Politics, State Ownership, and Corporate Investments

Abstract

We document a political cycle in the investment decisions of state-owned enterprises (SOEs) by using the constitutionally mandated election schedule in India as a source of exogenous variation in politicians’ incentive to cater to voters. Using a project-level investment database, we find that SOEs announce more capital expenditure projects in election years, especially in infrastructure, and in districts with close elections, high-ranking politicians, and left-wing incumbents. SOE projects in election years have negative announcement returns, suggesting a loss in shareholder value. These patterns are not seen in nongovernment firms or in off-election years. (JEL G31, G38, D72, D73, P16)

Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

State-owned enterprises (SOEs) compose a substantial fraction of the corporate sector in both developing and developed countries.¹ In OECD countries alone, SOEs employ over 6 million workers and have a combined value close to

2 trillion, with approximately 76% of the total value concentrated in nonfinancial sectors (Christiansen 2011). An intense academic debate centers on whether activities conducted by SOEs are better run by the private sector and, specifically, considers the decisions by several economies to denationalize the real sector. ² According to the theories on the politics of government ownership, SOEs are less efficient than private firms, because they pursue political objectives at the cost of firm value maximization (Shleifer and Vishny 1994; Shleifer 1998; Stiglitz 1989, 1994; Laffont and Tirole 1991; Tirole 1994). However, empirical evidence on what drives the efficiency wedge between SOEs and private firms remains ambiguous.³ In this paper, we provide micro evidence of politically motivated investments by nonfinancial SOEs.

The key econometric challenge in evaluating the role of political influence on SOE investment is in obtaining the counterfactual investment behavior in the absence of political interference. To circumvent this issue, we exploit the timing of elections in India as a source of exogenous variation in politicians’ incentives to influence SOE investments and track investments by both SOEs and nongovernment firms (placebo group) around election years. This empirical design comprises two novel components. First, the schedule of state and national elections in India is constitutionally mandated to be held once every 5 years. We use this schedule to instrument for actual elections, thus alleviating the concern that the timing of elections is endogenously determined by other economic factors.⁴ Moreover, state elections are staggered across the years, allowing us to exploit the variation in different 5-year cycles across states.

Second, we use a rich and unique project-level data set of capital investment projects announced in India by both SOEs and private firms. The data include the location of the investment, the industry, the cost, the identity of the promoters (owners), the project status (whether it was just announced, whether it is currently under implementation, whether it is completed, or whether it has stalled or been abandoned), and various dates associated with the project’s announcement and implementation. The announcement dates allow us to track the stock market announcement effects of the projects and provide an indication of the net present value (NPV) of these investments. We merge our project-level data with hand-collected district-level political variables from national and state elections held in India. Our panel data cover 18,981 investment projects announced over a period of 15 years (1995–2009) including 4 national and 93 state elections in 435 (594) national (state) electoral districts.

We see important differences in the nature and timing of the projects announced by SOEs versus private firms. In univariate statistics, SOE projects, on average, take a significantly longer time to implement and complete compared with projects by nongovernment enterprises. They are also larger in size and less likely to be stalled or abandoned compared with private firm projects. Focusing on election years, projects announced by SOEs are 17% larger compared with projects announced in off-election years. We see no difference in the likelihood of being abandoned or the time taken for completion for SOE projects announced in election years versus off-election years.

In multivariate analyses controlling for location fixed effects, we find that SOEs are not only more likely to announce projects in election years relative to off-election years but also announce a greater number of projects (“pork-barrel spending”). There is a 27.5% increase in the number of projects announced by SOEs owned by the local state governments (state SOEs) and a 17% increase in the number of projects announced by SOEs owned by the central government (central SOEs) in a district in election years. We find no such patterns in the placebo group of nongovernment firms.

We also see that the type of investment differs during political cycles. Election-year SOE projects are more likely to comprise infrastructure projects, such as construction and community development, that are more visible to voters, suggesting a change in the portfolio of SOE investments in election years. Our results point to the existence of a political investment cycle, where politicians manipulate the investment of public enterprises around elections to garner voter support.

Our results are not driven by general policy uncertainty, as measured by the Economic Uncertainty Index in Baker et al. (2016). Rather, our results are specifically driven by political motivations around election years. Policy uncertainty, on the other hand, adversely affects project announcements of nongovernment enterprises, consistent with the findings in Gulen and Ion (2016).⁵

These effects are not homogeneously spread across the country. Instead, SOE projects are more likely to be announced in districts with closely contested elections, specifically in election years. Close districts experience a 51% increase in investments by state SOEs during election years relative to less competitive districts. These results are consistent with theories on “tactical redistribution,” suggesting that to the extent that politicians care about winning elections, the incentives to affect economic variables are stronger when elections are more competitive (Lindbeck and Weibull 1987; Dixit and Londregan 1996).

We also see that political hierarchy and ideology play a role in SOE investments. Investment announcements by SOEs are higher in districts in which the electoral representative is a cabinet minister, and the increase in investment announcements by SOEs is exclusively driven by left-leaning incumbents, whereas some evidence suggests that right-leaning incumbents cut SOE investments.

When we focus on the consequences of election-year political interference, we see a positive impact on the outcome of elections in favor of the incumbent, especially with the announcement of visible infrastructure projects. On average, each additional project announced in a district leads to a 0.9% increase in the incumbent parties’ margin of victory. However, when we look at the stock market’s reaction to project announcements by “partially privatized SOEs,” announcement returns are negative, on average, and are lower for projects announced by SOEs during election years and for projects announced in politically competitive districts.⁶ Moreover, we see that although SOE investment is positively associated with investments by private firms, on average, this association is reduced in election years.

Our paper makes the following contributions. First, our findings relate to the growing literature linking politics to firms’ real decisions. Recent studies on state-owned banks (Khwaja and Mian 2005; Dinc 2005; Cole 2009; Sapienza 2004) and privatization (Dinc and Gupta 2011; Netter and Megginson 2001) find evidence consistent with political influence. Carvalho (2014) finds that, in exchange for government loans, Brazilian manufacturers expand employment and investment in politically attractive regions. In contrast with these studies, our paper focuses on the nonbanking sector to show that politicians can directly distort capital allocation in the real economy by influencing the investment decisions of SOEs. Furthermore, using granular project-level data allows us to disaggregate capital expenditures and show that political manipulation takes the form of changing the composition of SOE investment. SOE projects, on average, are larger and take a significantly longer time to implement and complete but are less likely to be stalled or abandoned than are projects by private firms. Importantly, we make a fundamental distinction between visible expenditures, with benefits or costs that are easily observed and verified by voters prior to an election, and less-visible expenditures, which generate benefits that are less easy to verify. SOEs announce a larger number of public infrastructure projects in election years, especially visible expenditures, such as building of roads, marketplaces, which are likely to sway voters. Our results suggest that it is not just the timing of the actual benefits that matters but also the visibility of those benefits prior to an election.

Second, our detailed project-level data allow us to assess the marginal cost (to a firm’s public shareholders) of politically motivated investment decisions by SOEs. We show that the SOE projects announced in election years, especially in close districts are associated with negative announcement returns, suggesting that the politicization of firm investment may prove detrimental to a firm’s public shareholders. The literature documents limited evidence of this save for Bertrand et al. (2018), who show that politically connected CEOs in France increase hiring and the rate of new plant openings in election years, but the accounting performance in these connected firms is lower than that in nonconnected firms. Furthermore, we show that although SOE projects are positively associated with the private sector investment, on average, this relation is dampened during election years. In this regard, our study complements Ru (2018), who studies the effect of government-directed lending programs in China and shows that government credit to strategic industries helps state SOEs expand but crowds out private firms in the same industry. Although the focus in that paper is very much on sorting out the crowding-out versus crowding-in effects of government credit, our paper focuses on showing that distortions also exist in capital expenditures: in project timing, where projects invest, and the investment type.⁷ The granular nature of our project-level data allows us to comment on how the portfolio of SOE investment projects changes during election years and the economic impact of political distortions using announcement returns. Such an investigation would not be possible with less granular data.

Our findings also add to the recent work on state-induced investment distortions; for example, Borisova et al. (2015) find government ownership to be associated with a higher cost of debt. The sovereign wealth fund (SWF) literature also shows sovereign wealth fund investments to be associated with poor monitoring (e.g., Knill et al. 2012), a SWF discount, and poor long-term operating performance (e.g., Bortolotti et al. 2015a). Karolyi and Liao (2017), on the other hand, study cross-border activities of sovereign acquirers and find that government acquirers are associated with higher announcement returns for the target firms and no higher failure rates. However, these papers do not consider investment decisions surrounding elections.

Third, our paper highlights heterogeneity across political competition (swing states vs. others), political ideology (left-wing vs. right-wing), and political hierarchy (Federal minister vs. others) in the investment projects of SOEs in election years. Our results are consistent with the tactical redistribution view, where politicians target resources toward swing voters during election years through SOE investments, as opposed to the core supporter view, which predicts that politicians target investments to their core supporters during off-election years. Considering political ideology, our results are specific to left-wing incumbents. Some evidence indicates that right-wing incumbents pressure SOEs to cut investment. Few other papers have focused on political ideology, with the exception of Bertrand et al. (2018), who find that although political favors appear to extend across party lines in France, some evidence suggests a partisan effect on the left wing of the political spectrum.

Finally, our paper shows that political considerations differently affect private firms versus SOEs. Julio and Yook (2012) and Durnev (2012) find that political uncertainty surrounding elections leads to a drop in investments and investment sensitivity to stock prices during election years. Our paper shows that SOEs increase investment during election years to target voters.

The privatization literature has shown improvements in newly privatized firms compared with state-owned firms (see Boycko et al. 1993; Megginson et al. 1994; Dewenter and Malatesta 1997, 2001). However, other studies have argued that the newly privatized SOEs in China run by politically connected CEOs still underperform nongovernment enterprises (Fan et al. 2007) and have weaker sensitivity of investment expenditure to Tobin’s q (Chen et al. 2011). In contrast to these studies, our paper uses granular data on investment projects to provide evidence of timing, location, and characteristics of the politically motivated negative NPV investments pursued by SOEs. We are also better able to address endogeneity concerns than are Fan et al. (2007) and Chen et al. (2011) by using election cycles to identify exogenous variation in politicians’ incentives to influence SOE investment.⁸ Overall, our findings complement those in the privatization literature by providing direct evidence of the underlying reason for poor investment efficiency of SOEs: political interference in the investments of these firms for electoral gains.

1. Hypotheses

Since the seminal work of Nordhaus (1975), extensive theoretical research has looked at political business cycles and political budget cycles, where incumbent politicians engage in pre-electoral manipulation of monetary policy and fiscal policy instruments to influence voting behavior (Rogoff 1990). The incumbent stimulates the economy close to election time to increase the probability of reelection, and, at the start of the new term, the inflationary effects of pre-electoral stimulation are eliminated with a recession. Empirically, there has been greater support in the literature for the manipulation of fiscal policy instruments (e.g., taxes, fiscal transfers, government spending) rather than monetary policy around elections (see Alesina and Sachs 1988; Drazen 2001; Brender and Drazen 2005; Cohen et al. 2011).

While both fiscal and monetary instruments can be used to boost economic conditions prior to an election, politicians also can try to influence the economy via the corporate sector. For instance, Shleifer and Vishny (1994) model the interests of politicians in having state-owned firms pay above-market wages and have excess employment to gain greater political support. Several papers have noted how politicians capture state-owned banks to distort credit allocation decisions, especially during election years (e.g., Sapienza 2004; Dinc 2005; Cole 2009; Carvalho 2014; Ru 2018). The focus of our paper is on a political investment cycle to determine whether politicians manipulate the investment decisions of SEOs to influence voting behavior. Although our focus is on the use of micro-level business decisions to further political goals, the underlying spirit of the political business/budget-cycle papers also applies to our setting.

We would expect to see a political investment cycle not just in the number of projects but also in the type of investment projects. If the voters care about employment and infrastructure and reward politicians for improvements in their socioeconomic well-being, then politicians can boost the quality of infrastructure and employment opportunities in the short run by coercing SOEs to undertake new investment opportunities in the run-up to the election. Some evidence in the public economics literature (e.g., Mauro 1998; Drazen and Eslava 2010; Brender and Drazen 2013) suggests that governments change the composition of aggregate government spending to influence voters by focusing on visible expenditures (Kneebone and McKenzie 2001; Brender 2003). Various mechanisms have been proposed in theoretical work to explain why voters respond favorably to visible expenditures including asymmetric information (Rogoff 1990; Robinson and Torvik 2005) and voter myopia (Healy and Malhotra 2009). Thus, we would expect SOEs to announce more visible expenditures such as infrastructure projects during election years.

To the extent that SOEs experience constraints on capital expenditures, such election-year investments will not be homogeneously spread throughout the economy but rather targeted toward certain regions. Distributive politics can be understood by two basic and opposing models. On the one hand, “tactical redistribution” theories suggest that incentives to woo voters will be greater when elections are more competitive, that is, in closely contested areas in which small changes in the share of votes received can substantially change the likelihood of reelection (see Lindbeck and Weibull 1987; Dixit and Londregan 1996). On the other hand, “core supporter” models (Cox and McCubbins 1986) predict that political parties may choose to reward a select group of party loyalists, because parties best know their preferences, whereas swing voters are riskier bets. A higher margin of victory indicates that the incumbent enjoys greater support among the voters (i.e., a higher number of core supporters) and as such weaker competition from opposing candidates. Which of these two models is at play in our context is an empirical question. While these models are static models and do not speak to the dynamics of electoral distribution, ⁹ one would expect that under “tactical redistribution,” politicians pressure SOEs to increase investment prior to the election, whereas the “core supporter” model seems to suggest rewarding supporters after elections. We take this into account when designing our tests.

Finally, we develop predictions on the costs of politically motivated investments by SOEs. First, a large finance literature has documented that political factors, such as uncertainty related to elections and political changes, should be reflected in asset prices¹⁰ and stock market volatility.¹¹ To examine whether these political factors are related to prices in our setting, we perform an event study around the announcement date of the projects by partially privatized SOEs. If election-year investments by SOEs are positive NPV investments undertaken to signal superior administrative competence of the incumbent politicians (Rogoff 1990), we would expect a positive stock price reaction. If, however, the investments by SOEs are negative NPV investments pursued because of political factors and at the expense of firm value (Shleifer and Vishny 1994), we would expect a negative stock market reaction to these projects. We would also expect these reactions to be larger for SOE projects in election years and in closely contested districts (if the “tactical redistribution” theory holds) or districts in which incumbents have greater voter support (if the “core supporter” theory holds).

We also examine whether the increase in SOE investments in election years complements or crowds out private investments. Economic theory suggests that SOE investment reduces the financing available for private investment, thus increasing interest rates and crowding-out private investment (Hicks 1937). Aschauer (1989), however, argues that the crowding-out effect of public investment is outweighed by a crowding-in effect associated with the role of public capital as a productive input and its complementarity with private capital. The social view of government ownership (e.g., Atkinson and Stiglitz 1980) also argues that government funding of high social return projects can have positive externalities. The empirical evidence is mixed with some studies showing that government expenditures dampen corporate investment (e.g., Cavallo and Daude 2011; Cohen et al. 2011), whereas others show it to be complementary (Greene and Villanueva 1990). The India-specific studies using macro data on government expenditures and private investment mostly document a crowding-out effect in the short run (Pradhan et al. 1990; Serven 1996; Mitra 2006; Agarwal et al. 2017). More recently, Ru (2018) argues that both effects are at play: government credits to SOEs in China crowds in private firms in other industries, while crowding-out private firms in the same industry. Using granular investment data from India, we exploit exogenous variation in SOE investment induced by election cycles to examine the relation between SOE investment and private investment in election and off-election years.

2. Data, Key Variables, and Summary Statistics

2.1 Electoral data

Our electoral data, from the Election Commission of India (ECI), cover 4 national elections and 93 state elections held across 30 states over the period 1995–2009. We aggregate all electoral data at the district level by matching electoral constituencies to districts based on ECI’s “Delimitation of Parliamentary and Assembly Constituencies Order” (2008 and 1977), because our data on project location are at the district level. We have 435 (594) districts for national (state) elections in our final sample.

We define all the dependent and independent variables in terms of fiscal years, including state-level real gross domestic product (GDP) growth, which is included as a control variable in our tests. Henceforth, unless otherwise specified, all references to years imply fiscal years.

Our main independent variables are as follows: Election is a dummy variable that takes a value of 1 for the fiscal year (April 1 of year |$t$|-1 to March 31 of year |$t$|⁠) associated with the calendar year |$t$| in which the election took place, regardless of the actual calendar month of the election. Thus, for instance, for any election held during any month of the calendar year 2009, Election takes a value of 1 for the fiscal year beginning April 1, 2008, and ending on March 31, 2009. In the following subsection, we discuss how we align the investment data to these dates.

While elections are held once every 5 years as per the Constitution of India, some elections are called early, typically because of changes in coalition leadership. In our sample, 1 of the 4 national elections and 13 of the 93 state elections were held before schedule.¹² Consequently, the different states have different 5-year election cycles, because, historically, some states have called early elections at varied points in time for various reasons. Figure B1 in the Online Appendix plots both the total number of state elections and the number of unscheduled elections each year. The figure also shows the relationship between the timing of state and national elections. If politicians call for early elections when the economy is doing particularly well, and investments are booming, we may observe a spurious correlation between election years and the number of investment projects announced. Hence, following Khemani (2004) and Cole (2009), we instrument Election using Scheduled, a dummy variable that takes the value of 1 if 5 years have passed since the last state election and 0 otherwise. Section 3.2 provides more details on the instrument.

Our measures of political competition are based on the difference in the share of votes received by the ruling coalition and opposition parties in a district in the previous election (margin of victory). Absolute margin is the absolute value of the margin of victory. A lower value of the absolute margin of victory indicates a more competitive election. Close is a dummy variable that takes a value of 1 if the absolute margin of victory or loss of the incumbent party in the previous election in a district was less than 5% and 0 otherwise. Less contested is a dummy variable that takes a value of 1 if the absolute margin of victory of the incumbent party in the previous election was above the 75th percentile of the entire sample of state elections and 0 otherwise. Thus, if elections occurred in the years 1999 and 2004, we assign the value of Close and Less contested realized in year 1999 to the years 2000 (fiscal year beginning on April 1, 1999, and ending on March 31, 2000) through 2004 (fiscal year beginning on April 1, 2003, and ending on March 31, 2004). Outcomes of the last election are a reasonable proxy for the expected level of competitiveness in the current election because of persistence in election outcomes. In our sample, districts that were closely contested in the previous national (state) election have a 34% (33%) chance of facing a close contest in the current election. Similar proxies have been used by Mian et al. (2010) and Carvalho (2014).

For each state and national election, we also collect data on the name, political affiliation, and share of votes received by all candidates in each electoral constituency, covering over 35,000 electoral contests and 40,000 unique candidates. Data on the members of the ruling party coalition were hand-collected from newspaper articles using the Factiva database. Information on the identity of the members of the federal cabinet, including information on Cabinet reshuffles was collected from archives of parliamentary debates.¹³ To measure political authority, we examine whether the electoral representative holds a ministerial position: Federal minister is a dummy variable that identifies districts in which the Member of Parliament is also a minister in the federal cabinet. Cabinets are often reshuffled before the termination of an electoral cycle, and individuals may lose their ministerial positions because of internal conflicts within the party or as a result of losing favor with the top party leadership. The Federal minister dummy variable captures all such changes.

Table 1 reports summary statistics on key electoral and investment variables for the national elections in panel A and state elections in panel B. The unit of observation is a district-year. For each election cycle, we drop those constituencies where both the winner and the losers were members of the ruling coalition.¹⁴ Doing so leaves us with an unbalanced panel of 5,081 (8,456) district-year observations for the national (state) elections.

Focusing on political variables, panel A of Table 1 shows that the median Absolute margin is 0.104 for national elections, suggesting stiff political competition at the district level. About one-quarter (27.2%) of national electoral district-years are classified as closely contested. Panel B of Table 1 shows that state elections are equally competitive with 37.3% of the state electoral district-years classified as closely contested. The mean value of Scheduled in panel B shows that 17.6% of our observations are for state elections that were held on schedule.

2.2 Investment and financial data

Data on new project announcements are obtained from the CAPEX database, maintained by the Centre for Monitoring Indian Economy (CMIE). CAPEX is updated daily and provides detailed information on announcement dates, locations, costs, sponsor identity, industry classifications, project status, and the expected time of completion for new and ongoing projects announced in India since 1995.¹⁵ This information is collected from multiple sources, including company annual reports, media reports, and government agencies when projects require bureaucratic approval. The database covers any project costing more than Rs. 10 million (⁠

0.15 million).¹⁶ Over 24,000 projects were announced during our sample period (1995–2009).¹⁷

For each investment project in the database, the CMIE identifies the entity with the majority equity stake (primary owner) and all other promoters (other owners) who have an equity stake in the project. Following this, we classify projects as owned by central SOEs (the central government has a majority equity stake), state SOEs (the state government has a majority equity stake), or nongovernment (the private owner has a majority equity stake).¹⁸ The classification of SOEs as central or state SOEs is provided by the CMIE, which we validate using the list of SOEs provided by the Department of Public Enterprise. State and central governments directly influence the decisions of SOEs under their respective control. Consequently, in our tests using national elections, we focus on projects announced by central SOEs, and, in the tests based on state elections, we focus on projects announced by local SOEs.

Our main dependent variables are as follows: Number of projects announced in a district in a fiscal year. By law, the fiscal year in India is from April 1 to March 31 for all firms, and the timing of the dependent variable aligns with the timing of our main independent variable, Election. Thus, for any election held in a calendar year |$t$|⁠, both Election and the Number of projects are defined over the period April 1 of year |$t$|-1 to March 31 of year |$t$|⁠. Other studies, including those of Cole (2009) and Julio and Yook (2012), also use fiscal years to define political and investment variables.

The majority of elections in our sample are held in April and May. Of the 4 national elections, 3 were held during April and May, and 1 was held in September. Of the 93 state elections, only 29 were held in the months September–December. To illustrate the timing issues in detail, we consider the elections in the calendar year 2009. For the elections in April or May of 2009, the Election dummy takes a value of 1 for the fiscal year starting April 2008 and ending in March 2009, and Number of projects is the total number of projects announced by firms in a district during April 2008 to March 2009. The projects in April and May 2009 are counted as part of the next fiscal year, that is from April 2009 to March 2010.¹⁹ For the other elections held during the months September–December 2009, again, the Election dummy and the Number of projects are defined over the period April 2008 to March 2009. Once again, the variables do not include projects announced between April 2009 and the actual election month (September–December of 2009). These projects are counted as part of the fiscal year from April 2009 to March 2010.

We define the Number of projects separately for project announcements by SOEs and by nongovernment firms. Announced is a dummy variable that takes a value of 1 if at least one project is announced in the district in a given year and 0 otherwise. Again, this variable is defined separately for SOEs and nongovernment firms. Percentage of government-owned projects is the ratio of the number of SOE projects announced to the total number of projects announced by all firms in that district in a particular year. We drop all projects with missing dates or districts. Doing so leaves us with a total of 18,981 projects announced during our sample period, of which 1,938 and 3,630 projects were initiated by central and state SOEs, respectively, and 13,413 projects by private firms. Figures B5 and B6 in the Online Appendix present the total number of projects and the total reported cost of all projects announced each year, respectively, with the dotted lines representing the year of the national elections.

Panels A and B of Table 1 show that, on average, approximately 2.623 projects are announced in a national electoral district-year. Of these, central SOEs account for 0.283 projects; state SOEs account for 0.475 projects; and private firms account for 1.865 projects on average. Similarly, in panel B, the mean total number of projects announced in a state electoral district-year is 2.111. Of these, state SOEs account for 0.396 projects; central SOEs account for 0.235 projects; and private firms account for 1.480 projects, on average. The Announced dummy shows that at least one project was announced in more than 50% (42%) of the national (state) electoral district-years.

For a smaller subsample, we also have project cost estimates. Panel A shows that, on average, project costing approximately Rs 1,207.929 million (⁠

17.732 million) are announced in a national electoral district-year, of which central SOEs account for 22.3%; state SOEs account for 14.1%; and the private firms account for the remainder. Similarly, in panel B, the average total investment in a state electoral district-year is Rs 995.723 million (⁠

$\$$

14.617 million), of which state SOEs account for 12.7%; central SOEs account for 22.6%; and private firms account for the remainder. We use two alternative dependent variables based on these cost estimates: Cost ratio, which is the ratio of the total cost of all SOE projects announced in a district to the total cost of all projects announced in the district, and ln(Cost), which is the natural log of one plus the total cost of all SOE projects announced in a district. We don’t present results using these variables as our main specification, because the cost of the project is missing for approximately 20% of the projects in our sample. ²⁰

3. Empirical Results

3.1 Project characteristics

Panel A of Table 2 compares and contrasts the characteristics of projects announced by SOEs (both state and central) versus nongovernment firms. First, our data allow us to identify whether the project is a substantial expansion of an existing plant, a completely new unit, or a minor renovation. Ninety-one percent of SOE projects are new units or substantial expansion units (as opposed to minor modifications or renovations), compared with 99% of private sector projects. Compared with private sector projects, SOE projects are, on average, 23% smaller in size (as seen by ln(Cost)), take longer to complete (3.68 years vs. 2.02 years for private firms), take longer to start following the announcement (1.64 years vs. 0.94 years for private firms), and are less likely to be stalled or abandoned. As an alternative classification, we classify projects as stalled or abandoned either (1) if they have been stalled or abandoned or (2) if more than 10 years have passed since the date of project announcement and the project status shows Announcement or Under implementation. Using this definition, we find that SOE projects are less likely to be stalled or abandoned compared with those announced by private firms.

In panel B of Table 2, we compare the nature of projects announced in election years versus off-election years. Focusing on Columns 1–3, election-year SOE projects are, on average, 17% larger and 3% more likely to be a new unit or substantial expansion compared with off-election- year SOE projects. We find no difference between election and off-election- year projects for the time taken to completion, time to start, or the likelihood of being stalled (using our primary definition). However, using our alternative classification, we find that a higher fraction of election-year projects (3%) announced by SOEs are stalled.

Overall, these findings are in line with our thesis that SOEs increase investments during election years to cater to voter preferences. We also see differences between election and off-election years for nongovernment firms. Election-year projects announced by nongovernment firms are 36% smaller and 1% less likely to be new units or substantial expansions. This finding is consistent with the evidence in Julio and Yook (2012) and Gulen and Ion (2016), who find that political and policy uncertainty is associated with a drop in firm investment. Along similar lines, our univariate statistics suggest that nongovernment firms refrain from substantial expansions during election years and typically announce smaller routine projects.

In panel C, we explore how the portfolios of SOE projects in election years differ from those of nonelection years. We classify projects based on the National Industry Classification (NIC) codes into Construction, Community Development (parks and recreation centers), Education, Health and Social Work, Transport, Storage and Communications, Utilities (includes Electricity, Gas, and Water), Hotels and Restaurants, Manufacturing, Mining, Real Estate Leasing, and Miscellaneous (includes Wholesale, Retail, and Financial Intermediation). We find that in election years, SOEs announce significantly more projects in Construction and Community development, which are visible expenditure projects that could influence voters. In off-election years, this composition shifts to projects in Education, Health and Social Work; Transportation, Storage and Communications; Hotels and Restaurants; and Real Estate Leasing. We find no difference in the number of projects announced in the other sectors.

3.2 Election cycle and investments

Panel A of Table 3 presents results for national elections, and panel B presents results for state elections. For the average district in our sample, the number of central SOE projects announced during election years increases by 17%.²¹ This finding is consistent with the idea that politicians manipulate investments of SOEs to serve their own political interests. In Column 2, we don’t find the same pattern in a placebo group of nongovernment firms. The estimates from Column 3 show that central SOEs announce a greater number of projects during elections relative to projects announced by other firms. Column 4 shows that the likelihood of a project being announced in a district by central SOEs is higher for election years. Again, this effect is not observed for projects announced by nongovernment firms in Column 5. ²² In Column 6, we find that the fraction of the total value of investments announced in a district by central SOEs is 1.5% greater in election years in absolute terms. This represents a 6.7% relative increase in the fraction of district-level investments by SOEs in the election year.²³ We obtain qualitatively similar results in Column 7 using ln(Cost) of SOE investments as the dependent variable.

Most of the elections are held on time, so Scheduled is a strong predictor of the actual occurrence of elections and clearly satisfies the inclusion restriction. The coefficient on Scheduled in the first stage regression (not reported here for brevity) is 0.95, and the first-stage Cragg-Donald Wald F-statistics reported in the last row confirms the strength of our instrument. Further, it is reasonable to assume that the schedule of elections has no bearing on investments of SOEs other than through the timing of the actual elections and therefore satisfies the exclusion restriction.

Panels B and C of Table 3 confirm the results in panel A for state elections. State SOEs announce a greater number of projects during election years, but we find no evidence of a political investment cycle for nongovernment firms. The coefficient estimates from both the ordinary least squares (OLS) (panel B) and instrumental variable specifications (panel C) are similar, suggesting that endogeneity in the timing of elections (early elections) does not bias our estimates. For the average district in our sample, the number of projects announced in a district during election years increases by 27.5%.²⁵ The results are qualitatively similar using other dependent variables in Columns 3, 4, 6, and 7. Again, we do not observe a similar pattern in the investments of nongovernment firms in Columns 2 and 5.

We undertake several additional tests to check the robustness of our results. First, we repeat our baseline tests at the monthly frequency in Table A1 of the appendix and find qualitatively similar results. Further, Figures A1 and A2 plot the times-series of the number and total value of projects announced by State SOEs and Nongovernment firms around the month of election. Second, our results are robust to repeating all our tests on off-national-cycle state elections as seen in Columns 1–3 of Table A2 of the appendix. Third, our results are not driven by unscheduled elections as seen in Columns 4–6 and 7–9 of Table A2 of the appendix, where we restrict the subsample to scheduled and unscheduled elections, respectively, suggesting that our estimates are primarily identified through a variation in timing of elections held on schedule. Finally, Table A3 of the appendix shows that we find qualitatively similar results when we repeat our analysis on a smaller sample of partially privatized SOEs for which we are able to examine announcement effects in Section 3.6.2.

Overall, these tests further strengthen the causal interpretation of our findings. Going forward, we mainly report results for state elections, unless otherwise specified, because the staggered nature of state elections across time allows for a cleaner identification.

3.3 Political competition, patronage, and investments

That the Election X Close coefficient in Column 1 of Table 4 is positive and significant indicates that, on average, state SOEs’ investments are especially greater in closely contested districts during election years. These effects are also economically significant, translating into a 51% increase in investments announced by state SOEs in closely contested districts in election years relative to other districts.²⁶ We find no evidence of the impact of political competition on investment decisions of nongovernment firms in Column 2. The estimates from Columns 3 and 4 show that both the percentage and the value of projects announced by SOEs relative to the investments made by other firms are higher for closely contested districts in election years. Finally, Column 5 shows that total cost of projects announced by SOEs is also greater in election years.

To examine the “core supporter” hypothesis, we rerun the above Equation (3) but replace Close with Less contested (LC). Focusing on the interaction term (Election X LC), in Columns 6, 9, and 10 of Table 4, we do not find empirical support for the “core supporter” hypothesis. Moreover, the negative and statistically insignificant coefficient on LC in Column 6 suggests that fewer SOE investments are announced in less contested districts during off-election years as well. This is likely because SOEs have limited capital to allocate across projects. By revealed preference, the incumbent party finds it optimal to target election-year investments toward closely contested regions as compared to areas in which they already enjoy greater electoral support. Our results are robust to including state-year fixed effects as shown in Table A4 of the appendix.

Overall, the results in this section provide support for the Tactical Redistribution hypothesis, where politicians target voters in closely contested districts by announcing a greater number of investment projects by SOEs in election years.²⁷

3.4 Intertemporal dynamics of SOE investments

Column 1 of Table 5 indicates that investment announcements by SOEs are lower in all off-election years relative to election years. These firms likely overinvest during election years, a choice that leads to a decrease in investments in the years following elections. The coefficient estimates vary from 0.085 (1 year before elections) to 0.203 (1 year after elections), which in percentage terms implies that SOE project announcements are 21% (51%) greater during election years as compared to the year before (the year after) elections. Focusing on nongovernment enterprises in Column 2, we find that the difference in the number of investments announced in election years and those announced 1 year before, 2 years before, and 1 year after is statistically indistinguishable from zero. However, we find that investments of nongovernment firms peak in the middle of the election cycle. This suggests that nongovernment enterprises conservatively invest immediately after and before elections, probably because of policy uncertainty (Julio and Yook 2012; Gulen and Ion 2016). Column 3 shows a 2% to 5% drop in the percentage of projects announced by state SOEs in off-election years. This finding is economically significant given that no project is announced by state SOEs in a median district-year and the mean value of the percentage of projects announced in a district-year is 10%.

Panels A and B of Table A6 of the appendix provide evidence against the core supporter model. Panel A shows that there is no significant difference in investments between closely contested districts and other districts in most off-election years except one year before an election, and panel B shows that the difference in investments between less contested districts and other districts is negative and statistically significant for all off-election years, except 1 year after an election.

Overall, the results in Table 5 show evidence of a political investment cycle, where the number of capital investment projects announced by SOEs increases in election years. The dynamic results also lend further support for the tactical redistribution hypothesis. We find no evidence of a political investment cycle in the investments of nongovernment firms.

3.5 Additional discussions and robustness tests

3.5.1 Policy uncertainty

To explore whether SOE investments are made in response to overall economic uncertainty, rather than just political manipulation around election years, we replace the Election dummy variable in our main specification in Table 3 with the Policy uncertainty index from Baker et al. (2016) for India in Table 6. While the election variable measures political uncertainty arising around election years, the policy uncertainty index measures the overall level of uncertainty in an economy arising from tax changes, monetary policy, and regulatory uncertainty. The policy uncertainty index for India is available at the national level from 2003. Hence, we restrict our sample to the period 2003–2009 and consider only national elections in these tests.

In Columns 1 and 2, we examine the impact of policy uncertainty on project announcements by SOEs in the current year and 1 year after, respectively, and find no impact of policy uncertainty on SOE investment announcements. In Column 3, when we control for both election dummy and policy uncertainty, we see that while policy uncertainty is negatively associated with SOE investment announcement, election dummy is positively associated with SOE investment announcement. We do not include the election dummy with additional lags of the policy uncertainty variable because, given the small sample of national elections, the Election dummy variable is fully determined once we include contemporaneous and lagged values of policy uncertainty. Columns 4–6 repeat these tests for projects announced by private firms. We find that policy uncertainty is negatively associated with investment announcements by private firms, both in the contemporaneous year in Column 4 and 1 year thereafter in Column 5. This is consistent with Gulen and Ion (2016), who document that policy uncertainty affects the investments of U.S. firms up to eight quarters into the future.

Overall, this section shows that SOE investments are sensitive to political interference, especially during election years, whereas investments of nongovernment firms are more sensitive to policy uncertainty, where the real effects last beyond election years.

3.5.2 Political authority and ideology

Governments’ budget constraints make it unlikely that politicians reward their supporters across all districts uniformly. Given finite resources and limited investment capital, incumbent parties may cherry-pick districts to suit the interests of their main leaders who rank higher up in the political hierarchy. To examine the importance of political authority, we use a specification similar to that of Equation (3), but we replace the Close dummy with the Federal minister dummy. These tests are carried out for national elections because we only have the list of ministers at the Federal level. Sufficient variation in the identity of ministers (and, therefore, in the districts associated with Federal ministers) allows us to exploit within-district variations in our tests.

Column 1 of Table 7 shows that the number of projects announced by central SOEs is 43% greater in the home districts of Federal ministers during off-election years as compared with other districts.²⁹ In Column 2, we do not observe a similar pattern for investments by private firms. Our results in Column 3 using the proportion of total projects announced by SOEs are consistent with the results from Column 1. Overall, these results suggest that SOEs announce a greater number of projects in the home district of ministers over the entire election cycle (both in the election and in off-election years).

Table 8 examines whether the political leaning (left/right) of the incumbent party affects SOE investments. India has a multiparty system but two major parties at the national level: the center-left Indian National Congress (INC) and the center-right Bharatiya Janata Party (BJP). In panel A, we focus on national elections and find that the increase in SOE investments is 67% greater during election years when the incumbent party is left leaning. The Election dummy itself has a negative and significant coefficient, indicating that, in districts with right-wing incumbents, the number of projects initiated by SOEs declines during an election year. In panel B, we focus on state elections. At the state level, a large number of smaller regional parties cannot be clearly classified as left-leaning or right-leaning parties, and, hence, we restrict our sample to those parties that are clearly identified with a left- or right-leaning political ideology. Here again, we find that left-leaning incumbents are associated with a greater increase in investments during election years and the Election dummy is negative, but not significant.

In summary, these results suggest that our results are specific to left-wing incumbents, and some evidence indicates that right-wing incumbents pressure SOEs to cut investment. Our results are consistent with the findings in Bertrand et al. (2018), who find that although political favors appear to extend across party lines in France, some evidence suggests a partisan effect on the left wing of the political spectrum.

3.5.3 Nature of election-year investments

The multivariate tests in Table 9 show that both the nature and the portfolio of SOE projects in election years differ from those of nonelection years. In panel A, controlling for industry fixed effects, we find that election-year SOE project announcements are more likely to be new units or substantial expansions and are larger compared with those announced by private firms. To the extent that labor and capital are complements, larger projects and new units could be used by politicians to signal the promise of greater employment opportunities in the future.

Next, we focus attention on the components of SOE investment that are most visible to the public and hence most likely to be used with political goals. For instance, plenty of anecdotal evidence suggests that politicians announce investment projects for “ribbon-cutting” publicity just prior to elections.³⁰ For instance, a few months preceding the 2019 elections, the incumbent Prime Minister of India inaugurated and announced a number of visible infrastructure projects in his home district of Varanasi (Times of India 2018). Theoretically, Rogoff (1990) argues that voters have only imperfect information about the competence level of each politician and hence extract information about the competence of an incumbent running for reelection from “visible” expenditures with benefits or costs that are easily observed and verified by voters prior to an election. The incumbent on his part signals competence by increasing expenditures in these “visible” programs. Other theoretical papers, such as those by Robinson and Torvik (2005) and Mani and Mukand (2007), use this framework to show why investment projects with negative social surplus, “white elephants” may be preferred to socially efficient projects if the political benefits are large compared to the surplus generated by efficient projects. Mani and Mukand (2007) argue that some expenditures are less visible either because the public good outcomes are intrinsically more difficult to directly observe (e.g., malnutrition in families) or because of the public good’s complexity where a large number of factors apart from government competence affects its outcome (e.g., degree of literacy in a population) and hence many have low visibility. We categorize projects in Construction and Community Development (e.g., parks and recreation centers) as visible expenditures and hence the most likely to be manipulated by the government for electoral purposes and the rest of the projects in other industry sectors as less-visible expenditures.

In Column 1 of panel B of Table 9, we find that election-year SOE projects are more likely to be announced in the visible industries. In Columns 2–12 of panel B, we find that relative to nongovernment firms, SOE projects are, on average, more likely to be in Construction, Education, Health & Social Work, Utilities, and Transport & Communication. That is, SOE projects focus on public infrastructure, whereas private firms mostly invest in the Hospitality (Hotels and Restaurants) and Manufacturing sectors. More importantly, we observe a shift in the portfolio of projects announced by SOEs in election years. Focusing on the Election |$\times $| SOE coefficient, SOE projects in election years are more likely to be announced in the Construction sector and less likely to be announced in Education; Health & Social Work; Transportation; Storage; and Communication.

Because these tests are at the project level, they capture the relative likelihood of a project being announced by an SOE in one industry as compared to other sectors and relative to nongovernment firms. However, to examine whether the level of investment in the visible and less-visible industries actually changes and whether these composition effects affect election outcomes, in Table A7 of the appendix, we repeat our baseline tests reported in panel C of Table A7 across visible and other industries. We find that both the total number of projects and the total cost of projects announced by SOEs in visible industries are greater during election years. In contrast, the number of projects and the total cost of projects announced by SOEs in less-visible industries are no different across election and off-election years. Looking at the ratios, we find that the relative percentage of both the number and cost of visible investment projects is greater during election years. Thus, while there is a decrease in the likelihood of these less visible investments by SOEs during election years, the absolute level of investments in these industries is unchanged.

3.6 Politically driven investments and outcomes

In this section, we examine the consequences of political interference. First, we examine whether election-year project announcements by SOEs favorably affect the outcome of elections for the incumbent. Next, we study the stock market’s reaction to project announcements to evaluate whether politically motivated investments enhance or destroy firm value. Finally, we see whether SOE investment crowds out private sector investment or whether there are positive externalities that crowd in investment by other firms.

3.6.1 Election outcomes

In Table 10 we restrict our sample to election years and look at the number of electoral constituencies in a district in which the incumbent party won the elections and their margin of victory in a district. To the extent that past election outcomes affect current election outcomes, we control for the margin of victory for the incumbent in the previous election.

Columns 1 and 2 show that election-year SOE project announcements positively affect both the number of constituencies in which the incumbent party wins and the margin of victory. Each additional project announced leads to 0.224 additional constituencies won (an average increase of 7.9%) and a 0.9% gain in the margin of victory for the incumbent party. Thus, if we compare the two districts associated with the same margin of victory for the incumbent during the previous election, election results will be more favorable for the incumbent in the district with higher current SOE investments.

Panel B of Table 10 differentiates between the effect of election-year visible versus other project announcements by SOEs on the outcome of elections. Election-year SOE visible project announcements positively affects both the number of constituencies in which the incumbent party wins and the margin of victory. Each additional visible project announced leads to 0.284 additional constituencies won (an average increase of 10%) and a 1.4% gain in the margin of victory for the incumbent party. We do not find a similar effect for SOE projects announced in other industries. Thus, the changing composition of election-year SOE investment toward more visible projects is associated with favorable outcomes for the incumbent politicians.

Table A8 of the appendix examines whether the positive impact of SOE investments on election outcomes is especially greater in Close districts. The estimates, although economically significant, are statistically indistinguishable from zero.

Overall, our results here confirm that politicians benefit from election-year-targeted investments.

3.6.2 Are political investments costly?

In this section, we study the stock market’s reaction to project announcements by focusing on the projects announced by partially privatized central SOEs.³¹ Our tests at the firm’s project-year level.³²

We use Excess returns and Abnormal returns around the day of the project announcement as a measure of the market’s perception about these investments. Abnormal returns are estimated as the difference between the return on a firm’s stock and the return predicted by the capital asset pricing model (CAPM) with the S&P Nifty as the benchmark market portfolio. The CAPM is estimated using daily returns on the firm’s stock and the S&P Nifty over the preceding 3 months. Excess returns are measured as the difference between the return on a firm’s stock and the return on the benchmark S&P Nifty index over the same time horizon. If these investments are primarily driven by political factors without regard to firm value, then we expect these project announcements to be associated with negative stock returns.

In Table 11, the event windows measured in days around the project announcement date are [-1,1], [-3,3], and [-15,15] in Columns 1, 2, and 3, respectively. Column 1 shows that relative to private firms, 1-day announcement returns are 1.5% lower for SOE projects announced in election years compared to off-election years. These results are starker over a 3-day (Column 2) and 15-day (Column 3) event window, suggesting some information leakage and, consequently, a negative stock reaction even prior to a formal project announcement.

To determine whether the negative stock price reaction is smoothed out over a longer duration, in Figure A3 we trace the daily cumulative excess returns for election-year projects over a 30-day period following the announcement. The figure shows that the cumulative excess returns are negative and continue to fall over the 30-day period for projects announced by SOEs. In contrast, the election-year projects of private firms are associated with positive cumulative returns that continue to rise over the 30-day period. When we focus on a 1- or a 3-year event window in Columns 4 and 5, respectively, of Table 11, we find that SOE projects announced in election years are associated with negative returns over the longer windows. We obtain similar results with Abnormal returns as the dependent variable.³³

We obtain similar results when we differentiate between projects announced in closely contested districts and those announced in other districts in Table 12. Specifically, compared with nongovernment firms, 1-day announcement returns are 1.5% lower for projects announced by SOEs in closely contested districts relative to projects announced in other districts. The results are qualitatively similar over longer horizon event windows. In unreported tests, we do not observe a significant effect of the size of the government stake on the sensitivity of firm investments to elections.³⁴

The estimated deadweight costs to shareholders of SOEs incurred because of politically motivated investments in India ranges anywhere between

2.4 billion to

$\$$

13 billion for the three elections (1999, 2004, and 2009), assuming a |$-$|1.5% stock price reaction, and between

$\$$

13 billion to

$\$$

69 billion, assuming a |$-$|8.0% stock price reaction. These measures are only a lower-bound estimate of the cost of political influence, because politically motivated projects could be announced even in off-election years, and we don’t focus on other channels through which politicians influence SOEs, such as hiring and firing decisions or procurement contracts.

However, these results could also reflect a timing issue, where all the negative NPV projects were announced in election years and the total amount of projects over 5 years is the same as in the counterfactual. To examine whether this timing manipulation has real economic consequences, in Table 13, we focus on whether SOE investment crowds in or crowds out private investment. Specifically, in Columns 1–4, we examine the impact of SOE project announcements on current project announcements by private firms (Column 1) and projects announced 1, 2, and 3 years after (Columns 2–4). The coefficient on the Number of SOE projects is positive and significant, suggesting that off-election-year SOE projects are positively correlated with investments by private firms. However, the negative and statistically significant coefficient on the interaction term suggests that the positive association between SOE investment and private firm investment is reduced in election years. As a robustness check, in Columns 5–8, we examine whether SOE project investments also affect the implementation of private sector investments. Again, we find that although SOE investments are positively associated with the number of private sector projects being implemented in both the short and the long run, this effect is reduced in election years.³⁶

Overall, our results show that while SOEs make less efficient investments than private firms, SOE projects do have some positive externalities in that they are positively associated with more private sector investment on average. However, this relation is significantly dampened during election years.

4. Conclusion

We examine the role of political influence on the investment decisions of state-owned enterprises by exploiting the timing of elections in India as a source of exogenous variation in politicians’ incentives to attract voters. Using a unique project-level data set of capital investments over the period 1995–2009, we compare the investment behavior of both SOEs and nongovernment firms in different districts of India across election and off-election years. We document compelling evidence of a political investment cycle in the corporate investment decisions of state-owned firms. Controlling for district and year fixed effects, the number of projects announced by government firms (depending on whether they are central or state SOEs) during election years increases by 17%–27.5%. We do not find a similar pattern for investment announcements by nongovernment firms. Further, these effects are particularly stronger for districts in which the previous election was closely contested. We also document a change in the composition of SOE investments: SOEs announce more visible expenditures, such as construction projects, in election years than in other years.

The project-level data also allow us to examine the consequences of the political manipulation of SOE investments. Election-year project announcements, especially visible expenditures are associated with positive election outcomes for the incumbent. While there are some positive externalities from SOE investment in being positively associated with private investment, consistent with SOEs forgoing value maximization to advance political goals, markets negatively react to projects announced by partially privatized SOEs in election years and located in politically competitive districts.

Overall, our results support the political view of government ownership. We show micro evidence of distortions in the investment behavior of SOEs for political reasons. Our findings have implications for the policy debate on the efficiency of state capitalism in emerging markets.

Appendix A

Variable definitions

• Abnormal return: The difference between the return on a firm’s stock and the return predicted by the CAPM model, with the S&P Nifty as the benchmark market portfolio. The CAPM model is estimated using daily returns on the firm’s stock and the S&P Nifty over the preceding 3 months

• Absolute Margin: The absolute value of difference between the percentage of votes received by the ruling coalition and the opposition parties in a district

• Announced: A dummy variable that takes the value of 1 if at least one project was announced in the district in a year

• SOE: A dummy variable that takes the value of 1 for government-owned firms

• Close: A dummy variable that takes the value of 1 if the absolute margin of victory or loss of the incumbent party in the previous election in a district was less than 5% and 0 otherwise

• Constituencies won: The number of constituencies in a district where the winner belonged to the incumbent party during the current elections

• Cost ratio: The ratio of the total cost of all investments announced by SOEs in a district to the total cost of investments announced by all firms

• Debt/assets: The ratio of total debt to total assets

• Election: A dummy variable that takes the value of 1 for the fiscal year (April 1 of year |$t$|-1 to March 31 of year |$t$|⁠) associated with the calendar year |$t$| in which the election took place, regardless of the actual calendar month of the election

• Excess return: The difference between the return on a firm’s stock and the return on the benchmark S&P Nifty index

• Expansion/new unit: A dummy variable that takes the value of 1 for projects that are a substantial expansion or a new unit and 0 for renovations and minor modifications

• Federal minister: A dummy variable that identifies districts where a Member of Parliament is also a minister in the federal cabinet

• Firm size: The natural log of (1+Total Assets)

• Less contested: A dummy variable that takes the value of 1 if the absolute margin of victory of the incumbent party during the previous election was above the 75th percentile of the entire sample of state elections and 0 otherwise

• ln(Cost): The natural log of one plus the total cost of all investments announced by SOEs in a district

• Margin of victory: The difference between the percentage of votes received by ruling party coalition and the opposition parties in a district

• Nongovernment firms: A dummy variable that takes the value of 1 for nongovernment firms

• Number of projects: The total number of projects announced by firms in a district in a fiscal year

• Percentage of government-owned projects: The ratio of the number of projects announced by SOEs to the total number of projects announced by all firms in a district in a fiscal year

• Per capita GDP growth: Annual state level per capita GDP growth

• Project size: The total estimated cost of a project

• Project value ratio: The ratio of the total cost of all projects announced by state SOEs in a district to the total cost of projects announced by all firms in the district

• ROA: The ratio of operating profits (EBITDA) to total assets

• Scheduled: A dummy variable that takes the value of 1 if 5 years have passed since the last election

• Stalled/abandoned (status): A dummy variable that takes the value of 1 for projects classified as stalled or abandoned

• Stalled/abandoned alternative (status): A dummy variable that takes the value of 1 for (a) projects explicitly classified as stalled or abandoned and (b) for projects in announcement or under implementation status for 10 or more years since the project was announced

• Years to completion: The time (in years) since announcement taken to complete a project

• Years to Implementation: The time (in years) since announcement taken to start implementation of a project

• Visible expenditure: A dummy variable that identifies projects in Construction and Community Development (parks and recreation centers) industry sectors

Acknowledgments

We thank Andrew Karolyi (Editor), two anonymous referees, Artem Durnev, Amit Seru, Radhakrishnan Gopalan, Kateryna Holland, William Megginson, Nagpurnanand Prabhala, Anjan Thakor, Robert Weiner, and seminar participants at Annual Meetings of the American Finance Association (Philadelphia), Olin Brown Bag Seminar Series, Indian School of Business, Hyderabad, University of New South Wales, and University of Florida for their helpful comments and suggestions. Any remaining errors or omissions are our own. Supplementary data can be found on The Review of Financial Studies web site.

Footnotes

References