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We investigate the influence of political and financial factors on the decision to privatize government-owned firms. The results show that profitable firms and firms with a lower wage bill are likely to be privatized early. We find that the government delays privatization in regions where the governing party faces more competition from opposition parties. The results also suggest that political patronage is important as no firm located in the home state of the minister in charge is ever privatized. Using political variables as an instrument for the privatization decision, we find that privatization has a positive impact on firm performance.
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Forthcoming in
Journal of Finance
The Decision to Privatize: Finance and Politics
I. Serdar Dinç and Nandini Gupta*
ABSTRACT
We investigate the influence of political and financial factors on
the decision to privatize government-owned firms using firm-level
data from India. We find that the government significantly delays
privatization in regions where the governing party faces more
competition from opposition parties. This result is robust to firm-
specific factors and regional characteristics. The results also
suggest that political patronage is important as no government-
owned firm located in the home state of the minister in charge is
ever privatized. Using political variables as an instrument for the
endogenous privatization decision, we find that privatization has a
positive impact on firm performance.
Key Words: Government Ownership, Political Economy, Emerging Markets, Economic
Reform, State-Owned Enterprise, Interest Groups, IPO.
* I. Serdar Dinc is at the Sloan School, Massachusetts Institute of Technology, dinc@mit.edu and Nandini Gupta is
at the Kelley School of Business, Indiana University, nagupta@indiana.edu. We are grateful to Sugato
Bhattacharyya, Benjamin Esty, Ray Fisman, Rick Harbaugh, Bill Megginson, Atif Mian, Paola Sapienza, Antoinette
Schoar, Anjan Thakor, Gregory Udell, and Kent Womack for valuable comments. We gratefully acknowledge
research assistance from Manvi Goel and Ajay Vutha. This paper has also benefited from the comments of
participants at the NBER Summer Institute Corporate Finance meeting, the NBER India Conference, Paris Spring
Corporate Finance Conference, India’s Financial Systems Conference at the Wharton School, the Western Finance
Association Meetings, the World Congress of the Econometric Society, and workshop participants at the University
of Amsterdam, Indiana University, University of Maryland, University of Michigan, MIT, Northwestern University,
University of Oregon, Southern Methodist University, Tilburg University, The World Bank, University of
Wisconsin, and York University. All remaining errors are our own.
The sale of government-owned firms to private owners has yielded more than $1 trillion in
revenues for governments, improved the performance of government-owned firms, and
facilitated the development of financial markets.1 Yet, governments still own a substantial
number of firms across the world (Megginson (2005)). Given the documented benefits from
privatization, why are there widespread delays in the process, with governments worldwide
choosing to sell some firms but not others to private owners?
To answer this question we investigate the role of firm-specific financial and political
factors in the selection of firms for privatization. Following the literature on the decision to go
public by private firms, we identify financial characteristics of firms that are likely to influence
the decision to privatize.2 However, the decision to sell government-owned firms is likely to
depend not only on financial factors, but also on political costs and benefits. While the benefits
of privatization, such as revenues from sale, financial market development, and efficiency gains,
tend to be dispersed across the population, the costs of privatization, such as layoffs of surplus
workers and the loss of private benefits of control for politicians, tend to be concentrated among
a small group.3 To understand how these concentrated costs can slow down the process of
privatization, we investigate the role of political competition and patronage in the privatization
decision.
Since the adverse effects of privatization, such as layoffs, are likely to be concentrated in
the region where a firm operates, the governing party may lose votes in that region because of
opposition from interest groups that are adversely affected, such as the local employees of
government-owned firms. Privatization may also be perceived negatively by the public as an
inequitable transfer of publicly-owned assets to private owners, which can result in a loss of
votes in the region. Since any decrease in voter support is likely to matter more for the governing
party if it is in a competitive race with opposition parties in a region, we expect the government
1
to delay privatization in regions where the governing and opposition parties face a close race.
This is consistent with the argument that politicians may allocate public funds to pivotal regions
to achieve electoral goals (Lindbeck and Weibull (1987) and Dixit and Londregan (1996)).
Since politicians obtain private benefits from controlling government-owned firms
(Shleifer and Vishny (1994) and Dinç (2005)), we also consider the role of political patronage in
the privatization decision. For example, politicians can influence the hiring decisions of
government-owned firms to favor supporters. Theory suggests that politicians may target
government programs to reward supporters with patronage (Cox and McCubbins (1986) and
Persson and Tabellini (2002)). Hence, rent-seeking politicians may delay privatization in regions
where their supporters are based.
To evaluate the effect of financial and political factors on the privatization decision we
use a firm-level dataset on Indian firms that includes both privatized firms and those that remain
fully government-owned. In many countries data on the latter are not available. The data provide
financial information for 92% of the firms owned by the federal government for the fiscal years
1990 to 2004. We observe all the privatizations that have occurred since the start of the program,
the majority of which were undertaken by the Congress government (1991 to 1995), and a
smaller number by the BJP government (1999 to 2003). Thus, the political results are drawn
mainly from the first privatizing government.
To investigate the role of politics we collect electoral data for each of the 543 electoral
districts in India from all the federal elections held since the start of the privatization program in
1991. We then hand-collect data on the address of the main operations of each firm, and use
digital geographic mapping techniques to match firms to electoral districts at varying distances
around their location.
2
Using India as the empirical context for studying the politics of financial reforms has
several advantages. First, it is a multi-party democracy with robust political competition among
its political parties. For example, the ruling party in the federal government was voted out of
power in four out of five elections held between 1991 and 2004. Second, since this is a single-
country study we can control for institutional differences across countries such as legal systems
and colonial legacies. Third, by using India as the empirical context we can exploit regional
differences across the different Indian electoral districts. The considerable political,
demographic, ethnic, and socio-economic diversity across the Indian subcontinent leads to
significant variation in support for the different political parties across the regions.
The results suggest that, similar to the IPO decision of private firms, larger firms are
more likely to be privatized early. We also find that privatization is significantly delayed for
firms with a large wage bill, suggesting that employees of firms with a large workforce may
block privatization. Unlike private firm IPOs, political factors also play an important role in the
privatization decision. In particular, we find that privatization is significantly delayed if a firm is
located in a politically competitive constituency where the governing and opposition party
alliances have won a similar share of the vote. For example, the rate of privatization is about 1.5
times higher for a firm located in a constituency at the 75th percentile of political competition
compared to a firm located at the 25th percentile, where the lower percentile indicates a more
competitive region. We find that the government also delays the privatization of firms that are
located in districts where the opposition party has more voter support. These results suggest that
the government acts to minimize the effects of a political backlash by delaying privatization in
districts where the governing party faces more competition from the opposition.4 Hence, the
dispersed benefits and concentrated costs of privatization appear to significantly influence the
pattern of privatization sales.
3
We check the robustness of the results in several ways. In particular, the specifications
control for firm-level sales, profitability, wages, year and industry effects, the relative
importance of the firm to the region, and regional differences in income, education, urbanization,
and growth.
To investigate the role of political patronage in the privatization decision, we examine
whether retaining control over a firm is a greater priority for the politician in charge of the firm if
the firm is located in the home state of that politician. We find that no firm located in the state
from which the minister with jurisdiction over that firm is elected is ever privatized. This result
suggests that political patronage has a significant impact on the privatization decision.
Our results suggest that firm characteristics, such as sales and workforce, are significantly
related to which firms are privatized. While there is a large literature documenting that
privatization leads to significant improvements in the performance of government-owned firms
(Megginson and Netter (2001) and Gupta (2005)), the majority of these studies do not account
for the endogenous selection of firms for privatization based on their performance. Using
political competition as an instrument, we correct for endogeneity of the privatization decision to
firm characteristics, and find that privatized firms experience significant improvements in
productivity and efficiency compared to firms that remain fully government-owned.
There is a growing empirical literature on the political economy of financial market
reforms.5 For example, Jones et al. (1999) show that governments adopt terms of sale that are
consistent with political objectives; Clarke and Cull (2002) find that the political affiliation of the
government does not have a robust impact on the probability of bank privatization in Argentina;
Bortolotti and Pinotti (2008) show that privatization is delayed in democracies with proportional
electoral systems; and, Dastidar, Fisman, and Khanna (2007) show that there is policy
irreversibility in the privatization process in India. In the related context of banking sector policy,
4
Kroszner and Strahan (1999) find that interest groups may influence the pattern of banking sector
deregulation across the different U.S. states; Sapienza (2004) shows that Italian government-
owned banks charge lower interest rates in areas where the government is politically strong; and,
Brown and Dinç (2005) document that governments are less likely to take over failing banks
prior to an election.
Our paper contributes to the political economy of finance literature in several ways. First,
the literature considers how differences in political institutions are correlated with patterns in
privatization, such as methods of sale. Our focus is different since we use data on both privatized
firms and those that remain government-owned to study how firms are selected for privatization
in a competitive democracy. Second, the political economy of finance literature is implicitly
motivated by the incentives of politicians. By investigating the role of political competition we
provide a direct test of this underlying assumption and show how politicians’ incentives shape
financial market policy. Third, we identify and connect politicians to the firms they control so as
to provide the first test of how political patronage affects privatization. Fourth, the literature
studying the effects of privatization often assumes that firms are selected randomly for
privatization, but our results indicate that privatization is likely to be endogenous to firm
characteristics. We show that political variables may be used as instruments to correct for this
endogeneity. These political measures are also likely to be useful for evaluating the impact of
other endogenously implemented reforms, such as banking sector and foreign investment
deregulation.
The paper is organized as follows: In section I we describe the Indian political system
and the privatization program, in section II we describe hypotheses based on the financial and
political factors likely to affect the privatization decision, and in section III we describe the data.
5
Section IV presents the regression results, section V describes robustness checks, in section VI
we discuss the impact of privatization, and in section VII we conclude.
I. Background on Privatization and the Political System in India
A. Government-owned Firms
In the post-independence era, government ownership of firms in India was justified by
concerns that the private sector would not undertake projects requiring large investments with
long gestation periods. In the late 1960s there was a period of rapid nationalization of firms in all
sectors, and by 1991, gross capital formation in federal government-owned firms accounted for
40% of total gross capital formation in the economy (Ministry of Finance (1996)).
We focus on firms owned by the federal government, which account for about 85% of the
total assets of all government-owned companies (Gupta (2005)). Government-owned firms are
typically overstaffed and their workers often earn more than workers in privately-owned firms.
For example, in 2003 over 10% of workers in the organized sector were employed in federal
government-owned firms (Ministry of Finance (2004)),6 and their average wages were twice as
high as in the private sector (Panagariya (2008)). This large wage difference suggests why
government firm workers vigorously oppose privatization. Describing this opposition a news
article reported: “Over 25,000 ONGC [Oil and Natural Gas Commission] staff observed ‘black
day’ and their union leaders went on hunger strike to mark their protest over the privatisation
move,” (The Financial Times, 1993). Over half the federal government-owned firms are loss-
making and perform far worse in comparison to private firms in the same industry (Department
of Disinvestment (2001)). For example, between 1990 and 1998 the ratio of profits after tax to
sales averaged -4.4% for government-owned manufacturing firms, and 6.7% among private firms
(Department of Disinvestment (2001)).
6
B. Political System
The most populous democracy in the world, India has a parliamentary system where
representatives are directly elected to the Lok Sabha, the lower house in the federal government.
Representatives are elected from 543 single-member districts distributed across 35 states, and the
political party or alliance of parties that wins the majority of districts forms the national
government. We include electoral data on five elections to the federal government held since the
start of the privatization program in 1991, namely the elections held in 1991, 1996, 1998, 1999,
and 2004.
On average, about 450 political parties participate in the elections. It is common for
national political parties to establish alliances with each other and smaller regional parties before
the elections in order to increase their chances of obtaining a majority. Hence, we study the
electoral performance of political party alliances. Following India’s independence from the
United Kingdom in 1947, the main political party was the ideologically center-left Congress
Party. The economic reforms of 1991 were initiated by the Congress Party, which along with its
allies won the 1991 elections and remained in power until the 1996 elections. The ideologically
right-wing Bharatiya Janata Party (BJP) was the main opposition during this period with the
second largest number of seats.
Between 1996 and 1998, there were successive short-lived governments, which collapsed
due to a lack of support from coalition members. Following the 1999 elections, a new coalition
led by the BJP formed the government and remained in power until 2004, with the Congress
Party alliance as the main opposition. In the 2004 elections, the Congress Party and its allies
obtained a winning majority with the BJP as the main opposition.
7
C. Privatization Process
In response to a balance of payments crisis in 1991, India undertook sweeping economic
reforms that included deregulation and privatization. Out of 280 non-financial firms owned by
the federal government, 50 firms were privatized between the fiscal years 1991 to 2006.7 The list
of firms to be privatized was decided at the Cabinet level and every government produced their
own list. The privatization program was initiated by the Congress government in 1991, and after
a brief hiatus was continued by the BJP government when it came to office in 1999. Below we
describe the official policies and actual progress made by the Congress and BJP governments.
First Phase (1991 to 1996): The official policy of the Congress government called for a
reduction in government ownership in most firms in non-strategic industries. The “Committee on
Disinvestment of Shares in Public Sector Units” recommended in 1993 that government
ownership should be reduced to 26%, the minimum equity holding necessary for certain voting
powers, in most non-strategic industries (Department of Disinvestment (2007)). However, in
1991, the Finance Minister said that the government would privatize only up to 20% of equity to
provide market discipline and raise money for the treasury (Department of Disinvestment
(2007)).
The Congress government undertook partial privatizations where it sold minority equity
stakes in 40 firms without transferring management control. While some of these firms sold
equity multiple times, we restrict our analysis to the first sale to avoid the endogeneity that may
arise if past equity sales affect the probability of subsequent sales. Like many countries around
the world (LaPorta, Lopez de Silanes, and Shleifer (2002) and Boubakri, Cosset, and Guedhami
(2005)), the majority of the privatizations undertaken by Indian governments involved the sale of
minority equity stakes in capital markets. Although the government continued to hold a majority
of shares, these firms became subject to market monitoring.8 Partially privatized firms are also
8
more likely to sell majority stakes subsequently (Gupta (2005)). Hence, politicians have an
incentive to resist partial privatizations both because increased monitoring reduces patronage
opportunities, and because these firms are candidates for the eventual sale of majority stakes to
private owners.
Privatization is deeply unpopular in India, as is demonstrated by the fact that it is
officially referred to as “disinvestment”. Despite the fact that both the Congress and BJP parties
have undertaken some privatization, neither have an ideological commitment to privatization and
both parties have used anti-privatization rhetoric to gain political advantage when in opposition.
For example, the conservative BJP frequently attacked the Congress government’s privatization
plans (Reuters News, 1992), and even joined forces with labor unions to protest privatization
(Reuters News, 1993). In 2004, sensing a public backlash against the BJP’s reform agenda, the
Congress party ran and won on a platform of limited privatizations.
Acknowledging the role of electoral politics the Congress government Prime Minister
said, “If you face immediate political problems - elections in four states - it is hard to push
ahead…We had to worry about the prospects of unemployment if public sector units faced
closure,” (Asia Times, 1997).
Second Phase (1999 to 2003): Following the defeat of the Congress government in 1996, the
privatization program remained in hiatus until the election of the BJP to the national government
in 1999.9 The BJP government established a new “Department for Disinvestment”, which
declared that the government would undertake majority sale privatizations with the transfer of
management control in all non-strategic industries. Between 1999 and 2003, the BJP government
privatized 10 firms that had not previously sold equity. The privatizations undertaken by the BJP
government include the sale of majority stakes and the transfer of management control to private
9
owners in 17 firms, some of which had previously been partially privatized. We also consider the
control transfer privatizations separately.
Political considerations may explain why so few privatizations were undertaken by the
BJP, since the opposition Congress Party campaigned against it. In fact, attributing the defeat of
the BJP-led National Democratic Alliance government in the 2004 elections to its disinvestment
[privatization] program, a major newspaper’s editorial opined, “The Indian voters…were
rejecting the National Democratic Alliance [NDA] government, which, as one poll slogan had it,
stood for the “National Disinvestment Agency” (The Hindu, 2004).
Following the BJP’s defeat, a coalition led by the Congress Party formed the government
in 2004 with the stated policy that it would not privatize any profitable firms (Department of
Disinvestment (2007)). During its tenure from 2004 to 2008, the Congress-led government
privatized just one new firm, in 2004.
II. The Role of Financial and Political Factors in the Decision to Privatize
In this section we develop empirical predictions about the main financial and political
factors that are likely to affect the decision to privatize. To develop predictions about financial
factors that may influence the privatization process we draw upon the literature on why private
firms go public (Pagano, Paneta, and Zingales (1998) and Ritter and Welch (2002)). However, a
major difference between the IPOs of private firms and the privatization of government-owned
firms is that political factors are likely to play a significant role in the latter case. We also
develop hypotheses regarding the role of politics in the privatization decision, which we test
using firm-level data on both privatized firms and firms that remain fully government-owned.
10
A. Financial Factors: Firm Size and Profitability
If investors are less informed than issuers about the value of a company then there may
be adverse selection in the quality of firms that choose to go public (Leland and Pyle (1977)).
Chemmanur and Fulghieri (1995) have argued that the cost of adverse selection is likely to be
greater for younger and smaller firms, which is supported by the results of Pagano, Panetta, and
Zingales (1998) who find that smaller firms are less likely to go public. In the privatization
context, comparing methods of sale in a cross-country sample of privatized firms, Megginson et
al. (2004) find that larger firms are more likely to be privatized through the sale of shares on
public rather than private capital markets. Hence, we investigate whether firm size has an impact
on the privatization decision.
Governments may prefer to privatize more profitable firms first to increase proceeds from
privatization (Gupta, Ham, and Svejnar (2008)), and to build public support with successful
initial sales (Dewenter and Malatesta (1997)). However, the evidence also suggests that
unprofitable firms experience the greatest efficiency improvements following privatization
(Claessens, Djankov, and Pohl (1997) and, Frydman et al. (1999)). Hence, this relationship will
depend on the relative emphasis placed by the government on proceeds and public support over
firm efficiency.
B. Political Factors
We investigate the role of politics using a political economy framework in which the
benefits of privatization, such as sale proceeds, are likely to be dispersed across the population,
while the costs of privatization, such as layoffs, tend to be concentrated among a small group. To
understand how these concentrated costs may slow down privatization, we investigate the role of
electoral considerations and political patronage on the decision to privatize.
11
Theory suggests that politicians may target public funds to pivotal regions with swing
voters to win elections (Lindbeck and Weibull (1987), Dixit and Londregan (1996), and Persson
and Tabellini (2002)). Empirically, Bertrand, et al. (2007) show that politically connected French
firms create more jobs in politically competitive regions; and, Dahlberg and Johansson (2002)
find that the distribution of grants in Sweden is concentrated in regions with more swing voters.
We investigate whether the privatization decision is affected by the closeness of the election in
the political competition hypothesis below.
Rent-seeking politicians may also allocate public funds to reward supporters with
patronage (Cox and McCubbins (1986) and Persson and Tabellini (2002)). For example,
Ansolabehere and Snyder (2007) show that governing parties skew the distribution of public
funds in favor of regions that support them. We investigate whether politicians use government
firms to benefit their supporters in the patronage hypothesis below. Note that politicians may
target both regions that support them as well as regions that are politically competitive
(Ansolabehere and Snyder (2007)).
B.1 Political Competition and Strength
The costs of privatization, such as layoffs, are likely to be geographically concentrated in
the region where a firm operates. As a result, voter support for the governing party in that region
may decrease because of opposition from government workers in the region, and negative public
perceptions about privatization. The effect of a political backlash on electoral outcomes is likely
to be greater if the governing and opposition parties have similar levels of voter support. When
the governing party faces strong competition from the opposition, a decrease in support may
cause it to lose seats from that region. Correspondingly, if the governing party has far more or far
less support than the opposition, then a political backlash may not have much impact on the
12
election. Thus, if political competition matters in the privatization decision, it follows that the
government will prefer to delay privatization in a region where the governing and opposition
parties are in a close race.
The governing party may also choose to minimize the effects of a voter backlash by
delaying the privatization of firms located in constituencies where the governing party does not
have strong support, or where the opposition party does. Alternatively, the government may
choose to reward its supporters by delaying privatization in regions where the governing party
has strong support. Hence, the question of the effect of the governing and opposition party’s
political strength on the privatization decision is an empirical one. Note that support for the
governing and opposition parties is negatively correlated but may not be exactly correlated in a
multi-party system.
B.2 Political Patronage
It has been argued that a principal cause of inefficiency in government-owned firms is
interference by politicians in the operations of the firm (Shleifer and Vishny (1994)). For
example, politicians can influence the hiring and purchase decisions of government-owned firms
so that they favor political supporters. If rent-seeking politicians obtain private benefits from
controlling these firms (Boycko, Shleifer, and Vishny (1996) and Dinç (2005)), then any loss in
these benefits following privatization may influence the decision to privatize. To examine if
political patronage affects the privatization decision, we investigate whether rent-seeking
politicians reward their supporters by delaying privatization in their home states.
13
III. Data
A. Financial Data
We observe financial data for 259 of the 280 manufacturing and non-financial service
sector companies owned by the federal government of India. To avoid attrition bias we do not
require the panel to be balanced. The data are collected by the Centre for Monitoring the Indian
Economy (CMIE) from company annual reports. We exclude three companies located in the
state of Jammu and Kashmir where the elections were not always held during the sample period
due to political unrest. Indian firms have to post profits for at least three out of the immediately
preceding five years to be able to list on the stock market (SEBI (2000), page 11). We restrict the
sample to firms that post positive profits for three years preceding privatization, for all years
except 1999 to 2003. Between 1999 and 2003, firms were privatized through private asset sales
and did not need to meet these listing requirements to qualify for privatization. The results do not
change if we impose the listing requirement for the entire sample.
The data used in the main regression analysis start in fiscal year 1990, one year prior to
the launch of the economic reforms of 1991, and end in fiscal year 2004 (March 2005). Data on
privatization transactions were obtained from the Disinvestment Commission of the Government
of India, and from news sources. We also hand-collect data on the address of the main operations
of each firm, which involved contacting many of these companies individually. About 80% of
companies have their main operations located in only one electoral constituency. For companies
with multiple plants in different locations, we define the main plant as the one with the largest
asset base and use its location as the location for the firm.
>>>>>>>>>>>>>>>>>>>>>Table I here<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
We observe financial data for 49 of the 50 federal government-owned firms that were
privatized between 1991 and 2004. Table I provides sample statistics for the main variables used
14
in the analysis and compares privatized firms with firms that remain fully government-owned
during this period. We include each privatized firm only until the year of privatization, defined
as the first sale to private owners, in order to avoid capturing the effect of privatization on firm
characteristics. All firms that remain fully government-owned are followed until the end of fiscal
2004, or the latest year that data are available.
Comparing the pre-privatization characteristics of privatized firms to firms that remain
fully government-owned, we note several differences. In Table I we report that the average
annual sales of privatized firms are more than four times larger than the average sales of firms
not chosen for privatization, with the difference being significant at the 1% level. This
comparison does not capture any performance improvements due to privatization because the
privatized companies are included in the sample only until the year in which they first sell
equity. Privatized companies also have lower wage expenses on average compared to their fully
government-owned counterparts, as measured by the ratio of the total wage bill to sales. We
control for these differences by including these firm characteristics in all the regressions.
B. Political Data
We collect electoral data for each of the 543 single member electoral districts on the vote
shares obtained by national and regional political parties in all the elections to the federal
government held since the start of the privatization program in 1991 until 2004. These data are
obtained from the Election Commission of India, which is in charge of conducting the elections.
Information on which parties belong to the main alliances is obtained from press sources and
election websites. In Figure 1 we provide a map of India’s electoral districts.
>>>>>>>>>>>>>>>>>>>>>>>>>>FIGURE 1 HERE<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
15
We identify the electoral district in which the main operations of each firm are located
and match the firm to the political and demographic data for that district. The role of political
considerations in the privatization decision may however extend beyond the immediate electoral
district to neighboring areas. In the main analysis, we construct the political variables for all the
electoral constituencies that are located within a 10 km radius around the firm’s main operations
using digital Geographic Information Systems (GIS) mapping. As a robustness check, we also
construct the political variables for electoral districts within 0, 5, 25, 50, and 100 km radii around
the firm. In Figure 2 we provide an example of the construction of the political measures at
varying distances around the main operations of firms located in the Indian state of Punjab.
>>>>>>>>>>>>>>>>>>>>>>>>FIG 2 HERE<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
To measure the political strength of the governing party alliance, we use the proportion of
votes received by the governing party and its allies in the most recent elections to the federal
parliament, in all the electoral constituencies within a 10 km radius around the firm (Govt Vote
Share). To measure the political strength of the largest opposition party alliance, we use the
proportion of votes received by the main opposition party and its allies in the most recent
elections to the federal parliament (Opposition Vote Share). Note that although the two variables
are related, Opposition Vote Share is rarely equal to 1-Govt Vote Share in a multi-party system.
From the summary statistics reported in Table II we also note that in the top quartile of
opposition vote share describing constituencies that strongly support the opposition, opposition
parties received just 42% of the vote, which suggests that the governing party may be
competitive even in the districts where the opposition is strongest.
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>TABLE II HERE<<<<<<<<<<<<<<<
To measure the extent of political competition between the governing and the opposition
party alliances we define Vote Share Difference as the difference between Govt Vote Share and
16
Opposition Vote Share, which assumes a lower value in constituencies where the governing and
opposition party are in a close race, or where the opposition party alliance is stronger. From the
summary statistics reported in Table II we note that the 25th percentile of Vote Share Difference
is equal to -0.006, i.e. it is close to zero in its lowest quartile. Hence, lower values of Vote Share
Difference is likely to represent competitive districts where the governing and opposition parties
have won a similar share of votes, rather than districts where the opposition party is far ahead of
the governing party. We also construct the political competition measure, Abs Vote Share
Difference, defined as the absolute value of the difference between Govt Vote Share and
Opposition Vote Share. A lower value of Abs Vote Share Difference indicates a more
competitive district.
>>>>>>>>>>>>>>>>>>>>>> FIG 3 HERE<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
In Figure 3 we provide a scatter plot and regression line describing the frequency of
privatization (Number of Privatized Firms/Number of SOEs) as a function of political
competition (Vote Share Difference) between the governing and opposition parties at the
electoral district level for the years 1991 to 1995. The graph suggests that there are fewer
privatizations in districts where the governing and opposition parties are in a close race as
captured by lower values of Vote Share Difference.
To investigate the role of political patronage, we hand-collect data from various sources
including the Comptroller and Auditor General of India (the auditing agency for government-
owned firms), and match each firm by the state in which it is located to the home state of the
cabinet minister with jurisdiction over that firm. The identity and the home state of the cabinet
ministers are obtained from the Election Commission of India. Up to 32 ministries are involved
with the management of these firms but the ministerial portfolios vary cross-sectionally. We
collect these data for the fiscal years 1991 to 1995 and 1999 to 2002, including the cabinet
17
assignments of the Congress and the BJP governments. Since the BJP government reshuffled its
Cabinet over a dozen times, we were unable to obtain data on Cabinet membership for the last
two years of its tenure. We describe the patronage results in Section IV D.
IV. Results
A. Regression Framework
In this section we use a regression framework to investigate the role of financial and
political factors on the likelihood of privatization. We use the Cox proportional hazard model
since it incorporates both the privatization of a given government-owned firm and the time of
privatization. More specifically, the hazard rate of privatization is given by
(
)
kk xxxthth
β
β
β
+
+
+
=...exp)()( 22110 , (1)
where are firm, constituency, and state-level explanatory variables, which include both
time-varying and time-invariant variables. A description of the proportional hazard model can be
found in Wooldridge (2001). The time of privatization is determined by the first sale of shares in
the firm. Throughout the paper we report the coefficients rather than the hazard ratios from the
estimations.
1
xk
x
To account for firm-specific characteristics that affect privatization we include annual
profits, sales, and the ratio of the wage bill to sales in the specifications, lagged one year. Notice
that in the Cox proportional hazard model the coefficient estimates are robust to any baseline
hazard function , which implies that the specification is robust to any time-specific
common factors, equivalent to controlling for year fixed effects. The regressions also include
fixed effects for 35 industries classified according to two-digit standard industrial classification
codes. Thus, the framework incorporates the fact that in some industries and in some years there
)(
0th
18
are no privatizations. Lastly, the heteroskedasticity-robust standard errors are corrected for
clustering at the state level.
B. Financial Factors
We start by exploring the influence of firm-specific factors on the privatization decision.
In particular, we include the logarithm of Sales as a measure of size, the ratio of Profit to Sales
as a measure of profitability, and the ratio of Wages to Sales as a proxy measure of the size of a
firm’s workforce. From the results reported in column (1) of Table III we note that larger firms
are significantly more likely to be privatized early. The size result is consistent with the
hypothesis that bigger firms face lower information costs and are therefore more likely to issue
equity. The result that privatization is likely to be significantly delayed for firms with a high
wage bill suggests that employees of firms with a large, organized workforce may be more
successful in delaying privatization.
>>>>>>>>>>>>>>>>>>>>>>>>>>>TABLE III HERE<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
C. Political Strength and Competition
Examining the role of electoral concerns in the privatization decision, we note from the
results reported in columns (2) and (3) of Table III that the coefficient of Govt Vote Share is
positive but not statistically significant, while the coefficient of Opposition Vote Share is
negative and statistically significant at the 1% level. For example, for a firm located in a
constituency at the 75th percentile of Opposition Vote Share (equal to 42%) the rate of
privatization is less than one-half of the rate for a firm located in a constituency at the 25th
percentile (equal to 16%), with the other variables evaluated at their regression mean in column
(3). Thus, privatization is significantly delayed in electoral constituencies where the opposition
party alliance has more voter support.
19
Considering the role of political competition next, we note from columns (4) and (5) of
Table III that the estimated coefficients of Vote Share Difference and Abs Vote Share Difference
are positive and statistically significant at the 1% level, indicating that privatization is
significantly delayed in constituencies where the governing and opposition party alliances are in
a close race, as captured by smaller values of these variables. From column (4) we note that the
rate of privatization for a firm located in a constituency at the 75th percentile of Vote Share
Difference (equal to 25%) is more than 1.5 times the rate for a firm located in a constituency at
the 25th percentile (equal to -0.6%), where the lower percentile indicates a more competitive
constituency since the two parties obtain a similar share of votes. From column (5), we estimate
that the rate of privatization is about 1.5 times higher for a firm located in a constituency at the
75th percentile of Abs Vote Share Difference (equal to 31%) compared to a firm located at the
25th percentile (equal to 7.6%).
Facing a trade-off between the locally concentrated costs and the dispersed benefits of
privatization, we find that the government’s decision to privatize some firms and not others
depends significantly on electoral concerns. In the “winner takes all” electoral system in India, a
small difference in vote shares implies that the seat from that district could flip to the other party
in the next election. Consistent with the theory (Lindbeck and Weibull (1987) and Dixit and
Londregan (1996)), our results show that the government delays the privatization of firms
located in more competitive constituencies where the difference in votes received by the
governing and opposition parties (Vote Share Difference) and the absolute value of this
difference (Abs Vote Share Difference) is small.
Rather than rewarding a supportive electorate, we find that the government delays the
privatization of firms that are located in districts where the opposition party has strong support.
We note that the election may be competitive even in districts where the opposition is strong. For
20
example, in constituencies where the opposition party vote share is in the 75th percentile of the
sample, these parties won just 42% of the vote, or less than a majority (Table II).
In addition to electoral factors, we investigate the influence of location-specific
demographic characteristics such as state-level income (Ln Per Capita Income) and growth
opportunities (Per Capita Income Growth, the annual change in per capita income) on the
decision to privatize. At the electoral district level, we consider the literacy and urbanization
rates in a 10 km radius around the firm’s main operations. From the results reported in Table III
it appears that the rate of privatization is significantly higher in districts with a more literate
population, suggesting that educated voters may favor reforms. Privatization is also significantly
delayed in more urban districts, which indicates a stronger presence of organized labor in
industrialized urban areas rather than agricultural rural districts. The political results are robust to
these regional differences in socio-economic characteristics.
The government may also delay privatization in a region with many government-owned
firms because there will be more workers opposed to privatization in that region. To investigate,
we consider the relative size of a firm in an electoral constituency with the variable Firm
Importance, the ratio of a firm’s sales to the total sales of all government-owned firms in the
region. As reported in Table III, the estimated coefficient of this variable is negative but not
statistically significant.
>>>>>>>>>>>>>>>>>>>>>>>>>>>TABLE IV HERE<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
D. Political Patronage
If a politician with jurisdiction over a firm is elected from the same state where the firm
is located he may be reluctant to privatize because he is likely to have more supporters in his
home state. To test this hypothesis, we identify the cabinet minister in charge of each firm for the
21
years 1991 to 1995 and 1999 to 2002, and compare the minister’s home state to the state where
the firm’s main operations are located.10
The results are presented in Table IV. In Panel A, we describe the tenure of the Congress
government (1991 to 1995), where the home state of the cabinet minister in charge of the firm
matches the state where the firm’s main operations are located in 15 cases. In Panel B, we
present the results for both the Congress and the BJP governments and find that the home state of
the cabinet minister in charge of a firm matches the state where the firm’s main operations are
located in 45 cases. The results show that no firm located in the home state of the Cabinet
minister in charge of that firm is ever privatized. The correlation between the incidence of
privatization and the match between a firm’s location and the minister’s home state is negative
and statistically significant at the 5% level. These results suggest that political patronage plays a
significant role in the privatization decision. Note that regression analysis is not possible because
of the lack of heterogeneity.
>>>>>>>>>>>>>>>>>>>>>>>>>>>TABLE V HERE<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
V. Robustness Checks
A. Political Variables at Different Distances
We check the robustness of the regression results by constructing the political variables at
varying distances around the main operations of the firm. In particular, we construct all the
political variables for electoral constituencies located within a 0, 5, 25, 50, and 100 km radius
around the main operations of the firm, and at the state level. From the results described in Table
V we note that the political competition results are robust to these alternative measures. Note that
here and in the robustness checks described below, the results are similar to those reported in
Table III for the other political measures. We provide the full set of tables in the Internet
Appendix.
22
B. Role of Election Years, Regional Communist Parties, and Regional Governments
If politics plays an important role, then the privatization decision may be affected by the
timing of elections. In Table VI, column (1) we estimate an exponential hazard regression to
investigate the presence of political business cycles with Election Year, a dummy variable that is
equal to one in the year of an election.11 Note that we control for government fixed effects in this
specification. The results suggest that privatization is significantly delayed in the year of an
election, while political competition remains significant at the 1% level. These results provide
additional evidence that privatization is politically costly, and the government seeks to minimize
the negative electoral impact by avoiding it in election years.
>>>>>>>>>>>>>>>>>>>>>>>>>>>TABLE VI HERE<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
Since the communist parties in India have consistently opposed privatization, we
investigate whether the strength of these parties in a constituency influences the privatization
decision. The results are reported in column 2 of Table VI. Communist Vote Share, defined as
the proportion of votes won by the Communist parties in all electoral districts within a 10 km
radius around the firm, does not have a statistically significant impact. This may be attributed to
the absence of broad-based support for Communist parties across the populace - these parties
received just 8% of the total vote on average across all elections. The coefficient of Vote Share
Difference retains its sign and significance, indicating that these results are not a proxy for the
influence of communist parties.
To investigate whether the federal government’s decision to privatize is influenced by the
government in power at the state level, we include a dummy variable, State Assembly Majority,
which is equal to one if the governing party in the federal parliament is also the governing party
in the state legislative assembly. From the results reported in column 3 of Table VI we note that
23
State Assembly Majority has a negative sign, although the coefficient is not statistically
significant.
>>>>>>>>>>>>>>>>>>>>>>>>>>>TABLE VII HERE<<<<<<<<<<<<<<<<<<<<<<<<<<<<
C. Privatization Method
Starting in 1999 the BJP-led government sold majority stakes and transferred
management control to private owners in 17 firms. To investigate whether the political results
are robust to the privatization method we separately consider control transfer privatizations,
which were all undertaken between 1999 and 2003. In Table VII we report the results from a Cox
proportional hazard specification, where the political variables are constructed using data from
the 1999 federal elections. Consistent with the results obtained using the full sample in Table III,
we find that control transfer privatizations are significantly delayed if the firm is located in a
region where the governing and opposition parties are in a close race as captured by a lower
value of Vote Share Difference.
D. Alternative Specifications
We conduct a number of additional robustness checks, which we report in the Internet
Appendix. To investigate whether the political variables are a proxy for other factors, such as
non-linear firm size effects, we include dummy variables for quintiles of firm sales in the
specification. The political variables all retain their sign and statistical significance. We also
estimate the regressions with the firm-specific variables winsorized a total of 5% to mitigate the
effect of potential outliers and find that the coefficients of the political variables are similar in
sign and statistical significance to Table III. Lastly, we investigate whether the political results
are robust to restricting the sample to the 15 largest Indian states and find that the estimated
coefficients of the political variables retain their sign and are highly statistically significant.
24
VI. Impact of Privatization on Firm Performance
Our results suggest that the financial characteristics of firms have a significant impact on
the government’s decision to privatize. This raises an identification issue for evaluating the effect
of privatization on firm performance. For example, if the government is more likely to privatize
profitable firms, then comparing the performance of privatized firms to firms that remain
government-owned may overstate the impact of privatization on profitability. Our analysis
provides a potential identification strategy using political variables as an instrument for the
privatization decision.
We estimate a two-stage least squares treatment effects regression by pooling data from
the Congress (1991-1995) and BJP years (1999-2003), and using the political competition
measure Vote Share Difference as an instrument for the variable Privatized, which takes the
value of one if the firm is privatized by the government in power.12 The regressions also include
all the firm-specific and demographic controls in Table III, as well as industry and year fixed
effects.13 The control group is firms that have not been privatized. From the first stage probit
regression results reported in Panel B of Table VIII, we note that Vote Share Difference has a
positive and highly statistically significant impact (at the 1% level) on the probability of
privatization.
In the second stage regressions, the dependent variables capture the change in firm
performance during the tenure of a particular government, and are winsorized 2.5% at each tail
to mitigate the effect of outliers. If a firm was privatized by a previous government, it is dropped
from the sample in the subsequent period.
>>>>>>>>>>>>>>>>>>>>>>>>>>>TABLE VIII HERE<<<<<<<<<<<<<<<<<<<<<<<<<<=
25
The results from the second stage of the instrumental variable regression are reported in
Panel A of Table VIII. From columns (1)-(3) we note that compared to firms that remain fully
government-owned, privatized firms experience a significant increase in productivity and
profitability. Specifically, Profits/Sales, Profits/Assets, and Profits/Wages increase significantly
following privatization. Using political variables to address the endogeneity of the privatization
decision to firm characteristics, our results suggest that privatization leads to a significant
improvement in the efficiency and profitability of government-owned firms. Thus, the
instrumental variable analysis allows us to identify the impact of privatization on firm
performance from the endogenous selection of firms based on performance into privatization.
VII. Conclusion
Based on the fact that most privatizing governments sell government-owned firms over
time or not at all, we investigate whether firm-specific factors and the political objectives of the
government are likely to affect the pattern of privatization. Using data on Indian government-
owned firms, which includes both privatized firms and firms that remain fully government-
owned, we use digital geographic mapping techniques to match firms based on their location to
electoral constituencies at varying distances around the main operations of the firms. We find
that the decision to privatize is affected by firm-level financial characteristics and location-
specific electoral considerations.
While the benefits of privatization, such as efficiency improvements, are dispersed across
the population, the costs are likely to be geographically concentrated among a small group, such
as the local employees of government firms. The public too may perceive privatization
negatively as an unequal transfer of public assets to private owners. This could result in a
decrease in voter support for the governing party in the region where the firm is located. The
26
effect of a backlash on electoral outcomes will be greater if the governing party faces a close
race with other political parties in that region.
The results suggest that larger firms and firms with a smaller wage bill are more likely to
be privatized early. We also find that political factors play a major role in the decision to
privatize. In particular, privatization is significantly delayed if the main operations of a firm are
located in electoral districts where the opposition party alliance is stronger, and where the
governing and opposition party alliances face a close race. The evidence also suggests that the
private benefits that politicians obtain from controlling government-owned firms can influence
the decision to privatize. In particular, we show that no government-owned firm located in the
home state of the politician in charge is ever privatized.
Lastly, our work has implications for the literature on privatization that studies the effect
of privatization by assuming (often implicitly) that firms are selected randomly for privatization.
This paper shows that selection for privatization is not random. Using political competition as an
instrument for the privatization decision, we find that the sale of government-owned firms leads
to significant improvements in the profitability and efficiency of these firms.
27
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33
1 (i) For recent surveys of the privatization literature see Megginson and Netter (2001) and Megginson (2005). (ii)
La Porta and Lopez-de-Silanes (1999) among others find that privatization leads to an improvement in the efficiency
of government-owned firms, and Gupta (2005) shows that even partial privatization leads to significant performance
improvements in Indian government-owned firms. (iii) Megginson et al. (2004) argue that share issue privatizations
facilitate the development of capital markets.
2 Pagano, Panetta, and Zingales (1998) investigate the determinants of the decision to go public by private firms. For
a recent survey of the IPO literature see Ritter and Welch (2002).
3 Ahmed and Varshney (2008) discuss why privatization has been difficult to implement in India, whereas other
policies such as stock market liberalization, have not: “Within economic policy… some issues are more likely to
arouse mass contestation than others. Privatization, a change in labor laws, withdrawal of agricultural subsidies...
Either a large number of people are negatively affected in the short run (agriculture), or those so affected, even when
not in large numbers, are well organized in unions (privatization and labor laws). It should now be clear why India’s
decision makers have…achieved limited privatization” (page 22).
4 Describing how the political costs of privatization can lead to delays one Indian Prime Minister noted, “If I do it
[privatization] immediately, I get into trouble. I get trouble from the workers. I get trouble from the political parties.
I get trouble from the general public,” (The Financial Times, 1994).
5 The question of how a government maximizing revenues will sequence the sale of firms is investigated
theoretically in an auction model by Chakraborty, Gupta, and Harbaugh (2006), and empirically using Czech data by
Gupta, Ham, and Svejnar (2008). Governments may also privatize gradually for strategic reasons. For example,
Perotti (1995) argues that governments may retain an ownership stake to signal to investors their commitment to not
implement policies that are adverse to the firm. Lastly, Biais and Perotti (2002) argue that share issue privatizations
may create more support amongst the median voter for the policies of conservative governments.
6 Total employment in the organized sector in 2003 was 27 million workers, about 7 to 8% of the total workforce
(Ministry of Finance (2004)). The organized sector refers to registered companies that are legally required to submit
financial statements.
7 Fiscal year t starts in April of calendar year t and runs through March of calendar year t+1.
8 Gupta (2005) shows that partially privatized Indian firms experience significant improvements in performance
relative to firms that remain fully government-owned, and the improvements are positively related to the amount of
equity sold. In contrast, in a “before-after” analysis of partially privatized Chinese firms, Sun and Tong (2003) find
that returns on sales decrease. However, using a different sample of Chinese firms, Song and Yao (2004) find that
earnings increase following partial privatization.
9 Two short-lived governments sold equity in four firms between 1996 and 1998, including the global depository
receipt issues in international markets of firms in the oil and telecommunications sectors. Since these firms had
previously sold equity between 1991 and 1995 to avoid endogeneity they are not included again in the regression
analysis.
10 If the same minister remains in charge of a given firm, an uninterrupted sequence of the minister’s home state for
that firm is taken as one observation due to the lack of independence across years. We also exclude from the sample
the industries in which no privatizations occur.
11 We estimate an exponential hazard model instead of the Cox proportional hazard model because the Cox model
controls for all the common elements at a given point in time, so it would drop the election year dummy.
12 We follow Wooldridge (2007, page 4) and fit a probit model with Privatized as the dependent variable, and use
the fitted probabilities from this model as an instrument for Privatized in a 2SLS estimation.
13 The regressions include two observations for each firm, one from the Congress period and the other from the BJP
era. The exception is firms that are privatized by the Congress government, which are dropped from the sample in
the subsequent BJP years to avoid endogeneity.
34
Table I. Comparing Privatized and Fully Government-Owned Firms
This table presents sample statistics of the firm-specific financial variables used in the analysis
for fiscal years 1990 to 2004, where fiscal year 2004 runs from April 2004 to March 2005.
Privatized denotes the companies in which the government sold shares during this period. It
includes firm-years until the first time a company sells shares and not after. Sales and Assets are
the annual sales and annual assets of the firm, respectively, and are in millions of Indian National
Rupees. Profit is the annual profit before interest, taxes, and depreciation; Wages is the firm’s
annual wage expenses. All the variables are lagged one year. Firms that do not meet the listing
requirement for all the years except 1999-2003 are excluded from the analysis and only firm-
years for which we have sales, profits, and wages data are included. Standard deviations are in
parentheses. *** indicates significance at the 1% level.
Variables Privatized Fully Government-Owned All Firms
Sales
Mean 28,862*** 6,777 8,910
Standard Deviation (51,935) (23,652) (28,407)
Number of Firm-years 153 1431 1584
Assets
Mean 51,545*** 14,436 18,025
Standard Deviation (114,022) (55,067) (64,103)
Number of Firm-years 153 1429 1582
Profit/Sales (%)
Mean .184 1.301 1.193
Standard Deviation (.189) (59.777) (56.816)
Number of Firm-years 153 1431 1584
Wages/Sales (%)
Mean .140*** .966 .887
Standard Deviation (.162) (9.176) (8.725)
Number of Firm-years 153 1431 1584
Number of Firms 49 191 240
35
36
Table II. Comparing Political Data across Privatized and Fully Government-Owned Firms
This table presents the summary statistics describing the political variables for the five federal
elections held in the fiscal years 1990, 1995, 1997, 1998, and 2003. Privatized denotes the
companies in which the government sold shares during this period. Govt Vote Share and
Opposition Vote Share are the proportion of votes in the most recent federal parliamentary
elections won by the governing party coalition and the opposition party coalition, respectively, in
the electoral constituencies located within a 10 km radius of the firm’s main operations; Vote
Share Difference is the difference between Govt Vote Share and Opposition Vote Share; and Abs
Vote Share Difference is the absolute value of that difference. Standard deviations are in
parentheses.
Variables All Firms Fully Government-Owned Privatized
Govt Vote Share
Mean 0.396 0.395 0.399
Standard Deviation (0.148) (0.149) (0.143)
75t
h
Percentile 0.487 0.489 0.470
25t
h
Percentile 0.372 0.362 0.396
Opposition Vote Share
Mean 0.308 0.310 0.291
Standard Deviation (0.151) (0.148) (0.172)
75t
h
Percentile 0.420 0.420 0.402
25t
h
Percentile 0.157 0.173 0.105
Vote Share Difference
Mean 0.088 0.085 0.108
Standard Deviation (0.228) (0.226) (0.238)
75t
h
Percentile 0.256 0.256 0.301
25t
h
Percentile -0.006 -0.011 -0.006
Abs Vote Share Difference
Mean 0.191 0.191 0.191
Standard Deviation (0.151) (0.148) (0.177)
75t
h
Percentile 0.314 0.312 0.329
25t
h
Percentile 0.139 0.142 0.099
Number of Firm-Years 1579 1426 153
Table III. The Decision to Privatize: The Role of Financial and Political Factors
This table presents results from estimating a Cox proportional hazard regression covering
fiscal years 1990-2004. The firm-specific variables are lagged one year. Ln (Sales) is the log of
annual sales; Profit is annual profit before interest, taxes and depreciation; Wages is the firm’s
annual wage expenses; all are lagged one year. Ln (Per Capita Income) is the log of annual per
capita income in the firm’s state; Per Capita Income Growth is the annual % change in Per
Capita Income; Literacy and Urbanization are the literacy and urbanization rates within a 10
km radius around the firm’s main operations. Firm Importance is the firm’s share in the total
sales of all government-owned firms located within a 10 km radius. Govt Vote Share and
Opposition Vote Share are the proportion of votes in the most recent elections won by the
governing and opposition party coalitions respectively, in the electoral constituencies located
within a 10 km radius; Vote Share Difference is the difference between Govt Vote Share and
Opposition Vote Share; and Abs Vote Share Difference is the absolute value of that difference.
Firms that do not meet the listing requirement except from 1999-2003 are excluded.
Heteroscedasticity-robust standard errors, clustered at the state level, are in parentheses. *, **,
*** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
(1) (2) (3) (4) (5)
Ln (Sales) 0.630*** 0.630*** 0.721*** 0.686*** 0.686***
(0.153) (0.147) (0.134) (0.138) (0.143)
Profit/Sales 0.024 0.024 0.019 0.02 0.022
(0.038) (0.037) (0.039) (0.041) (0.037)
Wages/Sales -3.336* -3.200* -3.169* -3.002* -3.233*
(1.965) (1.744) (1.755) (1.672) (1.905)
Ln (Per Capita Income) 0.304 0.296 0.342 0.312 0.375
(0.499) (0.485) (0.565) (0.534) (0.547)
Per Capita Income -0.261 -0.269 -0.127 -0.199 -0.183
Growth (0.517) (0.540) (0.628) (0.600) (0.620)
Literacy 3.039* 3.052** 2.669*** 2.787*** 2.718**
(1.590) (1.494) (0.995) (1.067) (1.122)
Urbanization -1.866*** -1.931*** -1.351** -1.669*** -1.664***
(0.715) (0.621) (0.639) (0.582) (0.627)
Firm Importance -0.402 -0.444 -0.405 -0.495 -0.464
(0.480) (0.393) (0.401) (0.374) (0.374)
Govt Vote Share 0.967
(1.386)
Opposition Vote Share -2.914***
(0.694)
Vote Share Difference 1.789***
(0.656)
Abs Vote Share Difference 1.680***
(0.554)
Industry FE Yes Yes Yes Yes Yes
Number of Firms 239 239 239 239 239
Number of Firm-years 1579 1579 1579 1579 1579
37
Table IV. The Decision to Privatize: The Role of Political Patronage
This table presents a two-way tabulation and correlation analysis between the decision
to privatize a firm and the home states of the ministers who have jurisdiction over that
firm. It excludes the industries in which no privatizations occur. Each minister-firm
pair is taken as a single observation regardless of the time length the firm remains
under that minister’s jurisdiction. Main Operations in Home State is a dummy
variable that is equal to one if the state where the firm’s main operations are located is
the same as the state from which the cabinet minister who has jurisdiction over that
firm is elected. Privatized is a dummy variable that is equal to one if the firm is
privatized while under the jurisdiction of a given minister. Once a firm is privatized it
is dropped from the sample. Firms that do not meet the listing requirement for all the
years except 1999-2003 are excluded from the analysis. ** denotes statistical
significance at the 5% level.
Panel A: Congress government (1991-1995)
Privatized
Main Operations in Home State No Yes Total
No 133 38 171
Yes 15 0 15
Total 148 38 186
Correlation -0.150**
Panel B: Congress & BJP governments (1991-1995 & 1999-2002)
Privatized
Main Operations in Home State No Yes Total
No 518 46 564
Yes 45 0 45
Total 563 46 609
Correlation -0.081**
38
Table V. Political measures at different distances from the main operations of the firm
This table presents results from estimating a Cox proportional hazard regression covering fiscal years 1990-2004, where the political variables
are measured for all electoral constituencies within different radii around the main operations of the firm. The political variable in the last
regression is constructed using all the electoral constituencies in the state where the main operations of the firm are located, regardless of the
distance. All the regressions include Firm-specific controls (Ln (Sales), Profit/Sales, Wages/Sales) and Demographic Controls (Ln (Per Capita
Income), Per Capita Income Growth, Literacy, and Urbanization at different distances). The variables are defined in Table III. Firms that do
not meet the listing requirement for all the years except 1999-2003 are excluded from the analysis. Heteroscedasticity-robust standard errors,
clustered at the state level, are in parentheses. *, **, *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
Maximum Distance between the electoral
constituencies and the firm 0 km 5 km 25 km 50 km 100 km State
Vote Share Difference 0.964* 1.528*** 1.843*** 1.535** 1.511** 1.540**
(0.547) (0.580) (0.705) (0.699) (0.742) (0.686)
Firm-specific and Demographic controls Yes Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
N
umber of Firm-years 1579 1579 1579 1579 1579 1579
N
umber of Firms 239 239 239 239 239 239
39
Table VI. Election Years, Communist Parties, and Regional Politics
This table presents results from estimating Exponential and Cox proportional hazard regressions
of the government’s decision to privatize, covering fiscal years 1990-2004. Election Year is a
dummy variable that takes the value of one in of the fiscal years 1995, 1997, 1998, and 2003.
Communist Vote Share is the share of votes won by the Communist parties in a 10 km radius
around the firm’s main operations in the most recent federal elections. State Assembly Majority
takes the value of one if the governing party also has majority in the local state government where
the firm is located and the remaining variables are as described in Table III. Firms that do not
meet the listing requirement for all the years except 1999-2003 are excluded from the analysis.
The first regression also includes government fixed effects. Heteroscedasticity-robust standard
errors clustered at the state level, are in parentheses. *, **, *** denote statistical significance at
the 10%, 5%, and 1% levels, respectively.
(1) (2) (3)
Ln(Sales) 0.702*** 0.693*** 0.685***
(0.126) (0.137) (0.138)
Profit/Sales 0.018 0.016 0.02
(0.046) (0.040) (0.041)
Wages/Sales -3.126* -3.049* -2.987*
(1.693) (1.689) (1.666)
Election Year -1.801*
(1.038)
Communist Vote Share 0.87
(1.065)
State Assembly Majority -0.292
(0.718)
Vote Share Difference 1.813** 1.700** 1.756***
(0.705) (0.705) (0.666)
Demographics Controls Yes Yes Yes
Industry FE Yes Yes Yes
Number of Firms 239 239 239
Number of Firm-years 1579 1579 1579
Hazard Model Exponential Cox Cox
40
Table VII. Privatization with the Transfer of Management Control (1999-2003)
This table presents the results from estimating a Cox proportional hazard regression of the
government’s decision to sell majority stakes and transfer management control in 17 firms starting
in 1999. The political variables are constructed using data from the 1999 federal elections and the
variables are described in Table III. Heteroscedasticity-robust standard errors, clustered at the state
level, are in parentheses. *, ** denote statistical significance at the 10% and 5% levels,
respectively.
(1) (2)
L
n(Sales) 0.169 0.167
(0.197) (0.203)
P
rofit/Sales -0.309 -0.280
(0.251) (0.238)
Wages/Sales -3.767 -4.014*
(2.390) (2.180)
L
n (Per Capita Income) 0.202 0.291
(0.442) (0.531)
P
er Capita Income Growth -1.329* -1.371*
(0.734) (0.727)
L
iterac
y
3.557* 5.034*
(2.025) (2.982)
Urbanization -2.663 -3.584**
(1.736) (1.391)
F
irm Importance -0.356 -0.490
(0.893) (0.915)
Vote Share Difference 2.422*
(1.305)
Industry FE Yes Yes
N
umber of Firms 223 223
N
umber of Fir
m
-years 878 878
41
Table VIII. Effect of Privatization on Firm Performance
This table presents the results from a pooled instrumental variable regression to analyze the
effect of privatization on firm performance. Panel A reports results from the second-stage 2SLS
regressions with firm performance measures as the dependent variables. The variable Privatized,
equal to one if the firm is privatized by the government in power, is instrumented by the fitted
probabilities obtained from a probit model, which has Privatized as the dependent variable and is
reported in Panel B. The variable included in the probit model but excluded in the performance
estimates is Vote Share Difference, which is the difference between the proportion of votes in the
most recent federal parliamentary elections won by the governing party coalition and the
opposition coalition, in the electoral constituencies located within a 10 km radius of the firm’s
main operations. The performance change variables measure the change in firm-level
characteristics from the most recent year before the government is elected to the last year it
remains in power. The dependent variables are winsorized 2.5% at each tail. If a firm is
privatized by a previous government, it is not included for performance evaluation in the
subsequent period. Firms that do not meet the listing requirement for all the years except 1999-
2003 are excluded from the analysis. The remaining variables are as described in Table III.
Standard errors are robust to clustering at the state level and all specifications include industry
and year fixed effects. *, **, *** denote statistical significance at the 10%, 5%, and 1% levels,
respectively.
Panel A. Second Stage Regressions with Firm Performance
D
ependent variable Change in
Profits/Assets Change in
Profits/Sales Change in
Profits/Wages
P
rivatized 0.415** 0.402** 2.480**
(0.170) (0.183) (0.981)
L
n(Sales) -0.088*** -0.110** -0.435
(0.034) (0.046) (0.285)
P
rofit/Sales -0.077** 0.107*** 0.290***
(0.038) (0.021) (0.103)
Wages/Sales 0.255** 0.479*** 1.216***
(0.102) (0.090) (0.335)
Constant 1.420* 1.654** 7.005**
(0.751) (0.769) (3.349)
Demographic Controls Yes Yes Yes
Industry and Year FE Yes Yes Yes
N
umber of Fir
m
-years 203 200 203
42
43
Table VIII continued
Panel B. First Stage Probit
D
ependent variable Privatized
Vote Share Difference 2.755***
(0.596)
L
n (Sales) 0.885***
(0.159)
P
rofit/Sales 0.063
(0.194)
Wages/Sales -1.277
(1.990)
Constant -11.410**
(4.946)
Demographic Controls Yes
Industry and Year FE Yes
Figure 1: Electoral District Map of India
44
Figure 2: Electoral Constituencies at the Firm Level
The figure describes how the political variables are constructed at varying distances around the
location of firms in one state.
45
46
Figure 3: Relationship between Frequency of Privatization at the Electoral Constituency
Level and Political Variables
The graph describes the relationship between the frequency of privatization in electoral
constituencies within 10 km of the main operations of all firms, constructed as the number of
privatized firms/number of government-owned and privatized firms in the region. The data is for
the fiscal years 1991-1995. Industries in which no firms are privatized and firms that do not meet
the listing requirement are excluded from the analysis.
0.2 .4 .6 .8 1
Frequency of Privatization
-.4 -.2 0.2 .4 .6
Difference in Vote Shares of Governing and Opposition Parties
... This dataset contains …nancial statements and other basic …rm-level information (e.g., the …rms'names, addresses, registration types, industry classi…cations, and the number of employees) for all SOEs as well as private …rms above the revenue threshold of 5 million RMB in the manufacturing sector. 21 We show its descriptive statistics in section 4.1 and further details in Appendices A.1 and A.2. ...
... That is, a new …rm ID is created and assigned to the privatized entity. If a researcher does not reconnect these multiple IDs across years, the raw 21 Only 57 (less than 0.1%) of non-private …rm-years fall under this threshold in our …nal sample. 22 "Back to business: Special Report on Business in China," September 12, 2015. ...
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