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 of KR 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 former KR, 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
with KR
”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
log(Bodies)
Lat × Lon polynomial
Province FE
Pre-KR commune controls
Dependent variable mean
N
(1)
(2)
Vote Share opposition
1.411∗∗∗
1.306∗∗
(0.483)
(0.311)
Yes
Yes
No
40.16
1,569
Yes
Yes
Yes
40.16
1,569
Standard errors clustered by 24 provinces in parentheses.
(3)
(4)
Pr[CPP Win]
−0.025
−0.034∗∗
(0.016)
(0.013)
Yes
Yes
No
0.61
1,569
∗
Yes
Yes
Yes
0.61
1,569
(5)
(6)
Turnout
0.001
0.008∗
(0.006)
(0.004)
Yes
Yes
No
0.79
1,569
Yes
Yes
Yes
0.79
1,569
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
• Employ KR’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 during KR
• 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
the KR 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 during KR
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 during KR correlated with other determinants of the
outcomes of interest?
Commune characteristics prior to KR
School in commune
Church in commune
Telephone in commune
Commune office in commune
Post office in commune
log(population density)
log(rice field area)
log(inundation area)
log(plantations area)
log(commune area)
log(distance to Phnom Penh)
log(distance to road)
log(distance to province capital)
log(distance to border)
log(km of roads in commune)
log(km of rails in commune)
Mean
0.709
0.035
0.005
0.396
0.017
1.542
2.22
0.929
0.128
3.818
4.479
0.531
2.588
3.682
1.844
0.193
Point estimate
−0.030
0.011
0.002
0.026
0.003
−0.227
−0.092
0.003
0.099
0.224
0.071
−0.032
0.147
−0.102
−0.055
−0.057
Standard error
(0.040)
(0.012)
(0.004)
(0.040)
(0.012)
(0.231)
(0.121)
(0.109)
(0.073)
(0.156)
(0.089)
(0.087)
(0.165)
(0.075)
(0.097)
(0.048)
T-stat
−0.74
0.90
0.54
0.65
0.23
−0.98
−0.76
0.03
1.35
1.43
0.80
−0.36
0.89
−1.36
−0.57
−1.18
22
Production shock and severity of killings
(1)
Neg. Production Shock during KR
−0.046
(0.019)∗∗
[0.018]∗∗
Neg. Production Shock 1972–1974
Neg. Production Shock 1978–1980
Lat × Lon polynomial
Province FE
Commune controls
Dependent variable mean
N
Yes
Yes
No
0.141
1,569
(2)
(3)
log(Bodies)
−0.057
−0.057
(0.018)∗∗∗ (0.018)∗∗
[0.019]∗∗∗ [0.021]∗∗
0.004
(0.024)
[0.020]
−0.012
(0.036)
[0.026]
Yes
Yes
Yes
0.141
1,569
Yes
Yes
Yes
0.141
1,569
(4)
−0.444
(0.181)∗∗
[0.140]∗∗∗
Yes
Yes
No
0.621
1,569
Standard errors clustered by 24 provinces in parentheses. Conley standard errors in brackets.
Continuous first stage
∗
(5)
(6)
Bodies
−0.478
−0.496
(0.175)∗∗
(0.181)∗∗
[0.150]∗∗∗ [0.170]∗∗∗
0.013
(0.135)
[0.123]
0.277
(0.243)
[0.188]
Yes
Yes
Yes
0.621
1,569
Yes
Yes
Yes
0.621
1,569
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Different dependent variable
Robustness
23
Political mobilization
(1)
Neg. Production Shock during KR
Neg. Production Shock 1972–1974
Neg. Production Shock 1978–1980
Lat × Lon polynomial
Province FE
Commune controls
Dependent variable mean
N
(2)
(3)
(4)
(5)
Vote share CNRP
Turnout
−5.328
−4.810
−4.814
−0.036
−0.032
(1.183)∗∗∗ (0.993)∗∗∗ (1.009)∗∗∗ (0.014)∗∗ (0.011)∗∗
[0.941]∗∗∗ [0.825]∗∗∗ [0.911]∗∗∗ [0.011]∗∗∗ [0.009]∗∗∗
−0.327
(1.289)
[1.256]
0.881
(1.447)
[1.226]
Yes
Yes
No
40.160
1,569
Yes
Yes
Yes
40.160
1,569
Yes
Yes
Yes
40.160
1,569
Yes
Yes
No
0.791
1,569
Standard errors clustered by 24 provinces in parentheses. Conley standard errors in brackets.
∗
Yes
Yes
Yes
0.791
1,569
(6)
−0.032
(0.011)∗∗
[0.010]∗∗∗
−0.010
(0.012)
[0.009]
0.012
(0.011)
[0.012]
Yes
Yes
Yes
0.791
1,569
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
Neg. Production Shock during KR
Lat × Lon polynomial
Province FE
Individual controls
Alive in 1978
Dependent variable mean
N
(1)
(2)
(3)
(4)
Trust in neighbor
Can influence government
0.090
0.151
0.159
0.059
(0.042)∗∗
(0.035)∗∗∗
(0.077)∗
(0.098)
[0.031]∗∗
[0.040]∗∗∗
[0.050]∗∗
[0.051]
Yes
Yes
Yes
No
0.480
991
Yes
Yes
Yes
Yes
0.462
450
Yes
Yes
Yes
No
2.731
1,849
Yes
Yes
Yes
Yes
2.660
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
Neg. Production Shock during KR
Lat × Lon polynomial
Province FE
Individual controls
Alive in 1978
Dependent variable mean
N
(1)
(2)
Has voted
−0.020
−0.016
(0.015)
(0.009)∗
[0.014]
[0.007]∗∗
Yes
Yes
Yes
No
0.921
1,963
Yes
Yes
Yes
Yes
0.968
1,294
(3)
(4)
Government has a paternal role
0.114
0.229
(0.028)∗∗∗
(0.089)∗∗
[0.028]∗∗∗
[0.084]∗∗
Yes
Yes
Yes
No
0.610
991
Yes
Yes
Yes
Yes
0.581
450
(5)
(6)
Supports political comp.
−0.049
−0.057
(0.022)∗∗
(0.023)∗∗
[0.022]∗∗
[0.020]∗∗
Yes
Yes
Yes
No
0.883
1,963
Yes
Yes
Yes
Yes
0.861
1,294
(7)
(8)
Voting makes a difference
0.101
0.093
(0.048)∗∗
(0.050)∗
[0.046]∗∗
[0.043]∗∗
Yes
Yes
Yes
No
0.428
1,484
Yes
Yes
Yes
Yes
0.456
906
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
27
Political knowledge
Neg. Production Shock during KR
Lat × Lon polynomial
Province FE
Individual controls
Alive in 1978
Dependent variable mean
N
(1)
(2)
Knows that voting is to elect Parliament
−0.101
−0.100
(0.037)∗∗
(0.040)∗∗
[0.036]∗∗
[0.040]∗∗
Yes
Yes
Yes
No
0.682
972
Yes
Yes
Yes
Yes
0.579
844
(3)
(4)
Knows election date
−0.057
−0.081
(0.038)
(0.046)∗
[0.040]
[0.050]∗
Yes
Yes
Yes
No
0.477
972
Yes
Yes
Yes
Yes
0.489
844
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
• Findings on trust, beliefs, and political knowledge consistent
with KR 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
Neg. Production Shock during KR
Lat × Lon polynomial
Province FE
Commune controls
Dependent variable mean
N
(1)
(2)
(3)
(4)
Commune has a
Commune has a community
community fishery
forest management scheme
0.046
0.047
0.044
0.033
(0.019)∗∗ (0.020)∗∗ (0.019)∗∗
(0.019)
[0.018]∗∗
[0.018]∗∗
[0.016]∗∗
[0.014]∗∗
Yes
Yes
No
0.059
1,564
Yes
Yes
Yes
0.059
1,564
Yes
Yes
No
0.079
1,564
Yes
Yes
Yes
0.079
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
Neg. Production Shock during KR
Lat × Lon polynomial
Province FE
Commune controls
Individual controls
Alive in 1978
Dependent variable mean
N
(1)
(2)
Has any debt
0.004
−0.000
(0.009)
(0.007)
Yes
Yes
Yes
Yes
No
0.277
273,456
Yes
Yes
Yes
Yes
Yes
0.247
95,016
(3)
(4)
log(Size of debt, KHR)
0.009
0.033
(0.055)
(0.055)
Yes
Yes
Yes
Yes
No
13.225
100,857
Yes
Yes
Yes
Yes
Yes
13.154
32,474
(5)
(6)
Informal lending
∗∗
0.033
0.034∗∗
(0.013)
(0.014)
(7)
(8)
Formal lending
∗∗∗
−0.039
−0.038∗∗∗
(0.012)
(0.013)
Yes
Yes
Yes
Yes
No
0.552
105,055
Yes
Yes
Yes
Yes
No
0.443
105,055
Yes
Yes
Yes
Yes
Yes
0.564
33,895
Yes
Yes
Yes
Yes
Yes
0.427
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)
Neg. Production Shock during KR
Lat × Lon polynomial
Province FE
Commune controls
Individual controls
Alive in 1978
Never migrated
Dependent variable mean
N
(2)
(3)
State employed
0.007∗∗
0.007∗
0.014∗∗
(0.003)
(0.004)
(0.005)
Yes
Yes
Yes
Yes
No
No
0.060
183,556
Yes
Yes
Yes
Yes
Yes
No
0.092
94,332
Yes
Yes
Yes
Yes
Yes
Yes
0.044
18,653
(4)
(5)
(6)
Private or self employed
−0.006
−0.007
−0.032∗∗
(0.006)
(0.007)
(0.014)
Yes
Yes
Yes
Yes
No
No
0.834
183,556
Yes
Yes
Yes
Yes
Yes
No
0.853
94,332
Yes
Yes
Yes
Yes
Yes
Yes
0.894
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)
Neg. Production Shock during KR
Lat × Lon polynomial
Province FE
Commune controls
Individual controls
Dependent variable mean
N
(2)
(3)
(4)
In-migration
All
Alive in 1978
0.017
0.025
0.016
0.027
(0.022)
(0.016)
(0.033)
(0.028)
Yes
Yes
Yes
No
0.322
199,501
Yes
Yes
Yes
Yes
0.322
199,501
Yes
Yes
Yes
No
0.564
79,931
Yes
Yes
Yes
Yes
0.564
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
Neg. Production Shock during KR
Lat × Lon polynomial
Province FE
Commune controls
N
−0.021
(0.055)
0.022
(0.048)
Yes
Yes
No
1,337
Yes
Yes
Yes
1,337
20–39
−0.069
−0.083
(0.094)
(0.094)
Yes
Yes
No
1,337
Yes
Yes
Yes
1,337
40-59
−0.123
−0.119
(0.086)
(0.094)
Yes
Yes
No
1,337
Yes
Yes
Yes
1,337
−0.045
(0.213)
Yes
Yes
No
1,337
≥ 60
−0.044
(0.213)
Yes
Yes
Yes
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
Neg. Production Shock during KR
Individual controls
N
Dependent variable mean
Neg. Production Shock during KR
Individual controls
N
Dependent variable mean
Neg. Production Shock during KR
Individual controls
N
Dependent variable mean
(1)
log(Size farm)
0.075
(0.136)
Yes
323,160
5.420
(2)
log(Value farm)
−0.021
(0.267)
Yes
323,160
9.316
(3)
log(p.c. consumption)
−0.005
(0.018)
Yes
366,013
11.413
(4)
log(Floor size)
0.015
(0.016)
Yes
296,265
3.629
Years of education
−0.103
(0.064)
Yes
334,832
4.380
Can read
−0.007
(0.009)
Yes
248,350
0.726
Can write
−0.010
(0.009)
Yes
248,338
0.697
log(Distance to primary school)
−0.012
(0.017)
No
1,561
0.764
log(Distance to market)
−0.029
(0.039)
No
1,561
1.555
# Markets
−0.021
(0.040)
No
1,564
1.211
Share of HH with elec.
0.055
(1.363)
No
1,136
19.951
Access to public elec.
−0.013
(0.022)
No
1,019
0.354
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
35
Summary
• Communes with more people killed/more severe persecutions
during KR
• 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 during KR 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)
Neg. Production during KR
Neg. Production 1972–1974
Neg. Production 1978–1980
Lat × Lon polynomial
Province FE
Commune controls
Dependent variable mean
N
(3)
(4)
(5)
log(Bodies)
Standardized within Province
Raw Data
−0.026
−0.027
−0.025
−0.124
−0.145
(0.012)∗∗ (0.011)∗∗
(0.012)∗∗ (0.034)∗∗∗ (0.046)∗∗∗
[0.009]∗∗∗ [0.010]∗∗
[0.043]∗∗∗ [0.042]∗∗∗
[0.010]∗∗
−0.010
(0.014)
[0.010]
0.006
(0.017)
[0.016]
Yes
Yes
No
0.141
1,569
(2)
Yes
Yes
Yes
0.141
1,569
Yes
Yes
Yes
0.141
1,569
Yes
Yes
No
0.141
1,569
Standard errors clustered by 24 provinces in parentheses. Conley standard errors in brackets.
p < 0.01
Yes
Yes
Yes
0.141
1,569
∗
(6)
−0.144
(0.051)∗∗
[0.046]∗∗∗
−0.071
(0.050)
[0.045]
0.013
(0.072)
[0.062]
Yes
Yes
Yes
0.141
1,569
p < 0.10, ∗∗ p < 0.05, ∗∗∗
Back
41
Different dependent variable
Table 2: Effect of production shock on severity of killings – different
dependent variable
Neg. Production during KR
Lat × Lon polynomial
Province FE
Commune controls
Leads and Lags
Dependent variable mean
N
(1)
(2)
log(Bodies) log(Bodies per capita)
−0.055
−0.058
(0.018)∗∗∗
(0.029)∗
Yes
Yes
Yes
Yes
0.141
1,569
Standard errors clustered by 24 provinces in parentheses.
Yes
Yes
Yes
Yes
0.233
1,569
∗
(3)
log(Bodies, Executed)
−0.059
(0.016)∗∗∗
(4)
log(Bodies per Area)
−0.129
(0.050)∗∗
(5)
Bodies per Capita
−1.277
(0.482)∗∗∗
(6)
Bodies per Area
−0.015
(0.006)∗∗
(7)
Mass graves
−7.180
(3.453)∗∗
Yes
Yes
Yes
Yes
0.086
1,569
Yes
Yes
Yes
Yes
0.405
1,569
Yes
Yes
Yes
Yes
2.393
1,569
Yes
Yes
Yes
Yes
0.025
1,569
Yes
Yes
Yes
Yes
12.079
1,569
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Back
42