paper-democracy

paper-dem.txt

Democratic Backsliding in the World’s Largest Democracy
Sabyasachi Das*
Ashoka University
July 3, 2023
Abstract
Democratic backsliding is a growing concern globally. This paper contributes to the discussion
by documenting irregular patterns in 2019 general election in India and identifying whether they are
due to electoral manipulation or precise control, i.e., incumbent party’s ability to precisely predict
and affect win margins through campaigning. I compile several new datasets and present evidence
that is consistent with electoral manipulation in closely contested constituencies and is less supportive of the precise control hypothesis. Manipulation appears to take the form of targeted electoral
discrimination against India’s largest minority group – Muslims, partly facilitated by weak monitoring by election observers. The results present a worrying development for the future of democracy.
JEL Codes: D72, D73, P00, Z12
Keywords: Electoral fraud, precise control, democracy, economics of religion
*Das: Economics Department, Ashoka University, National Capital Region, India. Email: sabyasachi.das@ashoka.edu.in.
The author wishes to thank Lakshmi Iyer, Ajay Shenoy, Sam Asher, Milan Vaishnav, Amrita Dhillon, Sourav Bhattacharya,
Siddharth George, Rohit Lamba, Gaurav Chiplunkar, Raphael Susewind, Jonathan Lehne, Dev Patel, Neelanjan Sircar, Aaditya Dar, Vimal Balasubramaniam and Sugat Chaturvedi for valuable comments. Rakesh Kumar provided excellent research
assistance. The author is responsible for errors, if any.
Electronic copy available at: https://ssrn.com/abstract=4512936
I Introduction
Free and fair elections are cornerstone of a democracy. Yet in many democracies, the fairness of elections is increasingly in doubt. In 2020, for example, the Constitutional court of Malawi declared the
Presidential election to be fraudulent.1 The 2019 Presidential election result in Bolivia is also reported
to be have been manipulated (Escobari and Hoover 2020). In the US, one-third of voters believe that
Joe Biden won the 2020 Presidential election solely because of voter fraud, even though there is no
evidence favoring such a claim.2 Even before the election, the share of American voters reported to have
confidence in the honesty of elections has been declining for several years, and in 2019, stood at only
40%.
3 This figure is 50% for the entire world, according to the Gallup World Poll (2007-13).
The global erosion of trust in electoral institutions coincides with the autocratizing tendencies of
several democracies, known as democratic backsliding or deconsolidation (Waldner and Lust 2018, Foa
and Mounk 2016, 2017a,b). Freedom House 2021 report points out that global freedom deteriorated for
15 consecutive years, with 75 percent of the world living in a country that experienced deterioration in
2020. Democracy Report (2020) mentions: “For the first time since 2001, democracies are no longer
in the majority. [...] The countries that have autocratized the most over the last 10 years are Hungary,
Turkey, Poland, Serbia, Brazil and India.” While the overall pattern of democratic backsliding is based
primarily on subjective evaluation by experts, objective evidence on this trend is lacking (Little and
Meng 2023).
I contribute to this important debate by examining objective evidence of democratic backsliding
in the form of electoral manipulation in the world’s largest democracy – India. India is a somewhat
unusual case for electoral fraud as it stands out in terms of the public trust its election authority enjoys.
Two-third of its voters reported to have confidence in the honesty of elections in 2019, based on the
Gallup Poll survey. Moreover, the confidence in elections is rising in India at least since 2006.4 The
level of confidence is also higher than many democracies with strong institutions, such as Japan (57%),
France (57%), UK (61%) etc. The independence and institutional strength of the electoral authority
in charge of conducting elections, the Election Commission of India (ECI), is an important factor that
can potentially explain such high degree of confidence.5 This makes the ECI one of the most powerful
election management bodies in the world.
In the past few years, however, the credibility of the ECI has been called into question, with allegations of bias in scheduling of elections (Ramachandran 2022) and arbitrary deletion of names of registered Muslim voters (Malhotra 2019, Trivedi 2019, Naqvi 2022), both favoring the ruling party. The
recent democracy reports of the V-Dem Institute highlight that various indicators of democracy in India,
including the autonomy of the ECI, has been declining. Democracy Report (2021) have consequently
classified India as an “electoral autocracy”. As the V-Dem report points out, decline in the autonomy
of the ECI was one of the important factors contributing to the reclassification of India’s regime type.
Similarly, Freedom House has changed India’s status in 2021 from Free to Party Free (Repucci and
Slipowitz 2021). The Supreme Court of India, in a recent judgement in 2023, acknowledged the dangers
1
https://www.nytimes.com/2020/02/03/world/africa/Malawi-president-election-fraud.html
2
https://www.monmouth.edu/polling-institute/reports/MonmouthPoll_US_031721/
3
https://news.gallup.com/poll/285608/faith-elections-relatively-short-supply.aspx
4
https://news.gallup.com/poll/248495/confidence-key-institutions-high-india-votes.aspx
5
Section II provides a brief discussion on the independence of election authorities in India and the contextual details of
India’s general elections.
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of a weak ECI and granted it significant autonomy and protection from executive overreach.6
In light of these developments, I first document that the 2019 general election in India that reelected
the incumbent party shows significant irregularities in the election data – the density of the incumbent
party’s win margin variable exhibits a discontinuous jump at the threshold value of zero. It implies that in
constituencies that were closely contested between a candidate from the incumbent party and a rival, the
incumbent party (BJP) won disproportionately more of them than lost. This is known as the McCrary
test and is now a standard check for manipulation of running variable in the regression discontinuity
design (RDD) method used in analysis of political economy (Prakash et al. 2019, Nellis et al. 2016,
Bhalotra et al. 2014). I do not find similar discontinuities in the previous general elections for either
BJP or INC (Indian National Congress), the other major national party, as well as for state assembly
elections held simultaneously with the 2019 general election and those held subsequently. Moreover,
BJP’s disproportionate win of closely contested constituencies is primarily concentrated in states ruled
by the party at the time of election.
Failure of McCrary test however does not necessarily imply electoral fraud. If the incumbent party,
due to its superior electoral machinery, was able to accurately predict and affect win margins in closely
contested constituencies – a phenomenon known as precise control (Jeong and Shenoy 2020, Vogl 2014),
then it could also generate such patterns. The incumbent party in India may have been able to exercise
precise control in 2019 since it had significantly built up its organizational capacity in several states,
subsequent to its 2014 general election victory. It mobilized active party workers at the level of polling
stations who monitored and shaped voter attitudes, backed by centrally managed teams analyzing the
collected information and suggesting campaign strategies (Jha 2017). Precise control in this context,
therefore, if exercised, is likely to be facilitated by localized and targeted campaigning facilitated by
grassroots presence of the party organization. This can explain the patterns described above. In the
subsequent analysis I attempt to look for evidence that may distinguish between the two competing
hypotheses.7
For my analysis, I put together several new datasets in addition to accessing the candidate level
general election results for 1977-2019 and state assembly election results for 2019-2021 from standard
sources. To examine precise control, I access the well-established post-poll survey – the National Election Survey (NES) of 2019 that gives micro data on election campaigning by political parties. To investigate election manipulation, I compile two different but official versions of constituency level Electronic
Voting Machine (EVM) turnout data (for 2019 general election) to directly measure data discrepancy.
The ECI initially released in its official website the “final” count of EVM votes polled for each Parliamentary Constituency (PC) for the first four out of seven phases of the 2019 elections (373 out of 543
PCs). Subsequently, it released constituency wise number of votes counted in EVMs, which did not
match the initial numbers. When the media pointed out the discrepancy, the ECI removed the earlier
figures from its website. I access copies of the earlier turnout data to measure discrepancy. I also put
together the list of counting observers assigned to each constituency by the ECI to monitor counting
of votes in 2019. The data provides various characteristics of the counting observers such as the state
where they work, their cadre (i.e., whether they are part of the central or state bureaucracy), year of
joining service etc. Additionally, I compile polling station level election outcomes for the 2019 general
6
https://www.thehindu.com/news/national/committee-of-pm-lop-cji-to-advice-on-appointment-of-electioncommissioners-supreme-court/article66570806.ece
7The excess mass of constituencies that BJP barely won could also arise purely due to chance.
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election by scraping and parsing the scanned PDFs containing the data, available from the official websites of election authorities in individual states. To examine targeted voter suppression of Muslims as a
potential mechanism of manipulation, I compute electorate share of Muslims at the level of Assembly
Constituencies (ACs) using a 3 percent representative sample of voter lists and applying a highly accurate religion prediction algorithm on their names, and match it to polling stations and ACs.8
. Section III
describes the datasets and their sources.
I use a new question added to the NES in 2019 to measure campaigning in the form of door-to-door
visits by BJP and other political parties in a representative sample of PCs to directly test for precise
control. I find that neither BJP nor any other party campaigned significantly harder in constituencies
that BJP barely won. Moreover, in BJP ruled states, campaigning by BJP does not exhibit statistically
significant discontinuity, while that for the other parties does. This makes precise control less likely to
be the primary mechanism.
Electoral manipulation, on the other hand, can take place at the stage of voter registration (registration manipulation) or at the time of voting or counting (turnout manipulation). To examine the mechanisms facilitating manipulation, I focus on Muslim voters who generally do not support BJP (Varshney
2019), and are easily identified in the voter list due to their culturally distanct names. Therefore, they
are potentially subject to both registration and turnout manipulation.9
I consider two channels; first,
strategic deletion of Muslim names from the list of registered voters or electoral rolls (Lehne 2022).
Second, strategic suppression of Muslim votes at the time of voting (or counting) (Neggers 2018). I do
not consider the possibility of manipulation of EVMs themselves as a mechanism, as Purkayastha and
Sinha (2019) have pointed out that given its technology, it is hard to manipulate them at scale.
To test for registration manipulation, I compute growth rate of electorate (i.e., number of registered
voters) for each Parliamentary Constituency (PC) between 2014 and 2019. I show that the growth rate
falls discontinuously by 5 percentage points (compared to mean of 0.09) in PCs barely won by BJP, and
the fall is concentrated in PCs with higher share of Muslim electorate. To examine turnout manipulation,
I first examine the absolute difference between the two official versions of EVM turnout data. The
discrepancies could be due to administrative errors during counting of votes. However, the extent of
discrepancy, in that case, should not exhibit any discontinuous change with respect to the incumbent’s
win margin at its threshold value of zero. I however find that there is a large discontinuous increase in
the magnitude of data revision at the threshold. Consistent with previous results, the discontinuity is
concentrated in BJP ruled states.
I interpret the evidence on turnout discrepancy as indicative of manipulation done locally at the
polling stations, rather than resulting from aggregation fraud at the constituency level (Callen and Long
2015). It is unlikely that ECI would engage in direct tampering of turnout data ex-post. Moreover,
barring one case, the magnitude of data revision is smaller than BJP’s absolute margin of victory. I show
that polling station level election outcomes in the relevant PCs exhibit irregularities consistent with local
8Census data on religious composition of population is not ideal in this case, since the lowest level of geographic unit for
which such data is available is tehsil, which (a) does not always map to a single AC and (b) hard to map to polling stations,
since the map of geographic area covered by a polling station is not available and data on location of polling stations is also
error-prone (Hintson and Vaishnav 2021). Additionally, electorate share of Muslims is the ideal measure, which can differ
from their population share because of various reasons such as differential fertility and child survival rates etc., which could be
correlated with their support for BJP.
9Religious identity, especially the Hindu-Muslim divide, is a salient political cleavage in India (Bhalotra, Clots-Figueras,
Iyer, and Vecci 2021, Varshney 2003). Moreover, the salience of religion has heightened under BJP’s rule since 2014 (Khosla
and Vaishnav 2022).
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manipulation.
To examine whether turnout manipulation was in part facilitated by weak monitoring of counting of
votes, I analyze the assignment of counting observers across PCs. I compute the fraction of counting
observers assigned in a PC who are from the State Civil Service (SCS), as opposed to the Indian Administrative Service (IAS).10 Since SCS officers are appointed by the state government, unlike the IAS
officers who are centrally appointed, they more likely to be politically pliable. I also compute the fraction of observers in a PC who are SCS and work in a BJP ruled state.11 I find that both fraction exhibits
large, positive and statistically significant discontinuity at the BJP win margin of zero. For the fraction
of SCS officers from BJP ruled states, the discontinuity is larger in magnitude in PCs of BJP ruled states,
while it is smaller and statistically insignificant for non-BJP ruled states. Additionally, in PCs won by
BJP, the fraction of counting observers who are SCS and come from BJP ruled states positively predicts
the extent of turnout data discrepancy in the PC; in PCs that BJP lost, no such relationship holds.
I analyze polling station level election results to test for local manipulation. For each polling station,
I compute the vote share of BJP at that polling station relative to its vote share in the PC; I refer to this
as the relative BJP vote share. This makes comparison of polling stations across constituencies easier.
I show that within a constituency, relative BJP vote share typically hovers around one across polling
stations with different turnout, except in closely contested constituencies barely won by BJP in BJP ruled
states. In those constituencies, the relative vote share of BJP exhibits a large spike in polling stations with
high turnout. The pattern is replicated with a polling station level indicator of BJP’s vote share exceeding
95th percentile of its distribution.12 I compute the distribution of second digit in the polling station level
vote tallies of candidates to measure departure from Benford’s law at the polling station level. Benford’s
law (Benford 1938) specifies distribution of digits in naturally occurring numbers, and departures of
the observed distribution from Benford’s specification is often used as an indicator of manipulation. I
show that the departures from Benford’s law exhibit the same pattern. Additionally, I perform tests on
the shape of the BJP’s vote share density, proposed in more recent research on electoral fraud, and find
results consistent with fraud. Moreover, the spike in the relative BJP vote share mentioned above is
higher in PCs with larger discrepancy in turnout data. While the first couple of results are consistent
with both mechanisms, the rest of the results indicate manipulation.
Finally, manipulation in the form of targeted electoral discrimination against Muslim voters would
imply that within a PC barely won by BJP, high vote shares of the party should be concentrated in
areas with higher Muslim presence. On the other hand, if precise control is the appropriate explanation,
then we should expect the opposite, as the increase in BJP’s vote share in 2019 relative to 2014 came
primarily from Hindus, especially from its lower caste groups, while its support among Muslims was
low and constant across the two elections (Varshney 2019).
I match the data on AC level electorate share of Muslims (described above) to polling stations to test
the above hypothesis. ACs are smaller than PCs, and each PC contains about 7 ACs on average. The
matched data therefore provide us within PC variation in electorate share of Muslims across polling stations located in different ACs. I find that in PCs that BJP barely lost, its vote share is less likely to exceed
the 95th percentile in polling stations located in high Muslim share ACs within the PC. However, this
negative relationship gets significantly reduced in PCs barely won by the party; in those PCs, the likeli10There are typically multiple counting centers in a PC, each of which is assigned a counting observer.
11Observers are deployed in a state different from where they work.
12In those polling stations, BJP on average received 90% of votes cast.
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hood of the event does not fall in ACs with higher Muslim share. This again supports the manipulation
hypothesis.
The paper is unable to comment on the overall extent of manipulation in the 2019 general election.
It focuses on closely contested constituencies as an empirical strategy to detect the presence of potential manipulation. Back of the envelope calculation shows that in PCs with BJP win margin less than
5%, BJP’s “excess” win is in about 11 PCs. Therefore, even if all the disproportionate wins of BJP in
closely contested PCs is due to manipulation, it likely would not have changed the government formation.13 Nonetheless, the results signify a worrying development for the future of democracy in India and
consequently, in the world at large.
This paper contributes to our understanding of democratic backsliding in consolidated democracies
using objective measures. Little and Meng (2023) argue that subjective evaluation of nature of democracy by experts may be subject to their biases. Therefore, claims about democratic backsliding need to
be grounded in more objective evidence. The authors examine objective measures of democracy and do
not find any evidence of systematic backsliding across democracies. They conclude, “[...] it may be the
case that major backsliding is occurring precisely in ways that elude objective measurement. However,
this is an extraordinary claim, which requires a stronger theoretical and empirical basis than has been
offered to date.”
Additionally, several studies examining democratic backsliding have focused on the “demand side”
issues, specifically, voters’ willingness to sacrifice democratic principles in the context of increased polarization (Braley et al. 2022, Fishkin et al. 2021, Graham and Svolik 2020), rise of populism (Martinelli
2016) etc., resulting in dismantling of check-and-balances (¸Sa¸smaz, Yagci, and Ziblatt 2022). The paper
shows that dilution of electoral integrity is also an important and “supply side” contributor to democratic
backsliding. Several consolidated or stable democracies, such as India14, Mexico15, Hungary (Scheppele
2022), have witnessed weakening of its electoral institutions in recent times. It is relevant to understand
whether and how this weakening contributes to democratic backsliding. There is little evidence in mature
democracies of direct electoral fraud typically observed in weaker democracies, such as ballot stuffing,
booth capturing or direct manipulation of data by election authorities. Incumbents in these countries are
likely to adopt subtler strategies, such as fragmentation of opposition (Arriola, Devaro, and Meng 2021)
or voter suppression (Manheim and Porter 2019) etc. My examination of the latest general election in
India adds to our understanding of this process.
Empirical analyses of electoral fraud have typically focused on weak democracies such as Afghanistan
(Callen and Long 2015), Ghana (Asunka et al. 2019), Nigeria (Onapajo and Uzodike 2014), Russia
(Enikolopov et al. 2013, Rundlett and Svolik 2016), Mexico during 1980s (Cantú 2019), nineteenth
century Germany (Ziblatt 2009), or local elections in robust democracies such as Japan (Fukumoto and
Horiuchi 2011). In these cases, the nature of fraud typically entails aggregation fraud, tampering of election documents at the polling station level etc. In case of India, my paper shows, manipulation took the
form of localized and targeted discrimination against a well-identified minority group, via manipulation
of voter registration as well as weaker monitoring of the election process.
Previous studies have employed several methods to detect electoral fraud – Cantoni and Pons (2020)
use sampled data on proven and suspected fraud cases, Asunka et al. (2019) and Enikolopov et al.
13BJP won 303 PCs and it needed 272 PCs to form the government.
14https://www.telegraphindia.com/india/election-commission-weak-kneed-say-former-officials/cid/1688448
15https://www.bbc.com/news/world-latin-america-64742733
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(2013) examine effect of poll observers on incumbent vote share, Christensen and Schultz (2014) analyze turnout behavior of specific voting groups more likely to be targeted for frauds, James and Clark
(2020) conduct survey of polling station workers etc. My paper contributes methodologically by analyzing irregularities across polling stations and constituencies with different demographic composition
of minority voters and applying regression discontinuity and difference-in-discontinuity designs. Additionally, papers on electoral fraud typically employ one specific method to detect fraud. In contrast,
this paper employs a combination of methods to demonstrate consistent results. This is of particular
importance given that the nature of irregularities is more subtle, as one may expect in a consolidated
democracy, and hence, requires a deeper examination.
II Background and Context
Autonomy of the Election Authority in India: Election Commission of India is the central authority
in charge of conducting national (and state) elections in India. It was established in 1950. Several
scholars have highlighted the exemplary role played by the ECI in ensuring free and fair elections and
consequently, in the consolidation of India’s democracy, in spite of its challenging social, cultural and
economic environment. Banerjee (2017), for example, says: “In contrast to the usual inefficiencies of
Indian public institutions, the well-oiled machinery of the Election Commission stands out because of
its excellent performance in conducting elections on an unimaginably large scale.” (p 410) Sridharan
and Vaishnav (2017) point out: “What has emerged over the past six-and-a-half decades is an Election
Commission that has significant powers, far greater than what its counterparts in many democracies have
at their disposal. [...] According to a 1996 poll conducted by the Centre for the Study of Developing
Societies, the ECI was the most respected public institution in all of India with 62 per cent of respondents
favourably disposed. A 2008 study found that an even higher percentage – nearly 80 per cent – of Indians
surveyed expressed a high degree of trust in the Commission, second only to the army among state
institutions.” (p 419 of Kapur et al. (2018)) Multiple researchers have shown that redistricting in India
does not suffer from gerrymandering, a common phenomenon in the US, thanks to the independence of
the Delimitation Commission of India from any political interference (Kjelsrud et al. 2020, Nath et al.
2017, Iyer and Reddy 2013). Eggers et al. (2015) in their study of elections across a number of countries
find no evidence of manipulation of election results in India for the period 1977-2004.
Elections in India: India follows a Parliamentary system. The Parliament has 543 legislatures or
Members of Parliament (MPs), each of whom is elected from a Parliamentary Constituency (PC) using
the first-past-the-post rule. The national or general elections in India are conducted every 5 years, unless
there is an early dissolution of the government. There are several parties that field candidates in the
general elections. The two main national parties are the BJP (Bhartiya Janata Party) and the INC (Indian
National Congress). Apart from the national parties, there are several regional or state parties that are
important political actors in specific states.
The ECI also conducts the state elections in India. In a state election, voters from each Assembly
Constituency (AC) elect one representative (Member of Legislative Assembly) to the state legislature.
The size of the legislature in a state depends on its population. Taken together, there are roughly 4, 300
ACs in India. An AC is always subsumed within a PC. The timing of state elections is not synchronized
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with the general elections. During every general election, a subset of states has their state assembly
elections simultaneously with it. But the subset of states changes over time due to either early dissolution
of state government, or the central government or both (Balasubramaniam et al. 2021). In 2019, the states
of Andhra Pradesh, Orissa, Arunachal Pradesh and Sikkim had simultaneous general and state elections.
General Election in 2019: The most recent general election in India happened in 2019 that reelected
the incumbent coalition (the National Democratic Alliance or NDA), led by the BJP, to power. There are
two significant developments related to the 2019 general elections that are worth highlighting. First, the
incumbent party, BJP, had built up its grassroots organizational presence significantly in the lead up to
the 2019 elections, especially in certain states. The party deployed it efficiently during its 2019 election
campaign, as discussed in Jha (2017). The author describes that the party created polling booth level
committees who were in charge of connecting with voters enrolled in the booth, organizing membership
drives, collecting household level data on various social, demographic and economic indicators, along
with their political attitudes. The households were classified according to their intention to vote for
the party to decide the party’s campaign strategy. This localized campaign, in conjunction with the
allocation of abundant campaign resources, gave the party an edge over the other parties.
At the same time, there were reports of mass deletion of voter names of minority groups from
electoral rolls (Malhotra 2019, Trivedi 2019, Naqvi 2022). Since the incumbent party enjoys lower
electoral support among the minority groups, such deletions may provide an electoral advantage to the
party. Additionally, subsequent to the elections, the ECI released two “final” versions of the PC level
EVM turnout data that did not match (Agarwal 2019). ECI did not provide any accounting of the data
discrepancy.16 These reports raise fears about possible electoral manipulation during the 2019 elections.
III Data
Aggregate Election Results: I first access the candidate level Parliamentary election results from
1977-2019 and state assembly election results of 2019-2021. It is published by the Election Commission
of India, and is compiled and made public by the Trivedi Centre for Political Data (TCPD) at Ashoka
University (Bhogale et al. 2019).17 The data contain for each PC (AC, in case of state election) and
each election year, details of candidate names, their party affiliations, votes received by candidates, total
turnout and electorate size.
Two Versions of EVM Turnout Data: The Election Commission of India (ECI) initially published
“Final Voter Turnout” figures for the first four (out of seven) phases of the 2019 general election.18
These figures reflect the PC wise number of votes polled in the Electronic Voting Machines (EVMs).
These numbers however do not match with the PC wise number of votes counted in the EVMs, as
available in the official website of the ECI. This is unusual as votes polled and votes counted in the
EVMs should be identical. The news media pointed out this discrepancy in the data, following which
16The Association for Democratic Reforms, an independent election watch body, has filed a petition in the Supreme Court
of India seeking reconciliation of the data: https://www.nationalheraldindia.com/india/adr-files-petition-in-supreme-court-onmismatch-in-evm-data.
17The data is publicly available from the TCPD’s website http://lokdhaba.ashoka.edu.in.
18For the rest of the PCs, it released the “estimated” turnout figures, and therefore, are not considered for analysis.
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the ECI removed the “Final Voter Turnout” figures from its website. The PDF copies of the data are
publicly available here: https://www.scribd.com/docu ment/411811036/EC-s-votes-polled-data-Phase1. I digitize the data and match it against the (revised) official EVM turnout figures available in the
ECI’s website: https://eci.gov.in/files/file/10969-13-pc-wise-voters-turn-out/.
Counting Observers in 2019: The ECI appoints officials who are responsible for overseeing and
monitoring the counting of votes in each counting center. They are referred to as counting observers.
The counting observers have the power to stop the counting process or not declare the results if they
find breach of counting procedure or if they suspect that some form of fraud has taken place. They
have to report to the ECI if they take such actions. I access the list of counting observers for 2019
general election from the official website of ECI. The data contain the names of officials assigned to
each PC, along with their ‘office state’, i.e., the state where they were currently working as a bureaucrat,
their ‘home state’, i.e., where they were born, whether they are an Indian Administrative Service (IAS)
officer or from the State Civil Service (SCS) and their year of joining the service. I am able to match
data for 539 PCs (out of 543) containing 1, 804 counting observers.
Polling Station Level Results: I put together polling station level election results for the 2019 general
election. Polling station level election records are available in each of the states’ Chief Electoral Officer’s
official website. The format of the data differs from state to state. While in one state the digitized data
is available, in most states the data come in the form of scanned PDFs containing the polling station
level results for each constituency. I scrape, digitize, clean and compile the results for 22 major states
of India covering more than 900, 000 polling stations. For each polling station, the data provide the PC
and AC it falls under, candidate-wise vote tallies (along with votes in favor of “None of the Above”)
and candidate’s party affiliation. This allows me to calculate the absolute turnout and vote share of BJP
at the polling station level. Except the state of Uttar Pradesh (UP), the data do not contain number of
electorates at the polling stations. Therefore, barring UP, it is not possible to calculate turnout rate at the
polling station level.
National Election Survey 2019: National Election Survey (NES) is a post-poll voter survey conducted by the Center for the Study of Developing Societies (CSDS). The surveys, conducted right after
every general election but before declaration of results, ask a representative sample of voters in a randomly selected sample of PCs questions about their political attitudes, knowledge and activities, among
other things. NES has been conducted regularly in India since 1990s and is a credible source of voter
preferences and political activities (Balasubramaniam et al. 2021, Banerjee et al. 2019, Thachil 2014). I
access the relevant sections of the NES 2019 data to examine the campaigning activities of parties. NES
2019 surveyed 24,236 voters across 208 PCs.
Muslim Electorate Share using Voter List: I create reliable estimates of Muslim electorate share
at the AC and PC level to examine electoral discrimination against Muslims as a potential source of
manipulation. For this, I use a 3 percent random sample of registered voters, representative at AC
level (about 25 million observations). I access this proprietary data from a private organization that has
compiled the full list of registered voters for the entire country using the electoral rolls published by
the ECI. The data uses electoral rolls published till 2018, and therefore, is not subject to any strategic
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deletion that may have happened during the 2019 revision prior to the general election. I use a religion
prediction algorithm (with 97 percent accuracy) developed by Chaturvedi and Chaturvedi (2023) to
predict each voter’s religion from their name, which allows me to compute Muslim share in each AC.19
Appendix Figure A1 plots the local polynomial relationship between vote share received by all Muslim
candidates running in an AC in a state assembly election (during 2008-2018) against the electorate
Muslim share and finds strong positive relationship. This indicates that my measure of AC level Muslim
share is reliable.
IV Irregularities in Aggregate Election Results
I first perform the McCrary test that checks for discontinuity in the distribution of win margin (of any
party) at the value of zero (Calonico et al. 2014, McCrary 2008) . The presence of discontinuity would
imply that there is disproportionately higher mass of closely contested electoral constituencies where
the party has barely won or lost, depending on whether the discontinuity is positive or negative. The
idea is that, if elections are fair, then conditional on an election being closely contested between party A
and any other party, the party A’s chance of winning would be close to 50%. This is because, whether
it ends up winning a really close election would effectively be random. I perform the McCrary tests by
computing the win margins for the two major national parties of India, namely BJP and INC. Any party
A’s win margin is defined as:
Party A win margin = (vote share of A - vote share of winner), if A loses
= (vote share of A - vote share of runner up), if A wins
BJP win margin therefore takes negative values in constituencies where it lost and positive values where
it won.20 When the variable takes values close to zero, it implies that BJP either lost or won the election
with a narrow margin. Similarly, a large negative (or positive) value would imply that BJP lost (or won)
that election with a large margin. Same is true for INC win margin. Figure 1a and 1b plot the densities
of BJP and INC win margins, respectively, for the 2019 general election. I observe a large discontinuous
jump in the density of BJP Win Margin just right of zero. This implies that conditional on a closely
contested election between a BJP candidate and another candidate, BJP was significantly more likely to
win that election than lose. I do not observe any discontinuity in the density of INC Win Margin.21
Before commenting on the interpretation of this finding, I wish to point out that failure of McCrary
test in electoral context is rare, both in India as well as internationally. I perform the test for past general
elections of India using BJP and INC win margins. Table 1 reports the estimated discontinuities in the
densities of the two variables for all general elections going back to 1977. I find that the BJP win margin
in 2019 is the only case exhibiting statistically significant estimate of the discontinuity. To illustrate
this point more clearly, Appendix Table A1 reports the number and percentage of constituencies BJP
won and lost in constituencies with small absolute BJP win margins for the past 4 general elections. I
consider three narrow win margin bands - within 0.05, 0.03 and 0.02. For each band, the 2019 elections
19I thank Sugat Chaturvedi for implementing the algorithm in the data.
20This is a standard definition in this kind of exercise. Nellis et al. (2016), for example, use the same running variable
defined for INC to estimate the causal effect of electing an INC politician on violence.
21It implies that the disproportionately higher wins of BJP candidates were primarily against regional parties.
10
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Figure 1—McCrary Tests Demonstrate Discontinuity in BJP Win Margin Distribution
(a) BJP Win Margin: All States (b) INC Win Margin: All States
(c) BJP Win Margin: BJP Ruled States (d) BJP Win Margin: Non-BJP Ruled States
show the most lop-sided share of win for BJP; the share of BJP victory is 69-74% in 2019, depending on
bandwidth. For each bandwidth, 2019 is the only year where the data rejects the null that the likelihood
of BJP victory is 0.5. In each case, the null hypothesis is rejected with with p-value less than 0.01, i.e.,
there is less than 1% probability of observing the patterns with BJP’s true probability of victory in close
elections being 0.5.
Nellis, Weaver, Rosenzweig et al. (2016) find that INC win margin passes the McCrary test in state
assembly elections for the period 1962–2000. Uppal (2009) find the same using incumbent win margin
as the running variable for state elections during the period 1975–2003. Moreover, the only evidence of
failure of McCrary test that has been documented in a robust democracy, is in the context of elections in
the US (Jeong and Shenoy 2020, Vogl 2014, Caughey and Sekhon 2011). Eggers et al. (2015), however,
have shown that it is in fact an exception as the test works in a number of countries (including the US
and India) and for different time periods. Hence, the failure of the test in the 2019 general election in
India warrants notice and additional investigation.
Interpretation: While the result is consistent with possible manipulation of the election results in
favor of the BJP, the incumbent party, it is not the only interpretation. Alternatively, it could be that BJP,
being the incumbent, was able to exercise precise control over win margin, i.e., it was able to precisely
predict win margins, especially in constituencies where a close contest was expected, and was able to
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Table 1—Estimates of the Discontinuity in the Density of BJP and Congress Win Margins
2019 2014 2009 2004 1999 1998 1996 1991 1989 1984 1980 1977
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Estimate of discontinuity: BJP Win Margin 1.51** -0.24 -0.83 1.88 2.41 -0.79 -1.20 0.43 -0.01
(0.75) (0.74) (1.15) (1.20) (1.63) (1.28) (0.94) (0.60) (1.24)
Estimate of discontinuity: INC Win Margin 0.78 -0.37 1.80 -1.02 -1.37 -0.24 0.66 -1.19 0.36 0.49 -0.54 0.89
( 0.60) (0.73) (1.30) (1.03) (0.91) (1.01) (0.79) (0.81) (0.77) (0.86) (0.73) (0.71)
Notes: The table reports the estimates of the discontinuity in the density at the threshold value of zero for two running variables – BJP Win Margin (first row) and
INC/Congress Win Margin (second row). The year in each column refers to the general election year. Each estimate, therefore, comes from a separate test for a given
of running variable in a given general election year. The estimates are computed using the method proposed by Calonico et al. (2014). The robust standard errors are
reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1
affect it, thanks to its comparative advantage in electoral campaigning and greater access to resources.
Notice that it is not enough for BJP to predict the constituencies where it will face a close fight to
generate failure of McCrary test. In such a case, it would campaign harder in all the constituencies
expected to have a close contest, resulting in a uniform shift of its win margin to the right and hence,
no discontinuity would emerge at zero.22 The party would have to accurately predict whether they are
ahead or falling behind in the close contest. Hence, for precise control to be the explanation, BJP had
to accurately predict the sign as well as the magnitude of the win margin, to be able to target the set
of constituencies where it expects to lose in a close contest. Jeong and Shenoy (2020) have shown that
incumbent parties in US state legislative elections do exhibit behavior consistent with precise control
and it can explain their ability to consistently win majority of close races. While election prediction in
India is still not as sophisticated as in developed countries such as the US, it is possible that BJP, due to
its superior electoral machine, was able to precisely predict and affect win margins.
State Assembly Elections: Seven states had their state assembly elections in 2019, including four
states where the state elections were held concurrently with the general election.23 BJP was the incumbent party in the government in three of the seven states. I compute the BJP win margin for state election
results for BJP and non-BJP ruled states separately. I find that it does not exhibit failure of McCrary
test (Appendix Figures A2a and A2b). Same is true for state elections held in 2020 and 2021 (Appendix
Figures A2c and A2d). If precise control is the mechanism responsible for Figure 1a, then we should
expect it at work at state level elections as well, at least in 2019. I however do not find that.
BJP vs. Non-BJP Ruled States: I now perform the McCrary test for the 2019 general election in
two sub-samples of constituencies – those in states that were ruled by the BJP at the time of the 2019
election and those in non-BJP ruled states.24 The two sub-samples have equal number of constituencies.
Figures 1c and 1d show the densities of BJP win margin for the two sub-samples respectively. I find
that for BJP ruled states, the density shows an even larger discontinuous jump to the right of threshold.
For non-BJP ruled states, the jump is muted. Appendix Table A2 reports the estimated discontinuities
in the densities for the two sub-samples of states separately. The estimate of the jump for BJP ruled
states is highly statistically significant (p-value = 0.007), while it is statistically insignificant (p-value
22Lee and Lemieux (2010) refer to this as imprecise control over the running variable and argue that the regression discontinuity design remains valid under imprecise control.
23The seven states are Arunachal Pradesh, Haryana, Maharashtra, Jharkhand, Andhra Pradesh, Odisha and Sikkim.
24In 2019, the BJP ruled states were Assam, Bihar, Goa, Gujarat, Haryana, Himachal Pradesh, Jharkhand, Maharashtra,
Manipur, Nagaland, Tripura, Uttar Pradesh, Uttarakhand.
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Figure 2—McCrary Test for General Elections in 2014 and 2009
(a) 2014 Election: BJP Ruled States (b) 2014 Election: Non-BJP Ruled States
(c) 2009 Election: BJP Ruled States (d) 2009 Election: Non-BJP Ruled States
= 0.84) for non-BJP ruled states. Therefore, the overall failure of McCrary test is primarily driven by
constituencies in the BJP ruled states. Those states, on the other hand, do not exhibit differential patterns
in the previous two general elections (Figure 2).
The results are consistent with both mechanisms. Having control over the state’s bureaucratic machinery can help a party target its manipulation efforts better, especially in a context where widespread
manipulation is hard to implement given the intense media attention during elections and vocal rival political parties.25 It is also consistent with precise control if being in power at the state government helps
in mobilizing party workers at the ground. Greater presence of party workers at the ground can generate
more precise information about a party’s expected vote share vis-a-vis the main rival party, which can
facilitate precise control.26
Comparability of PCs that BJP Closely Won and Lost: Table 2 reports the estimates of discontinuity of various PC level electoral variables at the BJP win margin threshold of zero. It finds no systematic
25Even though bureaucrats (Indian Administrative Service officers) are employees of the central government, their appointment and promotion are influenced by state governments (Iyer and Mani 2012). During general election, they report to the
ECI, but the pool of officers available is shaped by the state government, making them pliable to the interests of the incumbent
party at the state.
26Among the states not ruled by BJP, several of them, such as Madhya Pradesh, Rajasthan, Karnataka, Orissa, have strong
presence of the party. Appendix Figure A3 shows the discontinuity for that subsample of states. It does not exhibit a differentially larger discontinuity than the one in Figure 1d.
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differences between PCs that BJP barely lost and won. The variables examined are electorate size,
turnout rate27, number of candidates, reservation status for SC/STs, share of female candidates, share of
candidates switching political parties (i.e., turncoats), whether the incumbent is running in the election,
and whether BJP won the PC in the previous general election. The coefficient on BJP victory in 2014
is large and negative, though is noisily estimated. Therefore, in terms of various characteristics of PCs,
the ones that BJP barely lost vs won appear to be comparable.
Table 2—Comparability of PCs across BJP Win Margin Threshold
Electors Turnout #Candidates SC/ST Female Turncoat Incumbent BJP Won
rate reserved share share rerun in 2014
(1) (2) (3) (4) (5) (6) (7) (8)
BJP Won 0.178 -0.029 -0.931 0.125 -0.004 -0.002 -0.009 -0.170
(0.172) (0.036) (1.273) (0.154) (0.016) (0.010) (0.172) (0.181)
Mean Dep. var. 1.66 0.69 14.46 0.30 0.09 0.03 0.41 0.54
Bandwidth (h
∗
) 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16
Observations 189 189 189 189 189 189 189 189
Notes: The table reports RDD estimates using BJP win margin on various PC level variables, such as electorate size in
millions (column 1), turnout share (column 2), number of candidates (column 3), Reservation status for SC/ST (column 4),
share of female candidates (column 5), share of candidates who switched parties (column 6), whether the incumbent is running (column 7) and whether BJP won the PC in 2014 (column 8). The bandwidth used is the optimal bandwidth used for
McCrary test in Figure 1a and Table A2. Standard errors are reported in the parentheses. *** p<0.01, ** p<0.05, * p<0.1
V Evidence on Precise Control via Campaigning
BJP’s ability to exercise precise control is likely to arise from the party’s superior organizational strength
and the related election campaign strategy. It allowed the party to collect and utilize detailed and localized information about voters’ attitudes and voting intentions. This can result in precise control, as the
party can mobilize voters better than its opponent in closely contested elections, resulting in disproportionate wins. This is similar to Vogl (2014) who argues that in mayoral elections in southern US, Black
voters were better mobilized than White voters, resulting in disproportionate wins of Black candidates
in closely contested elections. Therefore, for precise control to be the primary explanation of Figure 1, I
expect the constituencies barely won by BJP to have significantly more campaigning by BJP relative to
other parties. It is well-established in the literature on political campaigning that a party’s relative campaigning, as opposed to its absolute campaigning activity, matters for its vote share (Bekkouche et al.
2022, Gerber 1998, Levitt 1994). Hence, the ideal test would estimate the discontinuity in the relative
campaigning by BJP at the BJP win margin value of zero.
There is no existing data on campaigning by political parties across constituencies in India. To
address this, I exploit a new question added to the post poll National Election Survey in 2019. In the
2019 round, the new question asks: “Did a candidate/party worker of the following parties come to your
house to ask for your vote in the last one month?” The survey listed the major political parties in each
state. The response to this question, therefore, reveals campaigning activity separately by individual
political parties in each of the sampled PCs.28 I use this question to define two dummy variables – home
27This is calculated using the revised turnout numbers as they are last version of data available with ECI.
28In previous rounds, the survey asked whether any candidate or party worker visited the respondent’s house for campaign14
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Table 3—Campaigning by Political Parties in Closely Contested Elections
Home Visit by Party Worker/Candidate
Full Sample BJP Ruled States Non-BJP Ruled States
BJP Any Other BJP Any Other BJP Any Other
Party Party Party
(1) (2) (3) (4) (5) (6)
Panel A: BJP Win Margin ≤ 0.191
BJP Won 0.03 0.00 0.05 0.34** 0.04 -0.09
(0.10) (0.13) (0.17) (0.17) (0.15) (0.19)
Mean Dep. Var. 0.41 0.49 0.33 0.37 0.48 0.58
Bandwidth (h
∗
) 0.191 0.191 0.191 0.191 0.191 0.191
Observation 8945 8945 3927 3927 5018 5018
No. of PCs 76 76 32 32 44 44
Panel B: BJP Win Margin ≤ 0.160
BJP Won -0.01 -0.01 0.10 0.38** -0.01 -0.14
(0.11) (0.14) (0.18) (0.17) (0.16) (0.20)
Mean Dep. Var. 0.41 0.49 0.33 0.39 0.47 0.57
Bandwidth (h
∗
) 0.160 0.160 0.160 0.160 0.160 0.160
Observation 7897 7897 3297 3297 4600 4600
No. of PCs 68 68 28 28 40 40
Notes: The sample is individual level survey data from the National Election Survey (post
poll) 2019. The dependent variable in columns (1), (3), (5) is a dummy variable that takes
value one if a BJP party worker or candidate visited the house of the respondent to campaign
for general election and is zero otherwise. The dependent variable in columns (2), (4) and (6)
is also a dummy variable that indicates whether party worker or candidate from any other party
visited the house for campaigning. BJP Won is an indicator of whether BJP is the winner of
the Parliamentary Constituency (PC). Sample in Panel A consists of PCs with BJP win margin less than 0.191 – the optimal bandwidth calculated using the MSERD method proposed
by Calonico et al. (2014), while Panel B uses the sample of PCs with BJP win margin less
than 0.16 – the optimal bandwidth used for McCrary test in Figure 1a and Table A2. Columns
(1) and (2) use the full sample of PCs within the respective bandwidths. Columns (3) and (4)
restrict the sample to states ruled by BJP during 2019 general election. The last two columns
use the sample of non-BJP ruled states. Standard errors are clustered at the PC level and are
reported in the parentheses. *** p<0.01, ** p<0.05, * p<0.1
visit by BJP, that takes value one if a BJP party worker or candidate visited the respondent’s house, and
home visit by any other party, that takes value one if any other party visited the house. The mean values
of the two variables are 0.38 (BJP) and 0.50 (any other party). Moreover, they are positively correlated
(r = 0.58), suggesting that parties tend to target similar set of “swing” voters.
I test whether likelihood of home visits by BJP and other parties increase discontinuously at BJP win
margin value of zero, and whether the increase for BJP is larger than other parties. Table 3 reports the
results. In Panel A, columns (1) and (2) report the RDD estimates for the two outcome variables using
the optimal bandwidth of 0.191. We find that both estimates are small in magnitude and statistically
insignificant. In columns (3) and (4), I restrict attention to BJP ruled states, as the failure of McCrary
test is concentrated in that sample. The coefficient for BJP home visits is 0.05, which is statistically
ing, i.e., it did not ask the question for each party separately.
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insignificant, while that for any other party is 0.34, which is statistically significant at 5%. Therefore, in
this sample, the estimate for BJP is not larger than that of other parties. For non-BJP ruled states, both
estimates again are statistically insignificant. The results remain same if we use 0.16 as the bandwidth
(Panel B). Therefore, differentially greater campaigning by BJP relative to other parties cannot be the
primary reason for its disproportionate win in closely contested constituencies.
Social Media and Election Outcomes: BJP and other major parties extensively used social media
during the 2019 election campaign. While smartphone penetration in India is not widespread, social
media could potentially play a pivotal role in shaping voting behavior. There is no micro-data on social
media campaigning by political parties. The NES 2019, however, asks individuals about their social
media usage. For each social media platform, I define a dummy variable (“social media user”) that takes
value one if an individual uses that platform at least once every day and zero otherwise. Appendix Table
A3 shows how social media usage predicts voting in favor of BJP in the full sample. I find that only
Facebook users are statistically significantly more likely to vote in favor of BJP, while users of other
social media platforms either vote less for BJP or vote for other parties with equal likelihood.29 For each
PC p, I then run the following regression:
I(Voted for BJP)ip = αp + βpF b_userip + θ
′
pXip + ϵip (1)
where the vector of controls Xip includes gender, age and caste categories. βp captures the propensity of
Facebook users in constituency p to vote differentially in favor of BJP. The estimate of βp therefore can
be interpreted as a proxy of BJP’s differential intensity of Facebook campaigning in the constituency.
I use βp as an outcome variable to test whether its value jumps discontinuously at BJP win margin of
zero. Appendix Table A4 column 1 reports the RDD coefficient. It is positive and statistically significant,
suggesting that constituencies barely won by BJP exhibited relatively more intense social media campaigning by the party. Columns 2 and 3 report the same coefficient when the RDD analysis is performed
on BJP ruled and non-BJP ruled states separately. The estimate in column 2 is small and statistically
insignificant, while that in column 3 is positive, comparable in magnitude to column 1 and statistically
significant. The evidence therefore cannot explain the patterns observed in the previous section that
failure of McCrary test is concentrated in the BJP ruled states.
VI Evidence on Manipulation
Manipulation of elections can take place at one of three stages of elections. First, at the time of voter
registration, in the form of targeted deletion of names of voters who are unlikely to vote for the incumbent party. I refer to it as registration manipulation. Second, at the time of voting, when polling officers
can strategically discriminate against registered voters, who are likely to vote against BJP. Finally, manipulation can take place at the time of counting of votes.30 Distinguishing between voting and counting
29This is consistent with the recent investigative media report that Facebook gave preferential rates to BJP for political ads
during 2019 campaign. See here: https://www.aljazeera.com/economy/2022/3/16/facebook-charged-bjp-lower-rates-for-indiapolls-ads-than-others.
30A fourth possibility is through manipulation of EVMs. However, some commentators have pointed about that widespread
manipulation of EVMs may be hard to achieve, given the technology (Purkayastha and Sinha 2019), making it an unlikely
mechanism.
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manipulation is difficult. Barring one analysis that comments directly on counting manipulation, the
rest of the evidence are consistent with both voting and counting manipulation. Hence, I refer to both
as turnout manipulation. Section II above mentions media reports of potential registration and turnout
manipulations. In the sections below, I discuss evidence consistent with each of them.
VI.I Registration Manipulation
To examine the presence of this channel, I compute the growth in the number of electorate (i.e., number
of registered voters) in a PC between 2014 and 2019. For each PC p, I define:
Gp ≡
Electoratep,2019 − Electoratep,2014
Electoratep,2014
If names were strategically deleted from the electoral rolls in an attempt to flip closely contested election
in favor of BJP, then we should expect the electorate growth rate to fall discontinuously at BJP win
margin value of zero. Moreover, if Muslims were the primary target of this strategic deletion, we expect
a greater fall in the electorate growth rate in PCs with higher Muslim electorate share. I implement the
regression discontinuity design on the full sample of PCs as well as on samples of PCs with Muslim
share greater and lower than the median of distribution of Muslim shares across PCs.31
Table 4—Electorate Growth Rate Smaller in PCs Barely Won by BJP
Electorate Growth Rate (Gp)
Full High Low Full High Low
Sample Muslim Share Muslim Share Sample Muslim Share Muslim Share
(1) (2) (3) (4) (5) (6)
BJP Won -0.05*** -0.06** -0.02 -0.05*** -0.07*** -0.03
(0.02) (0.02) (0.02) (0.01) (0.02) (0.02)
Mean Dep. Var. 0.10 0.10 0.09 0.09 0.09 0.09
Observations 123 72 51 181 101 80
Bandwidth (h
∗) 0.107 0.107 0.107 0.16 0.16 0.16
Notes: The data is at Parliamentary Constituency (PC) level. The table reports the regression discontinuity design
estimate using BJP win margin as the running variable. The dependent variable in all the columns is the growth rate
in the PC electorate between 2014 and 2019. BJP Won is a dummy indicating whether BJP won the PC in 2019.
Columns (1) and (3) use the full sample of PCs. Columns (2) and (4) use PCs where the electorate share of Muslims
is higher than the median, and column (3) and (6) use PCs where the share is lower than median. Columns (1)-(3) use
the optimal bandwidth using the MSERD method specfied by Calonico et al. (2014), while columns (4)-(6) use the
optimal bandwidth calculated for McCrary test in Figure 1a. Robust standard errors are are reported in the parentheses. *** p<0.01, ** p<0.05, * p<0.1
Table 4 shows the estimates for discontinuity for the three samples. Column (1) reports the RDD
estimate for the full sample using the optimal bandwidth 0.125. The estimated discontinuity is −0.05,
which is statistically significant at 1%. This implies that constituencies barely won by BJP had a 5
percentage points smaller growth rate in electorate between 2014 and 2019 compared to PCs that it
barely lost. This is a large fall, given the mean growth rate of 0.09. Moreover, in PCs with higher
Muslim share, the estimated fall is 6 percentage points (column (2)), while it is 2 percentage points (and
statistically insignificant) in PCs with lower Muslim shares. The difference between the two estimates
31I calculate PC level Muslim electorate share by taking a weighted average of AC level Muslim shares using electorate
share of an AC as the weight.
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in columns (2) and (3) is statistically significant at 10%. The result remains the same if we use the
bandwidth of 0.16. The result, therefore, is consistent with strategic deletion of Muslim names being an
important channel of manipulation.
Additionally, Appendix Table A5 reports the RDD coefficients for high and low Muslim share PCs
in the BJP ruled and non-BJP ruled states separately. Consistent with previous results, I find that the
only statistically significant coefficient is for the sample of high Muslim share PCs in BJP ruled states
(Column (1)). The Column (1) coefficient is also larger in magnitude than that low Muslim share in BJP
ruled states (Column (2)), though the difference is not statistically significant. The result for non-BJP
ruled states is also similar, though both the coefficients are noisily estimated.32
VI.II Turnout Manipulation: EVM Turnout Data Discrepancy
I compile the two different EVM turnout figures described in Section III for the 373 PCs covered in
the first four phases of election. In 64% of PCs the turnout was revised up, and in the rest of the
cases it was revised down. I compute the absolute difference in vote tallies between the two reports.
While the median difference is 358, the 90th and 95th percentiles of the difference are 3302 and 7357,
respectively. The largest mismatch is of 57,747 votes in the Gautam Buddha Nagar constituency in Uttar
Pradesh. I define a dummy variable called “large” turnout discrepancy: it takes value one if the absolute
discrepancy is larger than the 95th percentile and zero otherwise. If the mismatch occurred due to some
administrative errors or glitches in the EVM, then we expect the “large” discrepancies to be randomly
spread across PCs with different BJP Win Margins.
Figure 3—EVM Turnout Data Mismatch in Closely Contested Constituencies
(a) “Large” Data Discrepancy (b) Absolute Data Discrepancy
Figure 3a plots the relationship between the dummy variable and BJP win margin separately on
the two sides of the threshold value of zero. I find that the probability of “large” discrepancy jumps
significantly at zero. The estimate of the jump, using the method proposed by Calonico et al. (2014), is
0.26 (p-value = 0.008), implying that conditional on close election, the PCs that BJP barely won have 26
percentage point larger likelihood of having a “large” mismatch than PCs that BJP barely lost. This is
a large effect considering the average value of the dummy variable, by construction, is 0.05. The result
implies that the sample of closely contested constituencies that were disproportionately won by BJP
32The p-value of the Column (3) coefficient is 0.103.
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also has a disproportionately higher likelihood of “large” turnout revision. If the failure of McCrary test
demonstrated in the previous section involved manipulation of turnout figures, then we should expect this
pattern. Figure 3b plots the same graph directly using the absolute turnout discrepancy (in thousands)
and finds a similar pattern, though with a noisier estimate. The estimate of the discontinuity is 5.70
(p-value = 0.09).33 Hence, the result is consistent with the manipulation hypothesis. Moreover, it is not
obvious why precise control would lead to larger turnout revisions by the ECI in the closely contested
constituencies won by the BJP.
Table 5—Heterogeneity in RDD Estimates of Turnout Discrepancy
BJP Ruled States Non-BJP Ruled States
(1) (2)
Panel A: “Large” Turnout Discrepancy
BJP Won 0.45** 0.16
(0.19) (0.11)
Panel B: Absolute Turnout Discrepancy
BJP Won 15.54** -0.43
(7.42) (1.02)
Bandwidth (h
∗
) 0.153 0.153
Notes: The table reports RDD estimates for two dependent variables – the dummy variable “Large” Turnout Discrepancy (Panel
A) which takes value one if the absolute discrepancy in turnout
data is larger than the 95th percentile, and the absolute turnout discrepancy, in thousands (Panel B). The running variable is BJP Win
Margin. The sample only includes the 373 constituencies for which
turnout discrepancy information is available. Column 1 has states
ruled by BJP in 2019, while column 2 has the rest of the states.
Optimal bandwidth for Panel A is calculated using the MSERD
method proposed by Calonico et al. (2014) and is maintained in
Panel B. *** p<0.01, ** p<0.05, * p<0.1
BJP vs. Non-BJP Ruled States: Similar to the previous section, I test for heterogeneity across BJP
and non-BJP ruled states. Table 5 reports the RDD estimates for the two sub-samples for both outcome
variables. We observe in Panel A that the jump in the probability of “large” discrepancy is statistically
significant and large in magnitude for BJP ruled states, while it is statistically insignificant and smaller
in magnitude in non-BJP ruled states. The estimated jump in column 1 is 0.45 (and the value just to the
left of threshold is close to zero), i.e., the likelihood of “large” discrepancy in the PCs barely won by the
BJP is 9 times higher than what it would be under the random chance scenario. It is, on the other hand,
3 times higher for non-BJP ruled states. The results in Panel B are similar. While the estimated jump
in absolute discrepancy is more than 15, 000 votes (statistically significant at 5%) in BJP ruled states,
it is -430 (statistically insignificant) in non-BJP ruled states. The difference between the coefficients in
Panel A is not statistically significant, but in Panel B, it is significant at 5%.
33Appendix Figure A4 plots the same relationships using INC win margin and does not find discontinuity at the threshold.
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Discontinuity in Turnout Difference: Appendix Figure A6 shows discontinuity in turnout difference
at the BJP win margin threshold of zero for the past general elections. Turnout difference for a PC in
a general election is the difference between its turnout rates in the current and previous elections. We
observe that the discontinuity is positive and statistically significant for 2019. The discontinuity estimate
is 0.023 (p-value = 0.05). For previous elections going back to 1989, the discontinuity estimates are
statistically insignificant. This is consistent with the result that turnout data discrepancy went up in PCs
barely won by BJP.
Interpretation: It would be inappropriate to treat the data revision as aggregation fraud (Callen and
Long 2015), i.e., one where higher level ECI officials directly engaged in turnout manipulation while
aggregating turnout data from polling station level results. Such acts by the ECI is unlikely. Moreover,
in all cases, barring one, the revision is not larger than the win margin. Rather, the revisions are likely
indicative of possible manipulations committed locally, at the polling stations. The local manipulations
could either be at the time of voting or counting. Most electoral frauds are decentralized in nature, as
Rundlett and Svolik (2016) point out. The analysis in the following section suggests that it was at least
partly facilitated by weaker monitoring during counting, while the next section provides evidence that
local manipulation may explain part of the observed turnout manipulation.
VI.III Counting Manipulation: Assignment of Counting Observers
I examine assignment of counting observers in PCs across the BJP win margin threshold. All counting
observers are assigned to a state different from their ‘office state’ and ‘home state’, as defined in Section
III. 36% of observers are from the SCS cadre. The SCS officers typically work in lower ranked positions
in a state bureaucracy, as compared to the IAS officers with same experience (Iyer and Mani 2012).
They are also more likely to be politically pliable by the state government, since they are appointed
by them, as opposed to the IAS officers who are appointed by the central government. I compute the
fraction of counting observers in a PC who come from the SCS cadre. About 50% of PCs have at least
one SCS observer assigned. Since I know the ‘office state’ of each observer, I also compute the fraction
of observers who are SCS and work in a BJP ruled state. The mean fraction is 0.13.
Table 6 columns (1) and (4) report the estimates of discontinuity in the two outcomes variables at the
BJP win margin threshold of zero. In both cases, we find that the RDD estimate is positive – 0.24 and
0.22, respectively. They are large in magnitude and statistically significant at 5%. Columns (2) and (5)
report the results for BJP ruled states and columns (3) and (6) for non-BJP ruled states.34 All coefficients
are positive and 3 out of the 4 coefficients are statistically significant; coefficients for BJP ruled states are
larger in magnitude. Specifically, for the fraction of observers who are SCS and come from BJP ruled
states, the coefficient is 0.37 and is statistically significant at 1% for BJP ruled states (column (5)) but
is 0.17 and statistically insignificant for non-BJP ruled states (column (6)). Appendix Figure A5 depicts
the RDD graphs for the four cases. The results indicate that more politically pliant counting observers
were assigned in PCs barely won by BJP, and the pattern is concentrated in BJP ruled states.
I regress the absolute data discrepancy in turnout and the indicator for “large” turnout discrepancy
computed in Section VI.II above on the fraction of counting observers who are SCS and from BJP ruled
states, whether BJP won the PC and their interaction. I include all PCs in the sample to check whether in
34Here BJP and non-BJP ruled states refer to the PCs where the observers were deployed.
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Table 6—RDD Estimates of Characteristics of Counting Observers
SCS Observer SCS Observer from BJP States
All BJP Ruled Non-BJP All BJP Ruled Non-BJP
States States Ruled States States States Ruled States
(1) (2) (3) (4) (5) (6)
BJP Won 0.236** 0.335** 0.241* 0.224** 0.371*** 0.166
(0.100) (0.131) (0.136) (0.104) (0.0739) (0.154)
Mean dep. var. 0.36 0.31 0.40 0.17 0.17 0.17
Observations 188 83 105 188 83 105
Bandwidth (h
∗
) 0.16 0.16 0.16 0.16 0.16 0.16
Notes: The table reports RDD estimates for two dependent variables – share of counting observers assigned to a PC who are State Civil Service (SCS) officers (columns (1)-(3)) and share of counting observers who are SCS and work in BJP ruled states (columns (4)-(6)). The running variable is BJP Win
Margin. The columns (1) and (4) include all PCs with BJP win margin within 0.16, columns (2) and (5)
include PCs with the same win margin and are in BJP ruled states, columns (3) and (6) include PCs with
the same win margin and are in non-BJP ruled states. Robust standard errors are reported in the parentheses. *** p<0.01, ** p<0.05, * p<0.1
the full sample, assignment of politically pliant observers is correlated with data discrepancy. Appendix
Table A6 reports the results. I find that in PCs that BJP lost, the relationship between data discrepancy
and the fraction of SCS observers from BJP ruled states is negative. However, in PCs that BJP won, the
relationship turns positive for both outcomes. For the absolute discrepancy measure, the relationship is
statistically insignificant (p-value=0.104), while for the “large” discrepancy indicator, the relationship
is statistically significant at 5% (p-value = 0.041). This suggests that greater presence of politically
pliant counting observers in PCs barely won by BJP may have partly contributed towards discrepancy in
turnout data.
VI.IV Irregularities in Polling Station Outcomes
This section examines irregularities in polling station level election results for 2019 from 22 major states
of India.35 For each polling station, the data provide information on the total turnout and candidate wise
vote tallies. The data do not mention the number of electorates at the polling station level.36 If the
turnout discrepancy discussed above electorally benefited the incumbent party, then we should expect it
to be reflected in its vote share across polling stations. To examine this, I compute vote share of BJP
in each polling station in constituencies with a BJP candidate. To make polling station level BJP vote
shares comparable across PCs, I then compute the relative vote share of BJP in each polling station j in
each PC p:
Relative BJP vote sharejp =
BJP vote sharejp
BJP vote sharep
35The states are Andhra Pradesh, Assam, Bihar, Chhattisgarh, Delhi, Goa, Gujarat, Haryana, Himachal Pradesh, Jharkhand, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Punjab, Rajasthan, Tamil Nadu, Telangana, Tripura, Uttar Pradesh,
Uttarakhand, West Bengal.
36The only exception is Uttar Pradesh; it releases the electorate size of polling stations along with vote tallies of parties in
the same dataset.
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where BJP vote sharejp is BJP’s vote share in a polling station and BJP vote sharep is its vote share in
the entire constituency that the polling station belongs to. I use the revised turnout figures to calculate
vote shares, as that is the official data on turnout and are available for all PCs. Hence, relative BJP vote
share captures the party’s vote share in a polling station relative to its vote share in the constituency.
If its value is greater than one, then in the polling station, BJP’s vote share is higher than that in the
constituency. The average value of Relative BJP vote share should be approximately one.
Figure 4a plots, for the states ruled by BJP, the local polynomial relationship between the relative
BJP vote share and turnout at polling station level in closely contested constituencies, i.e., constituencies
where BJP’s win margin was less than 0.16 – the optimal bandwidth for the McCrary test performed in
Figure 1a. I estimate it separately for PCs where BJP won and lost, depicted by the solid and dashed
lines respectively. For comparison, Figure 4b plots the same graph for the states not ruled by BJP. Figure
4a shows that in both types of constituencies, the estimated relationship hovers around one for polling
stations with turnout 800 or below. In polling stations with higher turnout, relative BJP vote share spikes
in constituencies where BJP won, and falls in constituencies where BJP lost.37 In Figure 4b we do not
see such striking patterns.
I examine this directly by creating a dummy variable at the polling station level, called “high”
BJP vote share that takes value one if the BJP vote share in the polling station is higher than the 95th
percentile of the BJP vote share distribution in the entire sample. In these polling stations, BJP’s vote
share on average is 0.90. By construction, the average value of the dummy variable is 0.05. The dummy
variable essentially flags polling stations with “extreme” outcomes in favor of BJP. Figures 4c and 4d
plot the relationships for BJP ruled and non-BJP ruled states. The sample of PCs is same as before –
those with absolute BJP win margin within 0.16. We observe in Figure 4c that in both constituencies won
and lost by BJP, the average value of “high” BJP vote share is low in polling stations with turnout below
800. However, in larger turnout polling stations, the estimated relationships diverge. In constituencies
won by BJP, the average likelihood of “high” BJP vote share increases sharply, going beyond 0.4, while
the other graph remains flat. This is consistent with Figure 4a. Moreover, there is no such pattern
in the corresponding figure for non-BJP ruled states (Figure 4d). The pattern in Figure 4c is especially
noteworthy given the fact that I only consider closely contested constituencies for the estimation. Hence,
the rival party in these constituencies have received comparable vote share, which would make it less
likely for BJP to get “high” vote shares in any polling station.
Benford’a Law: I compute second digit distribution of absolute vote tallies across all candidates for
each polling station to check departures from the Benford’s law. Benford’s law specifies the distribution
of digits in different positions of naturally occurring numbers (Benford 1938, Raimi 1976). Manipulation of such numbers leads to a different distribution of digits, which allows analysts to detect the
manipulation (Hill et al. 1995). This method is used in a variety of contexts to detect fraud (Diekmann 2007, Nigrini 2012) – such as, income tax receipts, financial transactions, as well as elections.
In the context of election forensics, analysts usually focus on the distribution of second digits (Mebane
37The pattern for PCs lost by BJP is similar in constituencies that BJP lost with margin higher than 0.16 (Appendix Figure
A7). Therefore, in all the PCs that BJP lost, it got lower vote share in polling stations with high turnout. These polling stations
are likely to be located in urban centers. Since BJP’s primary support base is more urban than other parties, less support in
urban areas is a good indicator of its performance in a PC.
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Figure 4—Distribution of BJP vote share across Polling Stations – Win margin ≤ 0.16
(a) Relative BJP Vote Share: BJP ruled states (b) Relative BJP Vote Share: non-BJP ruled states
(c) “High” BJP Vote Share: BJP ruled states (d) “High” BJP Vote Share: non-BJP ruled states
(e) Benford distance: BJP ruled states (f) Benford distance: non-BJP ruled states
2008a,b).38 However, treating a significant deviation from Benford’s distribution in a given constituency
as evidence of fraud (in that constituency) can lead to misleading conclusions, as researchers have shown
that even in cases without fraud, empirical distributions of second digits can deviate from Benford’s law
(Shikano and Mack 2011). This can happen due to a myriad of reasons as discussed by Mebane (2011).
I therefore do not test for deviations from Benford’s distribution in each PC individually. I argue that
presence (or absence) of patterns in deviations across PCs and polling stations is a better statistical test.
38There are other digit-based tests of electoral fraud, for example, examining distribution of last digits (Beber and Scacco
2012) etc. However, Benford’s law is the most widely used method in this context.
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I compute the Euclidean distance between the second digit distribution of the vote tallies of candidates in each polling station and the “ideal” second digit distribution specified by Benford’s law. Figure
4e plots the Benford distance for each polling station against turnout for the same sample of PCs as Figure 4c. Figure 4f plots it for the sample of PCs used in Figure 4d. We observe that the Benford distance
typically falls with larger turnout, except for PCs won by BJP in BJP ruled states. In those PCs, the
Benford distance falls initially with turnout, but then rises for polling stations having turnout higher than
800. This is the same set of polling stations that exhibits irregular patterns, as discussed above. This
suggests that the “extreme” outcomes in favor of BJP observed in Figure 4c may have resulted from
some form of manipulation of vote tallies in that subset of polling stations.
Shape of vote share density: Some recent works on detection of electoral fraud examine the shape
of the density of vote share and turnout distributions using the booth (or precinct) level data. Rozenas
(2017), for example, test for presence of excess mass at coarse vote shares using a re-sampled kernel
density method. The method returns as output an estimated fraction of booths exhibiting fraudulent
results. I apply this method to the polling station level data from Uttar Pradesh (UP)– the only state
for which turnout rate as well as BJP’s vote share are available at polling stations, a requirement for
the analysis. Also, a significant number of PCs from UP are in the list of PCs with narrow win margin
(Appendix Table B1). I find that in the full sample, 0.13% booths are fraudulent. The share increases to
0.19% in PCs with BJP win margin less than 0.08 and won by BJP. The estimates are low but move in
the direction that is indicative of fraud. Additionally, for comparison, Rozenas (2017) analyze data from
Russian elections in 2011 and 2012 and find estimates of 0.94-0.97%. Klimek et al. (2012) argue that
the density of vote share (appropriately calculated and scaled) exhibits high kurtosis in case of fraud and
finds that Russian elections in 2011 and 2012 have kurtosis exceeding 10, while in other well-established
democracies in Europe, it is typically 5 or lower. In UP, the kurtosis is 29. Appendix Figure A8 shows
the density, which looks very similar in shape to those in Russian elections in 2011 and 2012 and unlike
those in other countries (Figure 2 in Klimek et al. (2012)).
Interpretation: The patterns observed in Figures 4a and 4c are consistent with both mechanisms. If
the incumbent party was able to accurately predict the win margins in closely contested constituencies,
and wished to affect them, it might be optimal for the party to target the larger polling stations, as they are
fewer in numbers and mostly located in urban areas, making voters easily accessible for campaigning.
This may result in high vote shares for the party in large turnout polling stations. Figure 4e and the
analysis of the shape of BJP’s vote share density, however, advance the manipulation hypothesis over
precise control.
VI.V Data Discrepancy and Irregularities at Polling Stations
To further distinguish between manipulation and precise control mechanisms, I utilize the fact that in
closely contested PCs barely won by BJP, the EVM data discrepancy is significantly larger. If discrepancies in turnout data and high vote share of BJP in large polling stations are both driven by manipulation,
then I should expect the two phenomena to be correlated, i.e., the irregular pattern in polling stations to
be primarily driven by constituencies with larger data revisions. However, if precise control is the explanation, then such patterns should be similar irrespective of whether data revision was large or small.
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This is because, in that scenario, larger data revisions only reflect administrative errors during counting of votes, which should be uncorrelated with BJP’s ability to exercise precise control at the time of
elections.
To test this hypothesis formally, I estimate the difference-in-discontinuity specification (Grembi et al.
2016) specified below:
Rel. BJP vote sharejp = α1 + γ1BJP_W onp + γ2BJP_W onp × Dp (2)
+ β1BJP_M arginp + β2BJP_M arginp × BJP_W onp
+ Dp × {α2 + β3BJP_M arginp + β4BJP_M arginp × BJP_W onp}+ϵjp
where Dp is the absolute discrepancy in turnout data in PC p, measured in unit of 10,000 votes and the
rest are as defined before. γ1 measures the RDD estimate for relative BJP vote share in PCs without any
turnout discrepancy. γ2 measures the differential discontinuity in the relative BJP vote share in PCs with
additional discrepancy in 10,000 votes. Our coefficient of interest, therefore, is γ2.
Table 7—Discrepancy in Turnout Data and Irregularity in Election Results
Relative BJP vote share
All BJP ruled Non-BJP
states states ruled states
(1) (2) (3)
BJP Won -0.063** -0.105*** -0.094*
(0.030) (0.034) (0.048)
Absolute Turnout Discrepancy -0.234* -1.738 -0.174*
(0.137) (1.059) (0.092)
Absolute Turnout Discrepancy * BJP Won 0.236* 1.737 0.196
(0.137) (1.060) (0.130)
Mean Dep. Var. 0.998 0.998 0.998
Observations 183,275 82,921 100,354
Bandwidth (h
∗
) 0.160 0.160 0.160
Notes: The data is at polling station level. The dependent variable in all columns is the
ratio of BJP vote share in a polling station and BJP vote share in the PC. Absolute Turnout
Discrepancy is the absolute mismatch (in unit of 10,000 votes) in EVM turnout data in
2019. Optimal bandwidth calculated for McCrary test in Figure 1a has been used in all
specifications. Standard errors are clustered at the constituency level and are reported in
the parentheses. *** p<0.01, ** p<0.05, * p<0.1
Before discussing the results, I emphasize that even though the difference-in-discontinuity method is
used to estimate heterogeneity in causal effect of some treatment, that is not the appropriate interpretation
in this context. The estimation of equation (3) allows us to examine whether irregular outcomes in the
polling stations are positively correlated with extent of turnout discrepancy in PCs barely won by the
BJP. Table 7 reports the results for the full sample (column (1)), BJP ruled states (column (2)) and nonBJP ruled states (column (3)). We find that estimate of γ1 is negative and statistically significant in
all columns, i.e., PCs barely won by BJP with no data discrepancy exhibits a fall in relative BJP vote
share. However, estimate of γ2 in column (1) is positive and statistically significant at 10%. Moreover,
the magnitude of γ2 is about 4 times larger than γ1, suggesting that in PCs with discrepancy larger
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than 2500 votes, the relative BJP vote share is higher in PCs won by BJP (relative to PCs lost by BJP).
Estimate of γ2 in column (2) is 17 times larger than γ1, while in column (3), it is twice as larger. The
coefficients in both columns however are noisily estimated. We therefore have weak and suggestive
evidence that turnout discrepancy (at the level of PCs) is positively correlated with irregular outcomes
at the polling stations in constituencies barely won by the BJP.
VI.VI Turnout Manipulation in High Muslim Share Areas
This section tests whether electoral discrimination of minorities, specifically Muslims, is a potential
source of turnout manipulation. Lehne (2022) shows, using individual voter level panel data on electoral
rolls from the state of Uttar Pradesh during 2012-2017, that in state assembly constituencies with BJP
incumbents (elected in 2012), Muslim voters have a significantly higher probability of being deleted
from the electoral rolls in 2017. Neggers (2018) shows using data from the state of Bihar, that polling
officers in charge of conducting election in a polling station exercise significant discretion in allowing
registered voters, specially from minority communities such as Muslims, to vote. Since Muslim names
are culturally distinct, Muslim voters are easily identified in the electoral roll. Therefore, they can
be subject to both strategic deletion (discussed above) and strategic discrimination. Moreover, such
exercises are easier in states controlled by the incumbent party, since the state government can influence
assignment of officials in charge of electoral roll revisions as well as polling officers. Hence, if fraud is
the appropriate explanation, I expect the polling station level irregularities to be concentrated in areas
within a PC that have high Muslim presence.
Precise control, on the other hand, would predict the opposite. This is because, the party historically
enjoyed minimal support among Muslim and consequently, spent significantly less effort in mobilizing
Muslim voters. Jha (2017), for example, points out that the party did not focus on areas with significant
Muslim presence, since it did not expect to get significant support from them. It instead directed its
efforts towards voters who could be converted to vote in favor of the party, especially those belonging
to lower castes among Hindus.39 This is consistent with Varshney (2019) who reports, using NES
data, that while support for the party increased substantially between 2014 and 2019, especially among
Scheduled Castes (SCs) and Other Backward Classes (OBCs) – two large disadvantaged caste groups
among Hindus, it remained constant among Muslims. In both elections, only 8 percent of Muslims are
reported to have voted for the BJP. Using the data on home visits by party workers, I also find that BJP
is significantly less likely to visit Muslim homes compared to non-Muslim homes, while other parties
are more likely to visit them (Appendix Table A7). Hence, if the polling station level irregularities are
due to exercise of precise control, we should expect it to be concentrated in areas within a PC that have
low Muslim share of the electorate.
Since Muslim electorate share at the polling stations is not known, I map each polling station to
the Assembly Constituency it falls under. Each AC is subsumed within a PC and each PC on average
contains about 7 ACs. The data on AC level Muslim electorate share (described in Section III) would
provide within-PC variation in Muslim electorate share across polling stations falling in different ACsegments. The final sample for this analysis contains more than 850, 000 polling stations mapped to 3098
ACs (76% of all ACs) covering 475 PCs. The mean Muslim share in an AC is 0.14. However, there is
39In Uttar Pradesh, for example, during BJP’s state-wide membership drive it did not focus on the 13, 000 polling stations
with significant Muslim presence, since it did not receive any votes in those areas.
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wide variation across ACs, with 5
th percentile at 0.01 and 95th percentile being 0.43. Appendix Figure
A9 shows the distribution across all ACs. Appendix Table A8 regresses polling station level BJP vote
share on AC level Muslim share and finds a sizable and statistically significant negative relationship, both
across PCs as well as within a PC. Now, to shed light on the mechanisms discussed above I focus on close
election PCs, i.e., those with absolute BJP win margin within 0.16 and run the following specification:
Yjap = ϕp + γBJP_W onp ∗ Muslim_shareap + δMuslim_shareap
+ β1BJP_W inM arginp ∗ Muslim_shareap
+ β2BJP_W onp ∗ BJP_W inM arginp ∗ Muslim_shareap + ϵjap
Table 8—Polling Station Level Irregularities Concentrated in High Muslim Share ACs
BJP Rel. BJP BJP share
vote share vote share ≥ 95th pctile
(1) (2) (3)
Panel A: All states
Muslim Electorate Share in AC -0.745*** -1.811*** -0.194**
(0.087) (0.300) (0.085)
BJP Won * Muslim Electorate Share in AC 0.327** 0.818** 0.199*
(0.127) (0.369) (0.101)
Observations 280,391 280,391 280,391
Panel B: BJP Ruled states
Muslim Electorate Share in AC -0.633*** -1.282*** -0.250**
(0.173) (0.373) (0.124)
BJP Won * Muslim Electorate Share in AC 0.367 0.695 0.269*
(0.224) (0.474) (0.147)
Observations 145,574 145,574 145,574
PC Fixed Effect YES YES YES
Bandwidth (h
∗
) 0.160 0.160 0.160
Notes: The data is at polling station level. The dependent variables are polling station level
BJP vote share (column 1), relative BJP vote share (column 2) and a dummy variable that
takes value one if the BJP vote share in a polling station exceeds the 95th percentile (column
3). BJP Won is an indicator of whether BJP is the winner of the Parliamentary Constituency.
Muslim Electorate Share in AC is the share of Muslim voters in the Assembly Constituency in
which a polling station is located. The sample in Panel A is PCs from all states while that in
Panel B is BJP ruled states. Optimal bandwidth calculated for McCrary test in Figure 1a has
been used in all specifications. Standard errors are clustered at the PC level and are reported
in the parentheses. *** p<0.01, ** p<0.05, * p<0.1
where j denotes polling station, a denotes AC and p denotes PC. Yjap is one of three outcome
variables – (i) vote share of BJP in a polling station, (ii) relative BJP vote share, as defined above, and
(iii) the indicator “high” BJP vote share defined above. ϕp is PC fixed effect and Muslim_shareap
is Muslim electorate share in AC a in PC p. The regression implements the difference-in-discontinuity
specification with PC fixed effects that subsume the running variable, the treatment BJP Won and their
interaction. It compares polling stations within a PC and checks if the BJP’s vote share is high or is more
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like to exceed its 95th percentile in AC segments with higher Muslim share and whether this relationship
is different between PCs that BJP barely won and lost. δ estimates the relationship in PCs lost by BJP.
γ is the differential estimate for PCs won by BJP and is our coefficient of interest. Precise control
hypothesis implies γ < 0, while manipulation would imply γ > 0.
Table 8 reports the results. The three columns correspond to the three outcome variables mentioned
above. Panel A reports the results for the full sample, while Panel B reports it for the BJP ruled states. As
before, I restrict attention to PCs with BJP win margin within 0.16. In Panel A, the estimates of δ in all
the columns is negative and statistically significant at 1% or 5%. However, the estimates of γ are positive
and statistically significant at 10% or 5%. In Panel B, the estimates of δ are also positive, but in columns
(1) and (2) they are noisily estimated. The estimate for the dummy indicating “high” BJP vote share in
a polling station (column (3)) is large in magnitude and statistically significant at 10%. The estimates of
γ and δ jointly indicate that the Muslim electorate share does not predict “extreme” outcomes in favor
BJP in PCs barely won by BJP, even though it strongly negatively predicts such outcomes in PCs barely
lost by the party.
Appendix Table A9 partitions the sample used in Panel B column (3) into polling stations with
turnout higher and lower than 800 and estimates the same specification. To allow comparison, column
(1) of Table A9 reports the same result as column (3) of Table 8. Columns (2) and (3) report the results for
the two sub-samples separately. We find that the the estimate of δ is large in magnitude and statistically
significant at 1% in column (2), while it is smaller in magnitude and statistically insignificant in column
(3). This is consistent with the graphs reported in Figure 4.
40
VII Concluding Remarks
The paper documents irregularity in India’s 2019 general election data by showing that the incumbent
party’s win margin distribution exhibits excess mass at zero, while no such pattern exists either in previous general elections or in state elections held simultaneously and subsequently. This implies that the
incumbent party in 2019 won a disproportionate share of closely contested elections. Moreover, the pattern is concentrated in the states ruled by the incumbent party at that time. While the result is consistent
with electoral fraud or manipulation, the incumbent party’s superior ability to predict and affect win margin (i.e., precise control), owing to its significant advantage in electoral campaigning over other parties
can also explain it. To isolate the two mechanisms, I conduct a series of analyses to check for presence
of precise control and manipulation. I do not find that the incumbent party did greater door-to-door
campaigning than other parties in constituencies barely won by it. On the other hand, I find evidence
consistent with electoral manipulation at the stage of voter registration as well as at the time of voting
and counting (turnout manipulation). In both cases, the results point to strategic and targeted electoral
discrimination against Muslims, in the form of deletion of names from voter lists and suppression of
their votes during election, in part facilitated by weak monitoring by election observers.
The tests are, however, not proofs of fraud, nor does it suggest that manipulation was widespread.
Proving electoral manipulation in a robust democracy is a significantly harder task that would require
detailed investigation of electoral data in each constituency separately. In the 1960 Presidential election
in the US, for example, there was reporting of possible fraud in Illinois state that may have resulted in
40The result remains the same if I use turnout threshold of 700 or 600, instead of 800.
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John F. Kennedy winning that state. Analysis of detailed data on recounting of votes from Cook county
showed patterns consistent with fraud, and yet, were not able to conclusively determine its magnitude
and whether it caused the result to flip in Kennedy’s favor (Kallina 1985). This case also highlights
that electoral fraud is often decentralized (Rundlett and Svolik 2016), as opposed to being implemented
centrally. Consequently, fraud may occur even in contexts where it would not have mattered for government formation. In 1960, Kennedy would have won the Presidential election even if he had lost Illinois.
Similarly, in my context, even if manipulation of election data drives all of the observed irregularities
in closely contested constituencies, the aggregate election outcomes in terms of government formation
would likely have remained unchanged. Appendix Table A10 reports the number of PCs with “excess”
BJP wins in closely contested PCs. It varies from 9-18, depending on the definition of a close contest;
the numbers are smaller than the lead of 31 PCs that BJP has over the threshold required to form government. Nonetheless, electoral fraud even in a single constituency would imply that such manipulations by
incumbent parties are possible. In view of the depletion of trust in electoral processes across the globe
and the exceptional integrity of India’s electoral institution in its past, the paper presents a worrying
development with potentially far-reaching consequences for the world’s largest democracy.
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Appendix
A Additional Figures and Tables
Table A1—Number of Constituencies BJP Won vs Lost in Close Elections
2019 2014 2009 2004
(1) (2) (3) (4)
Panel A: Absolute BJP Win Margin ≤ 0.05
# (%) of Constituencies BJP Won 41 (69%) 29 (60%) 49 (51%) 47 (59%)
# (%) of Constituencies BJP Lost 18 (31%) 19 (40%) 48 (49%) 33 (41%)
Total # (%) of Constituencies 59 (100%) 48 (100%) 97 (100%) 80 (100%)
Panel B: Absolute BJP Win Margin ≤ 0.03
# (%) of Constituencies BJP Won 28 (74%) 14 (58%) 29 (47%) 22 (56%)
# (%) of Constituencies BJP Lost 10 (26%) 10 (42%) 33 (53%) 17 (44%)
Total # (%) of Constituencies 38 (100%) 24 (100%) 62 (100%) 39 (100%)
Panel C: Absolute BJP Win Margin ≤ 0.02
# (%) of Constituencies BJP Won 20 (74%) 10 (53%) 21 (46%) 18 (64%)
# (%) of Constituencies BJP Lost 7 (26%) 9 (47%) 25 (54%) 10 (36%)
Total # (%) of Constituencies 27 (100%) 19 (100%) 46 (100%) 28 (100%)
Notes: The table reports the number and percentage of constituencies that BJP won and lost and
total number of constituencies in 2019 (column 1), 2014 (column 2), 2009 (column 3) and 2004
(column 4) general elections where the BJP’s absolute win margin was less than or equal to 0.05
(Panel A), 0.03 (Panel B) and 0.02 (Panel C).
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Table A2—Heterogeneity in Density Jump in BJP Win Margin
Panel A
BJP Ruled States Non-BJP Ruled States
(1) (2)
Density jump 3.14*** 0.21
(1.15) (1.03)
Bandwidth (h
∗
) 0.160 0.160
Notes: The table reports the estimates in the difference in densities
of BJP win margin at the threshold value of zero using the McCrary
test. Column 1 reports it for the states ruled by BJP in 2019, while
column 2 reports for the rest of the states. Optimal bandwidth is
calculated using the MSERD method proposed by Calonico et al.
(2014). *** p<0.01, ** p<0.05, * p<0.1
Table A3—Social Media Usage and Voting Behavior: NES 2019
Voted for BJP
(1) (2)
Facebook user 0.035*** 0.027**
(0.013) (0.013)
Twitter user -0.081*** -0.078***
(0.018) (0.017)
Whatsapp user 0.007 -0.008
(0.013) (0.013)
Instagram user 0.020 0.017
(0.014) (0.014)
YouTube user 0.001 -0.004
(0.013) (0.013)
Constant 0.335*** 0.367***
(0.004) (0.013)
Observations 22,037 22,037
R-squared 0.002 0.007
Notes: The sample is individual level survey data from the National Election Survey
(post poll) 2019. The dependent variable is
a dummy indicating whether the individual
reported to have voted for BJP in the 2019
election. For any social media platform, an
individual is defined to be “user” of the platform if they use it at least once daily. Column
2 controls for the gender, age and dummies
for caste categories of individuals. Robust
standard errors are reported in the parentheses. *** p<0.01, ** p<0.05, * p<0.1
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Table A4—Discontinuity in βp Estimates in Closely Contested Constituencies
Dep. Var.: βp
Full BJP Ruled Non-BJP
Sample States Ruled States
(1) (2) (3)
BJP Won 0.24*** 0.09 0.27*
(0.09) (0.09) (0.16)
Bandwidth (h
∗
) 0.16 0.16 0.16
Observations 68 28 40
Notes: The dependent variable is the estimate of the coefficient βp from running equation (1) for each PC. The table
reports the RDD estimates of a BJP victory using βp as the
outcome variable. Column 1 reports it for the full sample,
columns 2 reports it for the states ruled by BJP in 2019, and
column 3 reports for the rest of the states. Bandwidth used
is the optimal bandwidth used for McCrary test in Figure 1a.
*** p<0.01, ** p<0.05, * p<0.1
Table A5—Electorate Growth Rate Smaller in PCs Barely Won by BJP
Electorate Growth Rate (Gc)
BJP Ruled States Non-BJP Ruled States
High Low High Low
Muslim Share Muslim Share Muslim Share Muslim Share
(1) (2) (3) (4)
BJP Won -0.06* -0.04 -0.04 -0.01
(0.04) (0.03) (0.02) (0.02)
Mean Dep. Var. 0.11 0.10 0.08 0.07
Observations 53 30 48 50
Bandwidth (h
∗) 0.16 0.16 0.16 0.16
Notes: The data is at Parliamentary Constituency (PC) level. The table reports the regression discontinuity design estimate using BJP win margin as the running variable. The dependent variable in all the columns is the growth rate in the PC electorate between 2014 and
2019. BJP Won is a dummy indicating whether BJP won the PC in 2019. Columns (1) and
(2) use the sample of PCs in BJP ruled states, while columns (3) and (4) use PCs in non-BJP
ruled states. Columns (1) and (3) use PCs where the electorate share of Muslims is higher
than the median, and column (2) and (4) use PCs where the share is lower than median. All
columns use the optimal bandwidth calculated for McCrary test in Figure 1a. Robust standard errors are are reported in the parentheses. *** p<0.01, ** p<0.05, * p<0.1
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Table A6—Association between Turnout Data Discrepancy and Counting Observer Characteristics
Absolute “Large”
Turnout Discrepancy Turnout Discrepancy
(1) (2)
BJP Won -475.1 -0.0269
(623.9) (0.0229)
SCS Counting Observer from BJP Ruled State -463.2 -0.0471
(1,009) (0.0663)
BJP Won * SCS Counting Observer from BJP Ruled State 4,373* 0.210**
(2,589) (0.104)
Constant 1,712*** 0.0544***
(426.3) (0.0196)
H0 : β2 + β3 = 0 (p-value) 0.102 0.041
Observations 370 370
R-squared 0.012 0.016
Notes: The data is at Parliamentary Constituency (PC) level. The dependent variable in column (1) is absolute discrepancy in turnout data and in column (2) a dummy variable that takes value one when the absolute discrepancy
exceeds 95th percentile of its distribution. BJP Won is a dummy indicator whether BJP won the PC in 2019. SCS
Counting Observer from BJP Ruled State is the fraction of counting observers assigned to a PC who are SCS cadre
and work in BJP ruled states. Robust standard errors are are reported in the parentheses. *** p<0.01, ** p<0.05, *
p<0.1
Table A7—Campaigning among Muslim Voters by BJP and Other Parties
Home Visit by Party Worker/Candidate
from BJP from Any Other Party
(1) (2) (3) (4) (5) (6)
Muslim -0.14*** -0.12*** -0.12*** 0.05*** 0.04*** 0.03***
(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)
Mean Dep. Var. 0.38 0.38 0.38 0.50 0.50 0.50
Fixed Effect State PC AC State PC AC
Observations 24,230 24,230 24,230 24,230 24,230 24,230
No of PCs 208 208 208 208 208 208
R-squared 0.14 0.24 0.31 0.23 0.32 0.39
Notes: The sample is individual level survey data from the National Election Survey (post
poll) 2019. The dependent variable in columns (1)-(3) is a dummy variable that takes value
one if a BJP party worker or candidate visited the house of the respondent to campaign for
general election, and is zero otherwise. The dependent variable in columns (4)-(6) is also a
dummy variable that indicates whether party worker or candidate from any other party visited the house for campaigning. Muslim is dummy variable that takes value one if the survey
respondent is a Muslim. All columns control for the respondents’ age, age squared, gender
and education categories. Columns (1) and (4) have state fixed effects, (2) and (5) have PC
fixed effects, and (3) and (6) have AC fixed effects. Robust standard errors are reported in
the parentheses. *** p<0.01, ** p<0.05, * p<0.1
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Table A8—Correlation between AC Muslim Share and Polling Station Level BJP Vote Share
BJP vote share
(1) (2) (3)
Muslim Electorate Share in AC -0.37*** -0.31*** -0.43***
(0.02) (0.02) (0.02)
Mean Dep. Var. 0.46 0.46 0.46
Observations 674,253 674,253 674,253
State Fixed Effect NO YES NO
PC Fixed Effect NO NO YES
Notes: The data is at polling station level. The dependent variable in
all the columns is BJP vote share in a polling station. Muslim Electorate
Share in AC is the share of Muslim voters in the Assembly Constituency
in which a polling station is located. Column (1) has no other controls,
while columns (2) and (3) have state and AC fixed effects respectively.
Standard errors are clustered at the AC level and are reported in the parentheses. *** p<0.01, ** p<0.05, * p<0.1
38
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Table A9—Polling Station Level Irregularities Concentrated in Polling Stations with High Muslim
Shares
BJP vote share ≥ 95th pctile
BJP Ruled P. S. Turnout P. S. Turnout
States ≥ 800 < 800
(1) (2) (3)
Muslim Electorate Share in AC -0.250** -0.378*** -0.207
(0.124) (0.073) (0.137)
BJP Won * Muslim Electorate Share in AC 0.269* 0.701*** 0.224
(0.147) (0.174) (0.158)
Mean Dep. Var. 0.054 0.086 0.052
Observations 145,574 8,310 137,263
PC Fixed Effect YES YES YES
Bandwidth (h
∗
) 0.16 0.16 0.16
Notes: The data is at polling station level. The dependent variable in all the columns is a dummy
variable that takes value one if the BJP vote share in a polling station is larger than the 95th
percentile and zero otherwise. BJP Won is an indicator of whether BJP is the winner of the Parliamentary Constituency (PC). Muslim Electorate Share in AC is the share of Muslim electorate
in the Assembly Constituency in which a polling station is located. The sample in column (1) is
PCs in BJP ruled states with absolute BJP win margin within 0.16. Columns (2) and (3) samples
are partitions of the column (1) sample into polling stations with turnout higher than and less
than 800, respectively. All columns have PC fixed effect. The optimal bandwidth calculated for
McCrary test in Figure 1a has been used in all specifications. Standard errors are clustered at the
PC level and are reported in the parentheses. *** p<0.01, ** p<0.05, * p<0.1
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Table A10—Extent of “Excess” BJP Wins in Closely Contested PCs
BJP Win Margin
≤ 0.07 ≤ 0.05 ≤ 0.03
(1) (2) (3)
# Close Election PCs 82 59 38
# “Excess” BJP Wins 18 11 9
Notes: The table reports the number of closely contested
Parliamentary Constituencies (PCs) in 2019 and the “excess” number of wins by BJP in those PCs relative to
the benchmark of 50% chance of winning. The three
columns use three different bandwidths to define a close
contest. Column (1) considers BJP win margin within
0.07 while columns (2) and (3) consider 0.05 and 0.03
respectively.
40
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Figure A1—Correlation between Muslim Electorate Share and Vote Share of Muslim Candidates
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Figure A2—McCrary Test for State Assembly Elections
(a) 2019 Elections: BJP Ruled States (b) 2019 Elections: Non-BJP Ruled States
(c) 2020-21 Elections: BJP Ruled States (d) 2020-21 Elections: Non-BJP Ruled States
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Figure A3—McCrary Test for Non-BJP Ruled States with Strong BJP Presence
Figure A4—EVM Turnout Data Discrepancy in Closely Contested Constituencies
(a) “Large” Data Discrepancy (b) Absolute Data Discrepancy
43
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Figure A5—SCS Election Observers in Closely Contested Constituencies
(a) BJP Ruled States (b) Non-BJP Ruled States
(c) BJP Ruled States (d) Non-BJP Ruled States
44
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Figure A6—Turnout Difference in Closely Contested PCs in past General Elections
(a) 2019 (b) 2014
(c) 2009 (d) 2004
(e) 1998 (f) 1996
(g) 1991 (h) 1989 45
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Figure A7—Differential Pattern only in Closely Contested Elections Won by BJP
46
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Figure A8—BJP’s Vote Rate Density in Uttar Pradesh
47
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Figure A9—Distribution of Muslim Electorate Share
48
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B Constituency List
BJP Ruled State State Constituency BJP Won
(1) (2) (3) (4)
1 Assam KARIMGANJ 1
1 Assam NOWGONG 0
1 Bihar PATALIPUTRA 1
1 Goa SOUTH GOA 0
1 Haryana ROHTAK 1
1 Jharkhand DUMKA 1
1 Jharkhand KHUNTI 1
1 Jharkhand LOHARDAGA 1
1 Maharashtra CHANDRAPUR 0
1 Maharashtra NANDED 1
1 Manipur INNER MANIPUR 1
1 Uttar Pradesh SAHARANPUR 0
1 Uttar Pradesh MUZAFFARNAGAR 1
1 Uttar Pradesh MEERUT 1
1 Uttar Pradesh BAGHPAT 1
1 Uttar Pradesh FIROZABAD 1
1 Uttar Pradesh BADAUN 1
1 Uttar Pradesh SULTANPUR 1
1 Uttar Pradesh KANNAUJ 1
1 Uttar Pradesh KAUSHAMBI 1
1 Uttar Pradesh SHRAWASTI 0
1 Uttar Pradesh BASTI 1
1 Uttar Pradesh SANT KABIR NAGAR 1
1 Uttar Pradesh BALLIA 1
1 Uttar Pradesh MACHHLISHAHR 1
1 Uttar Pradesh CHANDAULI 1
1 Uttar Pradesh BHADOHI 1
0 Andaman & Nicobar Islands ANDAMAN & NICOBAR ISLANDS 0
0 Chhattisgarh RAIGARH 1
0 Chhattisgarh KORBA 0
0 Chhattisgarh BASTAR 0
0 Chhattisgarh KANKER 1
0 Dadra & Nagar Haveli DADRA AND NAGAR HAVELI 0
0 Karnataka KOPPAL 1
0 Karnataka BELLARY 1
0 Karnataka TUMKUR 1
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0 Karnataka CHAMARAJANAGAR 1
0 Madhya Pradesh CHHINDWARA 0
0 Odisha SAMBALPUR 1
0 Odisha MAYURBHANJ 1
0 Odisha BALASORE 1
0 Odisha BHADRAK 0
0 Odisha DHENKANAL 0
0 Odisha BOLANGIR 1
0 Odisha KALAHANDI 1
0 Odisha NABARANGPUR 0
0 Odisha PURI 0
0 Odisha BHUBANESWAR 1
0 Punjab HOSHIARPUR 1
0 West Bengal COOCH BEHAR 1
0 West Bengal RAIGANJ 1
0 West Bengal BALURGHAT 1
0 West Bengal MALDAHA DAKSHIN 0
0 West Bengal KRISHNANAGAR 0
0 West Bengal BARRACKPORE 1
0 West Bengal DUM DUM 0
0 West Bengal ARAMBAGH 0
0 West Bengal JHARGRAM 1
0 West Bengal BARDHAMAN DURGAPUR 1
Notes: The table lists the 59 constituencies where the absolute BJP win margin was within 0.05. The first column
indicates whether the constituency belonged to a state ruled by the BJP during the 2019 general elections. The last
column indicates whether the BJP won that constituency in 2019.
50
Electronic copy available at: https://ssrn.com/abstract=4512936