Distributive Implications of Competitive Clientelism:

Evidence from Bangladesh∗

Youjin Hahn† Kanti Nuzhat‡ Hee-Seung Yang§ Haishan Yuan¶

August 31, 2018

Abstract

In clientelistic democracies, political brokers may extract rents through interme-

diating the exchanges of political support and targeted transfers between voters and

parties. Using a regression discontinuity design, we find that having a member of par-

liament (MP) in the governing parties provides greater consumption for households

and reduces poverty in Bangladesh. However, the quantile treatment effects of having

a government MP are significantly greater at the high end of consumption distribution

than at the low end. The largest landowners by acre also benefit the most. Our findings

suggest that wealthy households disproportionately benefit from the rents brought by

a government MP.

Keywords: Distributive Politics; Clientelism; Bangladesh; Election; Elite Capture.

JEL Classification Numbers: D72, O12, I31, H42, H50, P16

∗We are grateful to Asadul Islam, Ethan Kaplan, Florian Ploeckl, Paul Raschky, and seminar/workshopparticipants at Monash University, Victoria University of Wellington, Australasian Development EconomicsWorkshop (2016), Deakin Workshop of Natural Experiments in History (2017), and Asian meeting of theEconometric Society (2018) for their helpful comments. We thank Jafar Iqbal and the Centre for UrbanStudies in Dhaka for helping us access the dataset.†Yonsei University, Republic of Korea; Email: youjin.hahn@yonsei.ac.kr‡North South University, Bangladesh; email: kanti.nuzhat@northsouth.edu§Corresponding author; KDI School of Public Policy and Management, Republic of Korea; Email:

heeseung.yang@kdischool.ac.kr; Address: 263 Namsejong-ro, Sejong-si 30149, Republic of Korea.¶University of Queensland, Australia; Email: h.yuan@uq.edu.au

1 Introduction

Electoral incentives often play an important role in the allocation of public expenditure.

After the third wave of democratization, many developing countries became electoral democ-

racies. In young democracies, distributive politics are often clientelistic, meaning that gov-

ernment policies include a high level of spending that targets narrow groups of voters and

provides a low level of public goods (Keefer, 2007). Moreover, the electoral targeting of pub-

lic resources in clientelistic democracies relies on local brokers to intermediate between the

governing parties and the voters. This is because political parties often lack the necessary

information to target narrow groups of voters and to ensure that those targeted with elec-

toral spending will turn out to vote for the incumbent parties (Finan and Schechter, 2012;

Stokes et al., 2013). Political parties may also lack the necessary credibility in implementing

electoral promises (Keefer and Vlaicu, 2007). The broker-mediated distribution of electorally

motivated spending provides ample opportunities for rent seeking. In a survey of Argentine

brokers by Stokes et al. (2013), more than 90% of respondents reported that at least some

brokers extracted resources intended for voters.

The clientelistic nature of distributive politics in many young developing democracies

poses two questions. First, does electorally motivated public spending increase the material

well-being of the targeted population? Patron-client relationships are reciprocal between

people with unequal social status. A patron provides protection and benefits for a person with

lower social status, namely the client, who in exchange offers general support and services to

the patron (Scott, 1972). But given the inherently unequal relationship between patrons and

clients, if patrons extract most rents through intermediating the targeted transfers between

parties and voters, and parties are unable to discipline their brokers, it is possible that

the electorally targeted transfers will have limited impacts on voters’ well-being. Second,

are targeted transfers pro-poor? Some have argued that when efficient and programmatic

redistributive policies are not feasible, clientelistic distribution may be the second-best policy

(e.g., Fukuyama, 2018). This is because it is arguably cheaper for parties to buy the votes

of the poor, and therefore clientelistic spending is likely pro-poor. However, while existing

2

evidence suggests that poor voters are more likely to be approached by parties for vote

buying, it is unclear that poor people are the primary beneficiaries of the transfers for vote

buying if rents can be extracted by patrons and brokers.

We aim to answer these questions in the context of Bangladesh. As discussed in the next

section, Bangladesh is a young democracy and is often characterized by competitive clien-

telism (Khan, 2013). Several features in Bangladesh’s fiscal and electoral institutions make it

an ideal setting for an empirical investigation of the distributive implications of competitive

clientelism. In particular, we first ask whether households benefit from having a member

of parliament (MP) affiliated with the governing parties. Using a regression discontinuity

(RD) design, we find strong evidence that having a governing party MP increases per-capita

household consumption by 20% and reduces poverty rates by 12%. We find some, albeit

weaker, evidence that having a government MP increases access to basic infrastructure such

as electricity, clean drinking water, and sanitary latrines. We do not find participation in

social safety programs to be different in constituencies represented by a government MP.

The increase in household consumption and the reduction in poverty do not necessarily

mean that electoral targeting with public spending by government MPs is pro-poor. Indeed,

we find the opposite. Moving beyond our initial focus on the average treatment effects of

having a government MP on consumption, we extend the RD design to examine the quantile

treatment effects (QTEs) of having a government MP on household consumption.

We find that the government MP treatment shifts up the household consumption through-

out the distribution, i.e., positive and statistically significant quantile treatment effects for

most quantiles of the household consumption distribution. However, the impacts of having

a government MP are substantially higher at the high end of the consumption distribution

than at the low end. Specifically, having a government MP increases per-capita household

consumption by 36% at the 90th percentile, but only 21% at the 10th percentile. We propose

a split-sample procedure of randomization inference to test the equality of QTEs at different

quantiles. The QTE at the 90th percentile is significantly higher than the QTE at the median

or the 10th percentile. The QTE at the 80th percentile is also significantly higher than the

QTE at the median or the 20th percentile.

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The QTEs on food consumption have a flat profile across quantiles, suggesting that

households’ food consumption increases by a relatively similar percentage throughout the

distribution. The flat profile of QTEs on food consumption suggests that transfers of in-

kind grain grants and other government spending do not target the most calorie-deficient

households. The higher QTEs on overall household consumption at the high end of the

consumption distribution are primarily driven by nonfood consumption.

These higher QTEs at the high end of the consumption distribution, however, do not

necessarily mean that transfers of public resources disproportionately favor the already rich

households. It is possible, though unlikely, that the counterfactual poor benefit so much from

having a government MP that they move from the bottom of the consumption distribution

to the top of distribution and become richer than the counterfactual rich. To address this

possibility, we further examine how having a government MP affects household consumption

by land ownership.

As is typical in agrarian societies, land in rural Bangladesh provides the basics of local

economies and political influence (Baland and Robinson, 2008). Landlords are traditional

patrons (Anderson et al., 2015), who build their patronage networks on land leases, share-

cropping, and hired labor, and extend their influence through the provision of credit, pro-

tection, and some public services such as dispute mediation to their supporters or “clients.”

If political positions and connections benefit the local elite the most, one should expect that

large landowners reap a disproportionate share of the rents from government transfers. In-

deed, we find that the top quartile of landowners by acre, accounting for about 10% of rural

households in our data, enjoys a significantly greater boost in consumption from having a

government MP than landless households do.

While incumbent candidates or parties in established democracies typically enjoy sub-

stantial electoral advantages in elections, incumbent candidates or parties often suffer dis-

advantages in developing democracies (Uppal, 2009; Klasnja, 2015). These incumbency

disadvantages reflect unfulfilled accountability to voters due to corruption or weak parties

(Klasnja and Titiunik, 2017). In the Bangladesh parliamentary elections, we find that in-

cumbent party candidates have neither electoral advantages nor significant disadvantages.

4

This lack of incumbency advantages suggests that additional electoral transfers from the gov-

ernment MPs distributed to buy votes are insufficient to overcome potential anti-incumbent

government sentiments among voters. Corruption is prevalent in Bangladesh: since 2001,

Bangladesh has been ranked among the most corrupt countries by Transparency Interna-

tional, based on the organization’s Corruption Perceptions Index. The major parties in

Bangladesh include anti-corruption in their platforms. Nevertheless, these weakly institu-

tionalized parties do not seem to be able to limit the rents extracted by local elites. Our

findings are consistent with findings in other young democracies: that weakly institution-

alized parties in developing democracies often lack the capacity to monitor brokers, while

brokers trade off election probability and rent extraction (Stokes et al., 2013; Larreguy et al.,

2016).

Our paper contributes to several strands of literature. A large literature on distributive

politics investigates whether local governments’ partisan alignment with upper-level govern-

ments increases intergovernmental transfers or grants allocated to local areas (e.g., Arulam-

palam et al., 2009; Sole-Olle and Sorribas-Navarro, 2008; Brollo and Nannicini, 2012). In the

presence of intermediated electoral targeting and political capture by local elites, it is unclear

whether greater national spending would translate to the improved material well-being of

the local population. Our study contributes to this literature by investigating the effects

of having a government MP on household consumption and access to basic infrastructure

and social programs using microdata. Another strand of literature investigates how exist-

ing socioeconomic inequality affects the allocation of public spending (e.g., Bardhan and

Mookherjee 2006; Araujo et al. 2008; Anderson et al. 2015). Our paper contributes to this

literature by documenting the causal effects in the opposite direction—electorally motivated

government transfers increase economic inequality across households. Recent studies explore

the roles of political brokers and the agency problems between political parties and brokers

in the distribution of clientelistic transfers (Stokes et al., 2013; Larreguy et al., 2016). We

make an empirical contribution to this literature by documenting how rents brought by a

government MP are distributed in patron-client networks. In addition, we make a method-

ological contribution by proposing a split-sample procedure of randomization inference to

5

test heterogeneous quantile treatment effects.

2 Background

2.1 Electoral Institutions

After gaining independence from Pakistan in 1971, Bangladesh briefly had a parliamentary

democracy from 1972 to 1975. In 1975, Sheikh Mujibur Rahman (Mujib), who led the

founding of Bangladesh and was the prime minister at the time, imposed a one-party socialist

rule. The assassination of Mujib in the same year and subsequent military coups led to a

prolonged period of military dictatorships under different regimes. Democracy was not

restored until 1991.

Since 1991, Bangladesh has maintained the Westminster system of a unicameral parlia-

mentary government. The prime minister of Bangladesh is the head of government and is

elected by the majority of the parliament. Members of the parliament are elected by univer-

sal suffrage for a term no longer than five years. Each of the 300 electoral districts (hereafter

constituencies) elects one member to the parliament under the first-past-the-post rule.1

Two parties, the center-left Bangladesh Awami League (AL) and the center-right

Bangladesh Nationalist Party (BNP), have dominated Bangladeshi politics and alternated

in power since 1991. In the general election of 1991, the BNP won 140 seats and, with the

support of the Jatiyo Party (JP), formed the government. The AL won 88 seats and became

the opposition party.

The next general election was held in February 1996, after the stipulated five-year term.

However, opposition parties boycotted this election to protest the BNP’s alleged electoral

manipulation in a by-election (special election) two years before. Despite winning all 300

seats in the February election, the BNP backed down under pressure from the opposition

1On top of the 300 members elected directly by the voters, Bangladesh’s parliamentary system mandatesa number of reserved seats for women. In 2000, 30 seats were reserved for women. Since 2008, the numberof reserved seats for women has been 50. These seats are allocated to parties proportional to the shares ofseats they won in the general election, and the parties allocate these reserved seats to their female members.However, they hold less power and have significantly less access to facilities than the directly elected MPs.In this paper, we focus on direct elections in the 300 constituencies.

6

through marches, demonstrations, and strikes, and agreed to hold a fresh election. The

BNP invoked a constitutional amendment and handed over power to a neutral caretaker

government to conduct a new parliamentary election. In June 1996, the AL won 146 of the

300 constituencies and, with the support of the JP, led a governing coalition.

In 2001, the BNP returned to power by winning 193 seats in that year’s general election,

while the AL retained only 62 seats. The next election was due in January 2007, but was

postponed by a military-backed caretaker government until 2008. In the 2008 election, the

AL led a 14-party grand alliance, which together won 263 seats. The AL alone won 230

seats.

In this paper, we focus on the competitive elections in 1996, 2001, and 2008, which

are considered to be, by and large, free and fair by domestic and international observers

(Chowdhury, 2009; Jahan, 2015). These elections also have high voter turnout rates of 75%,

76%, and 87%, respectively, in contrast to merely 55% in 1991 (Ahmed and Ahmad, 2003;

Chowdhury, 2009). Table 1 presents the number of seats the different parties, including

smaller parties, won in the three elections. Data are from the Election Commission of

Bangladesh and will be described in more detail in Section 3.1.

As shown in Table 1, the AL and the BNP alternated in power with large swings in the

number of seats they won. However, compared to swings in seat shares in the parliament,

the changes in national vote shares the two major parties received were modest. Figure 1

shows that despite a large increase in the number of seats won by the BNP in 2001, the

BNP’s national vote share increased by only 9 percentage points, from 33.6% to 42.7%. The

AL’s national vote share, far from dropping, increased slightly by 2.8 percentage points, from

37.4% in 1996 to 40.2% in 2001. Vote shares gained by the two dominant parties increased

at the expense of candidates from smaller parties.

In the 2008 election, the nationwide vote share of the incumbent BNP experienced a

significant drop, from 42.7% to 33.5%. The national vote share of AL increased from 40.2%

in 2001 to 48.8% in 2008. However, compared to the AL’s surge by 56 percentage points in

seat shares in the parliament, the change in AL’s vote share is rather modest.

Bangladesh’s majoritarian electoral system allows for large swings of partisan control in

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Table 1: Numbers of Constituencies Won by Each Party in General Elections

Party / Year 1996 2001 2008

Bangladesh Awami League (AL) 146 62 230Bangladesh Nationalist Party (BNP) 116 193 30Independent 1 6 4Islami Oikya Jote 1 2Jamat-E-Islami Bangladesh (JI) 3 17 2Jatiya Party (JP) 32 4 27Jatiya Samajtantrik Dal (Rab) 1 3Islami Jatiya Oikya Front 14Jatiya Party (manju) 1Krisak Sramik Janata League 1Bangladesh Worker’s Party 2Liberal Democratic Party (LDP) 1Bangladesh Jatiya Party (BJP) 1

Total 300 300 300

Notes: The 1996, 2001, and 2008 general elections were held, respectively, June 12, 1996; October 1, 2001;

and December 29, 2008. The constituencies won by the largest parties after each election are indicated by

bold numbers.

the parliament in the face of modest changes in partisan popularity. Swing constituencies are

therefore pivotal for the major parties to gain power. Electoral incentives that favor swing

districts in a majoritarian system have been well documented theoretically and empirically

(see, e.g., Stromberg, 2008; Banful, 2011).

2.2 Distributive Politics

Bangladesh has a unitary political system with a unicameral parliament and is fiscally highly

centralized. Each MP is entitled to propose the allocation of 150 million Taka (about 2.5

million U.S. dollars) as developmental grants for his or her constituency during the MP’s

parliamentary term. The grants can be used for infrastructure and development work in

the MP’s constituency, along with various social, educational, and religious activities. The

proposed use of these grants, however, is subject to the approval of the Local Government

Engineering Department (LGED) under the Ministry of Local Government, Rural Devel-

opment and Cooperatives. The LGED also monitors the implementation of the approved

8

Figure 1: Parliamentary Seat Shares and National Vote Shares of Two Major Parties

AL

BNP

AL

BNP

AL

BNP0

20

40

60

80

%

1996 2001 2008Election

AL (seat share) BNP (seat share)AL (vote share) BNP (vote share)

Notes: AL refers to the center-left Bangladesh Awami League. BNP indicates the center-right Bangladesh

Nationalist Party.

developmental projects. Each MP also has 600 tons of wheat or rice to allocate to his or

her constituency every six months. The grain could be distributed in the form of “food for

work” or poverty relief (Ahmed, 2012).

Each constituency on average has 182,000 voters during our sample period. The average

household consumption per capita in 2005 was $293 in our sample, which will be detailed in

the next section. Therefore, developmental grants amount to about 4.7% of a constituency’s

per-capita annual household consumption. Moreover, the in-kind grants amount to 6.6 kg

grain per voter per year.

In addition to the fixed amounts of cash and in-kind grants, MPs can also request addi-

tional grants. These special grants are often requested for infrastructure development, such

as building roads, bridges, schools, and religious centers. The LGED is also responsible for

approving these special grant requests. Therefore, MPs have considerable means to provide

constituency services. Moreover, MPs are powerful in local affairs. Frequently, “MPs dictate

instead of advising the affairs of local government” (Siddiqi and Ahmed, 2016). Government

9

MPs often appoint informal “coordinators” to oversee the implementation of development

projects when local officials are affiliated with opposition parties (Lewis and Hossain, 2017).

The two major political parties in Bangladesh lack distinct platforms; both are nation-

alistic and pledge anti-corruption measures. Though policy-making is highly centralized,

the parties are weakly institutionalized and rely on local elites to mobilize voters for sup-

port during elections and, often, through protests between elections (Islam, 2013; Ahsan and

Iqbal, 2017). Unsurprisingly, these conditions, which are not uncommon in young, developing

democracies, make it difficult for parties to credibly campaign on programmatic distributive

policies. Electorally motivated spending requires brokering by local patrons, whose social

connections ensure that electoral support at the polls and particularistic electoral promises

are delivered upon election. As is common in developing countries, patron-client relation-

ships have been central in the organization of Bangladesh society (Lewis, 2011). Patrons

are landlords, local business owners, local elected officials, village elders, political activists,

and other local elites who provide economic benefits, such as jobs, credit, protection from

physical violence, and judicial sanctions to their supporters or “clients” (Lewis and Hos-

sain, 2017). In return, clients offer general support and assistance, including turning out to

vote at the direction of their patrons. Patrons also mediate between citizens and MPs for

constituency services.

The effectiveness of turnout buying may depend on the party’s capacity to monitor bro-

kers (Larreguy et al., 2016). Partially to internalize such agency problems, the major parties

often nominate candidates who, in addition to past affiliations, have the local influence and

financial means to run expensive campaigns and mobilize voters. In recent parliaments,

businessmen and industrialists account for more than 50% of MPs (Amundsen, 2013).

Local elites may also seek office to enrich themselves as well. In election years, can-

didate application fees account for a large share of party income (Suri, 2007). National

newspapers have alleged corrupt activities that involve more than half of the MPs elected in

2008. By interviewing more than 600 local residents about 140 MPs in 2012, Transparency

International reported that more than 70% of MPs operated businesses, and 89% of these

businessmen obtained contracts for development projects in their constituencies. More than

10

70% of respondents also complained that MPs influence local public administration and

misuse development funds (Akram, 2012).

Clientelism is often linked to political monopoly and lack of electoral competition (Weitz-

Shapiro, 2012). However, electoral competition has yet to eliminate clientelistic practices

in Bangladesh. One explanation is the winner-take-all nature of Bangladesh’s polarized

politics. Sheikh Hasina and Khaleda Zia, the leaders of AL and BNP, respectively, have

ruled Bangladesh alternately since 1991 and have led their parties for even longer. Hasina

is the daughter of Mujib, the first (and assassinated) president of Bangladesh. Zia is the

widow of another assassinated president, Ziaur Rahman. With a personal vendetta between

the two party leaders, Bangladesh politics are antagonistic between the two dynastic parties,

which manifests in frequent boycotts of parliament sitting, violent protests, and road/railroad

blockages organized by opposition parties. Therefore, even without salient ethnic or religious

divisions, supporters of the two parties may allow inefficient policies and rent-extraction by

their leaders, fearing that the outcome under the opposition leader would be worse (Padro i

Miquel, 2007).

Clientelism in Bangladesh is also competitive in the sense that a single patron typically

is unable to dominate every local affair. As a complex web of informal social arrangements,

patronage networks may align with different parties, build on different kinship or social

relationships, and shift allegiances over time (Lewis, 2011). Competition among patrons

may also allow some rent sharing with clients within the patronage networks.

3 Data and Empirical Strategy

3.1 Data Description

We use data from various sources. Our main data source is the Household Income and

Expenditure Survey (HIES) of Bangladesh. The HIES is designed by the Bangladesh Bureau

of Statistics (BBS) to be comparable over time. We use 2000, 2005, and 2010 rounds for

measures of household consumption, access to publicly provided goods, and demographics.

11

We also use demographics from the 1995 round of the HIES for placebo tests.

In addition, we collect information on the Bangladesh National Parliamentary Elections

(candidate’s name, party affiliations, vote shares, and contested constituencies) from the

Election Commission of Bangladesh’s website and the Statistical Pocketbook by the BBS.

National elections in Bangladesh are usually held every five years, except for the 2008 elec-

tion, which was conducted seven years after the previous election. Election results for the

three years (1996, 2001, and 2008) are used in the analysis. We match the election results

from 1996 to HIES 2000, results from 2001 to HIES 2005, and results from 2008 to HIES

2010 via subdistricts identified in the HIES. Each constituency consists of one or more sub-

districts (known as upazilas or thanas), and one subdistrict may belong to more than one

constituency. Subdistricts consist of several unions, which are the smallest rural administra-

tive units in Bangladesh. For a small number of subdistricts that belong to more than one

constituency, we match the subdistrict to the constituency that contains a larger share of

the unions.

We also merge election results from 2001 and 2008 to subdistrict poverty rates in 2005

and 2010. The Bangladesh government sets two poverty lines. The higher one defines the

poverty rate, and the lower one defines the extreme poverty rate. Subdistrict-level poverty

rates for the years 2005 and 2010 are estimated by the BBS in collaboration with the World

Bank and World Food Program; poverty rates for 2000 are not available.

The HIES is a nationally representative survey, but it does not cover all constituencies.

Therefore, the number of constituencies in each wave is fewer than 300, which is the total

number of constituencies and therefore, we have an unbalanced panel. Table 2 shows the

number of households in each wave and provides summary statistics for the key variables

used in the empirical analysis. For instance, Bangladesh is young: the average age in our

sample is 26.8 years in 2005. The literacy rate has been low but is rising. Because of the

low literacy rate, ballot papers in Bangladesh typically use graphic symbols such as a boat

(AL) and paddy sheaf (BNP) to indicate the candidates’ affiliated parties.

Table 2 reports the mean and standard deviation of per-capita household consumption

by year. The figures are in 2010 dollars, after adjusting for inflation using CPI data from the

12

IMF. Household consumption refers to total household nondurable spending, including food

and nonfood consumption. Both food and nonfood consumption include goods and services

that are purchased, produced domestically, or received as gifts or in-kind wages.

Overall food and nonfood consumption adjusted for inflation has increased slightly over

time. Since 2000, average per-capita household consumption has been increasing, reaching

$438 in 2010. Slightly more than half of household consumption is in food; in rural areas, food

on average accounts for 62% of household consumption. The decline in average household

consumption from 1995 to 2000 is likely due to inflation adjustments using CPI, which may

be biased and noisy (Costa, 2001), rather than a decline in real consumption. The average

share of household consumption devoted to foods actually decreases from 1995 to 2000. The

well-established empirical relationship described by Engel curves suggests that households

on average became richer.

Beside household consumption, we also consider participation in social safety programs,

access to clean drinking water, improved sanitation, and electricity. The Bangladesh govern-

ment only started to set up social safety nets and welfare programs in recent years; before

2005, the HIES did not ask questions about program participation. In 2005, only 15% of

households had at least one member who participated in any such programs. Participation

increased to 24.5% in 2010. Programmatic redistribution programs are not supposed to

target geographically concentrated voters. In practice, however, access to these programs

through government agencies may be susceptible to clientelistic influence from local politi-

cians, as Weitz-Shapiro (2012) finds in Argentina.

The lack of clean drinking water and inadequate sanitation are leading causes of mortality

and disease in less-developed countries. The complementarity between clean drinking water

sources and improved sanitation, together with the positive epidemiological externalities of

clean water sources and improved sanitation, suggest that governments play a significant

role in basic public health infrastructure (Duflo et al., 2015). Tube wells have been a low-

cost way to obtain clean water, but the groundwater is often contaminated by naturally

occurring arsenic, continuous exposure to which is harmful. Given the spatial variation of

arsenic contamination within a village, a simple test-and-label process could ensure clean

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Table 2: Summary Statistics

1995 2000 2005 2010

Households

Age 24.247 25.768 26.823 28.435(9.694) (10.195) (10.772) (11.987)

Literacy 0.332 0.416 0.480 0.495(0.325) (0.353) (0.356) (0.323)

Female 0.503 0.501 0.504 0.513(0.185) (0.178) (0.180) (0.188)

Participation in Social Safety Programs - - 0.151 0.245- - (0.358) (0.430)

Access to Clean Drinking Water - - 0.583 0.559- - (0.493) (0.497)

Access to Improved Sanitation 0.324 0.350 0.530 0.523(0.468) (0.477) (0.499) (0.499)

Access to Electricity 0.284 0.371 0.459 0.575(0.451) (0.483) (0.498) (0.494)

Per-capita Household Consumption ($) 338.0 300.7 312.5 437.5(297.6) (256.2) (256.0) (351.0)

Per-capita Household Food Consumption ($) 187.0 159.2 166.8 233.0(104.9) (84.7) (82.4) (123.8)

Household Size 5.239 5.160 4.904 4.525(2.325) (2.193) (2.105) (1.876)

Number of Constituencies 242 230 258 237Number of Households 6,979 6,479 4,786 11,787

Subdistricts

Poverty Rate - - 0.417 0.308- - (0.163) (0.142)

Extreme Poverty Rate - - 0.268 0.174- - (0.142) (0.107)

Number of Subdistricts 423 421

Notes: The upper panel reports the means and standard deviations (in parentheses) of key variables in each

HIES wave as indicated by the column heading. Upper-panel statistics are at the household level, and the

lower panel includes subdistrict-level poverty rates. Age is the mean age in years in a household. Female is

the mean share of female in a household. Participation in Social Safety Programs is the share of residents who

report that their households benefited from a social safety program. Access to Clean Drinking Water is the

share of residents with access to pipe water or arsenic-tested tube-well water. Access to Improved Sanitation

is the share of residents with access to sanitary latrines. Literacy is the share of household members who can

read and write a letter. Per-capita (total and food) Household Consumption is an annual amount converted

to 2010 U.S. dollars.

14

drinking water (Bennear et al., 2013). Since the late 1990s, the Bangladesh government

initiated a mass program to test millions of tube wells throughout the country. We consider

piped water and arsenic-tested tube wells as sources of clean drinking water (other sources

of drinking water include ponds, rivers, waterfalls, and non-arsenic-tested tube wells). Our

summary statistics show that less than 60% of the sample households have access to clean

drinking water.

The National Sanitation Strategy (Local Government Division, 2005) set out the policy

for investing in and subsidizing in public sanitation infrastructure. Choudhury and Hossain

(2006) find that the majority of households set up sanitary latrines after receiving sanitary

materials from the government (61%) or the non-profit organization BRAC (21%). Therefore,

government intervention is an important driver for access to improved sanitation. We create

an indicator for access to improved sanitation using information based on the types of latrine

households use. Out of the six types of latrines used by households, a flush toilet, stable

(pacca) water-sealed latrine, and pit latrine are considered to be improved sanitation, while

an unstable (kacha) permanent latrine, temporary latrine, and open defecation are not. The

use of improved sanitation has increased from 32% to 52% between 1995 and 2010.

Similarly, the share of households that are connected to electricity shows a marked in-

crease from only 28.4% in 1995 to 57.5% in 2010, although a large proportion of households

still live without electricity. Finally, both poverty and extreme poverty rates have decreased

roughly 10 percentage points between 2005 and 2010.

3.2 Regression Discontinuity Design

To estimate the effects of having a parliamentary representative in the governing coalition

on household consumption, poverty, and access to government-provided goods and services,

we use a regression discontinuity (RD) design. A simple regression of outcome measures on

the indicator of having an MP in the governing coalition may provide biased estimates. The

error term in the regression may be correlated with both time-invariant and time-varying

factors that determine household outcomes. For example, places with different socioeconomic

15

conditions may support different parties, and hence have different likelihoods of having an

MP in the governing coalition. However, those economic conditions may also affect the

allocation of government spending and confound estimates of the causal impacts of having

a government MP.

Close elections provide quasi-experimental variation for identification via the RD design.

As long as there are some unpredicted random components that determine the vote shares

of candidates in an election, the treatment status in sufficiently close elections is assigned

as-if random (Lee, 2008). The RD design exploits this quasi-experimental variation. The

treatment we are interested in here is whether the representative of an electoral district (or

constituency) belongs to the governing coalition. To estimate the treatment effects on the

economic well-being of households in the constituency, we estimate the following equation:

Yijt = α + βGjt + f(Mjt) + µt + εijt, (1)

where i indicates household, j indicates constituency, and t indicates year of the HIES wave.

Yijt is the outcome variable of interest, such as per-capita household consumption. Gjt is

an indicator variable equal to one if, as a result of the previous election, constituency j

has a representative affiliated with the governing coalition at time t. Mjt is the margin

of victory of the candidate in the governing coalition, i.e., the vote share of the governing

coalition candidate over the most popular non-coalition candidate in the same race. If

Mjt is positive, Gjt = 1 and constituency j would have a representative in the governing

coalition; if Mjt is negative, Gjt = 0 and constituency j would not have a government MP.

f(Mjt) is a function of the running variable Mjt. In our preferable specification, we estimate

f(Mjt) nonparametrically using estimators from Calonico et al. (2014), which select the

optimal bandwidth by minimizing the means squared errors, adjust for asymptotic biases,

and provide robust inference. We also report estimates from specifications in which f(Mjt)

is linear or quadratic. Throughout this paper, standard errors are clustered by constituency.

µt is a survey year fixed effect to capture the different levels of the outcome variable over

time. εijt is an error term, and α and β are the coefficients to be estimated.

16

In a sharp RD design with a known cutoff, treatment status is a deterministic function

of the running variable. The only source of omitted variable bias in the neighborhood of

the cutoff is the running variable. In other words, the treatment status is selected on an

observable—namely, the running variable (Angrist and Rokkanen, 2015). Viewing an RD

design from this perspective, it is intuitive that the identification power of an RD design

for local average treatment effects extends to the identification of quantile treatment effects.

When treatment status is exogenous conditional on observables, the standard quantile regres-

sion proposed by Koenker and Bassett (1978) identifies the treatment effects on the various

quantiles of an outcome variable (Abadie et al., 2002). Frandsen et al. (2012) formally derive

conditions for nonparametric identification of quantile treatment effects in an RD design. In

Section 5, we extend the RD design to study how having a government MP changes the

different quantiles of the consumption distribution; namely, we study the quantile treatment

effects of having a government MP on the distribution of household consumption.

3.3 Tests of Identification Assumption

The identification assumption of a sharp RD design requires the continuity of unobservables

at the cut-off, which in conjunction with the running variable determines the treatment

status. Intuitively, this means that in the small neighborhood of the cut-off that determines

the treatment status, there are unpredictable random components that affect the running

variable. In our application, the running variable is the vote share margin of the governing

coalition candidate over his or her main competitor.

However, the identification assumptions of an RD design may be violated if the voting

results are manipulated. For example, an incumbent party may commit electoral fraud by

inflating their vote shares. Under the electoral system in Bangladesh, an incumbent party

would have particularly strong incentives to inflate the vote share of its candidate in a

constituency in which it is just below that of his or her opponent, since a small inflation

in the vote share of the incumbent government candidate could provide an extra seat to

the incumbent party. To test for potential electoral manipulation, McCrary (2008) suggests

17

examining the empirical density of the running variable. In particular, by assuming that the

distribution of the running variable is approximately continuous, a discontinuous jump of

empirical density around the electoral threshold indicates electoral manipulation.

The general elections of 1996, 2001, and 2008 resulted in the unseating of the incumbent

government by the new governing coalition. Since we define the margin of victory as the

vote share of the incoming governing party in the election over the incoming opposition,

constituencies on the left of the election threshold (Mjt < 0) were mainly won by the incum-

bent parties at the time of the election. In our context, therefore, a discretely higher density

on the left-hand side of the electoral threshold may indicate electoral manipulation of the

incumbent parties.

Figure 2: McCrary Density Test of Continuity at the Electoral Threshold

0

10

20

30

Num

ber o

f Con

stitu

enci

es

-1 -.5 0 .5 1Margin of Victory

Notes: The horizontal axis represents the vote-share margins of MP candidates from the governing parties

over their most popular competitors in the same electoral districts. The vertical axis indicates the num-

ber of constituencies in the 1996, 2001, and 2008 general elections. Each circle represents the number of

constituencies in a margin-of-victory bin of size 0.01. Purple dashed lines are local linear fits.

In Figure 2, we plot the density of the running variable. Each circle in the figure represents

the number of constituencies (y-axis) in a margin-of-victory bin of size 0.01 (x-axis) from the

general elections in 1996, 2001, and 2008. Following McCrary (2008), we choose a relatively

18

small bin size. The dashed lines on both sides of the electoral threshold indicate local linear

regressions of the numbers of constituencies in each bin on the midpoints of the margin-of-

victory bins.

Figure 2 does not show a discontinuous change in empirical density of the running variable

around the electoral threshold, which suggests no evidence of electoral manipulation. The

lack of manipulation is consistent with the fact that these elections have been certified by

domestic and international observers as free and fair (Chowdhury, 2009; Jahan, 2015), and

that election observers and the political establishment of both major parties in Bangladesh

were often surprised by the electoral outcomes (Ahmed and Ahmad, 2003; Chowdhury, 2009).

To further examine the validity of the RD identification assumptions, we perform a num-

ber of balancing tests. We replace the outcome variables in Equation (1) with predetermined

socioeconomic variables of constituencies, as well as the lagged outcome variables. Therefore,

constituency-level demographic variables from the 1995 HIES, 2000 HIES, and 2005 HIES

are regressed on 1996, 2001, and 2008 election outcomes, respectively. If the continuity

assumptions in our RD design are valid, having a representative in the governing coalition

should not significantly predict the predetermined variables once we control for the running

variable. As shown in Table 3, the partisan affiliations of MPs do not significantly predict a

constituency’s demographics, consumption, or access to government-provided goods before

the election. The coefficient estimate of household size is statistically significant at the 10%

level. However, given the large number of regressions reported in Table 3, this could happen

by chance.

4 Distributional Effects across Constituencies

4.1 Household Consumption

Due to domestic production of food and inaccurate reporting of income, it is well known that

household consumption is a better measure of economic well-being than income (Deaton,

1997; Banerjee and Duflo, 2007). To investigate how having a government MP affects mate-

19

Table 3: Balancing Tests of Predetermined Variables

Coefficient Std. Error # Obs.

Age 0.728 (0.637) 17,938Literacy -0.073 (0.051) 17,938Female -0.018 (0.011) 17,938Poverty Rate -0.052 (0.072) 399Extreme Poverty Rate -0.069 (0.056) 399Participation in Social Safety Programs -0.042 (0.065) 3,821Access to Clean Drinking Water 0.114 (0.174) 4,500Access to Improved Sanitation 0.051 (0.090) 17,938Access to Electricity 0.054 (0.097) 17,938Per-capita Household Consumption ($) -3.646 (47.94) 17,938Per-capita Household Consumption (log) 0.009 (0.110) 17,938Per-capita H.H. Food Consumption (log) 0.045 (0.082) 17,938Per-capita H.H. Nonfood Consumption (log) -0.023 (0.155) 17,938Household Size 0.308* (0.170) 17,938

Notes: Each row reports one nonparametric RD estimate for the coefficient of “GovMP,” which equals

one if the household resides in a constituency represented by a government MP and zero otherwise. The

running variable is the vote-share margins of MP candidates from the governing parties over their most

popular competitors in the same electoral districts. The second and third columns report the RD estimates

and robust standard errors of Calonico et al. (2014). Standard errors are clustered by constituency. The

last column reports the number of observations. Different rows report estimates with different pre-election

dependent variables, which are indicated in the first column and are defined similarly in Table 2. In the

regressions of poverty and extreme poverty rates, the observations are 2005 subdistrict poverty rates and

2008 election outcomes. Household characteristics from the 1995, 2000, and 2005 surveys are matched,

respectively, with 1996, 2001, and 2008 election outcomes. All regressions include year fixed effects.∗ p < 0.10; ∗∗ p < 0.05; ∗∗∗ p < 0.01.

rial well-being, we focus on per-capita household consumption. In Figure 3, we plot different

measures of per-capita household consumption against the governing party’s margin of vic-

tory. Each circle represents the average of households in a bin spanning 0.02 vote-share

margins.

In the top-left subplot, household consumption per capita is measured in 2010 dollars; in

the top-right subplot, per-capita consumption takes log values. In both subplots, household

consumption is considerably higher just to the right-hand side of the election threshold zero

than just to the left-hand side of the threshold. These RD graphics provide visual evidence

that having a government MP increases household consumption.

Furthermore, Table 4 reports the coefficient estimates of the binary variable that indicates

20

Figure 3: Government MPs and Household Consumption

0

1000

2000

3000

4000

5000

Num

ber o

f Hou

seho

lds

in E

ach

Bin

200

300

400

500

Hou

seho

ld C

onsu

mpt

ion

per c

apita

($)

-.4 -.2 0 .2 .4Margin of Victory

0

1000

2000

3000

4000

5000

Num

ber o

f Hou

seho

lds

in E

ach

Bin

5

5.2

5.4

5.6

5.8

6

Hou

seho

ld C

onsu

mpt

ion

per c

apita

(log

)

-.4 -.2 0 .2 .4Margin of Victory

0

1000

2000

3000

4000

5000

Num

ber o

f Hou

seho

lds

in E

ach

Bin

4.6

4.8

5

5.2

5.4

Food

Con

sum

ptio

n pe

r cap

ita (l

og)

-.4 -.2 0 .2 .4Margin of Victory

0

1000

2000

3000

4000

5000

Num

ber o

f Hou

seho

lds

in E

ach

Bin

4

4.2

4.4

4.6

4.8

5

Non

food

Con

sum

ptio

n pe

r cap

ita (l

og)

-.4 -.2 0 .2 .4Margin of Victory

Notes: Horizontal axes represent the vote-share margins of MP candidates from the governing parties over

their most popular competitors in the same electoral districts. Vertical axes of the subplots on the left

indicate measures of household consumption. From top left to bottom right, the vertical axes represent

per-capita annual household consumption in 2010 dollars; log per-capita total household consumption; log

per-capita household food consumption; and log per-capita household nonfood consumption. Each circle is

an average over districts in the margin-of-victory bin of size 0.02. Gray dashed lines are local linear fits.

Green bars at the bottom of each subplot indicate the number of constituencies in each bin.

a governing party constituency. In the first row, the outcome variable is the annual per-

capita household consumption in 2010 dollars; in the second row, the outcome variable is

the log per-capita household consumption. Column (1) reports estimates from our preferred

specification using local linear regressions and robust standard errors from Calonico et al.

(2014). Having a government MP increases household consumption by $72, or 20% (18.4 log

points). Both estimates are statistically significant at the 5% level.

For comparison, we also report estimates from parametric specifications in Columns (2)

to (5). In Column (2), the running variable is controlled through two linear terms: one

for values to the left of the zero cutoff and another for values to the right. In Column

21

(3), the running variable is similarly controlled by two quadratic terms. For both dollar

and log values of consumption, the quadratic specifications give slightly higher estimates

than the local linear regressions, while the linear specifications give lower estimates. The

quadratic specification suggests 23.7% (21.3 log points) increase in consumption while the

linear specification suggests an 11% increase.

Although the balancing tests in Table 3 show that pre-existing differences in house-

hold consumption between government constituencies and non-government constituencies

are small, statistically insignificant, and are not always positive, one may be concerned that

the differences affect our estimation in the finite sample. In Columns (4) and (5), we add

constituency fixed effects to the linear and quadratic specifications. These specifications

are very taxing for estimation as they rely on within-constituency variation as well as close

elections for identification. However, estimates from these specifications are similar to those

obtained from our preferred nonparametric specification and remain statistically significant.

In particular, the quadratic specifications also provide estimates close to the nonparametric

estimates. Given the caveats of Gelman and Imbens (2017), we refrain from using high-order

polynomials for the running variable.

For a low-income country such as Bangladesh, food consumption on average accounts for

more than half of total household consumption, and many poor households suffer malnutri-

tion. We decompose the household consumption into food and nonfood consumption to see

how having a government MP affects these two categories of consumption. In the bottom left

subplot of Figure 3, we show log per-capita household food consumption against the margin

of victory of the governing party candidates; in the bottom right subplot of Figure 3, we plot

the log per-capita household nonfood consumption. Discrete increases are visible in both

plots as the circles move rightward across the electoral cutoff for having a government MP.

The increase appears to be larger for nonfood consumption; indeed, the nonparametric RD

estimates suggest that having a government MP increases food consumption by about 11%,

but increases nonfood consumption by about 30%. Moreover, the estimate is statistically

significant at the 1% level for nonfood consumption but only significant at the 10% level for

food consumption.

22

Tab

le4:

Gov

ernm

ent

MP

san

dH

ouse

hol

dC

onsu

mpti

on

(1)

(2)

(3)

(4)

(5)

Per

-cap

ita

HH

Con

sum

pti

on($

)71

.7**

45.2

***

82.7

***

49.3

**59

.7**

(28.

2)(1

6.8)

(22.

0)(2

0.8)

(28.

8)

Per

-cap

ita

HH

Con

sum

pti

on(l

og)

0.18

4**

0.10

8**

0.21

3***

0.10

9***

0.15

4***

(0.0

74)

(0.0

43)

(0.0

60)

(0.0

41)

(0.0

56)

Per

-cap

ita

HH

Food

Con

sum

pti

on(l

og)

0.11

2*0.

070*

*0.

128*

**0.

063*

0.10

4**

(0.0

61)

(0.0

33)

(0.0

46)

(0.0

34)

(0.0

45)

Per

-cap

ita

HH

Non

food

Con

sum

pti

on(l

og)

0.29

9***

0.15

9**

0.32

9***

0.16

8***

0.21

3***

(0.1

01)

(0.0

63)

(0.0

88)

(0.0

59)

(0.0

80)

Runnin

gV

aria

ble

Non

par

amet

ric

Lin

ear

Quad

rati

cL

inea

rQ

uad

rati

cY

ear

Fix

edE

ffec

tY

YY

YY

Con

stit

uen

cyF

ixed

Eff

ect

YY

Num

ber

ofO

bse

rvat

ions

22,9

6922

,969

22,9

6922

,969

22,9

69

Not

es:

Th

eta

ble

abov

ere

por

tsth

eco

effici

ent

esti

mate

sof

an

ind

icato

rva

riab

leth

at

equ

als

on

eif

the

hou

seh

old

resi

des

ina

con

stit

uen

cyre

pre

sente

d

by

anM

Pfr

omth

ego

vern

ing

part

ies,

and

zero

oth

erw

ise.

Each

cell

rep

ort

son

ees

tim

ate

from

on

ere

gre

ssio

n.

Fro

mth

eto

pto

bott

om

pan

els,

the

outc

ome

vari

able

sar

e,re

spec

tive

ly,

(i)

annu

alp

er-c

ap

ita

tota

lh

ou

seh

old

(HH

)co

nsu

mp

tion

in2010

doll

ars

;(i

i)lo

gp

er-c

ap

ita

tota

lH

Hco

nsu

mp

tion

;

(iii

)lo

gp

er-c

apit

aH

Hfo

od

con

sum

pti

on;

and

(iv)

log

per

-capit

aH

Hn

on

food

con

sum

pti

on.

Colu

mn

sre

pre

sent

how

the

runn

ing

vari

ab

le,

the

marg

in

ofvic

tory

ofth

ego

vern

ing

par

tyM

P,

and

oth

erco

ntr

ol

vari

ab

les

are

mod

eled

.In

Colu

mn

(1),

the

run

nin

gva

riab

leis

mod

eled

non

para

met

rica

lly

usi

ng

loca

lli

nea

rre

gres

sion

(Cal

onic

oet

al.,

2014

).In

Colu

mn

s(2

)an

d(4

),tw

ose

para

teli

nea

rte

rms

of

the

run

nin

gva

riab

leare

incl

ud

edas

regre

ssors

,on

e

toth

ele

ftof

the

cuto

ffan

don

eto

the

righ

tof

the

cuto

ff.

InC

olu

mn

s(3

)an

d(5

),qu

ad

rati

cte

rms

of

the

run

nin

gva

riab

leare

incl

ud

ed.

All

regre

ssio

ns

incl

ud

eye

arfi

xed

effec

ts.

Col

um

ns

(4)

and

(5)

als

oin

clu

de

con

stit

uen

cyfi

xed

effec

ts.

Sta

nd

ard

erro

rsin

pare

nth

eses

are

clu

ster

edby

const

itu

ency

.∗p<

0.10

;∗∗p<

0.0

5;∗∗∗p<

0.01

.

23

As shown in Columns (2) to (5), the parametric estimates are qualitatively similar to

the nonparametric estimates, but are more precisely estimated. Again, the quadratic spec-

ifications provide estimates close to the nonparametric ones, and having a government MP

increases nonfood consumption more than food consumption.2

4.2 Poverty

Having a government MP also reduces poverty rates. Figure 4 plots the poverty rates against

the governing party’s margin of victory. The subplot on the left plots poverty rates set by

the upper poverty line. The subplot on the right plots extreme poverty rates, which are set

using the lower poverty line. Patterns from both the subplots are similar: there is a sharp

drop in poverty rates as one moves from the left-hand side of the cutoff to the right-hand

side of the cutoff.

The nonparametric RD estimates reported in Table 5 suggest that having a government

MP reduces the poverty rate by about 12 percentage points. The reduction in the extreme

poverty rate is slightly lower, at about 10 percentage points. Both estimates are statistically

significant at the 1% level.

2We also run the main regression on household consumption for each election year to check whether ourfinding is due to the party alignment or due to a specific party behavior since the two parties alternated inpower in every election. The non-parametric estimates are positive but imprecisely estimated for the firsttwo waves (2000 and 2005), and negative, small, and imprecisely estimated for the last wave (2010). Theparametric estimates are all positive and mostly statistically significant at the conventional level. Thus,there is no clear evidence that our finding is driven by a specific party behavior.

24

Figure 4: Government MPs and Poverty Rates

0

50

100

150

200

250

300

350

Num

ber o

f Sub

-dis

trict

s in

Eac

h Bi

n

.1

.2

.3

.4

.5

Pove

rty R

ate

-.4 -.2 0 .2 .4Margin of Victory

0

50

100

150

200

250

300

350

Num

ber o

f Sub

-dis

trict

s in

Eac

h Bi

n

.1

.2

.3

.4

.5

Extre

me

Pove

rty R

ate

-.4 -.2 0 .2 .4Margin of Victory

Notes: Horizontal axes represent the vote-share margins of MP candidates from the governing parties over

their most popular competitors in the same electoral districts. The vertical axis of the left subplot indicates

poverty rates in the subdistricts. The vertical axis of the right subplot indicates extreme poverty rates in the

subdistricts. Each circle is an average over districts in the margin-of-victory bin of size 0.02. Gray dashed

lines are local linear fits. Green bars at the bottom of each subplot indicate the number of subdistricts in

each bin.

Table 5: Government MPs and Poverty Rates

Poverty Rate Extreme Poverty Rate

GovMp -0.124*** -0.104***(0.044) (0.035)

Number of Subdistricts (N) 844 844

Notes: The table above reports the coefficient estimates of an indicator variable that equals one if the

household resides in a constituency represented by an MP from the governing parties, and zero otherwise.

The running variable is modeled nonparametrically using local linear regression (Calonico et al., 2014). In

the left-hand column, the dependent variable is the subdistrict-level poverty rate; in the right-hand column,

the dependent variable is the subdistrict-level extreme poverty rate. All regressions include year fixed effects.

Standard errors in parentheses are clustered by constituency.∗ p < 0.10; ∗∗ p < 0.05; ∗∗∗ p < 0.01.

25

4.3 Publicly Provided Goods and Services

Transfers from the national government to the constituencies can take many forms. In addi-

tion to local development projects, food-for-work grants, and poverty-relief food grants, MPs

may also increase the publicly provided goods and services to their constituencies. In this

section, we investigate whether having a government MP increases households’ participation

in social safety programs and access to basic infrastructures such as clean drinking water,

sanitary latrines, and electricity.

Figure 5: Government MPs and Publicly Provided Goods and Services

0

1000

2000

3000

4000

5000N

umbe

r of H

ouse

hold

s in

Eac

h Bi

n

0

.2

.4

.6

Soci

al S

afet

y Pr

ogra

m P

artic

ipat

ion

-.4 -.2 0 .2 .4Margin of Victory

0

1000

2000

3000

4000

5000

Num

ber o

f Hou

seho

lds

in E

ach

Bin

0

.2

.4

.6

.8

Acce

ss to

Cle

an D

rinki

ng W

ater

-.4 -.2 0 .2 .4Margin of Victory

0

1000

2000

3000

4000

5000

Num

ber o

f Hou

seho

lds

in E

ach

Bin

0

.2

.4

.6

.8

Acce

ss to

Impr

oved

San

itatio

n

-.4 -.2 0 .2 .4Margin of Victory

0

1000

2000

3000

4000

5000

Num

ber o

f Hou

seho

lds

in E

ach

Bin

0

.2

.4

.6

.8

Acce

ss to

Ele

ctric

ity

-.4 -.2 0 .2 .4Margin of Victory

Notes: Horizontal axes represent the vote-share margins of MP candidates from the governing parties over

their most popular competitors in the same electoral districts. Vertical axes in each subplot indicate the

share of population access to public infrastructure. From top left to bottom right, the public goods are,

respectively, social safety program participation, access to clean drinking water, access to sanitary toilets,

and access to electricity. Each circle is an average over districts in the margin-of-victory bin of size 0.02.

Gray dashed lines are local linear fits. Green bars at the bottom of each subplot indicate the number of

households in each bin.

In Figure 5, we first provide four RD graphs, one for each outcome variable. From top left

to bottom right, the subplots show safety net program participation, access to clean drinking

26

water, access to improved sanitation, and electricity. It appears that having a government

MP has some positive impacts on access to improved sanitation and electricity. However,

these RD graphs are noisier than those for household consumption. To formally estimate

the effects of having a government MP on publicly provided goods, we report the estimated

coefficients of the binary variable that indicates whether a constituency’s MP belongs to the

governing coalition in Table 6, which is similar to Table 4 in structure. In Column (1), we

report the nonparametric RD estimates. In Columns (2) to (5), we report estimates using

parametric assumptions for how the running variable affects the outcome variables.

In the top row, the outcome variable is a binary variable that indicates whether the house-

hold has a member participating in any social safety programs. The RD estimate suggests

that having a government MP has a positive but statistically insignificant impact on program

participation. The imprecision of the estimate may be because program participation is only

surveyed in the last two waves of HIES. The availability of, and hence participation in, social

safety programs was very limited before 2005. The parametric estimates from Columns (2),

(4), and (5) in the first rows are positive, but are only statistically significant at the 5%

level in Column (4), where the running variable enters in linear terms and constituency fixed

effects are included. If a government MP transfers more payments to the patronage networks

that support him or her, program participation rate may not increase. Estimates in the top

row suggest that the extensive margin of social safety programs is not the primary channel

through which a government MP reduces poverty and increases consumption.

We also do not find that having a government MP has a statistically significant impact on

household access to clean drinking water; the RD-estimated impact is positive but imprecise.

A potential reason for the lack of statistical precision may be the nonparametric estimation.

In Columns (2) to (5) of Table 6, we also report parametric estimates similar to those reported

in Table 4. The estimated effect on access to clean drinking water becomes statistically

significant at the 5% level in the linear and quadratic specifications without constituency

fixed effects. With a standard error of 0.093, the quadratic estimate suggests that having a

government MP increases a household’s probability of access to clean drinking water by 19

percentage points. Estimates from specifications with constituency fixed effects are positive

27

but not statistically significant.

For access to improved sanitation, the nonparametric estimate suggests that a government

MP has a positive but statistically insignificant effect. With one exception, parametric

estimates provide similar estimates: positive but not precisely estimated. In Column (3),

the quadratic specification without constituency fixed effects, the estimated effect on access

to improved sanitation is 15.6 percentage points and significant at the 5% level.

For access to electricity, the nonparametric estimate is also positive but statistically

insignificant. Three out of four parametric estimates, however, are significant at the 1%

level, and one is significant at the 10% level. The estimated effect varies from 8.7 to 21.1 log

points, depending on the specification.

These findings are consistent with a survey that found that respondents are substantially

more satisfied with government MPs than other MPs in fulfilling electoral promises, rep-

resenting the constituencies in the parliament, and promoting local development initiatives

(Akram, 2012). However, it appears that government MPs are more likely to increase the

provision of goods that are easier to target and more privately consumed. While access

to clean drinking water, particularly water delivered by the arsenic-tested tube wells, is a

local public good, the benefits may not be immediate or apparent. Ahmad et al. (2005) find

low willingness to pay for arsenic-free drinking water in rural Bangladesh. Although the

collective use of sanitary latrines is a public good, because improved sanitation has positive

externalities (Duflo et al., 2015), subsidized sanitary latrines are typically installed inside a

household and hence are primarily private goods. Access to electricity at the village level is

a public good; however, access to electricity inside a house is a private good. A government

MP may help with overcoming the bureaucratic red tape, which is a common barrier in

connecting to electricity.

28

Tab

le6:

Gov

ernm

ent

MP

san

dP

ublicl

yP

rovid

edG

oods

and

Ser

vic

es

(1)

(2)

(3)

(4)

(5)

Par

tici

pat

ion

in0.

041

0.05

0*-0

.024

0.10

8**

0.06

2Soci

alSaf

ety

Pro

gram

s(0

.060

)(0

.029

)(0

.039

)(0

.046

)(0

.061

)N

=15,7

27N

=15,7

27N

=15,7

27N

=15,7

27N

=15,7

27

Acc

ess

toC

lean

Dri

nkin

gW

ater

0.08

00.

150*

*0.

189*

*0.

062

0.11

6(0

.133

)(0

.064

)(0

.093

)(0

.061

)(0

.086

)N

=16,4

90N

=16,4

90N

=16,4

90N

=16,4

90N

=16,4

90

Acc

ess

toIm

pro

ved

San

itat

ion

0.05

70.

071

0.15

6**

0.05

7*0.

050

(0.1

14)

(0.0

50)

(0.0

72)

(0.0

33)

(0.0

47)

N=

22,9

69N

=22,9

69N

=22,9

69N

=22,9

69N

=22,9

69

Acc

ess

toE

lect

rici

ty0.

065

0.08

8*0.

211*

**0.

087*

**0.

109*

**(0

.081

)(0

.047

)(0

.064

)(0

.024

)(0

.032

)N

=22,9

69N

=22,9

69N

=22,9

69N

=22,9

69N

=22,9

69

Runnin

gV

aria

ble

Non

par

amet

ric

Lin

ear

Quad

rati

cL

inea

rQ

uad

rati

cY

ear

Fix

edE

ffec

tY

YY

YY

Con

stit

uen

cyF

ixed

Eff

ect

YY

Not

es:

Th

eta

ble

abov

ere

por

tsth

eco

effici

ent

esti

mate

sof

an

ind

icato

rva

riab

leth

at

equ

als

on

eif

the

hou

seh

old

resi

des

ina

con

stit

uen

cyre

pre

sente

d

by

anM

Pfr

omth

ego

ver

nin

gp

arti

es,

and

zero

oth

erw

ise.

Each

cell

rep

ort

son

ees

tim

ate

from

on

ere

gre

ssio

n.

Fro

mto

pto

bott

om

pan

els,

the

ou

tcom

e

vari

able

sar

e,re

spec

tive

ly,

(i)

anin

dic

ator

vari

able

that

equ

als

on

eif

the

hou

seh

old

part

icip

ate

sin

any

soci

al

safe

typ

rogra

ms;

(ii)

an

ind

icato

rva

riab

le

that

equ

als

one

ifth

eh

ouse

hol

dh

asac

cess

tocl

ean

dri

nkin

gw

ate

r;(i

ii)

an

ind

icato

rva

riab

leth

at

equ

als

on

eif

the

hou

seh

old

has

acc

ess

san

itary

latr

ines

;an

d(i

v)

anin

dic

ator

vari

able

that

equ

als

on

eif

the

hou

seh

old

has

acc

ess

toel

ectr

icit

y.C

olu

mn

sre

pre

sent

how

the

runn

ing

vari

ab

le,th

em

arg

in

ofvic

tory

ofth

ego

vern

ing

par

tyM

P,

and

oth

erco

ntr

ol

vari

ab

les

are

mod

eled

.In

Colu

mn

(1),

the

run

nin

gva

riab

leis

mod

eled

non

para

met

rica

lly

usi

ng

loca

lli

nea

rre

gres

sion

(Cal

onic

oet

al.,

2014

).In

Colu

mn

s(2

)an

d(4

),tw

ose

para

teli

nea

rte

rms

of

the

run

nin

gva

riab

leare

incl

ud

edas

regre

ssors

,on

e

toth

ele

ftof

the

cuto

ffan

don

eto

the

righ

tof

the

cuto

ff.

InC

olu

mn

s(3

)an

d(5

),qu

ad

rati

cte

rms

of

the

run

nin

gva

riab

leare

incl

ud

ed.

All

regre

ssio

ns

incl

ud

eye

arfi

xed

effec

ts.

Col

um

ns

(4)

and

(5)

als

oin

clu

de

con

stit

uen

cyfi

xed

effec

ts.

Sta

nd

ard

erro

rsin

pare

nth

eses

are

clu

ster

edby

const

itu

ency

.∗p<

0.10

;∗∗p<

0.0

5;∗∗∗p<

0.01

.

29

5 Quantile Treatment Effects on the Distribution of

Household Consumption

5.1 Quantile Treatment Effects from an RD Design

The previous section shows that having a government MP is on average beneficial to house-

holds in the constituency. However, it is unclear how having a government MP affects the

distribution of economic well-being. If extra public spending is primarily allocated to poverty

reduction, or poor voters are the main group targeted in the clientelistic transfers, then one

might expect that having an MP in the governing party mainly improves economic well-being

at the low end of the consumption distribution.

On the other hand, if a few patrons and local elites extract most of the rents from electoral

transfers and politicians use their positions to profit, then having a government MP may not

have strong positive effects throughout the consumption distribution. Moreover, if patrons,

brokers, and affluent households share the rents with less-affluent households but benefit the

most from having a government MP, this may shift the consumption distribution up, but

more so at high quantiles than elsewhere.

In this section, we investigate the distributive implications of having a government MP.

Again, we follow the literature to focus on household consumption as our measure of living

standards and economic well-being. Taking advantage of the HIES microdata, we extend

our RD design to examine quantile treatment effects on consumption.

To understand the distributive effects of having an MP in the governing coalition, we

estimate a set of conditional quantile regressions based on the following specification:

Qτ (Yijt) = ατ + δτGjt + fτ (Mjt) + µτ,t, (2)

where Gjt and Mjt are defined as before, i.e., a binary variable that indicates a governing

party constituency and the margin of victory, respectively; fτ (Mjt) is a function of the

running variable Mjt; µτ,t is a year fixed effect; Yijt is the log per-capita annual consumption

30

of household i in constituency j during year t; and Qτ (Yijt) is the τ -quantile of Yijt conditional

on Gjt, Mjt, and t:

Qτ (Yijt) = inf{y : Pr(Yijt < y|Gjt, Mjt, t) = τ}, for τ ∈ (0, 1).

The coefficients ατ and δτ , the function fτ (·), and year fixed effects µτ,t are all quantile-

specific; hence the subscript τ .

A quantile treatment effect (QTE) is the difference in the outcome variable between the

treated group and the control group at the τ -quantile. In our RD design, the key coefficient

δτ captures the QTE for the τ -quantile. In the framework of heterogeneous treatment effects

introduced by Imbens and Angrist (1994) and extended to the QTEs by Abadie et al. (2002)

and Frandsen et al. (2012), δτ captures the local QTE for the τ -quantile at Mjt = 0.

To estimate the QTEs on household consumption, we offer three specifications. First,

we estimate the conditional quantile model of Koenker and Bassett (1978). Given that

previous results suggest that specifications that control for quadratic running variables yield

estimates similar to nonparametric RD estimates, we use two quadratic polynomials for the

running variable, with one on each side of the cutoff zero. Using linear specifications gives

qualitatively similar results, which are not reported for brevity.

Second, we estimate the QTEs of government MP using a two-step estimator. In our

setting, treatment status varies at a group level and we have microlevel data. To estimate

conditional mean equations, least-squares estimators remain consistent in the presence of

classical measurement errors in the left-hand-side variables or group-level unobservables un-

correlated with the right-hand-side observables. This feature does not carry over to the

standard quantile regression estimator of Koenker and Bassett (1978).

An alternative estimator of QTEs on household consumption is a two-step grouped quan-

tile IV estimator proposed by Chetverikov et al. (2016). Since we are interested in uncon-

ditional QTEs, the first step of the grouped IV quantile estimator reduces to calculating

the quantiles within each group—i.e., a constituency-year—in our setting. In the second

step, because we extend the RD design to the quantile setting, selection into treatment is

31

exogenous conditional on the running variable. Therefore, the 2SLS in the second step is

reduced to OLS.

This two-step estimator yields a consistent estimate, even in the presence of group-level

unobservables or classical measurement errors of the left-hand-side variable (Chetverikov

et al., 2016). The consistency of this two-step estimator, however, relies on the number of

observations in each group, with the number of groups being large; however, the former needs

to grow to infinity at only a modest rate. In our sample, a constituency-year on average

includes 30 households. We use the two-step estimator with two specifications. In the first

specification, we control for the running variable using two quadratic terms as before, in the

second (OLS) step. In the second specification, we use a nonparametric estimator in the

second step and run local linear regression on both sides of the cutoff—namely, the Calonico

et al. (2014) estimator similar to those adopted previously in this paper.

5.2 Quantile Treatment Effects on Household Consumption

In Figure 6, we plot the estimates of δτ , for τ = 0.05, 0.10, · · · , 0.95 using blue solid lines.

Gray dashed lines indicate the 95% confidence intervals of the associated QTEs estimates,

which are based on standard errors robust to clustering by constituency. For the Koenker

and Bassett (1978) estimates in the first row, we calculate the bootstrap standard errors by

re-sampling constituencies for 250 repetitions. For the two-step estimates, standard errors

are the usual cluster-robust standard errors from the second step.

Subplots in the top row report QTEs from the traditional Koenker and Bassett (1978)

estimator. Subplots in the middle report QTEs using the two-step estimator and a quadratic

specification for the running variable in the second step. Subplots in the bottom row report

QTEs using the two-step estimator, in which the second step is nonparametric.

Subplots in the first column show QTEs on total household consumption measured by

log household consumption per capita. Subplots in the middle column plot the QTEs on

the food consumption measured by log per-capita food consumption. Subplots in the right

column plot the QTEs on the nonfood consumption, in which the outcome variable is log

32

per-capita nonfood consumption.

If we look at the top row of subplots, for the bottom half of the household consumption

distribution, QTEs are fairly stable and significant at the 5% level. Having an MP in

the governing coalition increases the median, as well as other quantiles below 0.5, of per-

capita household consumption by about 25%. For the 0.05-quantile, QTEs of having an

MP in the governing coalition are slightly lower. However, as one moves above the median,

QTEs increase with the quantile τ . The 0.9 and 0.95 quantiles of per-capita households

consumption are about 40% to 50% higher in government constituencies than elsewhere.

Estimated QTEs for the quantiles above 0.5 are all significant at the 5% level.

Profiles of QTEs on household consumption from the two-step estimators similarly have

an upward gradient as one moves from low quantiles to high quantiles. QTE estimates

from the first subplot in the second row are steadily increasing. QTE estimates from the

first subplot in the last row are quite stable from the 0.05-quantile to the 0.80-quantile,

but shapely increase as they move above the 0.80-quantile. QTE profiles from different

specifications suggest that while the low end of consumption distribution benefits from having

a government MP, the high end of consumption distribution increases more than the low end.

The increase in household consumption in the governing party constituencies is primarily

driven by nonfood consumption. Throughout the three specifications, the profiles of QTEs

on food consumption are fairly flat and only slightly upward sloping. In our sample, food

consumption accounts for about 60% of household consumption.

QTEs on nonfood consumption are considerably higher than those on food consumption.

For food consumption, QTEs in the bottom half of the distribution are always positive and

mostly statistically significant at the 5% level. The QTEs on nonfood consumption are

always positive and significant at the 5% level. From the 10th to the 50th percentiles, the

QTEs on both food and nonfood consumption are fairly stable. However, QTEs on nonfood

consumption increase as we move above the 50th percentile. At the 90th percentile, being a

governing party constituency increases per-capita household nonfood consumption by more

than 50%. Since the average treatment period is about four years, annualized QTEs on

nonfood consumption are more than 10%.

33

Fig

ure

6:D

istr

ibuti

onal

Impac

tson

Per

-Cap

ita

Hou

sehol

dC

onsu

mpti

on

0.2.4.6.8

Quantile Treatment Effect

0.2

.4.6

.81

Qua

ntile

Tota

l Con

sum

ptio

n

0.2.4.6.8

Quantile Treatment Effect

0.2

.4.6

.81

Qua

ntile

Food

Con

sum

ptio

n

0.2.4.6.8

Quantile Treatment Effect

0.2

.4.6

.81

Qua

ntile

Non

food

Con

sum

ptio

n

0.2.4.6.8

Quantile Treatment Effect

0.2

.4.6

.81

Qua

ntile

Tota

l Con

sum

ptio

n

0.2.4.6.8

Quantile Treatment Effect

0.2

.4.6

.81

Qua

ntile

Food

Con

sum

ptio

n

0.2.4.6.8

Quantile Treatment Effect

0.2

.4.6

.81

Qua

ntile

Non

food

Con

sum

ptio

n

0.2.4.6.8

Quantile Treatment Effect

0.2

.4.6

.81

Qua

ntile

Tota

l Con

sum

ptio

n

0.2.4.6.8

Quantile Treatment Effect

0.2

.4.6

.81

Qua

ntile

Food

Con

sum

ptio

n

0.2.4.6.8

Quantile Treatment Effect

0.2

.4.6

.81

Qua

ntile

Non

food

Con

sum

ptio

n

Not

es:

Th

efi

gure

sab

ove

plo

tth

ees

tim

ated

coeffi

cien

tsof

ab

inary

vari

ab

lefo

rh

avin

ga

rep

rese

nta

tive

inth

egov

ern

ing

coali

tion

from

anu

mb

erof

qu

anti

lere

gres

sion

sw

ith

vary

ing

qu

anti

les.

Dep

end

ent

vari

ab

les

inth

equ

anti

lere

gre

ssio

ns

on

the

left

,m

idd

le,

an

dri

ght

sub

plo

tsare

,re

spec

tive

ly,

log

per

-cap

ita

hou

seh

old

con

sum

pti

on,

log

per

-cap

ita

food

con

sum

pti

on

,an

dlo

gp

er-c

ap

ita

non

food

con

sum

pti

on

.E

xp

lan

ato

ryva

riab

les

incl

ud

ea

bin

ary

vari

able

for

hav

ing

are

pre

senta

tive

inth

egov

ern

ing

coali

tion

an

dye

ar

ind

icato

rs.

Th

esp

ecifi

cati

on

sin

the

top

two

row

sco

ntr

ol

for

the

run

nin

g

vari

able

wit

ha

qu

adra

tic

pol

yn

omia

lfo

rea

chsi

de

of

the

elec

tora

lth

resh

old

;th

esp

ecifi

cati

on

sin

the

bott

om

row

contr

ol

for

the

run

nin

gva

riab

le

wit

hlo

cal

lin

ear

regr

essi

ons.

Th

eru

nn

ing

vari

able

isth

em

arg

inof

vic

tory

of

the

gov

ern

men

tp

art

yca

nd

idate

.Q

uanti

les

vary

from

0.0

5to

0.9

5an

d

are

rep

rese

nte

dby

the

hor

izon

tal

axis

.T

he

vert

ical

axis

pro

vid

esth

esc

ale

for

the

coeffi

cien

tes

tim

ate

s,in

dic

ate

dby

the

blu

eli

ne.

95%

con

fid

ence

inte

rval

s,w

hic

har

ein

dic

ated

by

the

das

hed

lin

es,

are

con

stru

cted

usi

ng

stan

dard

erro

rsro

bu

stto

clu

ster

ing

by

const

itu

ency

.

34

5.3 Randomization Tests on Heterogeneous Quantile Treatment

Effects

The QTE profiles shown in Figure 6 may simply be due to sampling errors. To test whether

having a government MP increases the high end of the consumption distribution more than

the low end, we propose a randomization inference procedure. Specifically, we test whether

QTEs at the two quantiles are equal. Our test is a simple sign test similar to one proposed by

Dube et al. (2011), which builds on simple comparisons of two statistics and the associated

binomial distribution. To remove sampling correlation in the estimation of two QTEs from

the same sample, we adopt a split-sample procedure.

In particular, we would like to test the null hypothesis H0 that δτL = δτH against the

alternative hypothesis H1 that δτL < δτH , where δτL and δτH are two quantile treatment

effects captured by δτ in Equation (2), and τL and τH (where 0 < τL < τH < 1) indicate the

locations of the two quantiles. Our testing procedure consists of the following steps:

(i) Randomly split the sample in two with roughly equal size. In one sample, estimate δτL

to get δτL ; in the other sample, estimate δτH to get δτH . Compare δτL and δτH to get a

binary sign indicator 1(δτL < δτH ) for this simulation.

(ii) Repeat (i) with another randomly split sample 250 times. Each split sample yields an

indicator 1(δτL < δτH ). In the end, sum over the 250 sign indicators to obtain a test

statistic S.

(iii) Simulate 100,000 values of a random variable following a binomial distribution

B(250, 0.5). In other words, each random variable is a sum of 250 binary variables,

each of which follows independently a Bernoulli distribution with a probability of 0.5

taking value 1 and a probability of 0.5 taking value 0.

(iv) Compare the test statistics S to the simulated binomial distribution for the occurrences

in which S < Si, where i = 1, 2, · · · , 100, 000 indicates a draw from the simulated

binomial distribution. The frequency that S < Si among the 100,000 simulations

35

would be our one-side p-value under the null δτL = δτH . If the p-values are sufficiently

small, we reject the null in favor of the alternative, δτL < δτH .

When we split the sample in two, we split the constituencies rather than simply the

households. Splitting constituencies permits the within-constituency correlation in the error

terms, which is allowed in our previous estimations of standard errors, to carry over to the

randomization testing. Because we estimate δτL and δτH in separate samples, under the

maintained assumption that error terms between constituency clusters are independent and

the null that δτL = δτH , the probability that δτL < δτH should be 0.5. This observation

provides the basis for our randomization inference.

Because the split-sample procedure is demanding for sample size, we focus on the para-

metric specifications. In particular, given that quadratic specifications typically give es-

timates close to the nonparametric one, we use the specifications that generate estimates

plotted in the middle row of Figure 6 and focus on total household consumption. We test

several hypotheses that compare the QTEs of different quantiles and report the p-values of

these tests in Table 7.

Table 7: Randomization Tests of Heterogeneous Quantile Treatment Effects

τH vs. τL δτH δτL p-value

90 vs. 50 0.546 0.310 0.000080 vs. 50 0.434 0.310 0.000270 vs. 50 0.304 0.310 0.207560 vs. 50 0.273 0.310 0.2851

90 vs. 10 0.546 0.257 0.000080 vs. 20 0.434 0.286 0.000670 vs. 30 0.304 0.300 0.795460 vs. 40 0.273 0.307 0.6726

Notes: Each row represents the result of a randomization test, in which the null hypothesis is δτH = δτL and

the alternative is δτH > δτL . τH and τL in percentiles are indicated in the first column. One-side p-values

from the randomization tests are listed in the last column. Estimates of δτH and δτL are reported in the

second and third columns, respectively.

First, we compare QTEs at the 60th, 70th, 80th, and 90th percentiles to the QTE at the

50th percentile. While the QTE increases as the associated quantile increases, we find that

36

only QTEs at the 80th and 90th percentiles are significantly higher than the QTE at the

50th percentile at the 1% level. Furthermore, we compare QTEs at the 90th vs. the 10th

percentile, 80th vs. 20th percentile, 70th vs. 30th percentile, and 60th vs. 40th percentile.

Similarly, we find that only the QTE at the 90th percentile is significantly larger than the

10th percentile, and that the QTE at the 80th percentile is significantly larger than the 20th

percentile.

5.4 Heterogeneous Effects on Consumption by Land Ownership

While we find that having a government MP shifts up the consumption distribution at the

high end significantly more than at the low end, it does not necessarily mean that rich

households benefit more than poor households. The QTE at the τ -quantile is not equivalent

to the treatment effect on households whose potential consumption is at the τ -quantile unless

a household’s rank in the consumption distribution is invariant to the treatment status.

In other words, it is possible that the counterfactual poor benefit so much from having a

government MP that they become the rich and are richer than the counterfactual rich.

To address this possibility, we investigate whether land ownership affects the extent to

which households benefit from having a government MP. Despite the growth of the garment

industry in recent years, Bangladesh is still a predominantly rural country; two-thirds of the

population still live in rural areas, where land is the economic basis for income generation.

However, about 60% of households in our sample are landless. Even among landowners,

the distribution of land ownership is highly skewed and concentrated. Large landowners

hire landless laborers, arrange to sharecrop, provide credit, mediate local disputes, and

provide some local public goods. These economic and social relationships well position large

landowners to be local patrons who could broker for political parties (Anderson et al., 2015).

Moreover, land ownership evolves slowly. This is partially attributed to the lack of clear

property rights sponsored by the state, which is common in developing countries. While

a government MP might affect land ownership in his constituency, these changes are likely

slow and gradual. To mitigate the concern of reverse causality, we divide landowners into

37

four quartiles based on the size of their land holdings.

We estimate a model in which the treatment indicator interacts with the four landowner-

ship indicators Qx, for x = 1, · · · , 4, where Q4 indicates the largest landowners. Landowners

in the top quartile on average own 4 acres of cultivable land. For comparison, the median

cultivated area per farm household is 0.9 acres. We limit our sample to rural areas in these

estimations, whose results are reported in Table 8.

In Columns (1) and (3), we control for the running variable using linear terms. In

Columns (2) and (4), we control for the running variable using quadratic terms. In Columns

(1) and (2), land ownership quartiles are defined based on all landowners in our sample

throughout the sample period. Each quartile consists of roughly 10% of households be-

cause about 60% of the rural population are landless. In Columns (3) and (4), we define

landownership quartiles using ownership within the constituency of the households.

Notice that landless households are the benchmark category. Estimates in the first row

of Table 8 suggest that landless households benefit from having a government MP. The

quadratic specification suggests that their consumption increases by about 14%, which is

statistically significant at the 1% level. The linear specification indicates a 6% increase,

which is significant at the 10% level. In all specifications, we find that landowners enjoy

significantly greater consumption than landless households, and households owning more

land consume more.

In all four specifications, the estimated coefficients of the interaction terms monotonically

increase from Q1 to Q4, suggesting that larger landowners enjoy a greater consumption boost

from having a government MP. However, only estimates for the top quartile of landowners

interacting with the treatment indicator are statistically significant. With a linear running

variable, top-quartile landowners enjoy a 7.5% greater boost in consumption from the treat-

ment than the landless, which is significant at the 5% level. The estimate with a quadratic

running variable is similar, but only significant at the 10% level. These estimates are from

specifications using national landownership distribution to define ownership quartile.

Using local landownership to define quartile indicators, estimates for the interaction terms

between the treatment indicator and the top landownership indicator are larger at about

38

0.1 and statistically significant at the 1% level. From the quadratic specification, landless

households’ consumption increases by 14 log points, while top-quartile landowners enjoy 24

log points. The larger and more precise estimates of the Q4 interaction term in Columns (3)

and (4) suggest that the local measure of the largest landowners is a more precise proxy for

a household’s position in a patronage network from which one could extract rents from the

state.

Moreover, in both specifications, the differences between the coefficients of GovMP × Q4

and GovMP × Qx, for x = 1, 2, are statistically significant at the 5% level. However, the

difference between the coefficients of GovMP × Q4 and GovMP × Q3 is not statistically

significant. Interestingly, smaller landowners—those in the first quartile of land ownership

by acre—benefit slightly less than the landless. If smallholders do not rent land from large

landowners and have no patron-client relationships with landowners, then clientelistic trans-

fers may be less likely to be offered to them than to landless clients. However, the Q1

interaction term is not statistically significant at any conventional level.

39

Table 8: Government MPs and Household Consumption by Land Ownership

(1) (2) (3) (4)

GovMP 0.063* 0.146*** 0.059* 0.140***(0.036) (0.050) (0.035) (0.050)

Q1 0.135*** 0.137*** 0.079*** 0.081***(0.029) (0.028) (0.027) (0.027)

Q2 0.158*** 0.161*** 0.184*** 0.189***(0.031) (0.030) (0.028) (0.028)

Q3 0.224*** 0.230*** 0.247*** 0.254***(0.031) (0.032) (0.029) (0.029)

Q4 0.424*** 0.432*** 0.397*** 0.400***(0.031) (0.032) (0.029) (0.029)

GovMP × Q1 -0.050 -0.052 -0.015 -0.017(0.035) (0.035) (0.033) (0.033)

GovMP × Q2 0.047 0.044 0.018 0.014(0.036) (0.035) (0.032) (0.032)

GovMP × Q3 0.056 0.051 0.046 0.038(0.035) (0.036) (0.032) (0.032)

GovMP × Q4 0.075** 0.066* 0.098*** 0.095***(0.035) (0.036) (0.033) (0.034)

Running Variable Linear Quadratic Linear QuadraticQuartile Definition National National Local Local# Observations 15,026 15,026 15,026 15,026

Notes: Each column above reports the estimates of one OLS regression, where the sample is rural households.

Dependent variables are log per-capita household consumption. GovMP is an indicator variable that equals

one if the household lives in a constituency of a governing party MP. Q1 - Q4 are binary variables that

indicate, respectively, the bottom to top quartiles of cultivable land ownership in acres, conditional on the

household owning a positive area of cultivable agriculture land. Q1 - Q4 all equal to zero for households

owning no land. In Columns (1) and (2), quartiles are defined using the national distribution of land

ownership. In Columns (3) and (4), quartiles are defined using the distribution of land ownership within the

household’s electoral district. GovMP × Qx, for x = 1, 2, 3, 4, are interaction terms. In Columns (1) and (3),

two separate linear terms of the running variable are included as regressors, one to the left of the cutoff and

one to the right of the cutoff. In Columns (2) and (4), quadratic terms of the running variable are included.

All regressions include year fixed effects. Standard errors in parentheses are clustered by constituency.∗ p < 0.10; ∗∗ p < 0.05; ∗∗∗ p < 0.01.

40

5.5 Electoral Accountability

Political parties that engage in pork barrel politics target geographic areas with public spend-

ing. But unlike clientelistic spending, the pork barrel spending does not target individual

voters contingent on electoral support. Both pork barrel and clientelistic spending have been

found to provide electoral advantages to incumbents (see Golden and Min, 2013 for a review).

In Bangladesh, one may expect that incumbent party candidates may enjoy substantial elec-

toral advantages, given that a government MP increases consumption and reduces poverty

substantially in his or her constituency. However, local elites—particularly those at the very

top in terms of land ownership—also benefit the most from having a government MP. Notice

that log specification for the outcome variable means that consumption changes are mea-

sured by relative terms. These findings suggest that local elites benefit disproportionately

from having a government MP, which is likely because they extract rents while brokering

clientelistic transfers from the state.

Rent extraction, which likely involves corruption in practice, suggests that electoral ac-

countability may be weak. While citizens are more satisfied with government MPs than with

opposition MPs for fulfilling campaign promises and initiating local development projects,

citizens report overall low satisfaction with politicians in the same survey, and in particular

cite the pervasive corruption and misuse of public funds (Akram, 2012).

Recent studies have found that in places with weak parties and electoral accountabil-

ity, such as Romanian and Brazilian municipalities, incumbents often suffer incumbency

disadvantages in getting re-elected. These incumbency disadvantages reflect unfulfilled ac-

countability to voters that is due to corruption or weak parties (Klasnja, 2015; Klasnja and

Titiunik, 2017). To see whether similar patterns exist in Bangladesh, we study the electoral

prospects of incumbent party candidates.

In Figure 7, we plot the vote share of incumbent governing parties against their margin

of victory in the previous election. An incumbency advantage exists if there is a discrete

increase in the vote share of incumbent government candidates at the cutoff. In the top

left subplot, we pool all three elections from 1996 to 2008. In the top right to bottom right

41

subplots, we plot the 1996, 2001, and 2008 elections separately. In each election, we do

not find a discrete change of vote share for the incumbent party if the incumbent party

candidate narrowly won the constituency in the previous election. In the pooled sample, we

find a small decrease just to the right-hand side of the election threshold. The nonparametric

RD estimate is -0.025, suggesting that incumbent parties have a small disadvantage of 2.5%

of vote share if they won the constituency narrowly in the previous election. However, with

a standard error of 0.0148, the estimate is only significant at the 10% level.

Figure 7: Incumbency and Vote Share of Parliamentary Candidates

0

50

100

150

200N

umbe

r of C

andi

date

s in

Eac

h Bi

n

0

.2

.4

.6

Vote

Sha

re

-.4 -.2 0 .2 .4Margin of Victory

1996, 2001, and 2008 Elections Pooled

0

50

100

150

200

Num

ber o

f Can

dida

tes

in E

ach

Bin

0

.2

.4

.6

Vote

Sha

re

-.4 -.2 0 .2 .4Margin of Victory

1996

0

50

100

150

200

Num

ber o

f Can

dida

tes

in E

ach

Bin

0

.2

.4

.6

Vote

Sha

re

-.4 -.2 0 .2 .4Margin of Victory

2001

0

50

100

150

200

Num

ber o

f Can

dida

tes

in E

ach

Bin

0

.2

.4

.6

Vote

Sha

re

-.4 -.2 0 .2 .4Margin of Victory

2008

Notes: Horizontal axes represent the vote-share margins of incumbent governing parties over their most

popular competitors in the previous election. The vertical axis indicates the vote shares of the candidates

in the current election. In the top left subplot, all elections in 1996, 2001, and 2008 are included. The top

right, bottom left, and bottom right subplots, respectively, plot the elections of 1996, 2001, and 2008. Each

circle is an average over electorates in a margin-of-victory bin of size 0.02. Gray dashed lines are local linear

fits. Green bars at the bottom of each subplot indicate the number of candidates in each bin.

Several reasons may contribute to the lack of incumbency advantages. First, parties

are unable to ensure that voters would vote for them after they receive transfers, which is

42

why vote buying in young developing democracies requires intermediation through brokers

and patron-client networks. Patron-client networks resolve the commitment problem through

social norms and long-standing reciprocal relationships between unequals. Providing support

and assistance to patrons, including turning out to vote as directed, need not guarantee

followers tangible benefits, but failure to do so may result in exclusion from the patron-client

relationship and its associated benefits (Lewis, 2011). Additional transfers from electing a

government MP may have been expected, and therefore do not yield additional electoral

support.

Second, voters may not approve the distribution of rents if it disproportionately benefits

patrons, who may extract their rents through self-dealing, nepotism, and briberies. Any

electoral advantages from additional transfers to the constituency may be offset by anti-

incumbent sentiment, which arises from dissatisfaction with corruption associated with the

incumbent government.

Third, the anti-incumbent swings that generate the alternating governments may be

sufficiently large that candidates who marginally won their seats find it more profitable to

reap the immediate benefit rather than seek re-election by devoting a larger share of rents

to electorally beneficial transfers.

These reasons, together with how rents are distributed, suggest that electoral competition

in Bangladesh has only limited influence on checking the rent-seeking activities of politicians

and local elites.

6 Concluding Remarks

In this paper, we show that having a government MP in the Bangladesh parliament reduces

poverty, increases household consumption, and, to a lesser extent, improves access to elec-

tricity and basic health infrastructure. However, despite the common presumption that votes

of the poor are cheaper to buy, and hence the observation that clientelism is “poor people’s

politics” (Auyero, 2001), we find that wealthy households benefit substantially more than

poor households. Our findings have several implications.

43

First, clientelistic policies have been found to be associated with underprovision of broad-

based redistributive programs and local public services (Keefer, 2007; Khemani, 2015). Our

findings suggest that the clientelistic allocation of government spending also benefits wealthy

households substantially more than low-income households. While political parties and their

brokers may target poor voters with government spending for political support, poor house-

holds may not be the primary beneficiaries of clientelistic spending.

Second, elite capture has been a concern with fiscal decentralization. Our findings suggest

that in a fiscally centralized country like Bangladesh, rent extraction by political brokers in

a clientelistic environment may also divert a disproportionate amount of fiscal transfers

to wealthy locals. Because the national parties are weakly institutionalized, they rely on

patrons as brokers to target voters with government spending. This intermediation through

patron-client networks allows local elites to extract rents from the national governments.

Third, typically in majoritarian electoral systems, a constituency elects a single winner

under a plurality rule, while in proportional representation (PR) systems, multiple candi-

dates are elected from party lists. Because politicians from single-winner districts are held

accountable individually at the ballot box, majoritarian electoral systems are arguably more

effective in limiting corruption. Persson et al. (2003) show that majoritarian electoral systems

are correlated with less corruption across 80 countries. However, our findings suggest that

clientelistic practices likely mute any accountability advantages that single-winner elections

may have. Bangladesh’s competitive elections appear to be impotent in limiting rent-seeking

by politicians and their associated local elites. Given that the weak institutionalization of

political parties and polarized and confrontational politics may have sustained clientelistic

policies (Padro i Miquel, 2007), it is plausible that a PR system may offer greater constraints

on rent-seeking by individual politicians. According to Duverger’s law, a PR system tends to

have a larger number of effective parties, which may lead to less polarized political discourse

(Gandhi et al., 2016).

Last, pervasive patron-client relationships likely impede good governance and political

accountability, even in a competitive electoral environment. Nongovernmental policies that

weaken the patron-client relationships may be desirable. For instance, poor farming house-

44

holds traditionally rely on their patrons’ credit and assistance to deal with income risks

and consumption needs due to, e.g., bad weather and medical expenses, which allows pa-

trons to “buy” their clients’ votes (Anderson et al., 2015). In many developing countries

and Bangladesh in particular, migration to cities has been found to be effective in poverty

reduction. Even seasonal migration may substantially complement agricultural earnings and

mitigate income risk. A subsidy to relax the liquidity constraints on migration could be

highly cost-effective (Bryan et al., 2014). On the other hand, since patrons’ abilities to ex-

tract rents from the state depend on their abilities to mobilize their clients during elections,

patrons may prefer that clients remain in residence when elections take place. By subsidiz-

ing migration to cities, nongovernmental organizations may not only improve rural labor’s

earning potential but also mitigate earning risk by facilitating greater geographic mobility,

which weakens their reliance on patrons for insurance. In other words, with an exit option,

clients afford to be less loyal to their patrons (Hirschman, 1970).

45

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