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Long-Term Effects of the Korean War on Economic and Health Outcomes

Chulhee Lee Department of Economics Seoul National University

chullee@snu.ac.kr

November 2010 Preliminary Draft

Not to be cited without permission

Abstract This paper explores how prenatal exposure to the disruption in the early stages of the Korean War affected various socioeconomic and health outcomes at older ages. A number of socioeconomic outcomes, including educational attainment and the quality of occupation around age 50, were significantly lower for the birth cohorts who were in utero during the first nine months of the war. The disadvantages in socioeconomic outcomes experienced by this birth cohort are particularly large for those who were born in the Central Region that was more seriously devastated by the war than were other regions. Likewise, the cohorts who were in fetus at the early stages of the war exhibit poorer health outcomes at older ages, such as the increased probabilities of reporting bad health, being disabled, having limitations in daily activities, and stunting in height. These results support the fetal-origins hypothesis that has been established by recent studies that link many of the degenerative conditions of old age to in utero exposure to infectious disease, malnutrition, and other types of biomedical and socioeconomic stress. This study also suggests that early-life health is an important determinant of socioeconomic outcomes at older ages, and that the true costs of a war could be larger than suggested by the physical and human losses that occurred during the war, if its long-term consequences are considered.

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1. Introduction

A series of research across various disciplines have suggested that in utero exposure to

negative health shocks have strong and persistent effects on health and socioeconomic outcomes at

older ages. Since David J. Barker and his colleagues developed and popularized this argument in the

1990s, widely known as the fetal origins hypothesis (Barker 1992, 1994), a voluminous literature has

been accumulated, providing a variety of evidence in favor of this thesis (Currie and Hyson 1999;

Chay and Greenstone 2003; Behrman and Rosenzweig 2004; Black, Devereux, and Salvanes 2007;

Currie and Moretti 2007). In particular, it is remarkable that a growing number of studies have offered

semi-experimental evidence in recent years, investing the long-run consequences of some

exogenously-generated shocks to fetal health, such as the 1918 Pandemic Influenza (Almond 2006,

Almond and Mazumder 2005), the Dutch Famine (Neugebauer, Hoek, and Susser 1999; Roseboom,

Meulen, and Ravelli et al. 2001; Bleker et al. 2005), the Chinese Famine (St. Clair et al. 2005; Luo,

Mu, and Zhang 2006; Meng and Qian 2006; Chen and Zhou 2007; Almond et al. 2010), and the

Chernobyl disaster (Almond, Edlund, and Palme 2009).

The Korean War (1950-1953) offers a unique opportunity to examine the long-term effects of

disruptions caused by the conflicts, such as arduous refugee experiences, suffering under the North

Korean occupation, hunger, and direct exposure to combats. Though the war lasted more than three

years, major war damages experienced by civilians concentrated in the first nine months following the

sudden invasion of North Korea (late June 1950 to late March 1951), during which the front line

moved back and forth rapidly and unexpectedly (Halberstam 2007; Chung 2010; Yang 2010).

Furthermore, the severity of wartime experiences differed considerably by the region of residence.

The residents of the Central Region were hit particularly hard by the war, because the region is closer

to North Korea, and it was captured by the enemy twice. As will be discussed below in detail, these

features of the war help identify the effects of the war on maternal and fetal health by comparing the

adult outcomes across birth cohorts.

Taking advantages of these features of the Korean War, this paper explores how in utero

exposure to the war-related disruptions affected various adult outcomes in South Korea. For this

purpose, it is examined how various measures of socioeconomic performances and health at older

ages differ by the timing and place of birth. More specifically, it is whether the cohorts who were born

in 1950 or 1951 (and thus in fetus during the first nine months of the war) reveal discontinuous cohort

effects, and whether these cohort effects are more strongly revealed for those who were born in the

Central Region. Three micro datasets, including the micro samples of the Censuses for 2000 and 2005,

and the Korean Longitudinal Study of Aging (KLoSA), are used for constructing variables on adult

outcomes.

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The evidence provided below tends to support the fetal origins hypothesis. A number of

socioeconomic outcomes, including educational attainment and the quality of occupation around age

50, were significantly lower for the birth cohorts who were in utero during the first nine months of the

war. The disadvantages in socioeconomic outcomes experienced by this birth cohort are particularly

pronounced for those who were born in the Central Region. Likewise, the cohorts who were in fetus at

the early stages of the war exhibit poorer health outcomes at older ages, such as increased

probabilities of reporting bad health, being disabled, having limitations in daily activities, and

stunting in height.

2. Long-Term Consequences of In Utero Influences

It has been established by research across a number of disciplines that early-life health and

circumstances play an important role in determining health and economic outcomes at older ages.1

It is still a matter of debate if there is a certain age that is particularly critical for determining

health and human capital development over the life course. The findings that the effects of health and

investments in early childhood persist or even increase in magnitude as individuals age suggest that

the early years in one’s life is a distinct stage of health development in a sense that health shocks or

health investments that occur in these years are more influential and cannot be fully remedied by

compensatory health investments made at older ages.

A

series of studies by physicians and epidemiologists links many of the degenerative conditions of old

age to exposure to infectious disease, malnutrition, and other types of biomedical and socioeconomic

stress in utero and in the early years of life. Recent research by economists suggest that early-life

circumstances have strong and persistent influences on human capital accumulation and labor-market

performances, and that these effects are possibility mediated by deterioration in health and cognitive

ability.

A group of researchers pays particular attention to the long-term consequences of in utero

influences. In the 1990s, David J. Barker and his colleagues developed and popularized the argument,

widely known as the fetal-origins hypothesis, that disruptions to the prenatal environment are related

to various chronic health conditions at older ages, including coronary heart disease and diabetes

(Barker 1992, 1994). Of the two types of determinants of health in early ages, namely, post-natal

conditions and the initial health endowment (formed in utero), this hypothesis emphasizes the latter.

The fetal-origins hypothesis has been widely accepted by physicians and epidemiologists, and, as a

consequence, appeared in medical textbooks (Rudolph et al. 2003, Warrell et al. 2005).

1 See Almond and Currie (2010) for a comprehensive survey of the literature.

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Over the last two decades, a wide variety of evidence pertaining to the fetal-origins

hypothesis has been suggested. Experimental studies using animals provide evidence that maternal

malnutrition has a causal effect on subsequent health. A voluminous literature studies health and

socioeconomic consequences of low birth weight (LBW), a proxy of exposure to malnutrition,

infection, or toxic substances while in utero. Most of these studies find significant negative effects of

LBW on human capital accumulation and socioeconomic status as well as health outcomes (Currie

and Hyson 1999, Behrman and Rosenzweig 2004,, Black, Devereux, and Salvanes 2007, Currie and

Moretti 2007). Based on analyzing a Norwegian twin sample, for example, Black, Devereux, and

Salvanes (2007) found that LBW had long-run adverse effects on adult height, IQ, earnings, and

education. Currie and Moretti (2007) provided that LBW among individuals born in California had

modest but statistically significant negative impacts on educational attainment and the likelihood of

living in a wealthy neighborhood.

A possible concern confronted by studies of size at birth and subsequent outcomes is that the

positive correlation between measures of early-life and adult health could be biased by omitted

variables, such as genetic factors or post-birth investments.2

Almond (2006) used the 1918 influenza pandemic as a natural experiment for testing the

fetal origins hypothesis. His study found that cohorts in utero during the pandemic displayed reduced

educational attainment, increased rates of physical disability, lower income, lower socioeconomic

status, and higher transfer payments compared with other birth cohorts. Almond and Mazumder (2005)

discovered that cohorts in utero during the pandemic exhibit impaired health outcomes relative to

cohorts born a few months earlier or later, 65 to 80 years after the event. Kelly (2009) found that

prenatal exposure to 1957 “Asian Flu” in Britain diminished test scores.

A new line of research has attempted to

addresses this potential problem by exploiting unique opportunities offered by natural experiments in

which individuals of a particular background or cohort were randomly exposed to a type of disruption

in utero. If the probability of having the negative health shock in utero is uncorrelated to unobservable

determinants of health, estimated effects of the shock is not subject to omitted variable bias.

Historical famines have been studied as natural experiments, too. The cohort in utero during

the 1944 to 1945 Dutch Famine exhibited a broad spectrum of health problems at middle age, such as

poor self-reported health (Roseboom, Meulen, and Ravelli et al. 2001), coronary heart disease

morbidity (Roseboom, Meulen, and Ravelli et al. 2001, Bleker et al. 2005), and adult antisocial

2 Almond, Chay, and Lee (2005) compared the hospital costs, health at birth, and infant mortality rates between heavier and lighter infants from all twin pairs born in the United States to reduce potential bias arising from omitted variables, such as genetic factors. They obtained a much smaller effect of LBW on infant mortality and other health measures from a sample of twins.

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personality (Neugebauer, Hoek, and Susser 1999). Research on the Chinese Famine from 1959 to

1961 has reported a number of negative health and socioeconomic consequences of the famine,

including heightened risk of schizophrenia (St. Clair et al. 2005), obesity among women (Luo, Mu,

and Zhang 2006), height reductions (Chen and Zhou 2007), and reductions in hours of work (Meng

and Qian 2006). More recently, Almond et al. (2010) reported that higher famine intensity was

associated with greater risk of being illiterate, out of labor force, marrying later (for men), and

marrying spouses with less education (for women).

Impacts of prenatal exposure to pollution have been examined, exploiting some natural

experiments. Chay and Greenstone (2003) used variations in the air pollution across counties

exogenously produced by the implementation of the Clean Air Act of 1970 and the recession of the

early 1980s. They found that decline in particulates significantly reduced infant death rates. Almond,

Edlund, and Palme (2009) studied the effect of pollution from the Chernobyl disaster on the Swedish

cohort who was in utero at the time of disaster. They found that radiation exposure significantly

reduced academic performances.

3. The Korean War (1950-1953)

The Korean War began with North Korea’s surprise invasion of South Korea on June 25,

1950, and lasted three year until an armistice agreement was signed on July 27, 1953. Vastly

unprepared in terms of equipment and training, South Korean troops were repeatedly defeated, and

forced to retreat southward. Seoul, the South Korean capital, was captured by North Korean forces

just in three days. The UN troops arrived in the South upon the UN Security Council’s resolution

recommending military assistance to South Korea, but could not hold off the advance of the North

Korean Army. By late August 1950, in just two months after the war broke out, North Korea had come

to occupy about 90% of the entire Korean Peninsula, except for the vicinity of Pusan city, located in

southeast Korea.

In the resulting battle of Pusan Perimeter from August to mid September 1950, the Korean

and US forces managed to withstand fierce North Korean attacks meant to capture the entire

peninsular. On September 15, the UN troops countered with a successful landing at Inchon, which

forced the North Korean Army in the South to retreat northward to avoid a siege. UN troops liberated

Seoul on September 27, and continued northward to capture Pyongyang city, the North Korean capital

(October 19), and eventually reach the Amnok (Yalu) River that divides North Korea and China.

By late November 1950, it seemed that unification of the peninsula was only a matter of time.

However, Chinese communist forces, which had moved into North Korean territory secretly,

ambushed the UN troops, forcing them to fall back. Seoul was lost again on January 4, 1951, and a

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large number of refugees from both the South and the North had to leave their hometown, following

the retreating soldiers. Regrouped and reinforced, the UN forces stabilized the battlefront and fought

back. On March 14, 1951, the UN troops expelled the North Korean and Chinese armies from Seoul.

This was the city’s fourth conquest in just nine months. After exchanging several offensive actions

around the 38th parallel between March and June, the war fell into the stalemate that lasted until the

armistice of July 1953.

The Korean War is the bloodiest event that Korea has ever experienced in modern era, and

one of the major international wars that were fought after the World War II. Statistics on human losses

caused by the war are not highly reliable, and greatly differ by source. During the three years of

hostilities, according to the official statistics of South Korea published in 1955, the number of civilian

casualties totaled 990,968, including 373,599 persons killed, 229,625 injured, 84,532 abducted, and

303,212 missing in South Korea alone (Chung 2010). It is likely that these estimates are understated.

Military casualties were heavy, too. It is estimated that 36,940 US servicemen and 245,000 to 415,000

South Korean soldiers were killed while in service during the war.

In spite of its historical significance, the Korean War has drawn much less attention from the

people outside Korea, compared to World War II or the Vietnam War, even from the main actors

directly involved in the campaign, such as the United States. The Forgotten War, the title of one of the

best books written on it, befits its current status. There is a relatively rich literature on political

circumstances surrounding the origin, progress, and aftermath of the war (Cumings 1981, 1990, 2010).

Military actions are reasonably well documented, too (Halberstam 2007). However, social and

economic aspects of the conflict remain poorly understood. In particular, it is not well documented

how the war affected everyday lives of civilians, and how wartime experiences influenced the

subsequent health and socioeconomic performances of survivors over the life course. Studying social

history of the Korean War based on narratives of survivors has recently emerged as a promising way

of filling this gap in the literature (Kim 2009; Pyo et al. 2003). But it appears that its main focus has

been civil disputes and massacres caused by ideological conflicts.

Although quantitative evidence and systematic documentations are hard to come by, it is

not too difficult to imagine that the vast majority of Koreans went through horrible ordeals of various

kinds during the war. With the front line being pushed back and forth in a dramatic manner,

practically the entire Korean Peninsula experienced the ravage of war. Because of chaotic situation in

the early stages of the war, civilians were directly exposed to combat activities, which led to

tremendous toll in human life. In addition to physical wounds and illnesses, a few survivors of the war

may have suffered from mental damages such as post-traumatic stress disorder (PTSD) syndrome, as

in the case of soldiers who experience bloody battles.

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In the first several months of the war, a large number of South Koreans remained in the areas

that were occupied by North Korean forces. It is still uncertain how many civilians failed to escape

from the North Korean control. It is estimated that about one million (or 50% of) residents in Seoul

remained in the city during the first three months of captivity (Yang 2010. P. 576). Many of the people

under the communist control suffered from such atrocities as massacre, torture, imprisonment, and

abduction. Because of the learning from this terrible experience under the first occupation as well as

more organized efforts of evacuation by the government, virtually all residents of the Central Region

fled southward when the UN troops were pushed back again by the advance of the Chinese Army

from late 1950 to early 1951.

Refugee experience was no less arduous. Many refugees moved on foot for a few days with

carrying as much belongings as possible. People who were fortunate to take a train had to put up with

being packed into overly crowded cabins, and passengers who dangerously occupied the roof were

troubled by suffocating smoke blown out of the steam locomotive, especially when the train passed

through a tunnel. Away from home, the refugees had to be accustomed to chronic hunger and sleeping

without a proper shelter. Refugees who joined the retreat pushed by the Chinese attack suffered the

bleeding cold of the winter.3

The small areas in southeast Korea that managed to avoid the North’s occupation, especially

two large cities Pusan and Daegu, were the main destinations of refugees. The city of Pusan, for

example, with a population of about 400,000, became home to more than one million people in just

several months. Most refugees lived in a shabby shelter made with cardboard boxes, wood planks, and

makeshifts tarp covering. Some people who had close relatives or friends living there fared a little

better, cramming themselves into small rooms. Emergency aids provided by the UN forces and

distribution of rations by the government prevented mass starvation of refugees, but still could not

keep them from suffering from severe food shortage.

4. Data and Methods

A. Strategy for Identifying the Effect of the War

It is hypothesized in this study that various disruptions caused by the Korean War adversely

affected maternal and fetal health. A central issue related to examining this hypothesis is how to

measure the size of war-related negative health shock. There are no data available that can be used for

linking adult health and socioeconomic outcomes to detailed wartime experiences. The key strategy

for measuring the impact of the war employed in this study is to investigate how socioeconomic

3 See memoirs by Korean writers including Kim (2006) and Chun (2006).

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outcomes of individuals at old age (available from various micro data) differed by the timing and

place of birth.4

It is impossible to compare two birth cohorts while holding all other factors constant.

Because of the linear dependence of period, age, and cohort, changing the cohort necessarily changes

either age or period. Furthermore, given that most socioeconomic outcomes tend to change across

cohorts only gradually, it is difficult to identify a cohort effect from other smooth effects, such as the

effect of secular income change over time.

But one feature of the Korean War helps disentangle the cohort effect: civilians were directly

exposed to major disruptions of the war for a relatively brief period. Though the war lasted more than

three years, major war damages experienced by civilians concentrated in the first nine months

following the sudden invasion of North Korea (late June 1950 to late March 1951). During these

months, the front line moved back and forth rapidly and unexpectedly, forcing many civilians go

through either occupation by North Korea or refugee experiences. After the spring of 1951, on the

other hand, the war went into the stalemate around the 38th parallel, and the lives of civilian living in

the south of the battlefield became more or less stabilized. Thus, if maternal and fetal health was

actually affected by the Korean War, the adult outcomes should be particularly worse for the cohorts

who were born from the summer of 1950 through 1951.

The severity of wartime experiences differed by the region of residence, which offers another

opportunity to identify the effect of the war on maternal and fetal health. It is likely that wartime

experiences were particularly arduous for the residents of the Central Region, the northern provinces

of South Korea (including Seoul, Kyunggi, Kangwon, Chungbuk, and Chungnam), for the following

reasons. First, they had less time to respond to the surprise attack by the North Korean forces,

compared to the residents of the southern region because of its proximity to North Korea. As a

consequence, a larger fraction of the people living in the Central Region suffered from the occupation

by North Korea. Second, if they managed to escape from the communist control, the refugees from

the central region had to move a longer distance to get to the Pusan Perimeter. Finally, since a large

part of the Central Region was occupied by North Korea twice, many of the residents of the region

had to be displaced twice. Therefore, if the war negatively affected fetal health, it should be more

strongly revealed among those who lived in the Central Region when the war broke out.5

4 This closely follows the methods employed in major recent studies that examine the impact of the 1918 Pandemic Influenza by looking into the differences in adult outcomes by birth cohort (Almond 2005, 2006).

5 The place of birth could be different from the place of residence at the outbreak of the war because

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B. Data and Variables on Adult Outcomes

Three micro datasets, namely the 2% micro samples of the population censuses (Census,

hereafter) for 2000 and 2005, and the first wave of the Korea Longitudinal Study of Aging surveyed

in 2006 (KLoSA, hereafter), are utilized for the present study. Each of these sources has own

advantages and drawbacks in terms of the sample size and variables offered. A major advantage of the

micro samples of population census is large sample size. This allows me to estimate accurately adult

outcomes by birth cohort and place of birth. On the other hand, Census has two major limitations for

the purpose of this study. First, it only offers a restrictive set of variables on personal characteristics,

especially on health outcomes. Second, it does not report the month or quarter of birth, making it

difficult to identify the timing and duration of exposure to wartime disruptions in fetus.

Analyses offered in this study regarding the impact of the war on socioeconomic outcomes at

older ages largely rely on the 2000 census, because it provide a richer set of variables on adult labor-

market outcomes, and the cohorts who were born during the Korean War reached their prime ages

(ages 47 to 50) in 2000 in terms of socioeconomic status. Furthermore, the 2000 census reports

detailed geographic codes for the place of birth, which enables me to study how the impact of war

differed by the region of birth. The following measures of socioeconomic outcomes are considered: (1)

the years of schooling, (2) the probability of entering college, (3) the probability of having primary

school education or less, (4) the probability of having a professional job, (5) the probability of having

a manual job, and (6) the probability of using internet.

The 2000 census provides information on functional limitations only for individuals aged 65

and older. Thus, measures of adult health are obtained from the 2005 Census that reports whether a

person has disability and functional limitation for all respondents. More specifically, the following

two variables on health are considered: (1) the probability of having a disability, and (2) the

probability of suffering from limited daily activities. Since the micro sample of the 2005 census does

not report the place of birth, comparing these health outcomes across birth cohorts cannot be done

separately for each region of birth.

The KLoSA is perhaps inappropriate for the purpose of this study because its sample size

(about 10,000 persons aged 45 and older in 2006) is too small. The number of single-year birth cohort

by sex who were born during the Korean War ranges from 150 to 250. But it provides a much larger

sample of cohorts born around the war than other Korean micro data that contain more detailed

a few persons were perhaps born while taking refuge. However, since the majority of refugees returned home shortly after the North Korean forces were repelled, the place of birth should be a reasonably good proxy for the place of residence in June 1950.

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information on personal characteristics (e.g. the Korean Labor and Income Study). It also offers

highly rich information on health outcomes. Furthermore, since the month of birth is available from

the source, it is possible to examine how health outcomes change by the year and quarter of birth. For

these reasons, analyses of health outcomes are supplemented by the results based on the KLoSA in

spite of the weakness arising from the small sample size. The following measures of health outcomes

are obtained from this source and analyzed: (1) the score of self-reported health (one to five,

representing, respectively, the best and worst health status), (2) the probability of reporting poor

health (self-reported health is four or five), (3) the probability of having any disability, (4) height, and

(5) the probability of having shorter height (within the lowest quintile of height distribution). Table 5

presents sample means of the socioeconomic and health outcomes of the cohorts born from 1945 to

1959 for the entire sample, males, and females.

C. Two Types of Evidence for Cohort Effect

Two types of evidence for cohort effect are presented below. First, the raw data on adult

outcomes for each birth cohort are graphically displayed. It will be determined whether the cohorts

who were born in 1950 or 1951 reveal discontinuous cohort effects. For measures of socioeconomic

outcomes drawn from the 2000 Census (that reports the place of birth), the results for individuals who

were born in the central region are highlighted.

Second, deviations of census outcomes from smooth cohort trends are estimated

systematically for cohorts born between 1945 and 1959, employing the following regression equation:

yi = α + � βtIt + γ1YOB + γ2YOB2 + εi

1954

t=1950

where yi denotes the census outcome for individual i, It denotes the dummy variable that has value

of one if the individual was born in the year t, and zero otherwise, YOB denotes the last two digits of

birth year, and βt measures the departure of outcomes for the birth cohorts who were in utero during

the Korean War from the quadratic cohort trend. Similarly, deviations of KLoSA health outcomes

from the quadratic cohort trend are estimated as follows, including dummy variables for the quarter of

birth for the cohorts who were born between the third quarter of 1950 and the second quarter of 1954:

yi = α + � βtIt + γ1YOB + γ2YOB2 + εi

1954Q2

t=1950Q3

5. Major Preliminary Findings

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A. Socioeconomic Outcomes: Result from the 2000 Census

Figures 1 to 3 plot three measures of educational attainment for males and females who

were born in the Central Region from 1945 to 1959, namely, the years of schooling, the percentage of

individuals who entered college, and the percentage of people with primary schooling or less,

obtained from the 2000 Census. Appendix Figures A1 to A3 display these education measures for the

entire sample of cohorts regardless of their place of birth.

There was a strong upward trend in educational attainment for these cohorts. It is apparent

from the figures that the 1950 and 1951 birth cohorts lie off this steady trend. In particular, the

deviation of the 1951 cohort from the tendency of long-term increase in schooling is clearly revealed.

The 1951 cohorts received approximately two months less schooling than the cohort born in 1949.

They were less likely to enter college, and more likely to achieve the lowest educational attainment.

The departure of the 1951 birth cohort from the trend is also observed for the entire sample

(Appendix Figures A1 to A3), although the magnitude of the deviations is much smaller. Educational

attainment either stagnated (entrance to college or higher for males and females, and primary school

or less for females) or improved more slowly than predicted by the long-term trend (year of schooling

for males and females, and primary school or less for males) from the 1950 cohort to the 1951 cohort.

Figures 4 to 5 present two measures of success in the labor market for the male cohorts who

were born in the Central Region from 1945 to 1959, namely, the percentages of employed people who

had a professional occupation and those who were manual laborers, computed from the 2000 Census.6

Table 2 reports the estimates of βt in the above equation that show the magnitude of the

departure of outcomes for the male cohorts born between 1950 and 1954 from the quadratic cohort

trend. For binary outcomes (Panels B to F), logistic specification is employed. For the continuous

outcome variable (Panel A), OLS regression is conducted. The regression results confirm the

disadvantages experienced by the individuals who were born in 1951, especially those who were born

Because of the low labor force participation of females in these cohorts, the analysis is limited to

males. The same measures are presented for the entire sample in the appendix (Appendix Figures A4

to A5).

6 These measures of labor-market outcomes have been computed for the entire sample, including non-participants in the labor market and the unemployed. The unconditional percentages provide similar implications, implying that the results are not primarily driven by decisions on labor-force participation.

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in the Central Region, in educational attainment, occupational choice, and internet use.7

The regression results for females, reported in Table 3, are also matched well with the

patterns of cohort effects displayed by the figures. The 1951 birth cohort, born in the Central Region,

exhibits clear disadvantages in all three measures of educational attainment (Panels A to C of Col. 2).

Unlike males, however, the female cohort born in 1951 is not significantly different from other

cohorts in terms of internet use and the two measures of the quality of occupation. If born in the

Southern Region, the birth in 1951 had no negative effect on female socioeconomic outcomes.

For all six

measures, the 1951 cohort effect is statistically significant where the sample is limited to people born

in the Central Region (Col. 2 of Table 2). If the entire sample is used (Col. 1), the 1951 cohort effect is

still statistically significant (although much smaller in magnitude) for five out of six socioeconomic

outcomes. In sharp contrast, for individuals who were born in the Southern Region, whether occupied

by North Korea or not, the 1951 cohort effect is insignificant.

B. Health Outcomes: Results from the 2005 Census and the 2006 KLoSA

Figures 6 and 7 present the following two health outcomes of the male and female cohorts

born between 1945 and 1959, available from the 2000 Census: the percentages of individuals who

have any disability and of those whose daily activity is limited. Since the 2000 census does not

identify the place of birth, the patterns of cohort effects on health cannot be compared across the

regions of birth.

The plots suggest that, for males, the 1950 and 1953 cohorts experienced deviations from the

quadratic trend in the prevalence rates of disability and limitations in activity. For females, on the

other hand, there were no clear departures of the Korean War birth cohorts from the long-term trends

in the probability of having any disability and limitation in daily activities.

These patterns of cohort effects are confirmed by the results of logistic regressions reported

in Table 4, in which the quadratic cohort trend is controlled for. The estimates of βt suggest that the

1950 male birth cohort was more likely to have any disability and to have any limitation in daily

activities than other birth cohorts. No statistically significant cohort effects are found for females.

A crucial drawback of the present analysis based on the 2005 Census is that the cohort effect

cannot be estimated separately for people who were born in the Central Region, making it difficult to

assess the true effect of the war on fetal health. To provide a rough idea about the significance of this

limitation, the two measures of health are compared across birth cohorts who resided in the Central

7 The negative effect of birth in 1951 on the quality of occupation is not fully explained by the disadvantage of the cohort in educational attainment. Even if education is controlled for, the 1951 cohort effect remains statistically significant.

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Region in 2005. Of course, the region of 2005 residence is at best a very inaccurate proxy of the

region of birth given that the geographic mobility since the Korean War was high.8

To supplement the results obtained from the 2005 Census, several health outcomes available

from the 2006 KLoSA are plotted for each birth quarter from 1948 to 1955, namely, the average score

of self-reported health, the percentage of each cohort reporting that their health is poor, and the

percentage of each cohort having any disability. Since the place of birth is not given in the KLoSA,

only the results for the entire sample are given.

Nevertheless, the

plots given in the Appendix Figures A6 to A7 suggest that the negative 1950 cohort effect is likely to

be greater for those who were born in the Central Region. At least for males, the departure of the

outcomes for the 1950 cohort is more pronounced if the sample is limited to the individuals living in

the Central Region.

Although the measures of health considered in the analysis greatly fluctuate across the

quarters of birth (perhaps because of the small sample size), the male cohort born in the second

quarter of 1951 clearly deviates from the long-term trends in all three health outcomes. In contrast, the

plots for females do not reveal any clear cohort effect.

Table 5 offers the results of regressions in which the dummy variables of birth in each

quarter from the third quarter of 1950 to the second quarter of 1954 as well as quadratic cohort trend.

In addition to variables on self-reported health and disability, two measures on height (height

measured in centimeter and the probability that height belongs to the lowest quintile) are also

considered. Logistic regressions are performed if a binary variable is used as the dependent variable

(Panels A to C), whereas OLS model is applied to a continuous dependent variable (Panels D and E).

The regression results for self-reported health and disability are consistent with the patterns

of cohort effects displayed by the figures. The male cohort who were born in the second quarter of

1951 were more likely to report poorer health (Panels A and D) and to have any disability (Panel B).

Among the Korean War male cohorts, those who were born in the fourth quarter of 1951 and of 1952,

and the third quarter of 1953 were more likely to have any disability. Females who were in utero for

any length of time during the war do not exhibit any sign of significant disadvantages in self-reported

health and disability.

Several male and female cohorts who were exposed to the war in utero appear to experience

8 A comparison of the regions of birth and of residence in 2000 based on utilizing the 2000 census suggests that about 90% of the individuals who were born in the Central Region were still living there in 2000. But these original residents of the Central Region accounted for only 58% of the entire population of the region because of the inflow of migrants from the Southern Region after the Korean War.

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stunting in height, too. Males born in the fourth quarter of 1951 or the first quarter of 1953 were

shorter than the long-term trend by more than 1.7 centimeter. Females born in the quarter following

the outbreak of the war experienced a reduction of average height by 1.4 centimeter. Females born in

the second quarter of 1951 were much more likely to belong to the lowest quintile of the height

distribution.

6. Summary and Discussions

The Korean War (June 1950 to July 1953) likely damaged maternal and fetal health of people,

especially in the first nine months following the surprise invasion of North Korea when the front line

moved back and forth in dramatic manners, forcing so many civilians to go through severe disruptions

caused by the war. Based on the growing literature on the fetal origins hypothesis, this paper

investigates how in utero exposure to such arduous wartime experiences as refuge, occupation, hunger,

and combat, affected socioeconomic and health outcomes at older ages.

The preliminary evidence on the long-term effects of the Korean War offered in the paper

tends to support the fetal origins hypothesis. A number of socioeconomic outcomes available from the

2000 Census were significantly lower for the birth cohorts who were in utero during the first nine

months of the war. For instance, the 1951 birth cohorts (both males and females) received

significantly less schooling than predicted by the smooth cohort trends in several measures of

educational attainment. Males who were born in 1951 were less likely to use internet, less likely to be

employed in a professional job, and more likely to be a manual laborer in 1951 around age 50 (in

2000) than other male birth cohorts, controlling for the quadratic cohort trends in these measures.

The disadvantages in socioeconomic outcomes experienced by the 1951 birth cohort are

particularly pronounced for those who were born in the Central Region, perhaps because the people

who resided in this region when the war broke out received the biggest collateral damages: a larger

fraction of its residents had to endure occupation by North Korea; the refugees from the region had to

move a longer distance; and it fell into the communist control twice.

Likewise, the cohorts who were in fetus at the early stages of the war show poorer health

outcomes at older ages. The 1950 male birth cohorts were more likely to be disabled and to suffer

from limitations in daily activities in 2005. The males who were born in the second quarter of 1951

were much more likely to report poor health and to have a disability in 2006. Korean War birth

cohorts also exhibit stunting in stature, too. It is likely that these negative impacts of in utero exposure

to the Korean War on health are particularly strong for the people born in the Central Region, as in the

case of socioeconomic outcomes.

The preliminary results of this paper provide new evidence pertaining to the fetal origins

15

hypothesis, because it offers a rare episode of long-run consequences of direct exposure to a war in

utero. So far, the major focus of previous studies has been on how famines, infections, and exposure

to toxic substances in utero affect adult outcomes. Some of the observed effects of the Korean War

may in part capture the effects of malnutrition and infection while in fetus. However, the extent of

malnutrition during the Korean War should be much milder than what people experienced in the

Dutch Famine or the Chinese Famine. Despite the vivid memory of hunger among many survivors, it

is difficult to find any case of civilian death caused by starvation. There are no records of major

epidemics of infectious diseases during the war, either. Although further research is required to

determine the main mechanisms by which the war damaged fetal health, it is possible that such

wartime experiences as suffering from atrocities under the North Korean occupation, physical and

mental hardships confronted while taking refuge, and direct exposure to bloody combats, may have

played important roles.

This study also provides semi-experimental evidence for the link between health and

socioeconomic status, which is not common for South Korea. In particular, there is little empirical

evidence regarding the long-run socioeconomic consequences of fetal health suggested for South

Korea. Of course, it will have to be determined by further research how wartime damages to maternal

and fetal health differed by socioeconomic backgrounds. Nor is it clearly understood how conceptions,

live births, and survivals of the cohorts born during the Korean War were selected. Considering the

unexpected outbreak and highly chaotic progress of the war, however, it is likely that variations in

fetal health of the individuals born around the conflict were exogenously determined. In this sense, the

present study offers stronger case of the link between early-life health and adult outcomes. It is likely

that maternal and fetal health in South Korea greatly improved after it recovered from the war-caused

destruction. Thus, other things being equal, the post-war generations will perhaps be healthier at old

age than the previous cohorts who experienced in utero the Japanese colonial occupation, the Second

World War or the Korean War.

This study suggests that military conflicts, especially if civilians are directly exposed to

combats as in the case of the Korean War, can bring long-term negative health and economic

consequences by adversely affecting maternal and fetal health. This implies that conventionally

estimated costs of historical wars could be understated. In general, only physical damages and direct

human losses, such as wartime deaths and wounds, are counted in assessing economic costs of a war

(Goldin and Lewis 1975). Some studies take into account longer-term effects of damages to health on

socioeconomic mobility (Lee 2005, 2008). If long-run adverse impact on the health and

socioeconomic outcomes of the next generation is considered, the true total costs of modern military

conflicts, which tend to expose many civilians to various disruptions caused by the fight, should be

16

substantially larger than suggested by conventional estimates.

References Almond, Douglas (2006): “Is the 1918 Influenza Pandemic Over? Long-Term Effects of In Utero Influenza Exposure in the Post-1940 Population,” Journal of Political Economy 114(4): 672-712. Almond, Douglas, Kenneth Y. Chay, and David S. Lee (2005): “The Cost of Low Birth Weight,” Quarterly Journal of Economics 120(3), 1031-1084. Almond, Douglas, and Janet Currie (2010): “Human Capital Development before Age Five,” NBER Working Paper No. 15827. Almond, Douglas, Lena Edlund, Hongbin Li, and Junsen Zhang (2010): “Long-Term Effects of Early-Life Development: Evidence from the 1959 to 1961 Chinese Famine,” in Takatoshi Ito and Andrew Rose eds., The Economic Consequences of Demographic Change in East Asia, Chicago: University of Chicago Press, 321-350. Almond, Douglas, Lena Edlund, and Marten Palme (2009): “Chernobyl’s Subclinical Legacy: Prenatal Exposure to Radio Active Fallout and School Outcomes in Sweden,” Quarterly Journal of Economics 124(4), 1729-1772. Almond, Douglas, and Bhashkar Mazumder (2005): “The 1918 Influenza Pandemic and Subsequent Health Outcomes: An Analysis of SIPP Data,” American Economic Review Papers and Proceedings 95(2), 258-262. Barker, David, J. P. (1992): Fetal and Infant Origins of Adult Disease, London: British Medical Journal of Publishing Group. Barker, David, J. P. (1994): Mothers, Babies, and Disease in Later Life, London: British Medical Journal of Publishing Group. Behrman, Jere R., and Mark R. Rosenzweig (2004): “Returns to Birth Weight,” Review of Economics and Statistics 86(2), 586-601. Black, Sandra E., Paul J. Devereux, and Kjell G. Salvanes (2007): “From the Cradle to the Labor Market? The Effect of Birth Weight on Adult Outcomes,” Quarterly Journal of Economics 122(1), 409-439. Bleker, O.P., T.J. Roseboom, A.C.J. Ravelli, G.A. van Montfans, C. Osmond, and D.J.P. Barker (2005): “Cardiovascular Disease in Survivors of the Dutch Famine,” In G. Hornstra, R. Uauy, and X. Yang, eds., The Impact of Maternal Malnutrition on the Offspring: Nestle Nutrition Workshop Series Pediatric Program, vol. 55, 183-195. Basel, Switzerland: Karger. Chay, Kenneth Y. and Michael Greenstone (2003): “The Impact of Air Pollution on Infant Mortality: Evidence from the Geographic Variation in Pollution Shocks Induced by Recession,” Quarterly Journal of Economics 118(3), 1121-1167. Chen, Y., and L.A. Zhou (2007): “The Long-Term Health and Economic Consequences of 1959-1961 Famine in China,” Journal of Health Economics 26(4), 659-681.

17

Chung, B. J. (2010): “The Types and Characteristics of the Civilian Casualties in South Korea during the Korean War,” in The Group for Korean Historical Studies, ed., The Korean War in Historical Perspectives, Seoul: Humanist (in Korean), 471-504. Chun, S. K. (2006): “The Korean War that I Experienced,” D. Park, ed., The One Hundred Scenes of the Korean War, Seoul: Nunbit (in Korean), 153-160. Cumings, Bruce (1981): The Origins of the Korean War, I: Liberation and the Emergence of Separate Regimes, 1945-1947. Princeton, NJ: Princeton University Press. Cumings, Bruce (1990): The Origins of the Korean War, II: The Roaring of the Cataract, 1947-1950. Princeton, NJ: Princeton University Press. Cumings, Bruce (2010): The Korean War: A History. New York, N.Y.: Modern Library. Currie, Janet, and Rosemary Hyson (1999): “Is the Impact of Shock Cushioned by Socioeconomic Status? The Case of Low Birth Weight,” American Economic Review 89(2), 245-250. Currie, Janet, and Enrico Moretti (2007): “Biology as Destiny? Short- and Long-Run Determinants of Intergenerational Transmission of Birth Weight,” Journal of Labor Economics 25(2), 231-264. Goldin, Claudia D., and Frank D. Lewis (1975): “The Economic Cost of the American Civil War: Estimates and Implications,” Journal of Economic History 35(2), 299-326. Halberstam, David (2007): The Coldest Winter: American and the Korean War, New York: Hyperion. Kelly, Elaine (2009): “The Scourge of Asian Flu: In Utero Exposure to Pandemic Influenza and the Development of a Cohort of British Children,” Institute for Fiscal Studies Working Paper 09/17, University College London. Kim, Dong Choon (2009): War and Society, Pajoo: Dolbege. (in Korean). Kim, Won Il. (2006): “Three Months in Seoul Under the North Korean Occupation,” D. Park, ed., The One Hundred Scenes of the Korean War, Seoul: Nunbit (in Korean), 33-43. Lee, Chulhee (2005): “Wealth Accumulation and the Health of Union Army Veterans, 1860-1870,” Journal of Economic History 65(2): 352-385. Lee, Chulhee (2008): “Health, Information, and Migration: Post-Service Geographic Mobility of Union Army Veterans, 1860-1880,” Journal of Economic History 65, 352-385. Luo, Z., R. Mu, and X. Zhang (2006): “Famine and Overweight in China,” Review of Agricultural Economics 28(3), 296-304. Meng, X., and N. Qian (2006): “The Long-Run Effect of Childhood Malnutrition: Evidence from China’s Great Famine,” Working Paper. Brown University. Neuzgebauer, R., H. Wijbrand Hoek, and E. Susser (1999): “Prenatal Exposure to Wartime Famine and Development of Antisocial Personality Disorder in Adulthood,” Journal of the American Medical Association 281(5), 455-462.

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Pyo, I, et al. (2003): War and People, Pajoo: Han-ul (in Korean). Roseboom, T. J., J. H. P. Meulen, C. Osmond, D.J.P. Barker, A.C.J. Ravelli, and O.P. Bleker (2001): “Effects of Prenatal Exposure to the Dutch Famine on Adult Disease in Later Life: An Overview,” Twins Research 4(5), 293-298. Rudolph, Colin D., Abraham M. Rudolph, Margaret K. Hostetter, George Lister, and Norman J. Siegel, eds. (2003): Rudolph’s Pediatrics, New York: McGraw-Hill. St. Clair, D., M. Xu, P. Wang, Y. Yu, Y. Fang, F. Zhang, X. Zheng, N. Gu, G. Feng, P. Sham, and L. He (2005): “Rates of Adult Schizophrenia Following Prenatal Exposure to the Chinese Famines of 1959-1961,” Journal of the American Medical Association 294(5), 557-562. Warrell, David A., Timothy M. Cox, John D. Firth, and Edward J. Nenz, Jr. (2003): Oxford Textbook of Medicine, Oxford, U.K.: Oxford University Press. Yang, Y. C. (2010): “The Conditions of Refugees and Aid Activities in Deagu Area during the Korean War,” in The Group for Korean Historical Studies, ed., The Korean War in Historical Perspectives, Seoul: Humanist (in Korean), 569-594.

19

Table 1 Sample Means of Socioeconomic and Health Outcomes of the 1945-1959 Birth Cohorts

Sample Mean for the 1945-1959 Birth Cohorts

All Male Female The 2000 Census

Years of Schooling 10.7 11.6 9.8 % College or higher 18.9 26.6 11.2 % Primary or lower 20.0 12.9 27.1 % Professional job 19.8 25.7 9.0 % Manual job 21.8 24.1 17.4 The 2005 Census % Disabled 5.6 5.3 5.8 % Activity limited 3.6 3.5 3.7 The 2006 KLoSA Self-reported health score 2.7 2.5 2.8 % Reporting poor health 19.0 14.4 22.8 % Disabled 5.4 7.5 3.6 Height in Cm 162.8 169.3 157.4 Shorter height 32.1 15.1 46.4 Source: The 2% samples of the 2000 and 2005 censuses; The first wave of the Korea Longitudinal Study of Aging (2006 KLoSA). Note: See text for the definition of the variables.

20

Table 2 Deviation of the 1950-54 Male Birth Cohort Labor-Market Outcomes in 2000 from 1945-1959 Trend

Outcome

Regional Classification (1)

South Korea (N=86,777)

(2) Central Region

(N=26,000)

(3) Southern Region,

Occupied (N=30,207)

(4) Southern Region,

Unoccupied (N=7381)

A. Years of schooling Born in 1950 -0.050 -0.087 0.052 -0.113 Born in 1951 -0.135 ** -0.411 *** 0.040 0.014 Born in 1952 -0.026 -0.071 -0.025 -0.064 Born in 1953 0.136 ** 0.114 0.220 ** -0.056 Born in 1954 -0.003 -0.082 0.048 0.043 B. Primary or less Born in 1950 0.037 0.193 * 0.000 0.041 Born in 1951 0.105 ** 0.282 ** 0.050 -0.177 Born in 1952 0.012 0.019 0.033 0.262 Born in 1953 -0.054 -0.056 0.003 -0.036 Born in 1954 0.095 ** 0.247 ** 0.002 -0.182 C. College or higher Born in 1950 0.014 -0.025 0.097 -0.009 Born in 1951 -0.035 -0.153 ** 0.091 -0.023 Born in 1952 -0.007 -0.042 0.036 0.077 Born in 1953 0.107 *** 0.096 0.211 *** -0.037 Born in 1954 0.037 -0.037 0.090 -0.068 D. Internet use Born in 1950 -0.084 ** -0.063 -0.001 -0.173 Born in 1951 -0.103 *** -0.116 * 0.024 -0.139 Born in 1952 -0.022 0.003 -0.011 -0.039 Born in 1953 0.009 -0.021 0.119 * 0.010 Born in 1954 0.028 0.035 0.118 ** -0.139 E. Professional job Born in 1950 -0.023 -0.064 0.060 0.006 Born in 1951 -0.094 ** -0.255 *** 0.078 -0.094 Born in 1952 -0.012 -0.080 0.032 0.017 Born in 1953 0.038 -0.014 0.103 0.098 Born in 1954 0.000 -0.112 0.062 0.087 F. Manual job Born in 1950 0.077 * 0.036 0.055 0.014 Born in 1951 0.076 * 0.245 *** 0.025 -0.158 Born in 1952 0.016 -0.012 0.032 -0.132 Born in 1953 -0.023 -0.100 -0.053 0.019 Born in 1954 0.028 -0.004 0.061 0.153 Source: The 2% micro sample of the 2000 population census. Notes: (a) “South Korea” includes 547 persons who were born in North Korea and 22,642 persons for whom the place of birth is not reported; (b) Panel A reports the results of OLS regressions, and panels B to H partial effects (∂P/ ∂x)estimated from logistic regressions. (c) For panels E to H, the sample is limited to persons who were employed individuals at the time of census enumeration.

21

Table 3 Deviation of the 1950-54 Female Birth Cohort Labor-Market Outcomes in 2000 from 1945-1959 Trend

Outcome

Regional Classification (1)

South Korea (N=86,081)

(2) Central Region

(N=28,320)

(3) Southern Region,

Occupied (N=31,298)

(4) Southern Region,,

Unoccupied (N=8066)

A. Years of schooling Born in 1950 -0.046 -0.089 0.059 0.145 Born in 1951 -0.199 *** -0.463 *** 0.032 0.405 ** Born in 1952 -0.044 -0.247 *** 0.067 0.590 ** Born in 1953 0.074 -0.061 0.164 * 0.189 Born in 1954 -0.031 -0.087 0.042 0.028 B. Primary or less Born in 1950 -0.021 0.062 -0.074 -0.092 Born in 1951 0.113 *** 0.251 *** 0.045 -0.136 Born in 1952 -0.006 0.059 -0.067 -0.273 ** Born in 1953 -0.003 0.016 -0.001 0.093 Born in 1954 0.000 0.049 -0.035 0.039 C. College or higher Born in 1950 -0.001 0.005 0.131 0.029 Born in 1951 -0.068 -0.325 *** 0.322 *** 0.155 Born in 1952 -0.023 -0.175 *** 0.177 * 0.248 * Born in 1953 0.103 * -0.022 0.318 0.159 Born in 1954 -0.003 0.001 0.106 0.018 D. Internet use Born in 1950 0.002 0.010 0.099 0.021 Born in 1951 0.013 -0.095 0.126 0.242 Born in 1952 -0.001 -0.085 0.109 0.166 Born in 1953 0.077 0.151 * 0.107 0.007 Born in 1954 0.013 0.006 0.011 0.315 ** E. Professional job Born in 1950 0.015 0.071 -0.025 0.399 Born in 1951 0.092 0.028 0.254 0.511 Born in 1952 0.181 0.172 0.187 0.701 Born in 1953 0.079 0.140 0.170 0.222 Born in 1954 0.082 0.024 0.245 * 0.264 F. Manual job Born in 1950 0.069 0.169 0.048 0.123 Born in 1951 0.002 0.166 0.008 -0.247 Born in 1952 -0.059 ** -0.097 -0.054 0.161 Born in 1953 0.003 -0.086 0.006 0.176 Born in 1954 -0.025 0.039 -0.054 -0.265 Source: The 2% micro sample of the 2000 population census. Notes: (a) “South Korea” includes individuals who were born in North Korea and those for whom the place of birth is not reported; (b) Panel A reports the results of OLS regressions, and panels B to H partial effects (∂P/ ∂x)estimated from logistic regressions. (c) For panels E to H, the sample is limited to persons who were employed at the time of census enumeration.

22

Table 4 Deviation of the 1950-54 Birth Cohort Health Outcomes in 2005 from 1945-1959 Trend

Male Female

∂P/ ∂x P-value ∂P/ ∂x P-value A. Disabled Born in 1950 0.145 0.0494 0.044 0.5183 Born in 1951 0.077 0.3343 0.107 0.1522 Born in 1952 0.034 0.6385 -0.017 0.8048 Born in 1953 0.154 0.0499 0.065 0.3907 Born in 1954 0.093 0.2024 0.101 0.1706 B. Activity Limited Born in 1950 0.166 0.0640 0.019 0.8170 Born in 1951 0.027 0.7804 0.039 0.6729 Born in 1952 -0.041 0.6422 0.035 0.6930 Born in 1953 0.128 0.1793 0.055 0.5596 Born in 1954 0.093 0.2024 0.127 0.1653 Source: The 2% micro sample of the 2005 population census. Note: The sample includes 83649 males and 85994 females who were aged 46 to 60 in 2005.

23

Table 5 Deviation of the 1950-54 Birth Cohort Health Outcomes in 2006 from 1945-1959 Trend

Male Female

∂P/ ∂x P-value ∂P/ ∂x P-value A. Poor Self-reported Health Born in 1950 Q3 -0.405 0.4905 -0.275 0.3684 Born in 1950 Q4 0.436 0.4121 0.273 0.5129 Born in 1951 Q1 0.402 0.5090 0.066 0.8628 Born in 1951 Q2 2.365 0.0196 0.508 0.3142 Born in 1951 Q3 -0.720 0.2172 0.292 0.5195 Born in 1951 Q4 -0.132 0.7974 -0.349 0.3580 Born in 1952 Q1 0.816 0.1806 -0.169 0.5881 Born in 1952 Q2 0.651 0.2050 -0.594 0.0441 Born in 1952 Q3 -0.208 0.6688 0.340 0.4380 Born in 1952 Q4 0.635 0.3390 -0.746 0.0653 Born in 1953 Q1 0.039 0.9516 0.446 0.2874 Born in 1953 Q1 0.583 0.4153 -0.507 0.1936 Born in 1953 Q3 0.918 0.1702 0.517 0.2685 Born in 1953 Q4 -0.361 0.5495 -0.406 0.2938 Born in 1954 Q1 0.296 0.6812 -0.486 0.2839 Born in 1954 Q2 0.815 0.2407 -0.305 0.4224 B. Having a Disability Born in 1950 Q3 0.696 0.4120 0.489 0.6001 Born in 1950 Q4 >-999.999 0.9833 1.075 0.3397 Born in 1951 Q1 -0.596 0.3810 -0.028 0.9784 Born in 1951 Q2 2.234 0.0487 1.842 0.1780 Born in 1951 Q3 -0.523 0.4685 >999.999 0.9840 Born in 1951 Q4 1.342 0.0787 >999.999 0.9840 Born in 1952 Q1 -0.293 0.6450 0.433 0.6379 Born in 1952 Q2 -0.232 0.6718 0.579 0.5507 Born in 1952 Q3 0.625 0.3425 0.106 0.9236 Born in 1952 Q4 1.615 0.0685 2.224 0.1331 Born in 1953 Q1 0.031 0.9683 -0.085 0.9320 Born in 1953 Q1 0.120 0.8812 1.623 0.2118 Born in 1953 Q3 1.385 0.0945 0.124 0.9109 Born in 1953 Q4 >999.999 0.9854 0.192 0.8665 Born in 1954 Q1 >999.999 0.9866 >999.999 0.9854 Born in 1954 Q2 1.140 0.1772 1.044 0.3484 C. Height Lower 25% Born in 1950 Q3 -0.786 0.1352 0.540 0.1586 Born in 1950 Q4 -0.100 0.8210 0.068 0.8491 Born in 1951 Q1 0.432 0.4288 0.089 0.7966 Born in 1951 Q2 -0.438 0.4498 1.185 0.0592 Born in 1951 Q3 0.421 0.4702 0.639 0.1927 Born in 1951 Q4 0.558 0.2781 0.104 0.7893 Born in 1952 Q1 0.769 0.1497 0.329 0.3156 Born in 1952 Q2 -0.600 0.0887 -0.187 0.4859 Born in 1952 Q3 0.082 0.8495 -0.041 0.9038 Born in 1952 Q4 -0.448 0.3421 0.715 0.1916 Born in 1953 Q1 -0.126 0.8094 0.228 0.5139 Born in 1953 Q1 0.518 0.3912 0.271 0.5118 Born in 1953 Q3 0.881 0.1278 0.121 0.7403 Born in 1953 Q4 0.045 0.9316 -0.215 0.5001 Born in 1954 Q1 -0.230 0.6794 0.381 0.4214 Born in 1954 Q2 -0.210 0.6690 -0.290 0.3099

24

Table 5 Deviation of the 1950-54 Birth Cohort Health Outcomes in 2006 from 1945-1959 Trend (Continued)

Male Female

Parameter P-value Parameter P-value D. Self-reported health score Born in 1950 Q3 -0.119 0.5293 -0.037 0.7783 Born in 1950 Q4 0.065 0.6641 0.746 0.6276 Born in 1951 Q1 0.337 0.0280 0.040 0.7860 Born in 1951 Q2 0.470 0.0247 -0.052 0.7629 Born in 1951 Q3 -0.170 0.3424 -0.006 0.9724 Born in 1951 Q4 -0.101 0.5075 -0.135 0.4098 Born in 1952 Q1 0.067 0.9590 -0.112 0.3699 Born in 1952 Q2 0.091 0.4838 -0.254 0.0509 Born in 1952 Q3 0.083 0.5577 0.055 0.7187 Born in 1952 Q4 -0.096 0.5718 -0.439 0.0137 Born in 1953 Q1 -0.081 0.6407 0.028 0.8375 Born in 1953 Q1 0.029 0.8706 -0.219 0.1744 Born in 1953 Q3 0.112 0.4834 0.104 0.4939 Born in 1953 Q4 0.049 0.7720 -0.107 0.4958 Born in 1954 Q1 0.001 0.9962 -0.261 0.1404 Born in 1954 Q2 0.084 0.6065 -0.186 0.1973 E. Height Born in 1950 Q3 -0.032 0.9767 -1.402 0.0753 Born in 1950 Q4 -0.080 0.9272 -0.092 0.9198 Born in 1951 Q1 -1.092 0.2642 -1.225 0.1603 Born in 1951 Q2 0.130 0.9155 -0.739 0.4682 Born in 1951 Q3 -1.092 0.2978 -0.322 0.7400 Born in 1951 Q4 -1.863 0.0376 -0.322 0.7400 Born in 1952 Q1 -0.918 0.3003 -0.370 0.6171 Born in 1952 Q2 0.849 0.2661 0.634 0.4121 Born in 1952 Q3 -0.942 0.2542 -0.515 0.5700 Born in 1952 Q4 -0.573 0.5653 -1.440 0.1730 Born in 1953 Q1 -0.380 0.7119 -0.096 0.9077 Born in 1953 Q1 -1.739 0.0975 0.623 0.5148 Born in 1953 Q3 -1.137 0.2234 0.016 0.9863 Born in 1953 Q4 0.617 0.5349 -0.699 0.4526 Born in 1954 Q1 1.492 0.1701 -0.928 0.3781 Born in 1954 Q2 1.172 0.2216 0.445 0.6035 Source: The first wave of the Korea Longitudinal Study of Aging.. Notes: The sample includes 2060 males and 2444 females who were aged 46 to 60 in 2006.

25

11.2

11.4

11.6

11.8

12

12.2

12.4

12.6

12.8

13

13.2

19441945194619471948194919501951195219531954195519561957195819591960

Figure 1-AMale Years of Schooling in 2000 by Birth Cohort:

Born in the Central Region

8

8.5

9

9.5

10

10.5

11

11.5

12

19441945194619471948194919501951195219531954195519561957195819591960

Figure 1-BFemale Years of Schooling in 2000 by Birth Cohort:

Born in the Central Region

26

24

27

30

33

36

39

42

19441945194619471948194919501951195219531954195519561957195819591960

Figure 2-A% Males College or Higher in 2000 by Birth Cohort:

Born in the Central Region

6

8

10

12

14

16

18

20

19441945194619471948194919501951195219531954195519561957195819591960

Figure 2-B% Females College or Higher in 2000 by Birth Cohort:

Born in the Central Region

27

4

6

8

10

12

14

16

19441945194619471948194919501951195219531954195519561957195819591960

Figure 3-A% Males Primary School or Less in 2000 by Birth Cohort:

Born in the Central Region

81012141618202224262830323436384042

19441945194619471948194919501951195219531954195519561957195819591960

Figure 3-B% Females Primary School or Less in 2000 by Birth

Cohort: Born in the Central Region

28

25

26

27

28

29

30

31

32

33

34

35

36

19441945194619471948194919501951195219531954195519561957195819591960

Figure 4% Professionals among Employed Males:

Born in the Central Region

20

21

22

23

24

25

26

27

28

29

30

31

19441945194619471948194919501951195219531954195519561957195819591960

Figure 5% Manual Laborers among Employed Males:

Born in the Central Region

29

2

3

4

5

6

7

8

9

10

19441945194619471948194919501951195219531954195519561957195819591960

Figure 6-A% Males with Disability in 2005 by Birth Cohort

2

4

6

8

10

12

14

16

19441945194619471948194919501951195219531954195519561957195819591960

Figure 6-B% Females with Disability in 2005 by Birth Cohort

30

2

2.5

3

3.5

4

4.5

5

5.5

6

6.5

7

19441945194619471948194919501951195219531954195519561957195819591960

Figure 7-A% Males with Limited Activity in 2005 by Birth Cohort

1

2

3

4

5

6

7

8

9

10

19441945194619471948194919501951195219531954195519561957195819591960

Figure 7-B% Females with Limited Activity in 2005 by Birth Cohort

31

2

2.2

2.4

2.6

2.8

3

3.2

1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957

Figure 8-AMale Self-Reported Health in 2006

2

2.2

2.4

2.6

2.8

3

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3.4

1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957

Figure 8-BFemale Self-Reported Health in 2006

32

2

7

12

17

22

27

32

37

1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957

Figure 9-A% Males Reporting Poor Health in 2006

2

7

12

17

22

27

32

37

42

1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957

Figure 9-B% Females Reporting Poor Health in 2006

33

0

5

10

15

20

25

1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957

Figure 10-A% Males with Disability in 2006

0

1

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4

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6

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1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957

Figure 10-B% Females with Disability in 2006

34

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11

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12.5

13

19441945194619471948194919501951195219531954195519561957195819591960

Appendix Table A1-AMale Years of Schooling in 2000 by Birth Cohort

7

8

9

10

11

12

19441945194619471948194919501951195219531954195519561957195819591960

Appendix Figure A1-BFemale Years of Schooling in 2000 by Birth Cohort

35

15

18

21

24

27

30

33

36

39

19441945194619471948194919501951195219531954195519561957195819591960

Appendix Figure A2-A% Males College or Higher in 2000 by Birth Cohort

4

6

8

10

12

14

16

19441945194619471948194919501951195219531954195519561957195819591960

Appendix Figure A2-B% Females College or Higher in 2000 by Birth Cohort

36

10

15

20

25

30

35

40

45

50

55

19441945194619471948194919501951195219531954195519561957195819591960

Appendix Figure A3-B% Females Primary School or Less in 2000 by Birth Cohort

5

10

15

20

25

19441945194619471948194919501951195219531954195519561957195819591960

Appendix Figure A3-A % Males Primary School or Less in 2000 by Birth Cohort

37

20

21

22

23

24

25

26

27

28

29

30

19441945194619471948194919501951195219531954195519561957195819591960

Appendix Figure A4% Professionals among Employed Males

21

22

23

24

25

26

27

28

19441945194619471948194919501951195219531954195519561957195819591960

Appendix Figure A5% Manual Laborers among Employed Males

38

2

3

4

5

6

7

8

9

1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960

Appendix Figure A6-A% Males with Disability in 2005 by Birth Cohort:

Residing in the Central Region

2

4

6

8

10

12

14

19441945194619471948194919501951195219531954195519561957195819591960

Apendix A6-B% Females with Disability in 2005 by Birth Cohort:

Residing in the Central Region

39

2

2.5

3

3.5

4

4.5

5

5.5

19441945194619471948194919501951195219531954195519561957195819591960

Appendix Figure A7-A% Males with Limited Activity in 2005 by Birth Cohort:

Residing in the Central Region

0

1

2

3

4

5

6

7

8

9

10

19441945194619471948194919501951195219531954195519561957195819591960

Appendix Figure A7-B% Females with Limited Activity in 2005 by Birth Cohort:

Residing in the Central Region