Filling the Void: Evidence from Two Natural Disasters on the. Determinants of Taliban Support

Transcription

1 Filling the Void: Evidence from Two Natural Disasters on the Determinants of Taliban Support Federico Masera Hasin Yousaf This version: April 14th, 2014 Abstract Since the inception of war-on-terror there has been considerable increase in the Taliban insurgence and presence in Pakistan. In this paper we empirically analyze the determinants and effects of the political support for Taliban in Pakistan. We use evidence from two natural disasters: an earthquake in 2005 and a flood in 2010 which received different levels of international help. While the earthquake took place at a time when Pakistan was a close ally of United States in the war-on-terror and it received extensive international response, floods occurred at a time when Pakistan-U.S. relations had deteriorated and consequently it received little response. This had a direct effect on the political support for the Taliban in the affected areas. We show using the outcomes from Pakistan national elections of 2002, 2008 and 2013 that the support for Taliban decreased in the areas affected by the earthquake, while it increased in the areas affected by the flood. The evidence suggests competition between government and non-state actors for gaining support of people. Shortfall in the capacity of the government may lead to crowding out of government support and the non-state actors may fill this void and gain political support. 1

2 1 Introduction 2 Previous Literature 3 Context In this section we briefly discuss the context of our setting. Specifically, we give an overview of the political system of Pakistan, giving summary about the elections, major political parties and the Islamic parties. Then, we present summary of the flood of 2010, followed by the overview of the relations between U.S. and Pakistan during the flood. 3.1 Political System of Pakistan The Figure 1 shows the electoral districts of Pakistan along with the geographic details of its location. Pakistan shares its borders with Afghanistan, China, India, and Iran. The provinces of Khyber Pakhtunkhwa and Balochistan share the border with Afghanistan, while the provinces of Punjab and Sindh share the border with India. Islamabad is the capital city of Pakistan. Administratively, it is also equivalent to a province known as the Federal Capital. Lahore and Karachi are the two main urban areas of Pakistan with population of 15 million and 25 million respectively. [FIGURE ON ELECTORAL DISTRICTS] The governing structure of Pakistan is a parliamentary system. The parliament of Pakistan has bicameral structure composed of the Senate and the National Assembly (NA). The National Assembly of Pakistan and Senate are similar to the U.S. congress and senate respectively. The National Assembly has 342 members, of which 272 members are elected through voting 1. The tenure of the NA is 5 years, and the elections are held 1 Out of the 70 non-elected members, 60 seats are reserved for the women and 10 seats for the minorities. These seats are elected through an indirect proportional representation list system, whereby political parties submit their lists of women candidates for reserved seats to the Election Commission 2

3 after every 5 years. In our analysis, the elections were held in October 2002, February 2008 and May 2013 respectively 2. The 272 members of the NA are elected through the general elections held in 272 National Assembly constituencies. Out of the 272 electoral districts, 148 are in Punjab, 61 are in Sindh, 35 are in Khyber Pakhtunkhwa, 14 are in Balochistan, 12 are in the Federally Administered Tribal area (FATA), and 2 are in the Federal Capital, Islamabad 3. The electoral districts are very heterogeneous in terms of area, but have the same population in each electoral district 4. The voting structure is first-pass-the-post system. Each candidate can belong to at most a single political party or decide to run independently without any political affiliation. Historically, the two biggest parties in the political system of Pakistan are the Pakistan Muslim League Nawaz Sharif, PML (N) and the Pakistan s People Party (PPP). The PML (N) has strong base and support in the province of Punjab, while the PPP has its strngth in the province of Sindh. Since, 1989, the PML (N) and PPP has had major representation in three governments. PPP secured the most number of votes in the 2008 elections, while the PML (N) secured the most number of votes in the 2013 elections. Apart from the two main parties, there are several other political parties. Muttahida Qaumi Movement (MQM) and Pakistan Tehreek-e-Insaf (PTI) are among the other political parties. Apart from these four major political parties, there are several political parties with rightist and Islamic ideology. The three major Islamic parties include: Jamiat-e-Ulemae-Islam (JUI-F), Jamiat Ulema-e-Pakistan (JUP) and Jamaat-e-Islami Pakistan (JI). Jamiat-e-Ahle Hadith, and Pakistan Isami Tehrik (ITP) (formerly Tehriq-e-Jafaria (TeJ)) prior to the election (Constitution, Article 51). 2 The elections of 2008 were delayed by 6 months due to the assassination of Benazir Bhutto, the chairman of the second largest political party Pakistan Peoples Party. 3 The administrative division of Pakistan has several layers. The most coarse layer is the provinces. There are 6 provinces: Punjab, Sindh, Khyber Pakhtunkhwa, Balochistan, FATA and the Federal Capital. The Gilgit Baltistan and Azad Jammu & Kashmir are also divisions within Pakistan. Thus, Gilgit Baltistan and Azad Jammu & Kashmir are not represented in the National Assembly and Senate of Pakistan. However, they have an autonomous government which is part of Pakistan. The second layer is of the districts. There are roughly 30 districts in each province. 4 For instance, the Karachi city has 20 electoral districts, where as the whole province of Balochistan has only 14 electoral districts. 3

4 are among the other two Islamic political parties. In 2002, these five parties had a political alliance, Muttahida Majlis-e-Amal (MMA). The alliance was formed as a result of direct opposition to the policies led by President Pervez Musharraf to support for the United States war in Afghanistan (Adel et al. (2012)) Islamic Parties and connections with Taliban Since the inception of war-on-terror, the Jamiat-e-Ulema-e-Islam (JUI-F), Jamiat Ulemae-Pakistan (JUP) and Jamaat-e-Islami Pakistan (JI) have voiced their disapproval of the Pakistan s support to the United State equivocally. The three parties merged together in 2002 as a result of common opinion and to provide strong opposition to the President Musharraf s unconditional support to the United States for the war-on-terror Norell (2007). This pro-taliban stance was very popular among the province of Khyber Pakhtunkhwa, which shared the same ethnicity with the Afghanistan, in which the party gained majority. The MMA was able to form a coalition government in the province of Balochistan. All the individual parties in the MMA have links to militant groups, hence this coalition is of great interest when examining Pakistani links to the Taliban. Norell (2007) page 69 These parties emphasis on Islamic morals and principles in every day life. Jamiat Ulema-e-Islam has its roots in the anti-colonial movement and pro independent Muslim state movement back in the 1920s. It has strong presence in the Khyber Pakhtunkhwa and Balochistan. Jamaat-e-Islami Pakistan also traces back its roots before the independence of Pakistan. All of these parties have presence in the provinces of Khyber Pakhtunkhwa and Balochistan, which share the border with Afghanistan. 5 However, the political alliance was broken just before the 2008 elections. The JI wanted to boycott the elections, while the other two parties wanted to run for the elections. This difference of opinion led to the break-down of the alliance into the three political parties as they were before. 4

5 When the Taliban came to power in Afghanistan in 1996 it enjoyed support from the JUI-F, which in turn gained popular support from Pashtuns living in NWFP, Baluchistan and the Federally Administered Tribal Areas (FATA) (Norell (2007)) page 70 Norell (2007) presents detailed historical analysis of the relations between the Taliban and the MMA. Khyber Pakhtunkhwa, Balochistan and FATA are the areas sharing border with Afghanistan. The major political parties in these provinces are also the parties which are pro-taliban. 3.3 The natural disaster: Flood 2010 The 2010 floods in Pakistan have been called the greatest humanitarian crisis in recent history by the United Nations, with more people affected than were affected by the South- East Asian tsunami and the 2010 earthquake in Haiti combined Ferris (2011). In terms of economic damages, the flood of 2010 caused an estimated damage of $ 19 billion, while the earthquake caused an estimated damage of $ 6 billion. Pakistan was hit by abnormal monsoon rains in late July, 2010 which resulted in floods across all the provinces of Pakistan. Approximately, one-fifth of the Pakistan was flooded in terms of the area flooded and around 20 million people were affected due to the floods. It was the worst flood in the history of Pakistan, causing three times more people affected than the second worst flood which hit the country in In terms of the economic costs, it is the worst natural disaster to hit Pakistan ever. It caused more than three times the economic damage compared to the second worst natural disaster of Pakistan: the earthquake of [FIGURE ON FLOOD] The fig. 2 shows the extent of the flood in September As it is apparent from the figure, the flood were widespread all over Pakistan. The areas from all the major provinces were affected. Areas around the river Indus were severely affected, while rest of 5

6 the areas were moderately affected. Most of the eastern part of Punjab and south-western part of Balochistan was unaffected by the flood. Khyber Pakhtunkhwa was the earliest to get affected. On 20th July, Peshawar, the capital city of Khyber Pakhtunkhwa received heavy rain-fall. Soon, the rest of the province, in series of rain-falls, was affected. The flood in the mid-augusts, reached the southern part of the country and affected southern Punjab, northern Sindh and Balochistan. The direct damages to the infrastructure was estimated to be around $11 billion, while the cost of re-constructing the infrastructure was estimated to be around $ 8 billion. The flood caused large-scale damage not only to the houses and infrastructure of the area, but also resulted in wide-scale agricultural damages. More than 700,000 acres (3,000 km2) of cotton, 200,000 acres (800 km2) acres each of rice and cane, 500,000 tonnes of wheat and 300,000 acres (1,000 km2) of animal fodder were destroyed by the flood. The response by the Pakistan citizen, government and international agencies was no where as swift as the response to the earthquake in Doocy et al. (2013) surveyed households in the affected areas six months after the 2010 flood. They found that 95% of the households reported damage to their house, and 82% reported permanent damage to the house. Moreover, 85% of the households were displaced for more than two weeks. They showed that the need for flood aid was uniformly present in all the affected areas. But, only half of the affected areas reported receiving food aid ever. While half of the respondents cited as receiving aid in the first three months after the flood, only one-third of the respondents cited the same in the subsequent three months. More than 60% of the food needs were unmet across the flood affected areas six months after the flood. Farmers and daily-wage laborers were among the worst hit by the flood. They found that the common targeting practices were not kept in mind when distributing the food aid 6. The urban households were more likely to receive food aid despite the fact that rural households were the ones worst affected, both 6 That is, they showed that larger households, female headed households and internally displacement households were not significantly more likely to receive food aid compared to other households. 6

7 in terms of magnitude of people affected and magnitude of the damage. 3.4 Relations between Pakistan and U.S. The relations between Pakistan and the United States before 9/11 were tense. In 1998 Pakistan tested its nuclear technology despite strong international pressure. As a result Pakistan faced several sanctions and limitations from the international community. In 1999, the democratic government was overthrown by the military general, Pervez Musharraf, who declared himself the caretaker of the country until the next elections take place. United States disapproved the overthrow of democratic government by General Musharraf 7 Rennack (2001). However, after the 9/11 the image of Pakistan and Musharraf was completely transformed. Pakistan quickly joined the United States on the war-on-terror and became a key strategic ally of the U.S. As a result, image of Pakistan and General Musharraf was completely transformed from a rogue state and pariah to an ally and messiah Fair (2012). The sanctions and limitations on Pakistan was released and it received several billions in aid, military funds and loan forgiveness. The ties between the United States and Pakistan were cordial when the latter was hit by its worst earthquake in the history. The response from the international community lead by the U.S. was phenomenal. Within a day after the earthquake, the US Chinook helicopters arrived with food and relief supplies at the earthquake hit site. Within three days after the earthquake Flash Appeal for international help was sent out to the international community. The Secretary of State of U.S. at that time, Condoleezza Rice, said that the United States will support the country s earthquake relief efforts and help it rebuild after the Kashmir Earthquake. These prompt responses from the U.S. and the international community had significant impact on the perception of the locals about the foreigners and the United States. In 7 Infact when the President Clinton visited the South Asia in 2000, he spent only few hours out of a week long tour in Pakistan, and refused to shake hands with Musharraf. 7

8 May 2005, five months before the earthquake, the Pew poll results showed that only 23% of the Pakistanis expressed favorable views towards U.S.. However, in November 2006, one year after the earthquake, the result from the same polls indicated that 46% of the Pakistanis held favorable opinions about the U.S.. Similar results were formally shown by Andrabi and Das (2010). The authors randomly surveyed 126 villages around the faultline of the earthquake in They found that at the faultine, 70% of the individuals trusted foreigners (Europeans or Americans) compared to only 30%, the national average, 40 km away from the faultine. They showed that there was no such change in the level of trust for the local population. They attribute these findings to the on-ground presence and relief efforts of the foreigners and international agencies and argue that social preferences are not deep-rooted parameters, but are malleable to such experiences. On the other hand, the response after the flood of 2010 was inadequate. At first, the government mis-calculated the gravity of the situation. The first heavy rain of the monsoon which lead to flooding in the Khyber Pakhtunkhwa took place on 20th July However, the Flash Appeal for the relief and early recovery was not released until 20 days after the flood i.e. 9th August However, by that time most of the Pakistani land was under water. The poor rehabilitation and relief efforts was a result of both government incompetence and lack of international support. The government initially underestimated the effect of the floods and did not act promptly. The President of Pakistan at that time, Asif Ali Zardari, continued his trip to the United Kingdom despite the gravity of the situation. As of August 9, the international governments had committed less than $45 million. As a comparison, in the first ten days after the earthquake in 2005, the international governments had committed $247 million 8. 8 Other natural disasters around the world received intermediate amount of funding. For instance, in the first ten days of Cyclone Nargis, which hit Myanmar in May 2008, the international governments committed $110 million. Similarly, the earthquake in January 2010 in Haiti also saw a commitment of $742 million during the first ten days of the disaster. 8

9 4 Theoretical Framework The paper proposes lack of state mechanism to explain the differences in the level of support observed for the pro-islamic and pro-taliban party in the wake of the response to the natural disaster. Specifically, the paper argues that the government and nonstate actors compete in the developing countries for the political support. When the government is able to provide for the needs of the citizens it crowds out the non-state actors. On the other hand, government being unable to provide for these needs results in higher support for the non-state actors and crowding out of the government support. 5 Data Sources We use data sets from several different sources. The political outcome data set is constructed using the official election outcomes from the Election Commission of Pakistan. The electoral outcome in each electoral district is hand-picked and recorded. For each district, the number of votes won by each candidate and his/her political affiliation is recorded. In the 2002 elections, the Muttahida Majlis-e-Amal (MMA) participated as a single political party composed of the coalition of the following five parties: Jamiate-Ulema-e-Islam (JUI), Jamiat Ulema-e-Pakistan (JUP), Jamaat-e-Islami Pakistan (JI), Jamiat-e-Ahle Hadith, and Pakistan Isami Tehrik (ITP). The number of votes secured by the MMA over the total votes casted is used to determine the proportion of votes secured by the MMA in an electoral district. The candidate from the MMA appeared in 171 out of 272 electoral districts in Their representation is spread over all the provinces. The MMA is widely represented in the provinces of Balochistan and Khyber Pakhtunkhwa. They appeared in 13 out of the 14 electoral districts in Balochistan, and 32 out of the 35 electoral districts in Khyber Pakhtunkhwa. In the 150 electoral seats of Punjab and 61 electoral seats of Sindh, the MMA appeared in 90 and 36 electoral districts respectively. However, in the FATA the 9

10 MMA did not contest from any electoral district 9. Before the 2008 elections, the alliance of the five parties was broken due to internal power struggle among the parties. This clash also resulted in split of Jamiat-e-Ulema-e- Islam (JUI) into Jamiat-e-Ulema-e-Islam, Fazl-ur-Rehman (JUI-F) and Jamiat-e-Ulemae-Islam, Samiul Haq (JUI-S). In order to associate the votes secured by the MMA in 2008 (and subsequent election, i.e. 2013), the votes are aggregated for these six political parties. There are electoral districts in which some of these six parties do not have a candidate representation. There are electoral districts in which there is no candidate from any of the six parties, while there are districts with candidate from all the six parties. We compute the share of the MMA as the share of the political parties that present in that electoral district. We compute this share if two of the three major parties of the previous alliance MMA participate from that particular district. That is, if in any district less than the two major parties participate, we label it as if the MMA did not participate in that particular electoral district 10. The data on the flood of 2010 is gathered from two different sources: United Nation(UN) Office for the Coordination of Humanitarian Affairs (OCHA) and the National Disaster Management Authority (NDMA). 6 Empirical Methodology 6.1 Main Specification The natural disasters had effect on the affected population. We use the geographic heterogeneity of the effect of the flood along with difference in difference methodology to 9 This is not that surprising. The politics in FATA are more localized than other provinces. Usually, the representatives from FATA run without any political affiliation i.e. as independent candidates. 10 We tried with other definitions as well. For instance, labeling MMA as not participate only if all three major parties not participate from an electoral district; labeling MMA as not participate if two of the major parties do not participate in the electoral districts. The results with these different definitions of the share of MMA are presented in the Robustness section. All the different definitions of the vote share of MMA lead to the same qualitative result, and our consistent with our mechanism; the magnitudes are adjusted accordingly. 10

11 analyze if the flood had any effect on the political outcomes. Specifically, we estimate, MMA it = α i + λ t + β(affected i d2013 t ) + X itγ + u it, (1) where MMA it is the proportion of votes secured by the Muttahida Majlis-e-Amal in electoral district, i, at elections, t 11. The variable affected it is the variable denoting whether electoral district, i, was affected or not. X i are district level indicators of the average level of literacy in the district, percentage of people involved in agriculture, average household size, access to clean water and electricity, percentage of children immunized under 5 years of age, and percentage of urban area in the district. In addition to these demographic and economic variables, the control variables also include the distance from the Afghanistan, and the number of political parties in the election in that year. The time trend common to all the states is captured by the term λ t. α i are the electoral districts fixed effects 12. The methodology has several advantages. First, it controls for the pre-existing differences among the electoral districts through rich set of controls or electoral district fixed effects. Thus, any difference between the affected area and unaffected area only stems from the fact that the area is affected, rather than pre-existing differences. Second, the specification allows for difference in the result of election result for the MMA between the 2008 and 2013 elections through the term, λ t. 6.2 Advanced Specification In order to obtain consistent average treatment effect of the flood on the proportion of votes secured by the MMA in the 2013 elections, the treatment should be independent of the unobserved error term 13. That is, the areas which were flooded compared to the un- 11 Note that t = 2002, 2008, or Instead of using time invariant control variables, the specification also uses fixed effect specification to capture the effect of unobserved time invariant factors too. 13 For simplicity of the argument, consider that the flood has similar effect on all the flood affected areas. The argument presented below is valid for the heterogeneous effects of flood with minor changes. 11

12 flooded areas should not be systematically different across the unobservables. Formally, E(u it affected i = 1, X it, α i ) = E(u it affected i = 0, X it, α i ). This expression might not hold in our case. For instance, due to poor inundation system, heavy monsoon rains and melting of snow in the northern mountains in the summer the areas around the river Indus are flooded very frequently. In fact, some of the areas are flooded almost every year. Knowing that the areas around the flood are frequently flooded, people choosing to live around the river may be systematically different from the people choosing to live further away from the river. This would violate the above expression and generate inconsistent average treatment effects. In order to control for this potential factor that can lead to inconsistent estimates, we augment our specification above with the frequency of flood risk for each given electoral district. Specifically, we estimate the following specification: MMA it = α i + λ t + β 1 (affected i d2013 t ) + β 2 (frequency i d2013 t )+ β 3 (affected i d2013 t )(frequency i d2013 t ) + X itγ + u it, (2) where frequency i denotes a binary indicator for the frequently affected areas. The estimate of β 1 from the equation above would yield estimates which control for the propensity of flood in a given electoral district. That is, the above specification allows for the flood to have differential impact on the areas which are more likely (and are frequently) affected and areas which are usually not flooded by the flood, but were flooded in the 2010 flood. For these infrequently affected areas, the no selection bias expression, E(u it affected i = 1, frequency i = 0, X it, α i ) = E(u it affected i = 0, frequency i = 0, X it, α i ), is more likely to be satisfied. This is because the informal flood prevention mechanisms present in the frequently affected areas are most likely to be absent in the infrequently affected areas. Moreover, the infrequently affected areas are more likely to be surprised by the flood, and lack flood rehabilitation experience, which might make them more vulnerable to the flood. Since, the flood of 2010 took place on a massive scale which flooded almost one-third of Pakistan, there is enough variation in the term, 12

13 affected it frequency i. 7 Results 7.1 Flood and Political Outcomes The results from the estimation of Equation 1 are shown in table 1 below. table 2 shows the results. 7.2 Intensity of the floods Equation 3 illustrates the specification of interest. MMA it = α i + λ t + β s severe it + β m moderate it + u it, (3) where severe it is a binary indicator equal to one if the area was severely affected by the flood, and zero otherwise. moderate it indicates whether an area was moderately affected. The estimation controls for electoral-district fixed effects and time effects. Out of the 112 electoral districts that were affected by the flood, 55 were severely affected, while 57 were moderately affected. Table 3 shows the results. 8 Mechanism: Funding Gap In order to shed more light on the mechanism underpinning these changes, we utilize the funding gap data. The National Disaster Management Authority (NDMA) reported the funding gap present in the flood affected and other areas which were indirectly affected by the flood (for instance, due to rehabilitation in the neighboring electoral district). The 13

14 UNOCHA stored this information in form of a map representing the funding gap faced by an area in form of categorical variable. That is, the funding gap in a categorical variable with following categories: 0 20%, 20 40%, 40 60%, 60 80% and %. We use this information to generate funding gap corresponding to an electoral district 14. As a first step, we estimate the following specification: MMA it = α i + λ t + β(f undinggap i d2013 t ) + u it, (4) where F undinggap i represents the funding gap in district i. Funding gap is re-scaled between zero and one. Since funding gap is not entirely exogenous, we instrument funding gap with whether the area was affected, whether the area was severely affected and the distance from the national and provincial capital. (TALK ABOUT VALIDITY OF THE INSTRUMENT) Table 4 shows the results from both OLS and IV estimates. Column 1 reports unconditional estimates, Column 2 reports estimates with controls and Column 3 reports estimates with fixed effect. Column 4 to 6 report the unconditional, specification with controls and fixed effects for IV estimates. The results show that the funding gap is highly positively significant: an 10% increase in the funding gap is associated with 0.71% increase in the vote share of the MMA. The IV estimates are larger than the OLS estimates. IV estimates reveal that an 10% increase in the funding gap is associated with 1% increase in the vote share of the MMA. (TALK ABOUT ECONOMIC SIGNIFICANCE OF THE RESULTS) In order to identify the areas which are deriving the results, we estimate the equation above for different sub-samples. Table 5 shows the results from the estimation for different areas. The results indicate that the effect is not driven from a particular sub-sample, but rather is present in all the different regions. We interpret the results for the areas with low ex-ante propensity of flooding. Column 2 shows that an 10% increase in the funding 14 The funding gap information on the map is finer than the electoral district level. This helps us construct an estimate of the funding gap at the electoral district level. 14

15 gap is associated with 1.01% increase in the vote share of the MMA in the moderately affected areas, while the 10% increase in the funding gap is associated with 1.07% increase in the vote share of the MMA in the severely affected areas. (TEST IF THE RESULTS ARE STATISTICALLY DIFFERENT FROM EACH OTHER) These results highlight the importance of funding gap as one of the main mechanism through which the change in support for MMA is working. The areas which had higher funding gap witnessed greater increase in the vote share of the MMA. 9 Heterogeneity of results 9.1 Distance from Afghanistan The following equation captures the mechanism formally: MMA it = α i + λ t + βaffected it dist a fgh i + u it, (5) where dist afgh i is the distance of the electoral region, i, from the Afghanistan border. Figure 4 plots the marginal effects from the above equation. 9.2 Margin in previous election MMA it = α i + λ t + βaffected it margin i + u it, (6) where margin i is the margin of victory or loss of MMA in the electoral region, i, in the 2002 elections. Figure 5 plots the marginal effects from the above equation. 15

16 10 Robustness In this section we check for the robustness of our results. First, we replicate the main results using data from the provincial assembly. Second, we carry out falsification tests to confirm that the results are not driven from pre-existing trends in the data. Last, we partially test the parallel trends assumption required for consistent estimates of the difference-in-difference results Results from Provincial Assembly In the general elections, the individuals vote simultaneously for candidates from national and provincial assembly. The provincial assembly is finer than national assembly and overlooks the provincial matters. The provincial assembly has its own budget and it has discretion on how to allocate the budget across the province. We test if the results hold for provincial assembly, a more finer level 15. Table 6 shows the results of estimating equations 1 to 3 on the Provincial Assembly data. In order to shed light on the mechanism, Equation 4 is estimated using both OLS and IV. Table 7 shows the results for different areas using Provincial Assembly data Falsifications Tests In order to be sure that there no unobserved trends in the data driving our results, we carry out falsification tests. In particular, we carry out two different falsification exercises. The first falsification exercise randomly assigns the status of affected and unaffected to 15 Note, however, that the dynamics might be different in the provincial assembly. The provincial assembly might be better able to divert the blame of lack of rehabilitation to the national assembly and the government. Moreover, the provincial assembly politics are more localized in nature: MMA is not strongly represented in the provinces of Punjab and Sindh. Thus, pacifying the effect. On the contrary, the individuals might hold the provincial representatives more accountable. (CITE SOME- THING/ARGUE MORE). 16 The Table 5 used National Assembly data. 16

17 half of the districts and estimates the results from Equation 1. The exercise is repeated 999 times. The second falsification exercise is same as the first one, but randomly assigns affected status to the same number of electoral regions as those which were actually affected. The same exercises are carried out for the funding gap. In the first exercise, one-fifth are randomly assigned a value of zero, one, two, three and four. The Equation 4 is estimated and the exercise is repeated 999 times. In the second exercise, the funding gap values are assigned randomly to the actual number of areas (using the distribution of funding gap). Moreover, the equation with both affected and funding gap as the independent variables is also estimated in both cases. All the equations take electoral seat fixed effects and cluster the standard errors at the national assembly seat level. The Table 8 shows the mean, the standard deviation of the estimates obtained from each exercise in the first two rows. The third row reports the p-value from hypothesis test that the reported estimate is equal to zero. All the estimates are less than 0.001, and all of the p-values are greater than The estimates obtained strongly re-assure the fact that the results we got in the previous sections are not driven from pre-existing trends in the data Dropping Border areas 11 Earthquake and Political Outcomes 11.1 The Context The earthquake took place on 8th October 2005 near the city of Muzaffarabad, around 120 km north of Islamabad. The magnitude of the earthquake was very strong, as it was measured as 7.6 on the Richter magnitude scale. It was the worst earthquake to hit Pakistan ever, and the 15th worst earthquake around the world since The 17

18 earthquake caused 75,000 deaths, 70,000 were injured and 3.5 million people were left homeless. The estimated economic damage caused by the earthquake was around $ 6 billion. [FIGURE ON EARTHQUAKE] The fig. 3 shows the location of the epicenter of the earthquake. The figure also shows the 200 km and 350 km circle radius around the epicenter. Islamabad lies within the 200 km radius of the epicenter, while Peshawar and Lahore lie within the 350 km radius of the epicenter of the earthquake. The earthquake mainly affected the parts of Khyber Pakhtunkhwa, Punjab and Federal Capital. The earthquake show rapid response from the government, citizens and the international donors and community. Despite daunting challenges, including the difficult mountainous terrain and the race against time to provide basic shelter to the homeless prior to the arrival of the Himalayan winter, the humanitarian response to the earthquake was perceived by many to be one of the largest and most effective responses to a natural disaster to date. Wilder (2008) page 8 Within 24 hours of the earthquake the UN Disaster Assessment and Coordination (UNDAC) team flew into Islamabad and worked with the Government of Pakistan (GoP), donors, UN agencies, and NGOs to establish coordination structures. Within three days it had prepared and issued a Flash Appeal for $312 million to support a six-month emergency response, which two weeks later was increased to $550 million... Within 48 hours of the earthquake the first of 24 US helicopters arrived in Islamabad from Afghanistan. Soon approximately 1,200 US military personnel had arrived in Pakistan to assist with relief efforts, which included a medical team to run a US Army Mobile Army Surgical Hospital (MASH) in Muzaffarabad, and a 125-person Naval Mobile Construction Battalion to clear roads and debris, assist in setting up 18

19 IDP camps, and rebuild infrastructure. In addition to US military forces, the NATO Response Force also deployed approximately 1,200 specialist personnel including engineers and medical staff from 17 NATO countries to participate in NATO s first purely humanitarian mission. NATO forces also operated two air-bridges to fly relief supplies to Pakistan from bases in Germany and Turkey Wilder (2008) page Results In the earthquake 2005, the government and international organizations together provided a successful relief effort and rehabilitation program for the earthquake effectees as discussed above. The success of the government in the relief effort in response to a natural disaster, according to our mechanism, would imply crowding out of the non-mainstream political parties. In other words, the political support for the religious parties i.e. MMA should decrease. In order to test this implication, we use similar approach to the difference in difference methodology employed in the previous section. Specifically, we test whether the MMA lost more proportion of the votes in the 2008 elections in the areas affected by the earthquake compared to the areas which were unaffected by the earthquake. The following equation summarizes the equation of interest: MMA it = α i + λ t + βeq it + u it, (7) where EQ it is a measure for whether the electoral district, i, at election year, t, was affected by the earthquake or not. The estimation controls for electoral-district fixed effects and time effects. The table 9 shows the results from estimation of the above equation. The robust standard errors clustered at the electoral-district level are reported in the parenthesis below the estimates. Three different measures of whether an electoral district was affected 19

20 by the earthquake or not are used. The first two measures use a binary indicator for whether an area was affected or not. Specifically, in column 1, an area is coded as being affected by the earthquake if it is within the 250 km radius of the epicenter of the earthquake. Column 2 uses a stricter definition of affected by only coding areas within the 150 km radius of the epicenter of the earthquake as affected, and areas outside the 150 km radius as unaffected by the earthquake. Finally, column 3 uses a continuous definition of affected. That is, using the distance of the center of an electoral district to the epicenter of the earthquake as a measure of how much an electoral district was affected 17. In the column 1, of Table 9 shows the result from considering the 250 km radius around the epicenter as affected areas by the earthquake. 12 Discussion 13 Conclusion 17 We used several other measures of distance. For instance, the minimum distance of the earthquake epicenter to an electoral district, the maximum distance of the earthquake epicenter to an electoral district, and the distance by connecting roads from the epicenter to the electoral district. All these different measures of the distance yield similar results. The results are available upon request. 20

21 Figure 1: The National Assembly Electoral Districts of Pakistan 21

22 Figure 2: Flood Affected Districts 22

23 Figure 3: Earthquake Epicenter 23

24 Figure 4: Heterogeneous effect w.r.t. distance from Afghanistan Average Marginal Effect of 2010 Flood on MMA s share of votes Marginal Effect Distance of the centroid to the Afghan border in Km 24

25 Figure 5: Heterogeneous effect w.r.t. margin of victory of MMA in the previous elections Average Marginal Effect of 2010 Flood on MMA s share of votes Marginal Effect Margin of victory before the flood 25

26 Table 1: Estimates of effect of flood on the political support (1) (2) (3) (4) (5) (6) D (0.005) (0.005) (0.005) (0.005) (0.010) (0.005) Affected (0.016) (0.018) (0.018) (0.016) (0.015) Affected D (0.014) (0.014) (0.014) (0.014) (0.014) (0.014) Dist. Afgh (0.006) (0.007) (0.005) (0.005) Capital City (0.016) (0.029) (0.028) Development Index (0.001) (0.001) No. of Pol. Parties (0.002) Constant (0.007) (0.030) (0.030) (0.123) (0.122) (0.003) N

27 Table 2: Estimates of effect of flood on the political support controlling for ex-ante flooding risk (1) (2) (3) No Controls Controls FE D (0.005) (0.010) (0.005) Affected (0.021) (0.019) Affected D (0.019) (0.019) (0.019) High Prop. of Flood (0.035) (0.018) High Prop. of Flood D (0.021) (0.021) (0.021) High Prop. of Flood * Affected * D (0.030) (0.031) (0.030) Constant (0.006) (0.123) (0.003) N

28 Table 3: Varying effect of flood on the political support Whole Sample Low Propensity of Flood High Propensity of Flood No Controls Controls FE Controls FE Controls FE (1) (2) (3) (4) (5) (6) (7) D *** 0.016* 0.034*** 0.024** 0.034*** * (0.005) (0.009) (0.005) (0.010) (0.005) (0.028) (0.020) Moderately Aff *** (0.026) (0.019) (0.022) (0.066) Moderately Aff. * D ** 0.036** 0.033** 0.036* 0.034* (0.016) (0.017) (0.016) (0.022) (0.020) (0.034) (0.031) Severely Aff *** *** *** *** (0.016) (0.021) (0.029) (0.033) Severely Aff. * D * 0.041** 0.040* 0.067* 0.066* (0.021) (0.021) (0.021) (0.035) (0.035) (0.026) (0.025) Constant 0.020*** *** *** *** (0.007) (0.119) (0.003) (0.128) (0.004) (0.179) (0.005) N

29 Table 4: Estimates of effect funding gap on the political support OLS No Controls Controls FE No Controls Controls FE (1) (2) (3) (4) (5) (6) IV Funding Gap 0.084*** 0.070*** 0.071*** 0.204*** 0.193*** 0.100*** (0.015) (0.015) (0.015) (0.043) (0.045) (0.013) Constant 0.049*** *** 0.049*** 0.050*** 0.053*** (0.008) (0.112) (0.003) (0.008) (0.008) (0.008) N

30 Table 5: Varying effect funding gap on the political support for the Taliban with flood intensity and ex-ante flooding risk Low Propensity of Flooding High Propensity of Flooding Moderately Aff. Severely Aff. Moderately Aff. Severely Aff. OLS IV OLS IV OLS IV OLS IV (1) (2) (3) (4) (5) (6) (7) (8) Funding Gap 0.066*** 0.101*** 0.079*** 0.107*** 0.102** 0.112*** *** (0.017) (0.016) (0.019) (0.020) (0.039) (0.032) (0.038) (0.020) Constant 0.044*** 0.048*** 0.025*** 0.030*** 0.051*** 0.057*** 0.060*** 0.065*** (0.003) (0.011) (0.004) (0.006) (0.007) (0.021) (0.006) (0.022) N

31 Table 6: Effect of flood on the political support - Provincial Assembly (1) (2) (3) D (0.004) (0.005) (0.004) Affected D (0.009) (0.014) High Prop. of Flood D (0.000) Affetced * High Prop. of Flood D (0.001) Moderately Affected D (0.014) Severely Affected D (0.011) Constant (0.002) (0.002) (0.002) N Standard errors in parentheses p < 0.10, p < 0.05, p <

32 Table 7: Varying effect funding gap - Provincial Assembly Low Propensity of Flooding High Propensity of Flooding Moderately Aff. Severely Aff. Moderately Aff. Severely Aff. OLS IV OLS IV OLS IV OLS IV (1) (2) (3) (4) (5) (6) (7) (8) D *** 0.021*** 0.024* 0.026* (0.004) (0.003) (0.014) (0.014) Funding Gap D *** 0.073*** 0.068*** 0.099*** * (0.013) (0.016) (0.013) (0.013) (0.024) (0.024) (0.022) (0.014) Constant 0.039*** 0.044*** 0.025*** 0.031*** 0.045*** 0.050*** 0.039*** 0.043*** (0.003) (0.005) (0.002) (0.004) (0.005) (0.018) (0.004) (0.011) N

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