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

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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

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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.

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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-

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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.

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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

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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) -

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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).

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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.

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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.

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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.

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