Surviving the Killing Fields The Long-Term Consequences of the

Mathias Iwanowsky∗ Andreas Madestam†

February 28, 2016

Extended Abstract

What are the lasting effects of atrocities of war? While casualties are often measured in the number of dead and the destruction of physical and human capital, a majority of the population in war-torn countries is not directly affected by conflict. Yet many remain traumatized through indirect exposure to , with consequences for their trust in society’s ability to shield its citizens from harm. Civil war and is particularly damaging to trust in the government as representatives of the state often participate in the conflict. If indirect experience of state violence has a persistent impact on political views and behavior, this may be detrimental for a national government’s capacity to rebuild a postwar nation. Empirical evidence on the causal effects of indirect exposure to war is scarce. To begin with, it is difficult to disentangle direct and indirect experiences of violence. Moreover, conflict frequently occurs nationwide without credible counterfactuals. Finally, even when the intensity of violence varies, selective targeting of specific regions based on prewar political views may confound post-conflict measures of beliefs and behavior. It is thus an open question to what extent indirect exposure to conflict causes political change. This paper uses evidence from one of the worst in modern history, the terror regime of the Khmer Rouge, to study the causal effect of indirect experience of war on political beliefs, behavior, and trust almost 40 years after the genocide. Between 1975 and 1978, up to three million Cambodians were killed because of intellectual background, schooling, or urban dwelling. Demonizing modernization, the Khmer Rouge forced people in the cities to labor camps in the countryside to grow rice alongside the rural population. Toward the end of its reign, the regime killed a large share of the people in the camps creating vast Killing Fields throughout the country. The Cambodian experience allows us to investigate how indirect exposure to war atrocities affected the majority of its citizens as the Killing Fields represent a long-lasting trauma to the nearby rural population. Using the fact that the severity of killings varied across

∗IIES, Stockholm University and Harvard University, email: [email protected] †Stockholm University, email: [email protected] (see Figure 1), we investigate if the intensity of violence affected subsequent local political outcomes and measures of trust four decades later. To identify a causal effect, we rely on the Khmer Rouge’s documented desire to create an agricultural empire with rice production as the main staple using the city population as forced labor to grow the rice. We argue that the forced migration was based on rice production targets, with more people sent to areas experiencing higher agricultural productivity during the regime period. Using historic rainfall to generate exogenous variation in the productivity of rice fields throughout the reign of the Khmer Rouge, we find that fewer people were killed in less productive communes. Figure 2 displays the spatial distribution of rainfall during the period and columns (1) and (2) of Table 1 shows how the negative production shock led to a less people killed. The assumption is that conditional on the likelihood of rain in a commune, rainfall is a random event uncorrelated with other factors that affect political outcomes and beliefs. In addition, while we do not observe the number of laborers sent to the camps, we believe that the number of people killed serves as reasonable proxy for the size of the sites. Using highly detailed data based on US military maps from the early 1970s we first show that less productive communes during the Khmer Rouge are similar to more productive communes along a number of important socio-economic dimensions measured prior to Khmer Rouge period, confirming our identifying assumption (see Table 2). We then explore information from various sources to understand how the exposure to the Killing Fields affects people living in the affected communes in present-day Cambodia. We find that communes with more people killed during the Khmer Rouge have a higher turnout in the 2013 general elections 35 years later, primarily by favoring the main opposition party CNRP while punishing the long-ruling incumbent government [see columns (3)-(6) of Table 1]. To understand these findings we gauge individual-level survey data on people’s political beliefs and various indices of trust collected prior to the election in 2013. A first set of results demonstrate that an increase in the severity of the persecutions during Khmer Rouge as proxied by the production shocks increases mistrust today. Figure 3 shows that individuals living in communes experiencing more killings are significantly less likely to trust their neighbors or to agree with statements that the national government cares for its citizens. The lack of trust is not matched by political apathy however. These individuals are more inclined to support political competition and are better informed about the upcoming election and legislative procedures. We argue and show that the results are not driven by differences in present-day economic outcomes, as measured by consumption per capita and human and physical capital accumulation. In addition, the demographic structure and migration patterns in more or less affected communes are the same, supporting the assumption that the rural population were bystanders rather than victims killed in the labor camps. Taken together, our evidence demonstrates that indirect experience of war breeds persistent and long-lasting mistrust in general and in the state, as represented by the national government. The Killing Fields serves as an important reminder of the atrocities and the repression of a ter- rifying regime, generating a stronger demand for political alternatives to the ruling incumbent.

2 The findings suggest that a vibrant democracy may be particularly important in post-conflict societies plagued by civil war in order to restore the electorates’ trust in the state.

3 Figure 1: The Killing Fields

Figure 2: More and less productive communes in each province

4 Figure 3: Trust and political beliefs

Note: A shock to production implies less exposure to violence. 95%-CIs shown.

5 Table 1: Effect of production shock on severity of killings

(1) (2) (3) (4) (5) (6) First-stage effect Political mobilization log(Bodies) Vote share CNRP Turnout Neg. Production Shock during KR −0.057 −0.057 −4.810 −4.814 −0.032 −0.032 (0.018)∗∗∗ (0.018)∗∗ (0.993)∗∗∗ (1.009)∗∗∗ (0.011)∗∗ (0.011)∗∗ [0.019]∗∗∗ [0.021]∗∗ [0.825]∗∗∗ [0.911]∗∗∗ [0.009]∗∗∗ [0.010]∗∗∗ Neg. Production Shock 1972–1974 0.004 −0.327 −0.010 (0.024) (1.289) (0.012) [0.020] [1.256] [0.009] Neg. Production Shock 1978–1980 −0.012 0.881 0.012 (0.036) (1.447) (0.011) [0.026] [1.226] [0.012]

Lat × Lon Polynomial Yes Yes Yes Yes Yes Yes ProvinceFE Yes Yes Yes Yes Yes Yes CommuneControls Yes Yes Yes Yes Yes Yes Dependent variable mean 0.141 0.141 40.160 40.160 0.791 0.791 Mean Productive Communes 0.156 0.156 42.878 42.878 0.807 0.807 F-Test 9.835 9.180 N 1,569 1,569 1,569 1,569 1,569 1,569 All columns feature the shock realization ∈ {0, 1}, shown in Figure 2, as the main independent variable. In columns (1) and (2) we use the log of the bodies in thousands as the dependent variable, in columns (3) and (4) we use the vote share of the main opposition party, while in columns (5) and (6) we use voter turnout as the dependent variable. In columns (2), (4), and (6) we add lead and lag shocks to our specification which are expected to be insignificant. All columns include 2 2 the controls shown in Table 2. Lat × Lon Polynomial are: Latitude, Latitude , Longitude, longitude and Latitude × Longitude. Standard errors clustered by 24 provinces in parentheses. Conley Standard errors at 150km in brackets. ∗ p< 0.10, ∗∗ p< 0.05, ∗∗∗ p< 0.01

Table 2: Exogeneity check at the commune level

(1) (2) (3) (4) (5) (6) (7) (8) (9) School Church Telephone Commune Office Post Office log(Pop Density) log(km of Roads) log(km of Rails) log(Tons of Bombs) Shock During KR -0.030 0.011 0.002 0.026 0.003 -0.227 -0.055 -0.057 -0.361 (0.040) (0.012) (0.004) (0.040) (0.012) (0.231) (0.097) (0.048) (0.330)

(10) (11) (12) (13) (14) (15) (16) (17) log(Dist. ) log(Dist. to Road) log(Dist. to Province Capital) log(Dist. to Border) log(Rice fields) log(Inundation) log(Plantations) log(Commune area) Shock During KR 0.071 -0.032 0.147 -0.102 -0.092 0.003 0.099 0.224 (0.089) (0.087) (0.165) (0.075) (0.121) (0.109) (0.073) (0.156) N 1569 1569 1569 1569 1569 1569 1569 1569 Geographic Controls Yes Yes Yes Yes Yes Yes Yes Yes 2 2 Lat × Lon Polynomial are: Latitude, Latitude , Longitude, longitude and Latitude × Longitude. Standard errors clustered by 24 provinces in parentheses. ∗ p< 0.10, ∗∗ p< 0.05, ∗∗∗ p< 0.01

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