Intergenerational Effects of Shifts in Women's Educational Distribution in South Korea:...

Social Science Research 41 (2012) 1495–1514

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Social Science Research

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Intergenerational effects of shifts in women’s educational distributionin South Korea: Transmission, differential fertility, and assortative mating

Bongoh Kye a,⇑, Robert D. Mare b

a Kookmin University, Department of Sociology, Jeongneung Ro-77 Seoungbuk-Gu Seoul 136-702 Koreab University of California – Los Angeles, Department of Sociology, 264 Haines Hall, Box 951551, Los Angeles, CA 90095-1551, United States

a r t i c l e i n f o

Article history:Received 5 April 2011Revised 20 February 2012Accepted 10 May 2012Available online 18 May 2012

Keywords:Intergenerational effectsEducational mobilityBootstrap methodSouth Korea

0049-089X/$ - see front matter � 2012 Elsevier Inchttp://dx.doi.org/10.1016/j.ssresearch.2012.05.011

⇑ Corresponding author. Fax: +82 2 910 4429.E-mail addresses: [email protected] (B. Kye), m

a b s t r a c t

This study examines the intergenerational effects of changes in women’s education inSouth Korea. We define intergenerational effects as changes in the distribution of educa-tional attainment in an offspring generation associated with the changes in a parentalgeneration. Departing from the previous approach in research on social mobility that hasfocused on intergenerational association, we examine the changes in the distribution ofeducational attainment across generations. Using a simulation method based on Mareand Maralani’s recursive population renewal model, we examine how intergenerationaltransmission, assortative mating, and differential fertility influence intergenerationaleffects. The results point to the following conclusions. First, we find a positive intergener-ational effect: improvement in women’s education leads to improvement in daughter’seducation. Second, we find that the magnitude of intergenerational effects substantiallydepends on assortative marriage and differential fertility: assortative mating amplifiesand differential fertility dampens the intergenerational effects. Third, intergenerationaleffects become bigger for the less educated and smaller for the better educated over time,which is a consequence of educational expansion. We compare our results with Mare andMaralani’s original Indonesian study to illustrate how the model of intergenerationaleffects works in different socioeconomic circumstances.

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1. Intergenerational effects of education

This study examines how the changes in distribution of educational attainment in one generation lead to changes in thenext generation in South Korea. Changes in education affect intermediate demographic processes (e.g., marriage and child-bearing) as well as offspring’s educational attainment. Therefore, we need to account for differential population renewal pro-cesses to examine the changes in the distribution of education across generations. Following Mare and Maralani (2006), wedefine intergenerational effect as the expected change in distribution of the offspring’s educational attainment caused by thechange in distribution of educational attainment in the parental generation. The estimates of intergenerational association(e.g., Mare, 1981) could be directly converted to intergenerational effects if there were no socioeconomic differentials inreproduction rates. These differentials, however, do exist. Our approach accounts for educational differentials in reproduc-tion rates that determine distribution of family background in the next generation. Because groups with higher reproductionrates (usually less-educated groups) will be more represented in the next generation, accounting for such educational dif-ferentials is necessary in examining changes in the distribution of educational attainment across generations. Our approach

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1496 B. Kye, R.D. Mare / Social Science Research 41 (2012) 1495–1514

also allows for decomposing total intergenerational effect into three different pieces: assortative marriage, differential fer-tility, and intergenerational transmission.

Research in social mobility has not studied differential population renewal processes extensively until recently.1 Mostprevious studies on social mobility have focused on the relationship between parental and offspring’s socioeconomic outcomes(e.g., education, occupation, and income). This is common for the status attainment model (Blau and Duncan, 1967; Sewell et al.,1969; Hauser et al., 1983), occupational mobility studies (Erikson and Goldthorpe, 1992; Hout, 1988), and educational mobilitystudies (Mare, 1981; Shavit and Blossfeld, 1993). Estimating the net intergenerational association in socioeconomic outcomeshas been a central task in this line of research. Crucial methodological innovations have also been achieved to tackle this issue,including: handling measurement errors (Hauser et al., 1983), controlling for the influence of structural or distributional change(Erikson and Goldthorpe, 1992; Mare, 1981), and purging out unmeasured individual-specific heterogeneity (Mare, 1993). Theestimates of the net association tell us the expected changes in the offspring’s socioeconomic outcomes associated with thechanges in family backgrounds after controlling for other covariates. In other words, such estimates aim at quantifying the dif-ferences in the socioeconomic outcomes between individuals who, except for their parents’ socioeconomic status, are identical.As sociological research becomes keener to estimate causal effects from the counterfactual perspective (e.g., Morgan and Win-ship, 2007), we may expect that more efforts will be devoted to getting more refined estimates of the effects of family back-ground on children’s socioeconomic outcomes. Such estimates, although sophisticated, cannot be directly used to assess howthe changes in distribution of socioeconomic traits in one generation lead to changes in the next generation. To address thisquestion appropriately, we should account for differential population renewal processes.

Examining intergenerational effect is important in two respects. First, this provides a complete description of the inter-generational transmission of family backgrounds, which is a marked improvement over most previous mobility research thatdoes not address the question of differential population renewal. Second, the estimates of intergenerational effects will pro-vide a tool for evaluating long-term socioeconomic consequences of current education policies. For example, postsecondaryeducation expanded substantially throughout the world after World War II (Shavit et al., 2007). Such an expansion shouldhave long-term implications beyond improving the level of educational attainment in the current population. Sociologicalliterature has consistently shown that offspring’s educational attainment is positively associated with parental education(Mare, 1981; Shavit and Blossfeld, 1993) and that parents attempt to provide their offspring with at least the same levelof educational attainment as they have (Breen and Goldthorpe, 1997). Given such a strong intergenerational associationin educational attainment, educational expansion at a certain time point should have trickle-down effects on future educa-tional distribution in a population. This issue is particularly relevant to policy makers in developing countries who wouldplan to expand educational opportunity as a crucial strategy of socioeconomic development. However, the estimates ofnet intergenerational association from most previous research in social mobility cannot address such a policy issue becausedifferential population reproduction is difficult to account for in such approaches. Hence, the estimates of intergenerationaleffects would provide better guidance to policy makers in developing countries with regard to the long-term implications ofcurrent educational policies.

2. Goals and research questions

The first goal of the current study is to see how Mare and Maralani’s (2006) recursive population renewal model works inan institutional setting other than Indonesia. South Korea is suited for this purpose for several reasons. Substantively, SouthKorea experienced fast and fundamental socioeconomic and demographic changes over the past 50 years, making it partic-ularly relevant to apply a recursive population renewal model. First, educational opportunity expanded dramatically. Whilealmost none of the women over the age 25 in 1960 had ever attended college, nearly 60% of women of the same age in 2005received at least some college education (Korea Statistical Office, 2010). Such a rapid educational expansion yielded enor-mous upward educational mobility. For example, 45% of Korean people born between 1970 and 1985 whose father hadnot attended high school received at least some postsecondary education (Phang and Kim, 2002, p. 208). This rapid educa-tional expansion would yield small intergenerational effects because children are likely to attain high level of educationalattainment regardless of their parental education level. While upward mobility has been prominent, intergenerational asso-ciation in educational attainment decreased just modestly (Park, 2004, 2007). This is consistent with findings in other indus-trialized countries (Mare, 1981; Shavit and Blossfeld, 1993), which document invariant intergenerational association overtime and across countries. This persistent intergenerational association suggests that cohort changes in intergenerational ef-fects be modest. Second, spousal resemblance in educational attainment is strong from the comparative perspective (Parkand Smits, 2005). Such strong assortative mating should consolidate the rigidity of social inequality, strengthening intergen-erational effects. Third, South Korea transited from a high fertility country to a ‘lowest-low’ fertility county (Kohler et al.,2002) in less than a half century. During such rapid fertility transition, fertility differentials by education remained quite sta-ble (Jun, 2004). The persistent fertility differentials by education would dampen the intergenerational effects because better-educated women tend to produce fewer children than their less-educated counterparts. The contributions of assortative

1 Duncan (1966) recognized this weakness as early as the 1960s, pointing out that father’s generation in a mobility table does not refer to any representativebirth cohorts and that childless individuals are excluded from the mobility analysis. There were also studies that connected demographic processes and socialmobility in the 1950s and the 1960s (e.g., Prais, 1955; Matras, 1967). This line of research, however, has been largely overlooked in stratification research untilMare and his colleagues recently revived this tradition (Mare, 1997, 1996; Mare and Maralani, 2006, 2008; Choi and Mare, 2009; Kye, 2011).

B. Kye, R.D. Mare / Social Science Research 41 (2012) 1495–1514 1497

mating and differential fertility, however, would not be substantial because children are likely to attain high level of educa-tional attainment regardless of parental education due to the rapid educational expansion in South Korea. In this sense, wecan see if accounting for differential demographic behaviors makes a substantial difference in our understanding of educa-tional mobility when educational opportunity expanded rapidly. In the results section, we will discuss the implications ofrapid educational expansion for intergenerational effects in more detail in comparison with Mare and Maralani’s (2006) ori-ginal case, Indonesia.

Schooling and family formation patterns in South Korea also facilitate estimating Mare and Maralani’s model, which isanother reason to use the Korean data. Mare and Maralani’s model assumes that education affects the intermediate demo-graphic processes such as marriage and fertility. If women commonly get married or become a mother before finishingschool, such an assumption is difficult to hold. The schooling and family transition patterns among the Korean women, how-ever, were neatly ordered: marriage is almost universal in Korea despite the delay in marriage (Byun, 2004), the prevalenceof cohabitation is still very low despite the rising trend (Lee, 2008), non-marital fertility is still negligible (Korea StatisticalOffice, 2010), and the majority of marriage and childbearing occurs after leaving school (Kye, 2008). Transition patterns inother industrialized countries are much less ordered (Cohen et al., 2011), making it difficult to apply this type of recursivepopulation renewal model. These unordered transition patterns necessitate examining the causal direction in the oppositeway in the United States—the effect of timing of fertility on schooling (Lee, 2010; Stange, 2011). Of course, the ordered tran-sition patterns do not rule out a possible endogenous relationship in South Korea, because observed and unobserved con-founders would exist. The Korean case, however, is much better suited for applying Mare and Maralani’s recursivedemographic model than other industrialized countries.

Another goal is to elaborate Mare and Maralani’s model by using the bootstrap method to estimate standard errors ofintergenerational effects. Mare and Maralani (2006) used a recursive population renewal model that is based on severalregression models. Because the estimates of intergenerational effects are functions of parameter estimates in each regres-sion, it is challenging to compute the standard errors of intergenerational effects analytically. Because Mare and Maralani(2006) did not present standard error estimates, they cannot assess statistical significance of contributions of demographicelements to intergenerational effects. In this study, we address this problem by estimating bootstrap standard errors, whichallows for statistical tests. Having these goals in mind, we examine the following research questions.

1. How do changes in women’s educational distribution in one generation affect the educational distribution of the daugh-ter’s generation in South Korea? In other words, how strong are the intergenerational effects in South Korea?

2. How strong is the impact of assortative mating and differential fertility on the intergenerational effects in South Korea?3. How do the intergenerational effects change over time in South Korea?

3. Method: A recursive population renewal model

3.1. Baseline model

This study examines intergenerational effects, which must be assessed at the aggregate level because we are interested inthe expected change of the education distribution in the next generation associated with a change in the previous one. Infer-ring intergenerational effects based on standard regression analyses would be valid if reproduction rates are equal acrosssocioeconomic groups. This is hardly the case; demographic research has consistently shown socioeconomic differentialsin fertility (Bongaarts, 2003; Jejeebhoy, 1995; Skirbekk, 2008), which yield differential reproduction rates. Hence, an alter-native method is necessary to address the research questions asked.

We use a one-sex recursive ‘population renewal model’ (Mare and Maralani, 2006) to address this issue. In this model, thedistribution of offspring’s education is jointly determined by intergenerational transmission, differential fertility, and assor-tative mating. The intergenerational transmission rate of women’s educational attainment is expressed in the followingequation:

rjkji ¼ pHkjifikdikpD

jjiks ð1Þ

(where i: woman’s education, k: husband’s education, j: daughter’s education, i = 1, . . . , 4, k = 1, . . . , 4, and j = 1, . . . , 4). Here,we classify woman’s, husband’s, and daughter’s education into four categories: 0–8, 9–11, 12, and 13+ years. Students spend6 years in primary school, 3 years in junior high school, and 3 years in high school under the Korean educational system.Hence, each category represents less than junior high school graduate (0–8 years), less than high school graduate (9–11 years), high school graduate (12 years), and some tertiary education (13+ years). The rjk|i represents the joint distributionof husband’s (k) and daughter’s (j) educational attainment given women’s educational attainment (i). The pH

kji represents theprobability distribution of husband’s educational attainment conditional on wife’s educational attainment. The fik is the ex-pected number of children born to women in education group i and husbands in education group k. The dik is the proportionof daughters of children born to women in education category i and husbands in education category k. Finally, the pD

jjiks is theprobability distribution of daughter’s educational attainment (j) conditional on women’s education (i), husband’s education(k), and the number of siblings (s). Following Mare and Maralani (2006), we estimate the three equations separately: ordinallogistic regression models are used for husband’s education (pH

kji) and daughter’s education (pDjjiks), and Poisson regression for


the number of children (fik). The estimated parameters are used to compute each component, conditional probability distri-bution in Eq. (1). Using the estimated rjk|i and observed marginal distribution of women’s educational attainment, the mar-ginal distribution of daughter’s educational attainment is computed in the following way:

2 Thithe sibscontextstronglyrecentlygenerat

3 Theof a ‘‘onthis sengenerat

4 TheSubsequsimulat

Dj ¼X4

i¼1

X4

k¼1

rjkjiWi ð2Þ

(where Dj and Wi are marginal distributions of daughter’s and women’s educational attainment respectively).2

This model explicitly takes into account assortative mating and fertility differentials, which is distinctive from most priorsocial mobility research. First, we expect that assortative mating will amplify the intergenerational effects to some extent.Spousal resemblance in socioeconomic status has been studied extensively (Kalmijn, 1991; Mare, 1991; Schwartz and Mare,2005) because this shapes family backgrounds in the next generation (Mare and Schwartz, 2006). The degree of spousalresemblance should affect the distribution of children’s socioeconomic outcomes, but empirical studies show that this im-pact is not substantial. Mare (2000) showed that increasing spousal resemblance in educational attainment over time doesnot increase educational inequality substantially because intergenerational mobility is large enough to offset educationalassortative mating effects. Preston and Campbell (1993) also showed that equilibrium IQ distributions do not depend heavilyon mating patterns because of substantial intergenerational mobility. This evidence suggests a modest influence of assorta-tive mating on intergenerational effects. Second, differential fertility has two implications for the distribution of offspring’seducation. Because better-educated women tend to produce fewer children, differential fertility should reduce the intergen-erational effects of changes in women’s education. However, lower reproduction rates among better-educated women leadto a smaller sibship size among their offspring, which offsets the reduction of intergenerational effects to some extent. Inother words, although lower reproduction rates among better-educated women may lower the overall level of children’seducational attainment, their own children should benefit from the smaller family size, which may contribute to improve-ment in educational attainment in the next generation. Hence, whether or not differential fertility exerts a downward pres-sure on the education distribution in the next generation should be answered empirically and not a priori. This study willshow which forces are stronger in the Korean context.

3.2. Simulation

When we improve women’s educational attainment, daughter’s educational distribution may change in three differentways. First, improvement of women’s educational attainment directly enhances daughter’s educational attainment. Second,better-educated women are likely to marry better-educated husbands, and this improvement in husband’s education shouldpositively influence daughter’s educational attainment in addition to the direct impact of improvement of women’s educa-tional attainment. Finally, better-educated women tend to produce fewer daughters, which might dampen the benefits ofenhanced women’s educational attainment to the education distribution of daughters. The smaller family size among them,however, would reduce this dampening effect because family size is negatively associated with children’s educational out-comes (Guo and VanWey, 1999).

Here, we assess the intergenerational effect of changes in women’s educational distribution in six different scenarios: (1)‘‘Transmission only’’ (T only), (2) ‘‘Transmission + Fertility’’ (TF), (3) ‘‘Transmission + Marriage in an unconstrained marriagemarket’’ (TM), (4) ‘‘Transmission + Marriage in a constrained marriage market’’ (TMC), (5) ‘‘Transmission + Fertility + Mar-riage’’ in an unconstrained marriage market (TFM), and (6) ‘‘Transmission + Fertility + Marriage in a constrained marriagemarket’’ (TFMC). In each scenario, we redistribute the educational attainment of 5% of women in the sample from the lowereducation category to the higher education category in four different ways and examine how daughter’s educational attain-ment changes after these redistributions.3 In the (1) T only model, a change in women’s educational distribution does not entailany change in marriage and fertility behaviors at all. In other words, women who experience improvement of education still mar-ry the same kind of husbands and produce the same number of children. In this model, improvements in women’s educationalattainment only directly affect daughter’s educational distribution. In the (2) TF and (3) TM models, either fertility or marriagebehaviors respectively adjust to the change in the distribution of women’s education. In the (5) TFM model, both marriage andfertility behaviors alter according to the change in women’s education.4 Formally, we modify pH

kji (assortative marriage) and fikdik

s model is the same as Mare and Maralani (2006) except for two aspects. First, they dropped the number of siblings in the transmission equation becausehip size effects were not significant in the Indonesian context. However, sibship size matters for daughter’s educational attainment in the Korean. Second, whereas they included both sons and daughters in the transmission sample, we include daughters only. Mother’s socioeconomic status is more

related to daughter’s outcomes than son’s (Thomas, 1994), and educational attainment has been higher for men than women in South Korea until(Phang and Kim, 2002). Hence, the restriction of sample to daughters allows us to see changes in the distribution of educational attainment across

ions without complications due to gender dynamics.magnitude of redistribution,5% change, is chosen to illustrate how this model works. It represents an arbitrary unit, analogous to focusing on the effecte unit’’ change in a typical regression model. A 5 percent change is not intended to capture any realistic changes in educational attainment over time. Inse, the simulation analyses presented in this article show how differential demographic behaviors are important in educational mobility across

ions instead of projecting the educational attainment in the future generations.T only model is equivalent to assuming that associations among women’s education, husband’s education, and fertility are completely spurious.ent simulations assume that some associations are spurious. Although this study does not handle the endogeneity issue directly, this feature of

ion allows us to assess how large the bias would be in estimating intergenerational effect if education is endogenously determined.


(differential fertility) in Eq. (1) to capture these hypothetical conditions. Let us illustrate how the simulation works by consider-ing the scenario in which we redistribute 5% of women in the sample from 0–8 to 13+ years of schooling. This change leads tochanges in pH

kji and fikdik among those with 13+ years of schooling, leaving pHkji and fikdik unchanged for other education categories.

5 Thiconditiowomendistribuwomenregressiafter sim

pH NMkj4 ¼W4 � pH

kj4 þ :05� pHkj1 ð3Þ

f4kdNF4k ¼W4 � f4kd4k þ :05� f1kd1k ð4Þ

Eq. (3) shows the distribution of husband’s education if women’s education equals 13+ years when assortative mating is ab-sent after redistributing women’s education. This states that distribution of husband’s education of women with 13+ years ofschooling is a weighted average of pH

kj4 and pHkj1 where weights are given by the observed proportion of college-educated wo-

men (W4) and the simulated proportion of women who change their education from 0–8 to 13+ years, which is .05. Eq. (4)shows the expected number of children among college-educated women given husband’s education k when differential fer-tility is absent. This states that the expected number of children among college-educated women given husband’s educationequals the weighted average of f4kd4k and f1kd1k where weights are given in the same way as in Eq. (3). The expected daugh-ter’s educational attainment after simulation in each scenario is computed in following ways:

T only : Dj ¼X4

i¼1

X4

k¼1

pH NMkji fikdNF

ik pDjjiksW

0i ð5Þ

TF : Dj ¼X4

i¼1

X4

k¼1

pHkjifikdNF

ik pDjjiksW

0i ð6Þ

TM : Dj ¼X4

i¼1

X4

k¼1

pH NMkji fikdikpD

jjiksW0i ð7Þ

TFM : Dj ¼X4

i¼1

X4

k¼1

pHkjifikdikpD

jjiksW0i ð8Þ

(W 0i is simulated marginal distribution of women’s education).After simulations, we measure the intergenerational effects by the ratios of the simulated proportions to the baseline pro-

portions. If the ratio is equal to one, no intergenerational effect exists. If the ratio is greater than one, this means that a po-sitive intergenerational effect exists in a given category. If the ratio is smaller than one, this means that a negativeintergenerational effect exists. The greater the deviation from one, the larger the intergenerational effect. By comparingthe size of intergenerational effects in each scenario, we can assess how intergenerational effects depend on demographicand mobility components.

In the (3) TM and (5) TFM model, husband’s education is assumed to change following the improvement in women’s edu-cational attainment. This change is assumed to alter the marginal distribution of husband’s education automatically. How-ever, husband’s marginal education distribution may change differently; the change could be larger or smaller. Historically,improvement in women’s education has been more rapid than in men’s education in South Korea (Phang and Kim, 2002), sowe may expect smaller improvement in husband’s education. This gendered pattern of improvement of educational attain-ment may have important implications for our analyses. This could cause a lack of marriageable men among the highly edu-cated women (Raymo and Iwasawa, 2005), which may lead to delayed marriage, increasing never-married women, andincreasing childless women. These issues, however, are difficult to handle for a couple of reasons. First, our model doesnot account for the timing of marriage and childbearing. Second, we assume that every woman gets married. Because mar-riage is almost universal and childlessness is rare for the cohort of women considered in the present study (Byun, 2004; Jun,2004), the results may not change substantially if we account for them. In addition, it is difficult to predict the magnitude ofchange a priori. Hence, we experiment with two extreme scenarios: (1) marginal distribution of women’s education changesaccording to scenario without any restriction in the marginal distribution of husband’s education, and (2) husband’s mar-ginal distribution does not change at all and the association between women’s education and husband’s education, measuredby odds ratios, remains the same. Using the given marginal distribution and the set of odds ratios, we can compute a hypo-thetical distribution of husband’s education conditional on the changed marginal distribution of women’s education (seeAgresti, 2002, pp. 345–346).5 In this scenario, the marriage market is constrained in that the marginal distribution of husband’s

s technique is called raking, or table standardization. First, we estimate ordered logistic regression to obtain the distribution of husband’s educationnal on women’s education. Second, we compute the predicted joint probability distribution of women’s and husband’s education using observed

’s marginal distribution of education and predicted distribution of husband’s education conditional on women’s education. Third, we change marginaltion of women’s education according to the scenarios described above. Fourth, we re-estimate the distribution of husband’s education conditional on’s education using Poisson regression. The joint probability distribution of women’s and husband’s education predicted from the original ordered logisticon is used as an offset in this estimation. From this estimation, we can obtain the distribution of husband’s education conditional on women’s education

ulation while holding the association in spousal education and the marginal distribution of husband’s education the same as before simulation.


education is fixed regardless of changes in women’s educational distribution. However, the association between women’s andhusband’s education remains the same. We do this simulation both without differential fertility (TMC) and with differential fer-tility (TFMC). We may interpret the estimates from the unconstrained marriage market models as upper bounds and those fromthe constrained marriage market models as lower bounds because realistic husband’s educational attainment will be betweenthe unconstrained and the constrained models.

The intergenerational effect is determined by the parameter estimates in each regression model. As long as the parameterestimates are consistent, the estimates of intergenerational effects should be consistent. The sampling variability of intergen-erational effects, however, is difficult to assess analytically because it is not clear how sampling variability of parameter esti-mates in each regression model is translated into the sampling variability of the estimates of intergenerational effects.Bootstrapping is an option to assess sampling variability in this situation (e.g., Osberg and Xu, 2000). Because the sampleused in this study, the Korean Longitudinal Study of Aging (KLoSA), is a stratified multi-stage probability sample, a bootstrapmethod that assumes simple random sampling will yield biased estimates of standard errors (Lee and Forthofer, 2006). Weimplement the following steps to estimate bootstrap standard errors of intergenerational effects by accounting for samplingdesign of the data. First, we resample 1000 independent bootstrap samples from the original data set because 1000 replica-tions are sufficient to yield reliable bootstrap confidence intervals (Efron and Tibshirani, 1993). For each stratum defined byregion, type of areas, and type of housing (see the next section for description of data), we re-sample the same number ofprimary sampling units in the original data. By pulling the sample from each stratum together, we construct each bootstrapsample. Second, we compute intergenerational effects in each replication. That is, we go through the processes described inprevious paragraphs for each replication. Finally, we estimate standard errors by the standard deviation of intergenerationaleffects from the 1000 replications. In the results section, we report the estimates of intergenerational effects and bootstrapstandard errors.

3.3. Exogeneity of education

The assumption of exogeneity of education to marriage, fertility, and offspring’s education in the Mare and Maralani’smodel, which is necessary in estimation and decomposition of intergenerational effects, is likely to deviate from the reality.First, Mare and Maralani’s model assumes that women’s educational attainment determines husband’s educational attain-ment. However, the relationship between women’s and husband’s education should be reciprocal rather than causal. Second,the statistical association between women’s education and level of fertility may not be causal. There may be other factorsaffecting women’s education and demographic behaviors. For example, risk-taking tendency may explain variations in edu-cation and the timing of first marriage and first childbearing (Schmidt, 2008). Third, some unobservable genetic factors couldaffect both mothers’ and daughters’ education. Hence, assuming exogeneity of women’s education is not ideal.

Nevertheless, we make this assumption for two reasons. First, this is useful to illustrate demographic pathways throughwhich intergenerational effects are accrued while avoiding too much complication. Without making such a simplifyingassumption, estimation of intergenerational effects will be formidably difficult. Second, endogeneity of education doesnot threaten the validity of analysis too greatly in the South Korean context where most women finish schooling before mar-riage and childbirth. Furthermore, non-marital childbearing is very unusual (Kye, 2008). In a society where non-maritalchildbearing is not rare and marriage and childbearing among school enrollees are not extraordinary, assuming exogeneityof women’s education would be more problematic. The orderly pattern in South Korea does not rule out the possibility of theendogeneity of education, but makes the endogeneity issue less problematic.

4. Data

We use the first wave of the Korean Longitudinal Study of Aging (KLoSA), a biannual longitudinal survey of the non-insti-tutionalized Korean population aged 45 and older in 2006 (Korea Labor Institute, 2007). The survey interviewed all the indi-viduals aged 45 or over in the household. The data include information on broad socioeconomic circumstances of adults inSouth Korea. The KLoSA is a multistage probability sample, which stratified geographic areas by region, urbanicity, and typeof housing. First, it stratified 15 cities and provinces. Each city and province is first stratified by type of area (urban and rural),and then type of housing (apartment and ordinary housing). Out of 52 strata,6 1000 enumeration districts were selected, and1–12 households were interviewed in each enumeration district. This sampling design is accounted for when assessing the sam-pling variability of intergenerational effects.

This study constructs two analytic samples: a marriage-fertility sample and an intergenerational transmission sample.The marriage-fertility sample includes 4894 ever-married women aged 45–79 whose husband’s educational attainment isnot missing. When weighted, the married-fertility sample represents ever-married Korean women who were born between1927 and 1961 and still alive in 2006. The data do not include information on marital history. So we do not know for sure ifwomen and their husbands listed are the biological parents of the children. How serious the first problem is depends on theprevalence of marital disruptions and the educational resemblances among multiple partners for those who have remarried.If marital disruptions (divorce, separation, and widowhood) are prevalent, lack of information on biological fathers would be

6 There are 60 possible strata (15 � 2 � 2). However, some cities do not have rural areas.

Table 1Descriptive statistics for marriage-fertility sample, by birth cohort.

Women’s education Husband’s education (%) Number ofchildren (s.d)

Women’s marginal(N)

0–8 9–11 12 13+ Total

Cohort 1 (1927–1941)0–8 74.6 12.4 9.8 3.2 100.0 4.09 (1.73) 86.5 (1622)9–11 10.4 22.7 45.6 21.3 100.0 3.53 (1.61) 6.6 (137)12 4.9 5.5 40.6 49.1 100.0 3.37 (1.19) 5.5 (108)13+ 0.0 3.3 6.7 90.0 100.0 3.09 (0.94) 1.4 (30)

Total 65.4 12.6 13.8 8.2 100.0 4.00 (1.70) 100.0 (1897)

Cohort 2 (1942–1951)0–8 61.3 20.8 16.3 1.7 100.0 3.10 (1.27) 60.6 (842)9–11 9.6 29.4 52.1 9.0 100.0 2.85 (1.07) 20.2 (279)12 2.3 6.8 52.0 38.9 100.0 2.37 (0.91) 14.8 (199)13+ 0.0 0.0 10.8 89.2 100.0 2.19 (0.70) 4.4 (56)

Total 39.4 19.5 28.6 12.5 100.0 2.90 (1.20) 100.0 (1376)

Cohort 3 (1952–1961)0–8 53.7 26.8 18.1 1.5 100.0 2.41 (0.93) 23.3 (371)9–11 7.9 39.7 47.7 4.7 100.0 2.21 (0.79) 23.2 (383)12 0.9 5.7 60.7 32.8 100.0 2.06 (0.77) 43.8 (706)13+ 1.1 1.1 8.7 89.1 100.0 1.97 (0.64) 9.7 (161)

Total 14.9 18.0 42.7 24.4 100.0 2.16 (0.82) 100.0 (1621)

Entire sample0–8 66.4 17.8 13.5 2.3 100.0 3.46 (1.60) 54.4 (2835)9–11 8.8 33.8 49.2 8.2 100.0 2.61 (1.12) 17.3 (799)12 1.5 5.9 57.4 35.2 100.0 2.22 (0.91) 22.9 (1013)13+ 0.7 1.0 9.1 89.2 100.0 2.11 (0.74) 5.5 (247)

Total 38.0 16.9 29.5 15.7 100.0 2.95 (1.46) 100.0 (4894)


problematic. In South Korea, divorce rates have been low. Fewer than 5 divorces per 1000 married women occurred until theearly 1990s when most women in the sample were already married (Korea Statistical Office, 2010).7 Adult mortality, leadingto widowhood, is also fairly low (Kim, 2004). No study has studied educational resemblances among the sequential spouses inSouth Korea, and a study shows that educational assortative mating patterns are similar for first marriage and remarriage in theNetherlands (Gelissen, 2004, p. 370). This evidence, though not perfect, lessens the seriousness of this data issue to some ex-tent.8 The transmission sample includes 6902 daughters aged 20 and older of 3625 women aged 45–79 who have at leastone adult daughter with valid information on educational attainment. Among the 4894 women in the marriage-fertility sample,101 women are childless and 1168 women do not have an adult daughter. When weighted, the transmission sample representsall daughters who are 20 and older of ever-married surviving Korean women born between 1927 and 1961.

Because we rely on retrospective information from respondents aged 45 and older, we cannot address differential mor-tality appropriately: women who survived to age 45 and older are likely to differ from their original birth cohorts. In partic-ular, we speculate that better-educated women are overrepresented in the sample because of educational differentials inmortality. Table A.1 confirms this speculation, showing higher mortality for the less-educated women. So, we should be cau-tious in making inferences to the broader population from the analysis reported here. In addition, we can see that mortalitydifferentials decrease as women age. Actually, the existence of mortality differentials is more problematic for older womenbecause they have been exposed to differential mortality for a longer period of time. If mortality differentials grew over ages,our inferences for the older cohorts would be even more limited. The decreasing mortality differentials over ages at least donot worsen the sample selection problem. We can also see that mortality differentials by education increase over time, withlarger mortality differentials for the younger cohorts than the older. This suggests that ignoring mortality differentials ismore problematic for the younger cohorts. However, we should note that South Korea experienced rapid educational expan-sion and the share of less educated individuals decreased quickly over time (See Table 1). For example, while 86.5% of womenin the oldest cohort in the sample, born between 1927 and 1941, received 0–8 years of schooling, this figure amounts to only23.3% in the youngest cohort in the sample, born between 1951 and 1961. Although increasing differential mortality con-tributes to this reduction to some extent, the trends cannot be fully explained by differential mortality alone. Thus, the dis-

7 Divorce has increased rapidly in South Korea since the early 1990s. For example, about 15 divorces occurred per 1000 married Korean women in 2009(Korea Statistical Office, 2010). This rapid increase in divorce warrants more sophisticated methods to handle missing information on marital history foryounger women than the women examined in the present study.

8 We may be concerned about the possibility that children in non-intact families fare worse than those in intact families (McLanahan and Sandefur, 1994).Because women’s education is negatively associated with the chance of family disruptions (e.g., divorce/separation and widowhood), differential familydisruption may be one mechanism that creates intergenerational effects. Such effects are beyond the scope of the model used in this study.


tortion due to differential mortality is not particularly problematic for the younger cohorts. Although the existence of mor-tality differentials makes inferences to the entire birth cohort difficult, this limitation does not seriously impair the externalvalidity of this study.

In addition, the survey collected information only on the total number of surviving children, and not on the number oftotal births. Given the negative association between maternal education and child mortality in South Korea (Choe, 1987;Kim, 1988), using the number of surviving children would underestimate the educational differentials in fertility. This under-estimation has two implications for the present analysis. First, differential child mortality by mother’s education is not prob-lematic for the fertility model as long as we are interested in differential reproduction rates where reproduction should meanthe survival until adult ages and the completion of a certain level of schooling. This suggests that the number of survivingchildren would be an even more appropriate measure than the number of total births in this analysis. However, we shouldconsider adult mortality too because some daughters of the women in this sample survived until a certain adult age (e.g., age20) but died before the survey. If this mortality rate depends on maternal education, our daughter’s sample may not be rep-resentative of the population of interest. We speculate that there is a modest association between maternal education andyoung adult mortality because of (1) the intergenerational association in educational attainment, and (2) educational differ-entials in mortality. However, we do not find any empirical evidence for or against this conjecture. In addition, mortalityrates among young adults are quite low (Kim, 2004), making the influences of differential mortality by maternal educationsmall. Second, differential child mortality by mother’s education has implications for the transmission model because sibshipsize is underestimated, particularly for the less educated. Resource competition is claimed as a primary reason for the neg-ative association between socioeconomic outcomes and sibship size (Guo and VanWey, 1999). From this standpoint, earlydeaths (e.g., infant mortality) may not pose a significant problem for the transmission model. However, later deaths (e.g.,in teenage and young adult years) would be problematic, particularly if maternal education is strongly associated withthe chance of these deaths. We speculate modest association between these, but do not have empirical evidence for oragainst this conjecture.

The above discussion suggests that ignoring mortality differentials by education reduces the external validity of the pres-ent analysis to some extent. However, Kye (2011) found that the implication of differential mortality for the intergenera-tional transmission of women’s educational attainment is not great because of substantial intergenerational educationalmobility. This suggests that ignoring mortality differentials by education should not seriously distort the results.

5. Results

5.1. Descriptive results

Table 1 shows descriptive statistics for the marriage-fertility sample by women’s birth cohorts. First, we see strong edu-cational homogamy and hypergamy. For all cohorts, a significant percentage of women married husbands whose educationalattainments are the same as theirs. This is particularly true for college-educated women; about 90% of women with a collegeeducation married men with the same level of education. We can also see that women tend to marry up: upper diagonal cellstend to be bigger than lower diagonal cells. For example, more than one third of women with 12 years of schooling marriedmen with more than 13 years of schooling, but less than 10% of these women married down. Second, women’s education isnegatively associated with the number of children for all cohorts. While the mean number of children decreased across co-horts, educational differentials in the number of children are persistently observed for all cohorts. The existence of educa-tional differentials in fertility may dampen the intergenerational effects of increasing women’s educational attainment tosome extent. Finally, we can see that younger cohorts are better educated than older cohorts. For example, while less than7% of women in the oldest cohort (born in 1927–1941) attained a high school diploma, more than half of the women in theyoungest cohort (born in 1952–1961) did so.

Table 2 shows descriptive statistics for the transmission sample by birth cohorts. First, we can see that upward educa-tional mobility is prominent. While only about 20% of mothers and 40% of fathers finished high school, more than 80% ofdaughters attain at least a high school diploma. This strong upward mobility is observed for all birth cohorts. Secondly, apositive association between mother’s and daughter’s education is observed: better educated mothers tend to have daugh-ters with more schooling.

5.2. Parameter estimates

Table 3 reports parameter estimates of models for husband’s education, number of children, and daughter’s schooling.Weights and clustering within the primary sampling unit are taken into account in estimating each model. First, we seestrong educational assortative mating as expected from the descriptive analysis in Table 1: highly educated women are likelyto marry highly educated men.9 Second, there is a monotonic negative association between women’s educational attainment

9 The proportional odds or parallel regression assumption is violated in the model of husband’s education. In other words, the relative likelihood of marryinghusbands with higher education categories is not proportional. We also estimated a generalized ordered logistic regression, but this yielded virtually identicalsimulation results. Hence, we present the results using ordered logistic regression for the sake of simplicity.

Table 2Descriptive statistics for transmission sample, by birth cohort.

Women’s education Daughter’s education (%) Daughter’smarginal (N)

Father’smarginal (N)

Mother’smarginal (N)

0–8 9–11 12 13+ Total

Cohort 1 (1927–1941)0–8 17.9 16.8 47.5 17.8 100.0 15.9 (531) 65.3 (1020) 86.6 (1366)9–11 1.9 1.8 49.2 47.0 100.0 15.0 (509) 12.8 (214) 6.6 (114)12 0.8 1.5 26.7 71.0 100.0 46.2 (1686) 14.1 (224) 5.6 (93)13+ 0.0 0.0 6.3 93.7 100.0 22.9 (868) 7.9 (137) 1.3 (22)

Total 15.9 15.0 46.2 22.9 100.0 100.0 (3594) 100.0 (1595) 100.0 (1595)

Cohort 2 (1942–1951)0–8 3.6 8.3 54.1 34.1 100.0 2.4 (46) 41.3 (453) 62.6 (676)9–11 0.2 1.2 33.6 65.0 100.0 5.7 (101) 19.6 (215) 19.7 (211)12 0.0 0.0 11.7 88.3 100.0 43.3 (841) 27.3 (282) 13.6 (146)13+ 1.5 0.0 3.1 95.4 100.0 48.6 (951) 11.8 (123) 4.2 (40)

Total 2.4 5.7 43.3 48.6 100.0 100.0 (1939) 100.0 (1073) 100.0 (1073)

Cohort 3 (1952–1961)0–8 1.7 1.8 41.0 55.4 100.0 0.5 (6) 17.7 (169) 27.8 (259)9–11 0.0 0.3 29.6 70.1 100.0 0.7 (11) 19.9 (191) 26.6 (256)12 0.0 0.0 13.9 86.1 100.0 26.0 (349) 43.7 (414) 38.9 (375)13+ 0.0 1.8 7.1 91.1 100.0 72.7 (1003) 18.7 (183) 6.7 (67)

Total 0.5 0.7 26.0 72.7 100.0 100.0 (1369) 100.0 (957) 100.0 (957)

Entire sample0–8 11.4 12.4 49.0 27.2 100.0 7.7 (583) 42.4 (1642) 60.3 (2301)9–11 0.4 0.9 34.7 63.9 100.0 8.5 (621) 17.3 (620) 17.2 (581)12 0.1 0.2 14.9 84.8 100.0 40.5 (2876) 27.7 (920) 18.5 (614)13+ 0.6 0.7 5.3 93.3 100.0 43.3 (2822) 12.6 (443) 4.0 (129)

Total 7.7 8.5 40.5 43.3 100.0 100.0 (6902) 100.0 (3625) 100.0 (3625)


and fertility. However, we also see a curvilinear relationship between husband’s educational attainment and fertility. The least-educated men (0–8 years) have the most children. Once finishing junior high school (9 years+), more-educated men tend tohave more children. This result is consistent with findings in most demographic research which show that women’s socioeco-nomic status is negatively associated with fertility (Bongaarts, 2003; Jejeebhoy, 1995; Skirbekk, 2008) because it increases di-rect and opportunity costs of childbearing, but high status men are able to have more offspring because of their higherpurchasing power (Blake, 1968). Finally, we see that parental education is positively associated with daughter’s educationand that daughters growing up in larger families are worse off. Father’s education has a bigger impact on daughter’s educationthan does mother’s education. Combined with the results for husband’s schooling, this confirms previous research showing thatwomen’s educational attainment is a strong predictor of husband’s socioeconomic status but is not influential for family’s socio-economic status, net of husband’s status in South Korea (Lee, 1998).

From the coefficient estimates, we can speculate the magnitude of the intergenerational effects in each simulation. First,we expect bigger intergenerational effects in simulations with a corresponding change in marriage behavior (TM and TFM)than those without it (T and TF) because the effect of change in women’s educational attainment will be amplified via im-proved husband’s educational attainment in the TM and TFM model. Second, the inclusion of a fertility component in thesimulation may reduce or increase the magnitude of intergenerational effects. On one hand, this may reduce the intergen-erational effects because of higher reproduction rates of less-educated women compared to their better-educated counter-parts. On the other hand, the curvilinear relationship between husband’s education and fertility may offset the reduction inintergenerational effects in the TMF simulation because improvement in women’s education also increases the number ofhighly-educated husbands who have higher reproduction rates than the less educated. However, coefficient estimates fromthe Poisson regression suggest that women’s education matters more for reproduction rates than does husband’s education,indicating that the reduction in intergenerational effects due to this curvilinear relationship is not great. In addition, a neg-ative association between sibship size and educational attainment should reduce the negative impact of fertility differentialson the offspring’s educational attainment to some degree. Hence, the implications of differential fertility for intergenera-tional effects depend on the strength of fertility differentials by women’s education and the magnitude of the penalty thatlarge family sizes pose on daughter’s educational attainment.

5.3. Simulations

Tables 4 and 5 summarize the simulation results. Table 4 reports the ratios of simulated distributions to baseline distri-butions along with bootstrap standard errors. In the ‘‘Transmission only’’ (T only) simulation, we redistribute 5% of women’s

Table 3Parameter estimates for models of marriage, fertility, and intergenerational transmission.a

Husband’s schooling (ordered logit) Number of children (poisson) Daughter’s schooling (ordered logit)

b z b z b z

Women’s education0–8 (reference)9–11 2.291 29.66 �0.215 �9.95 0.699 6.6812 4.076 35.00 �0.401 �17.01 1.189 9.2813+ 6.709 27.26 �0.479 �14.14 1.253 3.51

Husband’s education0–8 (reference)9–11 – – �0.170 �7.94 0.821 8.6112 – – �0.102 �4.81 1.288 12.6113+ – – �0.050 �1.70 2.347 12.18

# of Siblings – – �0.394 �14.41Intercept – – 1.283 97.45 – –Cut point 1 0.628 – – – �3.381 –Cut point 2 1.863 – – – �2.402 –Cut point 3 4.607 – – – 0.194 –

Observations (N) 4894 4894 6902Pseudo log likelihood �4659.38 �12,421,922.00 �6364.16

a Weights and clusters are taken into account in estimating each model.


years of schooling according to four scenarios, but their marriage and fertility behaviors are assumed to remain the same. Forexample, the first scenario in the T only model (0–8 to 9–11) assumes that 5% of women change their years of schooling from0–8 to 9–11 years, but the characteristics of their husbands do not change accordingly and they produce the same number ofchildren as before. If the ratio is equal to one, this implies that the simulated change in women’s education distribution doesnot affect daughter’s education distribution (no intergenerational effect). The larger the deviation from one, the larger theintergenerational effects. Table 5 presents statistical tests of differences in intergenerational effects across models whenwe redistribute 5% of women’s schooling from 0–8 to 13+ years. The ‘‘Diff.’’ columns show the differences in intergenera-tional effects between models, and ‘‘s.e.’’ columns present bootstrap standard errors associated with these differences. Forexample, the difference in the proportion of daughters with 13+ years of schooling is 2.5% between T only model and the‘‘Transmission, Fertility, and Marriage’’ (TFM) model, with bootstrap standard error of .7%. In both Tables 4 and 5, we cansee that bootstrap standard errors are not substantial. None of the bootstrap standard errors are greater than 1%, suggestingthat the estimates of intergenerational effects are fairly precise.

The results summarized in Tables 4 and 5 point to the following conclusions. First, we can confirm positive intergener-ational effects. The intergenerational effects are largest when redistributing women’s education from 0–8 to 13+ years. Inaddition, the intergenerational effects are larger when redistributing the least-educated women: intergenerational effectsare greater when we redistribute 5% of women from 0–8 to 9–11 years than from 9–11 to 12 years, or from 12 to 13+ yearsof schooling. This is the case regardless of presence or absence of demographic elements. This suggests that providing moreopportunities for schooling for the least-educated group would have been most beneficial to the next generation. Finishingjunior high school (i.e., 9 years of schooling) makes more difference than do transitions in more advanced levels. Given thatmore than half of Korean middle-aged and elderly women (age 45 and above) in the sample belong to the least-educatedcategory (54.4%, see Table 1), we may conclude that finishing junior high school was very important for women’s socioeco-nomic and family-related outcomes that are associated with children’s educational successes.

Next, demographic factors are important in intergenerational effects: assortative mating makes a significant positive con-tribution to intergenerational effects whereas differential fertility operates in the opposite direction. For simplicity, we focuson the changes in the highest educated category, 13+ years of schooling. First, a change in marriage behavior makes animportant positive contribution to intergenerational effects. For example, intergenerational effects in the ‘‘Transmissionand Marriage’’ (TM) and TFM simulations are much greater than in the T only and ‘‘Transmission and Fertility’’ (TF) simula-tions. Whereas a 5% change in women’s educational attainment from less than a junior high graduate (0–8) to tertiary edu-cation (13+) would increase tertiary education (13+) by 4.2% in the T only simulation, this increases to 8.9% in the TMsimulation (Table 4). This 4.8% difference is statistically significant with bootstrap standard error of .08% (Table 5). This con-firms that the improvement in offspring’s educational attainment is due partly to the corresponding improvement of hus-band’s education in addition to the net effect of improvement in mother’s own educational attainment. However, in the‘‘Transmission and Marriage in a constrained marriage market’’ (TMC) simulation, where the marginal distribution of hus-band’s education remains the same, the size of the intergenerational effect is only 5.4% (Table 4), which is not much greaterthan the effect found in the T only simulation (4.2%) and the difference is not statistically significant (Table 5). The samepattern is observed for the comparison between TF and ‘‘Transmission, Fertility and Marriage in a constrained marriage mar-ket’’ (TFMC) simulation. This suggests that the change in the marginal distribution of husband’s education corresponding to

Table 4Intergenerational effects, ratios of the simulated proportions to the baseline predicted proportions of daughter’s educational attainments.

Daughter’s education

0–8 9–11 12 13+

Ratio s.ea Ratio s.ea Ratio s.ea Ratio s.ea

Simulationb

Transmission only, T0–8 to 9–11 0.939 0.007 0.946 0.012 0.990 0.003 1.029 0.0039–11 to 12 0.995 0.006 0.999 0.009 1.011 0.005 0.991 0.00612 to 13+ 1.002 0.004 1.001 0.004 0.998 0.004 1.001 0.0050–8 to 13+ 0.951 0.007 0.954 0.007 0.972 0.005 1.042 0.007

Transmission + Fertility, TF0–8 to 9–11 0.944 0.006 0.951 0.010 0.988 0.002 1.029 0.0039–11 to 12 1.011 0.006 1.014 0.009 1.020 0.005 0.978 0.00512 to 13+ 1.005 0.004 1.003 0.004 1.000 0.004 0.999 0.0040–8 to 13+ 0.959 0.003 0.961 0.004 0.977 0.002 1.035 0.003

Transmission + Marriage, TM(Unconstrained marriage market)0–8 to 9–11 0.923 0.003 0.934 0.005 0.972 0.002 1.050 0.0039–11 to 12 0.995 0.002 0.990 0.004 0.981 0.002 1.020 0.00312 to 13+ 0.996 0.003 0.995 0.003 0.990 0.003 1.010 0.0030–8 to 13+ 0.912 0.004 0.915 0.004 0.934 0.004 1.089 0.005

Transmission + Marriage, TMC(Constrained marriage market)0–8 to 9–11 0.955 0.004 0.960 0.006 0.983 0.002 1.030 0.0039–11 to 12 1.005 0.002 0.999 0.003 0.987 0.002 1.011 0.00312 to 13+ 0.999 0.003 0.999 0.003 0.997 0.003 1.003 0.0040–8 to 13+ 0.956 0.005 0.953 0.005 0.957 0.005 1.054 0.006

Transmission + Fertility + Marriage,TFM (Unconstrained marriage market)0–8 to 9–11 0.931 0.003 0.941 0.004 0.974 0.002 1.045 0.0029–11 to 12 1.002 0.002 0.997 0.003 0.985 0.002 1.013 0.00212 to 13+ 0.999 0.003 0.998 0.003 0.993 0.003 1.007 0.0030–8 to 13+ 0.932 0.003 0.935 0.003 0.952 0.003 1.066 0.004

Transmission + Fertility + Marriage,TFMC (Constrained marriage market)0–8 to 9–11 0.964 0.004 0.967 0.005 0.985 0.002 1.026 0.0039–11 to 12 1.012 0.002 1.006 0.003 0.992 0.002 1.004 0.00312 to 13+ 1.002 0.003 1.002 0.003 0.999 0.003 1.000 0.0040–8 to 13+ 0.977 0.004 0.974 0.005 0.976 0.004 1.030 0.004

Observed distribution (%) 7.7 8.5 40.5 43.3Baseline distribution (%) 4.6 6.3 39.8 49.3

a Bootstrap standard errors are reported (1000 replications).b Redistribution of 5% of women from lower to higher categories in each simulation.


the change in women’s education is crucial for explaining the importance of assortative mating in the intergenerationaltransmission process in South Korea. The significant impact of demographic elements is interesting because we expectedthat the influences of demographic elements may not be substantial due to the massive increase of educational attainmentover time. In terms of tertiary education among offspring, the difference in intergenerational effects between T only and TFMsimulation is 2.5%, which is statistically significant (Table 5). Hence, we can conclude that accounting for differential demo-graphic behaviors is still important for educational mobility even when educational opportunity expands dramatically.

Finally, the implications of differential fertility depend on the presence of assortative mating. For example, the compar-ison between T only and TF simulation shows no significant difference (Table 5). The insignificant difference between themsuggests that differential fertility is not large enough to offset the positive influence of smaller family size on daughter’s edu-cation when assortative mating is absent. Inclusion of the fertility component, however, becomes significant when assorta-tive marriage is present. Whereas a 5% change in women’s educational attainment from less than a junior high graduate (0–8) to tertiary education (13+) would increase the share of those with a tertiary education (13+) by 8.9% in the TM simulation(5.4% in TMC simulation), this amounts to 6.6% in the TFM simulation (3.0% in TFMC simulation). These differences are sta-tistically significant (Table 5). In sum, a change in marriage behaviors amplifies the intergenerational effect and changes infertility dampen the intergenerational effect when assortative mating is present.

Fig. 1 visually summarizes the results discussed above. It shows the size and the variability of intergenerational effects inthe most extreme scenarios examined: simulations in which 5% of women change their education from 0–8 to 13+. From thisgraph, we can see the maximum, 75 percentile, median, 25 percentile, and minimum for the ratios of the simulated to the

Table 5Differences in intergenerational effects across models (redistribution of 5% of women from 0–8 to 13+ years of schooling).

Models compared Daughter’s education

0–8 9–11 12 13+

Diff. s.ea Diff. s.ea Diff. s.ea Diff. s.ea

T only vs. TF 0.007 0.005 0.008 0.005 0.005 0.004 �0.007 0.005TM �0.039 0.008 �0.039 0.008 �0.038 0.006 0.048 0.008TMC 0.005 0.008 �0.001 0.008 �0.015 0.006 0.013 0.008TFM �0.019 0.007 �0.019 0.007 �0.020 0.005 0.025 0.007TFMC 0.026 0.007 0.020 0.008 0.004 0.005 �0.011 0.007

TF vs. TFM �0.026 0.003 �0.026 0.003 �0.025 0.003 0.032 0.003TFMC 0.018 0.004 0.012 0.004 �0.001 0.003 �0.004 0.004

TM vs. TMC 0.020 0.002 0.020 0.002 0.018 0.002 �0.023 0.003TFM 0.044 0.003 0.038 0.003 0.023 0.002 �0.035 0.003

TMC vs. TFMC 0.021 0.002 0.021 0.002 0.018 0.002 �0.024 0.003

TFM vs. TFMC 0.045 0.003 0.039 0.003 0.024 0.002 �0.036 0.003

T only: Transmission only.TF: Transmission + Fertility.TM: Transmission + Marriage in an unconstrained marriage market.TMC: Transmission + Marriage in a constrained marriage market.TFM: Transmission + Fertility + Marriage in an unconstrained marriage market.TFMC: Transmission + Fertility + Marriage in a constrained marriage market.

a Bootstrap standard errors are reported.


baseline proportion of each education category.10 We see that sampling variability of estimates is not great and that ranges donot include 1 in any simulations. This means that the estimates for intergenerational effects are precise and that the shift in wo-men’s education in each simulation has a statistically significant effect on daughter’s educational distribution. Substantively, thisgraph illustrates the implications of assortative mating and differential fertility for intergenerational effects discussed above.Again, we see that a change in marriage behaviors corresponding to educational upgrading is important. In the TM simulation,the proportion with the highest education increased the most and the proportions of other education categories decreased themost. The strong impact of assortative mating on the size of intergenerational effects has two important implications for inter-generational transmission of women’s educational attainment in South Korea. First, this suggests that marriage is an institutionthat consolidates educational inequality in South Korea. Without assortative mating, educational inequality in the next genera-tion is not strongly influenced by the educational inequality in previous one. Second, this also confirms husband’s primacy indetermining family’s socioeconomic status (Lee, 1998). Without a change in husband’s education, the increase in women’s edu-cation has a weaker impact on the distribution of daughter’s education. This is also evident from the substantially smaller inter-generational effects found in the simulation that impose marriage market constraints. Second, differential fertility dampens theintergenerational effect to some extent, as reproduction rates are higher among the less-educated women than the better-edu-cated women. The negative influence of large family size on daughter’s educational attainment does not fully offset differentialreproduction rates. The influences of differential fertility are insignificant when assortative mating is absent.

The intergenerational effects in South Korea show different patterns than Mare and Maralani’s (2006) Indonesian case,where the recursive demographic model for intergenerational effects was first applied. The comparison not only illustratesthe uniqueness of the Korean experience, but also improves our understanding of the process of distributional changes ineducation across generations. We can see two noticeable differences. First, the magnitude of intergenerational effects ismuch larger in Indonesia than in South Korea, although direct comparison between these two countries is not feasible be-cause of a different categorization of educational attainment. For example, whereas redistributing 5% of women from 0–8 years to 13+ years of schooling in South Korea leads to a 5.4% increase in percent of college-educated daughters (see Table4), much less drastic change in Indonesia (e.g., redistribution of 5% of women from 10–12 years to 13+ years of schooling)leads to a slightly larger change, a 5.9% increase (see Table A.1 in Mare and Maralani (2006, p. 562)). The smaller intergen-erational effects in South Korea are consistent with our expectation that rapid educational expansion yields small intergen-erational effects because children are likely to attain the high level of educational attainment regardless of their parents’education level. While Indonesia also experienced substantial change in the marginal distribution of education attainmentacross generations, the change is much more drastic in South Korea.11 The huge upward educational mobility driven by edu-cational expansion in South Korea is largely responsible for the smaller intergenerational effects.

10 We excluded outliers that are defined as the observations that fall more than 1.5 interquartile range above the upper quartile or below the lower quartile(Agresti and Finlay, 1999: p. 64). The lines that mark maximums and minimums actually indicate the points of 1.5 interquartile range above the upper quartileor below the lower quartile if such outliers exist. Among 1000 replications, only a small number of simulations yield outlying intergenerational effects, andthese are not much different from the cutoff points.

11 Percentage of those attaining tertiary education increased from 1.0% to 7.9% in Indonesia (See Table 1 in Mare and Maralani 2006, p. 551) whereas thisfigure changed from 4.0% to 43.3% in South Korea (See Table 2).

.9

1

1.1

.9

1

1.1

.9

1

1.1

.9

1

1.1

T on

ly TF TM

TMC

TFM

TFM

C

T on

ly TF TM

TMC

TFM

TFM

C

T on

ly TF TM

TMC

TFM

TFM

C

T on

ly TF TM

TMC

TFM

TFM

C

Years of schooling 0-8 Years of schooling 9-11

Years of schooling 12 Years of schooling 13+

Sim

ulat

ed/B

asel

ine

T only: Transmission only TF: Transmission + Fertility TM: Transmission + Marriage in an unconstrained marriage market TMC: Transmission + Marriage in a constrained marriage market TFM: Transmission + Fertility + Marriage in an unconstrained marriage market TFMC: Transmission + Fertility + Marriage in a constrained marriage market

Fig. 1. Intergenerational effects, proportional changes in daughter’s educational attainment when 5% of women change schooling from 0–8 to 13+ years.


Second, the implications of assortative mating and differential fertility are weaker in South Korea than in Indonesia. Thedifference in percentage of college-educated daughters between T only and TFM simulation in Indonesia is 17.8% when redis-tributing 5% of women’s education from no schooling to 13+ years (see Table A.1 in Mare and Maralani (2006, p. 561)). Thedifference in percentage of college-educated daughters between T only and TFM simulation in South Korea is 2.4% whenredistributing 5% of women’s education from 0–8 to 13+ years of schooling. Such a big difference suggests much smallerimplications of demographic processes for intergenerational effects in South Korea than in Indonesia. This is interesting be-cause substantial educational differentials in marriage and fertility also exist in South Korea. We also find that these ele-ments make a statistically significant difference in intergenerational effects in South Korea. This pattern is also related tothe rapid educational expansion in South Korea. As we have discussed, daughters in South Korea are likely to attain high lev-els of education due to the huge expansion of educational opportunity. Hence, demographic reproduction has only modest,while statistically significant, implications for intergenerational effects. In sum, the comparison with the Indonesian casehighlights the importance of educational expansion in determining the intergenerational effects in South Korea.

5.4. Cohort comparison

As discussed above, South Korea experienced rapid socioeconomic and demographic change in the past half-century. Thisleads us to expect sizeable transformation of intergenerational transmission, differential reproduction, and assortative mat-ing processes over time, which also influence the size of intergenerational effects. To test this idea, we separately estimatethe marriage, fertility, and transmission model by cohorts, and do the simulations for each cohort using the parameter esti-mates. The results for each cohort are reported in Tables A.2–A.4. The bootstrap standard errors from 1000 replications arereported. Bootstrap standard errors tend to be larger in cohort-specific simulations than simulations using an entire sampledue to smaller sample size. Here, we focus on the intergenerational effects for the least-educated and the most-educatedgroup from the most extreme simulations in which we change 5% of women’s education attainment from 0–8 to 13+.

Two general patterns are observed for all the cohorts. First, we see similar patterns observed in Table 4 and Fig. 1: sub-stantial intergenerational effects with a corresponding change in marriage behavior and weaker influences of differential fer-tility on intergenerational effects than assortative mating. This suggests persistent patterns of intergenerational effects ofchanging women’s educational attainment over time despite the changing magnitude. Second, the estimates of intergener-ational effects are less reliable for the least-educated youngest cohort (0–8, 1952–1961) and the most-educated oldest co-hort (13+, 1927–1941) than others. This is the case because the sample sizes for these groups are quite small.

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T only: Transmission only TF: Transmission + Fertility TM: Transmission + Marriage in an unconstrained marriage market TMC: Transmission + Marriage in a constrained marriage market TFM: Transmission + Fertility + Marriage in an unconstrained marriage market TFMC: Transmission + Fertility + Marriage in a constrained marriage market

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Fig. 2. Cohort comparison of intergenerational effects, proportional changes in daughter’s educational attainment when 5% of women change schoolingfrom 0–8 to 13+ years.


We can also see cohort changes in intergenerational effects. First, the upper panel in Fig. 2 shows that intergenerationaleffects increase across cohorts for the group of least-educated daughters. While a less than 10% decrease in the least-edu-cated daughters is expected from the simulation for the cohort born between 1927 and 1941, this amounts to 10–20% forthe youngest cohort (born in the 1950s). Second, intergenerational effects decrease across cohorts for the most-educatedgroup (lower panel in Fig. 2). For example, let us consider the TMF simulation. Whereas a 5% change in women’s educationalattainment from less than a junior high graduate (0–8) to having at least some tertiary education (13+) would increase thepercent tertiary (13+) by about 15% for the oldest cohort, this amounts to much less than 5% for the youngest cohort. Thechanging intergenerational effects across cohorts are interesting because the intergenerational association in education doesnot change much across cohorts. The persistent intergenerational association over time is also observed in previous studies(e.g., Park, 2007). Hence, the cohort changes in intergenerational effects largely reflect the rapid educational expansion inSouth Korea. The increasing intergenerational effect for the least educated suggests the disappearance of the least-educatedcategory over time. Among the youngest cohort, only 0.5% of women belong to this category. Because the least educated arecomposed of such a small portion of population, the small or modest decrease in this group leads to large proportionalchange. Hence, further improvement of women’s educational attainment and subsequent enhancement in daughter’s edu-cation attainment will lead to the disappearance of the least-educated group in the future. In addition, the decreasing inter-generational effects for highly educated women reflect a saturation of women’s educational attainment for the youngercohort. Whereas only 23% of daughters of the oldest cohort received a tertiary education, 73% of daughters of the youngestcohort attended college (see Table 2). Therefore, there is not much room for improvement in women’s educational attain-ment in the youngest cohort. The compositional constraints make intergenerational effects for highly educated women smal-


ler over time. In sum, changing intergenerational effects suggests the disappearance of the least-educated group and a sat-uration of women’s educational attainment in South Korea. This change also illustrates the importance of distributionalchanges for educational mobility.

Third, the impact of marriage on intergenerational effects decreased substantially. For example, the difference between TFand TFM is about 10% for the oldest cohort (1927–1941), about 5% for the middle cohort (1942–1951), and almost negligiblefor the youngest cohort (1952–1961). This suggests that the indirect benefits of increasing women’s educational attainmentvia a parallel upgrading of husband’s education have been decreasing over time in South Korea. As mentioned earlier, Lee(1998) showed that women’s educational attainment is a strong predictor of husband’s socioeconomic status but is not influ-ential for family’s socioeconomic status net of husband’s status in South Korea. The analysis of the entire sample in the cur-rent study also confirms the strong impact of assortative mating on intergenerational effects. Cohort comparison, however,suggests weakening of the impact of assortative mating on the intergenerational effects. We speculate a couple of explana-tions for this change: improvement in women’s socioeconomic status during the socioeconomic development (Mason, 1985)and changing labor markets conditions for men and women (Oppenheimer, 1988). We need more systematic investigationon this topic to test these ideas rigorously, which is beyond the scope of the current study.

6. Summary and discussion

The present study examines the intergenerational effects of women’s education by replicating Mare and Maralani’s(2006) model to South Korea, a nation that experienced rapid socioeconomic and demographic transformation over the lasthalf century. Examining intergenerational effects is important because this provides a complete description of the intergen-erational transmission of family backgrounds, which is a marked improvement over most previous mobility studies. The cur-rent study also improves Mare and Maralani’s (2006) original study by estimating bootstrap standard errors that allow fortesting statistical significance. The results point to the following conclusions. First, differential demographic behaviors areimportant intervening mechanisms in South Korea, similar to Mare and Maralani’s (2006) Indonesian case. When improve-ment in women’s education leads to the improvement in husband’s education, the intergenerational effects are amplifiedsubstantially. By contrast, differential fertility reduces intergenerational effects significantly. Second, the magnitude of inter-generational effects, however, is smaller in South Korea than in Indonesia. More drastic change in the distribution of educa-tional attainment in South Korea leads to weaker dependence of daughter’s education on mother’s education, which yieldssmaller intergenerational effects. Third, the contribution of assortative marriage and differential fertility to intergenerational

Table A.1Mortality differentials by cohort and education.a

Cohort Age

25–34 35–44 45–54 55–64

1926–1935No schooling – 1.15 1.17 0.95Primary – 1.09 1.02 1.15Secondary – 0.50 0.62 0.72Tertiary – 0.37 0.55 0.64

1936–1945No schooling 2.20 2.41 1.28 1.09Primary 1.12 1.07 1.19 1.13Secondary 0.44 0.51 0.20 0.77Tertiary 0.29 0.33 0.49 0.52

1946–1955No schooling 8.55 4.06 2.07 –Primary 1.49 1.64 1.39 –Secondary 0.46 0.67 0.79 –Tertiary 0.38 0.44 0.54 –

1956–1965No schooling 14.77 4.59 – –Primary 3.03 2.86 – –Secondary 0.74 0.89 – –Tertiary 0.50 0.45 – –

1966–1975No schooling 15.58 – – –Primary 8.54 – – –Secondary 1.11 – – –Tertiary 0.57 – – –

a Age-education-specific mortality ratios to overall age-cohort mortality assuming the same differential mortality in 10-year period. Sources: Kim (2004).

Table A.2Intergenerational effects, ratios of the simulated proportions to the baseline predicted proportions of daughter’s educational attainments (1927–1941).


0–8 9–11 12 13+


Simulationb








a Bootstrapping standard errors are reported (1000 replications).b Redistribution of 5% of women from lower to higher categories in each simulation.


effects is also weaker in South Korea than in Indonesia. The rapid educational expansion is also responsible for this weakercontribution. The level of daughter’s education in South Korea is so high that there is not enough room for differential demo-graphic behaviors to be influential for intergenerational effects. Finally, intergenerational effects change across cohorts,reflecting South Korea’s educational expansion over several decades. In particular, the size of intergenerational effects showsa decreasing trend for the highly educated daughters although the cohort difference is not statistically significant due tolarge sampling variability among the highly educated women in the older cohorts and the less-educated women in the youn-ger cohorts.

Research on social mobility has found that industrialized countries exhibit common patterns of intergenerational asso-ciation but specific parameters that vary by time and place (Erikson and Goldthorpe, 1992; Shavit and Blossfeld, 1993). Po-sitive intergenerational association exists almost universally, but the size of this association varies. One source of this cross-national difference is structural mobility, defined as changes in marginal distribution of socioeconomic status across gener-ations (Hout, 2004).12 This claim, however, cannot be assessed appropriately, in part because most mobility studies rely on datafrom existing parent-offspring dyads. These data cannot address the issue of differential reproduction, which affects a key ele-ment of structural mobility, the marginal distribution of socioeconomic status in the parental generation (Duncan, 1966). Thefindings of the current study, including the comparison between South Korea and Indonesia, suggest that the patterns of inter-

12 For example, Hout (2004) conjectured that modest intergenerational association in the United States despite its high level of inequality can be explained bythe strong structural mobility.


generational effects are consistent with intergenerational association: positive intergenerational effects are confirmed with theKorean and Indonesian data. However, the intergenerational effects are smaller in South Korea than in Indonesia. Because ourmodel accounts for differential demographic reproduction, we can link the varying intergenerational effects to structural mobil-ity more confidently. This is arguably an important advantage of the new model of intergenerational effects over previous ap-proaches in social mobility. Hence, we attribute the cross-national differences in intergenerational effects to the difference instructural mobility in educational attainment and find that greater improvement in educational attainment in South Korea thanIndonesia is responsible for this difference. In other words, the difference in distributional changes explains the cross-nationaldifferences in the magnitude of intergenerational effects. Further applications of this model to other countries will be necessaryto advance this claim further.

This study leaves a couple of issues unanswered. First, models for husband’s schooling, fertility, and daughter’s schoolingare not necessarily the best models for each process. In particular, we ignore cultural factors prevalent in Korea that influencethe family reproduction process. Some of these factors include strong son preference and more sex-discriminative educa-tional investment practices in Korean families than in Western ones (Choe and Park, 2006), although these have been weak-ening over time. Taking into account these elements in fertility and transmission models will provide a better description ofthe intergenerational transmission of educational attainment and will enhance our ability to assess intergenerational effects.However, such improvement will be achieved at the cost of much more complication in data analysis. Another point is theconstraints in the marriage market. The present study simply assumes that husband’s education distribution also changesaccording to the change in women’s education distribution (TM and TFM model), or that the association (e.g., odds ratios)

Table A.3Intergenerational effects, ratios of the simulated proportions to the baseline predicted proportions of daughter’s education attainments (1942–1951).


0–8 9–11 12 13+

Ratio s.eb Ratio s.eb Ratio s.eb Ratio s.eb

Simulationa








a Redistribution of 5% of women from lower to higher categories in each simulation.b Bootstrapping standard errors are reported (1000 replications).

Table A.4Intergenerational effects, ratios of the simulated proportions to the baseline predicted proportions of daughter’s education attainments (1952–1961).


0–8 9–11 12 13+


Simulationb








a Bootstrapping standard errors are reported (1000 replications).b Redistribution of 5% of women from lower to higher categories in each simulation.


is the same but the marginal education distribution of husbands does not change at all (TMC and TFMC model). Yet actualmatching mechanisms in marriage markets may differ from this. Using matching models that account for the opportunityand constraints of both sexes (e.g., Logan et al., 2008) will improve the understanding of marriage markets, which will helpus better assess the intergenerational effects of increasing women’s education.

This article, however, provides much richer description of educational mobility in South Korea than most previous stud-ies. Whereas earlier efforts focused on the intergenerational association based on existing parent-offspring dyads in SouthKorea (e.g., Park, 2004; Phang and Kim, 2002), we show how intermediate demographic processes contribute to the changingdistribution of educational attainment across generations. Hence, this study provides a better understanding of the rapiddemographic and socioeconomic changes in South Korea over the past half century than do previous studies.

Acknowledgments

Earlier versions of this article were presented at seminars in RAND, Santa Monica, CA in January 2010, the Research Com-mittee on Social Stratification of International Sociological Association meeting (RC 28) at Yale University, CT in August 2009,and the American Sociological Association meeting in San Francisco, CA in August 2009. The authors thank Kate H. Choi, Ju-dith A. Seltzer, Jenjira J. Yahirin, Andrew Cherlin, Yu Xie, and four anonymous reviewers for valuable suggestions and com-ments. We also thank Tom Rushmer for editorial help.


Appendix A

See Tables A.1–A.4.

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