Favoritism and Flooding: Clientelism and Allocation of River

Waters

by

Sabrin Beg

WORKING PAPER NO. 2017-02

WORKING PAPER SERIES

Favoritism and Flooding: Clientelism andAllocation of River Waters

Sabrin Beg∗

August 15, 2017

Abstract

Political favoritism is commonly documented in developing democracies, demon-strated by better outcomes in favored regions. There is little understanding of themechanisms driving such allocation of resources. I demonstrate favoritism in theallocation of agricultural resources, which eventually affect agricultural production.I use close elections to get random variation in a region’s alignment with the po-litical party in power and find that water may be diverted to favor areas alignedwith the ruling party. Water availability is in favor of upstream districts and againstdownstream districts when upstream districts are aligned with the ruling party. Theopposite is true when downstream regions are aligned with the ruling party. As aresult of this favoritism, floods (or droughts) are less likely to occur in downstreamregions, and agricultural yields are better there when the ruling party has incentivesto favor them. I argue that the ruling party’s influence over autonomous agenciesthat control water allocation allow them to favor their constituents.

Key words: Favoritism, Clientelism, Conflict, Environment, Irrigations, DamsJEL Codes: O15, Q25, D72, R11

∗University of Delaware (email: sabrin.beg@gmail.com). I acknowledge helpful feedback fromDan Keniston and Adrienne Lucas. I also thank the Water and Power Development Authority ofPakistan for the data required for this project. This research did not receive any specific grant fromfunding agencies in the public, commercial, or not-for-profit sectors.

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1 Introduction

Recent empirical literature in development economics highlights the differential

allocation of resources by rulers toward their favored regions. Political favoritism

and clientelism (politicians catering services and public goods to their clients) are

common phenomenon in developing countries, which happen to have weak politi-

cal institutions and under-educated voters. Finan (2004) finds evidence that fed-

eral deputies in Brazil reward municipalities based on political support. Burgess

et al. (2011) document that ethnic favoritism determines allocation of paved roads

in Kenya—leaders disproportionately invest in districts where their own ethnicity is

dominant. Hodler and Raschky (2014) demonstrate that leaders invest more in the

regions of their birth, which exhibit higher local development as measured by night-

time lights. Financial credit may also be offered based on the clientelistic incentives

of politicians—Khwaja and Mian (2004) conclude that access to credit and default

rates are influenced by political connections. In China, affiliation with the ruling

Communist party results in greater credit access and better performance for firms

(Li et al. 2008).

Asher and Novosad (2017) have reported another form of political favoritism—

benefits granted to regions that have a local politician who belongs to the party

controlling the state government. Utilizing data from India, the authors show that

aligned constituencies, where the local elected politician is a member of the ruling

party, experience higher private sector employment, higher share prices of firms, and

increased output as measured by night lights. While we have convincing evidence

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that regions favored by the ruler fare better in terms of outcomes, we do not fully

understand how this comes about. I document that ruling parties favor regions rep-

resented by their members by examining agricultural outcomes in the predominantly

agriculture-driven economy of Pakistan. In a model where representatives have an

incentive to maximize resources in the areas they represent, and affiliation with the

ruling party allows them to do so, we would expect more resources toward regions

that elect members of the winning party as their representative. The underlying

question to demonstrate political favoritism is: are regions more aligned with the

ruling party at the center favored in the allocation of certain public goods? Specif-

ically, I test if river water allocation is more or less favorable to upstream regions,

when the upstream regions are more or less aligned with the party in power relative

to downstream regions.

I study the strategic use of dams and link canals to control river water flow,

which matters in terms of access to irrigation water and for flood risk. I examine

water flow at two points along the Indus River in Pakistan—a point upstream near a

large dam and a point further downstream along the river—over the 37-year period

from 1977 to 2013. The main outcome variable of interest is ExcessDnstF low,

measured by the difference between the downstream flow rate and the upstream flow

rate; lower values indicate diversion of river water toward upstream regions, while

higher values indicate diversion toward downstream regions. The research question

explores whether ExcessDnstF low is affected by the regime in power, specifically

by the ruling party’s alignment with upstream or downstream communities during

any particular regime.

3

An aligned constituency is defined as one where a member of the ruling party

was elected. A regions’s alignment with a party is determined by the share of con-

stituencies aligned with that party. Each constituency corresponds to a government

seat, which is contested by candidates in direct, multi-party elections. The majority-

holding party heads the government.

I use variation in the ruling parties alignment with the upstream versus down-

stream regions over this 37-year period, during which there were 9 unique electoral

regimes—7 democratically elected governments and 2 spans of martial regimes.1 I

test whether the ruling party is able to influence the use of flow-control dams to favor

aligned regions by providing them differential access to water or by diverting water

away when flooding is imminent.

Since neither party membership nor electoral support are random, the number

or share of seats the ruling party wins from any region is endogenous to economic

outcomes. I use close elections to get plausibly exogenous variation in alignment

with the winning party during any specific regime. I use the share of close seats

won by the ruling party in any region as a measure of the region’s alignment with

the ruling party. Alternately, this is the share of closely contested constituencies

in a region that are aligned with the ruling party; this measure varies over time as

electoral regimes change.

The second source of exogenous variation comes from monthly rainfall, which

measures the likelihood of floods or droughts over the study period. I use regression

1The spans of military regimes are 1977-88 and 1999-2002. Elections occur in the followingyears: 1988, 1993, 1997, 1999, 2002, 2008, and 2013.

4

analyses to examine the difference between downstream and upstream flow rate and

how it is affected by the ruling party’s alliance with the upstream versus downstream

regions as rainfall is high or low. When mild rain makes drought imminent, an

upstream-aligned ruling party may have an incentive for diverting water away from

downstream regions to provide irrigation water for upstream regions. I test these

incentives by examining downstream versus upstream flow as a function of regional

alignment with the ruling party and rainfall shocks. I also test the affect of such

incentives for water allocation across regions on actual incidence of floods and on

realised agricultural yields.

Downstream water flow is a function of water diversion upstream; for instance,

if more water is blocked due to upstream embankments or reservoirs, then down-

stream flow will be lower. I find that downstream water availability does indeed

depend on downstream and upstream regions’ alignment with the party in power.

When rainfall is scarce and the downstream region is more aligned with the central

ruling party, more water is diverted to flow to the downstream regions to provide

much needed irrigation water. Alternately, when high rainfall increase the threat of

flooding, a downstream favoring rule will restrict water flow downstream in a effort

to prevent flooding. I also find that the likelihood of downstream flooding is higher

when the upstream regions are more aligned with the government relative to down-

stream regions. Additionally, due to the differential allocation of irrigation water,

agricultural yields are less sensitive to rainfall shocks in regions which are supported

by the center.

5

Water theft and corruption in the allocation of water in the Indus Basin has also

been documented by Fatima, Jacoby and Mansuri (2016) and Rinaudo (2002). Using

data collected in 420 canal outlets of a southern Punjab irrigation system in Pakistan,

Rinaudo (2002) notes that farmers in this region engage in informal negotiations and

extralegal transactions with irrigation-agency officials to obtain more water than

their legal quota. Fatima, Jacoby and Mansuri (2016) also recognize the occurrence

of illegal water diversion along canal channels in Punjab province. These papers

utilize hydraulic data along canals to study water diversion by farmer groups sharing

water from the same irrigation channel system. These local instances of corruption

may be facilitated by or be a result of overall ruling party incentives, and may cause

distortions in overall river water flow rates. This current paper aims to study river

water flow rates to provide evidence of political favoritism in river water sharing,

attempting to highlight the mechanisms through which political distortions may

result in misallocation of water resources. In doing so, I augment and corroborate

the documentation of corruption in the context of canal water sharing in the Indus

Basin.

The findings offer novel insights into the study of patronage and clientelism in the

political economy literature. On the one hand, I support research that documents

favoritism and government incentives toward rewarding certain voters or regions (Cox

and McCubbins 1986; Lindbeck and Weibull 1987). Simultaneously, the findings

point to an instance when the quality of institutions may inhibit the independent

action of government agencies. The irrigation authority that controls river water

is an independent organization whose responsibility is to maximize overall welfare

6

without regard to regional needs. In theory, the irrigation authority’s decisions are

not under the central government’s influence and should have no correlation with

the identity of the ruling party. But, as it turns out, river flow is dependent upon

who is in power, indicating a weak bureaucratic and institutional structure.

Water flow along the Indus river can be manipulated using a large dam and a

system of barrages and link canals. The Tarbela Dam along the Indus river was

constructed in 1977 and is the largest earth-and-rock-filled dam in the world, with

a volume of 142 million cubic meters (NASA 2002). Large dams are controversial

due to their substantial financial costs (Bacon et al. 1996; World Commission on

Dams 2000) and their environmental and social impacts (Duflo and Pande 2007;

McCully 1996; Ansar et al. 2014). Hydrological alterations due to dams can be

harmful for fish, riverine vegetation and other aquatic life (Nilsson et al. 2005; Ligon

et al. 1995). Researchers have particularly noted the adverse distributional impacts

of large irrigation dams. Richter et al. (2010) notes the negative consequences

for populations living downstream of dams due to dam-induced alterations of river

flows. In India, Dulfo and Pande (2007) show that better agricultural production and

lower poverty downstream from the dams comes at the expense of higher volatility

and lower income in the upstream districts.

This paper provides evidence of additional externalities of large dams through

political-economy mechanisms, which may explain the modest net economic benefits

of dams documented by Duflo and Pande (2007). To my knowledge, this is the

first empirical study that makes a link between water sharing and strategic political

7

agendas. It’s made possible by examining the correlation between flow rates, flood

incidence and electoral variables.

Beyond the political-economy insights, the findings have implications for envi-

ronmental and resource management policies. Water is a valuable natural resource

and its allocation is the subject of heavily debated national and international policies

(Bennett et al. 1998; Kilgour et al. 1996). Entire civilizations have settled and flour-

ished along rivers; however, regional boundaries cannot be demarcated to allocate

entire rivers to individual countries or provinces. Around 200 rivers flow through

more than one country (Barret 1994). River water sharing is thus an important

issue and is studied widely within the fields of geography and political science. This

paper provides new insights into how political distortions in a context where natural

resources are shared may lead to significant environmental impacts.

Lastly, this paper allows the scholarly community to understand the sources of

conflict in developing countries. A vast literature following Miguel, Satyanath, and

Sergenti (2004) has shown that rainfall shocks are correlated with the incidence of

conflict, through their effect on agricultural income. Sarsons (2015) argues that in

districts that are downstream from dams income is less sensitive to rainfall fluctu-

ations due to availability of irrigation, but rainfall shocks are still correlated with

conflict incidence. My results shed light on this direct effect of rainfall on conflict—

when river embankments may be used to deprive some regions of irrigation water,

negative rainfall shocks may result in riots due to the lower availability of irrigation

water, even if agriculture is not solely dependent on rain.

8

The possibility of upstream-downstream conflict is probable when regions with

upstream advantages can divert water in either direction to cause a drought or floods

downstream. The world has witnessed many conflicts between states over the alloca-

tion of river water, and as early as the 1980s, US government intelligence estimated

that there were at least 10 places in the world where war could break out over shared

water (Starr 1991). Using data on boundary-crossing rivers, a study by Toset et al.

(2000) indicates that a shared river increases the probability of militarized disputes

and armed conflict. My results illustrate the subtle yet significant connection be-

tween natural resources and conflict as well as the political economy of developing

countries. Water sharing is a crucial political-economy subject, given growing in-

ternational concerns over the consequences of potential water conflicts. Many have

remarked that while past wars were over oil, the next ones will be waged over water

(Soloman 2010).

The paper is organized as follows: section 2 describes the context and background,

section 3 presents the empirical strategy, section 4 describes the data, and section 6

the findings, while section 7 provides concluding remarks.

2 Background on Indus River

The Indus River is the main tributary of the Indus Basin, one of the largest

river basins in Asia with an area of approximately 1.1 million square kilometers. It

flows through the vast majority of the plains of three Pakistani provinces. Figure 1

is a map of the watershed and v shows the major tributaries and the national and

9

provincial boundaries that cross the river basin. In Pakistan, agriculture provides

about a quarter of the national income and employs over two-fifths of the workforce

(World Bank 2014). The agricultural activity is concentrated in the irrigated areas

of Punjab and Sindh, which account for nearly nine-tenths of wheat production and

virtually all of the cotton and rice grown (Gazdar 2005). As shown in figure 1, Punjab

is the north-eastern province of the country (upstream province), and Sindh is the

south-eastern (downstream province). There are two main agricultural seasons in

this part of the world: Rabi and Kharif. The Rabi season (October to March) is the

low rainfall season during which the most widely grown crop, wheat, is cultivated;

Kharif (April to September) is the high rainfall season. Agricultural activity in

this semi-arid environment experiences large seasonal variability in water supply.

For instance, the Indus River measured at Kalabagh station can change from 70 km3

during the summer to 12 km3 during the winter (Alam 2002). With high dependence

upon irrigation (Ahmad 1964), alongside increasing scarcity of water for agricultural

use (Briscoe and Qamar 2006), water sharing along the Indus is a contentious issue.

Indeed, water sharing across provinces is a central aspect of inter-provincial pol-

itics and a significant factor that divides political affiliations (Idrees 2011; Palijo

2003). The upstream-downstream rivalry along the Indus originates from the ability

of the upstream riparian region to divert water according to its needs, using dams,

barrages, and canals. The river flows down through the agricultural lands of Punjab

and then Sindh, where the river water is especially critical as rainfall is meager in

the lower Indus Valley. The Sindhis, facing typical downstream disadvantages, have

harbored mistrust and resentment toward their upstream neighbors (Indus River

10

System Authority). In 1991, an agreement was reached between all of Pakistan’s

provinces on the apportionment of Indus River water across the land. The Indus

River System Authority (IRSA) is a neutral entity responsible for distributing water

between provinces and assisting provinces in sharing shortages according to the Ap-

portionment Accord of 1991. Although water rights have been assigned through this

formal agreement, enforcement is challenging and the dispute over regional rights

continues.

Releasing water stored in reservoirs can result in flooding downstream, while ob-

structing water during dry spells results in low water levels in the river downstream.

Dams allow river water to be stored and transferred using link canals. An exten-

sive irrigation system exists in Punjab, allowing arid districts to receive water for

agriculture. This, however, is at the expense of water availability in downstream

districts—officials and politicians occasionally complain about the “disappearance of

Indus water” upstream at Tarbela (Kiani 2014). Other representatives from Sindh

have made accusations of “theft of [their] share of water,” calling out the use of

upstream embankments and link canals to divert water illegally (Kiani 2011, 2014).

Plans for dam construction in the upstream sections of the river are often hotly

debated and receive criticism from downstream residents in Sindh (Khan 2014).

Floods, which have hit the Indus Basin hard in recent years, are often followed by

blame games where local representatives allege that ruling party officials use their

influence on IRSA to protect their favored regions from getting hit too hard, thereby

causing floods downstream. In surveys, Sindhis are reportedly more likely to blame

the government for flood events (Gallup Pakistan 2010).

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Thus, water allocation is a prime political issue and, unsurprisingly, it shapes

voters’ political alliances. It turns out that the north-south divide in preferences for

water allocation is also mirrored by a divide in political party dominance. Democratic

politics in Pakistan has been intermittent, with several interruptions by military

leadership, and is characterized by a few dominant families and parties. In the most

recent 2013 elections, for instance, the two top parties together won 61% of the seats

in the National Assembly.2 These two major parties, Pakistan People’s Party (PPP)

under the Bhuttos and Pakistan Muslim League (PML) under the Sharifs, have held

the reigns of the government for most of the democratic history of the country. The

prominent Bhutto family, owning tens of thousands of acres of agricultural lands in

Sindh, have their loyalty with the downstream province (Baker 2008). The Sharifs,

on the other hand, lead the PML and are also owners of agricultural land in north

and north-east Punjab. Voting patterns also show a north-south divide. Figure 3

shows the seats won by each of the two major parties in the 2013 election. PPP is the

Bhutto family’s party, while PML is the second prominent party and was the ruling

party for that election. PML represents many constituencies in the northern province

of Punjab, while PPP unambiguously dominates the southern regions even when it

is not the winning party. Thus, the north and south are misaligned in not just their

preferences for river water distribution but also in their electoral alignments. The

ruling party at any time may have incentives to favor electorally aligned areas, and

to do this, may influence river flows, as water for agriculture is undoubtedly one of

the most important “public goods.”

2The National Assembly is the main legislative body under the Federal Parliamentary Systemof the country.

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3 Empirical Strategy

Water levels, or flow rates, at any point along the river depend on the following

factors: ground water, surface water (precipitation) and glacial melt as well as up-

stream water diversion. The usage of embankments and canals affects flow rates at

downstream points along the river. Flow rate data is measured in cubic feet per sec-

ond and is available at two points along the Indus—UpstreamFlow at Tarbela, the

location of the dam and DownstreamFlow at a point about 120 miles downstream.

As outlined in section 2, there are several opportunities, including the large Tarbela

dam and a system of canals to divert water between these two points.

Using GIS maps, I identify all the districts in the catchment area of the Indus

River and classify them into upstream and downstream districts based on their lo-

cation relative to the flow measurement station. Throughout the paper upstream

regions refers to areas along the Indus upstream from the station where the down-

stream flow rate is measured, and downstream regions refers to areas downstream

from the station (see section 4 and figure 2 for details). The regions are defined

as such because the flow at any point demonstrates water availability for all points

along the river downstream from the point. Thus upstream the upstream flow is

relevant for the upstream regions and downstream flow is relevant for downstream

regions.

Seasonal factors like rainfall and glacial melt should affect both upstream and

downstream flow; to control for such factors, my main outcome variable of interest is

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ExcessDnStF low, which is the difference between the flow rate at the two points.3

Since DownstreamFlow impacts downstream regions, while UpstreamFlow affects

upstream regions, the difference ExcessDnStF low is a measure of water available

for downstream regions that was not diverted for usage by upstream regions. Ef-

forts to store or divert water for canal irrigation in upstream regions should lower

DownstreamFlow, resulting in lower ExcessDnStF low. Thus ExcessDnStF low is

a measure of water availability for downstream regions relative to upstream regions.

Figure 4 shows the distribution of ExcessDnStF low; it is positive indicating

that the flow is higher as water moved downstream. It is more positive during the

rainy season when precipitation and glacial melt cause the flow to surge, but there

is considerable variation. Higher values of ExcessDnStF low during the can imply

higher risk of flooding downstream. Similarly, higher values of ExcessDnStF low

during the dry season implies that more water is available for downstream irrigation,

or conversely less water is diverted for irrigation upstream.

The general specification has the following form:

3The regression analysis also includes all appropriate controls for seasonal variations indepen-dent of electoral regimes.

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Log ExcessDnstF lowm,y,r = β0 + β1UpstreamAlignmentr ×Rainm,y,r

+ β2DownstreamAlignmentr ×Rainm,y,r

+ β3DownstreamAlignmentr × UpstreamAlignmentr ×Rainm,y,r

+ Γm,y,r + Λr + ηm + year + εm,y,r (1)

Log ExcessDnstF lowm,y,r is recorded each month m and year y during regime r.

Λr is dummy indicating each electoral regime over the time period 1977-2013. Γm,y,r

includes weather variables that may affect ExcessDnstF low. I include month fixed

effects to control for seasonality, and regime-fixed effects to account for the overall

effect of the electoral regime at any time. To account for more aggregate time trends,

I include an yearly trend. Since alignment is collinear with the regime fixed effect,

the individual effect of upstream or downstream alignment is not identified, but the

differential effect as rainfall varies is identified. β1 measures how flow rate changes

with rainfall variation when upstream areas are more aligned with ruling party while

downstream alignment is low; β2 measures this effect when downstream areas are

more aligned but upstream regions are not.

The research question I examine is whether flow rate is differentially affected

during democratic regimes that are more aligned with the upstream regions than

with the downstream regions. Stronger alignment with the ruling party is defined

as having a higher share of constituencies represented by members of the ruling

15

party. In general, the share of seats held by the winning party in any region may

reflect the party’s political standing or other characteristics of the region, which

may also be correlated with outcomes. More importantly there may be reverse

causality—seasonal factors or concerns about water availability may influence how

people vote and thus flow rates may be correlated with the electoral regime and

regional alignment of the ruling party.

To identify the effect of regional alignment I use random variation in the winning

representatives’ party alignment from close electoral races. The underlying assump-

tion is that in a close electoral race between candidates A and B, the winner is

plausibly random across A and B. In other words, a constituency barely won by the

ruling party candidate is similar to a constituency barely lost by the ruling party

candidate on all unobserved characteristics (Lee 2008; Lee and Lemieux 2010). Each

electoral race corresponds to one constituency, and at most one member of each

contesting party can run. If the identity of the winning candidate is exogenous in

a close race, the share of closely-contested constituencies in which the winner is a

ruling party member is also exogenous. This is a slight departure from the standard

regression discontinuity estimation, however, the underlying assumption in my design

is the same as in a standard RD design–a constituency barely won by the majority

candidate is similar to a constituency barely lost by the majority candidate.4 In line

with the literature I conduct the appropriate tests to ensure that this assumption is

not violated.

4Several authors in the past have used an RD strategy in the context of close elections to identifythe effect of incumbent identity (see for instance Figueras 2012 and Asher and Novosad 2017)

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I compile data on all electoral races held in the catchment area of the Indus

River. For each region (upstream and downstream), I calculate the share of seats

won by the ruling party in close electoral races. Following Lee 2008, closely contested

constituencies are classified as those where the margin of winning is less than 5% of

the total votes. Consider a close electoral race between a representative from the

winning party and another candidate: if the candidate representing the ruling party

wins, then the region has exogenously higher alignment with the winner. Thus, I

have an exogenous region-level measure for alignment with the ruling party, giving me

two variables, UpstreamAlignment and DownstreamAlignment which vary at regime

level. The two measures are normalized to 0 during a non-democratic regime.

To capture the incentives to favor any particular region, I examine the hetero-

geneity in excess downstream flow with respect to seasons and natural availability

of water. Rainfall is measured as a deviation from long term monthly mean pre-

cipitation. I obtain monthly total precipitation for the northern Punjab region, to

capture the extent of water from precipitation that can affect the ExcessDnstF low.

The difference between recorded monthly rainfall and its long term mean gives me

a measure of rainfall shock in for that month. In some specifications a categorical

variable for rainfall is also used. Rabi season (October to March) is the low rainfall

season, with plantings between October and December. During this time, farmers

rely on winter rains or water from irrigation for wheat cultivation, the most widely

grown crop in Punjab and Sindh. In the regression analysis, WinterCultivation indi-

cates the months from October-December, when the availability of water is crucial

for wheat harvest and risk of drought is highest.

17

I run the same specifications using the actual incidence of floods in downstream

regions as the left-hand-side variable. Downstream areas are prone to flooding when

excessive rain or excess water diverted from upstream regions causes rivers to over-

flow. Dams storage can be used to regulate downstream water flow and prevent

flooding. Ruling party’s alignment with regions at different points along the river

can affect actual incidence of floods. For instance, higher downstream alignment can

incentivises the ruling party to manipulate water flow to prevent downstream flood-

ing. The regressions allow me to examine how actual downstream flood incidence

is affected by downstream versus upstream alliance with the ruling party as natural

flood risk varies.

Finally, I examine the effect on regional alignment on actual agricultural yields in

the upstream and downstream regions. The yield (ton/ha) for any crop is measured

annually at district level.5 Specification (1) is adjusted to examine the effect of

regional alignment on district level yields.

Log Y ieldd,l,y,r = γ0 + γ1Alignmentl,r ×Rainl,y,r + γ2Rainl,y,r + γ3Alignmentl,r

+ Φd + ηy + εd,l,y,r (2)

The dependant variable is the log of yield for any crop for district d in region l in

agricultural year y during regime r; l is either upstream or downstream as classified

above and shown in figure 2. The agricultural year is from April to March; during

a year when the regime changes, the regime that is prevalent for majority of the

5Yield for cotton is measured as bales per hectre.

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duration of an agricultural year is matched with that y. Rain is calculated for

upstream and downstream regions as the deviation of the total precipitation over

agricultural year y from the long term mean precipitation for that region. γ1 will

indicate if higher alignment with any region can lower the effect of rainfall shocks on

yields through manipulation of irrigation water in way that is favorable for agriculture

in that region.

Flow rates are monthly observations and likely serially correlated, and standard

errors in specification (1) are allowed to be autocorrelated over 6 lags. I report

Newey-West standard errors in the regression tables for the effects on flow rate. For

outcomes that not serially correlated, I allow the errors to be correlated within a

regime and report standard errors clustered at the regime level. There are 9 regimes,

thus wild-bootstrapped standard errors are calculated using the procedure introduced

by Cameron, Gelbach and Miller (2008) to account for small number of clusters.

4 Data

The river water flow data is obtained directly from the Water and Power Devel-

opment Authority (WAPDA) of Pakistan. It measures the volume of water flow at

any point along the river. There are monthly observations in 1000s of cusecs (cubic

feet per second) from 1921 to 2013 at two stations, Tarbela and Kalabagh, on the

Indus River. The Tarbela station records flow rate at Tarbela Dam (UpstreamFlow)

while the Kalabagh station records the flow rate about 120 miles downstream from

the dam (DownstreamFlow). The dam can impound water flowing into it to sup-

19

press excess flow downstream to prevent floods, or to store the water for irrigation

and other uses. Irrigation water can be transferred from the reservoir into canals

that eventually flow into fields.

The flow rate at any point is a measure of water availability and relevant for

districts or locations downstream from the point where it is measured. Thus, the

DownstreamFlow measured at Kalabagh is significant for districts downstream from

Kalabagh, defined as downstream districts. The districts upstream from Kalabagh,

defined as upstream districts, are affected by UpstreamFlow in terms of water avail-

ability and flood risk. Thus the location of the downstream flow-measuring station

determines the upstream and downstream regions (see figure 2). It is important

to be careful about this classification of regions, because the upstream/downstream

points are typically defined with respect to the position of the dams. In this case,

the aim is to demarcate regions based on how they are affected by river water

flow; thus downstream districts are those that are influenced by DownstreamFlow,

while upstream regions are defined as such if UpstreamFlow matters for them while

DownstreamFlow is irrelevant for them. The flow rate regressions use the Upstream

and Downstream Flow data from 1977 to 2013; this period is used as election data

from before 1977 is not available.

The weather data comes from the Terrestrial Precipitation: 1900-2014 Gridded

Monthly Time Series, Version 4.01 database (Willmott and Matsuura 2002). This

data is available from the web and provides monthly precipitation totals at coordinate

level for a global grid of 0.5 x 0.5 degrees. Using GIS maps, I obtain the grid points

20

located in the districts identified as upstream and downstream, and I calculate the

average monthly precipitation for upstream and downstream regions. The rainfall

shock is obtained by calculating the difference between the log of monthly total

precipitation and the log of a 35-year average of monthly precipitation. The 35-year

period for calculating the historic long term mean is 1977-2012. The log rainfall

variable simplifies the interpretation and can be approximately interpreted as the

percentage deviation from mean rainfall (e.g. a value of 0.05 means that rainfall was

approximately 5% above long term mean).6 I also create a categorical rainfall shock:

it is 1 if the observed rainfall in any month is greater than the 75th percentile for

that month in that region, -1 if the actual rainfall is lower than the 25th percentile,

and 0 otherwise. Ordinarily, rainfall is used as a proxy for income; it deserves to be

pointed out that in this case the rainfall shocks allow me to measure the need for

irrigation water or the threat of floods in any particular region.

The Head of State of Pakistan is nominated by the Electoral College, which con-

sists of a National Assembly, Provincial Assemblies of four provinces, and the Senate.

Members of the National and Provincial Assemblies are directly elected in compet-

itive, multi-party elections. Provincial assemblies indirectly elect the members of

the Senate. I use constituency-based election results for the National and Provincial

Assemblies for the period of 1977-2013 from the website of the Election Commission

of Pakistan.7 The dates of the election and the identity of the winning coalition is

6The log variable is also used in other papers exploring rainfall shocks (e.g. Bjorkman-Nyqvist2013; Maccini and Yang 2009).

7The National and Provincial Assembly constitute all the directly elected members of the Elec-toral College.

21

also taken from the Election Commission website and is verified using news reports.

All the constituencies that lie along the Indus River are included in the analysis. For

each constituency I compile data on the winning and runner-up candidates, their

party affiliations and the margin of win.

The data on floods is from the Dartmouth Flood Observatory, which records

precise location and date of major flood events globally for the period from 1985

to present (Brakenridge 2010). The flood incidence regressions thus have fewer ob-

servations as only electoral variation after 1985 can be used. Flood incidence is an

indicator variable that equals one if a flood was recorded in the downstream regions

along the Indus during any specific month.

The data on agricultural yields is at district and year level obtained from the

Federal Bureau of Statistics of Pakistan. Yields for the main rabi crop (wheat) and

the main kharif crops (cotton, maize and rice) are used.

5 Balance Tests

I show balance between closely contested elections where the majority party rep-

resentative won and where he lost. Table 3 shows that electoral outcomes are not

significantly correlated with the likelihood that the majority party representative

wins in a close race. Table 4 shows that the share of seats won by majority party

candidates in close election in a district is also not predicted by any of the district

level baseline characteristics.

22

In addition, I conduct a McCrary test of discontinuity. Figure 6 shows the distri-

bution of win margin, the vote share of the majority party candidate minus the vote

share of next best opposition candidate. Figure 7 shows the fit of a McCrary test

of continuity in the density of win margin around the treatment threshold of zero

(McCrary, 2008). The p-value of the McCrary test of discontinuity is 0.13, allowing

me to reject that there is a discontinuity at the cutoff.

6 Results

As outlined in section 2, during the period under consideration, the electoral

regime shifts from democratic to military and back. Table 2 shows the different

regimes during this period, and figure 5 shows the constructed measures for upstream

and downstream alignment with the majority party during each regime. Exogenous

variation from regional alignment across these regimes and form monthly rainfall

shocks allows the investigation of allocation of river water. Table 5 shows that

downstream flow rate, or availability of water, changes differentially with rainfall

or natural supply of water when downstream districts are relatively more aligned

with the center. The coefficient on DownstreamAlignment × Rainfall is negative

and significant; this implies that when downstream regions are supported by the

ruling party, then negative rainfall shocks are associated with relatively higher flow

downstream. Conversely, months with positive rainfall shocks have relatively lesser

downstream flow when downstream alignment with the center is high. This suggests

that a downstream favoring ruling party diverts more downstream when the threats of

23

droughts, as measured by rainfall, is higher and restricts this downstream flows when

flood threat is high. A 10% increase in downstream alignment results in 6% higher

water availability during a negative rainfall shock (monthly precipitation is below

the 25th percentile) On the other hand, the coefficient on UpstreamAlignment ×

Rainfall is the opposite sign, but insignificant.

To reinforce the result from columns (1) and (2) of Table 5, I include an additional

interaction with a dummy for rabi (winter) season i.e. the period when wheat culti-

vation demands water but winter rainfall is scarce. Indeed, the electoral incentives to

favor more aligned areas are present when the demand for water is high. This is shown

by a positive coefficient on UpstreamAlignment × Rainfall ×WinterCultivation

and negative coefficient onDownstreamAlignment×Rainfall×WinterCultivation.

These coefficients indicate that when upstream alignment is higher and rainfall is low,

more water is diverted away and downstream flow rate is lower (and vice versa when

downstream alignment is higher). These coefficients are not significant, but have

opposite signs and are significantly different from each other. Thus, when the ruling

party favors the downstream regions, it offers access to water during dry months.

The opposite is true when the ruling party favors upstream regions: it has lower

incentive to prevent droughts downstream and higher incentive to store or divert

water for upstream irrigation.

Further, table 6 illustrates that a positive rainfall shock significantly increases

the likelihood of flooding when ruling party’s alignment with upstream districts is

higher and downstream alignment is lower. This affirms the effect of ruling party

24

incentives on water flow rates above. The effect of higher downstream alignment

is not significant, but it is worthwhile to note that when the ruling party has more

uniform alignment across the entire Indus Basin (i.e. both upstream and downstream

alignment is high) then the flood is risk is significantly lower, shown by the negative

coefficient on UpstreamAlignment × DownstreamAlignment × Rainfall. Thus

during periods when the regions has a lower electoral divide, the ruling party acts in

favor of the entire region, and flood risk in lower overall.

In summary, the above results demonstrate ruling party incentives to manipulate

water flow in the most crucial river system of the country. During an upstream-

favoring rule, the incentive to divert water away from downstream regions is higher

when droughts are probable. Likewise, during a downstream-favoring rule, the in-

centives for diverting water toward downstream regions are higher when irrigation

water is required. Similarly, high probability of flooding, as measured by rainfall,

incentivizes the upstream-favoring party to allow more water to flow to downstream

regions, increasing the chances of flooding there. In future drafts I intend to examine

reservoir levels to further investigate the incentives for water diversion.

A back-of-the-envelope calculation shows considerable environmental effects due

to the politically motivated manipulation of flow rates. If all seats in the upstream

regions are captured by the ruling party, the chances of downstream flooding are

70% higher.8 I imagine a counterfactual where political regime has no effect on the

flow rates or the incidence of flooding—that is, the only factor that could affect

flooding would be natural weather variations. The average downstream flow is 282.4

8I use the coefficient in UpstreamAlignment×Rain to calculate this statistic.

25

cusecs during flood events. Using rainfall and flow rate data representative of this

counterfactual scenario (I use data before the dam construction), I note that each

millimeter increase in monthly rainfall raises the downstream flow by 36.6 cusecs.

Therefore, rainfall that exceeds 7.7 millimeters would result in flooding. This has

happened only 10 times since 1985, but over the same period there were 22 flood

incidences. This suggests that 12 flood occurrences were plausibly preventable if it

were only nature at work—flood incidence could have been less than half of what it

actually ended up being.

Recent literature has found that political favortism results in better outcomes in

favored regions. In this case, we expect agricultural yield to be better in politically

aligned areas. Table 7 investigates this. Firstly, it is notable that rainfall better

for the production of most crops. The interaction between ruling party alignment

and rainfall demonstrates if higher affiliation with the center mitigates the effect of

negative rainfall shocks. The negative coefficient on Alignment × Rainfall shows

that indeed it does. When rainfall is lower, more aligned regions suffer less in terms

of agricultural production; in other words higher alignment causes relatively better

yield when a negative rainfall shock is experienced in that region. This result firstly

provides evidence of favoritism driven by alignment with ruling party, and addition-

ally corroborates the water diversion that I investigate above. Aligned regions seem

to be favored by ruling party and demonstrate better agricultural outcomes espe-

cially during more vulnerable times. The mechanism driving this favoritism is the

ruling party’s ability to influence autonomous bodies that are responsible for resource

management, which in this case is the use of the irrigation system.

26

7 Concluding Remarks

The paper has looked at mechanisms that drive ruling parties to favor certain

regions using the unique setting of the Indus River Basin in Pakistan. I explore

political and strategic incentives of the upper riparian region and how it drives the

patterns of water diversion and eventually flow rates in the lower riparian region.

Political favoritism may be manifested through targeted allocation of river water

access. I find that in the given context, the ruling party has incentives to divert

river water based on which communities it favors. This proves to be the mechanism

behind lower flood incidence and better agricultural yields for aligned regions.

This regional favoritism is perpetuating a longstanding conflict over water in the

Indus Basin. The results have profound implications for environmental and political

stability and expose how political incentives can result in inefficient management and

the misallocation of environmental resources. The results also provide clues about

the root causes of conflict in developing countries: environmental factors may impact

conflict (Miguel, Satyanath, and Sergenti 2004) but political-economy considerations

like favoritism by ruling parties can exacerbate it.

27

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8 Figures and Tables

Figure 1: Indus River and Major Tributaries

Source: Pakistan Water Getaway (www.waterinfo.net.pk)

31

Figure 2: Location of Flow Rate Gauges and Upstream/Downstream Regions

32

Figure 3: Seats won by two major parties

Figure 4: Distribution of Excess Downstream Flow

0.0

2.0

4.0

6D

ensi

ty

0 50 100 150Excess Flow

Rainy Season Dry Season

33

Figure 5: Alignment of Upstream and Downstream Regions during each DemocraticRegime

0 .2 .4 .6

PMLN

PPP

PPP/PMLQ

PMLN

PPP

IJI

PPP

Upstream Alignment Downstream Alignment

34

Figure 6: Distribution of Winmargin

0.005

.01

.015

.02

.025

Density

-100 -50 0 50 100winmargin

35

Figure 7: McCrary Test for Discontinuity at Threshold Determining Treatment

0.01

.02

.03

-100 -50 0 50 100 150

36

Table 1: Summary Statistics

Mean SD Min Max

Excess Flow Rate Upstream vs. Downstream (cubic feet per second) 36.53 28.03 -20.95 134Precipitation (Feeder Areas) 2.793 2.264 0.0512 11.88Precipitation (Upstream Areas) 6.531 5.733 0.0125 33.19Precipitation (Downstream Areas) 1.686 2.029 0 13.52Upstream Share of Ruling Party Seats (Close Elections) 0.140 0.177 0 0.474Downstream Share of Ruling Party Seats (Close Elections) 0.190 0.228 0 0.581Downstream Flood Events in a Month 0.0640 0.266 0 2Flood Events in a Month 0.0773 0.287 0 2Agricultural Yield: Cotton (bales/ha) 3.139 1.539 0.500 13.19Rice (ton/ha) 1.182 0.557 0 5Maize (ton/ha) 1.985 0.684 0.626 4.410Wheat (ton/ha) 1.874 0.759 0 4.725

Regimes between 1977 & 2013 11Democ Regimes between 1977 & 2013 8Length of Democ Regime (months) 44.8 18.6 21 67

Flood SeasonExcess Flow Rate Upstream vs. Downstream (cubic feet per second) 58.61 32.91 -20.95 134Precipitation (Feeder Areas) 2.444 1.669 0.228 9.316Precipitation (Upstream Areas) 12.38 6.527 0.523 33.19Precipitation (Downstream Areas) 3.489 2.680 0.0696 13.52Downstream Flood Events in a Month 0.207 0.435 0 2

Dry SeasonExcess Flow Rate Upstream vs. Downstream (cubic feet per second) 19.43 10.07 4.670 56.84Precipitation (Feeder Areas) 1.477 1.730 0.0512 11.85Precipitation (Upstream Areas) 2.453 2.622 0.0125 15.84Precipitation (Downstream Areas) 0.770 1.027 0 5.216Downstream Flood Events in a Month 0 0 0 0

37

Table 2: Electoral Regimes in Pakistan During 1977-2013

2 3 1Regime Start Regime End Majority Party

1977 1988 Non-Democ1988 1990 Pakistan People's Party (PPP)1990 1993 Islami Jamhoori Ittehad (IJI)1993 1996 Pakistan People's Party (PPP)1996 1999 Pakistan Muslim League (N)1999 2002 Non-Democ2002 2007 Pakistan People's Party (PPP)/ Pakistan Muslim League (Q)2007 2013 Pakistan People's Party (PPP)2013 present Pakistan Muslim League (N)

38

Table 3: Tests of Balance at Constituency Level

1 2(1) (2)

Dependant variable is an indicator for majority party winning in a close race Provincial NationalTurnout -0.162 -0.00104*

(0.269) (0.000576)Margin of win 0.00659 0.0519*

(0.0146) (0.0282)Number of candidates 0.00325 -0.00305

(0.00428) (0.0118)Turnout in previous election -0.0356 0.000233

(0.102) (0.000578)Margin of win in previous election 0.00226 -0.00213

(0.00142) (0.00275)Number of Candidates in previos election -0.000305 -0.00711

(0.00366) (0.0123)Notes: Regressions include all elections that had a close race over the sample period. The regresison include dummies for election years. The coefficients on these dummies are not significant.

39

Table 4: Tests of Balance at District Level

1Dependent variable is share of seats if all close races in the district won by the majority party candidate (1)Share of constituencies with close elections -0.124

(0.779)Share of close seats won by majority party in last election 0.0132

(0.221)Overall share of seats won by majority party in last election 0.223

(0.250)Cotton Yield -0.00415

(0.109)Rice Yield 0.0276

(0.106)Maize Yield 0.000180

(0.143)Wheat Yield 0.0874

(0.199)Rainfall -0.00624

(0.150)Notes: Regressions include all districts that had a close race over the sample period. The regresison include dummies for election years. The coefficients on these dummies are not significant.

40

Table 5: Effect of Electoral Regimes on Flow Rates

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

Dependant Variable: Water Available

Downstream Water Available

Downstream Water Available

Downstream Water Available

Downstream

Upstream Alignment x Rain -0.143 0.397 -0.597 0.135(0.469) (0.384) (0.631) (0.463)

Downstream Alignment x Rain -1.019*** -0.614** -0.794* -0.613*(0.266) (0.294) (0.422) (0.316)

Upstream Alignment x Downstream Alignment x Rain 2.021*** 0.473 1.990* 0.659(0.635) (0.805) (1.052) (0.884)

Upstream Alignment x Rain x Winter Cultivation 1.024 0.546(0.807) (0.950)

Downstream Alignment x Rain x Winter Cultivation -0.178 -0.467(0.498) (0.645)

Upstream Alignment x Downstream Alignment x Rain x Winter Cultivation -0.602 1.163

(1.196) (1.696)

Observations 432 432 432 432Notes: The regressions are at month-year level. Newey-West standard errors in paranthesis. Water available downstream is the difference in flow rate downstream relative to upstream. All regressions include fixed effects for rainfall, regime, month, an annual trend. and control for periods of transition between electoral regimes. Upstream Alignment and Downstream Alignment are share of ruling party seats in close elections in upstream and downstream regions, respectively. Rainfall shocks in columns (1) and (3) are calculated by taking the difference between the log total precipitation in a given month and the log 30-year long term average total precipitaion in that month. In colums (2) and (4) rainfall is an indicator that equals 1 if precipation is more than the 75 percentile, -1 if less than the 25th percentile and 0 otherwise.

41

Table 6: Effect of Electoral Regimes on Flood Incidence

1 2(1) (2)

Dependant Variable: Downstream Flood Downstream Flood

Upstream Alignment x Rain 0.374*** 0.411**(0.0830) (0.146)

Downstream Alignment x Rain 0.0438 -0.00308(0.0325) (0.0659)

Upstream Alignment x Downstream Alignment x Rain -0.673*** -0.696**(0.137) (0.278)

Observations 348 348Notes: All regressions include fixed effects for rainfall, regime, month and an annual trend. The regressions are at month-year level. Robust standard errors in paranthesis, clustered at regime level. All regressions include fixed effects for rainfall, regime, month, an annual trend. and control for periods of transition between electoral regimes. Rainfall shocks in column (1) are calculated by taking the difference between the log total precipitation in a given month and the log 30-year average total precipitaion in that month. In column (2) rainfall is an indicator that equals 1 if precipation is more than the 75 percentile, -1 if less than the 25th percentile and 0 otherwise.

42

Table 7: Effect of Electoral Regimes on Agricultural Output

1 2 3 4 5(1) (2) (3) (4) (5)

Dependant variable: log yield (production/hectre) for: All Crops Cotton Maize Rice Wheat

Alignment 0.127 -0.0220 -0.140 0.336 0.175(0.288) (0.284) (0.145) (0.558) (0.302)

Rain 0.0877*** -0.0751 0.0945** 0.0278 0.107***(0.0179) (0.119) (0.0386) (0.0803) (0.0264)

Alignment x Rain -0.214** -0.276 -0.204 -0.0681 -0.244*(0.0757) (0.389) (0.128) (0.197) (0.108)

Observations 3,006 538 816 693 959Notes: Regressions are at district year-level, including all districts that grow a particular crop in any year. Alignment is definded by region (upstream or downstream) and is the share of ruling party seats in close elections in upstream or downstream regions. All regression inculde dummies for district and year, and standard errors are clusered at district and regime level. Column (1) also includes dummies for each crop. Rainfall shock is an indicator that equals 1 if precipation is more than the 75 percentile, -1 if less than the 25th percentile and 0 otherwise.

43