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 functioningprogrammatic 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 asnatural 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 anexogenous shock and governance reforms provides evidence for economic mechanisms requiresthat the shock primarily impacts politicians’ incentives through that channel. Using evidencefrom the 2010-11 floods in Pakistan we show that this claim cannot be sustained for naturaldisasters, at least not in one critical case. Leveraging diverse data sources (geospatial measuresof flooding, election returns, and an original survey of 13,282 Pakistani households), we showthat 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 ofmilitant groups and small particularistic parties. In this case a major natural disaster whichcreated a transient economic shock also led to major changes in citizens’ attitudes and civicengagement. These results call into question the interpretation of a broad set of papers and tieinto a rich literature in political science showing that disasters can have complicated politicaleffects that are often quite divorced from their economic impacts.

∗The authors thank Tahir Andrabi, Eli Berman, Graeme Blair, Mike Callen, Jishnu Das, Rubin Enikopolov, AsimKhwaja, Rebecca Littman, Rabia Malik, Hannes Mueller, Maria Petrova, Paul Staniland, Basit Zafar, and seminarparticipants at the 2013 AALIMS Conference at Rice, the University of Chicago, and Yale for their helpful commentsand feedback. All errors are our own. This research was supported, in part, by the Air Force Office of ScientificResearch, 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.

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Introduction

How societies make the transition from autocracy to democracy and then on to functioning pro-

grammatic 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 disas-

ters – and democratization or public goods provision as a way to provide evidence on that process

(e.g., Bruckner and Ciccone, 2011; Ramsay, 2011; Bruckner and Ciccone, 2012). In the economics

literature, Bruckner 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 uncon-

tested (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 chan-

nel. 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 sug-

gests 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 concen-

trated 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

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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 his-

torically 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 excep-

tionally 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

1The EM-DAT International Disaster Database records approximately 20.4 million people affected and 1,985 killedfrom the 2010 floods.

2The 95% confidence interval on this effect ranges from 7% to 31% of the change.3This 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

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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 flood-

affected 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 theo-

retically 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.4The government was particularly effective at coordinating the large flows of foreign aid.5Though not their consequences, of course.

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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 patron-

client 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 citi-

zens’ 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

6The main exceptions are Cole, Healy and Werker (2011); Gallego (2012); Remmer (2013).7Pande (2011) provides a review of experimental evidence showing that providing voters with information improves

electoral accountability.8The latter was a result of the galvanized public, as evidenced by unprecedented and diverse (gender, age, educa-

tion) voter turnout, the rise of a competitive third party, and numerous highly contested races in previous one-partystrongholds.

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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

9Note, this interpretation is not consistent with the theoretical arguments that aid flows increase the risk of conflictby increasing the rents to be captured from control of the government (Besley and Persson, 2011).

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(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 Mon-

itor, 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 HERE10The next largest was the 1992 flood which affected 12.8 millions, displaced 4.3 million, and killed at least 1,446

people.

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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 spon-

taneously 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

11Author fieldwork in Nowshera and Dadu in December 2010 and January 2011.

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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

12Note 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).

13The United States provided an additional seven helicopters as part of their relief efforts.14By 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).15As 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 respondentswhere the groups did operate do not want to give them credit when talking to our enumerators is unknown.

16Compared 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.

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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

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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 district-

representative 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 Measurement

3.1 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.

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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.

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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

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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 respon-

dents 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.

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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.

17See Bullock, Imai and Shapiro (2011) for a measurement theory justification of this approach.18SSP 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 Islamistgovernance and regional autonomy in the Federally Administered Tribal Areas (FATA). The Afghan Taliban arefighting an insurgency against the American-backed government in Afghanistan and are commonly understood tooperate out of the border regions of Pakistan.

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 single-

member 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 con-

stituency 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

19The 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 con-

temporaneous 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 be-

cause 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 respon-

dents 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 re-

spondent 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 En-

dorsement 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

20We 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+ β1Fi + γd + BXi + εi, (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 es-

timator 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 peo-

ple 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 experi-

ment:

Yi = α+ β1Vi + β2Fi + β3(Vi × Fi) + γd + BXi + εi, (2)

where i indexes respondents, Yi represents a response to either the effectiveness or approval vari-

able, 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 im-

pact 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+ β1Ei,j + β2Fi + β3(Ei,j × Fi) + γd + ∆Xi + εi, (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

21Geographic controls that enter at the tehsil level include distance to major river from the tehsil centroid, adummy for bordering a major river, the mean elevation of the tehsil, the standard deviation of elevation within thetehsil, and the proportion of the population hit by the 2011 floods that occurred after 2010 but before our survey wasfielded. 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.

22Results are robust to clustering at the district level to account for the high possibility that the variance attitudesis 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+ β1Fi + γd + BXi + εi (4)

∆y2013,2008 −∆y2008,2002 = α+ β1∆Fi + BXi + εi, (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 Results

5.1 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

23We are currently coding satellite imagery on floods back to 2000 so that we can fully include observed floodexposure for prior years in our first-differenced regressions. Since the flooding prior to 2010 was modest we believethat 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 re-

spondents 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

24To see how the coefficient estimate β3 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

25Major 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 βR,

where R stands for Restricted, and the estimated coefficient from the second regressions βF , where

F stands for Full. Their proposed metric is then βF /(βR− βF ). 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

26Note that with district-vignette fixed effects in the model, the vignette-specific mean effect is absorbed.27Altonji, 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 providedin the working paper of their study, Bellows and Miguel (2008).

26

size of βR and βF determines the ratio’s sign. Positive ratios (i.e., βR > βF , as our coefficient

estimate of interest is positive) indicates that the added controls attenuated the treatment effect,

while negative ratio’s (i.e., βR < βF ) 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 rel-

ative 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 con-

trols. 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.

28In the knife-edge case when βR = βF 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 in-

creased 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 door-

to-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 constituen-

cies 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 per-

centage 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 esti-

mate 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

29Private 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 migra-

tion.

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 con-

stituencies 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, particu-

larly 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 non-

state 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 flood-

affected. 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.

30The 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 flood-

affected 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 responsive-

ness 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 pre-

vious 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 eco-

nomic 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 assum-

ing 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 interna-

tional 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 de-

mand 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 cap-

tured 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 at-

titudes 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 out-

number 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). Sev-

eral 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 expe-

rienced 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 institution-

alized 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 gov-

38

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 be-

haviors 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 rela-

tive 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

Figure 6: Major Party Vote Shares in Constituencies by Flood Exposure

46

Tables

Variables Unit Level N Median Mean Std. Dev. Min Max% Area Flooded 2010 & 2011 percent Tehsil 12994 0 0.083 0.157 0 0.928% Population Exposed 2010 & 2011 percent Tehsil 12994 0 0.059 0.136 0 0.897Population Exposed > Median == 1 dummy Tehsil 12994 0 0.222 0.416 0 1Population Exposed > 90th Percentile == 1 dummy Tehsil 12994 0 0.047 0.211 0 1Flood Exposure 4 pt. scale [0,1] Individual 12994 0.250 0.385 0.248 0.25 1Affected == 1 dummy Individual 12994 0 0.268 0.443 0 1Very Bad | Extremely Bad == 1 dummy Individual 12994 0 0.181 0.385 0 1Distance to River 100 kilometers Tehsil 12994 0.331 0.899 1.242 0.005 6.856Neighboring River == 1 dummy Tehsil 12994 0 0.469 0.499 0 1Std. Dev. of Elevation 1,000 meters Tehsil 12994 0.029 0.128 0.195 0.002 1.021Mean of Elevation 1,000 meters Tehsil 12994 0.191 0.540 0.724 0.005 4.247Male dummy Individual 12994 1 0.515 0.500 0 1Head of Household dummy Individual 12968 0 0.373 0.484 0 1Age 100 years Individual 12990 0.340 0.350 0.119 0.18 0.85Read dummy Individual 12931 1 0.545 0.498 0 1Math dummy Individual 12367 1 0.776 0.417 0 1Education 6 pt. scale [0,1] Individual 12889 0.167 0.275 0.281 0 1Sunni dummy Individual 12923 1 0.930 0.256 0 1Political Knowledge (PCA) index Individual 12994 2.120 2.024 0.741 0 3.006Political Knowledge (Additive) index [0,1] Individual 12994 0.700 0.673 0.227 0 1Satisfaction Government Relief Effort 4 pt. scale [1,4] Individual 12240 2 2.417 0.863 1 4Government Relief Comparison Other Towns 3 pt. scale [1,3] Individual 11671 2 1.838 0.644 1 3% Area Flooded 2010 & 2011 percent NA-Constituency 249 0 0.082 0.150 0 0.79Area Exposed > Median == 1 dummy NA-Constituency 249 0 0.253 0.436 0 1Area Exposed > 90th Percentile == 1 dummy NA-Constituency 249 0 0.052 0.223 0 1% Population Exposed 2010 & 2011 percent NA-Constituency 249 0 0.065 0.127 0 0.61Population Exposed <= Median == 1 dummy NA-Constituency 249 0 0.233 0.424 0 1Population Exposed > Median == 1 dummy NA-Constituency 249 0 0.249 0.433 0 1Population Exposed > 90th Percentile == 1 dummy NA-Constituency 249 0 0.052 0.223 0 1Affected (NDMA) == 1 dummy NA-Constituency 249 1 0.510 0.501 0 1% Population Exposed 2012 percent NA-Constituency 249 0 0.012 0.052 0 0.489Distance to River 100 kilometers NA-Constituency 249 0.260 0.482 0.715 0.016 5.517Neighboring River == 1 dummy NA-Constituency 249 1 0.522 0.501 0 1Std. Dev. of Elevation 1,000 meters NA-Constituency 249 0.007 0.093 0.198 0 1.097Mean of Elevation 1,000 meters NA-Constituency 249 0.179 0.351 0.575 0.004 3.922Turnout Diff. 2013-2008 [-1,1] NA-Constituency 249 0.114 0.113 0.079 -0.114 0.372Diff. in Turnout Diff. (2013-2008)-(2008-2002) [-1,1] NA-Constituency 249 0.083 0.086 0.141 -0.371 0.638PPP Vote Share Diff. 2013-2008 percent NA-Constituency 249 -0.158 -0.165 0.166 -0.59 0.388PML-N Vote Share Diff. 2013-2008 percent NA-Constituency 249 0.051 0.11 0.194 -0.489 0.669% Area Flooded 2010 & 2011 percent PA-Constituency 569 0 0.086 0.167 0 0.912Area Exposed > Median == 1 dummy PA-Constituency 569 0 0.220 0.414 0 1Area Exposed > 90th Percentile == 1 dummy PA-Constituency 569 0 0.042 0.201 0 1% Population Exposed 2010 & 2011 percent PA-Constituency 569 0 0.067 0.146 0 1Population Exposed > Median == 1 dummy PA-Constituency 569 0 0.206 0.405 0 1Population Exposed > 90th Percentile == 1 dummy PA-Constituency 569 0 0.042 0.201 0 1Affected (NDMA) == 1 dummy PA-Constituency 569 0 0.464 0.499 0 1% Population Exposed 2012 percent PA-Constituency 569 0 0.013 0.062 0 0.7Distance to River 100 kilometers PA-Constituency 569 0.269 0.579 0.893 0.001 6.353Neighboring River == 1 dummy PA-Constituency 569 0 0.373 0.484 0 1Std. Dev. of Elevation 1,000 meters PA-Constituency 569 0.006 0.084 0.175 0 1.059Mean of Elevation 1,000 meters PA-Constituency 569 0.179 0.388 0.598 0.002 4.243Turnout Diff. 2013-2008 [-1,1] PA-Constituency 569 0.112 0.103 0.094 -0.416 0.506Diff. in Turnout Diff. (2013-2008)-(2008-2002) [-1,1] PA-Constituency 569 0.077 0.072 0.150 -0.544 0.822

Table 1: Summary Statistics of the Covariates Across Datasets

47

(1) (2) (3) (4) (5) (6) (7)Independent % Area % Population Pop. Exposed > Pop. Exposed > Self-Reported Self-Reported Self-ReportedVariable: Flooded Exposed Median 90th Percentile Flood Exposure Affected Very | Extremely BadPanel 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 -0.215*** -0.216*** -0.220*** -0.204*** -0.248*** -0.221*** -0.209***(0.018) (0.018) (0.019) (0.016) (0.026) (0.018) (0.017)

β2: Flood Treatment 0.208** 0.058 0.040 -0.040 0.066* 0.010 0.050**(0.105) (0.107) (0.041) (0.047) (0.036) (0.022) (0.021)

β3: Violent * Flood 0.198** 0.282*** 0.089*** 0.118** 0.129*** 0.083*** 0.058*(0.086) (0.096) (0.033) (0.058) (0.049) (0.030) (0.031)

R-squared 0.237 0.235 0.235 0.232 0.237 0.236 0.236Observations 11963 11963 11963 11963 11963 11963 11963Clusters 1094 1094 1094 1094 1094 1094 1094Panel 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 -0.229*** -0.230*** -0.232*** -0.218*** -0.274*** -0.240*** -0.225***(0.019) (0.018) (0.019) (0.017) (0.027) (0.019) (0.018)

β2: Flood Treatment 0.076 -0.069 0.032 -0.121** -0.007 -0.039 0.017(0.116) (0.114) (0.041) (0.049) (0.039) (0.023) (0.023)

β3: Violent * Flood 0.213** 0.325*** 0.089*** 0.172*** 0.165*** 0.112*** 0.081**(0.088) (0.097) (0.034) (0.055) (0.052) (0.032) (0.032)

R-squared 0.252 0.253 0.253 0.251 0.253 0.253 0.252Observations 11963 11963 11963 11963 11963 11963 11963Clusters 1094 1094 1094 1094 1094 1094 1094Notes: 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

48

(1) (2) (3)Independent Self-Reported Self-Reported Self-ReportedVariables: Flood Exposure Affected Very | Extremely BadPanel A: Effectiveness of Junaid's/Mahir's Actions (mean = 0.65)

β1: Violent Vignette -0.256*** -0.224*** -0.215***(0.027) (0.019) (0.018)

β2: Flood Treatment 0.068** 0.019 0.043**(0.034) (0.021) (0.020)

β3: Violent * Flood 0.138*** 0.080*** 0.063**(0.046) (0.029) (0.029)

R-squared 0.306 0.305 0.305Observations 11963 11963 11963Clusters 1094 1094 1094Panel B: Approval of Junaid's/Mahir's Actions (mean = 0.62)

β1: Violent Vignette -0.283*** -0.241*** -0.230***(0.028) (0.020) (0.019)

β2: Flood Treatment 0.004 -0.025 0.016(0.038) (0.023) (0.022)

β3: Violent * Flood 0.182*** 0.107*** 0.093***(0.050) (0.031) (0.031)

R-squared 0.322 0.322 0.322Observations 11963 11963 11963Clusters 1094 1094 1094Notes: 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 TehsilFixed Effects

49

(1) (2) (3) (4) (5) (6) (7)Independent % Area % Population Pop. Exposed > Pop. Exposed > Self-Reported Self-Reported Self-ReportedVariables: Flooded Exposed Median 90th Percentile Flood Exposure Affected Very | Extremely BadPanel A: Effectiveness of Junaid's/Mahir's Actions (mean = 0.66)

β1: Violent Vignette -0.203*** -0.204*** -0.214*** -0.183*** -0.253*** -0.201*** -0.193***(0.027) (0.024) (0.026) (0.022) (0.034) (0.024) (0.023)

β2: Flood Treatment -0.027 -0.186* -0.087* -0.109*** -0.035 -0.038 -0.016(0.111) (0.099) (0.046) (0.035) (0.042) (0.028) (0.025)

β3: Violent * Flood 0.237** 0.409*** 0.152*** 0.171*** 0.218*** 0.124*** 0.125***(0.094) (0.093) (0.040) (0.055) (0.056) (0.038) (0.035)

R-squared 0.227 0.230 0.232 0.226 0.230 0.229 0.229Observations 5685 5685 5685 5685 5685 5685 5685Clusters 513 513 513 513 513 513 513Panel B: Approval of Junaid's/Mahir's Actions (mean = 0.62)

β1: Violent Vignette -0.230*** -0.230*** -0.241*** -0.209*** -0.283*** -0.229*** -0.216***(0.028) (0.025) (0.028) (0.023) (0.036) (0.025) (0.024)

β2: Flood Treatment -0.081 -0.224** -0.094** -0.171*** -0.120** -0.080** -0.060**(0.130) (0.109) (0.044) (0.046) (0.051) (0.031) (0.030)

β3: Violent * Flood 0.258** 0.434*** 0.163*** 0.207*** 0.235*** 0.145*** 0.124***(0.103) (0.098) (0.043) (0.057) (0.064) (0.041) (0.040)

R-squared 0.253 0.257 0.259 0.255 0.255 0.256 0.253Observations 5685 5685 5685 5685 5685 5685 5685Clusters 513 513 513 513 513 513 513Notes: 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

50

Comparison of Main Effect Controls in the Controls in the % Area % Population Pop. Exposed > Pop. Exposed > Self-Reported Self-Reported Self-Reportedto Bias Accounted for by Restricted Set Full Set Flooded Exposed Median 90th Percentile Flood Exposure Affected Very | Extremely Bad

Controling for individual District FE anddemographics District FE Demographics -23.603 -52.190 -14.622 30.514 -77.240 -59.049 -27.179

Controling for demographics Full Set of Controls and tehsil geography District FE as in Table 2 -15.523 -26.814 -9.939 59.602 -32.063 -32.737 -18.537

Controling for all common Geographic and Full Set of Controls district-specfic factors Demographics as in Table 2 2.773 5.376 -15.660 5.411 2.095 2.356 1.678

Controling for individual District FE anddemographics District FE Demographics -20.713 -50.145 -12.405 40.432 -81.275 -59.395 -32.242

Controling for demographics Full Set of Controls and tehsil geography District FE as in Table 2 -14.044 -29.854 -8.984 71.124 -37.358 -37.939 -22.221

Controling for all common Geographic and Full Set of Controls district-specfic factors Demographics as in Table 2 2.439 5.153 6.610 5.808 2.743 3.696 2.313

Independent Variables

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.

Panel A: Effectiveness of Junaid's/Mahir's Actions

Panel B: Approval of Junaid's/Mahir's Actions

Table 5: Using Selection on Observables to Assess the Bias from Unobservables

51

(1) (2) (3) (4) (5) (6) (7)Independent % Area % Population Pop. Exposed > Pop. Exposed > Self-Reported Self-Reported Self-ReportedVariable: Flooded Exposed Median 90th Percentile Flood Exposure Affected Very | Extremely BadPanel A: Index of Political Knowledge (Principal Component Analysis) (mean = 2.024, sd = 0.741)

Flood Treatment 0.168 0.453*** 0.077 0.213*** 0.149*** 0.101*** 0.062** (0.122) (0.132) (0.056) (0.058) (0.052) (0.030) (0.029)

R-squared 0.375 0.376 0.375 0.376 0.376 0.376 0.375Observations 12178 12178 12178 12178 12178 12178 12178Clusters 1097 1097 1097 1097 1097 1097 1097Panel B: Index of Political Knowledge (Additive) (mean = 0.673, sd = 0.227)

Flood Treatment 0.047 0.136*** 0.020 0.063*** 0.038** 0.025*** 0.016* (0.036) (0.039) (0.016) (0.018) (0.016) (0.009) (0.009)

R-squared 0.380 0.381 0.380 0.381 0.381 0.381 0.380Observations 12178 12178 12178 12178 12178 12178 12178Clusters 1097 1097 1097 1097 1097 1097 1097Notes: 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

52

(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 PercentilePanel A: Turnout Change (2013 - 2008) with Division Fixed Effects (mean = 0.11, sd = 0.08)

Flood Treatment 0.109* 0.034** 0.034 0.162** 0.016 0.054* (0.056) (0.016) (0.023) (0.070) (0.018) (0.028)

R-squared 0.306 0.308 0.299 0.313 0.294 0.305Observations 246 246 246 246 246 246Clusters 90 90 90 90 90 90Panel B: First Differences ((2013-2008)-(2008-2002)) (mean = 0.09, sd = 0.14)

Flood Treatment 0.234*** 0.074*** 0.089*** 0.308*** 0.055** 0.099**(0.072) (0.025) (0.033) (0.094) (0.026) (0.041)

R-squared 0.079 0.077 0.056 0.085 0.060 0.056Observations 246 246 246 246 246 246Clusters 90 90 90 90 90 90Panel C: First Differences ((2013-2008)-(2008-2002)) with Division Fixed Effects

Flood Treatment 0.249** 0.060* 0.079** 0.314** 0.032 0.084*(0.103) (0.030) (0.039) (0.127) (0.031) (0.046)

R-squared 0.279 0.271 0.267 0.280 0.258 0.266Observations 246 246 246 246 246 246Clusters 90 90 90 90 90 90Notes: 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 PercentilePanel A: Turnout Change (2013 - 2008) with Division Fixed Effects (mean = 0.10, sd = 0.09)

Flood Treatment 0.112*** 0.031*** 0.034** 0.110** 0.028** 0.022 (0.035) (0.011) (0.016) (0.046) (0.012) (0.019)

R-squared 0.311 0.305 0.300 0.308 0.030 0.298Observations 556 556 556 556 556 556Clusters 109 109 109 109 109 109Panel B: First Differences ((2013-2008)-(2008-2002)) (mean = 0.07, sd = 0.15)

Flood Treatment 0.237*** 0.084*** 0.092*** 0.246** 0.077*** 0.065*(0.068) (0.025) (0.035) (0.098) (0.024) (0.039)

R-squared 0.110 0.105 0.080 0.102 0.098 0.073Observations 556 556 556 556 556 556Clusters 109 109 109 109 109 109Panel C: First Differences ((2013-2008)-(2008-2002)) with Division Fixed Effects

Flood Treatment 0.222*** 0.065*** 0.067** 0.196** 0.063*** 0.038(0.062) (0.022) (0.032) (0.094) (0.022) (0.036)

R-squared 0.277 0.269 0.260 0.268 0.267 0.256Observations 556 556 556 556 556 556Clusters 91 91 91 91 91 91Notes: 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 PercentilePanel A: Turnout Change (2013 - 2008) Including Migration Estimate (mean = 0.11, sd = 0.08)

Flood Treatment 0.069 0.015 0.026 0.175** 0.024 0.052*(0.060) (0.016) (0.027) (0.082) (0.021) (0.027)

Migrated 0.186 0.197 0.170 0.199 0.186 0.169(0.201) (0.202) (0.205) (0.199) (0.200) (0.206)

R-squared 0.454 0.452 0.453 0.467 0.454 0.459Observations 158 158 158 158 158 158Clusters 52 52 52 52 52 52Panel B: Turnout Change (2013 - 2008) in NA-Constituencies Without Migration (mean = 0.12, sd = 0.07)

Flood Treatment 0.079 0.029** 0.024 0.134** 0.012 0.053*(0.053) (0.014) (0.023) (0.065) (0.017) (0.029)

R-squared 0.326 0.332 0.320 0.338 0.317 0.337Observations 157 157 157 157 157 157Clusters 69 69 69 69 69 69Panel C: Turnout Change (2013 - 2008) in PA-Constituencies Without Migration (mean = 0.12, sd = 0.07)

Flood Treatment 0.089*** 0.024** 0.030* 0.090** 0.022* 0.018(0.033) (0.011) (0.016) (0.044) (0.011) (0.018)

R-squared 0.314 0.307 0.305 0.312 0.306 0.302Observations 378 378 378 378 378 378Clusters 87 87 87 87 87 87Notes: 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

Geographic Extent:(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Political Party: PPP PML-N PPP PML-N PPP PML-N PPP PML-N PPP PML-NPanel A: Change in Vote Share (2013 - 2008)

Affected (NDMA) 0.062** 0.037 0.033 0.003 0.016 0.059* 0.089** 0.033 0.012 0.063**(0.030) (0.037) (0.092) (0.045) (0.094) (0.031) (0.038) (0.031) (0.038) (0.023)

R-squared 0.063 0.107 0.015 0.001 0.002 0.031 0.062 0.005 0.001 0.055Observations 249 249 13 13 34 34 143 143 57 57Clusters 91 91 11 11 22 22 36 36 21 21Panel B: Change in Vote Share (2013 - 2008) with Control Variables

Affected (NDMA) 0.054 0.017 0.003 -0.053 -0.028 0.071 0.096** -0.017 -0.012 0.043**(0.035) (0.041) (0.107) (0.115) (0.076) (0.046) (0.042) (0.053) (0.128) (0.020)

R-squared 0.087 0.124 0.456 0.348 0.162 0.235 0.086 0.128 0.059 0.265Observations 249 249 13 13 34 34 143 143 57 57Clusters 91 91 11 11 22 22 36 36 21 21Panel C: Change in Vote Share (2013 - 2008) with Control Variables (Baseline Categorie: Unaffected Constituencies)

Population Exposed 0.039 0.022 -0.003 -0.051 0.006 0.124** 0.068 -0.020 -0.039 0.037>0 and ≤ Median (0.038) (0.041) (0.103) (0.110) (0.061) (0.043) (0.047) (0.055) (0.148) (0.026)

Population Exposed 0.087* 0.030 0.117 -0.143 -0.081 0.153** 0.153* -0.011 0.090* 0.024> Median (0.044) (0.044) (0.123) (0.134) (0.088) (0.072) (0.079) (0.062) (0.051) (0.030)

R-squared 0.095 0.137 0.479 0.393 0.205 0.240 0.103 0.112 0.117 0.266Observations 249 249 13 13 34 34 143 143 57 57Clusters 91 91 11 11 22 22 36 36 21 21Notes: 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.

National Balochistan KPK Punjab Sindh

Table 10: Impact of Flooding on the Differences in the PPP’s and PML-N’s Vote Shares between 2013 and 2008

56

(1) (2) (3) (4) (5) (6) (7)Independent % Area % Population Pop. Exposed > Pop. Exposed > Self-Reported Self-Reported Self-ReportedVariables: Flooded Exposed Median 90th Percentile Flood Exposure Affected Very | Extremely BadPanel A: All Militant Groups (mean policy prefernce in control group = 0.62, sd = 0.26)

β3: Militant * Flood -0.144** -0.133* -0.062** -0.063 -0.043 -0.036 -0.019(0.068) (0.072) (0.031) (0.045) (0.043) (0.030) (0.026)

p-value for H0: β3>0 0.02 0.03 0.02 0.08 0.16 0.12 0.26

R-squared 0.308 0.308 0.308 0.310 0.306 0.307 0.306Observations 8443 8443 8443 8443 8443 8443 8443Clusters 828 828 828 828 828 828 828Panel B: SSP (mean policy prefernce in control group = 0.63, sd = 0.25)

β3: Militant * Flood -0.142 -0.143 -0.067* -0.068 -0.039 -0.041 -0.015(0.088) (0.088) (0.040) (0.052) (0.049) (0.032) (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 0.306 0.301 0.302 0.301Observations 4231 4231 4231 4231 4231 4231 4231Clusters 420 420 420 420 420 420 420Panel C: Pakistan Taliban (mean policy prefernce in control group = 0.63, sd = 0.25)

β3: Militant * Flood -0.209** -0.289*** -0.078* -0.153** -0.058 -0.061 -0.014(0.105) (0.109) (0.043) (0.068) (0.062) (0.041) (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 0.303 0.300 0.302 0.300Observations 4250 4250 4250 4250 4250 4250 4250Clusters 423 423 423 423 423 423 423Panel D: Afghan Taliban (mean policy prefernce in control group = 0.62, sd = 0.26)

β3: Militant * Flood -0.148* -0.115 -0.066 -0.040 -0.049 -0.025 -0.025(0.079) (0.081) (0.041) (0.046) (0.051) (0.034) (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 0.388Observations 4228 4228 4228 4228 4228 4228 4228Clusters 425 425 425 425 425 425 425Notes: 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

57

(1) (2) (3) (4) (5) (6) (7)Independent % Area % Population Pop. Exposed > Pop. Exposed > Self-Reported Self-Reported Self-ReportedVariable: Flooded Exposed Median 90th Percentile Flood Exposure Affected Very | Extremely BadPanel A: Did Government Do a Good Job? (mean = 2.024, sd = 0.741)

Flood Treatment 0.235 -0.037 0.108 -0.195** -0.032 -0.027 -0.019(0.190) (0.171) (0.081) (0.084) (0.070) (0.042) (0.040)

R-squared 0.207 0.206 0.207 0.207 0.206 0.206 0.206Observations 10848 10848 10848 10848 10848 10848 10848Clusters 1047 1047 1047 1047 1047 1047 1047Panel B: Did Other Town Get Better Relief? (mean = 0.673, sd = 0.227)

Flood Treatment 0.006 -0.166 0.027 -0.056 -0.028 -0.013 -0.018 (0.148) (0.151) (0.066) (0.068) (0.055) (0.031) (0.033)

R-squared 0.150 0.151 0.150 0.151 0.150 0.150 0.150Observations 10848 10848 10848 10848 10848 10848 10848Clusters 1047 1047 1047 1047 1047 1047 1047Notes: 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

58

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Appendix

(1) (2) (3) (4) (5) (6) (7)Independent % Area % Population Pop. Exposed > Pop. Exposed > Self-Reported Self-Reported Self-ReportedVariable: Flooded Exposed Median 90th Percentile Flood Exposure Affected Very | Extremely BadPanel A: Non-Militant GroupsTabliqhi Jamaat

Flood Treatment -0.026 0.207* 0.051 0.092 0.066* 0.045** 0.022(0.105) (0.121) (0.049) (0.065) (0.040) (0.020) (0.022)

Islamic Relief

Flood Treatment -0.255 -0.337* -0.097 -0.168 0.034 0.006 0.003(0.230) (0.195) (0.088) (0.115) (0.074) (0.043) (0.041)

Aga Khan

Flood Treatment -0.166 -0.211 -0.056 -0.080 0.007 -0.008 0.004(0.145) (0.150) (0.068) (0.089) (0.051) (0.029) (0.029)

Panel B: Militant GroupsJamaat-ud-Dawa (JuD)

Flood Treatment -0.230* -0.146 -0.108** -0.098 -0.020 -0.001 -0.035(0.122) (0.128) (0.054) (0.071) (0.062) (0.035) (0.036)

Sipah-e-Sahaba Pakistan (SSP)

Flood Treatment -0.331** -0.249 0.006 -0.098 -0.054 -0.050* -0.027(0.128) (0.156) (0.058) (0.070) (0.058) (0.030) (0.037)

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

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(1) (2) (3)Independent Self-Reported Self-Reported Self-ReportedVariables: Flood Exposure Affected Very | Extremely BadPanel A: Asset Index (mean = 0.49, sd = 0.16)

Flood Treatment -0.063*** -0.028*** -0.035***(0.013) (0.008) (0.007)

R-squared 0.439 0.438 0.439Observations 12178 12178 12178Clusters 1097 1097 1097Panel B:Monthly Household Income (log) (mean = 9.68, sd = 0.54)

Flood Treatment -0.075* -0.036 -0.034(0.042) (0.023) (0.024)

R-squared 0.291 0.291 0.291Observations 11563 11563 11563Clusters 1082 1082 1082Panel C: Monthly Household Expenditures (log) (mean = 9.50, sd = 0.62)

Flood Treatment -0.061 -0.016 -0.048*(0.043) (0.024) (0.025)

R-squared 0.249 0.249 0.250Observations 11708 11708 11708Clusters 1084 1084 1084Notes: 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

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(1) (2) (3) (4) (5) (6) (7)Independent % Area % Population Pop. Exposed > Pop. Exposed > Self-Reported Self-Reported Self-ReportedVariable: Flooded Exposed Median 90th Percentile Flood Exposure Affected Very | Extremely BadPanel A: Effectiveness of Junaid's/Mahir's Actions (mean = 0.65, sd in non-violent = 0.30, sd in violent = 0.38)No Controlsβ3: Violent * Flood 0.242** 0.306** 0.070* 0.127* 0.167*** 0.109*** 0.071**

(0.105) (0.123) (0.037) (0.076) (0.055) (0.032) (0.035)

Including Only District Fixed Effectsβ3: Violent * Flood 0.163* 0.246** 0.070** 0.103* 0.104** 0.074** 0.037

(0.091) (0.105) (0.034) (0.062) (0.051) (0.030) (0.032)

Including District Fixed Effects and Demographic Controlsβ3: Violent * Flood 0.194** 0.277*** 0.086*** 0.116** 0.127*** 0.082*** 0.057*

(0.086) (0.096) (0.033) (0.058) (0.049) (0.029) (0.030)Panel B: Approval of Junaid's/Mahir's Actions (mean = 0.62, sd in non-violent = 0.31, sd in violent = 0.39)No Controlsβ3: Violent * Flood 0.251** 0.340*** 0.081** 0.180** 0.190*** 0.127*** 0.087**

(0.105) (0.122) (0.039) (0.074) (0.059) (0.035) (0.037)

Including Only District Fixed Effectsβ3: Violent * Flood 0.156* 0.270** 0.063* 0.149** 0.132** 0.100*** 0.054

(0.094) (0.109) (0.035) (0.062) (0.054) (0.032) (0.033)

Including District Fixed Effects and Demographic Controlsβ3: 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

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(1) (2) (3) (4) (5) (6) (7)Independent % Area % Population Pop. Exposed > Pop. Exposed > Self-Reported Self-Reported Self-ReportedVariable: Flooded Exposed Median 90th Percentile Flood Exposure Affected Very | Extremely BadPanel A: Effectiveness of Junaid's/Mahir's Actions If the Respondents assessed Government Relief Effort as Goodβ3: 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 0.282Observations 5668 5668 5668 5668 5668 5668 5668Clusters 892 892 892 892 892 892 892If the Respondents assessed Government Relief Effort as Badβ3: 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 0.292Observations 5719 5719 5719 5719 5719 5719 5719Clusters 877 877 877 877 877 877 877Panel B: Approval of Junaid's/Mahir's Actions If the Respondents assessed Government Relief Effort as Goodβ3: 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 0.308Observations 5668 5668 5668 5668 5668 5668 5668Clusters 892 892 892 892 892 892 892If the Respondents assessed Government Relief Effort as Badβ3: 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 0.311Observations 5719 5719 5719 5719 5719 5719 5719Clusters 877 877 877 877 877 877 877Notes: 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 ofthe Government’s Relief Effort.

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(1) (2) (3) (4) (5) (6) (7)Independent % Area % Population Pop. Exposed > Pop. Exposed > Self-Reported Self-Reported Self-ReportedVariables: Flooded Exposed Median 90th Percentile Flood Exposure Affected Very | Extremely BadPanel A: Effectiveness of Junaid's/Mahir's Actions (mean = 0.65)

β2: Flood Treatment 0.230** 0.076 0.056 0.002 0.055* 0.008 0.048**(0.106) (0.111) (0.039) (0.043) (0.032) (0.021) (0.019)

β3: Violent * Flood 0.149 0.300* 0.090 0.004 0.118** 0.068* 0.048(0.156) (0.169) (0.070) (0.076) (0.058) (0.035) (0.032)

R-squared 0.290 0.289 0.289 0.287 0.291 0.289 0.290Observations 11963 11963 11963 11963 11963 11963 11963Clusters 1094 1094 1094 1094 1094 1094 1094Panel B: Approval of Junaid's/Mahir's Actions (mean = 0.62)

β2: Flood Treatment 0.122 -0.036 0.077* -0.080* -0.005 -0.034 0.020(0.118) (0.121) (0.042) (0.046) (0.037) (0.022) (0.022)

β3: Violent * Flood 0.096 0.307* 0.021 0.071 0.123** 0.084** 0.056(0.159) (0.174) (0.068) (0.076) (0.062) (0.037) (0.035)

R-squared 0.304 0.304 0.304 0.303 0.304 0.304 0.304Observations 11963 11963 11963 11963 11963 11963 11963Clusters 1094 1094 1094 1094 1094 1094 1094Notes: 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

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(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 PercentilePanel A: Turnout Change (2013 - 2008) with Division Fixed Effects (mean = 0.11, sd = 0.08)

Flood Treatment 0.112 0.038* 0.016 0.166** 0.008 0.055*(0.076) (0.019) (0.024) (0.083) (0.019) (0.029)

R-squared 0.315 0.323 0.302 0.327 0.301 0.320Observations 129 129 129 129 129 129Clusters 61 61 61 61 61 61Panel B: First Differences ((2013-2008)-(2008-2002)) (mean = 0.08, sd = 0.13)

Flood Treatment 0.207** 0.071** 0.057 0.266** 0.037 0.073*(0.091) (0.031) (0.038) (0.114) (0.030) (0.039)

R-squared 0.054 0.064 0.025 0.057 0.027 0.028Observations 129 129 129 129 129 129Clusters 61 61 61 61 61 61Panel C: First Differences ((2013-2008)-(2008-2002)) with Division Fixed Effects

Flood Treatment 0.292** 0.070* 0.049 0.356** 0.047 0.071 (0.146) (0.035) (0.042) (0.163) (0.037) (0.045)

R-squared 0.228 0.222 0.203 0.236 0.206 0.207Observations 129 129 129 129 129 129Clusters 61 61 61 61 61 61Notes: 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 AssemblyElections in Constituencies Neighboring Major Rivers

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(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 PercentilePanel A: Turnout Change (2013 - 2008) with Division Fixed Effects (mean = 0.10, sd = 0.08)

Flood Treatment 0.109*** 0.044*** 0.022 0.072 0.038** 0.004(0.039) (0.012) (0.018) (0.056) (0.015) (0.022)

R-squared 0.319 0.328 0.294 0.303 0.316 0.291Observations 209 209 209 209 209 209Clusters 63 63 63 63 63 63Panel B: First Differences ((2013-2008)-(2008-2002)) (mean = 0.07, sd = 0.13)

Flood Treatment 0.185** 0.077*** 0.067 0.116 0.067** 0.006(0.073) (0.026) (0.041) (0.115) (0.027) (0.045)

R-squared 0.075 0.087 0.029 0.029 0.062 0.012Observations 209 209 209 209 209 209Clusters 63 63 63 63 63 63Panel C: First Differences ((2013-2008)-(2008-2002)) with Division Fixed Effects

Flood Treatment 0.198*** 0.078*** 0.042 0.089 0.074*** -0.006(0.071) (0.024) (0.032) (0.111) (0.027) (0.045)

R-squared 0.198 0.208 0.168 0.170 0.199 0.163Observations 209 209 209 209 209 209Clusters 63 63 63 63 63 63Notes: 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 AssemblyElections in Constituencies Neighboring Major Rivers

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