How Natural Disasters Affect Political Attitudes and Behavior:
Evidence from the 2010-11 Pakistani Floods∗
C. Christine Fair†
Patrick M. Kuhn‡
Neil Malhotra§
Jacob N. Shapiro¶
This version July 27, 2013.
Abstract
How societies make the transition from autocracy to democracy and then on to functioning
programmatic politics is a key concern of scholars in both economics and political science.
A number of recent papers have studied the relationship between exogenous shocks – such as
natural disasters – and governance as a way of testing how economic conditions affect politicians’
incentives and behavior. Of course, the claim that an observed positive relationship between an
exogenous shock and governance reforms provides evidence for economic mechanisms requires
that the shock primarily impacts politicians’ incentives through that channel. Using evidence
from the 2010-11 floods in Pakistan we show that this claim cannot be sustained for natural
disasters, at least not in one critical case. Leveraging diverse data sources (geospatial measures
of flooding, election returns, and an original survey of 13,282 Pakistani households), we show
that flood exposure led Pakistanis to have more aggressive attitudes about civic engagement,
increased both turnout and vote share for the party in power in 2010, and led to a rejection of
militant groups and small particularistic parties. In this case a major natural disaster which
created a transient economic shock also led to major changes in citizens’ attitudes and civic
engagement. These results call into question the interpretation of a broad set of papers and tie
into a rich literature in political science showing that disasters can have complicated political
effects that are often quite divorced from their economic impacts.
∗
The authors thank Tahir Andrabi, Eli Berman, Graeme Blair, Mike Callen, Jishnu Das, Rubin Enikopolov, Asim
Khwaja, Rebecca Littman, Rabia Malik, Hannes Mueller, Maria Petrova, Paul Staniland, Basit Zafar, and seminar
participants at the 2013 AALIMS Conference at Rice, the University of Chicago, and Yale for their helpful comments
and feedback. All errors are our own. This research was supported, in part, by the Air Force Office of Scientific
Research, grant #FA9550-09-1-0314.
†
Assistant Professor, School of Foreign Service, Georgetown University; Email: c christine fair@yahoo.com.
‡
Pre-doctoral Fellow, Empirical Studies of Conflict Project, Princeton University; E-mail: pmkuhn@princeton.edu.
§
Associate Professor, Stanford Graduate School of Business; E-mail: Malhotra Neil@GSB.Stanford.edu.
¶
Assistant Professor, Woodrow Wilson School and Department of Politics, Princeton University, Princeton, NJ
08544; Email: jns@princeton.edu. Corresponding author.
1
Introduction
How societies make the transition from autocracy to democracy and then on to functioning programmatic politics is a key concern of scholars in both economics and political science. A number
of recent papers have looked to the relationship between exogenous shocks – such as natural disasters – and democratization or public goods provision as a way to provide evidence on that process
(e.g., Brückner and Ciccone, 2011; Ramsay, 2011; Brückner and Ciccone, 2012). In the economics
literature, Brückner and Ciccone (2011) prominently argue that the observed positive relationship
serves to test an opportunity-cost mechanism first articulated by Acemoglu and Robinson (2001),
in which negative income shocks increase citizens’ willingness to participate in rebellion, which in
turn creates incentives for politicians to democratize and provide public goods in order to avoid
paying the costs of repression.
The idea that natural disasters increase the risks of rebellious behavior has broad empirical
support (e.g., Nel and Righarts, 2008; Burke, Hsiang and Miguel, 2013), though it is not uncontested (e.g., Slettebak, 2012). But the claim that an observed positive relationship between natural
disasters and governance provides evidence for opportunity cost mechanisms requires that disasters
primarily impact citizen behavior (and therefore politicians’ incentives) through the economic channel. That claim is hard to sustain. Correlations between disasters and conflict (or democratization)
by themselves provide little direct evidence on the mechanism (Burke et al., 2009) and there is a
rich literature showing that disasters can have a wide-range of politically-relevant consequences. In
developed countries, researchers have long noted that voters punish politicians for natural events
beyond their control (Achen and Bartels, 2004; Healy and Malhotra, 2010), and new evidence suggests they do so in developing countries as well (Cole, Healy and Werker, 2011). Disasters also lead
to reactions by governments and other organizations that can shift both citizens’ beliefs and the
opportunities for would-be rebels. Andrabi and Das (2010), for example, find that the provision of
aid by outsiders in the wake of the devastating 2005 earthquakes in Khyber Pakhtunkhwa (KPK)
province and Azad Kashmir led to a substantial increase in trust towards outsiders. Berrebi and
Ostwald (2011) show that terrorism increases after natural disasters and that the effect is concentrated among less-developed countries, which they attribute to the twin effects of turmoil after a
catastrophe (which could allow militants to exploit the exacerbated vulnerabilities of the state) and
2
the government allocating resources away from maintaining order and towards disaster relief.
Using evidence from the 2010-11 floods in Pakistan, we demonstrate that natural disasters can
impact a range of politically relevant variables above and beyond citizens’ material well being.
The 2010 flood affected more than 20 million people, caused between 1,800 and 2,000 deaths, and
damaged or destroyed approximately 1.7 million houses, making it the worst flood in Pakistan’s
modern history.1 The 2010 floods were driven by an unusual monsoon storm that dropped historically unprecedented levels of moisture on the mountainous northwest regions of the country.
According to government accounts, Khyber Pakhtunkhwa (KPK) received 12 feet of rain from July
28 to August 3, four times the province’s average annual total (Gronewold, 2010). Those exceptionally high rainfall rates in mountainous areas compounded what was already an unusually rapid
snowmelt to trigger flash floods that vastly exceeded anything in historical memory. As the water
drained from KPK during the first week of August, a more typical monsoon storm inundated the
Indus flood plain, rendering it incapable of absorbing the dramatic inflows from the mountainous
regions and overwhelming many water-management structures. The following year Pakistan got
hit by an unusually strong monsoon storm, causing another round of devastating floods in the
southern plains. The surging waters hit some places more than others due in part to the random
combination of human action, prior differences in soil moisture, micro-topographic differences, and
complex fluid dynamics.
We leverage that plausibly exogenous variation and diverse data sources (geospatial measures
of flooding, election returns, and an original survey of 13,282 households) to show that Pakistanis
living in flood-affected places turned out to vote at much higher rates, that those hit hard by the
floods have more aggressive attitudes about demanding government services, and that they know
more about politics. These effects are substantively large. Turnout in the Pakistani national and
provincial elections rose roughly 11 percentage points between 2008 and 2013 (from 44% to 55%),
a massive increase. Our results suggest that approximately 19% of this change can be attributed
to the impact of the 2010 floods.2 Put differently the increased civic engagement due to the floods
led to a 2 percentage point change in the absolute level of turnout.3 We also find modest evidence
1
The EM-DAT International Disaster Database records approximately 20.4 million people affected and 1,985 killed
from the 2010 floods.
2
The 95% confidence interval on this effect ranges from 7% to 31% of the change.
3
This is almost half the 5.4 percentage point increase between the 2000 and 2004 U.S. presidential elections and
is larger than the 1.4 percentage point increase in turnout in the United States between 2004 and 2008, typically
3
that voters rewarded the government for what was generally considered to be an effective response
compared to previous floods in Pakistan.4 While the incumbent Pakistan Peoples’ Party (PPP)
lost massively in the 2013 election, losing 16.5 percentage points of vote share in the average
constituency, their loses were roughly 25% smaller in flood-affected constituencies. Voters in floodaffected areas also turned away from small particularistic parties and became less supportive of a
range of militant groups. We find no evidence that these results were driven by patronage goods
distributed differentially to flood-affected areas (or that if there was patronage in the guise of flood
relief it did not work).
Overall, this major natural disaster, which clearly created a transient economic shock, also led
to major changes in citizens’ attitudes and civic engagement. These results call into question the
interpretation of a broad set of papers and tie into a rich literature in political science showing
that disasters can have complicated political effects that are often quite divorced from economic
impacts.
Our results also speak to three additional literatures. First, this paper advances the broad
literature on the political impact of natural disasters on two distinct fronts. Researchers studying
whether voters objectively evaluate politicians’ performance and respond accordingly have become
increasingly interested in how voters react to natural disasters because their occurrence is exogenous
to politicians’ actions.5 Achen and Bartels (2004), for example, find that extreme droughts and
floods cost incumbent U.S. presidents about 1.5 percentage points of the popular vote. They theoretically interpret this empirical pattern as an example of “blind retrospection,” or voters failing to
hold leaders accountable only for conditions over which they have direct control and responsibility.
Healy and Malhotra (2010) similarly find that heavy damage from tornadoes decreases presidential
vote share by about 2 percentage points. Subsequent studies have found that voters may in fact be
reacting to the government’s actions in responding to the disaster, suggesting that citizens sanction
elected officials only when they fail to adequately address the negative effects of disasters (Gasper
and Reeves, 2011). For instance, politicians are rewarded for providing relief payments in the wake
of a disaster (Healy and Malhotra, 2009; Bechtel and Hainmueller, 2011; Cole, Healy and Werker,
2011) or even for providing distributive spending under the guise of relief efforts (Chen, 2013).
attributed to an unusually motivated electorate turning out in support of a historic candidacy.
4
The government was particularly effective at coordinating the large flows of foreign aid.
5
Though not their consequences, of course.
4
With a few exceptions this research is built on studies of the effect of natural disasters in advanced
developed democracies, a gap we help to fill by focusing on a young developing democracy with
weak democratic institutions and active militant groups.6 In addition, the vast majority of these
studies focuses on the political outcome of this exogenous event (e.g., Lay, 2009; Reeves, 2011;
Velez and Martin, 2013) ignoring the underlying causal mechanism. Ours is the first study we
know of which measures political attitudes in the period between a highly salient natural disaster
and a subsequent election. This enables us to measure how the flood impacted attitudes relevant
to interpreting the electoral outcome in the disasters aftermath. Consequentially, we shed light on
the mechanism through which disasters affect elections.
Second, we provide valuable evidence on the question of what drives governments from patronclient systems—which focus on providing targeted benefits to supporters at the cost of services
with larger collective benefits—to programmatic systems focused on effective service provision.
Most work on subject has focused on elite bargaining and left unexamined how changes in citizens’ preferences impact elite incentives (e.g., Shefter, 1977; Acemoglu and Robinson, 2012). Yet,
as Besley and Burgess (2002) show theoretically and empirically, more informed and politically
active electorates create strong incentives for governments to deliver services.7 The evidence from
Pakistan, a country long considered a stronghold of patronage politics, suggests that exogenous
events can create just such changes in the electorate. Situated on a range of geo-political fault lines
and with a population of more than 180 million people, scholars and analysts variously describe
Pakistan as a struggling military-dominated democracy, a revisionist nuclear power locked in a
security competition with a nuclearized India, and a failed or failing state (Ziring, 2009; Narang,
2013; Shah, 2013). The 2013 elections there were the first time a freely elected parliament served its
term and handed power to another elected government. It was, in other words, the first time that
voters were able to punish/reward politicians in something even remotely close to the democratic
ideal.8 And those hit hard by a natural disaster years before the election used that opportunity to
vote at higher rates and to reward a government that exceeded almost all expectations about how
6
The main exceptions are Cole, Healy and Werker (2011); Gallego (2012); Remmer (2013).
Pande (2011) provides a review of experimental evidence showing that providing voters with information improves
electoral accountability.
8
The latter was a result of the galvanized public, as evidenced by unprecedented and diverse (gender, age, education) voter turnout, the rise of a competitive third party, and numerous highly contested races in previous one-party
strongholds.
7
5
well it would respond (relative to the national trend, of course). Such changes augur well for the
emergence of programmatic politics and suggest the theoretical literature should explicitly consider
the conditions under which such changes lead to differences in actual policies.
Finally, our results are relevant to the emerging literature on the impact of natural disasters on
conflict. Scholars in this literature typically find a positive relationship between natural disasters
and conflict (see e.g. Miguel, Satyanath and Sergenti, 2004; Brancati, 2007; Ghimire and Ferreira,
2013), though there are exceptions (Berghold and Lujala, 2012). These findings worry many as
climate change is predicted by most models to lead to a long-run increase in the incidence of
severe weather-related disasters (Burke, Hsiang and Miguel, 2013). The evidence from Pakistan
suggests that effective response to such disasters can mitigate their negative political impact. In this
case, the international community provided a great deal of post-disaster assistance which the state
effectively coordinated. The net result was a differential increase in civic engagement by citizens in
flood-affected regions. The results thus provide micro-level evidence that aid in the wake of natural
disasters can turn them into events which enhance democracy, a possibility consistent with the
cross-national pattern identified in Ahlerup (2011) who finds that natural disasters are correlated
with democratization in countries that are substantial aid recipients.9
The remainder of this paper proceeds as follows. Section 1 provides the relevant background
on the 2010 Pakistan floods. Section 2 describes our various data sources. Section 3 outlines how
we measure key variables. Section 4 describes our strategy for data analysis. Section 5 presents
the results. Section 6 conclude by providing a theoretical interpretation of our results, discussing
policy implications, and laying out directions for future research.
1
Background on the Pakistan 2010 Flood
As the flood waters swept through the province of Khyber Pakhtunkhwa (KPK) and into Punjab
in the summer of 2010, journalists reporting on Pakistani politics worried the unfolding disaster
would be a boon for militant organizations. Militant organizations played into this concern, with
a Tehrik-i-Taliban Pakistan (TTP) spokesman famously offering to contribute $20 million to the
relief effort if the Pakistani government would eschew any Western aid (Associated Press of Pakistan
9
Note, this interpretation is not consistent with the theoretical arguments that aid flows increase the risk of conflict
by increasing the rents to be captured from control of the government (Besley and Persson, 2011).
6
(APP), 2010). Typical headlines at the time described a situation in which militants could step in
and win loyalty by providing badly needed services:
• “Militant groups have 3000 volunteers working around the country.” Christian Science Monitor, August 6.
• “Pakistani flood disaster gives opening to militants.” Los Angeles Times, August 10.
• “’Hardline groups step in to fill Pakistan aid vacuum.” BBC News, August 10.
• “Race to provide aid emerges between West and extremists.” Der Spiegel, August 16.
• “Pakistan’s floods: a window of opportunity for insurgents?” ABC News, September 8.
The scale of the disaster dwarfed anything in recent memory. The 2010 floods affected more
than 20 million people (i.e., about 11% of the total population), temporarily displaced more than 10
million people and killed at least 1,879, with the 2011 floods affecting another 5 million, displacing
another 660,000 people, and killing at least 5,050 more (Dartmouth Flood Observatory (DFO),
2013; Center for Research on the Epidemiology of Disasters (CRED), 2013). A survey of 1,769
households in 29 severely flood-affected districts found that 54.8% of households reported damage to
their homes, 77% reported at least one household member with health problems, and 88% reported
a significant household income drop (Kirsch et al., 2012). Figure 1 shows exactly how much of
an outlier the 2010 floods were within Pakistan’s flood history. Both graphs show standardized
values for the number affected, displaced, and killed for each flood between 1975 and 2012. The
upper graph uses data from the International Disaster Database (EM-DAT) hosted by the Center
for Research on the Epidemiology of Disasters (2013) (data range 1975-2012) and the lower graph
draws on data from the Global Active Archive of Large Flood Events of the Dartmouth Flood
Observatory (DFO) (2013) (data range 1988-2012). In terms of the number affected and the
number displaced, the 2010 floods were the largest in the modern history of Pakistan by several
orders of magnitude and according to the EM-DAT almost twice as devastating as the next largest
flood.10
For comparison, Hurricane Katrina killed 1,833 people in the Gulf Coast in 2005 even though
many fewer people were directly affected; approximately 500,000 according to the EM-DAT database.
INSERT FIGURE 1 HERE
10
The next largest was the 1992 flood which affected 12.8 millions, displaced 4.3 million, and killed at least 1,446
people.
7
The 2010 floods also led to an unprecedented reaction by Pakistani civil society; the central,
provincial, and district governments; as well as by the international community (Ahmed, 2010).
The overall leader for donor coordination was Pakistan’s Economic Affairs Division while Pakistan’s
National Disaster Management Agency (NDMA) which directed and coordinated the various relief
efforts. The NDMA maintained close working relationships with relevant federal ministries and
departments, Pakistan’s armed forces, and donor organizations supporting the relief efforts to
ensure that resources were mobilized consistent with local needs. At the provincial level, the chief
minister of each province was responsible for ensuring that various line ministries and the Provincial
Disaster Management Authorities acted in concert with each other and with the international and
domestic relief efforts. At the district level, district coordination officers were responsible for those
activities of local governance that are devolved to the district level (Office for the Coordination of
Humanitarian Affairs (OCHA), 2010; National Disaster Management Agency, 2011).
This complex system often made it difficult to discern who was responsible for success or failure.
In December 2010, one of the authors visited two relief camps, one each in Nowshera (KPK) and
Dadu (Sindh). The Pakistan military was most visible through their air, road and water missions
by uniformed personnel. Tents and other supplies were usually branded by the donor (e.g., the
United Nations, US AID, etc.), but the distribution of these goods was done through Pakistani
personnel. This was often deliberate as the intention of many donor and bilateral organizations
was to foster the impression that the Pakistani government was effectively managing relief efforts.11
In addition, there were spontaneous localized self-help efforts that emerged during the initial
phase of the crisis and continued throughout. These included victims and their kin’s own efforts to
save their belongings but also included survivor-led repair of local access roads and bridges after
the floods receded. This was in addition to an enormous civil society response that tended to spontaneously coalesce at very local levels (mohallas, union councils, villages, etc.). Such local groups
collected and distributed truckloads of relief items. Countless local as well as national organizations
set up collection sites for donations of goods and cash and then undertook the distribution of the
same. Individual philanthropists, professional bodies, and even chambers of commerce donated
money and supplies to the victims. Scholars associated with Pakistan’s Sustainable Development
Policy Institute note the importance of these local forms of assistance but contend that they are
11
Author fieldwork in Nowshera and Dadu in December 2010 and January 2011.
8
virtually unknown (and thus poorly documented) beyond the local level (Shahbaz et al., 2012).
Such volunteerism was not unique to the 2010 flood; rather, it is a common feature in Pakistan’s
domestic response to such calamities. Halvorson and Hamilton (2010), for example, document
extremely high levels of volunteerism following the 2005 Kashmir earthquake.12
By the end of July 2010 the government had appealed to international donors for help in
responding to the disaster, after having deployed military troops in all affected areas together with
21 helicopters and 150 boats to assist affected people (Khan and Mughal, 2010).13 In response,
the United Nations launched its relief efforts calling for $460 million to provide immediate help,
such as food, shelter, and clean water. Countries and international organizations from around
the world donated money and supplies, sent specialists, and provided equipment to supplement
Pakistani government’s relief efforts. According to the United Nations Office for the Coordination
of Humanitarian Affairs (UNOCHA) (2010), by November 2010, a total of close to $1.792 billion
had been committed in humanitarian support, the largest amount by the United States (30.7%),
followed by private individuals and organizations (17.5%) and Saudi Arabia (13.5%).14 Government
response (both internal and external) dwarfed anything non-state actors could do. Thus, if we
believe the state was in a competition over service provision with various militant groups, it should
have won hands down.15
The government’s effort and the massive influx of foreign aid made the response quite effective.
Calculating the ratio of killed to 1,000 people affected from the EM-DAT data and the ratio of
deaths to 1,000 people displaced for each flood between 1975 and 2012 from the DFO data, provides
a proxy for the effectiveness of the government’s response. For the 2010 flood, the ratios are 0.10
and 0.19, respectively, which is the smallest ratio in the DFO series (1988-2012) and the seventh
smallest ratio in the EM-DAT series (1975-2012).16 Strikingly, the 2010 ratio is only 21% of the
median ratio of killed to 1,000 displaced in the DFO data, so roughly one fifth as many people died
12
Note that this is not unique to Pakistan either. Scholars have also noted this elsewhere in South Asia (see e.g.,
Haque, 2004; Rahman, 2006; Ghosh, 2009).
13
The United States provided an additional seven helicopters as part of their relief efforts.
14
By April 2013, this total has increased to more than $2.653 billion with the three largest donor groups being the
United States (25.8%), private individuals and organizations (13.4%), and Japan (11.3%) (UNOCHA, 2013).
15
As Appendix Table 1 shows, respondents were less likely to say militant groups engage in disaster relief efforts
if they live in flood-affected areas, though whether this is because these groups were absent or because respondents
where the groups did operate do not want to give them credit when talking to our enumerators is unknown.
16
Compared to the 1992 flood, the only flood of comparable magnitude in the last 30 years, the 2010 ratio is 72%
smaller when using the DFO data and 8% smaller looking at the EM-DAT data.
9
as would have been expected given the median response in the last 37 years. Compared to previous
incidents, the government performance in handling the 2010 floods appears quite good.
2
Data Sources
We leverage three main data sources to measure variables of interest: (1) geocoded data on flood
exposure to measure the main treatment variable; (2) returns from the 2013 National and Provincial
Assembly elections (along with lagged election results) to measure the behavioral outcomes; and
(3) an original survey we conducted in early-2012 to measure attitudinal outcomes.
2.1
Geocoded Flood Data
Geo-spatial data on the 2010 and 2011 floods come from the United Nations Institute for Training
and Research’s (UNITAR) Operational Satellite Applications Program (UNOSAT), which provides
imagery analysis in support of humanitarian relief, human security, and strategic territorial and
development planning (United Nations Institute for Training and Research, 2003). For the 2010
flood UNOSAT provided a time series of satellite data recorded between the end of July and mid
September. For the 2011 flood in Sindh province UNOSAT provided a map of the standing flood
waters following heavy monsoon rain between mid-August and early October. Overlapping the
various images allowed us to generate a layer of maximal flood extent in the two years prior to the
survey data collection at the beginning of 2012.
Our spatial population data was taken from Oak Ridge National Laboratory’s (2008) Landscan
dataset, which provides high-resolution (1km resolution; i.e., 30” × 30”) population distribution
data for 2011. The digital administrative boundary data of all tehsils and electoral constituencies
for the national and provincials assembly were developed from constituency-level maps available
from the Electoral Commission of Pakistan (ECP) website.
2.2
2013 Election Data
We collected election data for the 2002, 2008, and 2013 National and Provincial Assembly elections
from the ECP website. The ECP website lists by-election results when a constituency has changed
hands due to the death of it’s representative or to him/her shifting to a new job. As by-election
10
contests are likely to have a different dynamic than those held during the general election we
replaced as many of the by-election results as possible with the original results from the main
polling date. For the National Assembly election, we managed to find general election data on all
contests except for five constituencies in 2008. For the Provincial Assembly election, we were able
to find polling results from the general elections for all by-elections in 2002 and 2008. For each
constituency we recorded turnout as well as the vote shares for all candidates on the ballot.
2.3
The Survey
Our survey included a large sample (n=13,282) of respondents in the four largest provinces of
Pakistan (Balochistan, Khyber Pakhtunkhwa (KPK), Punjab, and Sindh). We collected districtrepresentative samples of 155-675 households in 61 districts with a modest over-sample in heavily
flood-affected districts as determined by our spatial flood exposure data. We sampled 15 districts
in Balochistan, 14 in KPK, 12 in Sindh, and 20 in Punjab to ensure we covered a large proportion
of the districts in each province. Within each province we sampled the two largest districts and
then chose additional districts using a simple random sample. The core results below should be
taken as representative for our sample which, while large, does over-represent Pakistanis from the
smaller provinces. Weighted results using either sample weights calculated from Landscan gridded
population data or those provided by the Pakistan Federal Bureau of Statistics are substantively
and statistically similar.
Our Pakistani partners, SEDCO Associates, fielded the survey between January 7 and March
21, 2012. Our overall response rate was 71%, with 14.5% of households contacted refusing the
survey and 14.5% of the targeted households not interviewed because no one was home who could
take the survey. This response rate rivals those of high-quality academic surveys in the United
States such as the American National Election Study (ANES).
3
3.1
Measurement
Outcome Variables: Civic Engagement
We measure three outcomes that tap civic engagement: electoral turnout, attitudes towards using
violence as a means of achieving political ends, and political knowledge.
11
Turnout
We collected turnout data for the 2002, 2008, and 2013 elections for all 260 constituencies of the
National Assembly of Pakistan (excluding FATA constituencies) and the 577 constituencies of the
four Provincial Assemblies. Together with those five National Assembly constituencies for which we
could not find the original general results, we also dropped all constituencies that were uncontested
(i.e., no polling occurred because there was only one candidate), where elections were terminated
or re-polling was scheduled in 2013, and where the ECP did not report turnout and it could not be
inferred from the information given. This leaves us with 246 out of the 260 (i.e., 95.8%) National
Assembly and 560 out of the 577 (i.e., 97.1%) Provincial Assembly constituencies.
The Pakistan National and Provincial Assemblies combine members elected in single-member
first-past-the-post elections at the constituency level (272 for the National Assembly and 577 for the
Provincial Assemblies in the four main provinces) plus a number of seats reserved for women and
minorities (70 in the national assembly) that are allocated among parties according to a proportional
representation scheme. Most candidates align with a party but some run as independents and
affiliate with a party for coalition formation purposes after the election is complete. Candidates in
the 2013 election campaigns combined appeals to national issues and party platforms with locality
specific appeals and promises of patronage, with the mix varying by candidate.
Aggressive Political Action: Vignette Experiment
In order to measure support for aggressive political action we use a vignette experiment. This
approach circumvents three main challenges to measuring political attitudes. First, respondents
may face social desirability pressures to not explicitly support particular views (e.g. aggressive
civic protests). Second, concepts such as the political efficacy of aggressive protests are not easily
explainable in standard survey questions, but can be illustrated with examples. Third, respondent
answers to direct questions may not be interpersonally comparable (King et al., 2004). To overcome
these challenges, we wrote two vignettes describing concrete (but fictional) examples of two different
ways of getting the government’s attention: peaceful petition or violent protest. Respondents were
randomly assigned to receive one of the vignettes before answering the same two survey questions
on how effective they think the chosen method is and whether they approve of it.
12
More specifically, the vignette experiment works as follows. At the primary sampling unit (PSU)
level, respondents are randomized into one of the following two vignettes:
Peaceful Petition. Junaid lives in a village that lacks clean drinking water. He works
with his neighbors to draw attention to the issue by collecting signatures on a petition.
He plans to present the petition to each of the candidates before the upcoming local
elections.
Violent Protest. Junaid lives in a village that lacks clean drinking water. He works
with his neighbors to draw attention to the issue by angrily protesting outside the office
of the district coordinating official. As the government workers exit the office, they
threaten and shove them.
Following the vignette respondents are asked the following:
• Effectiveness. “How effective do you think Junaid will be in getting clean drinking water for
his village?” (response options: “extremely effective,” “very effective,” “moderately effective,”
“slightly effective,” “not effective at all”)
• Approval. “How much do you approve of Junaid’s actions?” (response options: “a great deal,”
“a lot,” “a moderate amount,” “a little,” “not at all”)
Our sample is well balanced across conditions in the vignette experiment on a broad range of
geographic, demographic, and attitudinal variables, as Figure 2 shows. The difference in means
between the groups within a region therefore provides an estimate of how effective/acceptable
citizens think the use of violence is to pressure their political representatives, which we referred to
earlier as a form of unconventional politics.
INSERT FIGURE 2 HERE
Political Knowledge
We construct a simple measure of political knowledge using a set of binary questions. To tap
awareness of political issues, we asked respondents whether they were aware of four policy debates
13
that were prominent in early-2012: whether to use the army to reduce conflict in Karachi; how
to incorporate the FATA into the rest of Pakistan; what should be done to resolve the disputed
border with Afghanistan; and whether the government should open peace talks with India. To
capture political knowledge, we asked six questions about politicians and scored whether respondents correctly identified the following: who led the ruling coalition in Parliament (the PPP); and
the names of the President, Prime Minister, Chief Minister of their Province, Chief of Army Staff,
and Chief Justice of the Supreme Court. Following evidence in Kolenikov and Angeles (2009) we
conduct principal component analysis on the polychoric correlation matrix of these items and use
the first principal component as our measure of political knowledge. That component accounts for
49.15% of the variance in the index of ten components, suggesting it does a good job of capturing
our underlying concept.
3.2
Outcome Variables: Government and Militant Support
We measure two outcomes that tap government support: vote shares for the ruling party in the
2008 and 2013 elections as well as support for a range of violent non-state militant groups that may
have served as substitutes for the government.
Incumbent Vote Share
Measuring vote shares is straightforward as described above. There were a number of reports of
fraud in the 2013 election, but most were concentrated in urban Karachi and none of the major
observers reported widespread or systematic vote fraud. Ultimately, vote fraud would affect our
inferences on government support to the extent that it was spatially correlated with flood impacts,
which appears highly unlikely given available evidence.
Support for Militant Groups: Endorsement Experiment
We also measured support for some of the violent non-state groups that are a key element of
Pakistani politics. The floods’ affects on support for militant groups was a prime concern of many
policy actors (Varner, 2010), and that support also provides a proxy for disaffection with normal
politics.
14
Drawing on an approach used in a range of papers studying sensitive attitudes in Pakistan and
Afghanistan we employ an endorsement experiment to measure attitudes towards militant groups
(Blair et al., 2012; Blair, Lyall and Imai, 2013).17 The goal of the endorsement experiment is to
measure support for militant groups without asking respondents directly about them. In a place like
Pakistan, direct questions on violent groups are subject to social desirability bias, have extremely
high rates of non-response, and threaten the safety of enumerators, particularly in rural areas where
militant groups are active.
In an endorsement experiment, the difference in respondents’ evaluations of a policy is compared
when they are presented with the policy alone and when they are told the policy is supported by a
specific group. The difference in means between the two groups is the measure of affect towards the
endorsing actor. We measured support for three militant groups—Sipah-e-Sahaba Pakistan (SSP),
the Pakistan Taliban, and the Afghan Taliban.18 Using a five-point scale ranging from “not at
all” to “a great deal” we asked respondents how much they support policies which were relatively
well known but about which they do not have strong feelings (as we learned during pretesting). In
this case we randomized equal proportions of the respondents into treatment conditions in which
they are told that one of the actors mentioned above supports the policies in question. The only
difference between the treatment and control conditions is the group endorsement.
The list of policies respondents were asked about were:
• Whether to use the army to deal with the violence and extremism in Karachi;
• Whether to make FATA a part of KPK and extend Pakistan’s constitution to this area;
• Whether to use peace jirgas to resolve boundary disputes with Afghanistan; and
• Whether the Pakistani government should continue engaging India in a dialogue to resolve
their differences.
As with the vignette, our sample in the endorsement experiment is well-balanced on a broad
range of geographic, demographic, and attitudinal variables, as Figure 3 shows.
17
See Bullock, Imai and Shapiro (2011) for a measurement theory justification of this approach.
SSP is a sectarian militant group that mostly targets the Shia minority in Pakistan. The Pakistan Taliban is an
umbrella label for an array of Pashtun militant organizations all of which advocate for some combination of Islamist
governance and regional autonomy in the Federally Administered Tribal Areas (FATA). The Afghan Taliban are
fighting an insurgency against the American-backed government in Afghanistan and are commonly understood to
operate out of the border regions of Pakistan.
18
15
INSERT FIGURE 3 HERE
There is minor imbalance due to chance on gender, education, and literacy in the endorsement
experiment, which we address by controlling for those variables in our core specification (Gerber
and Green, 2012).
3.3
Treatment Variable: Flood Impact
Figure 4 shows the combined maximum flood extents in 2010 and 2011 overlaid on a map of
Pakistan. The 216 tehsils in which we surveyed are highlighted in grey.19 As one can see the
surveyed areas include tehsils that were severely impacted, tehsils that had only small portion of
their territory affected, and those that were spared by the floods. As described below, we exploit
the randomness inherent in flood exposure to identify the impact of the 2010 and 2011 floods on
the variables described in the previous section.
INSERT FIGURE 4 HERE
We measure flood exposure with two different sets of variables: (1) objective measures based
on geo-spatial data; and (2) subjective measures asked of respondents as part of the survey.
Objective Measures
We calculate objective measures of flood exposure for each of the 409 tehsils, each of the 272 singlemember constituencies for the national assembly, and each of the 577 single-member provincial
assembly constituencies using a combination of: geo-spatial data on the maximum flood extents in
2010 and 2011 derived from overhead imagery by the UNOSAT project (http://www.unitar.org/unosat/);
high-resolution spatial population data; and a digital map of tehsil and national assembly constituency boundaries.
Based on these three input datasets we calculated two different objective measures of flood
exposure at the tehsil and constituency level: the percent of area flooded and the percent of
population exposed. Based on the population measure we also created two dichotomous variables;
one indicating whether at least 5% of a tehsil’s or electoral constituency’s population was exposed
19
The tehsil is the third level administrative unit in Pakistan, below provinces and districts.
16
to the flood (roughly the 50th percentile of all tehsils and electoral constituencies exposed) and
the other indicating whether at least 40% of a tehsil’s or or electoral constituency’s population
was affected by the floods (roughly the 90th percentile of all tehsils and electoral constituencies
exposed). As each method of assessing flood impacts entails some error we report all results for
multiple different measures.
These objectively calculated variables underestimate the floods’ impacts in steep areas where
the floodwaters did not spread out enough to be identified with overhead imagery but where contemporaneous accounts clearly show there was major damage in river valleys. In Upper Dir district
in KPK, for example, the UNOSAT data show no flooding but contemporaneous media accounts
and survey-based measurements clearly indicate the floods did a great deal of damage to structures
that were place well above the normal high-water mark but still close to rivers (see e.g. Agency for
Technical Cooperation and Development, 2010). Note that under the null that the floods impacted
citizen attitudes this kind of measurement error will attenuate our estimate of flood impacts because we are counting places as having low values on the treatment where the floods actually had
substantial effects.
Subjective Reports
To exploit variation in flood impacts at the household level, we also asked respondents how the
floods impacted them. In the analysis below, we use the following question to measure respondents’
subjective assessments of flood damage:
“How badly were you personally harmed by the floods?” (response options: “extremely
badly,” “very badly,” “somewhat badly,” “not at all”)
In addition to treating these responses as an ordinal variable, we created two dichotomous
variables of subjective flood exposure: one indicating whether a household was at least “somewhat
badly” affected and the other indicating whether a household was at least “very badly” affected.
Responses to this question correlate well with other self-reported measures. We asked respondents to rate how much money they lost as a result of the floods on an ordinal scale: less than
50k Pakistani Rupee (Rs.), 50k Rs. to 100k Rs., 100k-300k Rs., and more than 300k Rs. The
Pearson correlation between that loss and the subjective measure above is quite high (r = .73).
17
We further measured the relationship between self-reported flood impacts and three measures of
current economic outcomes: an asset index constructed from the household’s possession of 24 goods
not specific to agricultural production (cell phones, chairs, televisions, motorcycles, etc.), monthly
household income, and monthly household expenditures.20 All three are negatively correlated
with self-reported flood harm, even after controlling for a rich set of variables related to economic
outcomes including tehsil fixed-effects, education, gender, literacy, numeracy, and whether the respondent was the head of the household. As Appendix Table 2 shows, a one-point increase in the
four-point self-reported harm scale is associated with a .39 s.d. reduction in the asset index, a .14
s.d. reduction in log monthly income, and a .10 s.d. reduction in log monthly expenditures.
In the short run, we would expect the floods to have a significantly greater destructive impact
on household assets than human capital. The comparatively larger reduction in household assets
is thus just what we should expect if self-reported flood exposure is honest and accurate. Over the
1.5 years between the flood and our survey we could expect that rebuilding income would be easier
than regaining all pre-flood assets, especially given the absence of flood insurance and the bumper
crop Pakistan experienced following the floods which stabilized the rural economy (Looney, 2012).
4
Methodology and Research Design
This section describes our identification strategy, the statistical models we estimate, and the control
variables included. We first discuss our approach to estimating the floods’ effects on individual
outcomes—support of using more violent means to achieve government responsiveness, political
knowledge, and affect towards non-state militant groups—and then how we modify that strategy
to study the flood’s impact on aggregate-level outcomes—turnout and electoral behavior.
4.1
Individual-Level Outcomes: Political Knowledge, Vignettes, and the Endorsement Experiment
Our identification strategy at the individual level is to use district fixed-effects and respondent-level
controls to isolate the effect of local variance in flood impact that is unrelated to average flood risk.
We demonstrate that: (1) selection on unobservables would have to be unrealistically large to be
20
We omit assets related to agricultural production as many aid efforts provided help rebuilding those assets.
18
generating the main effect; and (2) the results are consistent across subsets of the data over which
the presence of likely confounders should vary a great deal. Specifically, when we compare places
that were similarly likely to be affected by the 2010 flooding due to proximity to rivers, some of
which were badly damaged and others spared, our core results remain consistent.
For the political knowledge index our estimating equation is a fixed-effect regression
Yi = α + β1 Fi + γd + BXi + i ,
(1)
where Fi is one of our flood exposure measures, γd is a district fixed-effect, and Xi is a vector
of demographic and geographic controls to further isolate the impact of idiosyncratic flood effects
by accounting for the linear impact of those variables within tehsils. We cluster standard errors at
the PSU level.
For the vignette experiment our measurement approach leverages a difference-in-difference estimator to answer a simple question: given that people are generally opposed to the pro-violence
vignette, is the difference in reactions between the pro and non-violence vignettes smaller for people in areas exposed to the flooding? To answer that question we need to control for a range of
potential confounders. For example, we might worry that in districts close to rivers (which are
most likely to be flooded), people are generally more accepting of violence because that location
is less desirable and so people living there tend to be poorer and more marginalized. It actually
seems unlikely that land near the rivers is undesirable; it is more fertile and population density is
substantially higher near major rivers. But, that then raises the concern that people more likely to
be affected would tend to be wealthier and less marginalized. In either case, we risk confounding
flood exposure with a more fixed characteristic of the region and the people who reside there, a
challenge given that we have survey data from a single cross section.
We therefore estimate the following as our core specification for analyzing the vignette experiment:
Yi = α + β1 Vi + β2 Fi + β3 (Vi × Fi ) + γd + BXi + i ,
(2)
where i indexes respondents, Yi represents a response to either the effectiveness or approval variable, Vi is an indicator for whether an individual received the protest vignette, Fi represents a
19
respondent’s flood exposure (either objective or self-reported), γd represents a district fixed effect
intended to capture regional differences in baseline propensities to express approval or perceived
effectiveness, and Xi is a vector of demographic and geographic controls to further isolate the impact of idiosyncratic flood effects.21 We again cluster standard errors at the PSU level since that
is the level at which the vignette was randomized.22
The estimate of β3 in these equations isolates the causal impact of the flood to the extent
that: (1) which vignette a respondent got was exogenous to their political attitudes; and (2) how
exposed one was to the floods depended on factors orthogonal to pre-existing political factors once
we condition on district-specific traits and the geographic controls. The first condition is true due
to random assignment of the survey treatment. The second condition is likely to be met for the
reasons outlined above. At the individual level, we can also exploit variation in flood effects at the
household level. Both our discussions with those involved in flood relief and surveys done to assess
post-flood recovery needs show there could be huge variation in damages suffered at the household
level (Kurosaki et al., 2011), likely due to minor features of topography that impacted flow rates,
how long areas were submerged, and so on.
For the endorsement experiment, we estimate a similar specification as equation (3):
Pi = α + β1 Ei,j + β2 Fi + β3 (Ei,j × Fi ) + γd + ∆Xi + i ,
(3)
where Pi represents average policy support and Ei,j is a dummy variable for whether the individual
was told that the policy was endorsed by group j ∈ {Sipah-e-Sahaba Pakistan (SSP), Afghan
Taliban, Pakistan Taliban}. In addition, we estimate an endorsement effect average across the
militancy groups by pooling together all respondents who received endorsements from militant
groups, in which case Ei,j = 1 for all respondents who received a militant group endorsement.
Additionally, we conduct a number of robustness checks to enhance the plausibility of our
claim that we have isolated the impact of the exogenous component of the floods. First, we show
21
Geographic controls that enter at the tehsil level include distance to major river from the tehsil centroid, a
dummy for bordering a major river, the mean elevation of the tehsil, the standard deviation of elevation within the
tehsil, and the proportion of the population hit by the 2011 floods that occurred after 2010 but before our survey was
fielded. Respondent-level demographic controls include gender, a head of household dummy, age, a literacy dummy,
a basic numeracy dummy (measured by a basic mathematical task), and level of education.
22
Results are robust to clustering at the district level to account for the high possibility that the variance attitudes
is highly correlated within districts as well as within PSUs.
20
that selection on unobservables would have to be unrealistically large relative to observables to
fully account for the result. Second, we restrict our analysis to tehsils alongside rivers as doing
so effectively compares places that were roughly similar ex ante in terms of the threat of flood
exposure.
4.2
Aggregate-Level Outcomes: Turnout and Incumbent Vote Share
Our identification strategy at the constituency level relies on a combination of fixed-effects and
constituency-level geographic controls to isolate the impact of local variation in flood intensity on
electoral turnout. In doing so, we need to control for a range of locality-specific confounders. We
might worry, for example, that it is easier for politicians to deliver patronage to constituencies
close to rivers (which are most likely to be flooded) through a combination of water-management
projects and prior flood relief, making them more likely to turn out. To avoid confounding flood
exposure with more fixed characteristic of constituencies we estimate the following two equations:
∆y2013,2008 = α + β1 Fi + γd + BXi + i
(4)
∆y2013,2008 − ∆y2008,2002 = α + β1 ∆Fi + BXi + i ,
(5)
where Fi is a measure of flood impact and ∆Xi is a vector of geo-spatial controls (i.e., distance
to major river from the constituency centroid, a dummy for constituencies bordering major rivers,
mean constituency elevation, and standard deviation of constituency elevation) plus the proportion
of population affected in the smaller 2012 floods that occurred after our survey data collection but
before the 2013 general elections. γd is a unit fixed-effect for the division, a defunct administrative
unit that was larger than the district but smaller than the province. We control for the 27 divisions
instead of districts because outside of Punjab, National Assembly constituencies are often aligned
with district boundaries or contain multiple districts. We also estimate the same equations using
turnout in the provincial elections which have roughly twice as many constituencies, and the results
become substantively and statistically stronger. The geo-spatial controls plus division fixed-effects
account for 72.2% of the variance in the area flooded among National Assembly constituencies
and 72.4% of the variance in population affected (and slightly less for the Provincial Assembly
21
constituencies at 64.2% and 66.2% respectively). We cluster standard errors at the district level to
account for the high probability that the cross-constituency variance in turnout changes may be
quite different across districts.
Our estimate of β1 identifies the impact of the floods to the extent that how exposed one was
to the floods depended on factors orthogonal to pre-existing political factors once we condition on
district-specific traits and the geographic controls. That condition seems likely to be met given two
facts. First, some areas were flooded due to unanticipated dam/levee failures (some intentional by
upstream and downstream land owners, others not) (e.g., Waraich, 2010). Second, the two most
readily observable indicators of flood risk are distance to major rivers and elevation. We surely
account for a large portion of the residual within-division differences between those who live in a
flood plain and those who do not by controlling for the linear impact of those variables.
Subject to our identifying assumptions, the first equation measures whether there are any
systematic changes at the constituency level between 2008 and 2013 associated with flood exposure.
The second specification checks whether the trends in turnout shift differentially in flood-affected
constituencies, effectively removing constituency-specific factors via differencing. Since the main
threat to identification here comes from location-specific trends in the political environment that
might be correlated with proximity to rivers, and not to time-invariant district-level political factors
which we could account for with fixed effects, equation (2) is our preferred specification. Subject to
the assumption that there was no major flood event between 2002 and 2008 (which there was not),
it effectively differences out all constituency-specific trends.23 We also test the robustness of the
second specification to adding a division fixed effect to account for level differences in the change
across divisions.
5
5.1
Results
Summary Statistics and Randomization Checks
Table 1 provides summary statistics for our key treatment and control variables. While slightly
more than 8% of the land area in our survey tehsils were flooded on average, those which were
23
We are currently coding satellite imagery on floods back to 2000 so that we can fully include observed flood
exposure for prior years in our first-differenced regressions. Since the flooding prior to 2010 was modest we believe
that doing so is unlikely to change the results.
22
exposed to the floods were substantially affected. Among the 22% of surveyed tehsils with above
median flood exposure in terms of population, the median tehsil had 18% of its population impacted
and the average percent of the population affected was almost 25%.
INSERT TABLE 1 HERE
5.2
Civic Engagement
We measure both behavioral and attitudinal indicators of civic engagement. Attitudinally we
study how the flood affected citizens attitudes towards engaging with government and their level
of political knowledge. Behaviorally we assess the impact of the floods on turnout.
Aggressive Political Action: Vignette Experiment
As shown in Table 2, respondents experiencing the economic shocks of the floods were more likely
to approve of violent political activity and to believe such activity to be efficacious. The estimate
of β1 measures the difference in the outcome variables between the pro-violence and non-violence
vignettes for people who scored a zero on the flood exposure measure. As shown in the top row
of Panel A, those who received the pro-violence vignette but did not experience flooding rated the
effectiveness of Junaid’s actions between .2 and .25 lower in the violent vignette on a 0-1 scale
controlling for a broad range of geographic and demographic controls, which is a movement of more
than .5 s.d. in all models. This is the case for the various objective measures of flooding (columns
1-4) and the self-reported measures (columns 5-7). We find results of similar magnitude for the
approval dependent variable (see Panel B of Table 2).
INSERT TABLE 2 HERE
Across a broad range of observational and self-reported measures, however, exposure to the flood
substantially and significantly lessened this disapproval. The estimate of β1 + β3 in the models tells
us the effect of the violent vignette among those highest on the flood index score and the estimate
of β3 indicates the moderating effect of flood exposure on the effect of the vignette. A one s.d.
movement in the proportion of the population exposed in tehsils with non-zero flood exposure (.17)
corresponds to a .16 s.d. increase in perceived effectiveness of the violent action and a .18 s.d.
increase in approval for the violent approach.
23
To benchmark these results consider the relationships between the vignette response and gender.
Existing research has shown that men are on average more likely to have pro-violent attitudes in
the context of normal social relations (Funk et al., 1999) and tend to have more extreme views
in some political settings involving violent contestation (Jaeger et al., 2012). The difference in
perceived effectiveness of the pro-violence vignette between men and women is approximately .08,
which equates to a .2 s.d. movement in effectiveness, and the difference in approval is of similar
size (i.e., .07). The difference between those affected by the flood and those who were not in terms
of approval is thus slightly smaller than the gender difference in the approval of violent action. The
gender difference in perceived effectiveness and approval across the two conditions is even smaller,
roughly .06 for effectiveness and .04 for approval, both of which are substantially smaller than all
the flood coefficients.24 Drawing on prior work we can also compare the flood affects to differences
across attitudes towards Islamist militants’ political positions. As in Fair, Malhotra and Shapiro
(2012) we measured individuals’ support for five political positions espoused by militant Islamist
groups and combined these in a simple additive scale ranging from 0 to 1. Moving from 0 to 1
on this scale equates to a .21 increase in approval for the violent vignette and a .11 increase in
effectiveness. The impact of a one s.d. move in flood exposure is similar in terms of approval to
that from going from agreeing with none of the Islamist policy positions to agreeing with all five
(and is much larger on the effectiveness measure), which implies it is a substantial shift.
Interestingly, the results are not a proxy for satisfaction with flood relief. We asked respondents
“In your opinion, did the government do a good or bad job in responding to the floods after they
occurred?” on a four-point scale ranging from “very bad” to “very good” with no midpoint so respondents were forced to assign a direction to their views of the government response. As Appendix
Table 4 shows respondents’ feelings about the violent vignette are not consistently correlated with
how respondents believe the government did in responding to the floods. For some measure of flood
effects the dif-in-dif is larger among the 6,158 respondents who felt the government did a poor job
of responding to the floods (about 50% of the sample), while for others it is higher among the
6,149 respondents who felt the government did a good job. Clearly we cannot interpret the lack
of a difference as falsifying a causal relationship between the quality of government response and
attitudes on the vignette. Individuals who rate the government response poorly may do so because
24
To see how the coefficient estimate β̂3 changes based on different sets of control variables, see Appendix Table 3.
24
they have some unobservable difference that also makes them more approving of violent protests to
gain political services. Nevertheless, the fact that there is no consistent correlation suggests that
the floods affected attitudes (and observable measures of civic engagement as we will see) through
some channel other than satisfaction with government performance.
The finding that flood victims approve of violent protests and believe they are more effective
in getting a government response is quite robust. One might be concerned, for example, that
there is unobserved heterogeneity between tehsils which is driving these results. To account for
this possibility and to exploit the substantial within-village variation noted by many observers
(Kurosaki et al., 2011), we estimated the impact of self-reported flood measures including tehsil
fixed effects. As Table 3 shows, the results on self-reported flood effects actually become stronger
once we account for tehsil-level variance in flood impacts.
INSERT TABLE 3 HERE
A second concern is that some enduring attitudinal difference between those who live in flood
plains and those who do not may be driving the results. Though this seems unlikely to be the case
given the results in Table 2, we estimated our core specification from Table 2 on the subset of 81
tehsils that border major rivers.25 The results in Table 4 are thus identifying off differences in the
specific course of the 2010 and 2011 floods among a set of places that are all unambiguously within
potential floodplains. Comparing what we can think of as lucky tehsils (i.e., those neighboring
rivers, but not flooded) with the unlucky ones (i.e., those neighboring rivers that were hit by the
flood) produces somewhat larger estimates of the flood effect than when we include the 103 tehsils
that do not border major rivers. This finding provides strong evidence that the result is not driven
by the proximity to rivers.
INSERT TABLE 4 HERE
One might also be concerned that district fixed effects only account for traits correlated with
the average response across the two vignette conditions, not for district-specific differences between
them. When we include district-vignette fixed effects to allow each district to have a different
response in each vignette condition, the core results are attenuated for some measures of flood
25
Major rivers include the Indus river and its arms (i.e., Chenab, Jhelum, Kabul, Ravi, Soan, and Sutlej).
25
impact, but the linear effect of the population proportion affected becomes substantively larger, all
coefficients remain positive, and two of three self-reported measures remain statistically significant
(see Appendix Table 5).26
Another worry is that what we have identified with the observationally based flood measures is a
compositional effect; in other words, those who are not willing to press the government hard moved
out of areas after the floods. This is unlikely to be driving our results. If it were, the results should
be statistically stronger using the observational measures than the self-reported measures, which are
not subject to this compositional bias. The opposite is the case. Moreover, there is little evidence
of large-scale migration to or from flood-affected areas. The floods clearly caused some short-term
displacement – focusing on 29 “severely” flood-affected districts Kirsch et al. (2012) found that 6
months after the floods 12.6% of urban respondents and 18.4% of rural ones were living outside
their home districts – but we could not find evidence this led to long-term movements. In a recent
study Mueller, Gray and Kosec (2013) find little evidence that flooding lead to long-term migration
in Pakistan.
Finally, we can also benchmark how large any unobserved factors linking flood exposure to the
attitudes we measure would have to be in order to fully account for the results using techniques
from Altonji, Elder and Taber (2005). They suggest gauging how strong selection on unobservables
would have to be relative to selection on observables to account for the full estimated effect.27 The
measure is calculated using two regressions: one with a restricted set of control variables and one
with a full(er) set of controls. Denote the estimated coefficient of interest in the first regression β̂ R ,
where R stands for Restricted, and the estimated coefficient from the second regressions β̂ F , where
F stands for F ull. Their proposed metric is then β̂ F /(β̂ R − β̂ F ). The intuition behind the formula
is easy to understand. First, the less the estimate is affected by selection on observables (i.e., the
smaller the denominator) the larger the (absolute) ratio and therefore the stronger selection on
unobservables need to be (relative to observables) to explain away the entire effect. Second, the
larger the absolute value of the numerator is, the higher the ratio is in absolute terms, indicating a
greater necessary impact of unobservables to explain the treatment effect away. Third, the relative
26
Note that with district-vignette fixed effects in the model, the vignette-specific mean effect is absorbed.
Altonji, Elder and Taber (2005) consider the situation where the explanatory variable is binary. Bellows and
Miguel (2009) adapt their test for the case where the variable is continuous. Their derivation of the metric is provided
in the working paper of their study, Bellows and Miguel (2008).
27
26
size of β̂ R and β̂ F determines the ratio’s sign. Positive ratios (i.e., β̂ R > β̂ F , as our coefficient
estimate of interest is positive) indicates that the added controls attenuated the treatment effect,
while negative ratio’s (i.e., β̂ R < β̂ F ) indicate that they increased the treatment effect.28 Negative
ratios thus imply that the bias we are removing with the fuller set of controls was pushing towards
a null result.
Again following Altonji, Elder and Taber (2005) we consider a range of comparisons to provide
intuition for the magnitude of the selection on unobservables required to account for the result relative to three intuitive measures: selection on demographic controls at the individual level within
district; selection on the combination of geographic and demographic controls within district; and
selection on district-specific effects after controlling linearly for geographic and demographic controls. The ratios for these three comparisons across our seven flood treatment variables and both
survey questions following the vignette (i.e., effectiveness and approval) are reported in Table 5.
INSERT TABLE 5 HERE
Of the 42 ratios reported in Table 5, none is between −1 and 1. Including the geographic and
demographic controls on top of district fixed effects slightly increases our coefficient estimates (the
vast majority of the ratios in the top two rows of each panel are negative). The overall median
ratio of −25.21 in these rows suggests that selection on unobservables would have to be very large
and push in the opposite direction of the bias we remove by adding geographic and demographic
controls to account for the result. Comparing the results with geographic and demographic controls
to the results include those controls plus district fixed effects, as we do in the bottom row of each
panel, provides a median ratio of 2.76. To attribute the entire flood impact to selection effects, the
impact of selection on unobservables within districts would have to be on average more than 2.5
times larger than that of all unobserved district-specific factors. In our view, that makes it highly
unlikely that the reported impact of the flood on approval and belief in efficacy of violent protest
is due to unobservables.
28
In the knife-edge case when β̂ R = β̂ F the metric is not defined.
27
Political Knowledge
Consistent with our interpretation of the floods increasing civic engagement, they also appear to
have weakly increased how much citizens know about politics. As Panel A of Table 6 shows,
our index of political knowledge is increasing in all our standard measures of flood impact and
statistically significantly so in 5 of 7 measures. The effects are generality statistically significant,
but small. A one s.d. increase in the proportion of the population affected by the floods (.136)
predicts a .062 increase in the knowledge index, a move of .08 s.d.. In order to ensure that the
results are not an artifact of the PCA procedure, panel B reports the same regressions with a simple
additive index of the 10 knowledge questions. The results are substantively similar.
INSERT TABLE 6 HERE
Turnout in the 2013 Election
The attitudinal changes above were accompanied by a behavioral response. Flood exposure increased electoral turnout, suggesting that these shocks triggered higher demand for government
responsiveness. In Panel B of Table 7, our preferred specification, the change in trends for the
National Assembly election turnout due to flooding is positive and statistically significant for all
objective measures of flood impact. The results are also quite large substantively. A one s.d.
increase in the proportion of the population affected by the 2010-11 floods (.127) predicts a 2.1
percentage point higher turnout in 2013 vs. 2008, which is almost a .25 s.d. increase in the outcome
(see Panel A, Column 4) and a 3.9 percentage point difference in the trend in turnout (see Panel
B, Column 4), roughly a .28 s.d. increase. Comparing Panel B and C in Table 2, we see that the
addition of division fixed effects makes most coefficients larger (and the estimates a bit noisier),
suggesting the unobserved location-specific factors that are consistent over time are unlikely to be
driving the result. These effects are in line with the effects observed in get-out-the-vote campaigns
in the United States. Green, Gerber and Nickerson (2003), for example, found that concerted doorto-door canvassing efforts in six sites (areas ranging from 8,000-43,000 people) yielded an average
turnout increase of 2.1 percentage points.
INSERT TABLE 7 HERE
28
These results remained consistent even when subsetting the data on the 129 electoral constituencies that border major rivers and thus are unambiguously within the flood plain. The results in
Panel A of Appendix Table 6 are substantively similar though noisier, as one would expect when the
sample size drops from 249 to 129. The results in Panel B, our preferred specification, are almost
identical within the restricted subset. The results in Panel C are similar in magnitude for both
continuous variables, though a bit less precise, and are substantively smaller for the dichotomous
exposure variables, though they retain the same sign and the t-statistics are all above 1.
In many countries, voting down ballot is an indicator of intensity of civic engagement as it
requires more time in the voting booth and it is harder to collect information on candidates for
less prominent offices. In the Pakistan context, voters received two ballots, one for the National
Assembly and one for the Provincial Assembly (PA), and turnout in the PA election was 2 percentage points lower (54.1% vs. 56.1%). Examining the impact of floods on PA turnout may thus
yield a better signal of the flood’s affects on civic engagement. Moreover, outside of Punjab the
National Assembly constituencies are often quite large geographically. Provincial Assembly (PA)
constituencies are typically much smaller, there are roughly twice as many across the country. We
therefore test the impact of the floods on turnout for PA constituencies as well. As Figure 5 shows,
there is substantial variance in flood impacts at the PA constituency level that is masked at the
NA constituency level.
INSERT FIGURE 5 HERE
Given that we have more observations in the PA elections, and that voting there is a clearer
signal of civic engagement, it should not be surprising that the results are stronger. As Table 8
shows, a one s.d. increase in the proportion of the population affected by the 2010-11 floods (.146)
predicts a 1.6 percentage point increase in 2013 vs. 2008, which is almost a .18 s.d. increase in the
outcome (see Panel A, Column 4) and a 3.6 percentage point difference in the trend in turnout (see
Panel B, Column 4), roughly a .24 s.d. increase. Comparing Panels B and C in Table 3, we see
that the addition of division fixed effects makes most coefficients a bit larger in columns 1 and 2
(and the estimates a bit noisier), suggesting that any unobserved division-specific factors which are
consistent over time are unlikely to be driving the result. On average, the flood impact increased
turnout in the 2013 PA election by 1.5 percentage points and based on our estimates the flood
29
accounts on average for 14.65% of the turnout increase (with a 95% confidence interval of 6.3% to
22.9%). As before, all results are robust within the subset of constituencies which border major
rivers (see Appendix Table 7).
INSERT TABLE 8 HERE
As before the results are consistent when we restrict attention to the 209 PA constituencies that
border major rivers. Our results are clearly not being driven by variance between places that are
always at risk and those which rarely are.
Is This Just a Compositional Effect?
An immediate concern with any results looking at the impact of a natural disaster which are not
based on panel data is that we may simply be picking up a compositional effect. If people who
moved out after the floods were systematically less likely to vote than those who stayed put (or
moved in), then the changes we are attributing to the flood’s impact on individual civic engagement
could actually be an artifact of those migration decisions. There in no evidence in surveys designed
to study migration that there were significant permanent population shiftings in Pakistan due to
the 2010-11 floods, either to or from flood-affected districts (Mueller, Gray and Kosec, 2013). Less
than 2% of those reporting their village was hit in the 2010 or 2011 floods in the Mueller, Gray and
Kosec (2013) nationally-representative panel study were living in a different village than in 2001.29
Fortunately, our survey provides some evidence on migration, allowing us to provide an estimate of how worried we should be about compositional effects driving our results. A number of
respondents reported suffering from flood damage who lived in places that were not affected by
the floods in 2010 or 2011 according to the combination of UNOSAT data and maps from the
Pakistan National Disaster Management Agency. Of the 1,201 respondents who reported being
hurt “extremely badly” by the floods only 75 lived in a tehsil that was not hit by the flood and of
the 2,360 who reported being “very badly” or “extremely badly” hit, only 170 live in a tehsil not
hit. These numbers are inconsistent with massive outmigration from flood-affected areas.
If we assume that all those reporting any damage who live in un-affected districts migrated
because of the flood, then we estimate that 4.6% of the population in unaffected districts are
29
Private communication with the authors.
30
migrants from the flood-affected regions and that a total of 2.05% of Pakistan’s population migrated
as a result of flood damage. This is surely an overestimate, many of those who report being affected
but live in districts with no flooding either moved for other reasons, are referring to damage suffered
by kin, or answered based on damage suffered from monsoon rains in the summer of 2010. Still,
we can use our estimates of migration to benchmark the difference in turnout attributable to the
impact of the flood.
The simplest way to do so is to estimate the migration rates for the 61 districts in our survey
(recall the sample was designed to be district representative) and add our estimates of the proportion
of migrants in a district to our National Assembly turnout regressions. If people who moved out
were less likely to vote, then we should see a negative conditional correlation between number of
migrants in unaffected communities and the change in turnout from 2008. As Panel A of Table 9
shows, the opposite is the case for National Assembly Constituencies. Instead, the coefficients on
migration is positive and insignificant in all regressions. We can also estimate our baseline turnout
regression for the 157 NA constituencies that we estimate did not receive any migrants (i.e. they
are in districts we surveyed that were either clearly hit by the floods or that had no one report
flood exposure). As Panel B shows the core results from Table 7 remain substantially unchanged
within that sub-sample. Panel C repeats the analysis of Panel B for the 378 PA constituencies
that were either hit or reside in a district where we found no migration. The results from Table 8
remain robust in that subsample.
INSERT TABLE 9 HERE
We should close this section by noting that the particulars of Pakistan’s voting system mean
that compositional changes consistent with the attitudinal results are unlikely to be driving the
results. The major door-to-door voter registration effort by the Electoral Commission of Pakistan
for the 2013 election occurred from August 22 to November 30, 2011 (mostly after the 2011 floods).
Voters were registered at the address on their national identity card and anyone not home during
the door-to-door drive could register until March 22, 2013 at their local electoral commission office
by providing a national identity card. Because changing the address on one’s national identity
card is a relatively cumbersome process (it requires visiting an office with either proof of property
ownership or a certificate from a local government representative), many people choose to vote
31
where they were registered rather than shifting that address. This registration process means that
if those who moved out were disproportionately inclined not to vote, then their registration would
likely remain in flood-affected areas (there would be, after all, no reason for them to shift their
registration if they do not plan to vote). That would introduce a downward bias to our estimates
of turnout there and therefore introducing a bias against our main result.
Overall, there is little evidence that we are detecting a compositional effect, though we cannot
rule it out without better information on migration patterns. As we saw above, the attitudinal
changes related to flood impacts are consistent across both objectively-measured and self-reported
flood exposure, suggesting again that we are not simply capturing the impact of differential migration.
5.3
Government Support
Given the broad literature on the impact of natural disasters on voter behavior we examined how
the floods affected support for various parties in the 2013 election.
Incumbent Vote Share
The Pakistan People’s Party (PPP) was the ruling party during the 2010-11 floods. The PPP
won 91 out of 272 contested seats in the 2008 election, almost a third more than the second
place Pakistan Muslim League (Nawaz) (PML-N) which won 69 seats. In the 2013 election the
PPP suffered a massive defeat, winning only 33 seats, while the PML-N scored a massive victory
winning 126 seats; almost four times the number of seats of the second-place PPP. Against this
background of massive defeat, we ask a simple question: was the PPP’s loss somewhat less stinging
in flood-affected constituencies? As Figure 6 shows, they were. We plot the vote share for PPP
(Panel A) and PML-N (Panel B) nationally and then by province, with the mean vote share by
constituency in each region along with the 90% confidence interval around the mean.
INSERT FIGURE 6 ABOUT HERE.
PPP losses are attenuated dramatically nationally and in Punjab (where they lost the most
relative to 2008) in flood-affected areas. On average the PPP vote share dropped 17.6 percentage
32
points of vote share nationally from 2008 to 2013, but they lost 21.1 percentage points in constituencies with nobody affected by the floods and only 13.9 percentage points in flood-affected
constituencies, a difference significant at the .005 level in a two-tailed t-test for difference in means.
For the PML-N, the main opposition party, the gains are larger in flood affected places, particularly in KPK and Sindh. Those gains come at the expense of ethnic and religious parties in those
places as the PPP lost a bit less in flood-affected areas in KPK and Sindh. Neither party saw any
significant difference across flood-affected regions in the provinces they have long dominated. In
Sindh, where the PPP has its stronghold, the party lost fewer votes in flood-affected places, but
the difference is marginal. In Punjab, which had been run by the PML-N for many years, the party
gained very slightly in flood-affected places, but again, the difference is small.
As Table 10 shows, these results are robust to include the same geographic controls as in the
other regressions plus a province fixed-effect to account for the parties’ differential organizational
capacities across provinces. While the results are not uniformly strong, they are inconsistent
with blind retrospection (Achen and Bartels, 2004) and provide modest evidence that the PPP
suffered less in flood-affected areas where they did not have an entrenched advantage and those
their performance could make a difference (i.e. Punjab).
INSERT TABLE 10 HERE
Critically, the PML-N campaigned in 2013 as the party of effective governance. Given that,
the large changes in their favor in flood-affected places are consistent with the possibility that
the floods shifted political behavior by dramatically reinforcing the importance of government.
In flood-affected areas, voters gave the incumbent some credit for its good relief efforts (as we
noted, the number killed was remarkably low given the extent of the devastation), but also moved
relatively more strongly towards the party which campaigned on effective governance and policy
execution. Flood exposure substantially decreased the vote shares of small parties that make ethnic,
sectarian, and locality-specific appeals. In the face of such obvious evidence that government can
competently address crises, people seem to have turned away from patronage politics in favor of
more programmatic parties.
33
Support for Militant Organizations: Endorsement Experiment
Contrary to the worries of many in the media, we find no evidence that support for militancy is
higher among those affected by the floods. Although the impact of flooding increased people’s
abstract support for violent political action, that did not appear to translate into a taste for nonstate actors. Across the same seven objective and subjective measures of flood impacts support for
militant groups (as measured by the endorsement experiment) is consistently lower among the floodaffected. Table 11 reports β3 from equation (3) across the core flood measures when we average
across militant groups (Panel A), and for each of the militant groups separately: the sectarian
group Sipah-e-Sahaba Pakistan (SSP) (Panel B), the Pakistan Taliban (Panel C), which claimed
to be administering flood relief in some parts of KPK, and the Afghan Taliban (Panel D) which
rarely, if ever, conduct violent activities in Pakistan. β3 is negative in all regressions and depending
on the measure of flood effects, we reject the one-tailed null that the floods had a positive impact
on support for militants on average at the 95% percent level for most objective measures and at
the 80% level on average for the subjective measures.
INSERT TABLE 11 HERE
Nor did militant service provision in flood-affected areas help them generate support. We
collected data on all locations where militants were providing relief and which organizations were
doing so using a Lexis-Nexis search of all flood coverage between July 1 and October 1, 2010. We
augmented these results by reviewing electronically-available Pakistani news sources.30 Despite
the international media coverage that seemed to suggest that militants were at the forefront of aid
delivery, we found fewer than two dozen sites of militant relief camps. Including a dichotomous
variable for whether a tehsil had militant relief camp in the regressions from table 11—either
interacted with the endorsement experiment or on its own—yields no evidence in the direction of
greater support for militant groups if they had a relief camp. The negative impact of flooding on
support for militant organizations is actually substantively larger in districts that had observed
militant relief camps and is more than twice as strong statistically.
30
The newspapers included in our search are: Pakistan Express Tribune, The Dawn, The News, and The Nation.
34
5.4
Ruling Out Patronage
So far we have seen that citizens in flood-affected areas have more aggressive attitudes about how to
petition for services, know more about politics, and turnout to vote at higher rates. An important
possibility to rule out is that these differences merely reflect the fact that patronage goods were
dispensed under the guise of flood relief. This is particularly important for assessing the results on
turnout and incumbent vote share.
The data provide several reasons to think the results are not driven by patronage. First,
as Table 12 shows, people were not discernibly happier with the government response in floodaffected areas as compared to non-affected ones. We asked respondents whether they thought the
government did a good job of dealing with the floods (on a 4-point scale from very bad job to
very good job)) and whether they thought their town was given better relief than other towns (on
a 3-point scale of better, the same, and worse). Neither indicator is consistently correlated with
flood exposure after adding our standard controls (if anything those in the hardest hit areas think
the government did a worse job), a fact that is not consistent with people voting at higher rates to
reward politicians for providing differential levels of patronage goods.
INSERT TABLE 12 HERE
Second, the flood effect on turnout is weakest in Sindh, where the PPP’s ability to distribute
patronage was strongest as it controlled both the national and provincial governments. Third, the
relative gain for the PPP in flood-affected areas vs. other areas of Sindh is very weak. In Sindh,
PPP actually loses votes in flood-affected constituencies relative to PML-N. Fourth, the impact of
the flood on political knowledge is strongly positive for the objective measures of those impacts
and weakly so for subjective ones, which is consistent with citizens living in flood-affected places
becoming more engaged but is not a prediction of the patronage argument. Finally, if the results
were driven by patronage we might expect that the probability that the incumbent party in 2008
lost in 2013 would be lower in flood-affected places where the Member of the National Assembly in
power had chances to use his/her development funds. That’s not the case, as there is no statistically
detectable difference between reelection probabilities across levels of flood exposure in NA or PA
elections and the turnover probability is generally a bit higher in flood-affected places.
35
6
Discussion
In assessing how the devastating 2010-11 floods affected political attitudes and behavior among
Pakistani citizens, we found five empirical regularities. First, citizens exposed to the 2010-11 floods
were more likely to think violent protests would be effective in achieving government responsiveness and more approving of this method than signing and presenting politicians with a petition.
Second, flood-affected citizens are more knowledgeable about politics. Third, flood victims did not
become more supportive of non-state militant groups, if anything they turned against them. Fourth,
turnout increased more in flood-affected constituencies from 2008 to 2013 than elsewhere. Fifth,
citizens living in flood affected areas in the country’s largest province appear to have given the previous incumbent some credit for its performance in responding to the floods, while citizens living
in flood-affected constituencies throughout the country turned away from parties making highly
particularistic appeals and towards the party that ran on a more programmatic platform. This
final section provides a possible theoretical interpretation of these empirical regularities, discusses
policy implications, and points towards avenues for future research.
6.1
Theoretical Implications
Our results provide an important note of caution for research using natural disasters to study economic mechanism that might explain how states make the transition from autocracy to democracy
and then on to functioning programmatic politics. In Pakistan a major disaster that created a
transient negative economic shock also led to a range of changes in citizens’ political attitudes
and behavior. A broad range of formal and informal theories suggest that informed and civically
engaged citizens are able to enforce better governance by providing politicians with incentives to
become more responsive and focus on public goods provision rather than relying on patronage or
particularistic and ethnic appeals to retain office. Researchers should therefore be careful of assuming a mono-causal, economic mechanism through which similar shocks affect governance. Disasters
may instead lead to a range of attitudinal and behavioral changes among citizens that alter the
costs and benefits politicians face from policy choices.
We believe the observed empirical patterns are consistent with the following interpretation.
Victims of the 2010-11 floods in Pakistan experienced a sudden and unexpected need for disaster
36
relief. Due to the massive scale of the disaster, the necessary coordination for managing international aid and equipment for getting it to people could only be provided by the government. This
event thus had unusual power for making citizens aware of how important it is to have a responsive
government providing basic services. This in turn increased their willingness to aggressively demand government services, but did not push people to abandon democratic institutions in support
of non-state actors. To the contrary, those hit hardest became more politically engaged, as captured by increased level of electoral turnout in the 2013 elections and their increased accumulation
of political knowledge.
The exact mechanism through which flood exposure caused this change in political attitudes,
however, remains open. We see at least two plausible mechanisms. The first is a straightforward
informational story. Through their experience, flood victims may have gained new information on
the proper role of government and its alternatives, which affected their beliefs about the importance
of good governance. For example, citizens hit by the flood experienced first hand how crucial the
effective provision of basic government services, such as disaster relief in form of shelter, sanitation,
food, and access to clean drinking, can be and learn that there is little alternative to government
action when it comes to providing theses services rapidly and at a large scale. This experience
and insight may have changed their attitudes towards government negligence and incompetence,
causing them to become more politically involved but also more supportive of unconventional forms
of demanding government action.
Alternatively, the mechanism linking flood exposure to the observed changes in political attitudes and behavior may lay in deep shifts of preferences and values. A considerable body of
psychological research finds that reports of “post-traumatic growth” experiences consistently outnumber reports of psychiatric disorders (Tedeschi and Calhoun, 2004). Among other domains of
personal growth after traumatic experiences, this line of research finds that victims experience a
changed sense of priorities, a greater sense of personal strength, and recognition of new possibilities
or paths for one’s life (e.g., Nolen-Hoeksema and Davis, 2002; Laufer and Solomon, 2006). Several recent papers in conflict research have found that exposure to violence is robustly linked to
greater political engagement and a higher willingness to contribute to a collective good. Bellows
and Miguel (2009), for example, observe that individuals in Sierra Leone whose households experienced more intense war violence are more likely to attend community meetings, more likely to
37
join local political and community groups, and more likely to vote. Using data from child soldiers
in Northern Uganda, Blattman (2009) shows that past experience of violence resulted in greater
political participation in the post-war period. Drawing on experimental data from 35 randomly
selected communities in Burundi, Voors et al. (2012) find that individuals that have been exposed
to greater levels of violence display more altruistic behavior towards their neighbors, are more risk
seeking, and have higher economic discount rates. Finally, Gilligan, Pasquale and Samii (2011)
find that members of communities with greater exposure to violence during Nepal’s ten-year civil
war exhibit significantly greater levels of social capital, measured by the subjects willingness to
invest in trust-based transactions and contribute to a collective good. Granted, victimization in
civil war is qualitatively different from being a victim of flooding. Yet, fearing for your own life
and the lives of loved ones in the wake of flash floods may nevertheless qualify as a traumatic event
that is sufficiently strong to unleash the described psychological process resulting in the change of
preferences, values, and ultimately political attitudes.
Through which mechanism do changes in political attitudes affect political behavior? We believe
that “expressive” theories of participation offer a possible explanation. These theories argue that
individuals engage in otherwise too costly political actions (e.g., protesting or voting) because they
value the act of political expression itself. Citizens, for example, turn out to vote not because of
purely materialistic incentives, but because of a “sense of civic duty” (e.g., Riker and Ordeshook,
1968) or because of the inherent value they place on the act of voting itself (e.g., Dhillon and Peralta,
2002; Feddersen, 2004). The origin of such a “ sense of civic duty” or inherent value of political
participation and the reason why it varies across citizens and time is not yet well understood. The
informational and psychological mechanisms above may help fill this gap. Traumatic events such
as the massive 2010-11 floods in Pakistan may contribute to changes in victims’ political attitudes
through either an informational process or a reordering of personal goals and priorities. These in
turn may increases their “sense of civic duty” and thus their willingness to vote.
6.2
Policy Implications
Our findings are good news for policy makers worried that natural disasters in weakly institutionalized countries will weaken fragile democratic institutions. As the Pakistani example shows, this
need not be the case. Exposure to natural disasters might actually highlight the necessity of gov38
ernmental services and strengthen citizen’s willingness to demand government responsiveness and
increase their engagement in democratic process. A key avenue for future research is the extent to
which these salutary effects are conditioned on an effective government response in the aftermath of
the disaster. At a minimum, these results highlight the importance of making sure governments can
respond effectively to disasters. Beyond the humanitarian imperative, supporting such a response
may enhance prospects for long-run improvements in governance.
There are, however, important limitations to our research.The political attitudes (i.e., way to
interact with the government, support for militants) measured in the survey and the political behaviors studied (i.e., electoral turnout and major party vote share) took place in an important,
but very specific context. In particular, Pakistani politics are very poorly institutionalized relative to many polities, with politicians historically switching parties to deliver their vote banks to
whomever offers them the best patronage opportunities. Though similar conditions are found in
other countries (e.g., parts of India) the specificity of the Pakistani context should be kept in mind
when generalizing the findings to political attitudes and behavior more broadly. Our results may
not apply to other countries with a different institutional structures, different types of militant
groups, and different levels of international support in the aftermath of a natural disaster.
6.3
Future Research
Still, our analysis highlights several avenues for future research. For instance, it is important to
uncover the causal mechanism underlying the impact of flood exposure on political attitudes and
behavior. Does a natural disaster alter victims’ preferences and/or political values or does it simply
provide useful information and a concrete experience that teaches victims the value of democracy
and government responsiveness? Distinguishing these two mechanisms is challenging as changes to
preferences and values are notoriously hard to measure.
Another avenue is to study how long the observed beneficial effects on political participation
persist. Will the effect sustain over time, suggesting that the flood caused an enduring change in
political participation? The fact that we were able to detect the effect three years after the floods
provides some evidence that the effect is quiet persistent.
Finally, we need to extend this analysis to further parse the mechanisms behind the effects
identified above. In particular, the National Disaster Management Agency of Pakistan collected
39
remarkably fine-grained data on what kinds of aid were delivered, where, and by whom. Future
research can assess how these results vary by average levels of aid provided.
40
Figures
Figure 1: Standardized Impact of Floods in Pakistan 1975 – 2012
41
Figure 2: Balance of Covariates for the Vignette Experiment
42
Figure 3: Balance of Covariates for the Endorsement Experiment
43
Figure 4: Composite Maximal Flood Extent in 2010 and 2011 and Surveyed Tehsils
44
Figure 5: Variance in Flood Impacts between National and Provincial Assembly Constituencies
45
46
Figure 6: Major Party Vote Shares in Constituencies by Flood Exposure
Tables
Variables
% Area Flooded 2010 & 2011
% Population Exposed 2010 & 2011
Population Exposed > Median == 1
Population Exposed > 90th Percentile == 1
Flood Exposure
Affected == 1
Very Bad | Extremely Bad == 1
Distance to River
Neighboring River == 1
Std. Dev. of Elevation
Mean of Elevation
Male
Head of Household
Age
Read
Math
Education
Sunni
Political Knowledge (PCA)
Political Knowledge (Additive)
Satisfaction Government Relief Effort
Government Relief Comparison Other Towns
% Area Flooded 2010 & 2011
Area Exposed > Median == 1
Area Exposed > 90th Percentile == 1
% Population Exposed 2010 & 2011
Population Exposed <= Median == 1
Population Exposed > Median == 1
Population Exposed > 90th Percentile == 1
Affected (NDMA) == 1
% Population Exposed 2012
Distance to River
Neighboring River == 1
Std. Dev. of Elevation
Mean of Elevation
Turnout Diff. 2013-2008
Diff. in Turnout Diff. (2013-2008)-(2008-2002)
PPP Vote Share Diff. 2013-2008
PML-N Vote Share Diff. 2013-2008
% Area Flooded 2010 & 2011
Area Exposed > Median == 1
Area Exposed > 90th Percentile == 1
% Population Exposed 2010 & 2011
Population Exposed > Median == 1
Population Exposed > 90th Percentile == 1
Affected (NDMA) == 1
% Population Exposed 2012
Distance to River
Neighboring River == 1
Std. Dev. of Elevation
Mean of Elevation
Turnout Diff. 2013-2008
Diff. in Turnout Diff. (2013-2008)-(2008-2002)
Unit
percent
percent
dummy
dummy
4 pt. scale [0,1]
dummy
dummy
100 kilometers
dummy
1,000 meters
1,000 meters
dummy
dummy
100 years
dummy
dummy
6 pt. scale [0,1]
dummy
index
index [0,1]
4 pt. scale [1,4]
3 pt. scale [1,3]
percent
dummy
dummy
percent
dummy
dummy
dummy
dummy
percent
100 kilometers
dummy
1,000 meters
1,000 meters
[-1,1]
[-1,1]
percent
percent
percent
dummy
dummy
percent
dummy
dummy
dummy
percent
100 kilometers
dummy
1,000 meters
1,000 meters
[-1,1]
[-1,1]
Level
Tehsil
Tehsil
Tehsil
Tehsil
Individual
Individual
Individual
Tehsil
Tehsil
Tehsil
Tehsil
Individual
Individual
Individual
Individual
Individual
Individual
Individual
Individual
Individual
Individual
Individual
NA-Constituency
NA-Constituency
NA-Constituency
NA-Constituency
NA-Constituency
NA-Constituency
NA-Constituency
NA-Constituency
NA-Constituency
NA-Constituency
NA-Constituency
NA-Constituency
NA-Constituency
NA-Constituency
NA-Constituency
NA-Constituency
NA-Constituency
PA-Constituency
PA-Constituency
PA-Constituency
PA-Constituency
PA-Constituency
PA-Constituency
PA-Constituency
PA-Constituency
PA-Constituency
PA-Constituency
PA-Constituency
PA-Constituency
PA-Constituency
PA-Constituency
N
12994
12994
12994
12994
12994
12994
12994
12994
12994
12994
12994
12994
12968
12990
12931
12367
12889
12923
12994
12994
12240
11671
249
249
249
249
249
249
249
249
249
249
249
249
249
249
249
249
249
569
569
569
569
569
569
569
569
569
569
569
569
569
569
Median
0
0
0
0
0.250
0
0
0.331
0
0.029
0.191
1
0
0.340
1
1
0.167
1
2.120
0.700
2
2
0
0
0
0
0
0
0
1
0
0.260
1
0.007
0.179
0.114
0.083
-0.158
0.051
0
0
0
0
0
0
0
0
0.269
0
0.006
0.179
0.112
0.077
Mean
0.083
0.059
0.222
0.047
0.385
0.268
0.181
0.899
0.469
0.128
0.540
0.515
0.373
0.350
0.545
0.776
0.275
0.930
2.024
0.673
2.417
1.838
0.082
0.253
0.052
0.065
0.233
0.249
0.052
0.510
0.012
0.482
0.522
0.093
0.351
0.113
0.086
-0.165
0.11
0.086
0.220
0.042
0.067
0.206
0.042
0.464
0.013
0.579
0.373
0.084
0.388
0.103
0.072
Std. Dev.
0.157
0.136
0.416
0.211
0.248
0.443
0.385
1.242
0.499
0.195
0.724
0.500
0.484
0.119
0.498
0.417
0.281
0.256
0.741
0.227
0.863
0.644
0.150
0.436
0.223
0.127
0.424
0.433
0.223
0.501
0.052
0.715
0.501
0.198
0.575
0.079
0.141
0.166
0.194
0.167
0.414
0.201
0.146
0.405
0.201
0.499
0.062
0.893
0.484
0.175
0.598
0.094
0.150
Table 1: Summary Statistics of the Covariates Across Datasets
47
Min
0
0
0
0
0.25
0
0
0.005
0
0.002
0.005
0
0
0.18
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0.016
0
0
0.004
-0.114
-0.371
-0.59
-0.489
0
0
0
0
0
0
0
0
0.001
0
0
0.002
-0.416
-0.544
Max
0.928
0.897
1
1
1
1
1
6.856
1
1.021
4.247
1
1
0.85
1
1
1
1
3.006
1
4
3
0.79
1
1
0.61
1
1
1
1
0.489
5.517
1
1.097
3.922
0.372
0.638
0.388
0.669
0.912
1
1
1
1
1
1
0.7
6.353
1
1.059
4.243
0.506
0.822
(1)
(2)
(3)
(4)
Independent
% Area
% Population
Pop. Exposed >
Pop. Exposed >
Variable:
Flooded
Exposed
Median
90th Percentile
Panel A: Effectiveness of Junaid's/Mahir's Actions (mean = 0.65, sd in non-violent = 0.30, sd in violent = 0.38)
β1: Violent Vignette
β2: Flood Treatment
β3: Violent * Flood
-0.215***
(0.018)
0.208**
(0.105)
0.198**
(0.086)
-0.216***
(0.018)
0.058
(0.107)
0.282***
(0.096)
-0.220***
(0.019)
0.040
(0.041)
0.089***
(0.033)
-0.204***
(0.016)
-0.040
(0.047)
0.118**
(0.058)
48
R-squared
0.237
0.235
0.235
0.232
Observations
11963
11963
11963
11963
Clusters
1094
1094
1094
1094
Panel B: Approval of Junaid's/Mahir's Actions (mean = 0.62, sd in non-violent = 0.31, sd in violent = 0.39)
β1: Violent Vignette
β2: Flood Treatment
β3: Violent * Flood
-0.229***
(0.019)
0.076
(0.116)
0.213**
(0.088)
-0.230***
(0.018)
-0.069
(0.114)
0.325***
(0.097)
-0.232***
(0.019)
0.032
(0.041)
0.089***
(0.034)
-0.218***
(0.017)
-0.121**
(0.049)
0.172***
(0.055)
(5)
Self-Reported
Flood Exposure
(6)
Self-Reported
Affected
(7)
Self-Reported
Very | Extremely Bad
-0.248***
(0.026)
0.066*
(0.036)
0.129***
(0.049)
-0.221***
(0.018)
0.010
(0.022)
0.083***
(0.030)
-0.209***
(0.017)
0.050**
(0.021)
0.058*
(0.031)
0.237
11963
1094
0.236
11963
1094
0.236
11963
1094
-0.274***
(0.027)
-0.007
(0.039)
0.165***
(0.052)
-0.240***
(0.019)
-0.039
(0.023)
0.112***
(0.032)
-0.225***
(0.018)
0.017
(0.023)
0.081**
(0.032)
R-squared
0.252
0.253
0.253
0.251
0.253
0.253
0.252
Observations
11963
11963
11963
11963
11963
11963
11963
Clusters
1094
1094
1094
1094
1094
1094
1094
Notes: Exposure measures calculated at the tehsil level in columns (1) - (4) and the individual level in columns (5) - (7). All regression include; district fixed effects; geographic controls at
the tehsil level, including distance to major river, dummy for tehsils boardering a major river, std. dev. of the tehsil's elevation, and mean tehsil elevation; and demographic controls including
gender, a head of household dummy, age, a literacy dummy, a basic numeracy dummy, education, and a Sunni dummy. Estimates significant at the 0.05 (0.10, 0.01) level are marked with **
(*, ***). Standard errors are clustered at the primary sampling unit.
Table 2: Impact of Flooding on Approval and Perceived Efficacy of Violent Protest
(1)
(2)
Independent
Self-Reported
Self-Reported
Variables:
Flood Exposure
Affected
Panel A: Effectiveness of Junaid's/Mahir's Actions (mean = 0.65)
β1: Violent Vignette
β2: Flood Treatment
β3: Violent * Flood
-0.256***
(0.027)
0.068**
(0.034)
0.138***
(0.046)
-0.224***
(0.019)
0.019
(0.021)
0.080***
(0.029)
R-squared
0.306
0.305
Observations
11963
11963
Clusters
1094
1094
Panel B: Approval of Junaid's/Mahir's Actions (mean = 0.62)
β1: Violent Vignette
β2: Flood Treatment
β3: Violent * Flood
-0.283***
(0.028)
0.004
(0.038)
0.182***
(0.050)
-0.241***
(0.020)
-0.025
(0.023)
0.107***
(0.031)
(3)
Self-Reported
Very | Extremely Bad
-0.215***
(0.018)
0.043**
(0.020)
0.063**
(0.029)
0.305
11963
1094
-0.230***
(0.019)
0.016
(0.022)
0.093***
(0.031)
R-squared
0.322
0.322
0.322
Observations
11963
11963
11963
Clusters
1094
1094
1094
Notes: Exposure measures calculated at the individual level. All regressions include; tehsil fixed
effects and demographic controls, including gender, a head of household dummy, age, a literacy
dummy, a basic numeracy dummy, education, and a Sunni dummy. Estimates significant at the
0.05 (0.10, 0.01) level are marked with ** (*, ***). Standard errors are clustered at the primary
sampling unit.
Table 3: Impact of Flooding on Perceived Efficacy and Approval of Violent Protest with Tehsil
Fixed Effects
49
(1)
(2)
Independent
% Area
% Population
Variables:
Flooded
Exposed
Panel A: Effectiveness of Junaid's/Mahir's Actions (mean = 0.66)
β1: Violent Vignette
β2: Flood Treatment
β3: Violent * Flood
-0.203***
(0.027)
-0.027
(0.111)
0.237**
(0.094)
-0.204***
(0.024)
-0.186*
(0.099)
0.409***
(0.093)
50
R-squared
0.227
0.230
Observations
5685
5685
Clusters
513
513
Panel B: Approval of Junaid's/Mahir's Actions (mean = 0.62)
β1: Violent Vignette
β2: Flood Treatment
β3: Violent * Flood
-0.230***
(0.028)
-0.081
(0.130)
0.258**
(0.103)
-0.230***
(0.025)
-0.224**
(0.109)
0.434***
(0.098)
(3)
Pop. Exposed >
Median
(4)
Pop. Exposed >
90th Percentile
(5)
Self-Reported
Flood Exposure
(6)
Self-Reported
Affected
(7)
Self-Reported
Very | Extremely Bad
-0.214***
(0.026)
-0.087*
(0.046)
0.152***
(0.040)
-0.183***
(0.022)
-0.109***
(0.035)
0.171***
(0.055)
-0.253***
(0.034)
-0.035
(0.042)
0.218***
(0.056)
-0.201***
(0.024)
-0.038
(0.028)
0.124***
(0.038)
-0.193***
(0.023)
-0.016
(0.025)
0.125***
(0.035)
0.232
5685
513
0.226
5685
513
0.230
5685
513
0.229
5685
513
0.229
5685
513
-0.241***
(0.028)
-0.094**
(0.044)
0.163***
(0.043)
-0.209***
(0.023)
-0.171***
(0.046)
0.207***
(0.057)
-0.283***
(0.036)
-0.120**
(0.051)
0.235***
(0.064)
-0.229***
(0.025)
-0.080**
(0.031)
0.145***
(0.041)
-0.216***
(0.024)
-0.060**
(0.030)
0.124***
(0.040)
R-squared
0.253
0.257
0.259
0.255
0.255
0.256
0.253
Observations
5685
5685
5685
5685
5685
5685
5685
Clusters
513
513
513
513
513
513
513
Notes: Exposure measures calculated at the tehsil level in columns (1) - (4) and the individual level in columns (5) - (7). All regressions include: district fixed effects; geographic controls at
the tehsil level, including distance to major river, std. dev. of the tehsil's elevation, and mean tehsil elevation; and demographic controls including gender, a head of household dummy, age, a
literacy dummy, a basic numeracy dummy, education, and a Sunni dummy. Estimates significant at the 0.05 (0.10, 0.01) level are marked with ** (*, ***). Standard errors are clustered at the
primary sampling unit.
Table 4: Impact of Flooding on Perceived Efficacy and Approval of Violent Protest in Tehsils Bordering Major Rivers
Comparison of Main Effect Controls in the
Controls in the
to Bias Accounted for by
Restricted Set
Full Set
Panel A: Effectiveness of Junaid's/Mahir's Actions
Controling for individual
District FE and
demographics
District FE
Demographics
Controling for demographics
and tehsil geography
Controling for all common
district-specfic factors
% Area
Flooded
% Population
Exposed
Pop. Exposed >
Median
Independent Variables
Pop. Exposed >
90th Percentile
Self-Reported
Flood Exposure
Self-Reported
Affected
Self-Reported
Very | Extremely Bad
-23.603
-52.190
-14.622
30.514
-77.240
-59.049
-27.179
District FE
Full Set of Controls
as in Table 2
-15.523
-26.814
-9.939
59.602
-32.063
-32.737
-18.537
Geographic and
Demographics
Full Set of Controls
as in Table 2
2.773
5.376
-15.660
5.411
2.095
2.356
1.678
51
Panel B: Approval of Junaid's/Mahir's Actions
Controling for individual
demographics
District FE
District FE and
Demographics
-20.713
-50.145
-12.405
40.432
-81.275
-59.395
-32.242
Controling for demographics
and tehsil geography
District FE
Full Set of Controls
as in Table 2
-14.044
-29.854
-8.984
71.124
-37.358
-37.939
-22.221
Controling for all common
district-specfic factors
Geographic and
Demographics
Full Set of Controls
as in Table 2
2.439
5.153
6.610
5.808
2.743
3.696
2.313
Notes: Each Cell in the table reports ratios based on the coefficient of β3 (i.e., Violent Vignette * Flood) from two individual-level regressions. In one, the covariates include the "restricted set" of control variables; call this coefficient βR. In the
other, the covariates include the "full set" of controls; call this coefficient βF. In both regressions, the sample sizes are the same. The reported ratio is calculated as: βF/(βR-βF). Geographic controls at the tehsil level include distance to major
river, dummy for tehsils boardering a major river, std. dev. of the tehsil's elevation, and mean tehsil elevation. Demographic controls include gender, a head of household dummy, age, a literacy dummy, a basic numeracy dummy, education,
and a Sunni dummy.
Table 5: Using Selection on Observables to Assess the Bias from Unobservables
(1)
(2)
(3)
(4)
Independent
% Area
% Population
Pop. Exposed >
Pop. Exposed >
Variable:
Flooded
Exposed
Median
90th Percentile
Panel A: Index of Political Knowledge (Principal Component Analysis) (mean = 2.024, sd = 0.741)
Flood Treatment
0.168
(0.122)
0.453***
(0.132)
0.077
(0.056)
R-squared
0.375
0.376
0.375
Observations
12178
12178
12178
Clusters
1097
1097
1097
Panel B: Index of Political Knowledge (Additive) (mean = 0.673, sd = 0.227)
(5)
Self-Reported
Flood Exposure
(6)
Self-Reported
Affected
(7)
Self-Reported
Very | Extremely Bad
0.213***
(0.058)
0.149***
(0.052)
0.101***
(0.030)
0.062**
(0.029)
0.376
12178
1097
0.376
12178
1097
0.376
12178
1097
0.375
12178
1097
52
Flood Treatment
0.047
(0.036)
0.136***
(0.039)
0.020
(0.016)
0.063***
(0.018)
0.038**
(0.016)
0.025***
(0.009)
0.016*
(0.009)
R-squared
Observations
Clusters
0.380
12178
1097
0.381
12178
1097
0.380
12178
1097
0.381
12178
1097
0.381
12178
1097
0.381
12178
1097
0.380
12178
1097
Notes: Exposure measures calculated at the tehsil level in columns (1) - (4) and the individual level in columns (5) - (7). All regressions include: district fixed effects; geographic controls at
the tehsil level including distance to major river, dummy for tehsils boardering a major river, std. dev. of the tehsil's elevation, and mean tehsil elevation; and demographic controls including
gender, a head of household dummy, age, a literacy dummy, a basic numeracy dummy, education. and a Sunni dummy. Estimates significant at the 0.05 (0.10, 0.01) level are marked with **
(*, ***). Standard errors are clustered at the primary sampling unit.
Table 6: Impact of Flooding on Political Knowledge
(1)
(2)
(3)
(4)
(5)
(6)
Independent
% Area
Area Exposed > Area Exposed > % Population Pop. Exposed > Pop. Exposed >
Variables:
Flooded
Median
90th Percentile
Exposed
Median
90th Percentile
Panel A: Turnout Change (2013 - 2008) with Division Fixed Effects (mean = 0.11, sd = 0.08)
Flood Treatment
0.109*
(0.056)
0.034**
(0.016)
0.034
(0.023)
R-squared
0.306
0.308
0.299
Observations
246
246
246
Clusters
90
90
90
Panel B: First Differences ((2013-2008)-(2008-2002)) (mean = 0.09, sd = 0.14)
Flood Treatment
0.234***
(0.072)
0.074***
(0.025)
0.089***
(0.033)
0.162**
(0.070)
0.016
(0.018)
0.054*
(0.028)
0.313
246
90
0.294
246
90
0.305
246
90
0.308***
(0.094)
0.055**
(0.026)
0.099**
(0.041)
0.060
246
90
0.056
246
90
0.032
(0.031)
0.084*
(0.046)
R-squared
0.079
0.077
0.056
0.085
Observations
246
246
246
246
Clusters
90
90
90
90
Panel C: First Differences ((2013-2008)-(2008-2002)) with Division Fixed Effects
Flood Treatment
0.249**
(0.103)
0.060*
(0.030)
0.079**
(0.039)
0.314**
(0.127)
R-squared
0.279
0.271
0.267
0.280
0.258
0.266
Observations
246
246
246
246
246
246
Clusters
90
90
90
90
90
90
Notes: Exposure measures calculated at the constituency level. All regressions include geographic controls at the constituency level
including distance to major river, dummy for constituencies boardering a major river, std. dev. of the constituency's elevation, and
mean constituency elevation, as well as the proportion of constituency population exposed to the 2012 floods. Estimates significant
at the 0.05 (0.10, 0.01) level are marked with ** (*, ***). Standard errors are clustered at the district level.
Table 7: Impact of Flooding on Turnout in the 2013 Pakistani National Assembly Elections
53
(1)
(2)
(3)
(4)
(5)
(6)
Independent
% Area
Area Exposed > Area Exposed > % Population Pop. Exposed > Pop. Exposed >
Variables:
Flooded
Median
90th Percentile
Exposed
Median
90th Percentile
Panel A: Turnout Change (2013 - 2008) with Division Fixed Effects (mean = 0.10, sd = 0.09)
Flood Treatment
0.112***
(0.035)
0.031***
(0.011)
0.034**
(0.016)
R-squared
0.311
0.305
0.300
Observations
556
556
556
Clusters
109
109
109
Panel B: First Differences ((2013-2008)-(2008-2002)) (mean = 0.07, sd = 0.15)
Flood Treatment
0.237***
(0.068)
0.084***
(0.025)
0.092***
(0.035)
0.110**
(0.046)
0.028**
(0.012)
0.022
(0.019)
0.308
556
109
0.030
556
109
0.298
556
109
0.246**
(0.098)
0.077***
(0.024)
0.065*
(0.039)
0.098
556
109
0.073
556
109
0.063***
(0.022)
0.038
(0.036)
R-squared
0.110
0.105
0.080
0.102
Observations
556
556
556
556
Clusters
109
109
109
109
Panel C: First Differences ((2013-2008)-(2008-2002)) with Division Fixed Effects
Flood Treatment
0.222***
(0.062)
0.065***
(0.022)
0.067**
(0.032)
0.196**
(0.094)
R-squared
0.277
0.269
0.260
0.268
0.267
0.256
Observations
556
556
556
556
556
556
Clusters
91
91
91
91
91
91
Notes: Exposure measures calculated at the constituency level. All regressions include geographic controls at the constituency level
including distance to major river, dummy for constituencies boardering a major river, std. dev. of the constituency's elevation, and
mean constituency elevation, as well as the proportion of constituency population exposed to the 2012 floods. Estimates significant
at the 0.05 (0.10, 0.01) level are marked with ** (*, ***). Standard errors are clustered at the district level.
Table 8: Impact of Flooding on Turnout in the 2013 Pakistani Provincial Assembly Elections
54
(1)
(2)
(3)
(4)
(5)
(6)
Independent
% Area
Area Exposed > Area Exposed > % Population Pop. Exposed > Pop. Exposed >
Variables:
Flooded
Median
90th Percentile
Exposed
Median
90th Percentile
Panel A: Turnout Change (2013 - 2008) Including Migration Estimate (mean = 0.11, sd = 0.08)
Flood Treatment
0.069
(0.060)
0.015
(0.016)
0.026
(0.027)
0.175**
(0.082)
0.024
(0.021)
0.052*
(0.027)
Migrated
0.186
(0.201)
0.197
(0.202)
0.170
(0.205)
0.199
(0.199)
0.186
(0.200)
0.169
(0.206)
R-squared
0.454
0.452
0.453
0.467
0.454
Observations
158
158
158
158
158
Clusters
52
52
52
52
52
Panel B: Turnout Change (2013 - 2008) in NA-Constituencies Without Migration (mean = 0.12, sd = 0.07)
Flood Treatment
0.079
(0.053)
0.029**
(0.014)
0.024
(0.023)
0.134**
(0.065)
0.012
(0.017)
R-squared
0.326
0.332
0.320
0.338
0.317
Observations
157
157
157
157
157
Clusters
69
69
69
69
69
Panel C: Turnout Change (2013 - 2008) in PA-Constituencies Without Migration (mean = 0.12, sd = 0.07)
Flood Treatment
0.089***
(0.033)
0.024**
(0.011)
0.030*
(0.016)
0.090**
(0.044)
0.022*
(0.011)
0.459
158
52
0.053*
(0.029)
0.337
157
69
0.018
(0.018)
R-squared
0.314
0.307
0.305
0.312
0.306
0.302
Observations
378
378
378
378
378
378
Clusters
87
87
87
87
87
87
Notes: Exposure measures calculated at the constituency level. All regressions include division fixed effects and geographic controls
at the constituency level including distance to major river, dummy for constituencies boardering a major river, std. dev. of the
constituency's elevation, and mean constituency elevation, as well as the proportion of constituency population exposed to the 2012
floods. Estimates significant at the 0.05 (0.10, 0.01) level are marked with ** (*, ***). Standard errors are clustered at the district
level.
Table 9: Evidence That Turnout Increase Is Not Due to a Composition Effect
55
National
(1)
(2)
Political Party:
PPP
PML-N
Panel A: Change in Vote Share (2013 - 2008)
Geographic Extent:
Affected (NDMA)
0.062**
(0.030)
0.037
(0.037)
Balochistan
(3)
(4)
PPP
PML-N
0.033
(0.092)
0.003
(0.045)
R-squared
0.063
0.107
0.015
0.001
Observations
249
249
13
13
Clusters
91
91
11
11
Panel B: Change in Vote Share (2013 - 2008) with Control Variables
Affected (NDMA)
0.054
(0.035)
0.017
(0.041)
0.003
(0.107)
-0.053
(0.115)
KPK
Punjab
Sindh
56
(5)
PPP
(6)
PML-N
(7)
PPP
(8)
PML-N
(9)
PPP
(10)
PML-N
0.016
(0.094)
0.059*
(0.031)
0.089**
(0.038)
0.033
(0.031)
0.012
(0.038)
0.063**
(0.023)
0.002
34
22
0.031
34
22
0.062
143
36
0.005
143
36
0.001
57
21
0.055
57
21
-0.028
(0.076)
0.071
(0.046)
0.096**
(0.042)
-0.017
(0.053)
-0.012
(0.128)
0.043**
(0.020)
0.128
143
36
0.059
57
21
0.265
57
21
R-squared
0.087
0.124
0.456
0.348
0.162
0.235
0.086
Observations
249
249
13
13
34
34
143
Clusters
91
91
11
11
22
22
36
Panel C: Change in Vote Share (2013 - 2008) with Control Variables (Baseline Categorie: Unaffected Constituencies)
Population Exposed
>0 and ≤ Median
0.039
(0.038)
0.022
(0.041)
-0.003
(0.103)
-0.051
(0.110)
0.006
(0.061)
0.124**
(0.043)
0.068
(0.047)
-0.020
(0.055)
-0.039
(0.148)
0.037
(0.026)
Population Exposed
> Median
0.087*
(0.044)
0.030
(0.044)
0.117
(0.123)
-0.143
(0.134)
-0.081
(0.088)
0.153**
(0.072)
0.153*
(0.079)
-0.011
(0.062)
0.090*
(0.051)
0.024
(0.030)
R-squared
0.095
0.137
0.479
0.393
0.205
0.240
0.103
0.112
0.117
0.266
Observations
249
249
13
13
34
34
143
143
57
57
Clusters
91
91
11
11
22
22
36
36
21
21
Notes: Exposure measures calculated at the constituency level. Regressions in Panel B and C include geographic controls at the constituency level, including distance to
major river, dummy for constituencies boardering a major river, std. dev. of the constituency's elevation, and mean constituency elevation, as well as the proportion of
constituency population exposed to the 2012 floods. National-level regressions (Models 1 and 2) also include province fixed effects. Estimates significant at the 0.05 (0.10,
0.01) level are marked with ** (*, ***). All standard errors are clustered at the district level.
Table 10: Impact of Flooding on the Differences in the PPP’s and PML-N’s Vote Shares between 2013 and 2008
(1)
(2)
(3)
Independent
% Area
% Population
Pop. Exposed >
Variables:
Flooded
Exposed
Median
Panel A: All Militant Groups (mean policy prefernce in control group = 0.62, sd = 0.26)
(4)
Pop. Exposed >
90th Percentile
(5)
Self-Reported
Flood Exposure
(6)
Self-Reported
Affected
(7)
Self-Reported
Very | Extremely Bad
β3: Militant * Flood
-0.144**
(0.068)
-0.133*
(0.072)
-0.062**
(0.031)
-0.063
(0.045)
-0.043
(0.043)
-0.036
(0.030)
-0.019
(0.026)
p-value for H0: β3>0
0.02
0.03
0.02
0.08
0.16
0.12
0.26
0.308
8443
828
0.310
8443
828
0.306
8443
828
0.307
8443
828
0.306
8443
828
R-squared
0.308
0.308
Observations
8443
8443
Clusters
828
828
Panel B: SSP (mean policy prefernce in control group = 0.63, sd = 0.25)
57
β3: Militant * Flood
-0.142
(0.088)
-0.143
(0.088)
-0.067*
(0.040)
-0.068
(0.052)
-0.039
(0.049)
-0.041
(0.032)
-0.015
(0.030)
p-value for H0: β3>0
0.05
0.05
0.05
0.09
0.21
0.10
0.31
R-squared
0.304
0.306
0.304
Observations
4231
4231
4231
Clusters
420
420
420
Panel C: Pakistan Taliban (mean policy prefernce in control group = 0.63, sd = 0.25)
0.306
4231
420
0.301
4231
420
0.302
4231
420
0.301
4231
420
β3: Militant * Flood
-0.209**
(0.105)
-0.289***
(0.109)
-0.078*
(0.043)
-0.153**
(0.068)
-0.058
(0.062)
-0.061
(0.041)
-0.014
(0.037)
p-value for H0: β3>0
0.02
0.00
0.03
0.01
0.17
0.07
0.35
R-squared
0.303
0.304
0.303
Observations
4250
4250
4250
Clusters
423
423
423
Panel D: Afghan Taliban (mean policy prefernce in control group = 0.62, sd = 0.26)
0.303
4250
423
0.300
4250
423
0.302
4250
423
0.300
4250
423
β3: Militant * Flood
-0.148*
(0.079)
-0.115
(0.081)
-0.066
(0.041)
-0.040
(0.046)
-0.049
(0.051)
-0.025
(0.034)
-0.025
(0.031)
p-value for H0: β3>0
0.03
0.08
0.06
0.20
0.17
0.24
0.21
R-squared
0.390
0.391
0.390
0.395
0.388
0.389
0.388
Observations
4228
4228
4228
4228
4228
4228
4228
Clusters
425
425
425
425
425
425
425
Notes: Exposure measures calculated at the tehsil level in columns (1) - (4) and the individual level in columns (5) - (7). All regressions include: district fixed effects; geographic controls at the
tehsil level including distance to major river, dummy for tehsils boardering a major river, std. dev. of the tehsil's elevation, and mean tehsil elevation; and demographic controls including
gender, a head of household dummy, age, a literacy dummy, a basic numeracy dummy, education, and a Sunni dummy. Estimates significant at the 0.05 (0.10, 0.01) level are marked with ** (*,
***). Standard errors are clustered at the primary sampling unit.
Table 11: Impact of Flooding on the Support for Militant Groups
(1)
(2)
Independent
% Area
% Population
Variable:
Flooded
Exposed
Panel A: Did Government Do a Good Job? (mean = 2.024, sd = 0.741)
Flood Treatment
0.235
(0.190)
-0.037
(0.171)
58
R-squared
0.207
0.206
Observations
10848
10848
Clusters
1047
1047
Panel B: Did Other Town Get Better Relief? (mean = 0.673, sd = 0.227)
Flood Treatment
0.006
(0.148)
-0.166
(0.151)
(3)
Pop. Exposed >
Median
(4)
Pop. Exposed >
90th Percentile
(5)
Self-Reported
Flood Exposure
(6)
Self-Reported
Affected
(7)
Self-Reported
Very | Extremely Bad
0.108
(0.081)
-0.195**
(0.084)
-0.032
(0.070)
-0.027
(0.042)
-0.019
(0.040)
0.207
10848
1047
0.207
10848
1047
0.206
10848
1047
0.206
10848
1047
0.206
10848
1047
0.027
(0.066)
-0.056
(0.068)
-0.028
(0.055)
-0.013
(0.031)
-0.018
(0.033)
R-squared
0.150
0.151
0.150
0.151
0.150
0.150
0.150
Observations
10848
10848
10848
10848
10848
10848
10848
Clusters
1047
1047
1047
1047
1047
1047
1047
Notes: Exposure measures calculated at the tehsil level in columns (1) - (4) and the individual level in columns (5) - (7). All regression include; district fixed effects; geographic controls at the
tehsil level, including distance to major river, dummy for tehsils boardering a major river, std. dev. of the tehsil's elevation, and mean tehsil elevation; and demographic controls including
gender, a head of household dummy, age, a literacy dummy, a basic numeracy dummy, education, and a Sunni dummy. Estimates significant at the 0.05 (0.10, 0.01) level are marked with **
(*, ***). Standard errors are clustered at the primary sampling unit.
Table 12: Impact of Flooding on Satisfaction with Government Relief
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63
Appendix
(1)
Independent
% Area
Variable:
Flooded
Panel A: Non-Militant Groups
Tabliqhi Jamaat
Flood Treatment
(2)
% Population
Exposed
(3)
Pop. Exposed >
Median
(4)
Pop. Exposed >
90th Percentile
(5)
Self-Reported
Flood Exposure
(6)
Self-Reported
Affected
(7)
Self-Reported
Very | Extremely Bad
-0.026
(0.105)
0.207*
(0.121)
0.051
(0.049)
0.092
(0.065)
0.066*
(0.040)
0.045**
(0.020)
0.022
(0.022)
-0.255
(0.230)
-0.337*
(0.195)
-0.097
(0.088)
-0.168
(0.115)
0.034
(0.074)
0.006
(0.043)
0.003
(0.041)
-0.166
(0.145)
-0.211
(0.150)
-0.056
(0.068)
-0.080
(0.089)
0.007
(0.051)
-0.008
(0.029)
0.004
(0.029)
-0.230*
(0.122)
-0.146
(0.128)
-0.108**
(0.054)
-0.098
(0.071)
-0.020
(0.062)
-0.001
(0.035)
-0.035
(0.036)
-0.249
(0.156)
0.006
(0.058)
-0.098
(0.070)
-0.054
(0.058)
-0.050*
(0.030)
-0.027
(0.037)
Islamic Relief
Flood Treatment
64
Aga Khan
Flood Treatment
Panel B: Militant Groups
Jamaat-ud-Dawa (JuD)
Flood Treatment
Sipah-e-Sahaba Pakistan (SSP)
Flood Treatment
-0.331**
(0.128)
Notes: Exposure measures calculated at the tehsil level in columns (1) - (4) and the individual level in columns (5) - (7). All regressions include district fixed effects. Estimates significant at
the 0.05 (0.10, 0.01) level are marked with ** (*, ***). Standard errors are clustered at the primary sampling unit.
Appendix Table 1: Survey Recall on the Provision of Assistance to Internally Displaced People by Militant and Non-Militant Groups
(1)
(2)
Independent
Self-Reported
Self-Reported
Variables:
Flood Exposure
Affected
Panel A: Asset Index (mean = 0.49, sd = 0.16)
Flood Treatment
-0.063***
(0.013)
(3)
Self-Reported
Very | Extremely Bad
-0.028***
(0.008)
-0.035***
(0.007)
R-squared
0.439
0.438
Observations
12178
12178
Clusters
1097
1097
Panel B:Monthly Household Income (log) (mean = 9.68, sd = 0.54)
0.439
12178
1097
Flood Treatment
-0.034
(0.024)
-0.075*
(0.042)
-0.036
(0.023)
R-squared
0.291
0.291
0.291
Observations
11563
11563
11563
Clusters
1082
1082
1082
Panel C: Monthly Household Expenditures (log) (mean = 9.50, sd = 0.62)
Flood Treatment
-0.061
(0.043)
-0.016
(0.024)
-0.048*
(0.025)
R-squared
0.249
0.249
0.250
Observations
11708
11708
11708
Clusters
1084
1084
1084
Notes: Exposure measures calculated at the individual level. All regression include;
tehsil fixed effects and demographic controls, including gender, a head of household
dummy, age, a literacy dummy, a basic numeracy dummy, education, and a Sunni
dummy. Estimates significant at the 0.05 (0.10, 0.01) level are marked with ** (*, ***).
Standard errors are clustered at the primary sampling unit.
Appendix Table 2: Impact of Flood Exposure on Household Assets, Income, and Expenditure
65
(1)
(2)
(3)
(4)
Independent
% Area
% Population
Pop. Exposed >
Pop. Exposed >
Variable:
Flooded
Exposed
Median
90th Percentile
Panel A: Effectiveness of Junaid's/Mahir's Actions (mean = 0.65, sd in non-violent = 0.30, sd in violent = 0.38)
No Controls
β3: Violent * Flood
0.242**
0.306**
0.070*
0.127*
(0.105)
(0.123)
(0.037)
(0.076)
Including Only District Fixed Effects
β3: Violent * Flood
0.163*
(0.091)
0.246**
(0.105)
0.070**
(0.034)
0.103*
(0.062)
66
Including District Fixed Effects and Demographic Controls
β3: Violent * Flood
0.194**
0.277***
0.086***
0.116**
(0.086)
(0.096)
(0.033)
(0.058)
Panel B: Approval of Junaid's/Mahir's Actions (mean = 0.62, sd in non-violent = 0.31, sd in violent = 0.39)
No Controls
β3: Violent * Flood
0.251**
0.340***
0.081**
0.180**
(0.105)
(0.122)
(0.039)
(0.074)
Including Only District Fixed Effects
β3: Violent * Flood
0.156*
(0.094)
0.270**
(0.109)
0.063*
(0.035)
0.149**
(0.062)
(5)
Self-Reported
Flood Exposure
(6)
Self-Reported
Affected
(7)
Self-Reported
Very | Extremely Bad
0.167***
(0.055)
0.109***
(0.032)
0.071**
(0.035)
0.104**
(0.051)
0.074**
(0.030)
0.037
(0.032)
0.127***
(0.049)
0.082***
(0.029)
0.057*
(0.030)
0.190***
(0.059)
0.127***
(0.035)
0.087**
(0.037)
0.132**
(0.054)
0.100***
(0.032)
0.054
(0.033)
Including District Fixed Effects and Demographic Controls
β3: Violent * Flood
0.208**
0.320***
0.086**
0.170***
0.162***
0.111***
0.079**
(0.087)
(0.097)
(0.034)
(0.055)
(0.052)
(0.031)
(0.032)
Notes: Exposure measures calculated at the tehsil level in columns (1) - (4) and the individual level in columns (5) - (7). Estimates significant at the 0.05 (0.10, 0.01) level are marked with **
(*, ***). Standard errors are clustered at the primary sampling unit.
Appendix Table 3: Impact of Flooding on Approval and Perceived Efficacy of Violent Protest with Different Sets of Control Variables
67
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Independent
% Area
% Population
Pop. Exposed >
Pop. Exposed >
Self-Reported
Self-Reported
Self-Reported
Variable:
Flooded
Exposed
Median
90th Percentile
Flood Exposure
Affected
Very | Extremely Bad
Panel A: Effectiveness of Junaid's/Mahir's Actions
If the Respondents assessed Government Relief Effort as Good
β3: Violent * Flood
0.171
0.262
0.122***
-0.080
0.220***
0.127***
0.104***
(0.120)
(0.167)
(0.037)
(0.091)
(0.058)
(0.036)
(0.035)
R-squared
0.278
0.277
0.283
0.277
0.284
0.283
0.282
Observations
5668
5668
5668
5668
5668
5668
5668
Clusters
892
892
892
892
892
892
892
If the Respondents assessed Government Relief Effort as Bad
β3: Violent * Flood
0.186**
0.237***
0.050
0.144***
0.057
0.046
0.017
(0.090)
(0.087)
(0.043)
(0.052)
(0.060)
(0.037)
(0.038)
R-squared
0.297
0.295
0.291
0.292
0.293
0.292
0.292
Observations
5719
5719
5719
5719
5719
5719
5719
Clusters
877
877
877
877
877
877
877
Panel B: Approval of Junaid's/Mahir's Actions
If the Respondents assessed Government Relief Effort as Good
β3: Violent * Flood
0.180
0.352**
0.129***
0.020
0.269***
0.160***
0.131***
(0.126)
(0.167)
(0.042)
(0.089)
(0.064)
(0.039)
(0.038)
R-squared
0.303
0.304
0.307
0.304
0.309
0.310
0.308
Observations
5668
5668
5668
5668
5668
5668
5668
Clusters
892
892
892
892
892
892
892
If the Respondents assessed Government Relief Effort as Bad
β3: Violent * Flood
0.209**
0.264***
0.054
0.169***
0.070
0.065*
0.027
(0.084)
(0.084)
(0.042)
(0.048)
(0.061)
(0.038)
(0.039)
R-squared
0.313
0.312
0.310
0.311
0.310
0.310
0.311
Observations
5719
5719
5719
5719
5719
5719
5719
Clusters
877
877
877
877
877
877
877
Notes: Exposure measures calculated at the tehsil level in columns (1) - (4) and the individual level in columns (5) - (7). All regression include; district fixed effects; geographic controls at
the tehsil level, including distance to major river, dummy for tehsils boardering a major river, std. dev. of the tehsil's elevation, and mean tehsil elevation; and demographic controls, including
gender, a head of household dummy, age, a literacy dummy, a basic numeracy dummy, education, and a Sunni dummy. Estimates significant at the 0.05 (0.10, 0.01) level are marked with **
(*, ***). Standard errors are clustered at the primary sampling unit.
Appendix Table 4: Impact of Flooding on Approval and Perceived Efficacy of Violent Protest Based on the Respondents’ Assessment of
the Government’s Relief Effort.
(1)
(2)
Independent
% Area
% Population
Variables:
Flooded
Exposed
Panel A: Effectiveness of Junaid's/Mahir's Actions (mean = 0.65)
(3)
Pop. Exposed >
Median
(4)
Pop. Exposed >
90th Percentile
(5)
Self-Reported
Flood Exposure
(6)
Self-Reported
Affected
(7)
Self-Reported
Very | Extremely Bad
0.076
(0.111)
0.300*
(0.169)
0.056
(0.039)
0.090
(0.070)
0.002
(0.043)
0.004
(0.076)
0.055*
(0.032)
0.118**
(0.058)
0.008
(0.021)
0.068*
(0.035)
0.048**
(0.019)
0.048
(0.032)
R-squared
0.290
0.289
Observations
11963
11963
Clusters
1094
1094
Panel B: Approval of Junaid's/Mahir's Actions (mean = 0.62)
0.289
11963
1094
0.287
11963
1094
0.291
11963
1094
0.289
11963
1094
0.290
11963
1094
β2: Flood Treatment
0.077*
(0.042)
0.021
(0.068)
-0.080*
(0.046)
0.071
(0.076)
-0.005
(0.037)
0.123**
(0.062)
-0.034
(0.022)
0.084**
(0.037)
0.020
(0.022)
0.056
(0.035)
β2: Flood Treatment
β3: Violent * Flood
68
β3: Violent * Flood
0.230**
(0.106)
0.149
(0.156)
0.122
(0.118)
0.096
(0.159)
-0.036
(0.121)
0.307*
(0.174)
R-squared
0.304
0.304
0.304
0.303
0.304
0.304
0.304
Observations
11963
11963
11963
11963
11963
11963
11963
Clusters
1094
1094
1094
1094
1094
1094
1094
Notes: Exposure measures calculated at the tehsil level in columns (1) - (4) and the individual level in columns (5) - (7). All regressions include: (vignette * district) fixed effects; geographic
controls at the tehsil level, including distance to major river, dummy for tehsils boardering a major river, std. dev. of the tehsil's elevation, and mean tehsil elevation; and demographic controls,
including gender, a head of household dummy, age, a literacy dummy, a basic numeracy dummy, education, and a Sunni dummy. Estimates significant at the 0.05 (0.10, 0.01) level are marked
with ** (*, ***). Standard errors are clustered at the primary sampling unit.
Appendix Table 5: Impact of Flooding on Perceived Efficacy and Approval of Violent Protest with Vignette-District Fixed Effects
(1)
(2)
(3)
(4)
(5)
(6)
Independent
% Area
Area Exposed > Area Exposed > % Population Pop. Exposed > Pop. Exposed >
Variables:
Flooded
Median
90th Percentile
Exposed
Median
90th Percentile
Panel A: Turnout Change (2013 - 2008) with Division Fixed Effects (mean = 0.11, sd = 0.08)
Flood Treatment
0.112
(0.076)
0.038*
(0.019)
0.016
(0.024)
R-squared
0.315
0.323
0.302
Observations
129
129
129
Clusters
61
61
61
Panel B: First Differences ((2013-2008)-(2008-2002)) (mean = 0.08, sd = 0.13)
Flood Treatment
0.207**
(0.091)
0.071**
(0.031)
0.057
(0.038)
0.166**
(0.083)
0.008
(0.019)
0.055*
(0.029)
0.327
129
61
0.301
129
61
0.320
129
61
0.266**
(0.114)
0.037
(0.030)
0.073*
(0.039)
0.027
129
61
0.028
129
61
0.047
(0.037)
0.071
(0.045)
R-squared
0.054
0.064
0.025
0.057
Observations
129
129
129
129
Clusters
61
61
61
61
Panel C: First Differences ((2013-2008)-(2008-2002)) with Division Fixed Effects
Flood Treatment
0.292**
(0.146)
0.070*
(0.035)
0.049
(0.042)
0.356**
(0.163)
R-squared
0.228
0.222
0.203
0.236
0.206
0.207
Observations
129
129
129
129
129
129
Clusters
61
61
61
61
61
61
Notes: Exposure measures calculated at the constituency level. All regressions include geographic controls at the constituency level,
including distance to major river, dummy for constituencies boardering a major river, std. dev. of the constituency's elevation, and
mean constituency elevation, as well as the proportion of constituency population exposed to the 2012 floods. Estimates significant
at the 0.05 (0.10, 0.01) level are marked with ** (*, ***). Standard errors are clustered at the district level.
Appendix Table 6: Impact of Flooding on Turnout in the 2013 Pakistani National Assembly
Elections in Constituencies Neighboring Major Rivers
69
(1)
(2)
(3)
(4)
(5)
(6)
Independent
% Area
Area Exposed > Area Exposed > % Population Pop. Exposed > Pop. Exposed >
Variables:
Flooded
Median
90th Percentile
Exposed
Median
90th Percentile
Panel A: Turnout Change (2013 - 2008) with Division Fixed Effects (mean = 0.10, sd = 0.08)
Flood Treatment
0.109***
(0.039)
0.044***
(0.012)
0.022
(0.018)
R-squared
0.319
0.328
0.294
Observations
209
209
209
Clusters
63
63
63
Panel B: First Differences ((2013-2008)-(2008-2002)) (mean = 0.07, sd = 0.13)
Flood Treatment
0.185**
(0.073)
0.077***
(0.026)
0.067
(0.041)
0.072
(0.056)
0.038**
(0.015)
0.004
(0.022)
0.303
209
63
0.316
209
63
0.291
209
63
0.116
(0.115)
0.067**
(0.027)
0.006
(0.045)
0.062
209
63
0.012
209
63
0.074***
(0.027)
-0.006
(0.045)
R-squared
0.075
0.087
0.029
0.029
Observations
209
209
209
209
Clusters
63
63
63
63
Panel C: First Differences ((2013-2008)-(2008-2002)) with Division Fixed Effects
Flood Treatment
0.198***
(0.071)
0.078***
(0.024)
0.042
(0.032)
0.089
(0.111)
R-squared
0.198
0.208
0.168
0.170
0.199
0.163
Observations
209
209
209
209
209
209
Clusters
63
63
63
63
63
63
Notes: Exposure measures calculated at the constituency level. All regressions include geographic controls at the constituency level,
including distance to major river, dummy for constituencies boardering a major river, std. dev. of the constituency's elevation, and
mean constituency elevation, as well as the proportion of constituency population exposed to the 2012 floods. Estimates significant
at the 0.05 (0.10, 0.01) level are marked with ** (*, ***). Standard errors are clustered at the district level.
Appendix Table 7: Impact of Flooding on Turnout in the 2013 Pakistani Provincial Assembly
Elections in Constituencies Neighboring Major Rivers
70