Post on 04-Jun-2020
NBER WORKING PAPER SERIES
PRIVATE RETURNS TO PUBLIC OFFICE
Raymond FismanFlorian Schulz
Vikrant Vig
Working Paper 18095http://www.nber.org/papers/w18095
NATIONAL BUREAU OF ECONOMIC RESEARCH1050 Massachusetts Avenue
Cambridge, MA 02138May 2012
We would like to thank Patrick Bolton, Ben Olken and seminar participants at the LSE-UCL developmentworkshop, Columbia and Warwick University. In addition, Vikrant Vig would like to thank the RAMDresearch grant at the London Business School for their generous support. Kyle Matoba and Jane Zhaoprovided excellent research assistance. The views expressed herein are those of the authors and donot necessarily reflect the views of the National Bureau of Economic Research.
NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies officialNBER publications.
© 2012 by Raymond Fisman, Florian Schulz, and Vikrant Vig. All rights reserved. Short sectionsof text, not to exceed two paragraphs, may be quoted without explicit permission provided that fullcredit, including © notice, is given to the source.
Private Returns to Public OfficeRaymond Fisman, Florian Schulz, and Vikrant VigNBER Working Paper No. 18095May 2012JEL No. D72,D73,D78
ABSTRACT
We study the wealth accumulation of Indian parliamentarians using public disclosures required ofall candidates since 2003. Annual asset growth of winners is on average 3 to 6 percentage points higherthan runners-up. By performing a within-constituency comparison where both runner-up and winnerrun in consecutive elections, and by looking at the subsample of very close elections, we rule out arange of alternative explanations for differential earnings of politicians and a relevant control group.The ``winner's premium" comes from parliamentarians holding positions in the Council of Ministers,with asset returns 13 to 29 percentage points higher than non-winners. The benefit of winning is alsoconcentrated among incumbents, because of low asset growth for incumbent non-winners.
Raymond FismanSchool of BusinessColumbia University622 Uris Hall3022 BroadwayNew York, NY 10027and NBERrf250@columbia.edu
Florian SchulzUCLA Anderson School of ManagementBox 951481Los Angeles, CA 90095-1481florian.schulz.2013@anderson.ucla.edu
Vikrant VigLondon Business SchoolRegent's ParkLondon NW1 4S, UKvvig@london.edu
An online appendix is available at:http://www.nber.org/data-appendix/w18095
1 Introduction
In economics and political science, there exists an enormous body of work — both theoretical
and empirical — that examines the motivations of politicians. Models of politician behavior
suggest many reasons for seeking office, including non-pecuniary benefits of public service,
financial gains that accrue after leaving office, and both salary and non-salary earnings, legal
or otherwise, while in office. Understanding politicians’ motivations is crucial for modeling
the pool of candidates - both the number and quality - that will seek office, and is also
important for designing policies to constrain politician behavior while in office.
In this paper, we look at the understudied though widely discussed issue of non-salary
earnings of public officeholders. We take advantage of data gathered via India’s Right to
Information (RTI) Act, which required all candidates standing for public office at all levels
to disclose the value and composition of their assets. Disclosure was mandatory, with punitive
consequences for misreporting. Using these records of politicians’ asset holdings across two
elections allowed us to calculate the asset growth of politicians that competed in consecutive
legislative assembly elections.
The Indian media has made much of the average asset growth of politicians - 208 percent
over a single election cycle, by one account.1 But how does this rate of wealth accumulation
compare to would-be politicians who failed in their election bids? Looking simply at the
average growth of assets fails to account for unobserved skills, resources, or inside information
that politicians may have access to, which are independent of holding office. And the average
gains to public office may obscure vast differences across politicians - if legislators do extract
high financial benefits from public office on average, which ones obtain the largest gains?
In our analysis, we focus on the subset of elections where both winner and runner-up
1http://www.hindustantimes.com/India-news/Mumbai/Access-to-key-info-makes-city-politicians-rich-Study/Article1-745426.aspx
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from the same constituency run in two consecutive elections, allowing us to calculate asset
growth for plausibly comparable political candidates. When we further limit our sample to
very close elections, we argue that our findings are very unlikely to be driven by unobserved
ability differences between winners and runners-up. In our baseline specifications, we find
that winning politicians’ assets grow at a rate that is 3 to 5 percent per year faster than
that of runners-up when we employ a basic regression framework; the “winner’s premium”
is slightly higher for politicians winning in close elections (we consider winning margins of
10, 5, and 3 percentage points). When we use a regression discontinuity design, we estimate
a winner’s premium of 6 percent.
This average benefit masks considerable candidate-level heterogeneity. Most strikingly,
the asset growth of high-level politicians - members of the Council of Ministers (COM) -
is 13 to 16 percent higher relative to control candidates, a difference that holds for very
close elections. Our regression discontinuity estimates imply a 29 percent premium relative
to control candidates. This is despite the fact that COM members earn virtually identical
salaries to other legislators. Once we control for obtaining a COM position, the winner’s
premium is much more modest, and statistically indistinguishable from zero, implying little
financial benefit of public office for most legislators.
Further, we find that there is a large difference in the winner’s premium between incum-
bents and candidates that had not previously held public office. There is little financial return
to winning for first-time politicians. Indeed, the point estimates imply a negative return for
non-incumbents, suggesting that their private sector outside options are comparable to or
even higher than the returns obtained through public office. By contrast, for incumbents our
estimate of the winner’s premium is 12.6 percent, primarily because of the very low returns
earned by incumbents that lose in their electoral bids. This provides suggestive evidence that
career politicians have relatively weak earning opportunities relative to public office.
A pair of robustness checks provide some evidence that our results are not driven by
selection problems. In the first of these, we focus on contests between pairs of politicians
3
where both had competed and been winner or runner-up in the two elections prior to 2003. We
argue that these “seasoned” politicians are unlikely to be affected by selection concerns, and
we obtain similar (though larger) estimates for the winner’s premium using this subsample.
We also look at a quasi-experiment in the state of Bihar where a hung parliament in February
2005 resulted in a follow-up election in October of the same year. By looking at candidates
that won in February but lost in October, and vice-versa, we argue that we come as close
as possible to providing a causal estimate of the returns to public office. The Bihar quasi-
experiment also yields similar (though somewhat larger) estimates of the winner’s premium,
relative to our main analysis.
Overall, our findings suggest little return to holding office for most politicians, while
high-level positions generate very high returns. This is broadly consistent with a tournament
model of politics in the spirit of Lazear and Rosen (1981), where participants compete for
the high returns that only a small fraction of entry-level politicians will attain. Further, our
results on how the winner’s premium is affected by incumbency indicate that becoming a
career politician may results in weaker private sector outside options.
In interpreting our findings, a few comments and caveats are in order. Most importantly,
our results necessarily account only for publicly disclosed assets, and hence may serve as a
lower bound on any effect (though we note that non-politicians may also engage in hiding
assets for tax purposes). This makes it all the more surprising that the data reveal such high
returns for state ministers. Additionally, we measure the returns to holding public office
only while a politician is in power. To the extent that politicians profit from activities like
lobbying and consulting after leaving office, we may consider our estimates to be a lower
bound on the full value of holding public office. Further, even if we assume transparent
financial disclosure, the relatively modest returns from winning public office do not imply
the complete absence of corruption among lower-level politicians. Given the low salaries of
legislators, they may be required to extract extra-legal payments merely to keep up with their
private sector counterparts. That is, what we aim to measure here is the financial returns of
politicians relative to private sector opportunities, and cannot directly measure the extent
4
of illegitimate financial returns of elected officials.
Our work contributes to the literature on politicians’ motivations for seeking public of-
fice. There exist numerous theoretical models describing politician motivation and behavior.
These include the seminal contributions of Barro (1973), Ferejohn (1986) and Buchanan
(1989), as well as more recent work by Besley (2004), Caselli and Morelli (2004), and Ma-
tozzi and Merlo (2008). A number of recent studies examine empirically the role of official
wages in motivating labor supply, including Ferraz and Finan (2011) and Gagliarducci and
Nannicini (forthcoming) for Brazilian and Italian mayors respectively; Kotakorpi and Pout-
vaara (2011) for Finnish parliamentarians; and Fisman et al (2011) for the Members of the
European Parliament. Diermeier et al (2005) further consider the role of career concerns
for Members of Congress in the United States. In contrast to these analyses that focus on
the effect of official wages, we compare the general wealth accumulation of winning versus
losing politicians to extract a measure of the broad financial benefits of holding public office,
relative to private sector employment.
Our work also relates to several studies that attempt to infer the non-salary financial ben-
efits of public office. Two recent papers examine the stock-picking abilities of U.S. legislators
over different time periods, and with widely disparate results - Ziobrowski et al. (2011)
reports high positive abnormal returns for Senators and members of the House of Repre-
sentatives, while Eggers and Hainmueller (2011) reports that Congress members’ portfolios
underperform the market overall, though outperforming the market for investments in donor
companies and those in their home districts. Braguinsky et al (2010) estimate the hidden
earnings of public servants in Moscow by cross-referencing officials’ salary data with their
vehicle registrations.
Several studies also examine the wealth accumulation of U.S. and British politicians. Lenz
and Lim (2009) compare the wealth accumulation of U.S. politicians to a matched sample
of non-politicians from the Panel Study on Income Dynamics. Their results suggest little
benefit from public office. Using a regression discontinuity design, Eggers and Hainmeuller
5
(2009) finds that Conservative party MPs benefit financially from public office while Labour
MPs do not. Finally, Querubin and Snyder (2009) examine the wealth accumulation of U.S.
politicians during 1850-1880 using a regression discontinuity design and find that election
winners out-earn losers only during 1870-1880. We view our work as complementary to these
studies in several ways. First, we focus on a modern context where abuse of public office
is plausibly a greater concern.2 Further, the mandatory disclosures of all Indian candidates
since 2003 help to mitigate selection issues that affect some of these earlier studies, and also
concerns over the use of wealth information provided on a voluntary basis.
Our work is closest to the study of Bhavnani (2012), which also examines politicians’
wealth accumulation in India based on mandatory asset disclosures. Given the similarities,
it is important to note how our work is distinguished from Bhavnani’s concurrent paper.
Bhavnani’s data include information on elections in 11 states, while we have a much more
comprehensive database covering elections in 24 states. This affords a number of crucial
advantages. Most importantly, we are able to include analyses that allow for constituency
fixed-effects, which helps to rule out many explanations for the winner’s premium based
on unobserved differences across candidates. Our sample is also less vulnerable to selection
concerns, since disclosures were matched across elections by hand rather than via a matching
algorithm. Our specifications also differ in a number of ways - for example, we focus on assets
net of liabilities, a standard measure of wealth, while Bhavnani focuses only on assets. This
distinction is potentially important in the presence of, for example, preferential loan access
of politicians which would mechanically inflate asset measures.
Finally, our work also contributes to the growing empirical literature that aims, often
via indirect means, to detect and measure corruption (See Olken and Pande, 2012, for a
recent survey). While we cannot detect corruption directly, the rapid wealth accumulation of
higher-level officials in our dataset necessarily implies access to income beyond official wages.
The rest of this paper is organized as follows: Section 2 provides a description of relevant
2For example, Transparency International’s Corruption Perceptions Index in 2000 ranked the United Kingdomand the United States as the 10th and 14th least corrupt countries out of the 91 countries in the Index. Indiaranked 69th.
6
political institutions and the data we employ, including those obtained through the Right
to Information Act. Section 3 presents our estimation framework. Section 4 presents our
empirical results, and Section 5 concludes.
2 Background and Data
We use hand-collected data from sworn affidavits of Indian politicians running as candidates
in state assembly elections (Vidhan Sabha). Prompted by a general desire to increase trans-
parency in the public sector, a movement for freedom of information began during the 1990s
in India. These efforts eventually resulted in the enactment of the Right to Information Act
(2005), which allows any citizen to request information from a “public authority,” among
others. During this period, the Association for Democratic Reforms (ADR) successfully
filed public interest litigation with the Delhi High Court requesting the disclosure of the
criminal, financial, and educational backgrounds of candidates contesting state elections.3
Disclosure requirements of politicians’ wealth, education and criminal records were de facto
introduced across all states beginning with the November 2003 assembly elections in the
states of Chhattisgarh, Delhi, Madhya Pradesh, Mizoram, and Rajasthan. The punishment
for inaccurate disclosures include financial penalties, imprisonment for up to six months, and
disqualification from political office.
Candidate affidavits provide a snapshot of the market value of a contestant’s assets and
liabilities at a point in time, just prior to the election when candidacy is filed. In addition
to reporting own assets and liabilities, candidates must disclose wealth and liabilities of the
spouse and dependent family members. This requirement prevents simple concealment of
assets by putting them under the names of immediate family members, and henceforth, our
measure of wealth will be aggregated over dependent family members. Further, criminal
records (past and pending cases) and education must be disclosed. While the relationship
linking wealth, education, and criminal activity to election outcomes is interesting in its own
3http://adrindia.org/about-adr/
7
right, we focus in this study on the effect of electoral victory on wealth accumulation over
an election cycle, of five years on average. Since reporting requirements are limited to those
standing for election, asset growth can only be measured for re-contesting candidates, i.e.,
those that contest - and hence file affidavits - in two elections. Therefore, our study is limited
to elections in the 24 states which had at least two elections between November 2003 and
December 2011, covering about 94 percent of India’s total electorate. Table 1 lists the 24
states in our sample along with descriptive information corresponding to the first of the two
elections.
The primary sources for candidate affidavits are the GENESYS Archives of the Election
Commission of India (ECI)4 and the various websites of the Office of the Chief Electoral
Officer in each state. The archives provide scanned candidate affidavits (in the form of
pictures or pdfs) for all candidates, though links to a few affidavits are non-functional. A
sample affidavit is shown in Online Appendix A. Except for the nine elections prior to October
2004, we are able to collect these data from the websites of the National Election Watch which,
in collaboration with ADR, provides digitized candidate affidavits.5 We extended the dataset
by collecting data for the remaining nine elections directly from the scanned affidavits.
In a first step, among all the candidates that contest in the first election in each state,
we filter out the winners and runners-up (our control group) using the Statistical Reports of
Assembly Elections provided by the Election Commission of India (ECI).6 We then match
these winners and runners-up with candidates that contest in the subsequent election in that
state. Due to large commonalities among Indian names as well as different spellings of names
across elections, matching was done manually. Overall, we are able to manually match a total
of 3622 re-contesting candidates (2303 winners and 1319 runners-up) based on variables such
as name, gender, age, education, address, and constituency, as well as a family member’s
name (usually the name of the father or spouse).7
4http://eci.gov.in/archive/5http://www.myneta.info/6http://eci.gov.in/eci main1/ElectionStatistics.aspx7A probabilistic matching algorithm, based on variables such as name and age, proved to be inefficient.To provide an example, in the Tamil Nadu Election of 2006, there are 2 runners-up with identical names
8
Of these initial 3622 matched candidates, we were unable to locate affidavits for both
elections for 53 candidates because of broken weblinks and hence discard them from our
sample. Further, we filter out candidates with affidavits that are poorly scanned, have
missing pages, or handwriting that is too unclear or ambiguous to get a clear picture of a
candidate’s reported financial situation. This drops a total of 561 candidates, or about 15.7
percent of the remaining sample matches.8 Next, we verify suspicious values and, since our
main focus is on growth in wealth, remove candidates that list significant assets without
corresponding market value information, leaving a sample of 2944 matched candidates (1872
winners and 1072 runners-up). Of these 2944 candidates, we have 633 constituencies in which
both the winner and the runner-up re-contest in the following election. This is shown by
state in the last 3 columns of Table 1.
From the affidavits, we compute the candidate’s net wealth, defined as the sum of movable
assets (such as cash, deposits in bank account, and bonds or shares in companies) and
immovable assets (such as agricultural land and buildings) less liabilities (such as loans from
banks), aggregated over all dependent family members listed on the affidavit. Finally, we
remove candidates with negative or extremely low net asset bases using a cutoff of beginning
net worth of Rs 100,000, and Winsorize net asset growth at the first and 99th percentiles.9
This leaves us with a final sample of 2741 matched candidates (1754 winners and 987 runners-
up) of which 1100 are constituency-matched pairs, i.e., we have 550 constituencies in which
both the winner and runner-up recontest.
We define a Criminal Record dummy equal to one if the candidate has pending or past
criminal cases at the time of the first election, and measure education based on years of
schooling (Years of Education). In addition to information gathered from candidates’ affi-
(RAJENDRAN.S), Age (56), and education (10th Pass) despite being identifiably distinct candidates. Wealso commonly encountered differential spellings of names between elections, for instance, Shakeel AhmadKhan (Bihar, 2005) and Shakil Ahmad Khan (Bihar, 2010).
8Affidavit availability and quality differs somewhat across states and tends to be slightly worse in the earlieryears. For example, out of 54 matched candidate in Delhi (2003), 27 percent of affidavits are unavailable orof very poor quality.
9None of these adjustments materially changes the quantitative nature of our results. Our findings are veryrobust to using different cutoff values (e.g., Rs 500,000), trimming instead of Winsorizing, or no adjustmentat all.
9
davits, we also collect data on election victory margins and incumbency from ECI’s Statis-
tical Reports of Assembly Elections. The reports also allow us to classify constituencies as
Scheduled Caste (SC), Scheduled Tribe (ST), or “general” constituencies. SC and ST con-
stituencies are reserved for candidates classified as SC or ST in order to promote members of
historically under-represented groups. That is, general candidates cannot compete in these
SC/ST-designated constituencies. We also distinguish among winning candidates based on
whether they held significant positions in the state government, using an indicator variable
for membership in the Council of Ministers, the state legislature’s cabinet.
As a measure of state-level opportunities for political rent extraction, we obtain a measure
of state-level corruption using the index reported in the 2005 Corruption Study by Trans-
parency International India. This report constructs a corruption index for 20 Indian states
based on perceived corruption in public services using comprehensive survey results for over
10,000 respondents. The index takes on a low value of 240 for the state of Kerala and a high of
695 for Bihar. Our sample covers 17 of the 20 states for which an index value is available and
we rescale the original measure by dividing it by 100. Finally, we collected a cross-section of
state legislature salaries during 2003-2008, and use the Base Salary of politicians to examine
more formally whether official salaries are an important determinant of wealth accumulation.
As we note in the introduction, these official salaries are likely too low to account for the
high levels of wealth accumulation of some politicians.
Table 2 lists definitions of the main variables used in the analysis and in Table 3, we show
some descriptive statistics for our constituency-matched sample of 1100 candidates (Panel
A) as well as for a subsample of elections decided by close margins (Panel B). Average
net assets are about Rs 9.7 million ($194,000 at an exchange rate of Rs 50 per dollar)
for winners and Rs 10.1 million (about $202,000) for runners-up. As a point of reference,
state legislators’ salaries, including allowances, are generally well under Rs 1,000,000 (about
$20,000) with relatively little variation as a function of seniority. Overall, winners and
runners-up in our sample appear to be similar in age, education, and gender. The two
groups differ based on incumbency - incumbents are less likely to win in this sample of
10
re-contestants, consistent with Linden’s (2004) finding of an incumbency disadvantage for
Indian politicians. The only other difference we observe is in net asset growth, which we
will explore in much more detail throughout the paper. About 14 percent of winners are
members of the state Councils of Ministers and 19 percent of the elections in our sample
are from SC/ST-designated constituencies. Runners-up in the subsample of close elections
tend to be slightly more educated than winners on average (14 years of educations vs. 13.8
for winners) though the median years of education is identical. Overall, based on these
observables, runners-up seem to constitute a reasonable comparable control group.10
3 Empirical Framework
Before proceeding to our regression results, it is worth emphasizing what it is that we are
attempting to measure as the returns to public office, and how our sample and specification
plausibly serve to estimate this. We wish to measure the percentage annual growth rate of
assets for an individual elected to public office, relative to the counterfactual where he was
not elected:
Ret. to Public Office = E(Netassetgrowthi|Winneri = 1)−E(Netassetgrowthi|Winneri = 0)
Of course, we cannot measure winner versus loser growth rates for a given politician, but
will rather make a comparison across observed winners and losers. We require that, condi-
tional on observables, assignment to the winner category is independent of returns to winning,
that is, [E(Netassetgrowthi|Winneri = 0),E(Netassetgrowthi|Winneri = 1)]⊥Winneri|Xi.
For the sample of politicians as a whole, this condition clearly fails - for example, politicians
that benefit most from winning will exert the greatest effort in campaigning, and those with
different unobserved (i.e., not in Xi) attributes may be of greater skill.
10On further investigating election expense for a subset of candidates, we also find no material differencesbetween winners and runners-up. Election expenditure on each candidate is further limited by law to aboutRs 1,000,000 in large states, and candidates generally receive lump sum grants from their political parties.
11
The subset of politicians that we may include in our analysis requires the further condition
that they choose to run at the end of an election cycle, regardless of whether they won the
first time around - otherwise, we observe only their initial asset levels, not their growth rates.
Hence, what we can plausibly estimate is the following:
(Ret. to Public Office|Rerun = 1) = E(Netassetgrowthi|Winneri = 1, Rerun = 1)
−E(Netassetgrowthi|Winneri = 0, Rerun = 1)
The independence of winning and the financial returns while in office is at least more
plausible with this subset of the pool of candidates - if these returns were much lower for
Winner = 0 candidates, they may choose not to run again. While this is a relevant subset
of the pool of candidates - those that make a career of running for office - it is likely one for
which the returns to public office are relatively high: if their outside options were sufficiently
good, such candidates may choose not to run again conditional on losing. We discuss this in
more detail in Section 4.4.
This does not necessarily mitigate concerns of unobserved skills correlated with winning,
and also with earnings ability. To make the closest comparison of like candidates, we focus on
a within-constituency comparison of winners and runners-up who choose to run in subsequent
elections, e = 1 and e = 2. This plausibly holds constant labor market opportunities, and
other local attributes affecting the earnings possibilities of winners and runners-up. That is,
we estimate the following fixed effects regression:
Net Asset Growthwc = αc + β ∗Winnerwc + log(NetAssetswc) + Controlswc + εwc (1)
where w ∈ {0, 1} indexes winners and runners-up, c indexes constituencies, αc is a con-
stituency fixed-effect, and εwc is a normally distributed error term.11 In our main empirical
11Note that an alternative formulation would be to ‘first difference’ the data, using the difference betweenwinner and runner-up net asset growth for each constituency as the outcome variable, as a function offirst-differenced covariates. For our main specifications, this approach yields virtually identical results tothose presented here.
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analysis, we present results on the full within-constituency sample, and also for the subset of
winner/runner-up pairs where the election was decided by a relatively slim margin. We argue
that the within-constituency close election estimation plausibly obviates many concerns of
within-pair unobserved differences.
We also employ a regression discontinuity research design (RDD) as an alternative empiri-
cal strategy, which effectively estimates the winner’s premium based on the winner-runner-up
difference in close elections. Under the identification assumption that outcomes of close elec-
tions are random, the difference in asset growth rates of winners and losers can be causally
attributed to holding public office.
The scatterplots and lines of best fit we show in our figures are produced using common
methods developed in the regression discontinuity literature (e.g., DiNardo and Lee (2004),
Imbens and Lemieux (2008) and Angrist and Pischke (2009)). Specifically, we are interested
in the extent to which winning causes a discontinuity in asset growth residuals at the win-
ning threshold. First we generate residuals by regressing growth in net assets on candidate
observables, including net assets, gender, and age, but excluding winner dummy and margin.
We next collapse the residuals on margin intervals of size 0.5 (margins ranging from -25 to
+25) and then estimate the following specification:
R̄i = α+ τ ·Di + β · f(Margin(i)) + η ·Di · f(Margin(i)) + εi (2)
where R̄i is the average residual value within each margin bin i, Margin(i)) is the mid-
point of margin bin i, Di is an indicator that takes a value of one if the midpoint of margin
bin i is positive and a value of zero if it is negative, and εi is the error term.12 f(Margin(i))
and Di · f(Margin(i)) are flexible fourth-order polynomials. The goal of these functions is
to fit smoothed curves on either side of the suspected discontinuity. The magnitude of the
discontinuity τ is estimated by the difference in the values of the two smoothed functions
12To address heterogeneity in the number of candidates and residual variance within each bin, we weighobservations by the number of candidates, and alternatively by the inverse of within-bin variance. Resultsare similar in both specifications.
13
evaluated at zero.
4 Results
4.1 Graphical presentation of results
We first present a series of figures that provide a visual description of our results. In Figure 1
we plot the Epanechnikov kernel densities of the residuals obtained from regressing growth in
net assets on candidate observables. Panel A uses the entire sample of constituency-matched
candidates while Panel B only uses candidates that were within a margin of 5 percentage
points.13 In both cases, the Kolmogorov-Smirnov test for equality of the distribution function
of winner and runner-up residuals is rejected at the 1 percent level and 5 percent level,
repectively. These figures thus depict a differential effect of election outcomes on net asset
growth between the treatment and control groups. In Panel C, we disaggregate winners
into ministers and non-ministers and plot kernel densities of these two groups as well as the
runners-up. The kernel density plots further suggest a long right tail for ministers, implying
that a relatively small number of these high-level politicians generate very high asset growth.
In Panels D and E, we disaggregate the sample based on whether an incumbent is standing for
reelection in the constituency. Panel D shows winner and runner-up densities for the sample
of constituencies where an incumbent was standing for reelection - the winner distribution is
clearly shifted to the right, implying a greater winner’s premium in races involving incumbents
(a test for equality of the distribution function is rejected at the 1 percent level). Panel E
shows densities for the subsample of non-incumbent constituencies - the winner distribution
is now shifted to the left and a test for equality of the distribution function is rejected at the
10 percent level (p-value of 0.086). We investigate in greater detail the patterns of net asset
growth among incumbents versus non-incumbents in our regression analyses below.
13The chosen bandwidth is the width that would minimize the mean integrated squared error if the data wereGaussian and a Gaussian kernel were used.
14
4.2 Regression Analyses
We now turn to analyze the patterns illustrated in Figure 1 based on the regression framework
described in the prior section. We use the basic specification shown in Equation 1, which
provides a within-constituency estimate of the winner’s premium, and present these results
in Table 4. In the first column, we show the binary within-constituency correlation between
Winner and Net Asset Growth. The coefficient of 0.0296, significant at the 5 percent level,
implies a winner’s premium in asset growth of about 3 percent. Adding log(Net Assets) as
a control in column (2) slightly lowers the point estimate to 0.0291, still significant at the 5
percent level. Column (3) adds controls for gender, incumbency, having a criminal record,
as well as quadratic controls for age and years of education; the point estimate is 0.0265,
significant at the 5 percent level. In columns (4) - (6) we examine the winner’ s premium in
close elections, defined by those where the vote share gap between winner and runner-up was
less than 10, 5, and 3 percentage points. In each case, the winner’s premium is estimated to
be around 3 - 5 percent and significant at the 5 percent level. The point estimate increases
for the 3 percent margin sample, where the coefficient on Winner is 0.0519 and significant
at the 5 percent level (p-value of 0.012).
In Table 5, we consider the returns to office as a function of potential influence in gov-
ernment. In the first column, we add an indicator variable, Minister, denoting whether the
constituency winner was appointed to the state’s Council of Ministers. The point estimate
on Minister is about 0.134, implying a 13.4 percent higher growth rate for Ministers relative
to the runner-up candidates in their constituencies. Further, the coefficient on Winner drops
to very close to zero. The point estimate is 0.01, with a standard error of 0.013, allowing us
to reject a winner’s premium of greater than 4 percent for those not appointed minister, at
the 5 percent level of significance. The results are robust to looking at narrow victory mar-
gins, as indicated by the results in columns (2) - (4). In columns (5) and (6) we include the
interaction of Winner and an indicator variable for whether a candidate’s party was part of
the state government; the small and insignificant coefficient on this interaction term suggests
15
no premium for merely being part of a ruling coalition.14 The coefficient on Minister remains
large and significant, implying extraordinary growth in wealth only for high-level positions.
It is worth emphasizing that it is problematic to assign a causal interpretation to the corre-
lation between Minister status and returns, since assignment to these posts is non-random.
At the same time, the very large effect of holding a Minister position on asset returns is such
that it is not easily explained by unobserved differences in abilities, and warrants further
investigation in future work.
In columns (7) and (8) we disaggregate asset growth into Movable Asset Growth through
holdings such as cash, bank deposits, and jewelry, and Immovable Asset Growth from land
and building assets (see the full definition in the Data section). We see a sharp difference
between the asset growth of Minister versus non-Minister politicians. The coefficient on
Winner is a highly significant predictor of growth in movable assets, implying a winner’s
premium of 5.23 percent. The magnitude of the coefficient on Minister in (7) implies a
further premium in movable asset growth of 4.2 percent, though this effect is not significant.
For immovable assets, the Minister growth premium is 8.8 percent and significant at the 10
percent level, while the winner’s premium is small in magnitude and statistically insignificant.
Note that immovable assets constitute, on average, about three quarters of a candidate’s
total assets. If the asset growth of politicians is the result of extra-legal payments, this
difference may simply reflect the fact that the scale of gifts is larger for ministers (e.g., cars
versus buildings). It may also result from access to low cost purchase of land for high-level
individuals as suggested by, for example, the case of Karnataka’s former Chief Minister B.S.
Yeddyurappa, who acquired land parcels at extremely favorable prices before selling them off
to mining companies.15 Such opportunities may only be available to high-ranking politicians.
In Table 6 we turn to assess how the winner’s premium differs as a function of incumbency,
by including the interaction term Winner*Incumbent. As suggested by the patterns in Fig-
14We also considered the effect of membership in the two main political parties - the Congress and the BJP -on the winner’s premium. The Winner*Congress interaction was marginally significant and positive, whilethe interaction of Winner*BJP was negative, though not significant at conventional levels.
15“Ministers stole millions in Karnataka mining scam,” BBC South Asia, July 21, 2011
16
ure 1, the winner’s premium comes exclusively from incumbents. The coefficient on Winner
is -0.053 and significant at the 5 percent level, implying that non-incumbent winners’ asset
growth is 5.3 percent lower than that of non-incumbent runners-up. The pattern is reversed
for incumbents, where there is a winner’s premium of nearly 12.6 percent (the sum of the
coefficients on Winner and Winner*Incumbent). One plausible interpretation of this differ-
ential winner’s premium by incumbency is that it reflects the relatively limited private sector
options available to career politicians. Alternatively, it may result from the greater skill with
which incumbents extract value from political office. The data are at least suggestive of the
first of these explanations - the large winner’s premium for incumbents is primarily the result
of the low earnings of incumbents that are not returned to office: incumbent winners have a
median asset growth of 0.205, virtually identical the median asset growth of non-incumbents
overall (0.204), while the median asset growth of incumbent runners-up is 0.15.
4.2.1 Electoral Accountability
The extent that legislators extract financial returns from their positions may be limited by
pressure from the electorate, particularly given the transparency afforded by the Right to
Information Act. We emphasize that the asset growth calculations we perform here are based
on data easily accessible via the internet, and their availability has been widely reported in
the Indian media. In Table 7 we examine whether there is any effect of high asset growth on
election outcomes, through the following specification:
Reelectionwc = αc + β1 ∗Winnerwc + β2 ∗Net Asset Growthwc (3)
+β3 ∗Winnerwc ∗Net Asset Growthwc + εwc
where Reelectionwc is an indicator variable that takes on a value of 1 if the candidate
won election e = 2 and 0 otherwise. While none of the coefficients are significant, the results
point, if anything, in the opposite direction - the coefficient on Net Asset Growth is positive
in Column (1), and its interaction with Winner, capturing the effect of asset growth among
17
election winners, is positive (Column 2). In results not reported, we also find that legislators
who win by large margins do not earn a higher winner’s premium. Such a specification
is, however, subject to extreme problems of unobserved heterogeneity - the large margin
may be because of a candidate’s effort or political skill, confusing the interpretation of the
Winner*Margin interaction. Finally, the negative coefficient on Winner is consistent with a
negative incumbency effect in India that was already observed in Table 3.
4.2.2 Exploring Cross-sectional Heterogeneity
In Table 8 we examine heterogeneity in the winner’s premium as a function of a number of
other candidate characteristics. In column (1) we look at the effect of state-level Corrup-
tion. The coefficient on the interaction term Winner*Corruption, while positive and hence
implying a higher winner’s premium in more corrupt states, is not statistically significant.
In column (2) we allow for a Minister*Corruption interaction; the coefficient on this term is
positive, again implying a larger asset growth premium in more corrupt states, but also not
significant. In column (3) we consider whether candidates with prior criminal records have
a higher winner’s premium. The coefficient on the interaction term is not significant.
In column (4) we consider the set of constituencies reserved for members of disad-
vantaged groups, so-called Scheduled Tribes and Castes (SC/ST). The interaction term
SC/ST Quota∗Winner is significant at the 1 percent level, and implies a winner’s premium
in asset growth of about 8 to 9 percent for constituencies reserved for SC/ST candidates.
There are two primary explanations for the relatively high winner’s premium for SC/ST-
designated constituencies. First, since these seats are reserved for a subset of potential
candidates, it may slacken electoral competition, allowing candidates to extract greater rents
without fear of losing their positions. Alternatively, SC/ST politicians may have less lucrative
private sector options as a result of discrimination, lower unobserved skill levels, or weaker
labor market opportunities in SC/ST-dominated areas. While we cannot include both the
direct effect of SC/ST Quota and constituency fixed effects in a single specification, in
18
column (5) we look at the direct effect of SC/ST quotas with a coarser set of fixed effects, at
the district level. There are approximately half as many districts as constituencies in our main
sample. We find a very similar coefficient on the interaction term SC/ST Quota ∗Winner
in this specification - approximately 0.09 - while the direct effect of SC/ST Quota is -0.073.
That is, it would appear that among runners-up, SC/ST politicians fare significantly worse
than other candidates, providing suggestive evidence that the differential SC/ST effect results
in large part from different private sector opportunities.
In column (7), we examine the effects of candidates’ education levels by including as
covariates the logarithm of years of schooling as well as its interaction with Winner. We
find a small positive direct effect of years of schooling, implying that for runners-up, asset
growth is higher for more educated candidates. However, this is more than offset by the in-
teraction term, log(Years of Education)*Winner. The sum of the coefficients on log(Years of
Education) and its interaction with Winner, while negative, is not significant at conventional
levels. This is broadly consistent with highly educated candidates having better private
sector opportunities, but not greater earning capacity as public officials.
We show the interaction of Female and Winner in column (6). The coefficient is positive,
though not statistically significant. Finally, in column (8) we interact Winner with log(Base
Salary). We find no evidence that the winner’s premium is higher in states with more
generous official salaries for legislators, implying that it is unlikely that official salaries play
a major role in the differential asset accumulation of elected officials.
4.3 Regression Discontinuity Design
Our main empirical identification strategy is effectively based on a regression discontinuity
design, with the winner’s premium identified from the winner-loser differential in close elec-
tions . In this section, we explicitly model the value of winning using regression discontinuity
methods, as described in Section 3. We first show a series of figures that depict our tests
for discontinuities around the winning threshold, followed by an analysis of the magnitudes
19
of winner-loser discontinuities. Note that we follow the approach outlined in Section 4.1 by
looking at net asset growth residuals which allows us to control for remaining differences in
covariates of candidates as well as observed and unobserved constituency heterogeneity. The
methodology in this section can thus be considered as conditional RD. Results are quantita-
tively similar when (unconditional) net asset growth in used.
In Figure 2, Panels A - E, we provide a visual description of this analysis and columns (1)
- (5) of Table 9 provide the corresponding discontinuity estimates of the winner’s premium.16
Panel A shows the sample of all winners and corresponding runners-up. Our estimated
regression indicates a jump in the residual values around the threshold. The point estimate
of τ is 0.065, and statistically significant at the 1 percent level (t-statistic of 2.8). Panel B only
includes ministers with corresponding runners-up - the point estimate of the discontinuity
increases to 0.287 (t-statistic of 5.26), a result qualitatively similar to that of the regression
analysis in the previous section, though somewhat larger in magnitude. On the other hand,
the subsample of winners not appointed to a Council of Ministers and corresponding runners-
up does not indicate a jump at all (Panel C) - the coefficient estimate of the discontinuity
is 0.0265 with a t-statistic of 1.13. In Panels D and E, we disaggregate the sample based on
whether an incumbent is standing for reelection in the constituency. Panel D shows results for
the sample of constituencies where an incumbent was running for reelection. The coefficient
estimate of the discontinuity is 0.08 and significant at the 1 percent level (t-statistic of 3.19).
By contrast, for the sample of non-incumbent constituencies, we observe no jump at the
threshold (the point estimate is 0.028 with a t-statistic of 0.79). Overall, these results are in
line with those obtained from standard regression analysis.
Finally, in Figure 3 we plot kernel densities of age and log(Net Assets) for the sample of
constituency-matched candidates that were within a Margin of 5 percentage points (“close
elections”). Panel A plots age densities for winners and runners-up and Panel B plots densities
for log(Net Assets). For both observables, the Kolmogorov-Smirnov test for equality of the
16Note that the apparent symmetries in the RD plots are the result of constituency fixed effects. Including con-stituency fixed effects allows us to control for observable and unobservable constituency-level heterogeneity,for example, differences in local labor markets or SC/ST Quota.
20
distribution function of winners and runners-up cannot be rejected at conventional levels,
providing some validation of our regression discontinuity design.
Based on these discontinuities, we can perform a simple back-of-the envelope calculation
to to approximate the winner’s premium in monetary terms. We do this by first calculating
how winners’ average wealth would have grown had they not won the election using the net
asset growth rate of all constituency-matched runners-up, and then comparing this average
to the level of wealth accumulation using the discontinuity estimates from the RD design.
Overall, for Winners as a group, the estimated annual premium is approximately Rs 1,500,000
(USD 30,000). However, for Ministers the winner premium is significantly larger, about Rs
10,750,000 per year (USD 215,000). By comparison, state-level legislators have salaries that
are much lower - generally under Rs 1,000,000 per year (USD 20,000). Further, these wealth
accumulation increments are relative to candidates’ initial assets that are, on average, only
about Rs 10,000,000 (USD 200,000), implying a very large impact in percentage terms.
4.4 Addressing Selection
Our analysis compares the returns of winners versus runners-up in constituencies where
both candidates run in two consecutive elections. While this sample allows us to include
constituency fixed effects and thus control for local constituency-level omitted variables, it
is important to consider whether these results are external valid for Indian legislators more
generally.
At the outset, we note that the constituencies that constitute our sample - where both
the winner and the runner-up contest both elections - are very similar on observables to
constituencies where only one of the candidates recontests. Specifically, the mean electorate,
percentage turnout, and percent SC/ST population for our winner/runner-up matched con-
stituencies are not significantly different from the rest of the population. Candidates in these
constituencies are also quite similar in attributes such as log of assets, age, and education.17
17For brevity, tables are not shown but are available from the authors upon request.
21
Thus, we believe that the local average treatment effects documented above can be likely
generalized to the population.
As noted earlier, our identification strategy – comparing candidates from the same con-
stituency in close elections – attempts to control for unobserved ability differences in can-
didates. By comparing the net asset growth of two otherwise similar candidates following
an election where one prevails by a narrow margin, we may calculate the private returns to
public office relative to a similar candidate that just lost the election. One significant concern
with this approach, however, is that electoral victory may itself influence the probability of
recontesting, and hence inclusion in the sample. Indeed, in Panel A of Figure 4, we find
that runners-up have a lower probability of re-contesting the second election when compared
to the corresponding constituency winners. The probability of recontesting is increasing in
margin, with a clear discontinuity at zero.18
In considering how this differential exit rate may affect our results, we note first that it
is not obvious a priori which direction any selection effect would bias our estimates. One
one hand, winners and runners-up that re-contest the second election are plausibly more
similar in terms of political ability than pairs where both contest the first election but one
subsequently chooses not to contest the second election. In this case, one might expect
that the ability differences between winners and runners-up are smaller in our sample of
constituencies than those without matched winners/runners-up, hence biasing our results
towards zero. 19 Alternatively, if candidates that exit have higher outside options compared
18In a separate analysis (not reported for brevity), we examine the recontesting decision of political candidatesusing a simple probit model. The dependent variable is one if we can match the candidate in a subsequentelection and zero if we only observe a candidate at election 1. We conduct our analysis separately for thesub-samples of winners and runners-up, and find that candidates that win the first election are significantlymore likely to re-contest in the subsequent election. For the sub-samples of both winners and runners-up,we find that wealthier and more educated candidates are more likely to rerun, whereas age is negativelyrelated to the decision to re-contest. The only variable that affects both groups differently is the winningmargin at the first election – runners-up who lose by wider margins are significantly less likely to re-contest,whereas for winners margin is not a significant predictor of running in the next election. There are twoready explanations for this difference - (1) if a candidate loses by a large margin, he may re-evaluate hischances of winning and not re-contest a second time, or (2) he may not get chosen to represent his party ifhe has shown little success in the previous attempt.
19Runners-up and winners in our sample have virtually identical chances of succeeding in the subsequentelection (42.08 percent and 41.89 percent, respectively), providing further support for similar political abilityof the two groups.
22
to candidates that decide to re-contest, neglecting the asset growth of unsuccessful candidates
that do not rerun may bias our analysis towards finding an effect even when none exists.
4.4.1 Evidence from Seasoned Candidates
To further assess the influence that differential exit rates may have on the estimated winner’s
premium, we analyze a restricted sample of constituencies where both winner and runner-
up are seasoned politicians, in the sense of both competing in at least two elections prior
to the elections we consider in our analysis, and where both were either winner or runner-
up in these earlier elections. Repeated contests of this sort between seasoned politicians
is surprisingly common in our sample. We provide one illustrative example below for the
Biswanath Assembly Constituency in the state of Assam. In this case, both candidates,
Prabin Hazarika and Nurjamal Sarkar, have contested all elections since 1991 and have been
either a winner or a runner-up in each instance. We argue that such career politicians are
less likely to exit because of party decisions or a reevaluation of future electoral success -
by construction, we include only politicians who have performed well as candidates in the
recent past. This subset of active seasoned politicians arguably represent more comparable
treatment and control candidates than the full sample of re-contesting politicians.
Biswanath Assembly Constituency (Assam)Year Winner %age Party Runner-up %age Party
2011 Prabin Hazarika 45.51 AGP Nurjamal Sarkar 44.09 INC2006 Nurjamal Sarkar 41.76 INC Prabin Hazarika 39.46 AGP2001 Nurjamal Sarkar 48.55 INC Prabin Hazarika 44.3 AGP1996 Prabin Hazarika 42.62 AGP Nurjamal Sarkar 31.76 INC1991 Nurjamal Sarkar 46.49 INC Prabin Hazarika 17.39 AGP
We focus our analysis on this set of active seasoned candidates in Panels B and C of
Figure 4. In Panel B, we find no differential probability of re-contesting the second election;
however, Panel C documents a jump in net asset growth rates around the winning threshold.
The point estimate of the discontinuity is 0.12 and significant at the 5 percent level. This is
consistent with differential exit rates of winners and runners-up creating a downward bias in
23
our main estimates on the returns to public office.
4.4.2 Evidence from Bihar’s Hung Parliament
We conclude this section by presenting some results from a quasi-experiment, albeit one
that involves a very limited sample of constituencies. In Bihar’s legislative assembly election
in February 2005, no individual party gained a majority of seats, and attempts at forming
a coalition came to an impasse. As a result of this hung parliament, new elections were
held in October/November of the same year.20 In a significant fraction of these contests,
repeated within less than a year of one another, the initial winner was defeated in the
follow-up election. For these constituencies, we come as close as possible to observing the
counterfactual of winners reassigned to runner-up, and vice-versa.
From the 243 constituencies contested in the February election, we sample those where
both the winner and runner-up matched up again in the October election of the same year
and emerged as winner/runner-up or runner-up/winner in this later election. This leaves
a sample of 260 candidates (130 constituencies) for which we analyze the probabilities of
winning the October election as a function of the winning margin at the February Election.
Results are shown in the Table below:
Bihar February 2005 Probability of Winning October 2005 Election
Winner 66.2% 63.2% 60.9% 58.6% 52.2% 50.0%Runner-Up 33.8% 36.8% 39.1% 41.4% 47.8% 50.0%
Margin (February 2005) < 20% < 15% < 10% < 5% < 1%
Elections 130 117 110 87 46 10
Overall, winners in the February 2005 election won in the later contest only 66.2 percent
of the time. Further, as on narrows the margin, this advantage decreases monotonically.
At the 5 percent threshold, the probability of winning is statistically indistinguishable from
20Bihar was under the direct rule of India’s federal government during this period.
24
50 percent for either candidate. This suggests a significant element of randomness to close
elections in this sample.21
To further sharpen our empirical strategy, we compare the net asset growth of two groups
– the treatment and control groups. The treatment group consists of candidates that were
runners-up in the February 2005 election but won in the October 2005 contest, while the
control group is comprised of candidates that were winners in February 2005 but runners-
up in the October election. These cases where winners and losers were switched owing to
the hung parliament provides a measure of the returns to public office with a relatively
straightforward causal interpretation. We look at all such candidates whose winner status
shifted between these two 2005 elections, and also chose to run again in 2010, so we can
calculate their asset growth rates. The resulting set of candidates is relatively small - 25
winners and 26 runners-up - which limits the types of statistical tests one can perform on
this sample. For this subset of candidates we find that the annual net asset growth of the
treatment group is on average 12.76% higher than that of the control group, a difference that
is significant at the 5 percent level. If we limit ourselves only to the constituency matched
samples where winner and runner-up status switched and both candidates ran in the 2010
election, the sample is reduced to 11 constituencies - 22 candidates - and we find a difference
in the net asset growth between winners and runners-up of approximately 6 percent, roughly
similar to the magnitudes we observe with the full sample. Given the small sample size, the
difference in asset growth for the sample of 22 candidates is not statistically significant.
5 Conclusion
In this paper, we utilize the asset disclosures of candidates for Indian state legislatures,
taken five years apart at two points across a five year election cycle, and accessed through
21Recent papers by Snyder (2005), Caughey and Sekhon (2010), Carpenter et al. (2011), and Folke et al.(2011) critically assess regression discontinuity studies that rely on close elections. There remains an activedebate on whether close elections can really be considered a matter of random assignment. If sorting aroundthe winning threshold is not random, but close winners have systematic advantages, then the RD designmay fail to provide valid estimates of the returns to office. The Bihar example provides at least suggestiveevidence that close elections are relatively random in the context we consider in this paper.
25
the country’s Right to Information Act. This has allowed us to compare the asset growth
of election winners versus runners-up to calculate the financial returns from holding public
office relative to private sector opportunities available to career politicians.
Our main findings suggest, at least in the Indian context, a relatively limited financial
benefit of public office for most politicians. By contrast, we find a 13-29 percent growth
premium for ministers in our sample, suggesting very strong earnings possibilities for higher-
level politicians. Looking at election winners not appointed to the Council of Ministers,
the asset growth premium for election winners is about one percent per year. Further,
this premium is derived entirely from the winner-loser differential among incumbents, with
incumbent runners-up earning unusually low returns when confronted with private sector job
opportunities; for non-incumbents, the winner’s premium is negative.
These findings have a number of implications for the modeling of the political process and
politicians’ behavior. First, our results suggest a sharp difference in the value of influencing
legislators at different levels in the Indian hierarchy: the votes of individual legislators have
relatively low value for private agents, while the influence of ministers is potentially very
valuable. At least in financial terms, one may thus think about prospective politicians being
motivated more by future rewards from gaining higher positions than by the initial returns of
holding office. This is broadly consistent with a tournament model of politics in the spirit of
Lazear and Rosen (1981), where participants compete for the high returns that only a small
fraction of entry-level politicians will attain.
Our work also presents several possible directions for future work. Given the high returns
we observe among ministers, it may be fruitful, with the benefit of additional data, to examine
whether particular positions within the Council are associated with high rents. And while
we do not observe a strong sensitivity of election outcomes to asset growth, one may assess
whether electoral accountability is affected by voter exposure to asset data, in the spirit of
Banerjee et al (2011). It may be interesting to explore the impact of the Right to Information
Act itself: disclosure requirements may induce exit by winners that have extracted high rents,
26
in order to avoid possible corruption-related inquiries. Finally, we are unable in this work to
uncover the mechanism through which asset accumulation takes place. We leave these and
other extensions for future work, which will be enabled either by experimental intervention
or the accumulation of new data via the Right to Information Act.
27
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29
Tab
le1:
Overv
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of
Sam
ple
Sta
tes
Note
s:T
his
Table
pro
vid
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over
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294
1896
152
(122)
94
(79)
57
(40)
Aru
nach
al
Pra
des
h2004
2009
-683,5
12
64.0
2%
60
168
55
(40)
22
(14)
19
(11)
Ass
am
2006
2011
5.4
217,4
34,0
19
75.7
7%
126
997
108
(98)
69
(58)
61
(45)
Bih
ar*
2005
2010
6.9
551,3
85,8
91
45.8
5%
243
2135
169
(134)
99
(67)
72
(35)
Ch
hatt
isgarh
2003
2008
4.4
513,5
43,6
56
71.3
0%
90
819
56
(27)
31
(15)
15
(4)
Del
hi
2003
2008
4.9
68,4
48,3
24
53.4
2%
70
817
46
(27)
8(3
)7
(2)
Goa
2007
2012
-1,0
10,2
46
70.5
1%
40
202
36
(34)
19
(18)
18
(17)
Hary
an
a2005
2009
5.1
612,7
35,8
88
71.9
6%
90
983
59
(50)
42
(37)
30
(18)
Jh
ark
han
d2005
2009
5.2
017,7
66,2
02
57.0
3%
81
1390
63
(43)
51
(38)
43
(22)
Karn
ata
ka
2004
2008
5.7
638,5
86,7
54
65.1
7%
224
1715
83
(51)
35
(23)
2(1
)K
erala
2006
2011
2.4
021,4
83,9
37
72.3
8%
140
931
102
(65)
23
(20)
18
(13)
Mad
hya
Pra
des
h2003
2008
5.8
437,9
36,5
18
67.2
5%
230
2171
126
(109)
51
(40)
29
(20)
Mah
ara
shtr
a2004
2009
4.3
365,9
65,7
92
63.4
4%
288
2678
196
(177)
98
(88)
75
(62)
Man
ipu
r2007
2012
-1,7
07,2
04
86.7
3%
60
308
47
(40)
33
(28)
28
(17)
Miz
ora
m2003
2008
-532,0
28
78.6
5%
40
192
31
(16)
17
(10)
15
(5)
Ori
ssa
2004
2009
4.7
525,6
51,9
89
66.0
5%
147
802
108
(88)
78
(69)
60
(46)
Pu
du
cher
ry2006
2011
-659,4
20
86.0
0%
30
218
25
(25)
17
(16)
14
(13)
Pu
nja
b2007
2012
4.5
916,7
75,7
02
75.4
5%
116
1043
87
(74)
59
(46)
46
(29)
Ra
jast
han
2003
2008
5.4
333,9
28,6
75
67.1
8%
200
1541
105
(76)
72
(52)
41
(18)
Sik
kim
2004
2009
-281,9
37
79.2
3%
32
91
12
(12)
14
(14)
2(2
)T
am
ilN
ad
u2006
2011
5.0
946,6
03,3
52
70.8
2%
234
2586
125
(101)
41
(30)
22
(12)
Utt
ar
Pra
des
h2007
2012
4.9
1113,5
49,3
50
45.9
6%
403
6086
297
(273)
216
(184)
168
(132)
Utt
ara
kh
an
d2007
2012
-5,9
85,3
02
59.4
5%
69
785
57
(48)
30
(27)
23
(17)
Wes
tB
engal
2006
2011
4.6
148,1
65,2
01
81.9
7%
294
1654
158
(142)
100
(96)
59
(52)
TO
TA
LS
631,9
67,3
97
3601
32208
2303
(1872)
1318
(1071)
924
(633)
Lok
Sab
ha
2004
2009
671,4
87,9
30
58.0
7%
543
5435
30
Table 2: Variable Definitions
Variable Description
Movable Assets (1) Sum of (i) Cash, (ii) Deposits in Banks, Financial Institutions and Non-Banking FinancialCompanies, (iii) Bonds, Debentures and Shares in companies, (iv) NSS, Postal Savings etc.,(v) Personal loans/advance given, (vi) Motor vehicles, (vii) Jewelry, and (viii) Other assetssuch as values of claims/interests as reported on the candidate affidavit. This item excludesthe value of life or other insurance policies (which are usually reported at payoff values).
Immovable Assets (2) Sum of (i) Agricultural Land, (ii) Non-Agricultural Land, (iii) Commercial Buildings and (vi)Residential Buildings (”Buildings and Houses”), and (v) Others as reported on the candidateaffidavit.
Total Assets Defined as the sum of (1) and (2).
Total Liabilities (3) Sum of (i) Loans from Banks and Financial Institutions, (ii) Loans from Individuals/Entitiesand (iii) any other liability, as well as (vi) any dues reported on the candidate affidavit.
Net Assets ”Net Worth” of the Candidate. Defined as the sum of (1) and (2) minus (3). We removecandidates with extremely low net assets bases (Net assets below Rs 100,000 as of election1).
Net Asset Growth Annualized Growth in Net Assets over an election cycle. Winsorized at the 1 and 99 per-centiles.
Winner Dummy variable taking on a value of 1 if the contestant won election 1.
Minister Dummy variable indicating whether the constituency winner was appointed to the state’sCouncil of Ministers.
Margin Vote share difference between winner and runner-up (scale of 0 to 100).
Incumbent Dummy variable taking on a value of 1 if the contesting candidate won the preceding con-stituency election.
Education Ordinary scale variable ranging from 1 to 9. We assign values based on the following educationbands: 1 = Illiterate, 2 = Literate, 3 = 5th Pass, 4 = 8th Pass, 5 = 10th Pass, 6 = 12th Pass,7 = Graduate or Graduate Professional, 8 = Post Graduate, 9 = Doctorate. This variableis missing if education information was not given.
Years of Education Number of years of education the candidate has received.
Criminal Record Dummy variable indicating whether the candidate has past or pending criminal cases.
Government Dummy variable indicating whether the candidate’s party is part of the ruling state govern-ment.
SC/ST Quota Dummy variable indicating whether the constituency of the candidate is that of disadvan-taged groups, so-called Scheduled Castes and Tribes (SC/ST).
Corruption Index Survey-based state corruption index (based on perceived corruption in public services) asreported in the 2005 Corruption Study by Transparency International India. The indextakes on a low value of 2.40 for the state of Kerala (perceived as ”least corrupt”)and a highvalue of 6.95 for Bihar (perceived as ”most corrupt”). We rescaled the original index bydividing it by 100.
Female Dummy indicating the gender of the candidate (1 = Female).
Base Salary Monthly base salaries of MLAs. Collected from states’ Salaries and Allowances and Pensionof Members of the Legislative Assembly (Amendment) Acts, official websites, and newspaperarticles.
31
Tab
le3:
Desc
rip
tive
Sta
tist
ics
of
Con
stit
uen
cy-M
atc
hed
Pair
s(1
100
Can
did
ate
s)
Note
s:P
anel
Ash
ows
des
crip
tive
stati
stic
sfo
rth
e1100
const
ituen
cy-p
air
edca
ndid
ate
sth
at
const
itute
our
main
sam
ple
(550
win
ner
sand
550
runner
s-up).
InP
anel
B,
we
only
incl
ude
candid
ate
sof
those
const
ituen
cies
that
are
dec
ided
by
aw
innin
gm
arg
inof
five
or
less
per
cent
(’cl
ose
elec
tions’
).E
xce
pt
for
net
ass
etgro
wth
,w
hic
his
mea
sure
dov
erth
ele
gis
latu
rep
erio
d,
all
vari
able
sare
as
of
the
firs
tof
the
two
elec
tions.
Vari
able
sare
defi
ned
indet
ail
inT
able
2.
The
last
colu
mn
show
st-
stati
stic
sof
diff
eren
cein
mea
ns
test
s.
Win
ner
an
dR
un
ner-u
pW
inn
er
Ru
nn
er-u
pD
iff.
inM
ean
sV
aria
ble
Mea
nM
edia
nS
td.
Dev
.M
ean
Med
ian
Std
.D
ev.
Mea
nM
edia
nS
td.
Dev
.(T
-sta
t)
Pan
el
A:
All
Con
stit
uen
cie
s
log(N
etA
sset
s)15.1
615.1
41.4
315.1
615.1
61.4
015.1
615.1
31.4
6-0
.08
Net
Ass
etG
row
th(a
nn
.)0.2
19
0.1
89
0.2
20.2
34
0.2
04
0.2
20.2
05
0.1
79
0.2
32.2
0F
emale
0.0
60
0.2
40.0
60
0.2
40.0
60
0.2
40.1
3A
ge
48.4
448
9.9
647.7
648
9.8
649.1
349
10.0
1-2
.28
Ed
uca
tion
6.6
17
1.3
06.5
77
1.3
86.6
57
1.2
3-1
.04
Yea
rsof
edu
cati
on
13.8
915
3.1
313.7
815
3.2
913.9
915
2.9
7-1
.07
Incu
mb
ent
0.3
70
0.4
80.3
40
0.4
70.4
10
0.4
9-2
.50
Cri
min
al
Rec
ord
0.3
00
0.4
60.3
00
0.4
60.3
00
0.4
60.0
2G
over
nm
ent
0.4
30
0.5
00.5
61
0.5
00.3
00
0.4
69.0
9
Min
iste
r0.0
70
0.2
50.1
40
0.3
5M
arg
in8.3
26.2
87.4
0S
C/S
TQ
uota
0.1
90
0.3
9
ML
AB
ase
Sala
ry16151
8000
20820
Pan
el
B:
Con
stit
uencie
sd
ecid
ed
by
Margin≤
5%
log(N
etA
sset
s)15.0
715.1
21.3
715.0
315.1
11.3
215.1
115.1
61.4
3-0
.60
Net
Ass
etG
row
th(a
nn
.)0.2
14
0.1
85
0.2
10.2
34
0.1
99
0.2
10.1
94
0.1
70
0.2
02.0
4F
emale
0.0
60
0.2
40.0
60
0.2
50.0
60
0.2
4-0
.20
Age
48.4
248.5
9.9
547.4
947
9.7
249.3
449
10.1
1-1
.95
Ed
uca
tion
6.6
67
1.3
06.5
37
1.4
56.8
07
1.1
2-2
.17
Yea
rsof
edu
cati
on
14.0
015
3.1
313.6
915
3.4
614.3
215
2.7
3-2
.12
Incu
mb
ent
0.3
70
0.4
80.3
40
0.4
70.4
00
0.4
9-1
.29
Cri
min
al
Rec
ord
0.3
10
0.4
60.2
90
0.4
60.3
30
0.4
7-0
.83
Gover
nm
ent
0.4
20
0.4
90.5
31
0.5
00.3
20
0.4
74.4
5
Min
iste
r0.0
60
0.2
50.1
30
0.3
4M
arg
in2.3
92.4
71.4
5S
C/S
TQ
uota
0.1
40
0.3
5
32
Table 4: Within-Constituency Effects of Winning the Election
Notes: The regression equation estimated is: Net Asset Growthwc = αc+β∗Winnerwc+log(NetAssetswc)+Controlswc +εwc. The dependent variable, Net Asset Growthwc, is the annualized growth rate in net wealth.αc is a constituency fixed-effect. Winnerwc is the dummy for winning the election (e=1). log(NetAssetswc)is the logarithm of the net assets of the politician. Controlswc include education (scaled from 1 to 9, with 9being the highest), criminal record (dummy if a criminal record were present as of the first election), gender,age, and incumbency. The regression is also run for close elections (Columns 4-6), where the vote share gapbetween the winner and the incumbent was less than 10, 5, and 3 percentage points. Robust standard errorsare given in parentheses. The reported constant is the average value of the fixed effects. Coefficients with***, **, and * are statistically significant at the 1%, 5%, and 10% levels, respectively.
(1) (2) (3) (4) (5) (6)Net Asset Net Asset Net Asset Net Asset Net Asset Net Asset
Variables Growth Growth Growth Growth Growth Growth
Winner 0.0296** 0.0291** 0.0265** 0.0352** 0.0341** 0.0519**(0.0133) (0.0123) (0.0132) (0.0137) (0.0163) (0.0203)
log(Net Assets) -0.0716*** -0.0720*** -0.0720*** -0.0740*** -0.0825***(0.0086) (0.0093) (0.0106) (0.0122) (0.0146)
Education -0.00777(0.0065)
Criminal Record 0.01(0.0219)
Female -0.0748*(0.0453)
Age -0.0047(0.0076)
Age2 3.80E-05(0.0001)
Incumbent 0.0144(0.0161)
Constant 0.205*** 1.291*** 1.404*** 1.291*** 1.312*** 1.436***(0.0094) (0.1300) (0.2350) (0.1600) (0.1840) (0.2190)
Close Elections: Margin ≤ 10 Margin ≤ 5 Margin ≤ 3
Observations 1100 1100 1060 740 436 268R-squared 0.511 0.585 0.598 0.604 0.656 0.661
33
Tab
le5:
Th
eE
ffect
of
Pote
nti
al
Infl
uence
inG
overn
ment
on
the
Retu
rns
toO
ffice
Note
s:N
etass
etgro
wth
of
the
politi
cian
isth
edep
enden
tva
riable
inco
lum
ns
1-6
.W
inner
is1
ifth
ep
oliti
cian
won
elec
tion
e=1
and
0if
the
politi
cian
did
not
win
.M
inis
ter
den
ote
sw
het
her
the
const
ituen
cyw
inner
was
app
oin
ted
toth
est
ate
’sC
ounci
lof
Min
iste
rs.
Log(N
etA
sset
s)is
the
logari
thm
of
the
politi
cian’s
net
ass
ets.
Inco
lum
ns
2-4
,cl
ose
elec
tions
are
exam
ined
,w
ith
the
vote
share
gap
bet
wee
nth
ew
inner
and
incu
mb
ent
bei
ng
less
than
10,
5,
and
3p
erce
nta
ge
poin
ts,
resp
ecti
vel
y.B
eing
part
of
the
ruling
gov
ernm
ent
isco
ded
as
Gov
ernm
ent;
we
furt
her
incl
uded
an
inte
ract
ion
term
bet
wee
nG
over
nm
ent
and
Win
ner
.In
colu
mns
7and
8,
ther
eis
adis
aggre
gati
on
of
ass
etgro
wth
into
two
cate
gori
es-
Mov
able
Ass
etG
row
th(h
old
ings
such
as
cash
,bank
dep
osi
ts,
and
jew
elry
)and
Imm
ovable
Ass
etG
row
th(l
and
and
buildin
gass
ets)
.R
obust
standard
erro
rsare
giv
enin
pare
nth
eses
.T
he
rep
ort
edco
nst
ant
isth
eav
erage
valu
eof
the
fixed
effec
ts.
Coeffi
cien
tsw
ith
***,
**,
and
*are
stati
stic
ally
signifi
cant
at
the
1%
,5%
,and
10%
level
s,re
spec
tivel
y.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Net
Ass
etN
etA
sset
Net
Ass
etN
etA
sset
Net
Ass
etN
etA
sset
Mov
able
Imm
ovable
Vari
able
sG
row
thG
row
thG
row
thG
row
thG
row
thG
row
thA
sset
Gro
wth
Ass
etG
row
th
Win
ner
0.0
107
0.0
221
0.0
197
0.0
332
0.0
242
0.0
291
0.0
523***
0.0
259
(0.0
130)
(0.0
144)
(0.0
173)
(0.0
216)
(0.0
342)
(0.0
344)
(0.0
185)
(0.0
162)
Min
iste
r0.1
34***
0.1
12***
0.1
11**
0.1
57***
0.1
39***
0.0
417
0.0
876*
(0.0
355)
(0.0
430)
(0.0
494)
(0.0
538)
(0.0
385)
(0.0
529)
(0.0
466)
log(N
etA
sset
s)-0
.0728***
-0.0
729***
-0.0
749***
-0.0
826***
-0.0
717***
-0.0
733***
-0.0
362***
-0.0
718***
(0.0
085)
(0.0
105)
(0.0
121)
(0.0
146)
(0.0
087)
(0.0
086)
(0.0
101)
(0.0
102)
Gov
ernm
ent
0.0
143
0.0
217
0.0
307
-0.0
048
(0.0
382)
(0.0
383)
(0.0
198)
(0.0
177)
Win
ner
*G
over
nm
ent
0.0
021
-0.0
442
(0.0
712)
(0.0
728)
Const
ant
1.3
09***
1.3
04***
1.3
26***
1.4
37***
1.2
87***
1.3
10***
0.7
80***
1.2
97***
(0.1
29)
(0.1
60)
(0.1
83)
(0.2
21)
(0.1
31)
(0.1
30)
(0.1
55)
(0.1
55)
Clo
seE
lect
ions:
Marg
in≤
10
Marg
in≤
5M
arg
in≤
3
Obse
rvati
ons
1100
740
436
268
1100
1100
1074
1042
R-s
quare
d0.5
96
0.6
12
0.6
64
0.6
77
0.5
86
0.5
96
0.5
22
0.5
65
34
Table 6: Incumbency
Notes: The table shows results for the constituency fixed-effects regression model and investigates the effectsof incumbency. Net asset growth of the politician is the dependent variable. Winner is 1 if the politicianwon election e=1 and 0 if the politician did not win. Incumbent is the dummy for incumbency. We alsoinclude an interaction term between Winner and Incumbent. Minister indicates whether the constituencywinner was appointed to the state’s Council of Ministers. Log(Net Assets) is the logarithm of the politician’snet assets. Robust standard errors are given in parentheses. The reported constant is the average value ofthe fixed effects. Coefficients with ***, **, and * are statistically significant at the 1%, 5%, and 10% levels,respectively.
(1) (2)Variables Net Asset Growth Net Asset Growth
Winner -0.0455* -0.0530**(0.0264) (0.0262)
Incumbent -0.0864*** -0.0819***(0.0312) (0.0311)
Winner*Incumbent 0.203*** 0.179***(0.0597) (0.0596)
Minister 0.117***(0.0366)
log(Net Assets) -0.0736*** -0.0737***(0.0086) (0.0085)
Constant 1.356*** 1.356***(0.1310) (0.1290)
Observations 1100 1100R-squared 0.596 0.604
35
Table 7: Effect of Asset Growth on Reelection
Notes: The following constituency fixed-effects model is run: Reelectionwc = αc + β1 ∗ Winnerwc + β2 ∗Net Asset Growthwc+β3∗Winnerwc∗Net Asset Growthwc+εwc where Reelectionwc is an indicator variablethat takes on a value of 1 if the candidate won election e = 2 and 0 otherwise. Robust standard errors aregiven in parentheses. The reported constant is the average value of the fixed effects. Coefficients with ***,**, and * are statistically significant at the 1%, 5%, and 10% levels, respectively.
(1) (2)Variables Reelection Reelection
Winner -0.00608 -0.0593(0.0378) (0.0639)
Net Asset Growth 0.141 0.0235(0.1190) (0.1640)
Winner*Net Asset Growth 0.242(0.2360)
Constant 0.392*** 0.416***(0.0362) (0.0428)
Observations 1098 1098R-squared 0.204 0.205
36
Tab
le8:
Oth
er
Can
did
ate
Ch
ara
cte
rist
ics
Note
s:O
ther
chara
cter
isti
csanaly
zed
incl
ude
corr
upti
on,
crim
inal
reco
rd,
const
ituen
cies
rese
rved
for
SC
/ST
candid
ate
s,gen
der
,and
yea
rsof
educa
tion.
Ther
eare
inte
ract
ion
term
sfo
rW
inner
andCorruption
,andMinister
andCorruption
,w
her
eco
rrupti
on
ism
easu
red
at
the
state
-lev
elas
outl
ined
inth
eva
riable
des
crip
tion.CriminalRecord
isa
dum
my
equal
toone
ifa
crim
inal
reco
rdw
as
pre
sent
as
of
the
firs
tel
ecti
on.SC/ST
Quota
isa
dum
my
for
whet
her
or
not
the
const
ituen
cyof
the
candid
ate
isth
at
of
adis
adva
nta
ged
gro
up,
so-c
alled
Sch
edule
dT
rib
esand
Cast
es(S
C/ST
).Fem
ale
isth
edum
my
for
the
gen
der
of
the
candid
ate
.log(Years
ofEducation)
isth
elo
gari
thm
of
the
yea
rsof
educa
tion
the
candid
ate
has
rece
ived
.R
obust
standard
erro
rsare
giv
enin
pare
nth
eses
.T
he
rep
ort
edco
nst
ant
isth
eav
erage
valu
eof
the
fixed
effec
ts.
Coeffi
cien
tsw
ith
***,
**,
and
*are
stati
stic
ally
signifi
cant
at
the
1%
,5%
,and
10%
level
s,re
spec
tivel
y.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Net
Ass
etN
etA
sset
Net
Ass
etN
etA
sset
Net
Ass
etN
etA
sset
Net
Ass
etN
etA
sset
Vari
ab
les
Gro
wth
Gro
wth
Gro
wth
Gro
wth
Gro
wth
Gro
wth
Gro
wth
Gro
wth
Win
ner
-0.0
464
-0.0
144
0.0
290*
0.0
134
0.0
141
0.0
256**
0.3
96**
0.0
448
(0.0
819)
(0.0
833)
(0.0
161)
(0.0
134)
(0.0
132)
(0.0
127)
(0.1
750)
(0.1
220)
Win
ner
*C
orr
up
tion
0.0
158
0.0
0651
(0.0
163)
(0.0
165)
Min
iste
r-0
.269
(0.2
660)
Min
iste
r*C
orr
up
tion
0.0
755
(0.0
534)
Cri
min
al
Rec
ord
0.0
144
(0.0
288)
Win
ner
*C
rim
inal
Rec
ord
0.0
00342
(0.0
392)
SC
/S
TQ
uota
*W
inn
er0.0
847***
0.0
875***
(0.0
324)
(0.0
313)
SC
/S
TQ
uota
-0.0
734**
(0.0
329)
Fem
ale
-0.0
956*
(0.0
570)
Win
ner
*F
emale
0.0
595
(0.0
770)
log(Y
ears
of
Ed
uca
tion
)0.0
414
(0.0
478)
log(Y
ears
of
Ed
uca
tion
)*W
inn
er-0
.139**
(0.0
656)
Win
ner
*lo
g(B
ase
Sala
ry)
-0.0
024
(0.0
130)
log(N
etA
sset
s)-0
.0725***
-0.0
730***
-0.0
714***
-0.0
714***
-0.0
699***
-0.0
709***
-0.0
730***
-0.0
734***
(0.0
092)
(0.0
092)
(0.0
087)
(0.0
084)
(0.0
068)
(0.0
085)
(0.0
091)
(0.0
093)
Con
stant
1.3
02***
1.3
09***
1.2
83***
1.2
87***
1.2
77***
1.2
86***
1.2
01***
1.3
18***
(0.1
390)
(0.1
380)
(0.1
340)
(0.1
280)
(0.1
030)
(0.1
290)
(0.1
760)
(0.1
400)
Ob
serv
ati
on
s960
960
1099
1100
1100
1100
1061
995
R-s
qu
are
d0.5
79
0.5
87
0.5
86
0.5
91
0.4
19
0.5
88
0.5
97
0.5
92
37
Tab
le9:
Regre
ssio
nD
isconti
nu
ity
Desi
gn
Note
s:In
this
table
,w
ere
port
resu
lts
from
regre
ssio
ndis
conti
nuit
ysp
ecifi
cati
on.
We
firs
tgen
erate
resi
duals
by
regre
ssin
ggro
wth
innet
ass
ets
on
candid
ate
obse
rvable
s,in
cludin
gnet
ass
ets,
gen
der
,and
age,
but
excl
udin
gw
inner
dum
my
and
marg
in.
We
then
collapse
the
resi
duals
on
marg
inin
terv
als
of
size
0.5
(marg
ins
rangin
gfr
om
-25
to+
25)
and
then
esti
mate
the
follow
ing
spec
ifica
tion:R̄
i=α
+τ·D
i+β·f
(Margin
(i))
+η·D
i·f
(Margin
(i))
+ε i
wher
eR̄
iis
the
aver
age
resi
dual
valu
ew
ithin
each
marg
inbin
i,Margin
(i))
isth
em
idp
oin
tof
the
marg
inbin
i,D
iis
an
indic
ato
rth
at
takes
ava
lue
of
1if
the
mid
poin
tof
marg
inbin
iis
posi
tive
and
ava
lue
of
0if
itis
neg
ati
ve,
andε i
isth
eer
ror
term
.f
(Margin
(i))
andD
i·f
(Margin
(i))
are
flex
ible
fourt
h-o
rder
poly
nom
ials
.T
he
goal
of
thes
efu
nct
ions
isto
fit
smooth
edcu
rves
on
eith
ersi
de
of
the
susp
ecte
ddis
conti
nuit
y.T
he
magnit
ude
of
the
dis
conti
nuit
y,τ,
ises
tim
ate
dby
the
diff
eren
cein
the
valu
esof
the
two
smooth
edfu
nct
ions
evalu
ate
dat
0.
Inco
lum
n(1
),w
ere
port
resu
lts
usi
ng
the
enti
resa
mple
of
const
ituen
cym
atc
hed
win
ner
sand
runner
s-up;
(2)
only
incl
udes
Ministers
wit
hco
rres
pondin
gru
nner
s-up,
and
(3)
only
incl
udes
win
ner
snot
app
oin
ted
toth
eC
ounci
lof
Min
iste
rsand
corr
esp
ondin
gru
nner
s-up.
Inco
lum
ns
(4)-
(5),
we
dis
aggre
gate
the
sam
ple
base
don
whet
her
an
incu
mb
ent
isst
andin
gfo
rre
elec
tion
inth
eco
nst
ituen
cy.
Colu
mn
(4)
show
sre
sult
sfo
rth
esa
mple
of
const
ituen
cies
wher
ean
incu
mb
ent
was
standin
gfo
rre
elec
tion;
colu
mn
(5)
use
sth
esa
mple
of
non-i
ncu
mb
ent
const
ituen
cies
.C
oeffi
cien
tsw
ith
***,
**,
and
*are
stati
stic
ally
signifi
cant
at
the
1%
,5%
,and
10%
level
s,re
spec
tivel
y.R
obust
standard
erro
rsare
giv
enin
pare
nth
eses
.
(1)
(2)
(3)
(4)
(5)
Net
Ass
etN
etA
sset
Net
Ass
etN
etA
sset
Net
Ass
etV
ari
able
sG
row
thR
esid
ual
Gro
wth
Res
idual
Gro
wth
Res
idual
Gro
wth
Res
idual
Gro
wth
Res
idual
τ0.0
652***
0.2
869***
0.0
265
0.0
799***
0.0
283
(0.0
233)
(0.0
546)
(0.0
234)
(0.0
250)
(0.0
358)
Sam
ple
:A
llW
inner
sM
inis
ters
Non-M
inis
ters
Incu
mb
ent
Non-I
ncu
mb
ent
Const
ituen
cies
Const
ituen
cies
Obse
rvati
ons
1065
144
921
797
268
Marg
inB
ins
98
68
95
91
83
R-s
quare
d0.1
19
0.3
99
0.0
60.2
10.2
84
38
Figure 1: Kernel Densities of Asset Growth Residuals
Notes: This figure plots Epanechnikov kernel densities of residuals obtained from regressing growth in netassets on candidate observables (characteristics such as net assets, gender, and age but excluding winnerdummy and margin) for the sample of constituency-matched candidates. Panel A uses the entire sample ofconstituency-matched candidates while Panel B only uses candidates that were within a margin of 5 percentagepoints (“close elections”). In both cases, the Kolmogorov-Smirnov test for equality of the distribution functionof winner and runner-up residuals is rejected at the 1% level and 5% level, repectively. In Panel C, we furtherdisaggregate winners into ministers and non-ministers and plot kernel densities of these two groups as well asthe runners-up. In Panels D and E, we disaggregate the sample based on whether an incumbent is standing forreelection in the constituency. Panel D shows winner and runner-up densities for the sample of constituencieswhere an incumbent was standing for reelection - test for equality of the distribution function is rejected atthe 1% level. Panel E shows densisties for the subsample of non-incumbent constituencies - test for equalityof the distribution function is rejected at the 10% level.
01
23
4D
ensi
ty
-.5 0 .5Residuals
WinnerRunner-up
Winners and Runner-upsPanel A
39
01
23
4D
ensi
ty
-.4 -.2 0 .2 .4Residuals
WinnerRunner-up
Winners and Runner-ups in Close Elections (Margin within 5%)Panel B
01
23
4D
ensi
ty
-.5 0 .5Residuals
Runner-upWinner (Non-Minister)Minister
Runner-ups, Ministers and Non-MinistersPanel C
40
01
23
4D
ensi
ty
-.5 0 .5Residuals
WinnerRunner-up
Constituencies with Incumbent standing for reelectionPanel D
01
23
Den
sity
-.5 0 .5Residuals
WinnerRunner-up
Constituencies without Incumbent standing for reelectionPanel E
41
Figure 2: Regression Discontinuity Design
Notes: This figure investigates residuals obtained from regressing growth in net assets on candidate observables(characteristics such as net assets, gender, age and incumbency but excluding winner dummy and margin) asa function of winning margin for the sample of constituency-matched candidates. We first collapse residualson margin intervals of size 0.5 (margins ranging from -25 to +25) and then estimate the following equation:R̄i = α+ τ ·Di + β · f(Margin(i)) + η ·Di · f(Margin(i)) + εi where R̄i is the average residual value withineach margin bin i, Margin(i)) is the midpoint of the margin bin i, Di is an indicator that takes a valueof 1 if the midpoint of margin bin i is positive and a value of 0 if it is negative, and εi is the error term.f(Margin(i)) and Di · f(Margin(i)) are flexible fourth-order polynomials. Panel A shows results using thesample of all winners sand runners-up; Panel B only includes Ministers with corresponding Runners-up; PanelC only includes winners that were not appointed to the Council of Ministers with corresponding Runners-up.In Panels D and E, we disaggregate the sample based on whether an incumbent is standing for reelection inthe constituency. Panel D shows results for the sample of constituencies where an incumbent was standingfor reelection; Panel E shows the subsample of non-incumbent constituencies.
-.2-.1
0.1
.2R
esid
uals
-20 0 20Margin Bin
Runner-ups and WinnersPanel A
42
-.4-.2
0.2
.4R
esid
uals
-20 0 20Margin Bin
Runner-ups and MinistersPanel B
-.20
.2R
esid
uals
-30 -20 -10 0 10 20Margin Bin
Runner-ups and Non-MinistersPanel C
43
-.20
.2R
esid
uals
-30 -20 -10 0 10 20Margin Bin
Constituencies with Incumbent standing for reelectionPanel D
-.50
.5R
esid
uals
-20 0 20Margin Bin
Constituencies without Incumbent standing for reelectionPanel E
44
Figure 3: Kernel Densities of Observables Characteristics in Close Elections
Notes: This figure plots Epanechnikov kernel densities of age and log(Net Assets) for the sample ofconstituency-matched candidates that were within a Margin of 5 percentage points (“close elections”). PanelA plots age densities for winners and runners-up and Panel B plots densities for log(Net Assets). For bothobservables, the Kolmogorov-Smirnov test for equality of the distribution function of winner and runner-upcannot be rejected at conventional levels.
0.0
1.0
2.0
3.0
4D
ensi
ty
20 40 60 80Age of Candidate
WinnerRunner-up
Winners and Runner-ups in Close Elections (Margin within 5%)Panel A
45
0.1
.2.3
.4D
ensi
ty
12 14 16 18 20 22Log(Net Assets)
WinnerRunner-up
Winners and Runner-ups in Close Elections (Margin within 5%)Panel B
46
Figure 4: Recontesting of Candidates
Notes: We investigate recontesting decisions for winners and runners-up. In a first stage we obtain residualsfrom regressing a binary recontesting dummy on candidate observables (characteristics such as age, gender,incumbency but excluding winner dummy and margin) and constituency fixed effects. Residuals are thencollapsed into margin bins and a polynomial is fitted to the data. In Panel A, we show results for the fullsample (5246 candidates), indicating a significant jump in recontesting around the winning threshold (pointestimate of 0.1328 with a t-statistic of 6.41). In Panel B, we show results for for the subsample of seasonedpoliticians (322 candidates) - no jump in recontesting can be detected around the threshold. In Panel C,we analyze net asset growth residuals for the subsample of seasoned politicians. The point estimate of thediscontinuity is 0.1206 and significant at the 5% level (t-statistic of 2.19).
-.4-.2
0.2
Res
idua
ls
-20 -10 0 10 20Margin Bin
Recontesting Probability ResidualsPanel A
47