, etc.); and a
grant for the administrative expenses of the GP. The GP has, in principle, complete flexibility
in allocating these funds. At this point, the GP has no direct control over the appointments of
government paid teachers or health workers, but in some states (Tamil Nadu and West Bengal,
for example), there are Panchayat-run informal schools.
In addition to devolving powers to the Panchayat, the 73rd Amendment also required onethird of the seats in all Panchayat councils, as well as one-third of the Pradhan positions, to
be reserved for women. Seats and Pradhan's positions were also reserved for the two disadvantaged minorities in India, "scheduled castes" (SC) and "scheduled tribes" (ST), in the form
of mandated representation proportional to each minority's population share in each district.
Reservations for women have been implemented in all major states except Bihar and Uttar
Pradesh (which has only reserved 25% of the seats for women in the 1995/96 elections).
States were instructed to ensure the random assignment of reservation for women Pradhan
across GPs. In West Bengal, for example, all GPs in a district are ranked in consecutive order
according to their legislative serial number (an administrative number pre-dating this reform).
They are then split in three separate lists, according to whether or not the Pradhan seat
had been reserved for disadvantages minorities (these reservations were also chosen randomly,
following a similar method). Using these three lists, every third GP starting with the first on the
list is reserved for a woman Pradhan for the first election.4 Chattopadhyay and Duflo (2004)
found that, in the 1998 elections, West Bengal strictly followed this rule in the reservation
assignment.
Table 3.1 compares the public goods available at the time of the 1991 census (well before
any reservation) in villages that were reserved for women in 1999-2000 to those that were not.
There are no statistical differences between villages located in reserved and unreserved GPs for
any of the village characteristics, suggesting that the woman Pradhan reservations were indeed
randomly assigned. A test for joint significance of the reservation variable in all the public
goods equations has a p-value of 0.68.
4
For the next election, every third GP starting with the second on the list was reserved for a woman, etc. The
Panchayat Constitution Rule provides tables indicating the ranks of the GPs to be reserved in each election.
132
3.3
Data
The main data source for this study is the "Millennial Survey," conducted by the Public Affairs
Centre, a non--government organization in Bangalore which is credited for starting the "report
card movement" in India. The "Millennial Survey" covered 36,542 households in 2,304 randomly selected villages in 24 states. The purpose of the survey was to provide an independent
assessment of key public services, using citizen feedback as well as direct evaluation of facilities.
The Millennial Survey focused on five basic public services: drinking water and sanitation,
health, education and child care, road transport and the public distribution system. An unusual
feature of the survey is that it contains both subjective measures of the quality and objective
measures of the quantity and quality of public goods provided in each village. This allows us
to compare women's performance as leaders, and how villagers evaluate this performance.
The PAC data consist of three parts: a household survey, an independent assessment of
facilities available in each village, and a village profile sheet. The household survey measured
the subjective evaluation of final users of public services: respondents answered questions about
access, quality, reliability and their overall satisfaction with public goods. The number of
respondents varies for each question, because citizens were only asked about services available
in their village. Household characteristics were also collected. Several questions were asked
about whether households found it necessary to pay bribes to obtain access to certain public
services. As the provision of some of these services is the GP's responsibility, these questions
present a measure of the incidence of corruption.
The household survey was complemented by independent site visits, which included assessments of select public facilities such as water sources, primary schools, clinics etc. Again, the
number of responses for these questions varies from question to question because a type of
public good could not be assessed in a particular village if the good was not available. For each
facility, a detailed survey was completed. We use the survey to construct a composite index
of quality (ranging between 0 and 1). The construction of each index is detailed in a footnote
to Table 3.2. To measure quantity we use either the number of available facilities (such as
handpumps, public taps, buses) or in the case of schools, public health centers and fair price
shops, an indicator of whether these public goods were available in the village. At the time we
had access to the Millennial survey, data on quantity of public drinking water facilities had not
133
yet been reliably entered for the states of Himachal Pradesh, Kerala and Punjab. As Punjab
and Kerala happen to be the two states where villagers overwhelming rely on private sources
of drinking water, we do not believe the omission of these states affects our findings.5
Because the Millennial data were not collected for the purpose of comparing female and
male GP leaders, many questions which might have shed light on leadership were not asked.
However, this also means that it is very unlikely that the surveyors induced any bias that would
complicate interpretation of the results regarding the gender of the leader.
The PAC data are supplemented with data from the 1991 Indian census, whose collection
was made prior to the implementation of reservations.
The census data allow us to check
whether reservation for women pradhan was in fact random.
Most difficult to obtain were data on which villages belonged to GPs which were reserved
for women. As the Millennial Survey was conducted in the end of 2000, we focus only on the
major states that held elections between 1995 and 2000 (the leadership term of the Pradhan
was set at 5 years after the 1973 amendment, but in some states elections were not held on
time). Fourteen states are represented in the PAC data and held elections between 1995 and
2000. We collected information on reservations from visits to the state election commissions
and rural development departments for 11 states in February 2003.6
The next step was to match villages to GPs. Systematic information in a central location
about which villages are in which GPs is typically not available, and in many cases it was
necessary to contact the district offices. For more than two-thirds of the villages in our sample,
we were able to both match the village to the GP and obtain information about the Pradhan
reservation status. This attrition is unlikely to bias our estimate of the impact of reservation,
since the unit of reporting was not the Panchayat, but rather the district, and the proportion
of GPs with women in each district was identical (by design) to the proportion in a state, or
in the sample. The main consequence of any differential selection would be to over-represent
wealthier districts, as well as those with more competent administrators.
7
5More than 90 percent of respondents indicated that they rely primarily on public sources for drinking water,
except in Kerala and Punjab where the percentage of people relying on public sources was 46 and 21 percent
respectively.
6
The 11 states included are Andhra Pradesh, Himachal Pradesh, Karnataka, Kerala, Maharashtra, Orissa,
Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh and West Bengal. Time limitations prevented collection of GP
reservation data for Gujarat, Assam and Manipur.
7
For Uttar Pradesh, we were able to match mostly large villages to Gram Panchayats. The regressions control
134
The Millennial data was collected over a period of several months, beginning in the end of
2000. Many states in our sample 8 had their first elections incorporating the 73rd Constitutional
Amendments in 1995. In all of these states, GP elections were due and were held in 2000. Due
to the rotational assignment of reservation, GPs which were reserved for women Pradhan were
'de-reserved" and new GPs were reserved for women. Since less than a year had passed between
the election and the survey, we used the 1995-2000 reservation status in all states. However,
for flow measures of quality of public services such as cleanliness, maintenance etc., we use the
reservation status of the current Pradhan, i.e. during the 2000-2005mandate. Information on
Pradhan reservation as of the end of 2000 was available for seven states. 9 Our sample thus
consists of approximately 810 villages when analyzing household satisfaction and availability of
public services, and 680 villages when analyzing the quality of public services.
3.4
3.4.1
Empirical Strategy and Results
Specification
Within each state, one-third of GPs were randomly chosen to be reserved for women. This
means that, conditioning on the state, any difference between the quality and quantity of
public goods in reserved and unreserved GPs can be confidently attributed to the reservation
policy. Likewise, any differences households report on their level of satisfaction with a public
service or necessity to pay a bribe for it can also be attributed to the policy.
Table 3.2 presents the means of the quantity, the quality and the measure of satisfaction for
five categories of public goods, and the coefficient on a woman Pradhan dummy in the following
regression, run separately for each good k.
Yjk = cak + /3kRj + Xjk
+ ijk
(3.1)
Where Yjk is the quantity (quality or satisfaction) of goods of type k in village j, Rj is a dummy
variable indicating whether or not the village was part of a GP where the position of Pradhan
for state fixed effects and village class dummies (a dummy of whether the village is small, medium or large).
"Andhra Pradesh, Himachal Pradesh, Kerala, Maharashtra, Rajasthan, Uttar Pradesh.
'Andhra Pradesh, Karnataka, Kerala, Maharashtra, Orissa, Punjab, Tamil Nadu, West Bengal.
135
was reserved for a woman as of the beginning of 2000, and Xj is a vector of control variables
(state fixed effects and a dummy for the size of the village). For easy comparison across types
of public goods, all the variables are expressed as standard deviations from the mean of the
distribution in the unreserved villages.
A central variable of interest is the average of these coefficients across all goods. We estimate:
5
k=l
where Nk is the number of observations used in the good k regression, and N is the sum of all
the observations in the five regressions.
The standard error for these averages is derived from the variance covariance matrix for the
5 coefficients obtained from jointly estimating the equations for the 5 public goods (see Katz,
Kling and Liebman (2004)).
We then estimate the coefficient /ok in the regression:
Yijk = Ck + 3kR + XjYk
+ Vjk + Eijk
(3.2)
where Yijk is a dummy variable indicating whether respondent i in village j is satisfied with
the quality of good k (in Table 3.2, columns (6) to (8)) or had to pay a bribe to get good k (in
Table 3.3).
The regression is run at the individual level, and we correct for clustering of the standard
errors at the GP level.1 0 In columns (7) and (8) of Table 3.2, and (3) and (4) in Table 3.3 we
report the coefficients separately for male and female respondents.
The average effects and the associated standard errors are obtained as described above.
3.4.2
Results
Consistent with the results in Chattopadhyay and Duflo (2004), reservation for women leads to
more investment in drinking water infrastructure. There are significantly more public drinking
water taps and handpumps when the GP is reserved for a woman, and there is also some
l°We have also run a specification where we control for a vector of household level covariates. The results are
essentially unchanged. They are reported in table 3 columns 5 to 7 for the incidence of bribes.
136
evidence that the drinking water facilities are in better repair (though this coefficient is not
significant at the 5% level). Consistent with these results as well (Chattopadhyay and Duflo
(2004) find that the other effects are either insignificant or are opposite in sign in the two states
they consider) there are no significant coefficients for the other public goods,
However, there are four positive coefficients and only one negative coefficient in the quantity
regression. In the quality regression, all coefficients are positive. Overall, the average effect
of reservation on the availability of public goods in a village is positive and significant (the
coefficient is 0.078 standard deviations, with a standard error of 0.041). The average effect
of the reservation on the quality of public goods is positive as well, but not significant (the
coefficient is 0.016 standard deviations, with a standard error of 0.011). To summarize, women
leaders do a better job at delivering drinking water infrastructure, and at least as good a job
at delivering the other public goods.
Table 3.3 reports the mean value of whether or not the respondent had to pay a bribe, and
the coefficient of the reservation dummy. For all types of bribes, respondents (both men and
women) are less likely to report that they needed to pay a bribe to obtain a service when the
GP is reserved for a woman than when it is not reserved. Overall, both men and women are
significantly less likely to have to pay a bribe to obtain a service if they live in a GP where the
position of Pradhan is reserved for a woman. Women do appear to be less corrupt than men.
In contrast, as reported in column (6) of Table 3.2, respondents are less likely to declare that
they are satisfied with the public goods they are receiving in villages with female Pradhans. On
average, they are 2 percentage points less likely to be satisfied. This number is significant at the
95% level, and it also corresponds to a large (25%) relative increase in the rate of dissatisfaction,
since the satisfaction ratings are overall very high. 1l
This is true for every good individually, and for female as well as male respondents. Particularly striking is the fact that individuals are less satisfied with water service, even though both
the quality and quantity of drinking water facilities is higher in reserved villages. The coefficient on dissatisfaction is 2.4%, with a standard error of 1.8%. Moreover, women are as likely
to be dissatisfied as men. Interestingly, respondents are also significantly less satisfied with the
quality of the public health services when the Pradhan is a woman. This is despite the fact
1 The fraction of respondents saying that they are satisfied is 82%, averaged across all goods.
137
that health services were centrally administered and not under the jurisdiction of Panchayats
in the 11 states in the study in this period. There was thus no reason the quality of health
services should be different in reserved Panchayats (indeed, our objective measures of quality
and quantity are uncorrelated with the reservation variable).
Chattopadhyay and Duflo (2004) show that women and men care about different public
goods and that female Pradhans tend to invest 'in goods preferred by women. This could
explain a general dissatisfaction among men with leadership when the Pradhan is a woman.
However, it does not explain why women are also less satisfied. Nor can it explain why both
women and men are less satisfied with the public goods they receive.
3.4.3
Discussion
While reservation is randomly assigned, the coefficient on the reservation for women in the
satisfaction regression does not necessarily reflect discrimination against women in politics:
though we observe that women invest more in observable water equipment (and no less in
others), one possibility is that women invest in the wrong kinds of repairs.
For example, they may spend more public money repairing the water facilities and building
new ones, but their repairs may not correspond to what villagers really need.
To assess to what extent the quality and quantity variables we include correspond to respondents' concerns, and to get some sense of how controlling for these variables affects the
evaluation of women, we run the following regressions:
Yijk = k + AkQjk + PLkQljk+ VkQjk * Rj + kQljk * Rj + Xjk + Vjk + eijk
where
Qjk
is the quantity of public good k in village j, and
Qljk
(3.3)
is the quality of public good
k in village j. The results are presented in Table 3.4. Each regression is in a different column,
with the coefficients in rows. Column (1) in the Table gives the average results across all public
goods. Columns (2)-(6) present the results for each individual good.
Across all goods, villagers' satisfaction is positively and significantly associated with quality,
but not with quantity. The coefficient on the reservation dummy is still negative. The interactions between the quality and the women reservation dummy and quantity and the women
138
reservation dummy are both negative, suggesting that women are given less credit for both
quality and quantity. However, they are given some credit: the sum of the quality variable and
its interaction with the women reservation variable is still positive and significant.
Not surprisingly, the coefficient on the reservation dummy is now larger than it was in Table
3.2 (-0.028 instead of -0.020). The overall effect of the reservation at the mean of the quality
and quantity variables is -0.022, very close to the -0.020 we estimated in Table 3.2.
The quality index ranges between 0 and 1. It is interesting to note that in the regression
across all public goods, the coefficient on the women reservation dummy is similar in magnitude
but opposite in sign to the coefficient on the quality variable. This implies that the effect of
having a female Pradhan on satisfaction is as large as the impact of transforming the average
quality of the public goods available in the village from entirely "good" to entirely "bad" (for
example a water source with no drain, no coverage, some leaks, etc...) in this scale.
The determinants of satisfaction with the provision of drinking water are of particular
interest. First, for most goods (such as public health facilities, public transportation),
quantity
is not controlled by the Panchayat, and changes very slowly over time. It is therefore not
surprising that, on average, satisfaction is more closely linked to quality rather than to quantity.
Water is an exception in the sense that the Panchayat can affect the quantity by increasing the
number of facilities. This is reflected in the data: satisfaction with drinking water facilities is
significantly associated with quantity, rather than with quality. Second, as we saw in Table 3.2,
there are significantly more drinking water facilities in villages that are reserved for women.
However, the coefficient on the interaction between quantity and reservation is negative, and
almost as large as the coefficient on the quantity variable (though not significant). Moreover,
the general level of satisfaction is lower among reserved GPs.
Two factors appear to contribute to the lower reported satisfaction with drinking water in
reserved GPs. First, women are not credited for the investment they are making as much as
men are. Second, the base level of satisfaction with women leaders (irrespective of quality or
quantity) is lower to start with.
139
3.5
Conclusion
Prima facie, women do not appear to be ineffective leaders for their communities. As a large
experimental literature suggests should be the case,1 2 they are also significantly less likely to
be corrupt. However, for all public goods, their performance is judged to be worse than that of
men. Overall satisfaction across all five public goods is significantly lower in villages reserved
for a female Pradhan.
There are various explanations for this finding. It could be that women's performance is
worse in important unobservable dimensions.
It could be that new leaders are judged less
favorably than established leaders.1 3 It could be that women have worse characteristics than
men. Chattopadhyay and Duflo (2004) show that women elected to reserved seats are poorer
than their male counterparts, they are less experienced, less educated, and less likely to be
literate. Voters may use these characteristics in forming their opinion on the quality of their
leaders. Finally, it could be that villagers generally expect women to be less effective leaders,
and these priors are slow to adjust, even in the face of facts.
The data do not allow us to distinguish among these different hypotheses. However, the
fact that even public goods beyond the jurisdiction of the Panchayat leader are judged to be
worse in woman-headed Panchayats implies that the first explanation is the least likely.
The results, however, suggest that women face an uphill battle in politics. This may explain
why they rarely win elections even though they appear to be at least as effective leaders along
observable dimensions, and are less corrupt. This may also help explain why women are not reelected once their seats are no longer reserved. In Udaipur district in Rajasthan, Chattopadhyay
and Duflo (2004) found that none of the women who had been elected on a reserved seat in
1995 were reelected in 2000.
The results also indicate that some caution is warranted when user-satisfaction reports are
used as a policy tool. "Citizen report cards" have increasingly been advocated as a means of
improving the quality of governance in developing countries. Reports by the general public are
used to pressure the state to improve the delivery of public services, or even to fire officials
12
Eagly and Crowley (1986), Eckel and Grossman (1998).
Linden (2004) finds that there is an incumbency disadvantage in India. However, two-terms incumbents are
treated more favorably than one-term incumbents.
13
140
implicated in wrongdoing. This in particular was a dominant theme in the last World Bank
Development Report on social services delivery (World Bank, 2004). Yet the data show that
citizens' opinions may be influenced by factors other than the quality of the public services they
are supposed to be evaluating.
3.6
Bibliography
British Council. "Effective leaders, view from Central and East Africa." mimeo, London, 2002.
Chattopadhyay, Raghabendra, and Esther Duflo. "Women as policy makers: Evidence from
a randomized policy experiment in India." Econometrica, 2004, 72(5), pp. 1409-1443.
Chaudhuri, Shubham "What difference does a constitutional amendment make? The 1994
Panchayati Raj Act and the attempt to revitalize rural local government in India." mimeo,
Columbia University, 2003.
Dollar, David, Raymond Fisman, and Roberta Gatti.
"Are women really the "fairer" sex?
Corruption and womenin government." Journal of Economic Behavior and Organization,
2001, 46(4), pp. 423-429.
Eagly, Alice H., and M. Crowley. "Gender and helping behavior: A meta-analytic review of
the social psychological literature." Psychological Bulletin, 1986, 100, pp. 283-308.
Eagly, Alice H., and Steven J. Karau.
"Role congruity theory of prejudice toward female
leaders." Psychological Review, 2002, 109, pp. 573-598.
Eckel, Catherine, and Philip Grossman. "Are women less selfish than men?: Evidence from
dictator experiments." Economic Journal, 1998, 108(448), pp. 726-735.
Ghatak, Maitreesh, and Maitreya Ghatak. "Recent reforms in the Panchayat system in West
Bengal: Toward greater participatory governance?" Economic and Political Weekly, 2002,
pp. 45-58.
Jones, Mark P. "Legislator gender and legislator policy priorities in the Argentine chamber of
deputies and the United States house of Represntatives." Policy Studies Journal, 1997,
25(4), pp. 613-629.
141
Katz, Lawrence F., Jeffrey R. Kling, and Jeffrey B. Liebman. "Moving to opportunity and
tranquility:
Neighborhood effects on adult economic self-sufficiency and health from a
randomized housing voucher experiment." mimeo, Princeton University, 2004.
Linden, Leigh. "Are incumbents really advantaged? Exploring the preference for nonincumbents in India." mimeo, MIT, 2004.
Norris, Pippa, and Ronald Inglehart. "Cultural barriers to women's leadership: A worldwide
comparison." IPSA 2000 paper, 2000.
Swamy, Anand, Stephen Knack, Young Lee, and Omar Azfar.
"Gender and corruption."
Journal of Development Economics, 2001, 64(1), pp. 25-55.
World Bank. Engendering Development: Through Gender Equality in Rights, Resources, and
Voice (Oxford University Press and World Bank), 2001.
World Bank. World Development Report 2004: Making Services Work For Poor People, 2004.
142
Table 3.1 Comparison of Reserved and Unreserved Villages in 1991
Reservation
Dependent Variable
Mean
Mean
Unreserved
Reserved
Difference
N
(1)
(2)
(3)
(4)
Effect with State
Fixed Effects
(5)
-
Total Population
2,817
2,805
-12
(229)
938
66
(120)
Literacy
0.396
0.378
-0.018
(0.012)
938
-0.012
(0.010)
Female Literacy
0.282
0.263
-0.019
940
-0.009
(0.010)
(0.013)
Male Literacy
0.502
0.486
-0.016
(0.012)
940
-0.012
(0.010)
Percentage of Irrigated Land
0.282
0.342
0.059
(0.032)
642
0.034
(0.023)
I if Village has a Bus or Train Stop*
0.627
0.554
-0.073
(0.034)
940
0.021
(0.025)
Number of Health Facilities*
0.604
0.685
0.081
809
0.126
(0.122)
(0.121)
1 if Village has Tube Well*
0.335
0.308
-0.027
(0.040)
789
-0.031
(0.031)
1 if Village has Hand Pump*
0.699
0.751
0.052
(0.034)
786
-0.009
(0.026)
1 if Village has Well*
0.724
0.703
-0.020
(0.032)
898
-0.032
(0.028)
1 if Village has Community Tap*
0.393
0.373
-0.020
(0.036)
825
0.026
(0.030)
Number of Primary Schools*
1.857
1.780
-0.077
(0.135)
919
-0.004
(0.106)
Number of Middle Schools*
0.714
0.689
-0.025
(0.065)
839
-0.021
(0.050)
Number of High Schools*
0.371
0.364
-0.007
(0.046)
808
0.026
(0.036)
Total Number of Schools
2.832
2.726
-0.105
(0.201)
920
-0.012
(0.142)
Notes:
a
Standard enrrorsbelow the coefficients
b
Regressions control for state fixed effects and village class dummies
C F-Test ofjoint significance of the variables marked with an asterix is 0.17 with 1 and 937 degrees of freedom (p-value 0.68).
Source:
Census of India, 1991
Table 3.2 Effect of Female Leadership on Public Goods Quality, Quantity, and Satisfaction
Quality
Quantity
Dependent Variable
A. OVERALL
Weighted Average
Mean
(1)
Norm.
Reservation
(2)
Mean
(3)
Reservation
(4)
Mean
(5)
Satisfaction
Reservation
Men
All
(7)
(6)
Women
(8)
4.35
0.078
(0.041)
0.569
0.016
(0.011)
0.818
-0.020
(0.010)
-0.020
(0.010)
-0.017
(0.013)
Water
20.11
(33.46)
633
0.191
(0.098)
0.392
(0.189)
611
0.016
(0.014)
0.835
(0.297)
6802
-0.024
(0.018)
-0.021
(0.022)
-0.027
(0.021)
Education
0.94
(0.24)
810
0.130
(0.064)
0.892
(0.242)
543
0.015
(0.021)
0.855
(0.198)
3661
-0.013
(0.011)
-0.010
(0.011)
-0.024
(0.023)
Transportation
2.26
(1.02)
635
-0.020
(0.082)
0.306
(0.292)
596
0.006
(0.025)
0.891
(0.189)
3868
-0.007
(0.016)
-0.007
(0.016)
0.008
(0.029)
Fair Price Shops
0.77
(0.42)
805
0.028
(0.069)
0.688
(0.289)
498
0.023
(0.027)
0.747
(0.309)
7212
-0.022
(0.015)
-0.026
(0.017)
-0.015
(0.022)
Public Health Facilities
0.65
(0.48)
809
0.066
(0.072)
0.654
(0.352)
355
0.017
(0.036)
0.803
(0.366)
741
-0.063
(0.033)
-0.086
(0.039)
-0.027
(0.053)
B. BY PUBLIC GOOD TYPE
Notes:
b
Standard deviation and number of observations below the mean, and standard errors (corrected for clustering at the GP level) below
the coefficients
All coefficients expressed in number of standard deviations of the independent variables
The standard errors of the weighted averages of the coefficients are obtained by jointly estimating the coefficient in a SUR framework
Regressions control for state fixed effects and village class dummies
f
h
The water quantity variables is the number of public drinking water taps and handpumps in the village
The water quality variable is a 0-1 index aggregating the responses to the following questions (by observations)
drain around source, no leakage, washing platform, caretaker, public latrine, drainage
The education quantity variable is an indicator of whether there is any education facility (school or non-formal education center) available in the village
The education quality variable is an index aggregating the answer to the questions:
quality of school's playground, blackboard, toilet and availability of drinking water
The transportation quantity variables is the number of public transportation facilities the village (public and private buses, vans, taxis etc.)
The transportation quality variable is a 0-1 index aggregating the responses to the following questions:
shelter at bus stand, information about bus, whether bus is new, whether the road repaired in the past 6 months
The Fair Price shop quantity variable is an indicator of whether there is a fair price shop available in the village
The Fair Price shop quality variable is a 0-1 index aggregating the responses to the following questions (responses obtained by observation)
prices displayed, prevalence of arguments and complaints, behavior of shopkeeper
The Public health quantity variable is an indicator of whether there is a public health center available in the village
The Public health quality variable is a 0-1 index aggregating the responses to the following questions (responses obtained by observation)
cleanliness of linens, floors, bathrooms and toilets and availability of safe drinking water for patients
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