DK: Administration and Legacy

From 1975 to 1979, DK abolished private property and currency, collectivized agriculture, closed formal schools, and forced citizens into highly stratified labor groups according to their age, sex, residence, and educational background. The regime’s development strategy hinged on a significant increase in rice production, which would be supported by building irrigation infrastructure through forced labor. Nominally, the regime pursued a mass literacy campaign, but labor requirements and the purging of former educators served a cross purpose. The regime relied on coercion to enact its social reorganization (Becker Reference Becker1998; Kiernan Reference Kiernan2008; Vickery Reference Vickery1984).

The DK regime was ideologically fragmented on a spectrum of moderate communists to repressive extremists. Fissures were consequential; after capturing Phnom Penh, it took months for combat commanders to come together to form a government. The fractionalization across ideology was highly regionalized: during the civil war from 1970 to 1975, commanders operated largely in isolation, even wearing different uniforms.

DK Zones: The Southwest and West

To manage tensions between regional commanders, the central party leadership divided the country into several different zones, which closely overlapped with combat theaters and at times ignored pre-existing provincial boundaries. The leaders of zones were called zone secretaries. Zones were organized with rigid hierarchy internally, but the ability of the central government to coordinate policy between zones was constrained by the influence of the zone secretaries within their territory (Vickery Reference Vickery1984, 68). Mass killing directives were handed down by party leadership, but zone secretaries ultimately carried out orders (Ea Reference Ea2005, 126). Since secretaries had a great deal of de facto authority, the implementation of regime mandates varied across zones based on the idiosyncrasies of personal leadership (Becker Reference Becker1998, 176).

I focus on a salient border that divided very different commanders: Mok in the Southwest and Sy in the West. Mok and Sy shared jurisdiction during the civil war, and their two respective zones were one area of operation during the civil war. After a major dispute concerning the brutality of Mok’s approach to the revolution, their jurisdiction was divided into two separate zones which they commanded in isolation, divided along National Road 4 (NR4).

The border road—NR4—was constructed cheaply by the United States in the 1950s, with the goal of connecting the port city of Sihanoukville to the capital Phnom Penh in the least costly way possible. In congressional hearings about the U.S.’s construction of the road, officials concede “[t]he principal justification was a political justification” unrelated to economics: the United States constructed the road as a favor to France and as a way to reduce Cambodian dependency on Vietnam for trade. Indeed, one engineer remarked “[t]he decision to give support to the construction of this highway in the first instance was not based on detailed studies of such matters as the volume of traffic and the precise economic benefits expected to result, nor indeed were they considered to be the determining factors under the circumstances” (House of Representatives 1961). Consistent with a lack of meticulous planning, the road quickly fell into disrepair, straining the US–Cambodian relationship (Howland and Kennedy Reference Howland and Kennedy1999).Footnote ²

Whereas the idea of the road was driven by political considerations, the exact location was less of a reflection of local economics and more of a matter of convenience. Figure 2 maps the Southwest and West zone. Binding geographic constraints outside of Kampong Speu (dashed lines) caused the road to bisect the province. The simplest route to connect the port and the capital was through a narrow stretch of flat land, across Kampong Speu. Building the road slightly differently would have led to the route to cross over elevated terrain (dark shading), adding cost and complexity to the project. As such, the location of the highway is largely arbitrary within Kampong Speu, since the terrain features that induced the road to be built in such a way are beyond the borders of the province. Within Kampong Speu, terrain, river, and road access appear largely similar on either side of NR4.

Figure 2.

West and Southwest Zones during Democratic Kampuchea Regime

Note: Zone border from Yale Genocide Studies Program (https://gsp.yale.edu/dk-zones-english). Shading shows elevation (in meters) in grid cells across the zones. Red dashed line is the provincial boundary of Kampong Speu, which was salient pre and post (but not during) the DK era. The thick black line shows the border between the West and Southwest zone, drawn over National Road 4, which was only a political border during the DK era. Brown dashed lines are major roads and thin blue lines are rivers.

The road was a natural choice for a border for DK since it provided a visible marker to divide rivals. Clear lines of demarcation of authority between areas was important because zone leaders were conflictual—trespassing on zones were known to cause skirmishes between troops in places were lines were less clear, such as the boundary between the Southwestern and Eastern zone (Vickery Reference Vickery1984, 77). One natural solution to this problem was the use of natural landmarks as borders, like roads, which DK also used to divide smaller Damban regions such as Damban 1 and 4 in the Northwest zone (Vickery Reference Vickery1984, 111), or rivers, such as the Mekong, which divided the North-Central and East zone in Kampong Cham.

Repression in the Southwest versus West

Both primary and secondary sources, along with descriptive quantitative evidence, point to a difference in repression intensity between the West and Southwest zones. Whereas Mok is credited with being among the most ruthless toward “new people”—individuals who were educated, urbanites, or in other ways connected to the Lon Nol or Sihanouk regime—Sy in comparison was less brutally violent toward these groups.

Sy and Mok are illustrative of the ideological divide within DK, which shaped how violent and repressive their rule was during the regime (Becker Reference Becker1998; Commission 1984). Sy was an intellectual, member of old reformist socialist groups, and less ruthless than the more extreme factions. In contrast to Mok and other extremists, he believed old regime members could be incorporated into DK rather than executed. During the civil war, interviewees report “Sangha Hoeun and Chou Chet [Sy] re-educated and taught these people [Lon Nol soldiers]. I saw this; they did not kill them. But Mok did kill such people, and he became angry with what the other two were doing” (Kiernan Reference Kiernan1989, 28).

Mok executed the moderate communist Sangha Hoeun, which created a strong division between him and Sy: an interviewee reported Sy “…didn’t agree with that [the execution of Sangha Hoeun] so he was transferred and the Southwest Zone was divided into two, and the Western Zone created” (Kiernan Reference Kiernan1980a, 511). Consistent with Sy’s approach of incorporating old regime members rather than executing them, party meeting reports in 1977 indicate that a “fair number” of cooperatives were staffed by individuals who were not members of the peasant class in Sector 32 of the West (Carney Reference Carney and Jackson1992, 86). Sector 32 of the West was Sy’s headquarters, which covered the half of Kampong Speu that was split into his zone. Staffing cooperatives with non-peasants is suggestive of Sy choosing to incorporate the social groups that Mok executed into government.Footnote ³

Primary source interviews corroborate the qualitative differences in cruel repression between Mok and Sy. One interviewee remarked “Mok was cruel since 1971–1972. Different from Chou Chet [Sy] and Phouk Chlag. Mok was fierce (khlang), a killer (Kiernan Reference Kiernan1980b, 2).”Footnote ⁴ A former Communist Party of Kampuchea (CPK) district chief expanded upon how the differences in ferociousness—khlang—impacted the intensity of DK cruelty: he said Sy was “not very harsh (khlang)…Sy didn’t set targets: when he took me to Tonle Sap, on the boat with me, he said ‘friend, grow 1,000 ha of rice here, if there is enough water. If water is short, well, it depends on the concrete situation.”’ (Kiernan Reference Kiernan1980c, 11). Given the excessive death caused by unrealistic project goals during the regime, this distinction suggests a critical difference in repression intensity. One interviewee who lived in Sector 32 of the West—Sy’s headquarters—reports “no killings” in 1975, and described the area as “softer” since “they didn’t kill many people in R32” (Kiernan Reference Kiernan1980d, 2).

Meanwhile, the Southwest committed most to the development of cooperatives according to the revolution’s ideology (Kiernan Reference Kiernan2008, 87–90). The zone was the “power core” of the regime (Vickery Reference Vickery1984, 86). Mok staffed his zone with close family members, who would remain loyal to his orders. Sector 33, run by Mok’s son, exemplifies the pattern: the area, in modern Kampong Speu province, was “the toughest sanctuary of the Khmer Rouge movement” (Vickery Reference Vickery1984, 98); affiliates of the old regime were forced to work in the fields (Vickery Reference Vickery1984, 93) and refugee reports suggest “new people” received less rations (Vickery Reference Vickery1984, 99). A U.S. embassy report quoted a refuge who said “[a]n error, if discovered, means death in the south” (Kiernan Reference Kiernan2008, 194).

Descriptive evidence is consistent with interviewee accounts of the differences between Mok and Sy. I use data on sibling deaths from the 2000 Demographic Health Survey round to estimate annual mortality trends by zone in Kampong Speu. I develop a predictive model of mortality based on gender, age, and residence (out of sample) and then use the model to compute expected mortalities by year. Figure 3 plots the difference between observed and expected mortalities by zone. In 1975, there is a differential spike in excess mortality in the Southwest, which corresponds with the year Pol Pot gave the order to begin murdering the educated and intellectuals indiscriminately (Kiernan Reference Kiernan2008).Footnote ⁵

Figure 3.

Estimated Mortality Trends by Zone

Note: Shaded region denotes the Khmer Rouge period (1975–79). Vertical axis is the difference between total and expected mortality. Horizontal axis is the year of reported death. Data from 2000 DHS survey round in Cambodia. Details on estimation of excess mortality in E.5 in the Supplementary Material.

To be clear, the West was not free of repression. Refugees report enforcement of the DK’s family separation policy, executions, and starvation (Kiernan Reference Kiernan1980b; Reference Kiernan1980c). From the reports that families were separated, one can reasonably infer education and labor policies did not sharply differ between Sy and Mok: indeed, such a difference would be such an affront to the official DK line it would have been easily observed and broadly recorded. The distinction between governance cannot be understood as night and day, violent or not. Instead, the difference between Sy and Mok is better characterized as a difference in the intensive margin of violence.

Long-Run Poverty

Poverty has persisted since DK-era repression (Kerbo Reference Kerbo2014). A key dimension of the poverty trap in Cambodia is characterized by a generational skill gap caused by executions. Intergenerational transfers of knowledge were either severely inhibited or eliminated due to the execution of middle aged intelligentsia, educators, and skilled workers (Jeong Reference Jeong2014). The paucity of educated mentors pushed individuals into precarious lines of work with lower economic returns which did not require education to obtain. Therefore, many worked on family farms without seeking specialized training or higher forms of education after the regime (Islam et al. Reference Islam, Ouch, Smyth and Wang2016), a trend which continues to drive poverty across the country (Kerbo Reference Kerbo2014).

Data

I study the degree of local poverty and economic activity at the village (phum) level in Kampong Speu province. Villages are the smallest unit; in Kampong Speu province, the median size of a village was 401 persons and 78 households in 1998. The compact size of villages provide a reasonable approximation of households distance to the border that separated the West and Southwest.Footnote ⁶

I compare the Southwest and the West within Kampong Speu for three reasons. First, the regions represent the ideological polarization within the regime; Mok was the staunchest ally of the party’s genocidal faction, and Sy was a typical moderate communist. Second, the border between zones did not overlap with other political boundaries unlike other DK zones, meaning a discrete change in the outcomes can be attributed to different zone leadership rather than differences between provinces. This mitigates the “compound treatment problem” commonly found in research designs that rely on geography. Third, the density of observations within a narrow bandwidth around the border means villages in the Southwest are being compared to an appropriate counterfactual, which is not true at other boundaries where villages are further from borders, or where the natural borders were excessively wide, like divisions created using the Mekong River.

Poverty data are from the Cambodian National Poverty Identification System (IDPoor). The data are collected in 2011 through a 16 question household survey conducted by elected village representatives who use questions regarding assets, living standards, means of transport, employment, education, and health to score household poverty on a continuous scale which is subsequently used to construct poverty categories. I measure the percentage of poor households. Further detail on the data collection process of IDPoor is available in Figure A.1 in the Supplementary Material.

Second, I use data on the nighttime luminosity of villages to estimate GDP within a $ 2\hskip0.3em \mathrm{km}\times 2 $ km grid cell surrounding the village center (Ghosh et al. Reference Ghosh, Powell, Elvidge, Baugh, Sutton and Anderson2010). Luminosity proxies both formal and informal economic activity, which is important given the role of informal commerce. I use the inverse hyperbolic sine (IHS) transformation to account for skewness and zeros.Footnote ⁷

Empirical Strategy

The nature of assignment into the Southwest versus West suggests a regression discontinuity (RD) approach which compares modern outcomes between nearby villages on either side of the boundary. The RD estimator is

(1) $$ {\displaystyle \begin{array}{l}{y}_v=\alpha +\gamma {\mathrm{SW}\ \mathrm{Zone}}_v+f\left({\mathrm{geographic}\ \mathrm{location}}_v\right)\\ {}\hskip1.7pc +\hskip2px \sum_{s=1}^nse{g}_v^s+{\varepsilon}_v\hskip5.55pt for\hskip5.55pt v\in bw,\end{array}} $$

where $ {y}_v $ is the outcome of interest, and $ {\mathrm{SW}\ \mathrm{Zone}}_v $ is a binary indicator scored 1 if a village was in the Southwest and 0 otherwise. $ f({\mathrm{geographic}\ \mathrm{location}}_v) $ is the forcing variable, which I define as the distance between the village v and the border that divided the Southwest and West.Footnote ⁸ I evaluate Equation 1 using distance in a linear and quadratic form.

I follow Dell (Reference Dell2010) and include boundary segment fixed effects $ {\sum}_{s=1}^nse{g}_v^s $ , which are computed by splitting the border into n segments s and then scoring 1 if a village is closest to segment s. Segment fixed effects compare villages that lie in the same neighborhood around the border, avoiding imprecise comparisons that may occur if villages have similar absolute distances at extreme ends of the boundary.Footnote ⁹

The robust error term is $ {\varepsilon}_v $ . I adjust standard errors for spatial heteroskedasticity and autocorrelation (HAC), following the data-driven procedure developed by Kelly (Reference Kelly2020) for selecting a HAC spatial kernel based on the spatial structure of each respective outcome. I estimate Equation 1 within narrow MSE optimal bandwidths $ bw $ (Calonico, Cattaneo, and Titiunik Reference Calonico, Cattaneo and Titiunik2014), however, results are robust to alternative bandwidth choices. I include the distance to the provincial capital as an adjusting covariate in some regressions to account for proximity to the provincial center.

The RD approach is advantageous relative to a selection on observables strategy. Since DK demographically targeted repression against the more well-off, repression levels are likely correlated with positive developmental trajectories, leading to an upward biased estimate of the effect on repression. Since administrative microdata before the regime was destroyed by DK cadres, one cannot adequately adjust for pre-existing development levels.

Since my RD design compares villages which were arbitrarily split into more or less extreme administrative zones, in expectation, confounding factors ought to be similar between villages within a narrow bandwidth, conditional on location. So long as the design assumptions hold, my strategy identifies a local average treatment effect (LATE) of exposure to an extreme DK administrator. I turn to discussing these assumptions now.

Design Assumptions and Inferential Threats

My design relies on two core assumptions: the smoothness of confounders at the discontinuity, and the absence of strategic line drawing to sort observations in a particular way.

Balance Tests

The assumption that confounders are smooth at the discontinuity is reasonable given the arbitrary placement of the road with respect to Kampong Speu. The historical record suggests one confounding feature: the Pol Pot faction wished to divide territory in a way to give Mok control over more productive areas and Sy with less well-endowed land, a goal that would be accomplished by splitting the relatively soil-poor Kampong Speu in two, leaving Mok with the entirety of the soil-rich Kampot province and leaving Sy with the more desolate Koh Kong and the more rugged areas in the far-north of Kampong Speu (Chandler, Kiernan, and Boua Reference Chandler, Kiernan and Boua1988; Vickery Reference Vickery1984).

Geographic variables are “slow moving” in the sense that they vary little within small bandwidths; given the arbitrary placement of the road, there is no reason to believe differences in productivity are sharp at the discontinuity despite the fact Mok was given more rich endowments in the far South outside of Kampong Speu.

I test balance on agro-economic, climatic, and topographic area when crossing from one side of the border to another on $ 1\hskip0.3em \mathrm{km}\times 1 $ km grid cells. Figure 4 shows the mean and variance of temperature and rainfall, forest and cropland cover, ruggedness, elevation, and soil fertility are similar on either side of the boundary. Critically, since the Cambodian economy was agriculture based in the lead-up to the Khmer Rouge, the similarity of factors of production on either side of the border suggest villages had similar access to sources of productive income.

Figure 4.

Balance Tests: $ 1\hskip0.3em \mathrm{km}\times 1 $ km Grid Cells

Note: Unit of analysis is the $ 1\hskip0.3em \mathrm{km}\times 1 $ km grid cell. Outcomes standardized reported in horizontal axis and vertical axis refers to each respective outcome. Spatial heteroskedasticity and autocorrelation consistent standard errors used to construct equivalence confidence interval (ECI). Equivalence range selected using the sensitivity approach $ \epsilon \pm 0.36\sigma $ . The null hypothesis is that areas differ from one another with at least a magnitude of 0.36 $ \sigma $ (Hartman Reference Hartman2021).

DK destroyed micro data from the 1962 Cambodian census, making balance tests on predetermined socioeconomic variables difficult. I use three data sources to probe for pre-existing economic differences from satellite data: estimated population, built-up areas, and road networks in 1975. As Figure 4 shows the equivalence confidence interval contains zero for these outcomes, however, the substantive size of the estimate could suggest some degree of initial imbalance. There is no reason to regard this imbalance as systematic evidence of manipulation rather than chance: first, it would make little sense for a border to be strategically drawn to give built-up areas—the locations DK was most concerned about repressing—to a moderate. Second, to the extent road density could be higher in the former Southwest, increased market access should boost development for former-Southwest villages, suggesting bias in the opposite direction of my main prediction. Balance tests are substantively similar with nonparametric estimator within an MSE optimal bandwidth (Figure A.1 in the Supplementary Material).

Threats to Inference

Robustness

I probe the robustness of my findings in several ways.

DHS Wealth Data To validate my measure of poverty at the village level with administrative data, I replicate my findings using survey data at the individual level collected by the Demographic Health Survey from 2000 to 2014. I show rural household wealth is lower in the former Southwest (B.1 in the Supplementary Material).

Two-Dimensional Forcing Variable Nonlinear spatial trends could be mistaken for discrete change in income levels if the univariate forcing variable masks higher-order changes across latitude–longitude space. I include a two-dimensional forcing variable (B.1 in the Supplementary Material) and estimate the RD along border points to account for this (B.2 in the Supplementary Material).

Power Analysis One may be concerned that the number of observations is small in a narrow neighborhood, reducing the statistical power of the tests. Since the effect size I uncover is substantial, not many observations are required. I show that I have sufficient power within an MSE optimal bandwidth to detect the main effects (B.2 in the Supplementary Material).

Falsification Test Spatial autocorrelation could explain the finding if village development clusters geographically. I show the bias-corrected CCT standard errors are robust to spatial noise simulations which create synthetic outcome data following the same spatial structure of the true data (Kelly Reference Kelly2020) (Figure B.8 in the Supplementary Material).

Donut RD I estimate a series of donut-hole RDs, dropping observations close to the border, and find similar results even after 10% of the data are dropped (Figure B.8 in the Supplementary Material). The result guards against the possibility that when approaching the road, villages experience a differential positive development shock unrelated to DK.

Repression and Poverty Traps: Conceptual Framework

Violence targeted at higher educated segments of society creates a skill gap between generations, leaving younger people without mentors who can transfer skills and knowledge. This type of violence can be found across autocracies and coerced labor regimes: autocrats prefer low-skilled loyalists to competent persons to extend their survival (Egorov and Sonin Reference Egorov and Sonin2011) and under coerced labor, principals are more violent toward productive and skilled persons, who have a larger outside option (Acemoglu and Wolitzky Reference Acemoglu and Wolitzky2011). The mechanism I study is therefore plausible in other contexts.

In the next sections, I show how poverty becomes self-perpetuating and persistent due to repression: a poverty trap. Educational attainment is lower in the former Southwest, with a particularly strong drop among the age cohort whose secondary schooling years were interrupted by the regime. This created an intergenerational poverty trap in two ways. Individuals in the Southwest were pushed into informal employment, which earns less income. The evidence is consistent with a model of poverty wherein individuals at time t remain poor in $ t+1 $ because their low income forces them into making decisions that reinforce their poverty, such as working low-earning jobs (Banerjee and Duflo Reference Banerjee and Duflo2011). Further, children born after the regime in the Southwest have worse health outcomes, which strongly predicts future income. This evidence highlights the intergenerational nature of the poverty trap: although children born in the former Southwest were never repressed by DK, they face deprivation because their parents were driven into poverty, perpetuating the cycle.

Human Capital Declined after DK

First, I evaluate whether trends in schooling by age changed in the Southwest zone. If human capital differentially declined in the Southwest due to the Khmer Rouge, one should observe relatively similar levels of schooling among age cohorts who finished schooling before the regime along with a sharp decline in schooling among villagers whose school-age years overlapped with the regime. I test this argument using a difference-in-differences design leveraging microdata on individual schooling and age from Cambodian Labor Force Surveys in 2000/2001. The common trends assumption is plausible in this setting, since all villagers educated before DK were in the same province, meaning the institutional differences between regions only emerged after the regime. I placebo test this assumption, regressing schooling on a series of separate cohort dummies among persons past schooling age in 1975, finding no evidence of large or significant breaks in educational trends (Table D.6 in the Supplementary Material).

I estimate the following model:

(2) $$ \begin{array}{rl}{y}_{i,(v),(c)}={\displaystyle \sum_{c\ne 20-24}^C}{\beta}_c\left(\right.{\mathrm{Cohort}}_{i,(c)}\times 1\{S{W}_{i,(v)}\}\left)\right.+{\mu}_v\hskip4.5em +\hskip0.35em {\lambda}_{iy}+{\displaystyle \sum_{k=1}^K}{\alpha}_k{x}_i^k+{\epsilon}_{i,(v),(c).}& \end{array} $$

The outcome is years of schooling, $ {y}_{i,(v),(c)} $ , measured for individual i in village v among age cohort c. The coefficients of interest are $ {\beta}_c $ , which capture the differential effect of an individual living in the Southwest zone in an age cohort who would have had primary or secondary schooling after DK, relative to the age cohort who would have completed primary or secondary schooling before the regime (aged 20–24 in 1975). Note $ {\beta}_c $ captures both pre-trends in educational differences among older cohorts and the dynamic effect of having overlapped with Mok’s rule during schooling age. I include birth-decade-by-commune fixed effects $ {\lambda}_{iy} $ which absorb decadal educational trends over space, village fixed-effects ( $ {\mu}_v $ ) to adjust for village-invariant factors, and K individual controls $ {\sum}_{k=1}^K{\alpha}_k{x}_i^k $ including age and its square and respondent gender. Standard errors are clustered at the village.

Figure 5 graphically displays the identification approach and results. Figure 5a shows the average years of schooling by cohort per zone, illustrating a (fitted) parallel trend in the pre period. Education levels then sharply decline in the former Southwest zone among 12- to 17-year olds (in 1975 years). Figure 5b corroborates the descriptive trend, showing the absence of a difference among older age cohorts between zones and a transitory decline in educational attainment for persons who were schooling age when the regime began. The evidence suggests schooling fell among school-aged Cambodians in the Southwest.

Figure 5.

Educational Attainment by Age Cohort and Zone

Note: X-axis records birth cohorts in reference to 1975—individuals born in 1985 are scored−10, whereas birth in 1965 is 10. Y-axis is years of education completed. Controls include age, age $ {}^2 $ , gender, decade-by-commune fixed effects, and village fixed effects. Panel a is the raw trend—averages of schooling by age cohorts and zone, with the 5-year moving average and linear fit separated by the pre- and post-period. Panel b plots difference-in-differences coefficients ( $ {\beta}_c $ per Equation 2). See Table F.2 in the Dataverse Appendix for partial derivatives and uncertainty for adjusting covariates.

If human capital decline persists overtime, one should observe lower human capital levels at the village level between zones. Table 3 shows the share of persons who have never attended school increases, whereas the literacy rate (of those over 15 years of age) declines. The estimates are largely commensurate with one another; whereas the percentage of persons who never attended school increases by 7% in the baseline, the share of literate persons over 15 declines by 8%. The results are substantively large, near 0.5 $ \sigma $ in the baseline estimates. In Table D.1 in the Supplementary Material, I show years of education decline by a year on average, and school attendance declines by 6% in 1998. The result is robust to adjusting for distance to schools (Table D.2 in the Supplementary Material). The education gap persists into 2008 through tertiary and secondary education (Table D.4 in the Supplementary Material).

Table 3.

Human Capital: Education and Literacy in 1998

Note: See Table 2. Outcomes are the percentage of the population without education and percentage over the age of 15 who can read and write. See Table F.3 in the Dataverse Appendix for full model results. $ {}^{***}p<0.001 $ , $ {}^{**}p<0.01 $ , $ {}^{*}p<0.05 $ , $ {}^{\dagger }p<0.10 $ .

The impacts of repression on schooling could vary by gender: parents may invest less in schooling daughters in the aftermath of repression, pushing female family members into care-taking roles instead of education. The pattern of lower educational attainment for females is consistent across Cambodia. Notably, this mechanism would not be a rival account to a poverty trap, rather, it would be a dimension by which the poverty trap is perpetuated. I find no consistent evidence of this pattern (Table D.5 in the Supplementary Material).

Labor Market Outcomes

Qualitative evidence suggests the absence of educated and skilled persons drove individuals to work in low-paying jobs (Jeong Reference Jeong2014). In the Cambodian context, less schooling strongly predicts individuals being “own account workers”—self employed, typically working agricultural jobs on small-scale family farms or in otherwise informal roles. This line of work is highly labor intensive, has a low level of productivity, and involves low levels of skill and technology (Arnold Reference Arnold2008). If the decline in education caused by DK repression reshaped local labor markets by pushing individuals into low-earning informal agricultural work, one may expect an increase in the probability an individual is an own-account worker and a commensurate decline in earnings and productivity.

Table 4 shows findings consistent with this pattern. Rural persons in the Southwest are far more likely to be own account workers—self-employed informal laborers (column 1). Consistent with broader patterns of employment and earnings, column 2 shows lower income from work as well. Finally, column 3 shows earnings per hour are also lower, meaning productivity for workers also diminishes. The evidence is consistent with qualitative accounts of how state repression shaped labor markets and workers in the wake of the human capital shock from the regime. In Table D.8 in the Supplementary Material, I show own-account workers are less educated and earn less.

Table 4.

Labor Market Effects of Repression

Note: Unit of analysis is employed working aged (11–59) individuals. Data from 2000 to 2001 Labor Force Survey. Pr(self-employed) is scored 1 for persons who are own account workers. Income is individual wages, remuneration, earnings, and tips reported from the last month in 10,000 riels and productivity is riels divided by working hours. Estimates are obtained using Calonico, Cattaneo, and Titiunik (Reference Calonico, Cattaneo and Titiunik2014) nonparametric RD within MSE optimal bandwidths and a triangular kernel. Covariates in local linear regressions include survey wave fixed effects, age, age squared, and gender of individual. Robust errors clustered at the village reported in (); wild cluster bootstrapped errors reported in []. $ {}^{***}p<0.001 $ , $ {}^{**}p<0.01 $ , $ {}^{*}p<0.05 $ , $ {}^{\dagger }p<0.10 $ . See Table F.4 in the Dataverse Appendix for full model results.

Intergenerational Consequences

Economic impacts can reverberate across generations by reducing child health. Childhood health determines later-life income levels and is partially determined by maternal economic well-being (Bleakley Reference Bleakley2010). I evaluate how the DK shock impacted subsequent generations by exploring the health of children between zones with four rounds (2000, 2005, 2010, and 2014) of Demographic Health Survey (DHS) data. Parents who lost schooling and were therefore poor as a result of Mok’s rule may have less healthy children as a result of their low income. Finding worse health outcomes for youth may highlight how repression creates persistent, negative human capital consequences beyond schooling and across generations that were never exposed to violence.

DHS randomly selects a subset of respondents and measures three critical dimensions of child health for persons aged 3–5: height for age (a measure of stunting), weight for age (a measure of wasting), and weight for height (a measure of being underweight). I create an index of health scores using the first principal component of these measures and evaluate each measure individually, and then estimate a version of Equation 1 which includes wave year fixed effects and maternal controls among rural households to maximize comparability.

Table 5 shows the health index declines by nearly 0.8 $ \sigma $ . The effect is driven by underweight and wasting children, rather than stunting, which suggests childhood food poverty drives health differences. In Table D.7 in the Supplementary Material, I show the effect is orders of magnitude larger for mothers without formal schooling, and that the difference between zones attenuates when mothers have some education, suggesting the education shock from the regime plays a crucial role in explaining health differences.

Table 5.

Intergenerational Effects: Child Health between Zones

Note: Unit of analysis is the 3- to 5-year-old individual from the 2000–2014 DHS survey waves—the children of the generation exposed to the Khmer Rouge. Health index (column 1) is the first principal component of individual health measures. Height/age is the standard deviations from the median of individual height for age (stunting), Weight/age is the standard deviations from the median of weight for age (wasting), and Weight/height is the standard deviations from the median of weight for height (underweight). Analysis within rural households to maximize comparability. Controls include the age of the mother, its square, and survey fixed effects. Robust standard errors clustered at the survey area reported in parentheses. Cluster standard errors reported in parentheses and wild cluster bootstraps reported in brackets. See Table F.5 in the Dataverse Appendix for full model results. $ {}^{***}p<0.001 $ , $ {}^{**}p<0.01 $ , $ {}^{*}p<0.05. $

Alternative Mechanisms

Social Capital

Cultural persistence via social trust has been shown to be an important persistence channel linking coerced labor to modern development (Lichter, Löffler, and Siegloch Reference Lichter, Löffler and Siegloch2021; Lowes and Montero Reference Lowes and Montero2021; Nunn and Wantchekon Reference Nunn and Wantchekon2011). I use data from Cambodia’s Violence Against Women survey, which asks respondent’s three questions about communal trust and social cohesion including: (1) “Do neighbors in COMMUNITY NAME generally tend to know each other well?,” (2) “If there were a street fight in COMMUNITY NAME would people generally do something to stop it?,” and (3) “If someone in your family suddenly fell ill or had an accident, would your neighbors offer to help?” I code 1 if a respondent answers “yes” and 0 otherwise. There is little variation across villages in different zones along these dimensions, with respondent’s reporting affirmative answers at high and nearly identical rates between zones (Table 6). This suggests social capital cannot explain the developmental divergence.

Table 6.

Social Cohesion and Trust

Source: WHO Women’s Health and Life Experiences Survey. Outcomes are the share of respondents reporting that they know their neighbors well (Know neighbors), whether the respondent believes people in the community would provide help if a fight broke out on the street (Help fight) and whether neighbors would help if one become sick or had an accident (Help sick). Each cell reports the proportion of respondents who reply in the affirmative divided by total respondents. SW is the Southwest zone and W is the West zone.

Property Rights

Weak property rights institutions are another channel commonly cited in the literature (Acemoglu, Johnson, and Robinson Reference Acemoglu, Johnson and Robinson2001; Dell Reference Dell2010). Since neither the DK ban on private property nor collectivization has persisted, it is unlikely formal institutional persistence explains contemporary maldevelopment. However, the destruction of records of land ownership could have increased the contestability of land, creating tenure insecurity and poverty. Social conflict as a result of extractive institutions has been shown to be a crucial persistence mechanism (Guardado Reference Guardado2018).

To measure respect for property rights, I collect data on the count of village land disputes from Commune Database Online. This measure captures a highly salient aspect of respect for property in the Cambodian context, where the highly agrarian economy has seen increasing poverty due to land grabs and unclear titling (Kerbo Reference Kerbo2014). I find no difference in land disputes at the discontinuity (Table 7).

Table 7.

Land Disputes

Note: RD estimates evaluating changes in land disputes. Outcomes are level of land disputes in 2008–2010 reported by the Commune Database Online. Column 1 is the count (levels), column 2 is the inverse hyperbolic sine of the count (IHS), and column 3 is a binary indicator for any dispute (binary). Standard errors reported in parentheses.

Political Legacy

A third class of explanations relates to the political dominance of former regime elites; historical coercive institutions may create politically uncompetitive environments, which outlast initial conditions. As explained, this mechanism is unlikely, as Mok and his cadres were driven from the region during Vietnam’s invasion. I measure the competitiveness of commune council elections in 2012 and 2017 (Herfindahl index, higher values represent less competitiveness) and the vote share for the ruling CPP in 2012/2017. Data are only reported at the commune level, of which there are 87 in Kampong Speu. Since the units are much larger than villages, I include all communes in the RD, but also include district fixed effects to absorb spatial heterogeneity.

Results in Table 8 show local elections are largely similar between zones. To the extent competitiveness of elections is different, elections appeared more competitive in the Southwest in 2017, where vote shares were less concentrated by 3.6 points. As such, political competition and partisan support are unlikely explanations. Another observable implication of elite capture and corruption—a key persistence mechanism in the study of extractive institutional persistence—is lower levels of public goods. In Table C.2 in the Supplementary Material, I show villages on either side of the border have similar access to hospitals, schools, and commune centers.

Table 8.

Commune Council Elections (2012/2017)

Note: RD estimates evaluating the political impact of the Southwest zone on commune council elections. Competitiveness is the share of vote shares squared (higher values mean more concentration and lower values more competitive). CPP vote share is the count of votes to CPP, the dominant ruling party, out of total votes. SHAC errors reported in parentheses.