Surviving the Killing Fields

The long-term consequences of the Khmer Rouge

Mathias Iwanowsky and Andreas Madestam Stockholm University June 15, 2016

Barcelona GSE Summer Forum Advances in Micro Development Economics Motivation

• Legitimacy of and trust in government are important for state building

• “Reservoir of loyalty”: increases and enhances citizens’ cooperation and compliance with rules and regulations even without incentives and sanctions

• In their absence, governments have to spend more resources on monitoring and enforcement to induce compliance

• Despite agreement on importance of legitimacy and trust, we know less of their origins

2 Motivation

• Legitimacy and trust key when rebuilding post-war societies as state institutions are weak • Civil war and genocide particularly damaging to trust in government as state representatives often participate in conﬂict • Memory of state involvement may prevent citizens from conferring authority on the state in fear of an abusive authority ⇒ What determines legitimacy and trust in general and in the government in war-torn societies? ⇒ What are the effects on the population at large beyond those directly affected by violence?

3 Empirical challenge

• Unclear whether experience of war causes changes in political beliefs and behavior • Conflict often nationwide without credible counterfactuals • Even if intensity of violence varies, (selective) targeting of specific regions based on prewar political views may confound measures of post-conflict beliefs and behavior • Difficult to disentangle direct and indirect experiences of violence

• Due to lack of empirical evidence, open questions are 1. Does (indirect) exposure to conﬂict cause political change? 2. If so, what are the mechanisms?

4 Cambodia

• Investigate genocide in Cambodia during Khmer Rouge (KR), 1975–1978, to study causal effect of experience of war on political beliefs, behavior, and trust almost 4 decades later • 1.5–3 million (20%) Cambodians killed • 63% were separated from family members • 30% observed torture, 22% killings • At end of reign, regime killed large share of urban pop residing in labor camps creating Killing Fields throughout country

• Allows us to study how indirect exposure to war atrocities affected majority of its citizens as Killing Fields represent long-lasting trauma to nearby rural population

5 Labor Camps and Killing Fields

Figure 1: Cambodia’s Killing Fields

6 What we do

We ﬁrst estimate whether the Killing Fields affected • Political mobilization in last national election in 2013 • Vote share for the long-ruling incumbent and opposition and turnout

To understand our ﬁndings, we then estimate impact of Killing Fields on • Measures of trust • Political beliefs • Knowledge of and interest in politics • Community engagement • Occupational choice • Credit market behavior • Investments in physical and human capital and public infrastructure

7 Why and how would (indirect) experience of atrocities and memory of Killing Fields matter today?

1. Witnessing atrocities ofKR and being reminded of experience via Killing Fields breed mistrust in general and in the state, as represented by national gov’t • Direct measures of social preferences and trust • Revealed-pref argument: if public institutions have low legitimacy, make investments that are less dependent on the state or contribute less to public good provision

2. Change in population and social structure • Systematic killings affect social and/or labor-land ratio (“Malthusian argument”)

8 Why and how would (indirect) experience of atrocities and memory of Killing Fields matter today?

3. Differential investments in public infrastructure • Recent gov’t provision of public goods affects legitimacy

4. “Post-traumatic growth” • Individual direct exposure to violence increases social cooperation and pro-social behavior, perhaps explained by increased prosociality toward in- over out-group members

9 Genocide

April 17th 1975,KR win 5-year long civil war by capturing Phnom Penh

• Immediately after, population is evicted from urban areas - Phnom Penh: 2 million were forced to leave within two weeks - Used as labor on rice ﬁelds • Population of Cambodia classiﬁed into two groups 1. Base people: farmers and peasants in rural areas 2. New people: city evacuees and those with education • New people were targeted and eventually killed

10 Genocide - ‘new’ vs ‘base’ people

Classiﬁcation was easily observable

• New people - Evicted urban population, in particular educated and former government ofﬁcials - Moved to compounds outside base people’s villages • Base people - Allowed to live in their own houses with basic rights - Limited interactions with new people but forced to watch beatings and killings - No planned extermination

11 Current political system

• Cambodians People’s Party (CPP) in power since 1985 - CPP leader Hun Sen, a formerKR, actively supported amnesty of KR cadres - Extensive cronyism and widespread corruption • In response, Cambodia National Rescue Party (CNRP) uniﬁed all opposition parties to oust CPP in 2013 national election - Its leader, Sam Rainsy, faces charges for accusing MPs of collusion withKR

”I not only weaken the Opposition, I’m going to make them dead ... and if anyone is strong enough to try to hold a demonstration, I will beat all those dogs and put them in a cage”

(Hun Sen, Jan 20, 2011 as a response to the Arab spring. Source: Human Rights Watch Report 2015)

12 Basic idea: correlation between severity of killings and 2013 national election

(1) (2) (3) (4) (5) (6) Vote Share opposition Pr[CPP Win] Turnout log(Bodies) 1.306∗∗ 1.411∗∗∗ −0.025 −0.034∗∗ 0.001 0.008∗ (0.483)(0.311)(0.016)(0.013)(0.006)(0.004)

Lat × Lon polynomial Yes Yes Yes Yes Yes Yes Province FE Yes Yes Yes Yes Yes Yes Pre-KR commune controls No Yes No Yes No Yes Dependent variable mean 40.16 40.16 0.61 0.61 0.79 0.79 N 1,569 1,569 1,569 1,569 1,569 1,569

Standard errors clustered by 24 provinces in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

• Areas with more people killed have higher turnout, favoring opposition • OVB problem • Positive bias: target opposition areas • Negative bias: target areas supportive of regime 13 Identiﬁcation strategy

• Rely on regime’s desire to create an agricultural empire with rice production as the main staple and use of city population as forced labor

• Regime moved urban population to areas experiencing higher (temporal) agricultural productivity

• Use historic rainfall to generate exogenous variation in rice productivity and, hence, variation in size of camps and subsequent killings

14 Data

• Cambodian Genocide Project (Yale, Ben Kiernan) Geocoding of 974,734 buried bodies in Cambodia • US Army maps series L7016, from 1969–1972 Detailed maps covering Cambodia prior to the genocide • Rainfall data at 0.25×0.25 degrees from 1951–2007 Aphrodite Monsoon Asia http://www.chikyu.ac.jp/precip/ • Voting outcomes from the 2013 national election • Individual-level survey outcomes on trust and political beliefs in 2003 and 2013, Asia Foundation • Cambodian Socio-Economic Survey 1996 – 2014 (12 waves) Repeated cross-section with information on socio-economic outcomes, occupations, migration • Population Census 1998/2008

15 Data

Figure 2: US Army map with commune characteristics and Killing Fields

16 Map accuracy Identiﬁcation strategy

• EmployKR’s plan to use forced labor to increase rice production

Source: Chandler et al (1988), Pol Pot Plans the Future: Conﬁdential Leadership Documents from Democratic Kampuchea, 1976-1977

17 Identiﬁcation strategy

• Rice is Cambodia’s main crop. Use local rainfall shocks to predict variation in production across communes duringKR • Yields are sensitive to excessive rain during harvest season

18 Identiﬁcation strategy

Assumptions • Conditional on likelihood of shocks, whether a commune had a shock during harvest season 1975–1977 is orthogonal to political outcomes today • Number of people killed approximates for size of site Intuition • Absence of a shock increases production, the size of labor camps, and subsequent killings Standardized rainfall (1) Calculate mean µc,p and standard deviation σc,p in rain using 1951 – 2007 in each commune c and standardize rainfall during theKR 1975, 1976, and 1977 (2) Use within-province mean and distinguish between positive and negative rainfall realizations

19 Distribution of rainfall

Figure 3: More and less productive communes duringKR 20 Main speciﬁcation

• Commune-level regressions

yc = βNeg. Production Shockc + µp + γXc + c

• Outcomes: people killed, voting, trust, political beliefs and knowledge, community investments, socio-economic and credit market measures • Neg. Production Shock: A dummy variable (= 1) if there was a negative shock to production (and, hence, fewer killings)

• µp, Xc: province FE and commune controls • All regressions control ﬂexibly for latitude and longitude with SEs clustered either at the province level or adjusted for spatial correlation using Conley at 1.5 degrees

21 Exogeneity check

• Is rainfall duringKR correlated with other determinants of the outcomes of interest?

Commune characteristics prior toKR Mean Point estimate Standard error T-stat School in commune 0.709 −0.030 (0.040) −0.74 Church in commune 0.035 0.011 (0.012) 0.90 Telephone in commune 0.005 0.002 (0.004) 0.54 Commune office in commune 0.396 0.026 (0.040) 0.65 Post office in commune 0.017 0.003 (0.012) 0.23 log(population density) 1.542 −0.227 (0.231) −0.98 log(rice field area) 2.22 −0.092 (0.121) −0.76 log(inundation area) 0.929 0.003 (0.109) 0.03 log(plantations area) 0.128 0.099 (0.073) 1.35 log(commune area) 3.818 0.224 (0.156) 1.43 log(distance to Phnom Penh) 4.479 0.071 (0.089) 0.80 log(distance to road) 0.531 −0.032 (0.087) −0.36 log(distance to province capital) 2.588 0.147 (0.165) 0.89 log(distance to border) 3.682 −0.102 (0.075) −1.36 log(km of roads in commune) 1.844 −0.055 (0.097) −0.57 log(km of rails in commune) 0.193 −0.057 (0.048) −1.18

22 Production shock and severity of killings

(1) (2) (3) (4) (5) (6) log(Bodies) Bodies Neg. Production Shock duringKR −0.046 −0.057 −0.057 −0.444 −0.478 −0.496 (0.019)∗∗ (0.018)∗∗∗ (0.018)∗∗ (0.181)∗∗ (0.175)∗∗ (0.181)∗∗ [0.018]∗∗ [0.019]∗∗∗ [0.021]∗∗ [0.140]∗∗∗ [0.150]∗∗∗ [0.170]∗∗∗ Neg. Production Shock 1972–1974 0.004 0.013 (0.024)(0.135) [0.020][0.123] Neg. Production Shock 1978–1980 −0.012 0.277 (0.036)(0.243) [0.026][0.188]

Lat × Lon polynomial Yes Yes Yes Yes Yes Yes Province FE Yes Yes Yes Yes Yes Yes Commune controls No Yes Yes No Yes Yes Dependent variable mean 0.141 0.141 0.141 0.621 0.621 0.621 N 1,569 1,569 1,569 1,569 1,569 1,569

Standard errors clustered by 24 provinces in parentheses. Conley standard errors in brackets. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Continuous ﬁrst stage Different dependent variable Robustness

23 Political mobilization

(1) (2) (3) (4) (5) (6) Vote share CNRP Turnout Neg. Production Shock duringKR −5.328 −4.810 −4.814 −0.036 −0.032 −0.032 (1.183)∗∗∗ (0.993)∗∗∗ (1.009)∗∗∗ (0.014)∗∗ (0.011)∗∗ (0.011)∗∗ [0.941]∗∗∗ [0.825]∗∗∗ [0.911]∗∗∗ [0.011]∗∗∗ [0.009]∗∗∗ [0.010]∗∗∗ Neg. Production Shock 1972–1974 −0.327 −0.010 (1.289)(0.012) [1.256][0.009] Neg. Production Shock 1978–1980 0.881 0.012 (1.447)(0.011) [1.226][0.012]

Lat × Lon polynomial Yes Yes Yes Yes Yes Yes Province FE Yes Yes Yes Yes Yes Yes Commune controls No Yes Yes No Yes Yes Dependent variable mean 40.160 40.160 40.160 0.791 0.791 0.791 N 1,569 1,569 1,569 1,569 1,569 1,569

Standard errors clustered by 24 provinces in parentheses. Conley standard errors in brackets. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

24 Channels

How can we understand these differences?

• Indirect experience and memory of atrocities breed persistent mistrust toward an abusive state, as represented by today’s ruling incumbent, and society in general • People get engaged to express discontent with authority, not to conﬁrm status quo

Other possible explanations

• Increase in pro-social behavior in general, possibly driven by differential feelings toward the local over central/state • Differential investments in public infrastructure • Change in population and social structure • Differential migration

25 Trust

(1) (2) (3) (4) Trust in neighbor Can inﬂuence government Neg. Production Shock duringKR0 .090 0.151 0.159 0.059 (0.042)∗∗ (0.035)∗∗∗ (0.077)∗ (0.098) [0.031]∗∗ [0.040]∗∗∗ [0.050]∗∗ [0.051]

Lat × Lon polynomial Yes Yes Yes Yes Province FE Yes Yes Yes Yes Individual controls Yes Yes Yes Yes Alive in 1978 No Yes No Yes Dependent variable mean 0.480 0.462 2.731 2.660 N 991 450 1,849 1,199

All columns feature shock realization ∈ {0, 1}, shown in Figure (2) as main independent variable. Lat × Lon polynomial: Latitude, Latitude2, Longitude, Longitude2, and Latitude × Longitude and province ﬁxed effects. Individual controls are age, age2 and male. SEs clustered by 24 provinces in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

26 Political beliefs

(1) (2) (3) (4) (5) (6) (7) (8) Has voted Government has a paternal role Supports political comp. Voting makes a difference Neg. Production Shock duringKR −0.020 −0.016 0.114 0.229 −0.049 −0.057 0.101 0.093 (0.015)(0.009)∗ (0.028)∗∗∗ (0.089)∗∗ (0.022)∗∗ (0.023)∗∗ (0.048)∗∗ (0.050)∗ [0.014][0.007]∗∗ [0.028]∗∗∗ [0.084]∗∗ [0.022]∗∗ [0.020]∗∗ [0.046]∗∗ [0.043]∗∗

Lat × Lon polynomial Yes Yes Yes Yes Yes Yes Yes Yes Province FE Yes Yes Yes Yes Yes Yes Yes Yes Individual controls Yes Yes Yes Yes Yes Yes Yes Yes Alive in 1978 No Yes No Yes No Yes No Yes Dependent variable mean 0.921 0.968 0.610 0.581 0.883 0.861 0.428 0.456 N 1,963 1,294 991 450 1,963 1,294 1,484 906

27 Political knowledge

(1) (2) (3) (4) Knows that voting is to elect Parliament Knows election date Neg. Production Shock duringKR −0.101 −0.100 −0.057 −0.081 (0.040)∗∗ (0.037)∗∗ (0.038)(0.046)∗ [0.036]∗∗ [0.040]∗∗ [0.040][0.050]∗

Lat × Lon polynomial Yes Yes Yes Yes Province FE Yes Yes Yes Yes Individual controls Yes Yes Yes Yes Alive in 1978 No Yes No Yes Dependent variable mean 0.682 0.579 0.477 0.489 N 972 844 972 844

• Findings on trust, beliefs, and political knowledge consistent withKR experience breeding general mistrust and demand for political alternatives to today’s ruling incumbent

28 Public good provision, credit market behavior, and occupational choice

To corroborate ﬁndings on trust, beliefs, and knowledge investigate indirect evidence of low general trust and lacking state legitimacy

• Low general trust • Communities less likely to solve collective action problems - fewer community ﬁsheries and community forest management schemes • Individuals less reliant on market transactions that build on trust - less informal lending from friends, family, moneylenders, and landlords and more anonymous formal transactions • Lacking legitimacy of public institutions • Make investments that are less dependent on the state (fewer asset-speciﬁc investment that relies on gov’t) - less likely to work for the government

29 Public good provision

(1) (2) (3) (4) Commune has a Commune has a community community ﬁshery forest management scheme Neg. Production Shock duringKR0 .046 0.047 0.044 0.033 (0.019)∗∗ (0.020)∗∗ (0.019)∗∗ (0.019) [0.018]∗∗ [0.018]∗∗ [0.016]∗∗ [0.014]∗∗

Lat × Lon polynomial Yes Yes Yes Yes Province FE Yes Yes Yes Yes Commune controls No Yes No Yes Dependent variable mean 0.059 0.059 0.079 0.079 N 1,564 1,564 1,564 1,564

All columns feature shock realization ∈ {0, 1}, shown in Figure (2) as main independent variable. Lat × Lon polynomial: Latitude, Latitude2, Longitude, Longitude2, and Latitude × Longitude and province ﬁxed effects. SEs clustered by 24 provinces in parentheses. Conley SEs in brackets. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

30 Credit market behavior

(1) (2) (3) (4) (5) (6) (7) (8) Has any debt log(Size of debt, KHR) Informal lending Formal lending Neg. Production Shock duringKR0 .004 −0.000 0.009 0.033 0.033∗∗ 0.034∗∗ −0.039∗∗∗ −0.038∗∗∗ (0.009)(0.007)(0.055)(0.055)(0.013)(0.014)(0.012)(0.013)

Lat × Lon polynomial Yes Yes Yes Yes Yes Yes Yes Yes Province FE Yes Yes Yes Yes Yes Yes Yes Yes Commune controls Yes Yes Yes Yes Yes Yes Yes Yes Individual controls Yes Yes Yes Yes Yes Yes Yes Yes Alive in 1978 No Yes No Yes No Yes No Yes Dependent variable mean 0.277 0.247 13.225 13.154 0.552 0.564 0.443 0.427 N 273,456 95,016 100,857 32,474 105,055 33,895 105,055 33,895

All columns feature shock realization ∈ {0, 1}, shown in Figure (2) as main independent variable. All columns include controls shown in Table 2 a Lat × Lon polynomial: Latitude, Latitude2, Longitude, Longitude2, and Latitude × Longitude and province ﬁxed effects. Individual controls are age, age2 and male. SEs clustered by 24 provinces in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

31 Occupational choice

(1) (2) (3) (4) (5) (6) State employed Private or self employed Neg. Production Shock duringKR0 .007∗∗ 0.007∗ 0.014∗∗ −0.006 −0.007 −0.032∗∗ (0.003)(0.004)(0.005)(0.006)(0.007)(0.014)

Lat × Lon polynomial Yes Yes Yes Yes Yes Yes Province FE Yes Yes Yes Yes Yes Yes Commune controls Yes Yes Yes Yes Yes Yes Individual controls Yes Yes Yes Yes Yes Yes Alive in 1978 No Yes Yes No Yes Yes Never migrated No No Yes No No Yes Dependent variable mean 0.060 0.092 0.044 0.834 0.853 0.894 N 183,556 94,332 18,653 183,556 94,332 18,653

All columns feature shock realization ∈ {0, 1}, shown in Figure (2) as main independent variable. Lat × Lon polynomial: Latitude, Latitude2, Longitude, Longitude2, and Latitude × Longitude and province ﬁxed effects. Commune controls are shown in table 2. Individual controls are age, age2 and male. SEs clustered by 24 provinces in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

32 Alternative explanations: migration

(1) (2) (3) (4) In-migration All Alive in 1978 Neg. Production Shock duringKR0 .017 0.025 0.016 0.027 (0.022)(0.016)(0.033)(0.028)

Lat × Lon polynomial Yes Yes Yes Yes Province FE Yes Yes Yes Yes Commune controls Yes Yes Yes Yes Individual controls No Yes No Yes Dependent variable mean 0.322 0.322 0.564 0.564 N 199,501 199,501 79,931 79,931

All columns feature shock realization ∈ {0, 1}, shown in Figure (2) as main independent variable. Lat × Lon polynomial: Latitude, Latitude2, Longitude, Longitude2, and Latitude × Longitude and province ﬁxed effects. Commune controls are shown in table 2. Individual controls are age, age2, and male. SEs clustered by 24 provinces in parentheses. Conley standard errors at 150km in brackets.∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

33 Alternative explanations: population

(1) (2) (3) (4) (5) (6) (7) (8)

Age distribution: mean age in group

0–19 20–39 40-59 ≥ 60 Neg. Production Shock duringKR −0.021 0.022 −0.069 −0.083 −0.123 −0.119 −0.045 −0.044 (0.055)(0.048)(0.094)(0.094)(0.086)(0.094)(0.213)(0.213)

Lat × Lon polynomial Yes Yes Yes Yes Yes Yes Yes Yes Province FE Yes Yes Yes Yes Yes Yes Yes Yes Commune controls No Yes No Yes No Yes No Yes N 1,337 1,337 1,337 1,337 1,337 1,337 1,337 1,337

All columns feature shock realization ∈ {0, 1}, shown in Figure (2) as main independent variable. Lat × Lon polynomial: Latitude, Latitude2, Longitude, Longitude2, and Latitude × Longitude and province ﬁxed effects. SEs clustered by 24 provinces in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

34 Alternative explanations: investment in physical and human capital and infrastructure

(1) (2) (3) (4) log(Size farm) log(Value farm) log(p.c. consumption) log(Floor size) Neg. Production Shock duringKR0 .075 −0.021 −0.005 0.015 (0.136)(0.267)(0.018)(0.016) Individual controls Yes Yes Yes Yes N 323,160 323,160 366,013 296,265 Dependent variable mean 5.420 9.316 11.413 3.629 Years of education Can read Can write log(Distance to primary school) Neg. Production Shock duringKR −0.103 −0.007 −0.010 −0.012 (0.064)(0.009)(0.009)(0.017) Individual controls Yes Yes Yes No N 334,832 248,350 248,338 1,561 Dependent variable mean 4.380 0.726 0.697 0.764 log(Distance to market) # Markets Share of HH with elec. Access to public elec. Neg. Production Shock duringKR −0.029 −0.021 0.055 −0.013 (0.039)(0.040)(1.363)(0.022) Individual controls No No No No N 1,561 1,564 1,136 1,019 Dependent variable mean 1.555 1.211 19.951 0.354

35 Summary

• Communes with more people killed/more severe persecutions duringKR • Higher turnout in 2013 elections, favoring main opposition party • Turnout and greater political knowledge as a response to limit current gov’t rather than evidence of pro-social behavior • Less public good provision and fewer investments relying on gov’t or local trust

• Persistent and long-lasting impact • While some effects are stronger for individuals that experienced KR, intergenerational memory of Killing Fields also matter

36 Additional results Data

Figure 4: US Army map with commune characteristics and Killing Fields 38 Data

Figure 5: Satellite picture 1975

Back 39 Placebos

• Running placebos using rainfall in any given three-year period during 1951–2007 • If rainfall duringKR has a causal effect, point estimate is an outlier in placebo distribution

Back

40 Continuous variables

Table 1: Effect of production shock on severity of killings – continuous rainfall

(1) (2) (3) (4) (5) (6) log(Bodies) Standardized within Province Raw Data Neg. Production duringKR −0.026 −0.027 −0.025 −0.124 −0.145 −0.144 (0.012)∗∗ (0.011)∗∗ (0.012)∗∗ (0.034)∗∗∗ (0.046)∗∗∗ (0.051)∗∗ [0.010]∗∗ [0.009]∗∗∗ [0.010]∗∗ [0.043]∗∗∗ [0.042]∗∗∗ [0.046]∗∗∗ Neg. Production 1972–1974 −0.010 −0.071 (0.014)(0.050) [0.010][0.045] Neg. Production 1978–1980 0.006 0.013 (0.017)(0.072) [0.016][0.062]

Lat × Lon polynomial Yes Yes Yes Yes Yes Yes Province FE Yes Yes Yes Yes Yes Yes Commune controls No Yes Yes No Yes Yes Dependent variable mean 0.141 0.141 0.141 0.141 0.141 0.141 N 1,569 1,569 1,569 1,569 1,569 1,569

Standard errors clustered by 24 provinces in parentheses. Conley standard errors in brackets. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

41 Back Different dependent variable

Table 2: Effect of production shock on severity of killings – different dependent variable

(1) (2) (3) (4) (5) (6) (7) log(Bodies) log(Bodies per capita) log(Bodies, Executed) log(Bodies per Area) Bodies per Capita Bodies per Area Mass graves Neg. Production duringKR −0.055 −0.058 −0.059 −0.129 −1.277 −0.015 −7.180 (0.018)∗∗∗ (0.029)∗ (0.016)∗∗∗ (0.050)∗∗ (0.482)∗∗∗ (0.006)∗∗ (3.453)∗∗

Lat × Lon polynomial Yes Yes Yes Yes Yes Yes Yes Province FE Yes Yes Yes Yes Yes Yes Yes Commune controls Yes Yes Yes Yes Yes Yes Yes Leads and Lags Yes Yes Yes Yes Yes Yes Yes Dependent variable mean 0.141 0.233 0.086 0.405 2.393 0.025 12.079 N 1,569 1,569 1,569 1,569 1,569 1,569 1,569

Standard errors clustered by 24 provinces in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Back