The Effect of War on Local Collective Action:

Evidence from the Korean War∗

Hyunjoo Yang†

March 15, 2017

Abstract

Does war have important long-term economic consequences? Existing literature sug-

gests 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 investi-

gate 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.

Keywords: Political Purges, Social Capital, South Korea

JEL Codes: O10, D74, N45, R11

∗I thank Nathaniel Baum-Snow, Pedro Dal Bo, Andrew Foster, Raphael Franck, Oded Galor, SteliosMichalopoulos, Sri Nagavarapu, Louis Putterman, David Weil, and participants at Brown Macro LunchSeminar for their comments. Dahae Yang provided excellent research assistance. All errors are mine.†Korea Development Institute, Department of Public Finance and Social Policy, Namsejong-ro 263, Se-

jong, South Korea (email address: hyang@kdi.re.kr).

The Effect of War on Local Collective Action: Evidence from the Korean War

1 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) sta-

tioned 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

1Investigating the role of social division on conflict is important, given mounting evidence of the effectof contemporary social divisions on economic outcomes such as income, investment, corruption, institutionalefficiency 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).

1

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 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,

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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).

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

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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.

2 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

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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 Vietnam 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

2The 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).

3See Hastings (2010) for details on General MacAurthur’s Inchon landing on September 15, 1950.

5

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:

One of the most important consequences of the war was the hardening of ideo-

logical ... lines. The antipathy ... was deepened into a blood feud among family

members, extending from political leaders to the bulk of the ordinary people ...

The thirteen-hundred-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 vil-

lages. Accusations were typically made by village members, and people who were labeled as

anti-communists were executed onsite, often by their accusers (Park, 2005). Due to the pres-

ence 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:

4UN Forces did not take part in eliminating NKPA troops hiding in the mountains. The U.S. Joint Chiefsof Staff issued a directive to the Chief of the UN Command that “guerrilla activities should be dealt withprimarily by the forces of the Republic of Korea, with minimum participation by United Nations contingents(Schnabel, 1972, p. 183).”

6

“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 anti-communist 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.

3 Empirical Strategy

To identify the effect of conflict on community cooperation and population trends, I em-

ploy two different specifications. First, to estimate the effect of war on the propensity for

cooperation within community, I use the following cross-sectional empirical specification:

cooperationi = α + β conflicti +Xiγ + θc + ei, (1)

where cooperationi is the measure of cooperation in township i, conflicti 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

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provides a consistent estimate of β.5

Second, to investigate whether population which experienced a short-term reduction dur-

ing 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 con-

flicts. 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 +Xitν + 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 6= 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 6= 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.

4 Data

Primary data sources for the analysis are population censuses in various years and the New

Village Comprehensive Survey (NVCS).

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

5While it is possible that the NKPA chose hiding places based on town characteristics, such as overalldegree of politically left-leaning tendencies. However, during the war, it might be difficult to acquire accurateinformation on political preference of residents. Moreover, the urgency of finding hiding place during thewar may resulted in more random choice of hiding locations. Perhaps the most important determinants ofthe choices of hiding places would be ruggedness and altitudes which may prevented easy access from UNforces. I plan to include extensive geographic controls.

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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, gen-

der, 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). (3)

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 migra-

tion 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, al-

most 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

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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 em-

pirical strategy of including 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

6If the reduction in population during the war captures the severity of conflict, I expect that ∆pop49,55is relatively uncorrelated with ∆popt, the population changes in periods before and after the war pe-riod. I calculate corr(∆popt−1,∆popt) for every population census year data from 1925 to 1990. I findthat corr(∆pop44,49,∆pop49,55) is not only approximately zero, but also it has lowest value among allcorr(∆popt−1,∆popt) from other census periods.

7Village council members decided how to use cement then decided the usage of cement through votesfrom the head of each village household.

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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

village-level 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 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

8It was particularly difficult to donate private agricultural field for road improvement, such as wideningvillage road because the average cultivated area was already quite low. Korea had a successful land reformsin the late 1940s. Each farmer could own land only up to 3 hectares.

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which provides information at town level which is the lowest administrative unit. Urban re-

gions 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.

5 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).

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

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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.

9The results in this paper contrast with existing literature on limited long-run impacts through destruc-tions in physical capital (Davis and Weinstein, 2002; Brakman et al., 2004; Miguel and Roland, 2011).

13

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 β 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

10Instrument variable strategy will alleviate this concern in more systematic way. Work on IV strategyis on progress.

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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).

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 conver-

gence 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

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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 con-

verge to pre-war population trend. The table shows the estimates of δt from various specifi-

cations. 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 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

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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 5 and 6 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, the sign of, and possibly the magnitude of, the estimated coefficients, β, would

also have been similar across provinces.

6 Conclusion

In this paper, I find evidence of persistent economic and social consequences of war. Specif-

ically, 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

17

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 major 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.

18

Figures and Tables

Table 1: Descriptive statistics

obs mean SD min max

Independent Variable

public use 216 0.477 0.109 0.139 0.921

Explanatory Variable

∆pop4955 220 0.001 0.080 -0.381 0.213

Control Variables

population 1949 227 11613 5783 4046 60251illiteracy rate 227 0.799 0.047 0.639 0.896% ag occupation 227 0.853 0.097 0.400 0.960family clan Herfindahl Index 227 0.008 0.012 0.000 0.126% Japanese pop 227 0.010 0.016 0.000 0.111% native born 227 0.736 0.088 0.339 0.955% single individuals 227 0.479 0.017 0.437 0.521% individuals w/ age 0-14 227 0.403 0.014 0.360 0.438% individuals w/ age 15-24 227 0.171 0.008 0.143 0.202

19

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∗∗∗ 0.30*** 0.20** 0.16*(0.11) (0.11) (0.08) (0.09)

log(population 1949) -0.03 -0.08*** -0.29**Illiteracy rate 0.01 0.24% ag occupation -0.19 -0.09Family clan Herfindahl Index -0.13 0.29% Japanese pop 1.47 1.59% native born 0.44*** 0.19% single individuals -0.19 -0.71% individuals w/ age 0-14 0.34 0.73% individuals w/ age 15-24 1.40 0.75

County FE N N N YObservations 216 216 216 216R-squared 0.04 0.05 0.13 0.47

Selection ratio: 1.27 (βROLS, β

FFE), 1.99 (βR

OLS, βFOLS), 5.06 (βR

FE, βFFE)

Notes: Robust standard errors in parentheses. Standard errors in Column 4 are clustered at thecounty level. Data are from population censuses and the New Village Comprehensive Survey. Thepublic use variable equals one if a village used cement from the government to produce village levelpublic 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 theweight. ∆pop49,55 is changes in town population between 1949 and 1955. This measure is a proxyfor the severity of conflict within a township. Selection ratios are based on (Altonji et al., 2005).* p < 0.1, ** p < 0.05, *** p < 0.01

20

Table 3: Falsification checks using ∆pop in pre- & post-war periods

Dep. var.: public use

Explanatory variable β1 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 regressionswith the specification identical to Table 2 Column 4 except the regressor. Each row shows thechanges in population in various years as regressors. For example, ∆pop 1925-1930 is a measure ofpopulation changes between year 1925 and 1930.* p < 0.1, ** p < 0.05, *** p < 0.01

21

Table 4: Effect of war on population trends

Dep. var.: log(population)(1) (2) (3)

Before the War

treatment*year1930 -0.02 -0.02 -0.02treatment*year1935 -0.05 -0.05 -0.05∗∗∗

treatment*year1944 -0.02 -0.02 -0.02treatment*year1949 -0.03 -0.03 -0.03

After the War

treatment*year1955 -0.15∗∗ -0.14∗∗ -0.14∗∗∗

treatment*year1960 -0.14∗∗ -0.13∗∗ -0.13∗∗∗

treatment*year1966 -0.13∗ -0.13∗∗ -0.12∗∗∗

treatment*year1970 -0.15∗∗ -0.15∗∗ -0.14∗∗∗

treatment*year1975 -0.14∗∗ -0.14∗∗ -0.14∗∗∗

treatment*year1980 -0.19∗∗ -0.19∗∗∗ -0.19∗∗∗

treatment*year1985 -0.19∗∗ -0.21∗∗∗ -0.21∗∗∗

treatment*year1990 -0.16∗ -0.18∗∗ -0.17∗∗∗

Controls N Y YCounty FE N N Y

Observations 2808 2808 2808R-squared 0.21 0.48 0.52

Notes: Robust standard errors in parentheses. Standard errors in Column 3 are clustered at thecounty level. Data are from population censuses and the New Village Comprehensive Survey. Iconstruct a panel data set in which a township has multiple observations for each years. The treat-ment dummy equals one if a township experienced negative growth between just before and justafter the war (∆pop49,55) and zero otherwise. The treatment group experienced relatively moreconflict 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 theaverage population differences in two groups. Controls variables are identical to Table 2.∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

22

Figure 1: Location of South Jeolla

South Jeolla province is located in southwestern part of South Korea. The province is highlighted in graycolor in the map. The map shows township boundaries.

23

Figure 2: The Korean War and UN Forces

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

24

Figure 3: Major battle sites

Note: The numbers in the map show the location of major battles between the NKPA and UN Forces duringthe Korean War. South Jeolla province (southwestern region) escaped major battles.Source: The 8th Army Staff Historian’s Office (1972)

25

Figure 4: Severity of conflicts

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

26

Figure 5: Effect of conflict on public use in South Jeolla

.2.4

.6.8

1public_use

-.4 -.2 0 .2Δpop4955

The figure shows bivariate relationship between ∆pop1949,1955 and public use in South Jeolla province.

27

Figure 6: Effect of conflict on public use in North Kyungsang

.2.4

.6.8

1pu

blic

_use

-.4 -.2 0 .2 .4 .6Δpop4955

β_hat = -0.036, s.e. = 0.146, R2 = 0.0007

The figure shows bivariate relationship between ∆pop1949,1955 and public use in North Kyungsang province.

28

Figure 7: Effect of conflict on population trend

-.3-.2

-.10

coef

f. of

inte

ract

ion

term

s

1930 1940 1950 1960 1970 1980 1990year

The graph shows that the reduction in the civilian population caused by the war did not converge back topre-war trend after 40 years. The solid line shows the differences in population between treated group andcontrol group by plotting the estimated coefficients of the interaction term, treatment · year dummy. Thedotted lines indicate the 95% confidence interval. Treated group is defined as townships that experienceda negative population growth during the war. Townships in control group did not experience populationreduction. 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 duringthe war persisted 40 years after the war.

29

Figure 8: The Pusan Perimeter

Note: The Pusan Perimeter shown in the map is the battle lines between the NKPA and UN forces. Thehighlighted region in the southeastern part of the map indicates the only part of South Korea which was notoccupied by the NKPA.Source: Stueck (2002)

30

Figure 9: Map of North Kyungsang province

North Kyunsang province is located in southeastern part of South Korea. The province is highlighted in redcolor in the map. The map shows township boundaries.

31

Figure 10: Effect of bombing on population trends

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

32

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