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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.
1
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
2
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).
11
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.
12
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
13
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.
14
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)
16
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.
18
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
References
Ahmad, K. S. U. (1964). A geography of Pakistan. Karachi, Oxford University Press.
Alam, U. Z. (2002). Questioning the water wars rationale: a case study of the induswaters treaty. The Geographical Journal 168 (4), 341–353.
Ansar, A., B. Flyvbjerg, A. Budzier, and D. Lunn (2014). Should we build morelarge dams? the actual costs of hydropower megaproject development. EnergyPolicy 69, 43–56.
Asher, S. and P. Novosad (2017). Politics and local economic growth: Evidence fromindia. American Economic Journal: Applied Economics 9(1), 229–73.
Bacon, R. W., J. E. Besant-Jones, and J. Heidarian (1996). Estimating construc-tion costs and schedules: experience with power generation projects in developingcountries. World Bank.
Baker, A. (2008, February 13, 2008). Landowner power in pakistan election.Time. http://content.time.com/time/world/article/0,8599,1712917,00.
html. (accessed on January 10, 2017).
Barret, S. (1994). Conflict and cooperation in managing international water re-sources. Technical report, The World Bank.
Bennett, L. L., S. E. Ragland, and P. Yolles (1998). Facilitating international agree-ments through an interconnected game approach: The case of river basins. InConflict and cooperation on trans-boundary water resources, pp. 61–85. Springer.
Bjorkman-Nyqvist, M. (2013). Income shocks and gender gaps in education: Evi-dence from uganda. Journal of Development Economics 105, 237–253.
Brakenridge, G. R. (2010). Global active archive of large flood events. Dart-mouth Flood Observatory, University of Colorado. Available online: http://
floodobservatory.colorado.edu/index.html (accessed on 10 September 2016).
Briscoe, J. and U. Qamar (2006). Pakistan’s water economy: running dry. OxfordUniversity Press World Bank document.
Burgess, R., R. Jedwab, E. Miguel, A. Morjaria, and G. P. i Miquel (2011). Ethnicfavoritism.
28
Clots-Figuerasa, I. (2012). Are female leaders good for education? American Eco-nomic Journal: Applied Economics 4 (1), 212–244.
Fatima, F., H. G. Jacoby, and G. Mansuri (2016). Decentralizing corruption? irri-gation reform in pakistan’s indus basin.
Gallup Pakistan (2010). Gilani research foundation opinion poll. September 2, 2010.
Gazdar, H. (2005). Baglihar and politics of water: a historical perspective frompakistan. Economic and Political Weekly 40 (9), 813–817.
Khan, M. (2014). Kalabagh dam - a pivotal need for pakistan?The World Post . http://www.huffingtonpost.com/mariam-khan/
kalabagh-dam-a-pivotal-ne_b_5901028.html (September 30, 2014).
Khwaja, A. I. and A. Mian (2005). Do lenders favor politically connected firms? rentprovision in an emerging financial market. The Quarterly Journal of Economics ,1371–1411.
Kiani, K. (2011). Water storage capacity to be raised by 20maf. Dawn.com, May19, 2011 .
Kiani, K. (2014). Discrepancy in river flow records sparks protests. Dawn.
Kilgour, D. M. and A. Dinar (1996). Are stable agreements for sharing internationalriver waters now possible? Technical report, The World Bank.
Lee, D. S. (2008). Randomized experiments from non-random selection in us houseelections. Journal of Econometrics 142 (2), 675–697.
Lee, D. S. and T. Lemieuxa (2010). Regression discontinuity designs in economics.Journal of economic literature 48 (2), 281–355.
Li, H., L. Meng, Q. Wang, and L.-A. Zhou (2008). Political connections, financingand firm performance: Evidence from chinese private firms. Journal of developmenteconomics 87 (2), 283–299.
Ligon, F. K., W. E. Dietrich, and W. J. Trush (1995). Downstream ecological effectsof dams. BioScience 45 (3), 183–192.
Maccini, S. and D. Yang (2009). Under the weather: Health, schooling, and economicconsequences of early-life rainfall. American Economic Review 99 (3), 1006–1026.
29
McCrary, J. (2008). Manipulation of the running variable in the regression disconti-nuity design: A density test. Journal of econometrics 142 (2), 698–714.
McCully, P. et al. (1996). Silenced rivers: The ecology and politics of large dams.Zed Books.
NASA (2002). TARBELA DAM, PAKISTAN. http://visibleearth.nasa.gov/
view.php?id=5539 (accessed December 30, 2016).
Nilsson, C., C. A. Reidy, M. Dynesius, and C. Revenga (2005). Fragmentation andflow regulation of the world’s large river systems. Science 308 (5720), 405–408.
Palijo, R. B. (2003). Sindh-punjab water dispute 1859-2003. Hyderabad: Center forPeace and Human Development .
Richter, B. D., J. V. Baumgartner, D. P. Braun, and J. Powell (1998). A spatialassessment of hydrologic alteration within a river network. Regulated Rivers: Re-search & Management 14 (4), 329–340.
Rinaudo, J.-D. (2002). Corruption and allocation of water: the case of public irriga-tion in pakistan. Water Policy 4 (5), 405–422.
Starr, J. R. (1991). Water wars. Foreign policy (82), 17–36.
Toset, H. P. W., N. P. Gleditsch, and H. Hegre (2000). Shared rivers and interstateconflict. Political Geography 19 (8), 971–996.
Willmott, C. J., K. Matsuura, and D. Legates (2001). Terrestrial air tem-perature and precipitation: Monthly and annual time series (1950-1999).Center for climate research version online: http://climate.geog.udel.edu/ cli-mate/html pages/README.ghcn ts2.html 1.
World Bank (2014). World development indicators.
World Commission on Dams (2000). Dams and Development: A New Framework forDecision-making: the Report of the World Commission on Dams. Earthscan.
30
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
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