Evidence from the Korean War [PDF]

The Effect of War on Local Collective Action: Evidence from the Korean War∗ Hyunjoo Yang† September 16, 2017

Abstract Does war have important long-term economic consequences? Existing literature suggests a lack of long-term effects related to the short-term destruction of physical capital and population reduction. Increased ideological and social division as a result of war, on the other hand, may produce persistent economic and social outcomes. I investigate the effect of the 1950-1953 Korean War on cooperation within rural communities in South Korea. Combining census data and unique data on village level collective action, I find that residents of townships that experienced more intense conflicts due to the prolonged presence of the North Korean Army and communist influences during the war were less likely to cooperate 20 years after the war ended. Further, I provide evidence that the reductions in township populations due to the conflict persisted over 40 years. The empirical results suggest that the impacts of the war persisted in the form of increased ideological and social division.

JEL Classification: O10, D74, N45, R11 Keywords: Political Purges, Social Capital, South Korea

∗

I thank Nathaniel Baum-Snow, Pedro Dal Bo, Andrew Foster, Raphael Franck, Oded Galor, Stelios Michalopoulos, Sri Nagavarapu, Louis Putterman, David Weil, and participants at Brown Macro Lunch Seminar for their comments. Dahae Yang provided excellent research assistance. All errors are mine. † Brown University, Department of Economics, 64 Waterman Street, Providence, RI 02912, USA (email address: hyunjoo [email protected]).

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Introduction

War can cause immense damage and lead to countless deaths of both military personnel and civilians. As a result of the destruction of physical capital and the loss of human capital, production and income both decrease in the short term. Uncertainty remains, however, as to whether there are persistent economic consequences of war. If a war causes short term disturbances of physical capital accumulation or reduces the population level, the neoclassical growth model predicts no changes in the growth path (i.e., the economy will quickly converge back to its pre-war state). On the other hand, if a war changes fundamental aspects of an economy, such as institutions and social norm, the long-run growth path of the economy can be permanently altered. The 1950–1953 Korean War provides compelling historical evidence of community-level social division. During the war, members of the North Korean People’s Army (NKPA) stationed in South Korea executed a significant number of civilians labeled as anti-communists. At the time, typical farmers were functionally illiterate and lacked knowledge of communism. Yet, they had to side with either anti- or pro-communist groups, often involuntarily. This unprecedented social division severely damaged community-level social cohesion. In this paper, I investigate whether the ideological conflict inflicted during the Korean War is associated with lasting damage to the social fabric of affected communities using census data and a novel data on collective action.1 As a measure of the severity of conflict, I use the changes in the civilian population that occurred during the period from 1949 (just before the war) to 1954 (immediately after the war) following Davis and Weinstein (2002). As a measure of community cooperation, I use the Korean government’s evaluations of the use of public resources distributed to each village under the 1970-1971 New Village Beautification Project. Each village received bags of cement intended for the production of village public goods. A year later, the government systematically evaluated each village’s cement usage and assigned one of three grades: A, B, or C. A village received an A grade if it produced relatively more public goods than a village with a B grade. A village received a C grade if it produced few public goods. Since the production of public goods requires voluntary labor and private contributions, I use the probability of receiving either an A or B cement project grade as a proxy for community cooperation. I demonstrate that the severity of conflict has an impact on community cooperation 20 years after the war ended. A 10% reduction in a township’s civilian population was associated with a 2 percentage point reduction in the probability of using cement for the production of public goods. A township is an administrative unit comprised of 10 to 20 villages. The effect is statistically 1

Investigating the role of social division on conflict is important, given mounting evidence of the effect of contemporary social divisions on economic outcomes such as income, investment, corruption, institutional efficiency and public goods provision (Knack and Keefer, 1997; Alesina et al., 1999; Alesina and La Ferrara, 2000; Banerjee et al., 2005; Miguel and Gugerty, 2005; Khwaja, 2009).

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significant and the magnitude is economically meaningful. A one standard deviation decrease in the civilian population is associated with a decrease of one-fifth of the standard deviation in the cement measure. I then analyze whether the reduction in population level during war was short term and whether the population converged back to the pre-war trend. For the analysis, I divide townships into two groups depending on whether a township experienced a decrease in the civilian population or not during the war. Using the population trend of the group without population reductions during the war as a counterfactual population trend, I show that the population reduction from the war persisted for more than 40 years. I then turn to investigate whether the social division is a channel through which the conflict affected community cooperation. The unique Korean context allows me to compare the effects of war through social divisions and through destruction of physical capital from conventional battles within the same national boundary. South Jeolla province did not experience conventional war battles, but political purges were frequent. On the other hand, North Kyungsang province suffered by military battles between NKPA and the UN forces, but it experienced little purges. Consistent with the hypothesis that the social division has lasting influences on cooperation, I find that the severity of conflict was associated with community cooperation only in South Jeolla. In North Kyungsang, I find little association between conflict and cooperation. This work contributes to the literature on the impact of violence on social capital in two ways. First, I introduce ideological conflict within community as a novel explanation for the association between violence and social capital. In the existing literature, the evidence of the effects of conflicts on social capital is mixed, suggesting the existence of various channels through which conflicts could influence social capital. Some scholars find the positive effects of violence on social capital, typically measured by trust from survey data (Bozzoli et al., 2011; Cassar et al., 2011; Becchetti et al., 2014). Other scholars find positive effects of civil conflicts on social capital, such as political participation and measurement from experiments (Bellows and Miguel, 2006, 2009; Blattman, 2009; Gilligan et al., 2014). Second, I provide empirical evidence of the persistence of the effect of conflict on social capital. While there is a lack of extensive research on the topic of persistent damages in social capital, my empirical results contrasts with De Luca and Verpoorten (2015) who find that armed conflict in Uganda decreased trust and associational membership only temporarily. They document that the negative effect lasted only a few years. One possible reason of the different degree of persistence could be related to whether perpetrators of violence were just following orders (returning soldiers in Uganda) or whether they actively destroyed community social fabric (political purges by community residents in South Korea).

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Additionally, this work is broadly related to literature on the effects of war on economic outcomes today. In the existing literature, scholars find little evidence of long-run effects of war associated with the destruction of physical capital (Davis and Weinstein, 2002; Brakman et al., 2004; Miguel and Roland, 2011). However, my empirical results resonate with the literature on the effect of civil conflicts in African countries that document the existence of long run effects from conflict (Blattman and Annan, 2010; Voors et al., 2012; Besley and Reynal-Querol, 2014). My work also contributes to literature on political purges in general as well as purges perpetuated by communists (Getty, 1987; Chandler, 1999; Strauss, 2002; Acemoglu et al., 2011). To my knowledge, this is one of the first empirical papers on the effect of political purges on social capital, as well as on the effect of the Korean War on economic and social outcomes. The rest of the paper is organized as follows. In the next section, I describe the context of the study on the Korean War and anti-communist purges. Then I explain my empirical strategy in section 3. In section 4, I describe data before proceeding to empirical results in section 5. I provide some concluding remarks in section 6.

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Context

2.1

The Korean War (1950-1953)

After obtaining independence from the Japanese colonial government in 1945, the Korean peninsula was divided into two governments, one in the north backed by Soviet Union and the other in the south supported by the United States. North Korea invaded South Korea on June 25th, 1950, with support from the Soviet Union and communist China. The South Korean army was ill prepared. On the other hand, the NKPA possessed Russian T-34 tanks and had support from heavy artillery. Hastings (1987) observed that “communists...[were] checked more by terrain and natural obstacles than by the [South Korean] forces as they forged through the gaps in the hills.” Just two months after the war began, when the Joint United Nations Forces intervened to counter the North Korean attacks, most parts of South Korea were already occupied by the NKPA. The battles ended with the armistice in 1953. Damages from the war were severe. The total value of property losses in South Korea was estimated to be approximately similar to the entire gross national product of South Korea in 1949. It is estimated that 3 million people were either killed, wounded or missing during the war. Furthermore, approximately 5 million refugees fled war-torn areas (Oberdorfer, 1997). The number of deaths and casualties from the Korean War was significant compared to other major wars. While the number of battle deaths during the Korean War was smaller than battle deaths during the Viet-

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nam War or during the World War I, non-battle deaths totaled 21,000, almost twice the number from the Vietnam War (Edwards, 1998).2 Moreover, the number of North Korean and Chinese casualties exceeded 500,000 (Edwards, 2003).

2.2

War damages in South Jeolla province

South Jeolla province, which is the focus of this study, is located in the southwestern corner of the country (see Figure 1). The province was mostly poor and agrarian throughout Korean history (Wickham, 1999). During the war, the province experienced a disproportionately large number of civilian deaths compared to the rest of the nation. According to one estimate, more than 70% of total civilian deaths occurred in this region (Park, 2005). When the UN forces launched their counterattack, they landed in the west near Seoul, the capital city of South Korea, and Pusan in the southeast, which is the second largest city.3 As a result, some members of the North Korean army were trapped in South Jeolla Province because their escape routes were cut off (see Figure 2). The NKPA was essentially trapped in this region whereas North Korean soldiers in other regions were able to retreat back to the North more easily because they had easy access to escape routes back through mountains and the east coast (Gibney, 1992). It was reported that 15,000 NKPA soldiers and local communist supporters remained in the South (Korea Institute of Military History, 2001). Even by the middle of May in 1951, 11 months after the war began, the guerrilla forces were not completely eliminated (Korea Institute of Military History, 2001). The NKPA was able to linger in the mountains because the strategic priority of UN Forces was not to eliminate trapped NKPA soldiers, but to recapture the capital city of South Korea and force the NKPA to retreat back to the north.4 As a result, while civilian deaths did occur, no major battles between the NKPA and UN Forces took place in South Jeolla during the war (see Figure 3).

2.3

Anti-communist purges

The prolonged presence of North Koreans in South Jeolla severely damaged social cohesion and increased tensions and hostility within communities. Oberdorfer (1997, p. 10) notes: 2

The total battle deaths during the Korean War was estimated to be 34,000. During the Vietnam War, the battle deaths were 47,000. During the World War I, the battle deaths were 54,000 (Edwards, 1998). American casualties during the Korean War were 50,000 dead and 291,000 wounded (Edwards, 2003). 3 See Hastings (2010) for details on General MacAurthur’s Inchon landing on September 15, 1950. 4 UN Forces did not take part in eliminating NKPA troops hiding in the mountains. The U.S. Joint Chiefs of Staff issued a directive to the Chief of the UN Command that “guerrilla activities should be dealt with primarily by the forces of the Republic of Korea, with minimum participation by United Nations contingents (Schnabel, 1972, p. 183).”

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One of the most important consequences of the war was the hardening of ideological ... lines. The antipathy ... was deepened into a blood feud among family members, extending from political leaders to the bulk of the ordinary people... The thirteenhundred-year-old unity of the Korean people was shattered ” The NKPA set up ad hoc courts called people’s courts to purge anti-communists in villages. Accusations were typically made by village members, and people who were labeled as anticommunists were executed onsite, often by their accusers (Park, 2005). Due to the presence of the NKPA, one had to take sides with either the pro-communist or anti-communist group, often involuntarily. This ideological divide severely damaged social cohesion. For example, once the NKPA came to town, an elementary school teacher who was a communist sympathizer killed his own pupils whose parents were thought to be anti-communists (Kim, 2003). Some historians have documented that existing conflicts within communities were amplified as some rival groups exploited the people’s courts and accused other groups of being anti-communists (Park, 2005; Park, 2010). Park’s memoirs provide a vivid story related to the people’s courts (Park, 1999, p. 59). He was accused of being an anti-communist by another village member who had a personal grudge toward him. Park wrote: “This is of of the vilest enemies!” he yelled, grabbing my hair and shaking my head mercilessly. ... [he] was raving happily with this opportunity for revenge. I was moved to the second cell and there I found the principal of Songlim Girls’ Middle School. He was imprisoned on the accusation that he had been a leading figure in anticommunist education. In the areas like Naju and Muan, the communists held a people’s court several days earlier. When prisoners were dragged out and presented before the people, the leftists and families holding grudges gathered and called out, ”Yes, yes. Kill that one, too!” They shouted out together influenced by the mass psychology. It was rare for one or two prisoners to survive out of several hundred. Those unfortunate people ... were falsely accused as a result of personal animosity or intrigue by their own neighbors.

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Empirical strategy

To identify the effect of conflict on community cooperation and population trends, I employ two different specifications. First, to estimate the effect of war on the propensity for cooperation within 6

community, I use the following cross-sectional empirical specification: cooperationi = α + β conf licti + Xi γ + θc + ei ,

(1)

where cooperationi is the measure of cooperation in township i, conf licti is the measure of conflict severity in township i during the war, Xi is a vector of controls, and θc is the county fixed effects. To identify the effects of conflict, one needs to ensure that selection into conflicts are based on unobserved but fixed county-level characteristics. If this assumption holds, Equation 1 provides a consistent estimate of β.5 Second, to investigate whether population which experienced a short-term reduction during the war converged back to pre-war trend, I divide the sample into two groups. First group is the treatment group that had more conflicts. The control group had relatively less conflicts. The precise definition of the measure of severity of conflicts will be discussed in section 4. The following specification is used: log(popit ) = α + β treatmenti + γt yeart + δt yeart · treatmenti + Xit ν + θc + ϵit

(2)

where the outcome is log population at town i and year t. Xit is a vector of controls. θc is the county fixed effects. The coefficient of interest is δt which shows differences in the level of population between the treatment and control group. δt ̸= 0 implies that the mean of the population of the

treatment group in year t is different from the mean of the population of the control group in the same year. If δt ̸= 0 after the end of war, it implies that the population level of the treatment group

does not converge back to pre-war level at year t.

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Data

Primary data sources for the analysis are population censuses in various years and the New Village Comprehensive Survey (NVCS). 5

While it is possible that the NKPA chose hiding places based on town characteristics, such as overall degree of politically left-leaning tendencies. However, during the war, it might be difficult to acquire accurate information on political preference of residents. Moreover, the urgency of finding hiding place during the war may resulted in more random choice of hiding locations. Perhaps the most important determinants of the choices of hiding places would be ruggedness and altitudes which may prevented easy access from UN forces. I plan to include extensive geographic controls.

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4.1

Population censuses

I use population censuses from year 1925 to 1990 to measure the changes in the number of civilian populations at the township level. Population census was collected every five years on average. The data contain various township characteristics including the number of the population, the number of illiterate population, the number of people with agriculture-related occupations, the number of Japanese population, the number of single and married people, and the number of people with different age groups, for example, between 0 and 14, and between 15-24. It would be ideal to have detailed breakdowns of population changes such as by age, gender, education level and migration destinations to assess detailed effects of war on population movement. However, the census data do not have more detailed population breakdowns. Therefore, I use overall population trend for the analysis.

4.2

Severity of conflict

The explanatory variable of interest is the severity of conflicts due to NKPA. To capture the severity of conflict within a township, the ideal data would be the the number of people’s courts held in a township. Unfortunately, these data are not available. Instead. I use census data and calculate the changes in civilian population right before the war (1949) and right after the war (1955) as a measure of severity of conflict, following Davis and Weinstein (2002). The severity measure, ∆pop49,55 is defined as ∆pop49,55 = log(pop1955 ) − log(pop1949 ). The changes in population reflect both war casualties and the reduction in population who migrated out to avoid conflict. Additionally, the population change also reflects other migration flows as well as births and other deaths. During the war, however the most prominent factor of population changes could be war-related migration and deaths. In my data, almost half of the townships experienced a reduction in population during the war. Before the breakout of the war, however, there were few townships that experienced a reduction in population.6 Figure 4 shows the spatial variation in ∆pop49,55 . It shows that there are multiple pockets of regions where there is a large concentration of a relatively large reduction in the civilian population. While there is no centrally concentrated regions with a large decline in the population within the province, the existence of concentrations requires me to employ empirical strategy of including If the reduction in population during the war captures the severity of conflict, I expect that ∆pop49,55 is relatively uncorrelated with ∆popt , the population changes in periods before and after the war peI riod. I calculate corr(∆popt−1 , ∆popt ) for every population census year data from 1925 to 1990. find that corr(∆pop44,49 , ∆pop49,55 ) is not only approximately zero, but also it has lowest value among all corr(∆popt−1 , ∆popt ) from other census periods. 6

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county fixed effects to eliminate across-county variations driving empirical results.

4.3

The NVCS data

To construct a measure of community cooperation, I use a government publication, the New Village Comprehensive Survey (NVCS) in 1972 which recorded the government assessment on the the production of public goods under the New Village Beautification Project, a rural intervention program in 1970. I digitized the data into an electronic format for analysis. Under the New Village Beautification Project, each village was given the same amount of bags of cement bags by the government to produce village-level public goods. Since only cement was provided by the government, other resources such as land, labor, and equipments were voluntarily supplied by village members. Further, the usage of cement was collectively decided by village members.7 The government systematically evaluated each village the following year and classified villages depending on the actual usages of cement. Some villages used cement for production of public goods such as improving village roads and building common laundry facilities. These villages received an A or B grade. Other villages used cement privately, such as kitchen floor improvements, and received a C grade. Using data on the government classification of cement projects, I construct the public use variable which takes the value one if a village received an A or B grade and zero otherwise. Since the unit of analysis in this paper is township, I compute the weighted average of villagelevel public use dummy at the township level. The weight is the number of households of each village. This measure could be a reasonable proxy for cooperation among village members for a couple of reasons. First, without any agreement among village members to use cement for public goods, it would be difficult to produce public goods. Second, even conditional on agreement to produce public goods, village members still have to voluntarily provide land and labor.8

4.4

The family clan data

I use Family Names in Chosun, a part of population census in 1930 by the Japanese Colonial Government to construct a lineage diversity measure at the village-level. The family clan data 7

Village council members decided how to use cement then decided the usage of cement through votes from the head of each village household. 8 It was particularly difficult to donate private agricultural field for road improvement, such as widening village road because the average cultivated area was already quite low. Korea had a successful land reforms in the late 1940s. Each farmer could own land only up to 3 hectares.

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contain the number of households belong to each family clan in a village as long as the clan household share exceeds 10% of the total number of households. Using the household share of each clan in a village, I construct the family clan Herfindahl Index for measuring clan concentration and include it as a control variable in the analysis. As the unit of study is township, I take the weighted average of the Herfindahl Index of villages in the same township. The weight is the household share of each village in a township. The study region is South Jeolla province which experienced the most severe conflicts during the war because of the extended period of presence of NKPA. South Jeolla province has population of 1.7 million in 2010 and the size is roughly similar to the state of Connecticut in the U.S. The analysis is at the township level because the population census is the main data set which provides information at town level which is the lowest administrative unit. Urban regions in the province are excluded from the sample because the outcome variable, public use, is only available in rural townships. Table 1 presents summary statistics of townships. According to the 1949 population census, a township had population of roughly 10,000 on average. Agricultural occupation consisted of 85% of all occupations of township residents. The population was relatively immobile with 74% of population were born in the same township they resided when census was conducted. This is not surprising because farmers often inherited land from ancestors, and they were reluctant to sell ancestors’ land and move elsewhere. The illiteracy rates were high, almost approaching 80%. The mean and the median of the main explanatory variable, ∆pop49,55 was approximately zero and the standard deviation was 0.08.

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Empirical results

This section presents two sets of estimation results. I first show estimates of the effect of the conflicts during the war on cooperation within community. I then show whether the reduction of the civilian population during the war persisted.

5.1

Effects of war on cooperation

To test whether ideological conflicts had adverse effects on community cooperation, I examine the relationship between ∆pop49,55 and public use. Figure 5 plots ∆pop49,55 and public use. It suggests that there is a positive relationship between these two variables. The figure shows that a township that experienced more severe conflict (lower value of ∆pop49,55 ) was less likely for its population to cooperate 20 years after the end of the war (lower value of public use). 10

To confirm the patterns shown in the figure, I estimate the empirical specification in Equation 1. Table 2 shows results. I correct for heteroskedasticity in standard errors. When county fixed effects are used, I cluster standard errors at the county level. For the analysis, I use the population census data and the NVCS data. Column 1 does not include any control variable. The coefficient of ∆pop49,55 indicates that one percentage point decrease in the population during the war – more severe conflict – is associated with a decrease of the probability of using government-provide cement for public use by 29 percentage points. The estimate is highly statistically significant at 1 percent level. Column 2 adds the pre-war population level right before the war as a control. The coefficient changes only slightly. Column 3 adds pre-war township controls, and Column 4 include county fixed effects. The estimated coefficient is 0.16 and it is statistically significant at 10 percent level. While the magnitude of the coefficient decreased as more controls were added, the coefficient of ∆pop49,55 remains practically large given the standard deviation of the outcome variable is 0.1. These results suggest that internal social division is associated with community cooperation and its consequences could be harmful and long-lasting.9

5.2

Alternative explanations

In this section, I evaluate alternative explanations on the relationship between the reduction of civilian population during the war and community cooperation. These include location specific amenities, migration to avoid conflict, and more generally, selection on unobserved variables. 5.2.1

Location specific amenities

It is possible that the regressor ∆pop49,55 predicts cooperation because of the existence of a third factor, such as time-invariant location specific amenities. While county fixed effects take differences in amenities at the county-level into account, there is still a possibility that township-level differences may still exist. These amenities could draw people into a township and also make town residents more likely to cooperate. This could drive spurious results. I carry out a placebo test that uses ∆pop in pre-war periods in South Jeolla province. If a presence of a third factor drives the results, I expect to see that placebo ∆pop in other periods will be also positively predict the outcome measure, that is β > 0. Table 3 shows that data do not support evidence that a time-invariant third factor drives my results. I substitute ∆pop49,55 with ∆pop with different time periods in my preferred econometric specification, Column 4 of Table 2. Each row of Table 3 represents the estimated coefficients βˆ 9

The results in this paper contrast with existing literature on limited long-run impacts through destructions in physical capital (Davis and Weinstein, 2002; Brakman et al., 2004; Miguel and Roland, 2011).

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for each separate ∆pop. The results show that ∆pop in pre-war periods do not predict community cooperation. Except ∆pop49,55 , the coefficients of ∆pop of pre-war periods are statistically insignificant and the sign of the coefficients are mostly the opposite of the results I find in the main results in Table 2. 5.2.2

Migration to avoid conflict

I also evaluate whether the relationship between ∆pop49,55 and public use shown in the previous section is due to migration of township residents to avoid conflict during the war, i.e., ∆pop49,55 captures migrations to avoid actual conflict instead of civilian casualties due to the war. Because the NKPA advanced to this region, it is reasonable to assume that the capitalists or the anti-communists were more likely to leave townships to avoid purges. However, these selective out-migration of people with right-leaning ideology would result in less ideological diversity of the remaining township residents, i.e., a negative relationship between ∆pop49,55 and public use. This contrasts with the positive association that I find in the data. 5.2.3

Selection on unobserved variables

While I employ county fixed effects to eliminate across-county differences driving results, the concern of potential biases still remains from the selection on unobserved variables.10 The effect of conflicts on outcome could be driven by selection because the magnitude of main coefficients of Table 2 does change as more controls are included. I use a statistical test suggest by Altonji et al. (2005) to check whether unobserved characteristics could dominate the main coefficient of ∆pop49,55 . Table 2 includes selection test statistics. The tests indicate that it seems unlikely that estimated coefficient is mostly due to selection. Conditional on county fixed effects being included, the explanatory power of unobserved characteristics should be at least five times greater than the explanatory power of control variables used in my study to claim that the estimate is entirely due to selection.

5.3

Effects of war on population size

Existing literature on the effect of war typically show that the population reduction during the war is temporary and it converges back to pre-war trend level quickly. For example, Davis and Weinstein (2002) document a rapid recovery of population in Japanese cities from bombing. Nagasaki took less than 15 years for the population recovery. Similar results of the convergence of population were shown in the case of bombing in rural regions in Vietnam (Miguel and Roland, 2011). 10

Instrument variable strategy will alleviate this concern in more systematic way. Work on IV strategy is on progress.

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Unlike the effects of bombing and destruction of physical capital, an increase in social divisions during the war may have lasting effects on the population and prevent the convergence of population. Residents may not wanted to live socially divided villages, or potential residents could be more reluctant to move into villages with uncooperative residents. To test the convergence of population after the war is ended, I compare the population trend of a group of townships which experienced the decline in civilian population during the war (treatment group) and another group without the population reduction (control group), which serves as a counterfactual population trend. That is, treatement group is consists of townships with ∆popi,4955 < 0. Control group has ∆popi,4955 ≥ 0.

Figure 7 plots the difference in the average population size of the two groups by year. I calculate

the differences by estimating Equation 2 using the census data. The estimated δt captures the differences in the population sizes between the two groups for year t. I plot the δt in the figure. Prior to the beginning of the war in 1950, there was no differences in the average population size. Between 1950 and 1960, there was a 15% drop in population in the treatment group. This initial drop is expected because the treatment and control group are defined based on whether a township faced a reduction in population during the war. The reduction in the population, however, were sustained 40 years after the war up to 1990. The differences in the population size reached 20% by 1980 and the differences are statistically significant. Table 4 presents quantitative evidence that the township population size does not converge to pre-war population trend. The table shows the estimates of δt from various specifications. Column 1 has no control variables. Column 2 and 3 adds controls and county fixed effects. The results shows that there was little difference in the population size before the war. The estimates are mostly statistically insignificant. After 1955, however, the population gap persisted up 1990.

5.4

Comparison of the effects of conflict through social division and conventional battles

The main hypothesis of this paper is that heightened social divisions during the war are associated with community cooperation. On the other hand, if the civilian casualties are from conventional war battles, rather than through social division, then there could be a lack of such effect on cooperation. South Korea provides an unique context to compare the social division effect and the war battle effect within the same country. North Kyungsang province experienced military battles between NKPA and UN forces during the war. It contained Pusan Perimeter, a heavily fought battle lines (see Figure 8). Unlike Jeolla South, political purges were rare because North Korean soldiers

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could easily retreat back to north through the east coast and through mountains when UN forces successfully fought back (see Figure 2). Since purges were mostly absent in North Kyungsang but were frequent in South Jeolla, I expect that the relationship between the conflict and cooperation only holds in South Jeolla. This is because North Kyungsang did not expect much social divisions due to few political purges. To test this intuition, I run the same regression, using Equation 1, in North Kyungsang. The results are consistent with the idea that the war effect through social division lowers cooperation but not through civilian casualties due to battles: the reduction in the civilian population in North Kyungsang did not predict community cooperation. The estimates using North Kyunsang province show a negative association of ∆pop49,55 and public use, and estimates are not statistically significant. Figure 6 and 7 compare the bivariate relationship between ∆pop49,55 and public use in South Jeolla and North Kyungsang. While South Jeolla shows a relatively strong positive relationship, North Kyungsang shows weakly negative or no relationship. Additionally, through this exercise of the comparison of different provinces with various channels of war damages, I am able to reject an alternative hypothesis that there is a common omitted variable drives spurious correlation between ∆pop and cooperation across provinces. Otherwise, ˆ would also have been the sign of, and possibly the magnitude of, the estimated coefficients, β, similar across provinces.

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Conclusion

In this paper, I find evidence of persistent economic and social consequences of war. Specifically, I find a robust association between the severity of conflict a community experienced during the Korean War and cooperation within that community 20 years after the war ended. Further, the reduction in the population during the war did not converge back to the pre-war trend, even 40 years after the war ended. Evidence of the consequences of war through social division, however, is far from complete in this paper. I examine only two outcomes: community cooperation and the population trends. Assessing other important economic, political and social outcomes would provide further understandings of the effects of war. In this paper, I examine the effects of political purges by communists during the Korean War. The general message—that political purges could have negative long-run consequences—could apply to other contexts such as political purges instigated by Mao in communist China, or by Stalin in the Soviet Union. The results in this paper also have the potential to provide policy guidance. Identifying the ma14

jor channel of war damages can help policymakers use recovery funds more efficiently. For regions with heavy reductions in physical capital, it would be advisable to boost capital accumulation and rebuilding efforts. However, for regions with damaged social cohesion, it might be necessary to develop policies for rebuilding trust and confidence among community members. Spending resources in rebuilding physical capital alone in these regions may not produce the intended results of revitalizing the regional economy. As this paper has shown, people may not migrate to regions where residents do not trust each other, fail to cooperate, or exhibit lower levels of social capital.

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Brakman, Steven, Harry Garretsen, and Marc Schramm (2004) “The strategic bombing of German cities during World War II and its impact on city growth,” Journal of Economic Geography, Vol. 4, pp. 201–218. Cassar, Alessandra, Pauline Grosjean, and Sam Whitt (2011) “Civil war, social capital and market development: Experimental and survey evidence on the negative consequences of violence,” Working Paper 1917111, SSRN. Chandler, David Porter (1999) Voices from S-21: Terror and history in Pol Pot’s secret prison: University of California Press. Davis, Donald R and David E Weinstein (2002) “Bones, Bombs, and Break Points: The Geography of Economic Activity,” American Economic Review, Vol. 92, pp. 1269–1289. De Luca, Giacomo and Marijke Verpoorten (2015) “Civil war, social capital and resilience in Uganda,” Oxford Economic Papers, Vol. 67, pp. 661–686. Edwards, Paul (2003) The Korean War: A Historical Dictionary (Historical Dictionaries of War, Revolution, and Civil Unrest, No. 23): Scarecrow Press. Edwards, Paul M (1998) The Korean War: An Annotated Bibliography, No. 10: Greenwood Publishing Group. Getty, John Arch (1987) Origins of the great purges: the Soviet Communist Party reconsidered, 1933-1938, Vol. 43: Cambridge University Press. Gibney, Frank (1992) Korea’s quiet revolution: from garrison state to democracy: Walker. Gilligan, Michael J, Benjamin J Pasquale, and Cyrus Samii (2014) “Civil War and Social Cohesion: Lab-in-the-Field Evidence from Nepal,” American Journal of Political Science, Vol. 58, pp. 604–619. Hastings, Max (1987) The Korean War: Simon and Schuster. Khwaja, Asim Ijaz (2009) “Can good projects succeed in bad communities?” Journal of Public Economics, Vol. 93, pp. 899–916. Knack, Stephen and Philip Keefer (1997) “Does social capital have an economic payoff? A crosscountry investigation,” Quarterly Journal of Economics, pp. 1251–1288. Miguel, E. and M.K. Gugerty (2005) “Ethnic diversity, social sanctions, and public goods in Kenya,” Journal of Public Economics, Vol. 89, pp. 2325–2368. 17

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Table 1: Descriptive statistics Obs

Mean

SD

Min

Max

216

0.477

0.109

0.139

0.921

220

0.001

0.080

-0.381

0.213

227 227 227 227 227 227 227 227 227

11613 0.799 0.853 0.008 0.010 0.736 0.479 0.403 0.171

5783 0.047 0.097 0.012 0.016 0.088 0.017 0.014 0.008

4046 0.639 0.400 0.000 0.000 0.339 0.437 0.360 0.143

60251 0.896 0.960 0.126 0.111 0.955 0.521 0.438 0.202

Independent Variable public use Explanatory Variable ∆pop4955 Control Variables population 1949 illiteracy rate % ag occupation family clan Herfindahl Index % Japanese pop % native born % single individuals % individuals w/ age 0-14 % individuals w/ age 15-24

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Table 2: OLS and FE estimates of the effects of the conflict on cooperation Dependent variable: public use (mean 0.48, s.d. 0.11) (1) (2) (3) (4) ∆pop49,55

0.29∗∗∗ (0.11)

0.30∗∗∗ (0.11) -0.03

0.20∗∗ (0.08) -0.08∗∗∗ 0.01 -0.19 -0.13 1.47 0.44∗∗∗ -0.19 0.34 1.40

0.16∗ (0.09) -0.29∗∗ 0.24 -0.09 0.29 1.59 0.19 -0.71 0.73 0.75

N 216 0.04

N 216 0.05

N 216 0.13

Y 216 0.47

log(population 1949) illiteracy rate % ag occupation family clan H Index % Japanese pop % native born % single individuals % individuals w/ age 0-14 % individuals w/ age 15-24 County FE Observations R2

Selection Ratio (Altonji et al 2005)

R R F 1.27 (βOLS , βFF E ), 1.99 (βOLS , βOLS ), 5.06 (βFRE , βFF E )

Notes: Robust standard errors in parentheses. Standard errors in Column 4 are clustered at the county level. Data are from population censuses and the New Village Comprehensive Survey. The public use variable equals one if a village used cement from the government to produce village level public goods and zero if cement was used for private usage. As the analysis is at the township level, village level public use is averaged at the township level with the number of village household as the weight. ∆pop49,55 is changes in town population between 1949 and 1955. This measure is a proxy for the severity of conflict within a township. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

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Table 3: Falsification Checks Using ∆pop in Pre- & Post-War Periods

Explanatory variable

Dep. var.: public use βˆ1 s.e.

∆pop 1925-1930

0.04

0.13

∆pop 1930-1935

-0.01

0.09

∆pop 1935-1944

-0.10

0.06

∆pop 1944-1949

-0.11

0.11

∆pop49,55 (original regressor)

0.16∗

0.09

∆pop 1955-1960

-0.23

0.14

∆pop 1960-1966

-0.18

0.12

Notes: Robust standard errors in parentheses. Standard errors are clustered at the county level. Data are from population censuses and the New Village Comprehensive Survey. I run regressions with the specification identical to Table 2 Column 4 except the regressor. Each row shows the changes in population in various years as regressors. For example, ∆pop 1925-1930 is a measure of population changes between year 1925 and 1930. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

21

Table 4: OLS Estimates of Differences in Population Trends between treatment group (∆pop < 0) and control group (∆pop ≥ 0) Dep. var.: log(population) (3) (1) (2) Before the War treatment*year1930 treatment*year1935 treatment*year1944 treatment*year1949 After the War treatment*year1955 treatment*year1960 treatment*year1966 treatment*year1970 treatment*year1975 treatment*year1980 treatment*year1985 treatment*year1990

-0.02 -0.05 -0.02 -0.03

-0.02 -0.05 -0.02 -0.03

-0.02 -0.05∗∗∗ -0.02 -0.03

-0.15∗∗ -0.14∗∗ -0.13∗ -0.15∗∗ -0.14∗∗ -0.19∗∗ -0.19∗∗ -0.16∗

-0.14∗∗ -0.13∗∗ -0.13∗∗ -0.15∗∗ -0.14∗∗ -0.19∗∗∗ -0.21∗∗∗ -0.18∗∗

-0.14∗∗∗ -0.13∗∗∗ -0.12∗∗∗ -0.14∗∗∗ -0.14∗∗∗ -0.19∗∗∗ -0.21∗∗∗ -0.17∗∗∗

N N

Y N

Y Y

2808 0.21

2808 0.48

2808 0.52

Controls County FE N R2

Notes: Robust standard errors in parentheses. Standard errors in Column 3 are clustered at the county level. Data are from population censuses and the New Village Comprehensive Survey. I construct a panel data set in which a township has multiple observations for each years. The treatment dummy equals one if a township experienced negative growth between just before and just after the war (∆pop49,55 ) and zero otherwise. The treatment group experienced relatively more conflict compared to the control group. The variables year19XX indicates a dummy for year 19XX. The table shows interaction terms between treatment dummy and year dummies to indicate the average population differences in two groups. Controls variables are identical to Table 2. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

22

M &

" J

¯

Towns in South Jolla Province

& M " J

Seoul Pusan

Figure 1: South Jeolla province is located in southwestern part of South Korea. The province is highlighted in gray color in the map. The map shows township boundaries.

23

Figure 2: UN forces landed near Seoul (west) and also counter attacked the NKPA from the southeast toward northwestern direction (direction of arrows). As a result, some of the NKPA units were trapped in the southwest area. (Source: Department of History, US Military Academy)

24

Figure 3: The numbers in the map show the location of major battles between the NKPA and UN Forces during the Korean War. South Jeolla province (southwestern region) escaped major battles. (source: The 8th Army Office of the Staff Historian, 1972)

25

South Jolla Province Delta pop 49 55 < -2.5 Std. Dev.

¯

-2.5 - -1.5 Std. Dev. -1.5 - -0.50 Std. Dev. -0.50 - 0.50 Std. Dev. 0.50 - 1.5 Std. Dev. 1.5 - 2.5 Std. Dev. > 2.5 Std. Dev.

Figure 4: The figure shows the spatial variation of ∆pop49,55 , changes in township population right before and the right after the Korean War. Red colors indicate townships that experienced reductions in population (more severe conflict). Green colors shows townships with a positive increase in population. Yellow colors show minimal changes in population. A darker red implies a more reduction in population. A darker green implies a more increase in population.

26

1 .8 public_use .6 .4 .2

-.4

-.2

0

.2

Δpop4955

.2

.4

public_use .6

.8

1

Figure 5: Bivariate relationship between ∆pop1949,1955 and public use in South Jeolla province shows a positive correlation (social division effect)

-.4

-.2

0

.2 .4 Δpop4955 β_hat = -0.036, s.e. = 0.146, R2 = 0.0007

.6

Figure 6: Bivariate relationship between ∆pop1949,1955 and public use in North Kyungsang province shows zero or a slight negative correlation (war battle effect)

27

0 coeff. of interaction terms -.2 -.1 -.3

1930

1940

1950

1960 year

1970

1980

1990

Figure 7: The reduction in the civilian population caused by the war did not converge back to pre-war trend after 40 years. The solid line shows the differences in population between treated group and control group by plotting the estimated coefficients of the interaction term, treatment · year dummy. The dotted lines indicate the 95% confidence interval. Treated group is defined as townships that experienced a negative population growth during the war. Townships in control group did not experience population reduction. The differences in population between treatment and control group before the war (1950-1953) were little and were mostly statistically insignificant. On the other hand, the reduction in population during the war persisted 40 years after the war.

28

Figure 8: The Pusan Perimeter shown in the map is the battle lines between the NKPA and UN forces. The highlighted region in the southeastern part of the map indicates the only part of South Korea which was not occupied by the NKPA. (source: Stueck, 2002)

29

Figure 9: North Kyunsang province is located in southeastern part of South Korea. The province is highlighted in red color in the map. The map shows township boundaries.

30

Figure 10: Davis and Weinstein (2002) provide evidence of a rapid recovery of population from bombing. Nagasaki took less than 15 years for its population trend to reach the pre-war trend. Hiroshima took 30 years. (source: Davis and Weinstein, 2002)

31