Development or Rent-Seeking?:
How Political Influence Shapes Infrastructure Provision in India
Anjali Thomas Bohlken1
This Draft: February 2017. Comments Welcome.
Abstract
How do incumbents with influence over infrastructure programs balance their in-centives to gain electoral support with their proclivities for rent-seeking? I argue thatgovernment elites in parliamentary systems manage this trade-off by concentratingrent-seeking opportunities in their own hands while facilitating efficient public goodsprovision in the constituencies of their more junior partisan colleagues. Analyses usingfine-grained data on road construction in India based on a variety of causal inferencestrategies support the argument. While ruling party incumbents showed higher levelsof road provision in their constituencies regardless of ministerial status, road projectsin ministers’ constituencies showed higher levels of rent-seeking than those in the con-stituencies of other ruling party legislators. Moreover, consistent with the mechanism,ruling party legislators’ diminished access to rent-seeking opportunities is shown to belargely driven by the influence of co-partisan ministers. The findings illuminate howpoliticized distribution can sometimes mitigate inefficiencies in infrastructure provision.
1 Assistant Professor, Sam Nunn School of International Affairs, Georgia Tech. For helpful comments
and feedback, I am grateful to Sam Asher, Thad Dunning, Robin Harding, Mark Schneider, Jacob Shapiro,
Emmanuel Teitelbaum, Johannes Urpelainen and seminar participants at the UBC Comparative Politics
workshop, at the Indian Political Economy workshop at the Carnegie Endowment for International Peace
and at the Center for Advanced Study of India at the University of Pennsylvania. I am also thankful to
Ashish Ranjan for his assistance in conducting interviews in Uttar Pradesh and Bihar and to Himanshu
Mistry at NYU Data Services for his assistance with the GIS maps.
1
In recent decades, governments across the developing world have embarked on significant
investments in infrastructure to attempt to improve citizens’ access to basic services such
as electricity, schools, clean water and roads. Yet, especially because these public infras-
tructure programs often occur in contexts where the rule of law is weak and where political
influence over the bureaucracy runs rampant, the question of whether these programs lead
to development or whether they simply result in the creation of rent-seeking opportunities is
often debated. On the one hand, some studies have argued that local infrastructure projects
serve mainly as cash cows, used by political elites to generate kickbacks for their own pri-
vate ends (Lehne et al. 2016, Boas et al. 2014, Khemani 2010, Samuels 2002, Wilkinson
2006, Rose-Ackerman 1999). On the other hand, however, previous studies have argued that
when incumbents face competitive elections, they have an incentive to use their control over
infrastructure programs to improve the provision of public goods (Lake & Baum 2001, Buen-
odeMesquita et al. 2003, Stasavage 2005, Harding 2014). This paper provides an argument
supported by new evidence from the Indian context to reconcile these two competing views
of public infrastructure provision.
How does political influence shape the implementation of infrastructure programs? This
paper takes as its starting point the notion that although non-programmatic distribution may
be less normatively desirable than programmatic distribution (e.g. Stokes et al. 2013), some
types of non-programmatic distribution may be far more beneficial to ordinary citizens than
others (e.g. Auerbach & Sinha 2013). In particular, although non-programmatic distribution
of government goods and services is often referred to derogatorily as ‘pork’, these ‘outputs’ of
public infrastructure programs often improve the lives of ordinary citizens (e.g. Dinkelman
2
2011, Asher & Novosad 2015). Conversely, non-programmatic distribution that results in
the ‘inputs’ of infrastructure provision being allocated so as to allow for politicians and
bureaucrats to extract substantial rents in the process often occurs at the expense of ordinary
citizens. In some cases, rent-seeking may occur by allocating spending for public goods which
is in excess of what is actually required to provide the goods (e.g. Golden & Picci 2005,
Kunicova & Rose-Ackerman 2005). In other cases, rent-seeking may occur through the
under provision of inputs and may thus detract from the quality of public goods provision
(e.g. Wilkinson 2006). In all of these cases, rent-seeking is a source of inefficiency that will
generally be to the detriment of the average citizen.
Consequently, although political elites often seek to use their influence to extract private
rents, such rent-seeking behavior may come into conflict with their desire to appeal to voters
through the provision of public goods. How do political elites manage this trade-off? To
address the question, I build on the insight that parliamentary systems with single member
districts create a situation in which an individuals’ success in gaining and maintaining office
is closely tied with the electoral success of her party as a whole (e.g. Cain et al. 1984). I
argue that, in this context, the incentives of individual members within a ruling party should
be aligned when it comes to ensuring the delivery of public goods to their constituents, but
not when it comes to allowing the creation of rent-seeking opportunities.
When it comes to providing public goods to constituents, I argue that members of the
governing coalition who exert control over the governmental machinery for implementing
infrastructure projects should seek to use their influence to ensure that all their fellow party
members are able to deliver the infrastructure necessary to win their seats. However, when it
3
comes to creating rent-seeking opportunities, the incentives for co-operation between mem-
bers of the same party the same party should diminish. Specifically, since the benefits of
rent-seeking opportunities are primarily private, government elites with control over the
machinery for implementing infrastructure projects should have an incentive to concentrate
rent-seeking opportunities within their own hands and in the hands of a select set of powerful
party colleagues. Meanwhile, since rent-seeking could detract from public goods provision
and ultimately hurt the ruling party’s electoral prospects, those with control over infrastruc-
ture provision should seek to minimize, to the extent possible, the rent-seeking opportunities
available to their less powerful co-partisan colleagues. Thus, rather than partisan criteria
determining access to rent-seeking opportunities, the argument suggests that there should
be a division of roles within ruling parties between those who are given access rent-seeking
opportunities and those who are made to deliver infrastructure more efficiently. In a parlia-
mentary context where ministers typically have either formal power over the governmental
machinery used to implement development projects or informal leverage with party lead-
ers or both, I argue that this division of roles is largely determined by a party member’s
ministerial status.
The paper provides support for this argument using evidence from a large-scale rural roads
development scheme in India launched in 2000 which is known by the acronym PMGSY.2
The dataset used in this paper consists of a range of information for over 30,000 road projects
across seven states in North India3 that were geocoded and located in individual politicians’
2 The acronym stands for Pradhan Mantri Gram Sadak Yojana or Prime Minister’s Rural Roads Scheme.
3 The states chosen are Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Rajasthan, Uttar Pradesh and
4
constituencies. Since state governments were responsible for administering this scheme,
analyses were conducted both at the state constituency level as well as at the individual
road level, focusing on how the implementation of this roads scheme varied across state
constituencies based on the ministerial status and partisan alignment of the state legislators
in those districts. A focus on the PMGSY scheme is beneficial since it furnishes rich and
fine-grained data on a host of characteristics of thousands of road projects, thus providing
a rare window into the inner workings of public infrastructure programs and how they are
manipulated through political influence. At the same time, large-scale public infrastructure
projects are common in many countries (e.g. Harding 2014, Lewis-Faupel et al. 2015, Golden
& Picci 2005, Rose-Ackerman 1999.) and the argument and findings of this research can shed
light more broadly on the contexts in which such programs are likely to be successful.
Analyses using the above-mentioned data provides compelling support for the argument
showing that incumbents aligned with the ruling party show a greater ability to deliver
infrastructure outputs regardless of whether they are ministers or ordinary legislators. How-
ever, when it comes to rent-seeking opportunities - measured using spending leakages (e.g.
Golden & Picci 2005) and further corroborated using evidence on inefficiencies in contractor
selection, on the quality of road construction and on expenditures on unproductive projects
- ministers’ constituencies consistently benefit at the expense of the constituencies of ordi-
nary legislators from the ruling party. The use of a variety of techniques - (i) constituency
level fixed effects, (ii) a regression discontinuity design based on close elections and (iii) an
Uttarakhand that together comprise a population of over 500 million - over 40% of India’s total population.
Section 4 below discusses the rationale behind the selection of these states.
5
instrumental variables approach that captures exogenous changes in partisan alignment and
ministerial status - increases confidence that these results are not driven by unobservable
confounding factors.
Further results rule out alternative explanations for the observed differences in access to
rent-seeking opportunities between ministers and ordinary legislators such as (a) differences
in the degree of electoral competition faced by ministers vs. ordinary legislators and (b)
differences between ministers and ordinary legislators in terms of their formal control over
infrastructure provision. Instead, it is shown that the results indicating that ministers have
greater opportunities for rent-seeking are driven not only by those with formal powers over
infrastructure provision but by other ministers as well.
To provide support for the precise mechanisms implied by the argument, further analyses
are used to show evidence for several additional observable implications, two of which are
highlighted here: (i) First, the results show that although ruling party aligned ordinary legis-
lators suffered a disadvantage over other legislators in accessing expenditures for unproductive
projects, these legislators had an advantage over other legislators in accessing discretionary
expenditures for productive road projects in their constituencies. This result supports the
argument that the ability of ordinary legislators aligned with the ruling party to gain access
to patronage resources depends crucially on whether these resources are used for develop-
ment or for rent-seeking. (ii) Second, the results show that ordinary legislators aligned with
the ruling party showed significantly lower expenditure on unproductive projects in their con-
stituencies when they were subject to greater oversight from certain key co-partisan ministers
- that is, when they shared a bureaucratic jurisdiction with a co-partisan minister whose de-
6
partment is charged with rural road provision. This finding supports the argument that the
lower prevalence of rent-seeking in the constituencies of ordinary legislators aligned with the
ruling party is largely driven by the influence of government elites in their own party. Thus,
in contrast to the conventional wisdom that politicized distribution is inherently inefficient,
the overall findings highlight the conditions under which government elites use their influence
over the bureaucracy to reduce inefficiencies in public goods provision.
The findings contribute to the vast and growing literature on the politics of public goods
provision. Several studies have emphasized the role that democracy and electoral competition
play in shaping political elites’ incentives to provide public goods (e.g. Lake & Baum 2001,
BuenodeMesquita et al. 2003, Chhibber & Nooruddin 2004, Stasavage 2005, Harding 2014,
Min 2015). The present study shows, however, that public goods provision depends not only
on electoral considerations but also on the incentives for co-operation between members of
the ruling party when it comes to improving outcomes for the benefit of ordinary citizens
and when it comes to extracting rents. In doing so, this research relates to previous studies
highlighting the importance that local bureaucrats play in delivering public services (e.g.
Kumar et al. 2017, Bhavnani & Lee 2015, Bussell 2012, Gulzar & Pasquale 2015), as well
those that have examined how the degree of control that political incumbents have over
the bureaucracy shapes developmental outcomes (e.g. Golden & Min 2013, Alkon et al.
2016, Bhavnani & Jensenius 2015 Asher & Novosad 2016). The present research builds on
these insights by shedding light on the question of how political elites with control over the
bureaucracy manage the trade-off between vote-seeking and rent-seeking.
This research also relates to the growing literature that focuses on how political elites dis-
7
tribute government goods and services (e.g. Golden & Min 2013, Stokes et al. 2013, Vaishnav
& Sircar 2010, Keefer & Khemani 2009, Dunning & Nilekani 2013, Besley et al. 2004). While
these previous studies have largely focused on how distribution is governed by partisan con-
siderations, this research sheds light on the conditions under which divisions within parties
rather than divisions between parties matter for distributive outcomes. In doing so, it sheds
new light on the factors shaping the distribution of key outcomes associated with infrastruc-
ture provision.
Finally, the present research helps contribute to a better understanding of the mechanisms
through which the institutional context can shape incentives for public goods provision as
opposed to rent-seeking. For example, while there is an ongoing debate on whether par-
liamentary or presidential systems provides more scope for corruption and inefficiency (e.g.
Kunicova & Rose-Ackerman 2005, Gerring & Thacker 2004, Persson & Tabellini 2005), this
paper provides micro-level evidence to support the argument that one often overlooked mech-
anism through which parliamentary systems can reduce inefficiency in public infrastructure
provision is by aligning the political fates of government elites and ordinary legislators from
the same party.
The rest of the paper is organized as follows. Section 1 describes the argument in greater
detail. Section 2 provides background on the context and data, Section 3 describes the
research design and Section 4 presents the results of the main analyses as well as additional
analyses designed to investigate alternative explanations and provide further evidence on
mechanisms. Section 5 concludes with a discussion of the broader implications of the findings.
8
1 Argument
Parliamentary systems with single member districts are known to create strong incentives
for co-operation between ministers and their partisan colleagues in the legislature who are
not part of the government (Denemark 2000, Carey & Shugart 1995, Cain et al. 1984). This
paper argues, however, that whether these incentives for co-operation between ministers
and ordinary legislators exist depends on what is being distributed. On the one hand,
the ‘outputs’ of infrastructure provision - such as roads, electricity, health care centers and
schools - are quasi- public goods that are non-excludable at least to citizens within a certain
area. On the other hand, rents from infrastructure provision are rival and excludable. Thus,
politicians should - following the logic of selectorate theory (BuenodeMesquita et al. 2003) -
prefer to use the ‘outputs’ of infrastructure provision to gain electoral support from a broad
base of citizens, but should prefer to use the rents they derive from infrastructure provision
for their own enrichment or for the enrichment of a small set of cronies.
While parties are often treated as unitary actors, there is often a substantial division of
power within ruling parties in terms of members’ abilities to exercise control over govern-
ment machinery for delivering public goods and services. How do government elites with
control over the implementation of infrastructure programs choose to exercise their influence?
Since government elites in a parliamentary system are affected by the electoral fortunes or
misfortunes of their co-partisan colleagues (e.g. Cain et al. 1984), and since the provision of
public goods in turn affects these electoral fortunes, the interests of government elites and
their co-partisan colleagues should be aligned when it comes to ensuring the provision of
9
public goods. Thus, the argument suggests:
H1: All else equal, there should be more road provided in constituencies that have an
incumbent who belongs to a governing party - regardless of her status within the party -
than in constituencies that have an incumbent who belongs to an opposition party.
In addition to providing politicians with the ability to target public goods, however, public
infrastructure programs also provide politicians with opportunities for rent-seeking. In turn,
these rents often serve as private goods for politicians and their families, allowing them to
enrich themselves (e.g. Fisman et al. 2014) and their family members. Even if politicians use
these rents to fund their election campaigns (e.g. Kapur & Vaishnav 2011, Samuels 2002,
Wilkinson 2006), the rents still largely represent a private benefit to politicians in allowing
them to defray the costs of electoral campaigns that may otherwise have been financed out
of their own pockets or through other costly activities. Moreover, even if politicians use the
rents extracted to fund the electoral campaigns of their co-partisans, the opportunity to be
a ‘residual claimant’ on the rents extracted from public infrastructure provision is in itself a
private good. Thus, unlike the provision of a road which provides a broad benefit to citizens,
rents extracted from infrastructure typically serve mainly as private goods for bureaucrats,
political elites and their cronies.
Because of the ‘private good’ nature of rents, I argue, the interests of ruling party members
are far less aligned when it comes to using political influence over infrastructure provision to
create rent-seeking opportunities. In particular, a ruling party incumbent with control over
infrastructure provision should seek to concentrate rent-seeking opportunities in her own
10
hands by creating such opportunities in areas where she has the knowledge and connections
to ensure that she can derive a personal profit from these opportunities - such as in her
constituency. She should also seek to allow access to such opportunities to a select set of other
partisan colleagues - especially those who could offer their own lucrative sources of rents in
exchange or those whose loyalty she or her party leaders wish to earn or keep. Since ministers
in a parliamentary system tend to have both formal control over departments as well as
informal leverage with the head of government or other important leaders, the key observable
criterion that should determine a party member’s access to rent-seeking opportunities from
infrastructure provision is her ministerial status.
Yet, while rent-seeking can provide private benefits to incumbents, it can also be costly. First,
if rent-seeking is financed through excess expenditures, it typically imposes a cost to the
government budget which could, in turn, hurt the ruling party as a whole. Yet, because the
benefits of rent-seeking opportunities are primarily private rather than electoral, ministers
should typically prefer to minimize the costs of rent-seeking by minimizing the rent-seeking
opportunities available to their fellow party members rather than by curtailing their own
rent-seeking.
Second, if rent-seeking occurs through the under provision of inputs or through the hiring
of inefficient contractors, then it could be electorally costly for the incumbent in whose
constituency such rent-seeking is taking place. Indeed, although voters may not observe rent-
seeking practices directly, these types of rent-seeking could often detract from the quality
of the road provided or from the timeliness of road completion (e.g. Lehne et al. 2016) -
11
outcomes that are typically visible and salient to voters.4 In this case, a minister typically
faces a tradeoff between, on the one hand, extracting rents in the short-term and decreasing
her chances of getting re-elected and, on the other hand, foregoing rent extraction in the
short-term and, as a result, increasing her chances of getting re-elected. If ministers face a
high underlying electoral risk - that is, a high risk that she will fail to be re-elected regardless
of her rent-seeking behavior while in office - this could make her more apt to favor extracting
rents in the short-term despite the electoral costs of doing so (e.g. Achen & Bartels 2002,
Healy & Malhotra 2013).
At the same time, if rent-seeking by a minister’s party colleagues is a private good, a minister
should have little incentive to help her junior party colleagues gain access to rent-seeking
opportunities. Even if the junior party colleagues’ had little chance of being re-elected,
facilitating such rent-seeking could hurt the reputation and therefore electoral prospects of
the ruling party while yielding little personal benefit to the minister. Thus, in a context
where incumbents face a sufficiently high underlying electoral risk, we should expect that:
H2: Rent-seeking opportunities in road projects should be more prevalent ministers’ con-
stituencies than in constituencies of ordinary legislators who are members of the ruling party
or coalition.
Since existing studies in the Indian context suggest that the underlying electoral risk faced
4 Anecdotally, our conversations with villagers at PMGSY construction sites showed them to be greatly
attuned to whether the quality of the road construction was sufficiently good to allow the road to withstand
heavy monsoon rains.
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by Indian state incumbents is generally high (e.g. Nooruddin & Chhibber 2008, Uppal 2009)
and since further investigation suggests that state incumbents who are ministers face a high
risk as well5, the Indian context may - in general - meet the necessary scope conditions
for H2. At the same time, an additional implication is that electorally costly rent-seeking
opportunities in road projects should be more prevalent in ministers’ constituencies where
there is a higher underlying electoral risk. This implication is tested in Section 4.1.
The tests of H2 are predicated on the idea that a common way that rent-seeking occurs in
infrastructure provision is through kickbacks. If ministers exercise control over the careers of
bureaucrats, they can exert pressure on these bureaucrats to select certain contractors who
then must pay off not only the bureaucrat but also the relevant politicians in charge (e.g.
Boas et al. 2014, Wade 1982.). In turn, contractors generate the funds required to pay off the
bureaucrats and politicians by either inflating their expenses or under-providing inputs and
thereby saving on the costs of the inputs (Wade 1982). Thus, one observable implication of
rent-seeking opportunities in infrastructure provision is the prevalence of spending leakages
or spending inefficiencies - whereby there would be spending on roads in excess of some
‘baseline’ level of expenditure required to produce a given amount of road (e.g. Golden
& Picci 2005). At the level of the individual road project, one egregious manifestation
of spending leakages is the incurring of expenditure on unproductive road projects - that
is, projects that do not end up resulting in the construction of a road. A second and
related observable implication of rent-seeking opportunities is that roads would be of lower
5 For example, of the 45 ministers in Rabri Devi’s government in Bihar who stood for re-election in
October 2005, only 40% won re-election (Author’s own calculations).
13
quality even after controlling for the expenditure on the road and other road characteristics.
This reflects the possibility that, rather than inflating costs, contractors may have skimped
on materials required to ensure the quality of the road construction. A third implication
of observable implication of rent-seeking activity is nepotism towards contractors whereby
contractors are selected on the basis of their personal loyalties or familial connections with the
incumbent in a given constituency as opposed to their qualifications and experience. Absent
data on these personal connections and loyalties, one indication that a given politician is
engaging in this type of rent-seeking is that the contractors that they select to execute
road works in their constituencies will be less successful at winning contracts overall than
the ones selected in other constituencies. While each of these measures is a relative rather
than absolute measure of rent-seeking, each allows us to test H2 which posits that rent-
seeking activity is higher for road projects in the constituencies of ministers than those in
the constituencies of ordinary legislators from the ruling party.
Note that H2 leaves open the question of whether rent-seeking associated with road projects
in ministers’ constituencies will be higher or lower than the rent-seeking associated with
road projects in the constituencies of opposition party incumbents. This, in turn, would
depend on whether and how government elites can exert control over the bureaucracy in
the constituencies of opposition parties. Section A.16 in the Appendix further explores this
question.
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2 Background and Data
This paper focuses on the Pradhan Mantri Gram Sadak Yojana (PMGSY) - a rural roads
scheme that was launched by the Indian national government in December 2000. The scheme
was launched in recognition of the fact that, according to government estimates in the year
2000, around 40% of habitations6 all over India remained unconnected by all-weather roads.7.
The scheme was thus designed to provide roads that connect habitations with market centers
and administrative headquarters as well as roads that connect habitations to each other and
to the nearest major road or highway. The scheme also provides for existing roads of poor
quality to be upgraded, but prioritizes the provision of new connectivity over upgrades.
Like many of India’s development schemes, the PMGSY is funded by the Indian central
government but its implementation is left upto Indian state governments. India has a federal
parliamentary system that currently has 29 states and 9 Union Territories. Indian states
also have a parliamentary form of government where the head of government is the chief
minister. Members of the state legislative assembly - known as MLAs - are elected through
a First Past the Post electoral system with Single Member Districts.
With the PMGSY, each state is given a fixed allocation of funds from the central government
and the state government is responsible for deciding where and how the roads are to be built
6 Habitations are administrative units at the sub-village level in India
7 PMGSY Scheme and Guidelines, Section 1. Government of India, Ministry of Rural Development,
Accessed October 10, 2014.
15
in the state and how the allotted expenditures are to be utilized.8 Decisions regarding how
much should be spent on the road and how the road should be executed fall under the
purview of a state-level bureaucratic agency.9 In particular, the day to day implementation
of the scheme is carried out by a “Programme Implementation Unit” (PIU) of this executing
agency that oversees either one administrative district or a group of districts. Meanwhile,
decisions regarding where the roads are allocated are decided by elected councils below the
state level with input from national and state level legislators.10
The analysis in this paper focuses on seven states in North India - Rajasthan, Madhya
Pradesh, Uttar Pradesh, Bihar, Chattisgarh, Uttarakhand and Jharkhand - that together
comprise over 40% of India’s overall population. These states have historically lagged behind
in terms of human development and have been categorized as BIMARU states - an acronym
commonly used to refer to this group of states that plays on the Hindi word bimar or sickness.
A focus on these states is useful because of their broad socio-economic similarities but also
because they present a ‘least likely case’ for the specific argument that the paper seeks
to examine. Specifically, given that these states are notorious for their poor governance
records, one would expect rent-seeking to be widespread. However, the argument of the
paper suggests that government elites should seek to minimize rent-seeking in road projects
8 Section 5 of the PMGSY Scheme and Guidelines.
9 PMGSY Scheme and Guidelines, Section 7.
10 PMGSY Scheme and Guidelines, Section 6.
16
located in the constituencies of their co-partisans.
Data were collected for all roads that were sanctioned in the above-mentioned states under
the Pradhan Mantri Gram Sadak Yojana (PMGSY) between 2000 when the scheme began
until 2014. The data on roads were scraped from the website housing the online management,
monitoring and accounting system for the PMGSY maintained by the National Rural Roads
Development Agency (NRRDA). Section A.1 in the Appendix discusses the mechanisms put
in place by the NRRDA to ensure that the data in the online system reflects the on-the-
ground realities of PMGSY implementation with reasonable accuracy. Section A.2 in the
Appendix provides further details on the matching procedure. Since a key component of the
research design is to isolate the effect of the partisan alignment of legislators while holding
other confounding factors constant, the analyses are restricted to elections in the time period
before constituency boundaries were redrawn in India. Section A.3 in the Appendix describes
the constituencies, states and years included in the analysis.
2.1 Scope for Political Influence in PMGSY: Ministers and Ordi-
nary Legislators
India is a federal country where state governments can exert significant control over the bu-
reaucracy responsible for administering development schemes at the state level (Wade 1982,
Iyer & Mani 2012). Thus, although PMGSY is a centrally sponsored scheme and although
a national government agency oversees the administration of the scheme, state governments
can still exert a significant amount of control over how the scheme is implemented at the
17
local level. This control results from state governments’ significant influence over the careers
of bureaucrats including their ability to transfer of officers from one post to another (Wade
1982, Iyer & Mani 2012). State governments could also exercise leverage over bureaucrats
by arranging the suspension of uncompliant bureaucrats on some trumped up grounds or
by intervening to prevent a bureaucrat who is charged with malfeasance or negligence from
being suspended.11
The final official authority over transfers lies with the state chief ministers.12 In practice, the
chief minister often issues transfer and suspension orders at the behest of ministers in the
state government who control the department to which a bureaucrat belongs.13 In the case of
the PMGSY, therefore, the chief minister as well as the minister of the relevant departments
concerned with rural roads provision in the given state would have the most direct formal
control over the transfers of bureaucrats responsible for administering the PMGSY. Other
ministers may also, however, use their informal leverage with party leaders to exercise control
over bureaucrats in departments other than their own.14
11 See Hindustan Times, 16 September 2015, Notice to Haryana minister, govt on transfer of an official;
Press Trust of India, March 18, 2011.
12 The order to transfer bureaucrats is signed by the Chief Secretary (the top bureaucrat) who reports
directly to the Chief Minister of the state (Iyer & Mani 2012).
13 See, for example, RB Minister faces allegations of nepotism in promotions, Early Times Report, 20
February 2013; Press Trust of India, March 18, 2011, BJP MLA charges minister of supporting ’corrupt’
engineer.
14 For example, see Multiple power centres make bureaucratic transfers a game of musical chairs in
18
Ministers could use their leverage over the bureaucracy to manipulate the implementation
of the PMGSY in a number of ways. In some cases, these ministers could use their influence
to enhance the efficiency of public goods provision in a given constituency - for example,
they could pressure PMGSY officials in a given district to speed up the completion of road
projects through their negotiations with contractors.15
In other cases, however, state government ministers could use their influence over the bu-
reaucracy in a given district to facilitate the creation of rent-seeking opportunities. They
could do this by pressuring bureaucrats to select favored contractors16, by encouraging of-
ficials to turn a blind eye to the under-provision of inputs or by encouraging officials to
sanction unnecessary expenditures. Indeed, although there are several mechanisms in place
to prevent the misuse of funds under the PMGSY17, there have been significant irregularities
noted with regard to the implementation of the scheme. Audits by a central government
agency uncovered numerous instances of irregularities in several states including the selec-
Akhilesh’s UP; New Indian Express, 15 November 2014, Cold War Between Ministers Causes Officers Odd
Moments.
15 Interview with Executive Engineer, Uttar Pradesh, December 2015.
16 Interview with Executive Engineer, Uttar Pradesh, December 2015.
17 See, for example, PMGSY Guidelines Section 9 and 15.
19
tion of unqualified contractors18, the submission of payment against fake invoices19, and the
incurring of expenditures that were well in excess of those necessary to complete the road
works according to the prescribed guidelines.20
While both ministers as well as ordinary legislators may wish to benefit from rent-seeking
opportunities generated by the irregularities described above, doing so usually requires the
ability to pressure a bureaucrat to bend the rules to manipulate the process of contractor
selection to the politician’s advantage, to overlook fake invoices or to inflate expenditures to
allow for kickbacks. However, bending the rules is often risky for bureaucrats especially given
the mechanisms in place to monitor and audit the implementation of the PMGSY at the
local level.21 Thus, although there may be some baseline level of corruption that falls under
the radar or that is even implicitly tolerated by everyone involved,22 an ordinary legislator
who does not have the backing of a minister may not be able to pressure a bureaucrat to
18 Report of the Comptroller and Auditor General of India for the year ended March 13, 2013, Government
of Jammu and Kashmir, see also (Lewis-Faupel et al. 2015).
19 Village Roads go nowhere, The Telegraph, August 30, 2011
20 Report of the Comptroller and Auditor General of India for the year 2011-12, Government of Chhat-
tisgarh, Chapter 4.
21 PMGSY Guidelines Section 6 and 15, Interview with PMGSY Official, New Delhi, December 2015.
22 For example, we heard anecdotally that the junior engineer gets a standard 20% “commission” from
each road project (Interview with Contractor Staff, Uttar Pradesh, December 2015).
20
incur the risk involved in going beyond this baseline level. Conversely, a bureaucrat would
typically be more likely to egregiously bend the rules at the behest of a minister who has
the power to, on the one hand, offer her protection from suspension and reward her with a
desirable promotion or transfer if she complies and to, on the other hand, get her transferred
to an undesirable post or get her suspended on trumped up charges if she fails to comply.
3 Research Design
The paper uses data from PMGSY described above to test the hypotheses linking the partisan
identity and ministerial status of the incumbent legislator with roads provision and road
spending leakages. However, there are likely to be a host of factors that could confound the
association between the characteristics of incumbent legislators and characteristics of road
provision in a constituency. For example, ruling party legislators may be more commonly
found in areas with stronger bureaucratic capacity or in areas with a more politically active
population. Consequently, a simple OLS regression would likely lead to a biased estimate
of the effect of ruling party alignment and road provision. Thus, in order to arrive at an
estimate of the causal impact of the characteristics of incumbent legislators I utilize two
complementary approaches: a Regression Discontinuity (RD) design and an Instrumental
Variables (IV) approach described in further detail below.
To examine H1, I start by using the RD design (Imbens & Lemieux 2008) relying on close
races in which the ruling party either won or lost by a small margin (Lee 2008).23.Thus,
23 For an application in the context of Indian state elections, see Uppal 2009
21
the ‘forcing’ variable is the vote-margin of the chief minister’s party’s candidate in the
given constituency i in the most recent election. The variable is positive when the chief
minister’s candidate just won the election and negative when a chief minister’s candidate’s
party just lost the election.24 The dependent variable is the total road length sanctioned
and completed in the given constituency and electoral term. The cut-point of the forcing
variable is a 0% vote margin which separates constituencies with a ruling party incumbent
from other constituencies. The use of the RD design relies on the assumption of as-if-random
assignment of the treatment close to the cutpoint. Section A.7 in the Appendix describes a
series of diagnostic tests undertaken to assess the validity of this assumption by investigating
balance in pre-treatment covariates as well as by looking for evidence of strategic sorting at
the cut-point. As described in the appendix, these tests affirm confidence in the RD design.
Denoting the forcing variable by V and denoting the variable indicating alignment with the
chief minister’s party as T , the RDD involves estimating an equation of the following form
for constituency i and electoral term t25.
yi,t =p∑
k=0
(αkVki,t) + Ti,t
p∑k=0
(πkVki,t) + βXi,t + us,t + εi,t (1)
24 In cases where the party of the chief minister changed in the middle of the electoral term, the coding
captures the party of the chief minister who was in place for the majority of the electoral term. Constituencies
where the ruling party did not run are omitted.
25 The equation is similar to that used in Brollo & Nannicini 2012.
22
where Xi,t refer to control variables measured at the level of constituency i and electoral
term t and us,t refer to state-electoral term fixed effects. The dependent variable yi,t is the
total length of road sanctioned and completed in constituency i during election term t. If
there were no roads sanctioned during the electoral term in constituency i, then yi,t equals
0. The order of the polynomial chosen is p. The interaction of T with each term in the
polynomial allows for a separate estimation of the relationship between yi,t and V to the left
and right of the cut-point. The estimated coefficient π̂0 identifies the treatment effect right
at the cut-point of 0.
While the RDD derives inferential leverage from legislators who win or lose their seats with
close margins, I also utilize an IV approach that derives inferential leverage from legislators
who survive in office for more than a term. Thus, examining both approaches simultaneously
allows us to assess the scope conditions or ‘substantive relevance’ (Dunning 2012) of the
identification strategies. Moreover, while the RDD is not well-suited to enable a comparison
between the influence of ordinary legislators and that of ministers, the IV analysis allows for
just such a comparison.
The IV approach is based on a ‘differences-in-differences’ logic which involves taking first
differences of each of the variables in the analysis. This approach allows us to control for
constituency specific factors that could influence the attractiveness of rural roads as well
as influence the type of incumbent legislator - either their partisan affiliation or ministerial
status. The dependent variable in the analysis is the difference in the total road length
sanctioned and completed in the given constituency between the current electoral term and
the previous electoral term. The independent variables of interest are ∆ CM Party Align-
23
ment which measures the change in alignment of the incumbent legislator in the relevant
constituency and ∆ Ministerial Status which measures the change in the ministerial status
of the incumbent legislator in the relevant constituency. The instrument for the change in
alignment capture changes in the partisan alignment of an incumbent in a constituency that
are produced by a change in the partisan identity of the government in the state capital
and not by a change in the identify or partisan affiliation of an incumbent in a given con-
stituency. Similarly, the instrument for ministerial status also captures changes induced only
by a change in the partisan identity of the government in the state capital. The use of these
instruments helps ensure that the results are not being driven by time-varying confounding
factors within a constituency that produce a change in the identity or partisan affiliation of
the incumbent legislator and also independently influence the provision of rural roads. At
the same time, however, consistent with the paper’s argument, this approach allows for the
possibility that the ministerial status could be either a cause of access to rents or symptom
of underlying power that in turn leads to rents.
With these instruments, the main threat to identification would be if the change in the
partisan composition of the state government in the capital affected the provision of roads in a
constituency other than through the change in partisan alignment of the incumbent legislator
in the constituency. To address this threat, I include state-electoral term fixed effects analysis
to ensure that the results are not being driven by changes in the partisan composition of
the state government that affect the state as a whole. Section A.8 in the Appendix provides
further details on the IV specification and on how each variable is constructed. A range of
control variables described in Section A.4 are also included.
24
4 Results
Table 1 shows the results of the RDD analysis used to test H1. Columns (1) and (2) shows
the results of a linear regression restricting the sample to constituencies whose vote margin
in the most recent election was 5% and 2.5%, respectively. Column (3) shows the results
of the estimation of Equation 1 with the full sample and a fourth order polynomial.26.
Section A.4 in the Appendix provides a description of the control variables used in the
analyses and provides summary statistics. Standard errors in each of the specifications are
heteroskedasticity consistent and clustered by state assembly constituency.
The coefficient on CM Party Alignment is statistically significant across all three specifica-
tions showing evidence of a treatment effect around the 0% vote margin cutpoint. According
to Column (3), the alignment of an incumbent with the ruling party increases the road sanc-
tioned and completed within a constituency in a given electoral term by 6.36 kilometers.
Since the median length of a road in a constituency is about 3.4 kilometers and since the
average size of the habitations benefited by the road is a 1000, these estimates indicate
that alignment with a ruling coalition party benefits about 2000 additional people in the
constituency even after controlling for a host of other factors.
Figure 1 shows an RDD plot using the data-driven method recommended by Calonico et al.
2015. The x-axis shows the ‘forcing variable’ as described above. Following Lee & Lemieux
(2010), p331), the dependent variable - the total road length completed within the term in
26 A similar specification is used by Brollo and Nannicini (2012).
25
Table 1: The Effect of a Ruling Party Alignment on the Total Road Length CompletedDuring the Electoral Term
Dependent Variable: Total Road Completed During Term
Margin=5% Margin=2.5% All
CM Party Alignment 4.95*** 3.69* 6.36**(1.82) (1.92) (2.92)
New Connectivity Proportion −0.82 1.81 −2.60(2.38) (2.95) (1.89)
Domestic Collab. (Proportion) −27.63** −14.76* −11.33(11.54) (8.91) (8.49)
Village Illiteracy (Average) 3.26 −4.93 9.48(8.23) (10.12) (7.21)
SC/ST Percentage (Average) 0.04 0.28* 0.005(0.14) (0.16) (0.03)
Habitation Size (Average) −0.0002 0.0006 −0.002**(0.00) (0.00) (0.00)
Forcing −168.60**(66.04)
Forcing2 −1108.62**(475.93)
Forcing3 −2399.16**(1106.71)
Forcing4 −1603.03**(771.03)
Forcing* CM Party 127.89(117.73)
Forcing2* CM Party 1807.34(1110.50)
Forcing3* CM Party −523.12(3322.61)
Forcing4* CM Party 5003.09(3333.60)
Constant 27.14** 16.49* 7.01(11.73) (9.80) (9.65)
Sanction Year Fixed Effects Yes Yes Yes
State-Electoral Term Fixed Effects Yes Yes Yes
Observations 549 292 1573
Significance levels : ∗: 10% ∗∗: 5% ∗ ∗ ∗: 1%. The unit of analysis is the state constituency term.Forcing refers to the forcing variable which is the vote margin of the candidate from the chief minister’sparty. It is positive when the candidate from the chief minister’s party just won and negative when thecandidate from the chief minister’s party just lost. Constituencies where a candidate from the chiefminister’s party did not run are dropped from the analysis. Heteroskedastic-consistent standard errorsclustered by state constituency are shown in parentheses.
26
the constituency - is ‘residualized’ to reduce sampling variability.27The figure shows evidence
of a sharp discontinuity at the the 0% vote margin cutpoint indicating that there is a clear
difference in the quantity of road completed by ordinary legislators aligned with the ruling
party than by ordinary legislators who are not aligned. Figure A2 in the Appendix shows
that we also see a discontinuity at the 0% vote margin cutpoint when we use the ‘raw’ rather
than the ‘residualized’ dependent variable.
Figure 1 also indicates that the treatment effect at the cut-point is largely driven by the
constituencies of opposition legislators who won against a ruling party candidate by a small
margin experiencing a decline in their access to completed roads during the electoral term.
The evidence therefore is suggestive of manipulation on the part of the state government to
‘tie the hands of’ opposition party incumbents in close races such as in Brollo & Nannicini
(2012)).
Section A.6 in the Appendix shows the estimates and associated 95% confidence intervals
using the optimal bandwidth suggested by Imbens & Kalyanaraman 201228 as well as using
the robust confidence intervals and the MSE-optimal bandwidth chosen recommended by
Calonico et al. 2014. Both methods show that the estimated treatment effect evaluated at
the cutpoint is statistically significant at the 95% level.
27 In particular, residuals are taken from a regression of total road length completed within the term in
the constituency and electoral term on all the control variables shown in Table 1, except of course for CM
Party Alignment.
28 The optimal bandwidth suggested given our data is 11.17%
27
Figure 1: (RD) Design: The Effect of Ruling Party Incumbent on Road Length Sanctionedand Completed within Term
-50
510
Tota
l Roa
d Le
ngth
Com
plet
ed w
ithin
Ter
m (R
esid
ual)
-.2 -.1 0 .1 .2Vote Margin of Ruling Party
Sample average within bin 4th order global polynomial
Effect of Ruling Party Alignment on Road Provision
The figure shows a RDD plot generated using the data-driven method recommended by Calonico, Cattaneoand Titiunik (2015). The x-axis shows the vote-margin of the ruling party in the given constituency in themost recent election. Constituencies where the ruling party did not run are omitted. The y-axis shows theresiduals of a regression of the total length of road completed in the constituency during the relevant electoralterm on control variables described in the text. The dots represent the mean of the residuals within binsof the forcing variable whose widths are chosen by the evenly spaced bin selection method recommended byCalonico, Cattaneo and Titiunik (2015).
Turning to the IV analysis, Table 2 shows the results of estimating Equation 1 using both OLS
(Column 1) and Two Stage Least Squares (2SLS) (Columns 2-5). The analyses account for
the dependence of errors within a given constituency using heteroskedastic-consistent stan-
dard errors clustered by state constituency. In both Columns (1) and (2) the coefficient on ∆
CM Party Alignment is positive and statistically significant. Interestingly, the substantive
effects as found in the IV are similar to those found in the RDD estimates. Figure A4 in the
Appendix shows that the patterns in the raw data confirm the results of the IV analysis.
28
Column (3) presents the results of a 2SLS analysis which considers the effect of being a
minister separately by including the variable ∆ Ministerial Status. Since ministers may
either belong to the chief minister’s party or to another party within the governing coalition,
Column (3) includes the variable ∆ Ruling Coalition Alignment to separate out the effect of
being a minister from the effect of being aligned with a party in the ruling coalition. Thus,
the coefficient on ∆ Ruling Coalition Alignment represents the effect of being an ordinary
legislator aligned with a party in the ruling coalition while the coefficient on ∆ Ministerial
Status represents the effect of being a minister over and above the effect of being aligned
with a ruling coalition party. Column (3) shows that the effect of ruling coalition party
alignment is positive although not significant at conventional levels. Moreover, the lack of
significance of the coefficient on ∆ Ministerial Status indicates that there is no significant
difference between ruling coalition aligned incumbents with ministerial status and those
without. Additional calculation shows, however, that the overall effect of being a minister is
positive and statistically significant.29
Taken together, the results in Columns (1)-(3) support H1. They suggest that even after
controlling for constituency characteristics as well as time-varying factors that could cause
both a change in roads provision as well as a change in the incumbent in the constituency,
the alignment of an incumbent with a ruling coalition party increases the length of road
sanctioned and completed in the constituency during the electoral term. Consistent with
the main argument, the results also suggest that there is no significant difference between
29 This is calculated by adding the coefficient on ∆ Ruling Coalition Alignment and the coefficient on ∆
Ministerial Status. Standard errors are calculated using the Delta Method.
29
ministers and ordinary legislators aligned with the ruling party in their ability to provide
completed roads to their constituents.
The specifications in Columns (4) and (5) turn to tests of H2. The columns show the esti-
mates derived from 2SLS regressions examining the effect of alignment and ministerial status
on completed roads after controlling for the actual expenditures on the road completed dur-
ing the term, for the budgeted amount for the given road projects, as well as for several other
road characteristics that could influence the cost of the road. Thus, the coefficient on ∆ CM
Party Alignment in these specifications represents the effect of having an aligned incumbent
legislator on the length of completed road produced holding constant the expenditure on the
roads as well as several other factors that could influence the cost of the road.. Column (5)
shows the results obtained when separating out the effect of alignment with a ruling coalition
party with the effect of being a minister. Thus, the these specifications shed light on how
alignment and ministerial status affects the efficiency of road production during the electoral
term.
Column (4) indicates that alignment with the chief minister’s party produces a statisti-
cally significant improvement in the efficiency of road production during the electoral term.
Column (5) shows that the effect of being aligned with the ruling coalition is positive and
significant indicating that alignment with a ruling coalition party on average increases the
efficiency of road production in a constituency during the electoral term by an average of 4.7
kilometers. However, interestingly, the coefficient on ∆ Ministerial Status is negative and
significant indicating that even holding constant the expenditure on roads in the constituency
and other factors, road production when the incumbent is a minister is 3.8 kilometers lower
30
on average than when the incumbent is an ordinary legislator aligned with a ruling coalition
party. If spending inefficiencies or leakages are indicative of the availability of rent-seeking
opportunities, these results provide evidence in favor of H2 which suggests that rent-seeking
opportunities should be more prevalent in ministers’ constituencies than in the constituen-
cies of ordinary legislators aligned with the ruling coalition party. Note, however, that the
effect of being a minister as opposed to being a member of an opposition party is small and
not statistically significant. I explore this finding further in Section 4.1 and in Section A.16
in the Appendix.
Note that the results in Table 2 include all ministers regardless of whether they control
departments responsible for rural roads provision. However, Section A.11 in the appendix
shows, consistent with the argument, that the finding that ministers produce roads less
efficiently is not entirely driven by ministers with formal power over rural roads provision,
but also by other ministers.
At the same time, a possible concern is that the above results simply reflect the difference in
the formal powers accorded to ministers and ordinary legislators under the PMGSY scheme.
In particular, while it is the influence over how roads are built that is most likely to lead
to the creation of rent-seeking opportunities, ordinary legislators only have formal input in
terms of where roads are built while ministers, through their control over the bureaucracy,
have influence both over the where and the how. Section A.13 in the Appendix shows,
however, that an ordinary legislator’s alignment with the state ruling coalition has an effect
on the timely completion of road projects (i.e. how roads are built) even after controlling for
the initial allocation of road projects in the constituency (i.e. where roads are built). This
31
result, in turn, strongly suggests that ministerial intervention is likely to play a key role in
helping ordinary legislators aligned with the ruling party to deliver roads more effectively.
Section A.12 in the Appendix shows the results of analyses examining two additional ob-
servable implications of rent-seeking at the level of the individual road project. The first
specification shows that even after controlling for the value and size of the individual road
project, the total value of all contracts won by contractors hired in ministers’ constituen-
cies is significantly less than the total value of all contracts won by contractors hired in
the constituencies of ordinary legislators aligned with the ruling coalition. These results are
consistent with ministers’ propensity to hire contractors with whom they may have personal
connections but who may not otherwise have the necessary qualifications and experience
to execute road projects and who may therefore be less likely to be selected to execute
road projects in other constituencies. The second test also described in Section A.12 in
the Appendix shows that, after controlling for expenditures and other factors that could
influence the quality of road construction, road construction in ministers’ constituencies is
rated to be of lower quality than in the constituencies of ordinary legislators aligned with the
ruling coalition. These results are consistent with a form of rent-seeking financed through
the underprovision of inputs necessary for satisfactory road construction (e.g. Wade 1982).
Taken together, the results in Column (5) in Table 2 as well as in Section A.12 in the Ap-
pendix show remarkably robust evidence consistent with H2 which posits that rent-seeking
is more prevalent in road projects located in ministers’ constituencies than in constituencies
of ordinary legislators aligned with the ruling party.
Table A10 in the Appendix examines whether these differences between ordinary legislators
32
Table 2: The Effect of a Change in Alignment and Ministerial Status on the Change in theTotal Road Length Completed During the Electoral Term
Dependent Variable: ∆ Total Road Completed in Term
(1) (2) (3) (4) (5)OLS 2SLS 2SLS 2SLS 2SLSFull Full Full Conditional on Conditional on
Sample Sample Sample Road Char. Road Char.
∆ CM Party Alignment 2.12** 5.32*** 2.13**(0.93) (1.83) (1.03)
∆ Ministerial Status 2.84 −3.95**(3.08) (1.89)
∆ Ruling Coalition Alignment 4.52 4.82**(3.30) (1.95)
∆ Vote Margin −6.03 −8.14 −6.72 2.00 3.10(11.61) (11.28) (11.93) (5.25) (5.50)
∆ Vote Share −22.02** −24.00** −24.62** −4.85 −5.38(10.55) (10.76) (11.06) (5.74) (5.88)
∆ MP National Gov’t Alignment 1.93* 1.88 1.83 1.54** 1.39**(1.15) (1.14) (1.14) (0.72) (0.70)
∆ MP State Gov’t Alignment −1.91* −2.37** −2.10** −0.04 −0.37(1.01) (0.99) (0.97) (0.52) (0.54)
∆ New Connectivity Proportion −4.40*** −4.83***(1.22) (1.32)
∆ Domestic Collab. (Proportion) −2.43 −2.94(2.53) (2.59)
∆ Village Illiteracy (Average) 5.45 5.69(4.78) (4.86)
∆ SC/ST Percentage (Average) 0.02 0.01(0.02) (0.02)
∆ Habitation Size (Average) 0.0007* 0.0007(0.0004) (0.0004)
∆ Total Expenditure in Term 0.04*** 0.04***(0.003) (0.003)
∆ Total Expenditure to Date −0.0009 −0.0008(0.0006) (0.0006)
∆ Total Sanctioned Cost 0.0004 0.0004(0.0004) (0.0005)
Sanction Year Fixed Effects Yes Yes Yes Yes Yes
State-Electoral Term Fixed Effects Yes Yes Yes Yes Yes
Observations 1799 1799 1799 1383 1383
Significance levels : ∗: 10% ∗∗: 5% ∗ ∗ ∗: 1%. The unit of analysis is the state constituency term.Columns (4) and (5) drop all constituencies in which there were no road projects sanctioned in any of theelectoral terms. Each variable is the difference in the value of the given indicator or measure for the stateconstituency i between the current electoral term t and the previous electoral term t− 1. The instrumentused for ∆ CM Party Alignment is ∆ CM Party Alignment - Instrument, the instrument used for ∆ RulingCoalition Alignment is ∆ Ruling Coalition Alignment -Instrument and the instrument used for ∆Ministerial Status is ∆ Ministerial Status -Instrument. Heteroskedastic-consistent standard errorsclustered by state constituency are shown in parentheses.33
and ministers observed above are driven by differences in the competitiveness of their con-
stituencies. The results show that there is no significant effect of the partisan alignment of
ordinary legislators on rent-seeking regardless of the electoral competitiveness of their con-
stituencies. Meanwhile, they show that there is a consistently significant effect of ministerial
status on rent-seeking both in relatively competitive as well as relatively safe constituencies.
4.1 Additional Observable Implications: Unproductive Expendi-
tures and The Role of Ministers
Beyond the simple similarities and differences between ministers and ordinary legislators
highlighted in H1 and H2, the paper’s argument also suggests three additional implications.
First, since the argument suggests that the incentives for ministers to co-operate with their
junior party colleagues in terms of distributive politics differs based on the type of good in
question, it suggests that ruling party aligned ordinary legislators’ constituencies should be
deprived of access to expenditure on unproductive projects that could be used for the purposes
of rent-seeking, but should have an advantage in accessing expenditure for productive projects
(H3). Second, since the argument emphasizes the influence of ministers in minimizing ac-
cess to rent-seeking activities in the constituencies of their non-ministerial co-partisans, the
argument also suggests that rent-seeking in the constituencies of ordinary legislators aligned
with the state ruling party should be less prevalent in cases where the relevant co-partisan
ministers exercise more influence and oversight. (H4). Last, since there is likely a trade-off
between rent-seeking and vote-seeking, the argument suggests that ministers should be more
34
likely to engage in electorally costly rent-seeking when the underlying electoral risk they face
is higher (H5).
This section examines these additional implications of the argument by focusing on expendi-
tures incurred on unproductive road projects - that is, road projects sanctioned at least five
years30 prior to data collection but that remained incomplete at the time of data collection.
Although projects may remain incomplete for a variety of reasons other than corruption, high
levels of expenditure on unproductive projects are often a result of payments made against
fake invoices - a common type of corruption that occurs in road projects in India.31 Indeed, if
projects remain incomplete due to land disputes, material shortages or other reasons, officials
and state ministers should - in the absence of corrupt intentions - seek to curb expenditures
on these projects soon after they became aware of the obstacles.32 Consequently, an exami-
nation of the constituencies that have unproductive projects that incur systematically higher
levels of expenditure even after controlling for a host of other factors should provide a good
indication of which types of constituencies are benefiting from rent-seeking opportunities in
road provision. In relation to H5, this measure should also provide a good indication of
which types of incumbents are willing to suffer the potential electoral cost associated with
30 Five years is chosen as a cut-off since it is the length of the typical electoral term.
31 See, for example, Village Roads Go Nowhere, The Telegraph, August 30, 2011; Business Standard,
August 1, 2013,
32 Interview (conducted on behalf of the author) with PMGSY Assistant Engineer, Bihar, December 2015.
35
failing to deliver a completed road in exchange for the rents associated with engaging in
corrupt activity.
Table 3 uses the logic described above as a basis upon which to test H3 and H4. In each
specification, the unit of analysis is the individual road project and the sample is restricted
to road projects sanctioned at least five years prior to data collection but that remained
incomplete at the time of data collection. The dependent variable Expenditure - Incomplete
is the total expenditure incurred on the given incomplete road project to date. Section A.5
in the Appendix provides details on the control variables used in this analysis. In each
specification, constituency fixed effects are used to address the possibility that unobserved
differences across constituencies could be driving the results.
Table 3, Column (1) examines the first part of H3 that emphasizes the differences in the rent-
seeking opportunities available in the constituencies of ministers and ruling party aligned
ordinary legislators. Consistent with the hypothesis, the results show ruling party aligned
ordinary legislators have significantly lower expenditure on unproductive projects in their
constituencies than other legislators. The coefficient on the Minister variable falls short of
statistical significance, but Column (2) - which breaks down the type of minister - shows that
the coefficient on the variable indicating a minister from the chief minister’s party is positive
and statistically significant suggesting that road projects in these ministers’ constituencies
are more likely to result in rent-seeking opportunities than road projects in the constituencies
of ordinary legislators aligned with the ruling party.
Additional results in Table A11 show further results consistent with H3. First, Table A11,
36
Column (1) in the appendix shows that the disadvantage that ruling party aligned ordinary
legislators experience in accessing expenditures for unproductive projects in their constituen-
cies does not hold when it comes to accessing expenditures for productive projects in their
constituencies that get completed within a ‘normal’ timeframe of two years. Meanwhile,
consistent with H3, Table A11, Column (2) in the appendix shows that, for such productive
projects, ruling party aligned ordinary legislators experience an advantage over other legisla-
tors when it comes to accessing ‘expenditure premiums’ for road projects in their constituen-
cies. Our interviews revealed that such expenditure premiums require special administrative
approval from higher level agencies33 and, thus, accessing such premiums may specifically
depend on intervention by ministers. The results suggest that ministers intervene on behalf
of their partisan colleagues to allow them access to discretionary expenditure that would
facilitate their timely delivery of infrastructure to their constituents.
To examine H4, Table 3 Column (3) investigates whether ‘aligned’ ordinary legislators’ con-
stituencies incur less unproductive expenditures when ministers in charge of departments in-
volved with rural road works exercise more oversight. To operationalize ministerial oversight,
the analysis takes advantage of the fact that the administration of the PMGSY occurs at the
level of the administrative district which encompasses multiple state assembly constituencies.
Since ministers should typically have a special interest in their own constituencies, we would
expect them to have closer relationships with bureaucrats in the administrative district that
overlaps with their own constituency. Thus, ministers should exercise greater oversight over
33 Author interviews with PMGSY officials, Uttar Pradesh, December 2015.
37
road projects in constituencies within this administrative district than in constituencies out-
side. Consequently the analysis is thus built on the premise that a minister should have
more of the information required to curb expenditure on wasteful projects in those con-
stituencies of her co-partisan colleagues that lie within her own administrative district than
in the constituencies that lie outside of her administrative district.
Consequently, Table 3 Column (3) examines, in a sample that excludes ministers’ constituen-
cies, the effect of an interaction between ruling party alignment and sharing the adminis-
trative district of a minister involved with rural roads provision who is also from the ruling
party. The interaction term is negative and statistically significant indicating that ruling
party ‘aligned’ ordinary legislators suffer a particular disadvantage with regard to accessing
rent-seeking opportunities when their constituencies share a bureaucratic jurisdiction with
the constituency of a co-partisan minister whose department is charged with rural roads
provision. Meanwhile, we observe that ‘aligned’ ordinary legislators do not suffer such a
disadvantage when their constituencies do not overlap with the administrative district of
such a minister’s constituency. The results thus suggest that the lower levels of expenditure
on unproductive projects in the constituencies of ruling party aligned ordinary legislators is
largely driven by the influence of the relevant ministers in their own party. This finding is
consistent with the notion that ministers seek to minimize the likelihood that road projects
in the constituencies of their co-partisans remain incomplete due to corruption.
If rent-seeking imposes an electoral cost, then the argument also suggests that ministers
should be more willing to tolerate rent-seeking in the constituencies of opposition party
legislators than in the constituencies of legislators from their own party. Several pieces
38
of evidence are consistent with this view, First, recall that the results in Table 2 and in
Table A8 fail to show any significant difference in the prevalence of rent-seeking in minister’s
constituencies relative to the constituencies of incumbents who are members of opposition
parties. Section A.16 in the appendix undertakes a further exploration of these findings
and uncovers evidence that rent-seeking in opposition held constituencies is greater when
ministers from the ruling party exercise greater oversight. One interpretation of these results,
along with the findings of the RDD analysis as well as the findings of Iyer & Mani (2012), is
that ministers may manipulate the bureaucracy to ensure that road projects in opposition
held constituencies remain incomplete and then may allow bureaucrats located in these
constituencies to benefit from the rent-seeking opportunities associated with these projects
as a reward for their cooperation.
Finally, Table A10, Column 1 in the Appendix presents analyses that shed light on H5.
Specifically, if H5 is right, expenditures on incomplete projects should be on average higher in
those ministers’ constituencies that had displayed a higher level of previous competitiveness
which should indicate a higher level of underlying electoral risk (i.e. risk independent of the
incumbent’s current performance). Consistent with H5, the results in Table A10, Column
1 in the Appendix show that the average expenditure on incomplete projects is significantly
higher in the constituencies of incumbent ministers with previously high levels of electoral
competition than in the constituencies of incumbent ministers with previously lower levels of
electoral competition.
39
Table 3: Analysis of Expenditures on Incomplete Road Projects
(1) (2) (3)Dependent Variable: Expenditure on Road Projects Incomplete for at least Five Years
Minister 23.84(16.01)
Minister (CM’s Party) 49.50**(23.13)
Minister (Coalition Partner) 29.58(21.48)
Cabinet Minister −31.98(23.47)
Member of Chief Minister’s Party −35.55*** −38.93*** −11.98(12.28) (12.79) (11.12)
Member of CM’s Party * Admin. District of Road Works Minister (CM’s Party) −82.04**(33.71)
Admin. District of Road Works Minister (CM’s Party) −69.62***(6.45)
Member of Coalition Partner −31.92* −29.01(17.98) (18.31)
Electronic Procurement 53.49 55.51 52.86(78.11) (78.41) (73.75)
Vote Margin −41.66 −61.54 −26.12(84.89) (86.31) (70.95)
Vote Share −6.73 9.28 12.28(118.75) (117.89) (109.06)
Road Length (Kms) 4.11 4.05 6.06*(2.54) (2.55) (3.15)
Sanctioned Cost 0.24*** 0.24*** 0.21***(0.07) (0.07) (0.08)
MP in CM’s party −23.34* −21.21 −30.77**(13.29) (14.24) (14.60)
MP in PM’s party −30.18 −30.39* −23.01(18.75) (18.40) (21.22)
Illiteracy of Village −12.64 −12.67 −16.48(23.03) (22.95) (24.84)
SC/ST Percentage −0.01 −0.01 0.05(0.07) (0.07) (0.06)
Habitation Size 0.001 0.001 0.001(0.001) (0.001) (0.001)
New Connectivity 5.05 6.41 5.88(10.55) (10.62) (11.44)
Domestic Collaboration 14.57 15.38 15.49(21.28) (21.07) (27.78)
Years Since Sanctioned −23.74*** −23.86*** −28.76***(4.85) (4.79) (5.33)
Constituency Fixed Effects Yes Yes YesState Fixed Effects Yes Yes YesSanction Year Fixed Effects Yes Yes Yes
Observations 1985 1985 1759
Significance levels : ∗: 10% ∗∗: 5% ∗ ∗ ∗: 1%. The unit of analysis is the individual road project.Constituencies in which a minister was an incumbent are excluded from Column (3).Heteroskedastic-consistent standard errors clustered by state constituency are shown in parentheses.
40
5 Conclusion
How do government elites use their influence over public infrastructure projects? Overall,
the findings support the story that government elites in a parliamentary system seek to
derive private benefit from their influence, while at the same time ensuring that their party
members are able to provide the infrastructure to their constituents necessary for their party
to be successful at the polls. Thus, government elites have an incentive to maximize their
own control over rent-seeking opportunities while minimizing the degree to which their less
powerful co-partisan colleagues have access to such opportunities. Thus, when it comes to
the managing the trade-off between rent-seeking and vote-seeking, the results suggest that
ruling party elites who have control over the government machinery can - in a sense - have
their cake and eat it too.
A possible alternative interpretation of the results is that, although rent-seeking opportuni-
ties are systematically more prevalent in road projects in ministers’ constituencies, the rents
extracted from these road projects may be used to generate campaign funds for the entire
party rather than for the private benefit of individual ministers. However, the notion that
ministers help fund the campaigns of their junior partisan colleagues is not consistent with
existing accounts of Indian politics suggesting that most Indian state legislators self-finance
their campaigns and do not rely on their parties for funds (e.g. Gowda & Sridharan 2012).
Meanwhile, we do have evidence that Indian state ministers derive private benefit from their
office to a degree far more than ordinary legislators (e.g. Fisman et al. 2014).
This research advances our understanding of distributive politics in part by offering a cor-
41
rective to the widespread assumption that political influence over the bureaucracy leads to
inefficiency. Instead, it suggests that although political influence over the implementation of
development projects may result in non-programmatic distribution, the presence of electoral
pressures in combination with incentives for intra-party co-operation in a parliamentary sys-
tem can mitigate the prevalence of rent-seeking in public infrastructure provision. Thus, the
findings offer a more nuanced view of public infrastructure provision in patronage-dependent
systems, suggesting that while government elites in these systems may wish to use their influ-
ence over the bureaucracy to extract personal rents, they also seek to mitigate inefficiencies
in public goods provision in the districts of their co-partisans. These findings have impor-
tant implications for our understanding of the merits of public programs for infrastructure
provision in the developing world.
References
Achen, C. H., & Bartels, L. M. (2002). Blind retrospection: electoral responses to drought,
flu, and shark attacks. Annual Meeting of the American Political Science Association.
Alkon, M., Kennedy, P., & Urpelainen, J. (2016). Partisan alignment and development
outcomes in a federal system: Evidence from india’s rural electrification, 2001-2011. Un-
published Manuscript, Columbia University.
Asher, S., & Novosad, P. (2015). The employment effects of road construction in rural india.
Dartmouth College. Unpublished.
42
Asher, S., & Novosad, P. (2016). Politics and local economic growth: Evidence from india.
Forthcoming, American Economic Journal: Applied Economics.
Auerbach, A., & Sinha, A. (2013). Does developmental clientelism exist? degrees of clien-
telism in the world’s largest democracy. Paper prepared for the 2013 Conference on South
Asia in Madison, Wisconsin.
Besley, T., Pande, R., Rahman, L., & Rao, V. (2004). The Politics of Public Good Provision:
Evidence from Indian Local Governments. Journal of the European Economics Association
Papers and Proceedings , 2 (2-3), 416–426.
Bhavnani, R., & Jensenius, F. (2015). Voting for development? ruling coalitions and literacy
in india. Unpublished.
Bhavnani, R., & Lee, A. (2015). Local embeddedness and bureaucratic performance:
Evidence from india. Manuscript. University of Wisconsin-Madison and University of
Rochester.
Boas, T. C., Hidalgo, F. D., & Richardson, N. P. (2014). The spoils of victory: Campaign
donations and government contracts in brazil. The Journal of Politics , 76 (2), 415–429.
Brollo, & Nannicini (2012). Tying your enemy’s hands in close races: The politics of federal
transfers in brazil. American Political Science Review , 106(4), 742–761. Page 73.
BuenodeMesquita, B., Smith, A., Siverson, R. M., & Morrow, J. D. (2003). The Logic of
Political Survival . Cambridge, MA: MIT Press.
43
Bussell, J. (2012). Corruption and Reform in India: Public Services in the Digital Age. New
York: Cambridge University Press.
Cain, B., Ferejohn, J., & Fiorina, M. (1984). The constituency service basis of the personal
vote for u.s. representatives and british members of parliament. American Political Science
Review , 78 (1), 110–125.
Calonico, S., Cattaneo, M., & Titiunik, R. (2014). Robust nonparametric confidence intervals
for regression-discontinuity designs. Econometrica, 82 (6), 2295–2326.
Calonico, S., Cattaneo, M., & Titiunik, R. (2015). Optimal data-driven regression disconti-
nuity plots. Journal of the American Statistical Association, forthcoming.
Carey, J., & Shugart, M. (1995). Incentives to cultivate a personal vote. Electoral Studies ,
14 (4), 417–439.
Chhibber, P. K., & Nooruddin, I. (2004). Do Party Systems Count? The Number of Parties
and Government Performance in the Indian States. Comparative Political Studies , 37 (2),
152–187.
Denemark, D. (2000). Partisan pork barrel in parliamentary systems: Australian
constituency-level grants. The Journal of Politics , 62 (3), 896–915.
Dinkelman, T. (2011). The effects of rural electrification on employment: New evidence from
south africa. American Economic Review , 101 (7), 3078–3108.
Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach.
New York: Cambridge University Press.
44
Dunning, T., & Nilekani, J. (2013). Ethnic Quotas and Political Mobilization: Caste, Parties,
and Distribution in Indian Village Councils. American Political Science Review , 107 (1),
35–56.
Fisman, R., Schulz, F., & Vig, V. (2014). The private returns to public office. Journal of
Political Economy , 122 (4), 806–862.
Gerring, J., & Thacker, S. (2004). Political institutions and corruption: The role of unitarism
and parliamentarism. British Journal of Political Science, 34 (2), 295–330.
Golden, M., & Min, B. (2013). Distributive politics around the world. Annual Review of
Political Science, 16 (1), 73–99.
Golden, M. A., & Picci, L. (2005). Proposal for a new measure of corruption, illustrated
with italian data. Economics and Politics , 17 (1), 37–75.
Gowda, M. R., & Sridharan, E. (2012). Reforming india’s party financing and election
expenditure laws. Election Law Journal , 11 (2), 226–240.
Gulzar, S., & Pasquale, B. (2015). The political economy of oversight: Evidence from india’s
employment guarantee. Unpublished Manuscript.
Harding, R. (2014). Attribution and accountability: Voting for roads in ghana. World
Politics. Forthcoming.
Healy, A., & Malhotra, N. (2013). Retrospective voting reconsidered. Annual Review of
Political Science, 16 , 285–306.
45
Imbens, G., & Kalyanaraman, K. (2012). Optimal bandwidth choice for the regression
discontinuity estimator. The Review of Economic Studies . Doi: 10.1093/restud/rdr043.
Imbens, G., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice.
Journal of Econometrics , 142 (2), 615–635.
Iyer, L., & Mani, A. (2012). Traveling agents: political change and bureaucratic turnover in
India. Review of Economics and Statistics , 94 (3), 723–739.
Kapur, D., & Vaishnav, M. (2011). Quid pro quo: Builders, politicians, and election finance
in india. Center for Global Development, Working Paper 276.
Keefer, P., & Khemani, S. (2009). When do legislators pass on pork? The role of parties in
determining legislator effort. American Political Science Review , 103 (1), 99–112.
Khemani, S. (2010). Political economy of infrastructure spending in india. Policy Research
working paper ; no. WPS 5423. Washington, DC: World Bank.
Kumar, T., Post, A., & Ray, I. (2017). Transparency “fixes” for local public services: Field
experimental evidence from bangalore’s water sector. Manuscript. University of California
Berkeley.
Kunicova, J., & Rose-Ackerman, S. (2005). Electoral rules and constitutional structures as
constraints on corruption. British Journal of Political Science, 35 (4), 573–606.
Lake, D., & Baum, M. (2001). The invisible hand of democracy: political control and the
provision of public services. Comparative Political Studies , 34 , 587–621.
46
Lee, D. S. (2008). Randomized experiments from non-random selection in us house elections.
Journal of Econometrics , 142 (2), 675–697.
Lee, D. S., & Lemieux, T. (2010). Regression discontinuity designs in economics. Journal of
Economic Literature, 48 (2), 281–355.
Lehne, J., Shapiro, J., & Eynde, O. V. (2016). Building connections: Political corruption
and road construction in india. Unpublished Manuscript, Princeton University.
Lewis-Faupel, S., Neggers, Y., Olken, B., & Pande, R. (2015). Can electronic procurement
improve infrastructure provision? evidence from public works in india and indonesia.
Unpublished Manuscript.
Min, B. (2015). Power and the Vote: Elections and Electricity in the Developing World .
New York: Cambridge University Press.
Nooruddin, I., & Chhibber, P. K. (2008). Unstable politics: Fiscal space and electoral
volatility in the indian states. Comparative Political Studies , 41 (8), 1069–1091.
Persson, T., & Tabellini, G. (2005). The Economic Effects of Constitutions . Cambridge,
MA: The MIT Press.
Rose-Ackerman, S. (1999). Corruption and Government: Causes, Consequences, and Re-
form. New York: Cambridge University Press.
Samuels, D. J. (2002). Pork barreling is not credit claiming or advertising: Campaign finance
and the sources of the personal vote in brazil. The Journal of Politics , 64 (3), 845–863.
47
Stasavage, D. (2005). Democracy and education spending in africa. American Journal of
Political Science, 49 (2), 343–358.
Stock, J., Wright, J., & Yogo, M. (2002). A survey of weak instruments and weak identifi-
cation in generalized method of moments. Journal of Business and Economic Statistics ,
20 (4), 518–29.
Stokes, S., Dunning, T., Nazareno, M., & Brusco, V. (2013). Brokers, Voters, and Clien-
telism: The Puzzle of Distributive Politics . New York: Cambridge University Press.
Uppal, Y. (2009). The disadvantaged incumbents: estimating incumbency effects in indian
state legislatures. Public Choice, 138 (1), 9–27.
Vaishnav, M., & Sircar, N. (2010). The politics of pork: building schools and rewarding
voters in tamil nadu. Department of Political Science, Columbia University.
Wade, R. (1982). The System of Administrative and Political Corruption: Canal Irrigation
in South India. Journal of Development Studies , 18 (3), 302–304.
Wilkinson, S. I. (2006). The politics of infrastructural spending in india. University of
Chicago mimeo.
48
A Online Appendix
A.1 Description of the Reliability of the Data Source
The data on road provision used in this paper scraped from the Online Management, Mon-
itoring and Accounting System (OMMAS) of the PMGSY.34 A bureaucrat at the district
level - the PIU - is responsible for updating the data online on a monthly basis and the
online system is actively monitored by the NRRDA officials at the central level35. Notably,
the data on the online system are used as a basis for releasing funds to the state and district
(ibid., PMGSY Scheme and Guidelines, Section 16) and is also used by bank branches as
a basis for disbursing payments (ibid., PMGSY Scheme and Guidelines, Section 18). Our
interviews showed that district bureaucrats are often held to task by officials in the NRRDA
to make sure that the data are entered and updated in a timely manner.36 Moreover, the
data entered are verified by independent monitors who regularly visit the road construction
sites.37 Thus, although there are sometimes clerical errors arising from the fact that the
34 Available at omms.nic.in.
35 Interview with PMGSY Official, NRRDA, New Delhi, December 2015; Interview with PMGSY Executive
Engineer, Uttar Pradesh, December 2015.
36 Interview (on behalf of the author) with Assistant Engineer, Bihar; Interview with Executive Engineer,
Uttar Pradesh.
37 Author Interview with NRRDA Official, New Delhi, December 2015; Author Interview with PMGSY
Assistant and Executive Engineer, Uttar Pradesh, December 2015, Interviews with PMGSY contractor staff
and laborers, Uttar Pradesh, December 2015.
49
data are entered with a bit of a time lag38, it is very likely that the data reflect - with a
substantial degree of accuracy - the actual on the ground implementation of the PMGSY at
the local level. Field visits to three PMGSY construction sites in Uttar Pradesh also verified
that the information on the online system with regard to the locations of the road projects
and the stage of completion were also accurate.
With that said, the information on expenditures may often not reflect ‘productive’ expen-
ditures. In particular, bureaucrats may have incentives to find ways to allocate more ex-
penditure on projects than is actually deserved, to make payments against fake invoices
submitted by contractors, or to otherwise allocate expenditures on a given project to un-
productive rather than productive uses. Indeed, these types of behaviors form the premise
behind the measures of spending leakages that are employed in the analyses.
A.2 Description of Data Collection and Matching Procedure
The initial dataset included all projects sanctioned under the PMGSY from 2000 until the
time of data collection in October 2014 from the seven states that are the focus of this
research - Bihar, Chattisgarh, Jharkhand, Madhya Pradesh, Rajasthan, Uttar Pradesh and
Uttarakhand.
To match the individual road projects from the Online Monitoring System of the PMGSY to
38 Interview (on behalf of the author) with Assistant Engineer, Bihar
50
individual assembly constituencies, I utilized information obtained from the online system
on which habitation(s) each road benefited. I then used information from the National
Habitation Survey published in 2003 by the Ministry of Drinking Water and Sanitation
to locate each habitation within a village. Incidentally, this was the same survey used by
PMGSY officials to identify and locate habitations.39 To match the habitation names, I used
a program for fuzzy matching developed in R that matched the habitation name contained
in the PMGSY online monitoring system to the habitation name in the National Habitation
Survey containing information on the villages to which the habitations belonged.40 Matching
of habitations was done by block and district. Where there was more than one benefited
habitation listed on the website, the program looped through each of the names to obtain
a match. If there was more than one match obtained, only the first match on the list of
benefited habitations was used. Thus, each road project is assigned to only one constituency.
Section A.17 presents additional analyses showing, however, that the main results are not
an artifact of this assignment procedure.
While the fuzzy matching program was used to generate the initial matches, the matches
were manually checked and retained only if they were accurate. A conservative approach
was used whereby matches were discarded if there were doubts about the similarity of the
names or because there was more than one habitation within the block and district that bore
the same name. The remaining accurate matches then provided information on the villages
in which the relevant roads were located. This list of village names was then matched
39Interview (on behalf of the author) with PMGSY Assistant Engineer, Bihar, December 2015.40Where there were no benefited habitations listed, I used the name of the road to provide information on
the benefited habitations.
51
with a list of census villages geocoded by MLInfomaps. Using GIS maps of state assembly
constituencies also provided by MLInfomaps, I was then able to locate each of the villages
in the relevant assembly constituencies. The procedure yields accurate results because,
although the information on roads was available by habitation and not village, assembly
constituency boundaries do not cut across village boundaries. Using this procedure, 74% of
the total roads in the sample could be identified in terms of their village location.
For the remaining roads whose village location could not be identified, I used GIS maps
of 2001 block boundaries to examine the overlap between the block in which the road was
located and the assembly constituencies. While there is in general a relatively weak overlap
between administrative blocks and assembly constituencies, some blocks are almost perfectly
contained by a single assembly constituency. By selecting those blocks whose areas over-
lapped with a single state assembly constituency by at least 99%, I was able to match an
additional 11% of road projects. Thus, the total proportion of road projects whose con-
stituency locations could be determined was 85%.
In addition to information on roads, this dataset included information on 525 bridges which
were excluded from the sample. The dataset also included duplicate entries for road projects
in cases where more than one contractor was assigned to an initial road project. While this
information is taken into account for the Total Contract Value Won by Contractor variable
below, the duplicate entries were otherwise removed from the analyses. Once these entries
were excluded, the dataset included information on 59,272 road projects in the seven states,
of which 87% could be matched by village location. The analyses in the paper are restricted
to the 38,865 road projects in the seven states that took place in the time period before the
52
first state elections under the newly delimited state electoral boundaries took place. In this
sample, 88% of road projects could be matched by village location.
To match individual road projects to time-varying characteristics such as the partisan iden-
tity of the incumbent legislator in the constituency, I used information on the fiscal year
in which the road was sanctioned and on the month and year in which the state election
took place. If the fiscal year in which the road was sanctioned occurred during an election
year, I assigned the road to the electoral term that had the greatest overlap with the fiscal
year.41 Using this procedure, I was thus able to match each of the roads with constituency
level electoral information While the partisan affiliations of the individual legislators were
available from the Election Commission of India42, the identification of which legislators
were ministers required additional data collection as described in Section A.4. Since a key
component of the research design is to isolate the effect of the partisan alignment of legis-
lators while holding other confounding factors constant, most of the analyses in the paper
are restricted to elections in the time period before constituency boundaries were redrawn
in India. Section A.3 describes the constituencies, states and years included in the analysis.
A.3 Data Description
The first set of results in the paper pertain to data that is aggregated at the level of the
constituency-electoral term. The table below shows how the sample used in the first differ-
41For example, suppose a road was built in the fiscal year 2002-2003, and an election was held in Augustof 2002. Since the Indian fiscal year begins on April 1st, the road would be assigned to the legislator thattook office after the July 2002 election and not before.
42Data from the Election Commission of India were compiled by the Bhavnani State Election Dataset.
53
Figure A1: Road Projects Sanctioned under the PMGSY Development Scheme in the BI-MARU states
Note: Each red dot in the above figure represents the village location (the centroid of the village polygon)of the first listed habitation benefited by a road project sanctioned under the PMGSY scheme between 2000and 2014. Each road project pertains either to a new road or an upgrade to an existing road that is inneed of repair. The figure represents 74% of the road projects whose village location could be determinedin Bihar, Chattisgarh, Jharkhand, Madhya Pradesh, Rajasthan, Uttar Pradesh and Uttarakhand. Thepolygons outlined in black are the state assembly constituencies.
54
enced results in the main text (i.e. Table 2) are broken down by state and election year.
Note that the analysis is limited to the time period before the first election in the state
held under newly delimited constituency boundaries. This delimitation of constituencies
took effect in 2007 and elections in all states in the sample held after 2007 used the newly
constituency boundaries with the exception of the election in Jharkhand in 2009 which used
the old constituency boundaries.
Note that the differences in the number of constituencies between elections for Bihar, Mad-
hya Pradesh and Uttar Pradesh reflect the fact that the states Chhattisgarh, Jharkhand and
Uttarakhand were carved out of these states in 2000. There were 90 constituencies in Mad-
hya Pradesh that became part of Chhattisgarh in 2000 and there were 81 constituencies in
Bihar that became part of Jharkhand in 2000. Since the boundaries of these constituencies
remained unchanged, these constituencies could be treated as single units comparable across
multiple electoral terms. Thus, the IV analysis which involved taking first differences, com-
pared the data for the constituencies Chhattisgarh (Jharkhand) after 2000 with the same
constituencies that were part of Madhya Pradesh (Bihar) prior to 2000. For Uttarakhand,
however, the number of constituencies significantly increased and, thus, the constituencies
in Uttarakhand in 2002 were not comparable to the constituencies in Uttar Pradesh in 1996.
Thus, the data for Uttarakhand in the electoral term beginning in 2002 are omitted from
the IV analysis. They are, however, included in the other analyses.
55
Table A1: States and Election Years in the Sample
State Start of Electoral Term Number of ConstituenciesBihar 2000 324Bihar 2005∗ 243Chhattisgarh 2003 90Jharkhand 2005 81Madhya Pradesh 1998 320Madhya Pradesh 2003 230Rajasthan 1998 200Rajasthan 1998 200Uttar Pradesh 1996 424Uttar Pradesh 2002 403Uttar Pradesh 2007 403Uttarakhand 2002 70Uttarakhand 2007 70
∗:There were two elections held in Bihar in 2005 - one in February and one in October. This paper uses thedata from the election in October 2005.
A.4 Variable Descriptions and Sources - Constituency Level Vari-
ables
Total Road Completed in Term: This variable is calculated by summing up the road
lengths for all individual road projects in the constituency that had a completion date prior
to the end of the electoral term in question. Source: PMGSY Monitoring System
CM Party Alignment: This variable is coded 1 if the winner of the most recent election in
the constituency was aligned with the chief minister’s party for the majority of the electoral
term, and 0 otherwise. Source: Election Commission of India as compiled by Bhavnani
(2014). Information on Chief Ministers and their parties was obtained from http://www.
worldstatesmen.org/India_states.html.
Forcing Variable: This variable is equal to the vote margin of the candidate from the chief
56
minister’s party in the constituency. It is positive is the candidate from the chief minister’s
party won the election and negative if the candidate from the chief minister’s party lost the
election. It is missing if a candidate from the chief minister’s party did not run. Source:
Same as Above.
New Connectivity Proportion: The proportion of road projects in the constituency in
the given electoral term that involved establishing new connectivity rather than upgrades of
existing roads. Source: PMGSY monitoring system
Domestic Collab. (Proportion): The proportion of road projects in the constituency in
the given electoral term that involved a domestic collaboration rather than a collaboration
with an international agency such as the World Bank. Source: PMGSY Monitoring System
Village Illiteracy (Average): The average illiteracy rate in the villages that were served
by the road projects in the given constituency and the given electoral term. Source: Census
of India (2001)
Habitation Size (Average): The average population size of the habitations that were
served by the road projects in the given constituency and the given electoral term. Source:
National Habitation Survey (2010)
Average Sanction Year: The average of the sanctioning year of the road projects allocated
in the given constituency and electoral term (rounded). Source: PMGSY Monitoring System
Vote Margin: The difference in the votes obtained by the winning and runner-up candidate
in the constituency as a proportion of the total vote in the constituency. Source: Election
57
Commission of India as compiled by Bhavnani (2014)
Vote Share: The proportion of vote obtained by the winning candidate in the constituency
and electoral term. Source: Same as Above.
MP National Government Alignment. An Indicator for whether the Member of Parlia-
ment (National Legislator) whose constituency encompasses the relevant state constituency
shares the same party as the Prime Minister. Source: Same as Above.
MP State Government Alignment: An Indicator for whether the Member of Parliament
(National Legislator) whose constituency encompasses the relevant state constituency shares
the same party as the Chief Minister. Source: Same as Above.
∆ CM Party Alignment: The variable is equal to 1 if CM Party Alignment changed from
0 in the previous electoral term to 1 in the current electoral term, it is equal to -1 if CM
Party Alignment changed from 1 in the previous electoral term to 0 in the current electoral
term and is equal to 0 if there was no change in CM Party Alignment. Source: Constructed
∆ Ruling Coalition Alignment: This variable is analogous to the one above but is based
on the variable Ruling Coalition Alignment which is coded 1 if the winner of the most recent
election in the constituency was aligned with either the chief minister’s party or another
party in the governing coalition for the majority of the electoral term, and 0 otherwise.
Source: Constructed.
∆ Ministerial Status: This variable is analogous to the one above but is based on the
variable Ministerial Status which is coded 1 if the winner of the most recent election in the
58
constituency belonged to the state council of ministers for more than one year during the
electoral term, and 0 otherwise. Source: Coded by the Author Based on Information from
State Government Website Archives and News Sources.
∆ CM Party Alignment - Instrument: This instrument is equal to the variable ∆ CM
Party Alignment in cases where the identity of the incumbent and her partisan affiliation
remained the same in the constituency across the current and previous electoral terms. In
all other cases the variable is equal to 0. Source: Constructed.
∆ Ruling Coalition Alignment - Instrument: This instrument is equal to the variable
∆ Ruling Coalition Alignment in cases where the identity of the incumbent and her partisan
affiliation remained the same in the constituency across the current and previous electoral
terms. In all other cases the variable is equal to 0. Source: Constructed.
∆ Ministerial Status - Instrument: This instrument is equal to the variable ∆ Ministerial
Status as long as (a) the identity of the incumbent and her partisan affiliation remained the
same in the constituency across the current and previous electoral terms and (b) there was
a change in the alignment of the incumbent with ruling coalition between the previous and
current electoral term. In all other cases the variable is equal to 0. Source: Constructed.
A.5 Variable Descriptions and Sources: Individual Road Project
Variables
Road Length: The length of road in Kilometers sanctioned under the road project.
59
Table A2: Summary Statistics - Constituency Level Variables
Variable Obs Mean Std. Dev. Min Max MedianVariables used in RDD Analysis
Total Road Completed in Term 1976 10.829 30.879 0 413.77 0CM Party Alignment 1976 .572 .495 0 1 1Forcing 1976 .022 .129 -.79 .543 .019New Connectivity Proportion 1631 .808 .248 0 1 .913Domestic Collab. (Proportion) 1631 .853 .29 0 1 1Village Illiteracy (Average) 1582 .577 .109 .218 .936 .573SC/ST Percentage (Average) 1616 3.45 16.716 0 414.46 .445Habitation Size (Average) 1630 1032.011 767.962 15 6049.25 879.314Average Sanction Year 1631 2003.773 3.292 2000 2012 2004
Variables used in Instrumental Variables Analysis∆ Total Road Completed in Term 1799 10.438 34.071 -77.945 413.77 0∆ CM Party Alignment 1799 .023 .685 -1 1 0∆ Ruling Coalition Alignment 1799 .015 .725 -1 1 0∆ Ministerial Status 1799 -.037 .577 -1 1 0∆ CM Party Alignment - Instrument 1799 -.006 .431 -1 1 0∆ Ruling Coalition Alignment - Instrument 1799 .005 .427 -1 1 0∆ Ministerial Status - Instrument 1799 .012 .29 -1 1 0∆ New Connectivity Proportion 1461 .033 .29 -1 1 0∆ Domestic Collab. (Proportion) 1461 -.233 .318 -1 .333 -.083∆ Village Illiteracy (Average) 1391 .004 .062 -.25 .329 .003∆ SC/ST Percentage (Average) 1440 .024 15.394 -369.692 125.383 .038∆ Habitation Size (Average) 1457 -266.413 620.333 -5336.675 3154.2 -177.843∆ Vote Margin 1799 -.011 .112 -.622 .392 -.007∆ Vote Share 1799 -.019 .096 -.361 .268 -.019∆ Total Expenditure in Term 1799 250.829 722.926 -2176.68 7004.02 0∆ Total Sanctioned Cost 1799 1185.974 2333.555 -8101.15 14449.02 840.16∆ MP National Gov’t Alignment 1799 -.202 .697 -1 1 0∆ MP State Gov’t Alignment 1799 .057 .624 -1 1 0Average Sanction Year 1493 2006.417 2.453 2003 2012 2006
Note: The above table shows the summary statistics for the constituency level analyses in the paper. Theunit of analysis is constituency electoral term. Note that, for the RDD analyses, the constituencies inwhich a ruling party candidate did not run are ommitted. For the instrumental variables analysis, thevariables involve taking a difference between the current and lagged values of the relevant variables. Thus,the variables are missing for the first electoral term in the sample.
Road Quality Rating: The variable reflects the rating of the quality of the road project
done by an independent monitor - either the State Quality Monitor or the National Quality
Monitor. In cases where ratings by both monitors exist, the rating of the State Quality
Monitor is used. The variable is coded 1 if the road is rated as being “Satisfactory” and
0 if the road is rated as ‘Unsatisfactory’ or ‘Required Improvement’. Source: PMGSY
60
Monitoring System.
Total Contract Value: The variable pertains to the contractor hired to execute the given
road project. It is calculated by sorting the individual road projects within a state by the
name of the contractor and adding up the sanctioned cost for each road project won by the
relevant contractor. The variable is missing in cases where the name of the hired contractor
is missing. Source: PMGSY Monitoring System.
Expenditure Premium: The expenditure incurred on the individual project over and
above the sanctioned cost. If the expenditure incurred was less than or equal to the sanc-
tioned cost, this variable is coded as 0. Source: PMGSY Monitoring System.
Minister: An indicator for whether the road project located in constituency i was sanctioned
in a fiscal year during which the incumbent in the constituency belonged to the state’s council
of ministers. This variable is coded 1 even if the incumbent resigned from her ministerial
position in the middle of the fiscal year. Source: Author’s Coding based on Fisman et. al.
(2014) and State Government Website Archives and News Sources.
Cabinet Minister: An indicator for whether the road project located in constituency i
was sanctioned in a fiscal year during which the incumbent in the constituency belonged to
the state’s council of ministers and was of cabinet rank. Source: Author’s Coding based on
State Government Website Archives and News Sources.
Minister of State: An indicator for whether the road project located in constituency i
was sanctioned in a fiscal year during which the incumbent in the constituency belonged to
61
the state’s council of ministers and was not of cabinet rank but was a ”Minister of State”.
Source: Author’s Coding based on State Government Website Archives and News Sources.
Road Works Minister: An indicator for whether the road project located in constituency
i was sanctioned in a fiscal year during which the incumbent in the constituency was a
minister associated with a department that was either partially or wholly responsible for
rural roads provision under PMGSY in the state. The relevant departments in each state
were identified through a perusal of PMGSY websites for the given states and through
interviews with PMGSY officials. To guard against misattribution of expenditures (since
these are not available by fiscal year), the analyses in Section 4.1 codes only those road
works ministers who remain in their position for more than two years. The departments
include the PWD (Public Works department), Rural Development and Rural Engineering
Services. Source: Author’s Coding based on State Government Website Archives and News
Sources.
Alignment with Chief Minister’s Party: An indicator for for whether the road project
located in constituency i was sanctioned in a fiscal year during which the incumbent in the
constituency was a member of the chief minister’s party. Election Commission of India.
Alignment with Ruling Coalition: An indicator for for whether the road project lo-
cated in constituency i was sanctioned in a fiscal year during which the incumbent in the
constituency was a member of a party that was a member of the state governing coalition.
This variable includes, but is not limited to, the chief minister’s party. Source: Election
Commission of India. Information on Membership in the Governing Coalition coded from
62
news sources.
Alignment with Coalition Partner: An indicator for for whether the road project lo-
cated in constituency i was sanctioned in a fiscal year during which the incumbent in the
constituency was a member of a party that was a coalition partner in the state government.
This variable excludes membership in the chief minister’s party. Source: Election Com-
mission of India. Information on Membership in the Governing Coalition coded from news
sources.
Alignment with Opposition Party: An indicator for whether the road project located in
constituency i was sanctioned in a fiscal year during which the incumbent in the constituency
was a member of a party that did not belong to the governing coalition. Source: Election
Commission of India. Information on Membership in the Governing Coalition coded from
news sources.
Administrative District of Minister (CM’s Party): An indicator for whether the
constituency in which the road project was part of a district that contained a constituency
of a minister belonging to the chief minister’s party at some point during the electoral term
during which the road was sanctioned.
MP in CM’s party: An indicator for whether the state assembly constituency in which
the road project was located was part of a national parliamentary constituency for which
the MP was a member of the state chief minister’s party at some point during the electoral
term in which it was sanctioned. Source: Election Commission of India. Information on
boundaries of state assembly constituencies and parliamentary constituencies were obtained
63
using maps from MLInfoMaps.
MP in PM’s party: An indicator for whether the state assembly constituency in which
the road project was located was part of a national parliamentary constituency for which the
MP was a member of the Prime Minister’s party at some point during the electoral term in
which it was sanctioned. Source: Election Commission of India. Information on boundaries
of state assembly constituencies and parliamentary constituencies were obtained using maps
from MLInfoMaps.
Latitude: The latitude location of the village served by the road project. Source: PMGSY
Monitoring System and MLInfoMaps.
Longitude: The longitude location of the village served by the road project. Source:
PMGSY Monitoring System and MLInfoMaps.
Sanctioned Cost: The total amount allocated for the road project in lakhs of Indian rupees.
1 lakh is equal to a 100,000. Source: PMGSY Monitoring System.
Total Expenditure till Present: The total expenditure actually incurred on the given
road project until data collection in 2014 in lakhs of Indian rupees. 1 lakh is equal to a
100,000. Source: PMGSY Monitoring System.
No Progress: An indicator for whether the road project is recorded as having undergone
‘No Progress’ at the time of data collection in 2014. Source: PMGSY Monitoring System
Years Since Sanctioned: The number of years since the road project was initially sanc-
64
tioned. Source: PMGSY Monitoring System.
Completed: An indicator for whether the road project is recorded as having been complete
at the time of data collection in 2014. Source: PMGSY Monitoring System.
Illiteracy of Village: The proportion of illiterate adults in the village connected by the
road project. Source: Census of India 2001 as made availably by MLInfoMaps.
SC/ST Percentage: The proportion of members of Scheduled Caste and Scheduled Tribe
in the habitation connected by the road project. Source: National Habitation Survey (2010).
Habitation Size: The total population of the habitation connected by the road project.
Source: National Habitation Survey (2010).
New Connectivity: An indicator for whether the road project provides new connectivity
as opposed to being an upgrade of an already existing road. Source: PMGSY Monitoring
System.
Domestic Collaboration: An indicator for whether the road project involves a domestic
collaboration rather than a collaboration with an international agency. Source: PMGSY
Monitoring System.
Electronic Procurement: An indicator for whether the state had rolled out electronic
procurement at the time the road project was sanctioned. Source: Lewis-Faupel et. al.
(2015).
65
A.6 (RD) Design: Additional Results
A.6.1 Comparison of Plot with Raw Dependent Variable and Plot with ”Resid-
ualized” Dependent Variable
Figure A2 shows a comparison of the RD Plot with the ”residualized” dependent variale and
the RD plot with the ”raw” dependent variable. The figure reveals a discontinuous jump
at the 0% vote margin in both plots, although the jump is somewhat larger in the case
of the plot with the residualized dependent variable. Moreover, as expected, the sampling
variability is substantially higher in the plot showing the raw dependent variable than in the
plot showing the residualized dependent variable.
A.6.2 RDD: Non-Parametric Estimates
Table A4 shows the non-parametric estimates of the treatment effect from the (RD) De-
sign. The first row shows the estimates using the Imbens and Kalyanaraman (2011) optimal
bandwidth. The results are estimated using the rd program in STATA developed by Nichols
(2014). The second and third rows show the estimates using the Calonico, Cataneo and
Titiunik (2014) optimal bandwidth and are estimated using the rdrobust package in STATA
developed by the same authors. The bandwidth refers to the width of the vote margin share
used. (L) and (R) refer to the number of observations to the left and right of the cut-point
respectively.
66
Figure A2: (RD) Design: The Effect of Ruling Party Incumbent on Road Length Sanctionedand Completed within Term
-10
010
2030
-.2 -.1 0 .1 .2Vote Margin of Ruling Party
Residualized Dependent Variable
-10
010
2030
-.2 -.1 0 .1 .2Vote Margin of Ruling Party
Raw Dependent Variable
Effect of Ruling Party Alignment on Road Provision
The figure shows two RDD plots generated using the data-driven method recommended by Calonico, Cat-taneo and Titiunik (2015). The x-axis shows the vote-margin of the ruling party in the given constituencyin the most recent election - constituencies where the ruling party did not run are omitted. The y-axis onthe first graph shows the residuals of a regression of the total length of road completed in the constituencyduring the relevant electoral term on control variables described in the text. The y-axis on the second graphshows the “raw” dependent variable - that is, the total length of road completed in the constituency duringthe relevant electoral term. The dots represent the mean of the residuals within bins of the forcing variablewhose widths are chosen by the evenly spaced bin selection method recommended by Calonico, Cattaneoand Titiunik (2015).
A.7 RDD: Balance Tests and Tests of Strategic Sorting
As recommended by Imbens and Lemieux (2008) and as implemented by Lee, Morelli and
Butler (2004), I examine the validity of the RDD design by investigating whether there is
a significant difference in the pre-determined characteristics of our treated and control con-
stituencies - i.e. those with and without a ruling party aligned incumbent respectively. I
test for differences in a host of covariates including several characteristics of road projects
67
in the given constituency during the previous electoral term such as the length of road com-
pleted, the length of road sanctioned, the expenditure incurred and the amount of funding
sanctioned. I also examine covariates that capture the previous political situation in the
constituency such as whether it had an incumbent aligned with the chief minister’s party
and the vote share obtained by the winning candidate in the election prior to the most
recent one. Finally, I also include covariates capturing socio-economic features that could
affect rural roads provision - the level of illiteracy and the percentage of rural population - as
measured by the 2001 Indian census. Since census data only report socio-economic variables
by administrative block, I examine these factors as they pertain the block that most closely
overlaps with the given constituency.
Table A5 shows the results obtained when estimating the effect of alignment with the chief
minister’s party substituting the dependent variable of interest with each covariate. In each
case, the estimates fail to reject the null hypothesis of no difference between the treated
and control groups. These results are similar to what we would observe if the assignment of
ruling party alignment across constituencies across the threshold in our sample were random
and provide increased confidence in the assumptions underlying our RD design.
As a final check on the validity of our identifying assumption, we also conduct the McCrary
test (McCrary 2008) to check whether there is a systematic difference in the density of our
forcing variable around the threshold. As noted by McCrary (2008), such evidence would
indicate the possibility that certain types of incumbents in close races can strategically
manipulate their vote margins to facilitate their electoral victories. Figure A3 however
shows no evidence of strategic sorting at the cutpoint, thus further increasing confidence
68
Figure A3: (RD) Design: Density Test by McCrary (2008)
01
23
4
-1 -.5 0 .5 1
The figure shows the density test of the forcing variable recommended by McCrary (2008) as implementedby the STATA command DCdensity by the same author.
that the observed results are not driven by the ability of certain politicians to manipulate
their chances of victory in close races.
A.8 Further Details on the Instrumental Variables Methodology
and Analysis
The instrumental variables analysis involves estimating the following equation:
yi,∆t = α0 + α1Alignmenti,∆t + α2Ministeri,∆t + α3Xi,∆t + us,t + εi,∆t(2)
69
where i refers to the state assembly constituency, t refers to a given electoral term and
∆t refers to the difference in the relevant variable in the electoral term t and the previous
electoral term t− 1. The dependent variable yi,∆t is the difference in the total length of road
sanctioned and completed in constituency i between electoral term t and t−1. Alignmenti,t
is an indicator for whether the incumbent legislator in constituency i is aligned with a
ruling party in electoral term t.43 Thus, Alignmenti,∆t = Alignmenti,t − Alignmenti,t−1.
While the main results focus on alignment with the chief minister’s party in the state,
I also examine in some specifications the results obtained when considering the effect of
alignment with any party in the governing coalition. The other key independent variable is
Ministeri,∆t = Ministeri,t−Ministeri,t−1 where Ministeri,t is an indicator for whether the
incumbent legislator in constituency i was a minister during electoral term t.44 Thus, the
coefficient on Ministeri,∆t represents the effect of being a minister over and above the effect
of being an ordinary legislator aligned with the state ruling coalition.
The instrumental variables regression, estimated using two stage least squares (2SLS) in-
volves using AlignmentInstrumenti,∆t as an instrument for Alignmenti,∆t where the in-
strument is equal to Alignmenti,∆t only if there is no change in the identity or partisan
affiliation of the incumbent legislator between t and t − 1 and is equal to 0 otherwise.
Thus, the instrument captures only those changes in ruling party alignment that result from
changes exogenous to the constituency - that is, changes to the partisan composition of the
state government. In a similar fashion, MinisterInstrumenti,∆t is used as an instrument for
43In cases where the chief minister’s party changed during the course of the electoral term, I capture thealignment of the legislator with the party to which the chief minister was aligned for the majority of theelectoral term.
44This variable is coded 1 as long as the incumbent legislator was a minister during electoral term t for aperiod of more than one year.
70
Ministeri,∆t where MinisterInstrumenti,∆t is equal to Ministeri,∆t if there is no change
in the identity or partisan affiliation of the incumbent legislator between t and t − 1 and
if there is a change in the alignment of the incumbent with a ruling coalition party. In all
other cases, MinisterInstrumenti,∆t is equal to 0. Thus, the instrument captures changes
in ministerial status induced by changes in the composition of the state government that are
exogenous to the constituency. This approach allows us to isolate the effect of ministerial
status from the effect of constituencies in which ministers tend to run45 and from the effect of
candidate qualities associated with being a minister46. However, consistent with the paper’s
argument, this approach allows for the possibility that the ministerial status could be either
a cause of access to rents or symptom of underlying power that in turn leads to rents. To
control for electorally induced changes in the partisan composition of state government that
could affect the state as a whole, us,t represents dummies for each state s and electoral term
t.
In each of the specifications, Xi,∆t = Xi,t −Xi,t−1 where Xi,t refers to covariates pertaining
to the constituency i during electoral term t. These variables capture the political charac-
teristics of the constituency such as its electoral competitiveness and the alignment of its
incumbent member of national parliament with the chief minister’s party and the prime
minister’s party. In some specifications, variables capturing the average characteristics of
road projects in the constituencies are also included. These specifications involve dropping
constituencies in which no PMGSY road projects were sanctioned. Section A.4 in the ap-
pendix contains a detailed description of the variables included in each specification and
45Since we are taking first differences, this differences out any constituency specific effects.46Since we are comparing the same incumbent in both electoral terms.
71
provides summary statistics.
The validity of the instrumental variables approach relies in part on the assumption that
the instrument(s) are highly correlated with the endogenous regressors. This assumption is
explored through an analysis of the first stage of the two stage least square analysis described
in Section A.9.
A.9 Instrumental Variables: First Stage Diagnostics
Table A6 reports the results of first stage regressions of the 2SLS instrumental variables
analysis shown in Table 2 in the main text. The two endogenous regressors in these specifi-
cations are ∆ Ruling Coalition Alignment and ∆ Ministerial Status. The instruments used
for each of these endogenous regressors are ∆ RulingCoalitionAlignment - Instrument and ∆
Minister Instrument respectively. Section A.4 in the Appendix describes how these variables
are coded and the main text discusses the rationale for the instrument. A key criterion for
the validity of a given instrument is the degree to which it is correlated with the endogenous
regressor. The so-called ‘weak instrument problem’ arises when an instrument is only weakly
correlated with the endogenous regressor, thus resulting in biased estimates. Stock, Wright
and Yogo (2002) propose using the F-statistic in the first stage regression as an indicator of
whether an instrument is weak and suggest an F-statistic of 20.65 based on the number of
endogenous regressors and the number of exogenous variables. The F-statistics for each of
the specifications shown below are well above this threshold value, thus providing reassur-
ance that the weak instrument problem is not likely to be a source of concern for the 2SLS
72
results. Specifically, the F-statistics are well above the recommended benchmark of 1047 and
thus avoid the weak instrument problem that could cause instrumental variable estimates to
be biased toward OLS estimates.
A.10 The Effect of a Change in Alignment on the Change in Roads
Provision: A Visual Examination of the Raw Data
Figure A4 illustrates the instrumental variables research design in the state of Madhya
Pradesh. Each dot represents a village in which a road was sanctioned and completed during
the relevant electoral term. The green dots represent villages that fell in the constituency of
an incumbent legislator aligned with the Congress party and the red dots represent villages
that fell in the constituency of an incumbent legislator aligned with the BJP. The first column
shows the roads that were sanctioned and completed during the electoral term marked by
the 1998 assembly election during which he Congress party was the ruling party in the state.
The second column shows the roads that were sanctioned and completed during the electoral
term marked by the 2003 assembly election during which the BJP was the ruling party in
the state. Consistent with the research design described above, the dots are depicted only
for constituencies in which there was no change in the identity or partisan affiliation of the
incumbent legislator from the 1998 electoral term to the 2003 electoral term.
If H1 is correct, we would expect that there should be a greater increase in the provision of
roads from 1998 to 2003 for the BJP than for the Congress. Indeed, we observe that while
47See Stock et al. 2002
73
Figure A4: Road Projects Sanctioned under the PMGSY Development Scheme in MadhyaPradesh
(a) 1998 to 2003 (State Ruling Party=INC) (b) 2003 to 2008 (State Ruling Party=BJP)
Note: Each dot in the figure represents a village in which a road project was newly sanctioned in the givenelectoral term. The black lines depict state assembly constituency boundaries. Green dots represent roadprojects sanctioned in constituencies where the incumbent legislator belonged to the Indian NationalCongress (INC) and red dots represent road projects sanctioned in constituencies where the incumbentlegislator belonged to the Bharatiya Janata Party (BJP). The dots pertain only to those constituencies inwhich there was no change in either the identity or the partisan affiliation of the incumbent legislator inthe constituency in both electoral terms.
there is a significant increase in the number of red dots (representing the BJP) between
1998 to 2003 there is comparatively much less of an increase in the number of green dots
(representing the Congress) over the same time period. Moreover, since we are restricting
attention only to cases where the identity of the incumbent legislator did not change across
time periods, the results cannot be driven by changes in the quality of the incumbent or by
changes in the partisan identity of the incumbent that, in turn, could be produced by other
constituency specific confounding factors. Thus, the observed changes in road provision over
time in the given constituencies are most likely causally related to a change in the alignment
of the incumbent legislator with the state level ruling party.
74
A.11 Effect of Alignment and Ministerial Status on Completed
Roads, Dropping Road Works Ministers and Chief Minis-
ters.
This section shows the results of the same specifications shown in Table 2 in the main text,
but with dropping constituencies in which the incumbent was a chief minister or a minister
whose department was wholly or partially responsible for the provision of rural road works.
As shown in Table A7, the main results are robust to the exclusion of these ministers.
A.12 Additional Observable Implication of Rent-Seeking
.
To examine further evidence regarding H2, Table A8 examines additional observable impli-
cations of rent-seeking at the level of the individual road project. For these analyses, the
following equation is estimated:
yr,i,t = α0 + α1Alignmentr,i,t + α2Ministerr,i,t + α3Xr,i,t + ui + γt + εr,i,t (3)
where r refers to the individual road project, i refers to the constituency in which the road
was located and t refers to the year in which the road was sanctioned. Alignmentr,i,t is
coded 1 if the road r in constituency i was sanctioned in a year during which the incumbent
75
legislator of constituency i was aligned with the chief minister’s party. Ministerr,i,t is coded 1
if the road r in constituency i was sanctioned in a year during which the incumbent legislator
of constituency i was a belonged to the state government’s council of ministers. Xr,i,t refer to
road-specific and constituency-electoral term specific control variables. With the inclusion
constituency-level fixed effects ui that help control for characteristics of constituencies that
do not vary over time, the coefficients can be interpreted as within constituency effects. The
use of dummies for the sanction year γt help control for temporal shocks and state dummies
help to control for fixed characteristics of states.
Controls include measures of the electoral competitiveness in the constituency and elec-
toral term - Vote Margin and Vote Share as well as indicators for whether the MP in the
overlapping constituency is aligned with the Prime Minister’s Party (MPNationalRuling)
or the Chief Minister’s Party (MPStateRuling). In addition to these variables capturing
political factors, several control variables that could influence the cost of the road or the
difficulty of executing the road project are included. Specifically, indicators for whether the
road project provides new connectivity rather than an upgrade (New) and for whether it
involves a domestic rather than international collaboration (Domestic Collaboration) and for
whether it was introduced after the state had implemented electronic procurement of con-
tracts (EProcure).48 Also included are variables capturing the illiteracy level of the village
in which the road is located (Illiteracy), the population size of the habitation that the road
connects (Habitation Size) and the proportion of Scheduled Castes and Scheduled Tribes
48See Lewis-Faupel et al. 2015 for a discussion of electronic procurement. Note that this variable couldnot be included in the specifications with the quality ratings as a dependent variable since there were noquality rating observations available for periods after electronic procurement took effect.
76
in the Habitation (SC/ST Percentage in Habitation). Finally, indicators for the status of
completion of the road project (Completed and NoProgress) and a measure of the number of
years since the project was sanctioned (YearsSinceSanctioned) are also included. Appendix
Section A.5 provides further information on these variables and their sources. To account for
the dependence of standard errors within constituencies, heteroskedastic-consistent standard
errors clustered by constituency are used.
Table A8, Column (1) shows the results of an OLS model with the dependent variable
measuring the total value of contracts won by the contractor hired for the specific road project
r in the state.49 The coefficient on Minister is negative and significant indicating that the
total value of contracts won by contractors hired in ministers’ constituencies is significantly
lower than the total value of contracts won by contractors hired in the constituencies of
ordinary legislators aligned with the ruling coalition. The inclusion of constituency level fixed
effects ensure that these results are not driven by unobserved differences in the characteristics
of constituencies of ministers and ordinary legislators. These results provide further evidence
consistent with a greater prevalence of rent-seeking in ministers’ constituencies relative to
constituencies of ordinary legislators from the ruling party.
Table A8, Column (2) and Column (3) show the results of a linear probability model with
the dependent variable indicating the quality of the road as rated by an independent agency.
The PMGSY requires that independent monitoring is conducted by Quality Control Units
who are set up by the state government and who are independent of the executing agencies.
49Specifically, the variable is measured by summing up the total sanctioned cost of the road projectsawarded to the relevant contractor in the given state during the period before constituency boundaries wereredrawn.
77
These State Quality Monitors are generally retired persons who do not belong to the district
that they are assigned to monitor. There are also independent monitors at the national level
who are designated by the National Rural Roads Agency who are responsible for conducting
random inspections (National Quality Monitors). Column (2) investigates only the ratings
by the State Quality Monitors while Column (3) examines the combined ratings of the State
and National Quality Monitors where the rating of the State Quality Monitor is included
in the case of a discrepancy. The coefficient on Minister is negative and significant in
both specifications while the coefficient on Alignment with Chief Minister’s Party is positive
and significant in the specification in Column (2). These results indicate that roads in
ministers’ constituencies have a significantly lower quality rating than road projects in the
constituencies of ordinary legislators aligned with the state ruling party. Moreover, Column
(2) shows that road projects in the constituencies of ordinary legislators aligned with the
state ruling party are rated to be of significantly higher quality than road projects in the
constituencies of opposition legislators.
Note, however, that across all three specifications, there is no significant difference on aver-
age between ministers’ constituencies and the constituencies of legislators from opposition
parties. Thus, the key difference in rent-seeking is between the constituencies of ministers
and the constituencies of ordinary legislators aligned with the ruling coalition. Section A.16
further explores this finding.
78
A.13 Allocation vs. Completion
The results in this section shed light on whether the effect of alignment on completed roads
uncovered in Table 2 in the main text is a function of the influence of ‘aligned’ ordinary
legislators on the initial allocation of road projects or whether it is instead a function of
their ability to ensure the timely completion of road projects once they are sanctioned.
Accordingly, TableA9 shows the results from an instrumental variables analysis similar to
the one in Table 2 in the main text, but replacing the dependent variable first with the
proportion of road projects completed in the constituency during the relevant term (Column
1) and then with the total length of road sanctioned in the constituency during the relevant
electoral term (Column 2). Column (1) includes a control for the total road length sanctioned
during the electoral term. To allow for a focus on ordinary legislators, constituencies with
incumbent state legislators who were ministers are dropped from the analyses.
The results from Column 1 show that the coefficient on ∆ Ruling Coalition Alignment is
positive and significant indicating that, after controlling for the road length sanctioned within
the constituency, alignment with a party that belongs to the governing coalition in the state
increases the proportion of roads completed within the electoral term by 5 percentage points.
Column 2 shows that when it comes to the sanctioning of road projects, alignment with a
party that belongs to the governing coalition also has a positive and significant effect. The
results thus suggest that the influence of an ‘aligned’ ordinary legislator on completed roads
is driven both by the effect of alignment on the completion of already sanctioned roads
(which in turn is a function of the efficiency of the bureaucracy and the contractors they
79
select) as well as by the initial allocation of road projects (which is a function of inputs of
district level representative bodies and the bureaucracy.).
A.14 The Effect of Alignment and Ministerial Status on Rent-
Seeking, Conditional on Underlying Electoral Risk
As discussed in the main text, one observable implication of the argument is that ministers
should have greater incentives to engage in electorally costly rent-seeking when they are
subject to greater underlying electoral risk. Table A10 , Column 1 provides a test of this
argument by examining whether the level of expenditure on incomplete projects - an indica-
tion of an incumbent’s willingness to engage in corruption at the cost of providing completed
roads to voters - is greater when ministers’ constituencies have previously experienced higher
levels of electoral competition. In particular, Column 1 presents an analysis similar to that
of Table 3 but includes an interaction of Vote Margin with the variable Minister and with
the variable Alignment with Ruling Coalition respectively.
The results show that the interaction of Vote Margin and Minister is negative and statis-
tically significant indicating that ministers are significantly less likely to incur expenditure
on incomplete projects when they face a lower underlying electoral risk (i.e. higher vote
margins in the previous election). Note, however, that additional calculation shows that we
do observe a positive and significant effect of ministerial status on rent-seeking regardless
of whether previous vote margins were at the 25th percentile in the sample or at the 75th
percentile of the sample. At the same time, we also observe a negative and significant effect
80
of the ruling coalition alignment of ordinary legislators on rent-seeking regardless of whether
previous vote margins were at the 25th percentile in the sample or at the 75th percentile of
the sample.
The results in Table A10 are also used to examine one plausible alternative explanation
for the observed differences between ministers and ordinary legislators in terms of their
propensity to engage in rent-seeking. As discussed in the main text, it is possible that the
results are driven by the fact that ministers face, on average, less intense electoral competition
in their constituencies than ordinary legislators. Indeed, the median vote margin in the
sample in ministers’ constituencies is 8.5% while the median vote margin in the constituencies
of ordinary legislators is 6.7%. The results in the table shed light on whether and how the
intensity of electoral competition in the constituency - measured by the vote margins in the
constituency - modify the effect of ‘alignment’ or ministerial status on rent-seeking.
Accordingly, Table A10, Columns 2, 3 and 4 show the results of specifications similar
to those estimated in Table A8, but also include an interaction of Vote Margin with the
variable Minister and with the variable Alignment with Ruling Coalition respectively. Across
all of these three specifications in addition to the specification in Column 1, we find that
the interaction of Vote Margin and Alignment with Ruling Coalition is not significant at
conventional levels indicating that the prior level of underlying electoral risk does not exert
a modifying effect on the effect of ruling party alignment of ordinary legislators on rent-
seeking. Moreover, in contrast with the results in Column 1, the results in Columns 2, 3 and
4 show that the interaction of Vote Margin and Ministerial Status is also not significant,
although they are positive.
81
A.15 Expenditures on Road Projects Completed within Two Years
Table A11 seeks to examine the affect of ministerial status and ruling party alignment on
expenditures on road projects completed within two years. The unit of analysis is the indi-
vidual road project and constituency fixed effects are included in each of the specifications.
Table A11, Column 1 analyzes the effect of ministerial status and alignment on total ex-
penditures incurred on the project to date. The results show that there is no significant
effect of membership in the chief minister’s party or of ministerial status on expenditures
incurred. Column 2 analyzes the effect of ministerial status and alignment on expenditure
premiums - that is, expenditure in excess of the sanctioned cost of the project that typically
require special administrative approval from higher-level agencies.50 Here, we observe that
alignment with the chief minister’s party has a positive and significant effect. Meanwhile, the
coefficient on Minister is positive but insignificant indicating that ministers and state ruling
party members do not differ much on average in their ability to gain access to expenditure
premiums for productive projects.
A.16 Explaining Road Provision Inefficiencies in the Constituen-
cies of Opposition Legislators
A remaining question is whether road projects in the constituencies of opposition legislators
appear to be inefficient in spite of, or because of, the influence of the relevant ministers.
50Interview with PMGSY Official, Uttar Pradesh, December 2015. See Section A.5 for further informationon how the dependent variable used in this analysis is coded.
82
Recall that although the results in Table 2 and in Table A8 show that rent-seeking is more
likely in ministers’ constituencies than in the constituencies of ordinary legislators aligned
with the ruling coalition, they fail to show any significant difference in the prevalence of
rent-seeking in minister’s constituencies relative to the constituencies of incumbents who
are members of opposition parties. Thus, inefficiencies appear to be just as prevalent in
opposition-held constituencies as in minister’s constituencies.
Table A12 probes this result further at the level of the individual road project - in an
analysis similar to that in Table 3 in the main text - by focusing on expenditures on road
projects that remain incomplete for at least five years in a sample that exclude ministers’
constituencies. The variable Alignment with Opposition Party is a dummy variable that
equals 1 if the incumbent is a member of a party that is not the chief minister’s party or
one of the other parties belonging to the governing coalition. Table A12, Column (1) shows
that this variable is positive and significant indicating that expenditures on unproductive
road projects are significantly higher in constituencies in which a member of an opposition
party is the incumbent. Table A12, Column (2) seeks to explore if this greater unproductive
expenditure is in any way driven by ministerial influence. In particular, if rent-seeking in
opposition constituencies is a result of ministerial influence, we should see more evidence
of rent-seeking in those opposition constituencies that share an administrative district with
a minister responsible for rural road works. Indeed, the results in Table A12, Column (2)
show that the interaction term between an indicator for belonging to the opposition party
and an indicator for sharing an administrative district of a minister responsible for rural
road works is positive and significant. Thus, interestingly, the results show that inefficiency
83
in road projects in the constituencies of opposition party incumbents occurs at least partly
because of, and not despite, the influence of government elites.
These results, in turn, could mean that government elites are able to use their bureaucratic
leverage to extract rents from wasteful road projects in the constituencies of opposition
legislators when they are located in their own administrative district. Indeed, the notion
that government elites seek to control the bureaucracy in opposition-held constituencies is
consistent with the findings of Iyer & Mani (2012) who show that chief ministers are more
likely to effect the transfer of bureaucrats in districts that are not controlled by incumbents
from their own party than in districts that are controlled by their party incumbents. Thus,
government elites may have an incentive to use their bureaucratic leverage to prevent road
completion in opposition-held constituencies and then to extract rents from these projects.
This interpretation is also consistent with the results of the RDD design presented in the
main text which shows evidence of bureaucratic manipulation in opposition constituencies
with close races to reduce the prevalence of completed roads in those constituencies.
A.17 Robustness of Main Results to Dropping Roads that Benefit
Multiple Habitations
A possible concern with the assignment procedure used in this research is that road projects
that benefit multiple habitations that could possibly straddle more than one constituency
are assigned to only one constituency. Table A13 shows however that the results of the
instrumental variables analysis are robust to dropping road projects that benefit more than
84
one habitation. Table A14 shows that the results on expenditures on incomplete road projects
at the level of the individual road project are also robust to dropping road projects that
benefit more than one habitation. These additional results show that the main results are
not an artifact of the assignment procedure.
85
Table A3: Summary Statistics - Road Project Level Variables
Variable Obs Mean Std. Dev. Min Max MedianRoad Length (Kms) 41088 3.82 4.726 0 255 2.7Minister 41088 .167 .373 0 1 0Member of Chief Minister’s Party 41088 .497 .5 0 1 0Member of Ruling Coalition 41088 .547 .498 0 1 1Member of Coalition Partner 41088 .049 .216 0 1 0Vote Margin 41086 .098 .086 0 .79 .078Vote Share 41086 .411 .094 .163 .874 .403Latitude 36274 25.297 2.076 18.297 31.157 25.505Longitude 36274 79.95 4.121 70.137 88.267 79.69Latitude*Longitude 36274 2021.467 183.848 1492.264 2437.991 2058.883MP in CM’s party 41088 .412 .492 0 1 0MP in PM’s party 41088 .275 .446 0 1 0Sanctioned Cost 38581 94.193 104.769 0 995.54 60.57Total Expenditure till Present 38783 75.735 90.874 0 992.37 47.52No Progress 41088 .042 .2 0 1 0Years Since Sanctioned 41088 4.107 2.5 0 14 4Completed 41088 .89 .313 0 1 1Illiteracy of Village 36196 .582 .139 .122 1 .578SC/ST Percentage 37624 3.538 29.104 0 1480 .272Habitation Size 40955 1009.362 1216.935 1 26153 704New Connectivity 41088 .812 .391 0 1 1Domestic Collaboration 41088 .863 .344 0 1 1Electronic Procurement 41088 .067 .251 0 1 0Road Quality Rating (State Quality Monitor) 7262 .591 .492 0 1 1Road Quality Rating 7441 .588 .492 0 1 1Total Contract Value 39903 4065.055 4830.561 0 27201.5 2148.37Cabinet Minister 41088 .094 .292 0 1 0Minister of State 41088 .073 .26 0 1 0Road Works Minister 41088 .012 .109 0 1 0Minister (CM’s Party) 41088 .123 .328 0 1 0Expenditure Overrun 39890 2.453 13.445 0 588.06 0Member of Opposition Party 41088 .453 .498 0 1 0Administrative District of 41088 . .628 .483 0 11Minister (CM’s Party)Administrative District of 41088 .052 .221 0 1 0Road Works Minister (CM’s Party)
Sample of Incomplete Projects Sanctioned at least Five Years PriorTotal Expenditure till Present 2636 68.216 100.623 0 921.86 31.11Minister 2764 .114 .318 0 1 0Road Works Minister 2764 .017 .128 0 1 0Minister (CM’s Party) 2764 .085 .28 0 1 0Member of Chief Minister’s Party 2764 .406 .491 0 1 0Administrative District of Minister (CM’s Party) 2764 .652 .476 0 1 1Administrative District of Road Works Minister (CM’s Party) 2764 .025 .156 0 1 0
86
Table A4: The Effect of Alignment with Chief Minister’s Party on Completed Roads (Resid-ualized)
Method Est. Treatment P-Value Chosen ObsEffect Bandwidth (L),(R)
Conventional, IK Bandwidth 5.07 0.027 11.5
Conventional, CCT Bandwidth 5.33 0.033 9.5 503, 641
Robust, CCT Bandwidth 5.72 0.05 9.5 503, 641
The first row shows the estimates using the Imbens and Kalyanaraman (2011) optimal bandwidth. Theresults are estimated using the rd program in STATA developed by Nichols (2014). The second and thirdrows show the estimates using the Calonico, Cataneo and Titiunik (2014) optimal bandwidth and areestimated using the rdrobust package in STATA developed by the same authors. The bandwidth refers tothe width of the vote margin share used. (L) and (R) refer to the number of observations to the left andright of the cut-point respectively.
Table A5: The Effect of Alignment with Chief Minister’s Party on Covariates: Balance Tests
Name of Covariate Estimated Effect P-Value Optimal BandwidthRoad Length Completed During Previous Term 1.96 0.14 8.06
Road Length Sanctioned During Previous Term 4.4 0.36 9.78
Expenditure Incurred During Previous Term 103.06 0.33 8.05
Total Amount Sanctioned During Previous Term 156.11 0.19 9.90
Alignment of Incumbent with Chief Minister’s Party During Previous Term −0.04 0.57 10.85
Maximum Vote Share in Constituency in Previous Election 0.008 0.52 5.75
Illiteracy Rate of Block Most Closely Overlapping with Constituency 0.035 0.77 12.16
% Rural Population of Block Most Closely Overlapping with Constituency 0.02 0.61 19.12
Note: The estimates use the Imbens and Kalyanaraman (2011) optimal bandwidth and are estimated usingthe rd program in STATA developed by Nichols (2014).
87
Table A6: Instrumental Variables First Stage Regressions
(1) (2)
∆ Ruling Coalition Alignment ∆ Ministerial Status
∆ Ruling Coalition Alignment - Instrument 0.74*** −0.06***(0.02) (0.01)
∆ Ministerial Status - Instrument 0.12*** 1.03***(0.02) (0.01)
∆ Vote Margin −0.07 0.22(0.16) (0.16)
∆ Vote Share 0.51*** 0.25(0.20) (0.20)
∆ MP National Gov’t Alignment 0.04* −0.01(0.02) (0.02)
∆ MP State Gov’t Alignment 0.11*** −0.03(0.02) (0.02)
State-Electoral Term Fixed Effects Yes Yes
Observations 1799 1799F-Statistic (14, 1784) 523.06 1113.74
Significance levels : ∗: 10% ∗∗: 5% ∗ ∗ ∗: 1%. The unit of analysis is the constituency-electoralterm. Heteroskedastic-consistent standard errors clustered by state constituency are shown in parentheses.
88
Table A7: The Effect of a Change in Alignment and Ministerial Status on the Change in theTotal Road Length Completed During the Electoral Term: Dropping Road Works Ministersand Chief Ministers
Dependent Variable: ∆ Total Road Completed in Term
(1) (2)
∆ Ruling Coalition Alignment 5.23 4.69**(3.34) (1.90)
∆ Ministerial Status 2.21 −3.64**(3.42) (1.86)
∆ Vote Margin −6.81 3.03(11.92) (5.50)
∆ Vote Share −26.58 ∗ ∗ −5.67(11.33) (5.95)
∆ MP National Gov’t Alignment 1.63 1.40**(1.14) (0.71)
∆ MP State Gov’t Alignment −2.20** −0.29(0.98) (0.54)
∆ New Connectivity Proportion −4.97***(1.35)
∆ Domestic Collab. (Proportion) −2.95(2.64)
∆ Village Illiteracy (Average) 5.43(4.90)
∆ SC/ST Percentage (Average) 0.02(0.02)
∆ Habitation Size (Average) 0.00(0.00)
∆ Total Expenditure (Completed in Term) 0.04***(0.00)
∆ Total Expenditure to Date −0.00(0.00)
∆ Total Sanctioned Cost 0.00(0.00)
Sanction Year Fixed Effects No Yes
State-Electoral Term Fixed Effects Yes Yes
Observations 1775 1363
Significance levels : ∗: 10% ∗∗: 5% ∗ ∗ ∗: 1%. The unit of analysis is the state constituency term.Each variable is the difference in the value of the given indicator or measure for the state constituency ibetween the current electoral term t and the previous electoral term t− 1. The instrument used for ∆ CMParty Alignment is ∆ CM Party Alignment - Instrument, the instrument used for ∆ Ruling CoalitionAlignment is ∆ Ruling Coalition Alignment -Instrument and the instrument used for ∆ Ministerial Statusis ∆ Ministerial Status -Instrument. Further details on the instruments are given in the text.Heteroskedastic-consistent standard errors clustered by state constituency are shown in parentheses.
89
Table A8: The Effect of a Coalition Alignment and Ministerial Status on Rent-Seeking:Analysis of Individual Road Projects
(1) (2) (3)Total Contract Value Road Quality (SQM) Road Quality (Combined)
Minister −638.25** −0.07** −0.07**(266.60) (0.03) (0.03)
Member of Ruling Coalition 223.40 0.03* 0.03(233.56) (0.02) (0.02)
Vote Margin −1542.68 −0.22 −0.25(1953.58) (0.16) (0.16)
Vote Share 1512.02 0.03 0.09(2091.97) (0.16) (0.16)
Road Length (Kms) −56.37*** −0.00 −0.01*(20.19) (0.00) (0.00)
MP in CM’s party −105.69 −0.02 −0.02(221.25) (0.02) (0.02)
MP in PM’s party −1164.69*** −0.00 −0.01(201.44) (0.03) (0.03)
Total Expenditure till Present −2.22** 0.0003** 0.0003***(1.10) (0.00) (0.00)
Sanctioned Cost 7.76*** −0.0009 −0.0009(1.08) (0.00) (0.00)
Illiteracy of Village −288.64 0.08 0.09(253.67) (0.06) (0.06)
Habitation Size 0.01 0.00 0.00(0.03) (0.00) (0.00)
SC/ST Percentage 0.49 −0.001*** −0.001***(1.04) (0.00) (0.00)
New Connectivity 1788.52*** 0.05 0.04(197.73) (0.04) (0.04)
Domestic Collaboration −271.30* −0.07* −0.06(154.12) (0.04) (0.04)
Completed −321.00 0.04 0.04(205.97) (0.02) (0.02)
Years Since Sanctioned −82.11*** 0.01 0.01(26.67) (0.01) (0.01)
Electronic Procurement 1484.99*** . .(317.32) . .
Unit Fixed Effects Constituency District DistrictState Fixed Effects Yes Yes YesSanction Year Fixed Effects Yes Yes Yes
Observations 29782 5192 5314
Significance levels : ∗: 10% ∗∗: 5% ∗ ∗ ∗: 1%. The unit of analysis is the individual road project.Columns (2) and (3) includes all road projects in the sample for which a quality rating is available.Heteroskedastic-consistent standard errors clustered by state constituency are shown in parentheses.
90
Table A9: The Effect of a Change in Alignment and Ministerial Status on Completion vsAllocation
Dependent Variable: Proportion Completed Within Term Total Road Length Sanctioned
∆ Ruling Coalition Alignment 0.05** 4.96*(0.03) (2.80)
∆ Total Road Length Sanctioned 0.0001 0.20**(0.0001) (0.09)
∆ Total Sanctioned Cost −0.000007 0.0004(0.000006) (0.003)
∆ MP National Gov’t Alignment 0.02 0.45(0.02) (1.67)
∆ MP State Gov’t Alignment −0.02 −2.23*(0.01) (1.22)
∆ Vote Margin 0.05 2.74(0.12) (14.02)
∆ Vote Share −0.21 −21.81(0.14) (13.68)
∆ New Connectivity Proportion −0.12*** −3.26(0.04) (2.67)
∆ Domestic Collab. (Proportion) 0.10* −4.29(0.05) (5.55)
∆ Village Illiteracy (Average) 0.17 1.35(0.14) (10.59)
∆ SC/ST Percentage (Average) −0.000009 0.03(0.0004) (0.04)
∆ Habitation Size (Average) 0.00001 0.003**(0.00001) (0.0009)
Sanction Year Fixed Effects Yes Yes
State-Electoral Term Fixed Effects Yes Yes
Observations 1118 1118
Significance levels : ∗: 10% ∗∗: 5% ∗ ∗ ∗: 1%. The unit of analysis is the state constituency term.All constituencies with ministers are dropped from the analysis. Each variable is the difference in the valueof the given indicator or measure for the state constituency i between the current electoral term t and theprevious electoral term t− 1. The instrument used for ∆ CM Party Alignment is ∆ CM Party AlignmentIncumbent, the instrument used for ∆ Ruling Coalition Alignment is ∆ Ruling Coalition AlignmentIncumbent. Heteroskedastic-consistent standard errors clustered by state constituency are shown inparentheses.
91
Table A10: The Effect of Alignment and Ministerial Status on Rent-Seeking: Conditionalon Underlying Electoral Risk
(1) (2) (3) (4)DV: Expenditure Total Value Road Quality Road Quality
Incomplete Projects of Contracts Rating (SQM) Rating (Combined)
(1) (2) (3) (4)
Minister 51.24** −712.16 −0.11*** −0.10**(20.09) (446.91) (0.04) (0.04)
Ministerial Status * Vote Margin −240.87** 684.33 0.40 0.31(102.85) (3061.05) (0.31) (0.31)
Member of Ruling Coalition * Vote Margin −59.81 −181.72 −0.11 −0.13(134.39) (2572.37) (0.25) (0.25)
Member of Ruling Coalition −24.13 239.31 0.04 0.04(17.47) (324.58) (0.03) (0.03)
Vote Margin 29.41 −1592.73 −0.27 −0.27(103.84) (2579.96) (0.18) (0.18)
Road Length (Kms) 11.48*** −56.52*** −0.00 −0.01*(1.76) (20.24) (0.00) (0.00)
MP in CM’s party −28.27* −100.78 −0.02 −0.02(14.40) (223.00) (0.02) (0.02)
MP in PM’s party −30.41* −1163.54*** −0.00 −0.01(17.49) (200.51) (0.03) (0.03)
Illiteracy of Village −6.99 −287.59 0.08 0.09(23.24) (254.26) (0.06) (0.06)
SC/ST Percentage −0.01 0.49 −0.00*** −0.00***(0.06) (1.04) (0.00) (0.00)
Habitation Size 0.00 0.01 0.00 0.00(0.00) (0.03) (0.00) (0.00)
New Connectivity 11.20 1788.93*** 0.06 0.04(16.02) (198.01) (0.04) (0.04)
Domestic Collaboration 30.49 −271.25* −0.06* −0.06(27.79) (154.09) (0.04) (0.04)
Years Since Sanctioned −22.21*** −82.04*** 0.01 0.01(4.97) (26.66) (0.01) (0.01)
Electronic Procurement 1487.15*** . .(317.85) . .
Vote Share 1516.92 0.05 0.11(2087.72) (0.16) (0.16)
Total Expenditure till Present −2.22** 0.00** 0.00***(1.10) (0.00) (0.00)
Sanctioned Cost 7.77*** −0.00 −0.00(1.08) (0.00) (0.00)
Completed −320.69 0.04 0.04*(206.30) (0.02) (0.02)
Unit Fixed Effects Constituency Constituency District DistrictState-Electoral Term Fixed Effects Yes Yes Yes YesSanction Year Fixed Effects Yes Yes Yes Yes
Observations 2055 29782 5192 5314
Significance levels : ∗: 10% ∗∗: 5% ∗ ∗ ∗: 1%. The unit of analysis is the individual road project.Heteroskedastic-consistent standard errors clustered by state constituency are shown in parentheses.
92
Table A11: The Effect of Alignment and Ministerial Status on Expenditures on RoadProjects Completed within Two Years
(1) (2)Total Expenditure till Present Expenditure Premium
Minister −0.37 0.15(0.84) (0.23)
Member of Chief Minister’s Party 0.92 0.46**(0.59) (0.18)
Member of Coalition Partner −2.00 0.46*(2.55) (0.23)
Electronic Procurement 18.00*** −0.65(3.52) (0.95)
Vote Margin 6.38 0.97(5.06) (1.39)
Vote Share −12.20* −4.10**(6.32) (1.89)
Road Length (Kms) 1.67*** 0.17***(0.30) (0.07)
MP in CM’s party −0.63 −0.24(0.70) (0.21)
MP in PM’s party 0.72 −0.29(0.78) (0.26)
Sanctioned Cost 0.76*** −0.01***(0.02) (0.00)
Illiteracy of Village 1.81 0.22(1.63) (0.68)
SC/ST Percentage −0.00 −0.00(0.00) (0.00)
Habitation Size −0.00 0.00(0.00) (0.00)
New Connectivity 1.36 0.42(0.93) (0.44)
Domestic Collaboration −0.60 −0.30(0.90) (0.32)
Years Since Sanctioned −0.24 0.14(0.43) (0.21)
Constituency Fixed Effects Yes YesState Fixed Effects Yes YesSanction Year Fixed Effects Yes Yes
Observations 8557 8557
Significance levels : ∗: 10% ∗∗: 5% ∗ ∗ ∗: 1%. The unit of analysis is the individual road project.Only road projects completed in two years are included in the sample Heteroskedastic-consistent standarderrors clustered by state constituency are shown in parentheses.
93
Table A12: Analysis of Expenditures on Incomplete Road Projects: Opposition Party Leg-islators
(1) (2)Dependent Variable: Expenditure on Road Projects Incomplete for at least Five Years
Opposition Party Constituency 31.06** 22.31*(13.81) (12.53)
Opposition Party Constituency * Administrative District of Road Minister 135.00***(33.32)
Administrative District of Road Minister −116.49***(29.14)
Electronic Procurement 50.97 52.34(74.65) (73.87)
Vote Margin 13.13 −24.53(94.63) (76.50)
Vote Share −25.24 12.76(144.64) (115.09)
Road Length (Kms) 5.64* 6.18*(3.24) (3.27)
MP in CM’s party −20.27 −23.79(20.72) (19.31)
MP in PM’s party −45.84** −36.85**(18.74) (17.88)
Sanctioned Cost 0.22*** 0.21***(0.08) (0.08)
Illiteracy of Village −18.27 −17.20(23.94) (23.97)
SC/ST Percentage 0.05 0.04(0.07) (0.07)
Habitation Size 0.00 0.00(0.00) (0.00)
New Connectivity 4.76 6.17(11.75) (12.02)
Domestic Collaboration 15.29 15.40(27.87) (27.65)
Constituency Fixed Effects Yes YesState Fixed Effects Yes YesSanction Year Fixed Effects Yes Yes
Observations 1759 1759
Significance levels : ∗: 10% ∗∗: 5% ∗ ∗ ∗: 1%. The unit of analysis is the individual road project.Constituencies in which a minister was an incumbent are excluded from Column (3).Heteroskedastic-consistent standard errors clustered by state constituency are shown in parentheses.
94
Table A13: The Effect of a Change in Alignment and Ministerial Status on the Change inthe Total Road Length Completed During the Electoral Term, Dropping Roads that BenefitMultiple Habitations.
Dependent Variable: ∆ Total Road Completed in Term
(1) (2) (3) (4) (5)OLS 2SLS IV 2SLS 2SLSFull Full Full Conditional on Conditional on
Sample Sample Sample Road Char. Road Char.
∆ CM Party Alignment 1.39* 3.28** 1.47*(0.73) (1.45) (0.85)
∆ Ministerial Status −0.71 −4.25**(2.82) (1.72)
∆ Ruling Coalition Alignment 3.95 4.05**(2.92) (1.65)
∆ Vote Margin −1.44 −2.84 2.92 −1.36 3.59(9.97) (9.61) (4.85) (10.01) (4.99)
∆ Vote Share −13.41 −14.40 −6.77 −15.61* −6.84(8.71) (8.86) (5.12) (9.29) (5.20)
∆ MP National Gov’t Alignment 2.04** 2.03** 1.37** 2.00** 1.22*(0.91) (0.91) (0.65) (0.91) (0.64)
∆ MP State Gov’t Alignment −1.84** −2.09*** −0.02 −2.02*** −0.26(0.82) (0.80) (0.49) (0.78) (0.50)
∆ New Connectivity Proportion −3.64 ∗ ∗∗ −3.86 ∗ ∗∗(0.96) (1.03)
∆ Domestic Collab. (Proportion) −0.92 −1.52(1.88) (1.95)
∆ Village Illiteracy (Average) 3.76 3.70(3.67) (3.75)
∆ SC/ST Percentage (Average) 0.01 0.00(0.01) (0.01)
∆ Habitation Size (Average) 0.00** 0.00**(0.00) (0.00)
∆ Total Expenditure in Term 0.04*** 0.04***(0.00) (0.00)
∆ Total Expenditure to Date −0.00*** −0.00***(0.00) (0.00)
∆ Total Sanctioned Cost 0.00 0.00*(0.00) (0.00)
Sanction Year Fixed Effects Yes Yes Yes Yes Yes
State-Electoral Term Fixed Effects Yes Yes Yes Yes Yes
Observations 1715 1715 1272 1715 1272
Significance levels : ∗: 10% ∗∗: 5% ∗ ∗ ∗: 1%. The unit of analysis is the state constituency term.Each variable is the difference in the value of the given indicator or measure for the state constituency ibetween the current electoral term t and the previous electoral term t− 1. The instrument used for ∆ CMParty Alignment is ∆ CM Party Alignment - Instrument, the instrument used for ∆ Ruling CoalitionAlignment is ∆ Ruling Coalition Alignment -Instrument and the instrument used for ∆ Ministerial Statusis ∆ Ministerial Status -Instrument. Further details on the instruments are given in the text.Heteroskedastic-consistent standard errors clustered by state constituency are shown in parentheses.
95
Table A14: Analysis of Expenditures on Incomplete Road Projects, Dropping Road Projectsthat Benefit Multiple Habitations
(1) (2) (3)DV: Expenditure-Incomplete Projects
Minister 28.57(17.45)
Member of Chief Minister’s Party −43.57*** −45.98*** −9.91(16.54) (16.79) (19.52)
Member of CM’s Party * Admin. District of Road Works Minister (CM’s Party) −72.36*(38.47)
Admin. District of Road Works Minister (CM’s Party) −67.62***(11.27)
Member of Coalition Partner −54.04*** −52.15***(18.52) (19.42)
Vote Margin −30.70 −51.50 −9.38(91.03) (92.73) (73.18)
Vote Share 5.38 24.59 14.84(112.41) (112.25) (96.34)
Road Length (Kms) 2.29 2.19 3.33(2.60) (2.64) (4.69)
Sanctioned Cost 0.29*** . 0.30***(0.07) . (0.09)
MP in CM’s party −16.41 −15.06 −32.27(15.12) (16.29) (20.92)
MP in PM’s party −40.28* −41.06** −33.53(21.12) (20.65) (24.33)
Sanctioned Cost ..
Illiteracy of Village −18.87 −18.95 −23.24(23.68) (23.40) (25.50)
SC/ST Percentage −0.01 −0.01 −0.01(0.03) (0.03) (0.03)
Habitation Size 0.00 0.00 0.00(0.00) (0.00) (0.00)
New Connectivity −0.91 0.26 0.92(11.77) (11.88) (12.36)
Domestic Collaboration 22.30 23.00 17.94(25.00) (24.77) (31.19)
Years Since Sanctioned −20.88*** −20.98*** −26.84***(4.88) (4.83) (5.62)
Minister (CM’s Party) 50.70**(24.04)
Minister (Coalition Partner) 36.66(25.65)
Cabinet Minister −30.86(23.96)
Unit Fixed Effects Constituency Constituency ConstituencyState-Electoral Term Fixed Effects Yes Yes YesSanction Year Fixed Effects Yes Yes Yes
Observations 1502 1502 1309
Significance levels : ∗: 10% ∗∗: 5% ∗ ∗ ∗: 1%. The unit of analysis is the individual road project.Constituencies in which a minister was an incumbent are excluded from Columns (2), (3) and (4).Heteroskedastic-consistent standard errors clustered by state constituency are shown in parentheses.
96