gender norms and housework time allocation among dual

Gender Norms and Housework Time AllocationAmong Dual-Earner Couples

Jisoo Hwang∗ Chulhee Lee† Esther Lee‡

February 27, 2017

Abstract

This paper studies the effect of parental gender norms on the allocation of house-hold work among dual-earner couples. Using regional variation in the strength of sonpreference in Korea, we proxy parental gender norms with sex ratio at birth in one’sbirthplace (POB SRB). We find that both the total and the wife’s housework timeincrease when the husband is from a province with higher sex ratio at birth, whereasthe husband’s housework time is unaffected by the couple’s POB SRB. We explore po-tential mechanisms, including human capital accumulation, preference for householdproduction, and selection in the marriage market.

JEL Codes: D13, J16, Z10Keywords: household work, sex ratio at birth, time use, gender norms

∗Department of International Economics and Law, Hankuk University of Foreign Studies, 107 Imun-ro,Dongdaemun-gu, Seoul 02450, Korea. Email: [email protected].†Department of Economics, Seoul National University, Seoul, Korea. Email: [email protected].‡Department of Economics, Seoul National University, Seoul, Korea. Email: [email protected].

We thank Eleanor Jawon Choi, Youjin Hahn, Jinyoung Kim, Seik Kim, Ki Seong Park, Heonjae Songand participants at the 2016 Korean Allied Economic Associations Annual Meeting, and SJE Interna-tional Conference on Human Capital and Economic Development for helpful comments and discussions.Hwang acknowledges research grant from Hankuk University of Foreign Studies. C. Lee received supportfrom the National Research Foundation of Korea Grant (SSK), which is funded by the South Korean gov-ernment (NRF-2016S1A3A2924944) and the Institute of Economic Research of Seoul National University.E. Lee acknowledges support from the BK21Plus Program (future-oriented innovative brain raising type,21B20130000013) funded by the Ministry of Education (MOE, Korea) and the National Research Foundationof Korea (NRF). All remaining errors are our own.

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mailto:[email protected]



1 Introduction

The gender gap in education, including test scores and college enrollment rates, have nar-

rowed down or reversed in many developed countries. Female labor supply, on both the

extensive and intensive margins, increased over the decades. Yet the glass ceiling at work-

place persists, with very few women reaching the top of the ladder and women getting paid

less than men even in the most gender-equal countries. There are numerous explanations

for the “stalled revolution,” ranging from gender discrimination in the labor market, the

structure of occupations, gender differences in psychological factors, to social norms about

gender roles (e.g., Hochschild, 1989; Blau and Kahn, 2006; Bertrand, 2011; Goldin, 2014).

By studying the allocation of household work among dual-earner couples, this paper

illustrates how norms about gender roles at home could hinder their convergence outside the

home. To test the effect of parental gender norms on housework time, we exploit the regional

variation in sex ratios at birth in South Korea (henceforth, Korea). The ratio of male to

female births captures the prevalence of son preference, or more broadly, traditional gender

norms. We find that both the total and the wife’s housework time increase significantly

when the husband is from a province with higher sex ratio at birth. The difference in sex

ratio at birth between Jeon-buk (109) and Gyeong-buk (125), for example, implies about 6

more hours of housework per week for the working wife. The husband’s housework time,

on the other hand, is unaffected by his or his wife’s hometown. Results remain robust to

alternative specifications, such as accounting for the couple’s actual working hours, number

and age of children, earnings, and proxies for intra-household bargaining.

The findings are difficult to reconcile with standard models of marriage and household

production, which predict specialization in the market or non-market sector based on spouses’

relative productivity. Instead, they highlight the importance of parental gender norms even

among working couples. We explore three potential mechanisms. First, there may be differ-

ences across regions in gender-specific human capital accumulation. In regions with stronger

son preference, boys may grow up to have lower household productivity because they are less

exposed to domestic tasks. Second, preference for home goods (versus market goods) may

differ by one’s parental gender norms. Those from more traditional households may pre-

fer the wife to do housework regardless of her productivity or available market substitutes.

Third, there may be selection in the marriage market with regards to one’s productivity or

preference regarding household work.

The paper contributes in several ways to the growing literature on the effect of gender

norms on economic outcomes. First, we exploit regional variation in sex ratios at birth within

a country. This has at least two important advantages compared to prior studies that mainly

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use cross-country differences in female labor force participation, fertility rates, or survey

responses to gender attitude questions among immigrants (e.g., Fortin, 2005; Fernandez

and Fogli, 2009; Alesina and Giuliano, 2010). Although these measures are correlated with

each other, they are also affected by the source country’s stage of economic and political

development.1 The epidemiological approach also faces limitations because immigrants from

different countries vary not only in their cultural background but also in their human capital

(e.g., language barrier) and reasons for migration. By exploiting regional variation within

an ethnically homogeneous country like Korea, we are less concerned about such omitted

variable bias.2

Second, our data, the Korean Labor Income Panel Survey (KLIPS), includes time use

and demographic characteristics of both the husband and the wife. Studies using the Amer-

ican Time Use Survey, for instance, cannot conduct couple-level analysis because the survey

collects information from only one member of the household (e.g., Aguiar and Hurst, 2007;

Gimenez-Nadal and Sevilla, 2012; Hwang, 2016b). Using the KLIPS, we are able to distin-

guish whether a woman does more housework because there is more work in total or because

her husband does less than other men.3

Lastly, by exploring potential mechanisms through which parental gender norms influence

housework time allocation, we aim to go further than documenting that norms matter per

se. In the seminal work, Akerlof and Kranton (2000) incorporate identity, “a person’s sense

of self,” into economics and theoretically show how prescriptions about appropriate gender

roles can affect occupation choice and household division of labor by evoking discomfort (in

oneself and in others) when they are violated. Most empirical papers on this topic, however,

do not discuss as deeply about the relationship between gender norms and the outcomes

of interest. Complementing recent work like Bertrand et al. (2015), we provide suggestive

evidence that it is difficult to explain our findings without differences in preferences about

how much housework should be done by whom.

The rest of the paper is organized as follows. In Section 2, we describe the regional

variation in sex ratios at birth in South Korea. In Section 3, we present the data and

empirical framework. Section 4 shows the main results and their robustness. In Section 5,

we discuss potential mechanisms and conduct further analyses. Finally, Section 6 concludes.

1For example, post-Communist countries tend to have high female labor force participation but this maynot necessarily imply egalitarian gender norms.

2According to the 2015 Population Census, the “Korean” ethnic group accounts for 97.3% of the totalpopulation of Korea.

3There are few papers that make use of data on time use of spouses. Using the Canadian Census ofPopulation, Frank and Hou (2015) for example, document both the absolute and relative measures of couples’labor activities. Gimenez-Nadal and Molina (2013) also analyze at the couple level using the MultinationalTime Use Study of Spain and the UK, although their focus is on parents’ educational childcare time.

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2 Background

As in many other Asian countries, son preference has persisted across generations in Korea.

Influenced by Confucianism, sons (the eldest son, in particular) are expected to “carry on

the family line” by serving the family’s ancestors. Economic returns also differ by child

gender, as Korea lags behind other developed countries in female labor force participation

and male-to-female earnings ratio.4

*** Figure 1 here ***

The prevalence of son preference is revealed most starkly by the imbalance of sex ratio

at birth that emerged after the introduction of ultrasound technology, enabling sex-selective

abortion. As depicted in Figure 1, sex ratio at birth began to increase in the 1980s in Korea,

reaching a peak in 1990 with 117 boys being born per 100 girls. After the early 1990s,

however, the ratio began to decline until it reached the natural level of about 106 boys per

100 girls in 2007.5


Throughout this period, there was (and still is) substantial regional variation in the

strength of son preference. As observed in Figure 2, sex ratio at birth is remarkably higher

in southeastern regions. In 1988, the provinces with the highest sex ratio at birth in Korea

were Daegu (134.5) and Gyeong-buk (125.2), both in the southeast. In 2000, the two highest

were again Gyeong-buk (113.6) and Daegu (113.4). Although inter-province differences have

diminished over time, the ranking of the provinces by sex ratio at birth remains stable.

In this paper, we use the regional variation in sex ratios at birth in the early 1990s as a

measure of the average parental gender norms of individuals born in that region. By parental

gender norms, we refer to the gender roles an individual was exposed to from childhood by

one’s parents (and other adults in the region). There are several reasons why we use sex

ratio at birth, and this period, in particular. First, sex ratio at birth represents parents’

child gender discrimination not only before birth but after birth as well. Lin et al., 2014,

Hu and Schlosser, 2015, and Lee and Lee, 2015 show that girls’ outcomes improve (they

receive more parental investments) when sex-selective abortions become prevalent in a given

4According to the OECD Employment Outlook 2014, employment to population ratio among womenaged 15–64 in Korea ranks 27th out of 34 OECD countries (at 54.9 percent) and the gender earnings gapremains the largest (at 37 percent).

5The re-balancing of sex ratio at birth in Korea is attributed to a number of factors—economic develop-ment, relative improvements in social and economic status of females, increasing disadvantage of males inthe marriage market, and weakening son preference (Chung and Gupta, 2007; Edlund and Lee, 2013; Lee,2013; Choi and Hwang, 2016).

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region, because then girls are only born to parents who have less gender bias. Another

way to check this is to compare female outcomes across regions with different sex ratios

at birth, among cohorts preceding the introduction of prenatal sex-selective technology. In

regions with higher sex ratios at birth in Korea, for example, women have lower educational

attainment, are less likely to be employed, and are less likely to represent leadership positions

in governments.6

Second, an important assumption in using the regional variation in sex ratios at birth is

that there are no significant differences across regions in legal or technological constraints re-

garding prenatal sex selection. Abortion clinics remained accessible and inexpensive across

the country, although it has always been prohibited by law except under special circum-

stances. The diffusion of prenatal sex diagnostic technology (e.g., ultrasonic tests) was

completed by the late 1980s in Korea; by 1989, sex ratio at birth reached over 112 nationally

and over 109 in 12 out of 15 provinces. The 1990s is therefore conservatively the earliest time

period in which regional variation in sex ratios at birth captures differences in the strength

of son preference, rather than differences in the availability of sex-selection technology.

Lastly, norms tend to be persistent through intergenerational transmission. Sex ratios

at birth in the early 1990s would not be a good representation of parental gender norms of

a region if gender norms shifted substantially prior to the 1990s. Figure 2 above and Lee

and Lee (2015) confirm that there is a strong positive correlation in provincial sex ratios at

birth across time. Even if there are changes in son preference, as suggested by the recent

decline in sex ratio at birth, the parent generation of the currently married sample precedes

this new trend. Thus, different views about gender roles among younger cohorts today do

not threaten the validity of our identifying assumption. Note again that sex ratio at birth in

one’s hometown captures the effect of parental gender norms, which may or may not align

with one’s own gender attitudes.

6According to estimates using the 2 percent sample of the 2000 Census, the percentage female amongthose with college education born between 1960 and 1980 is 50 percent in the top 5 regions but 57.1 percentin the rest of the country, and female employment rate is 46.2 and 52.2 percent, respectively. The ratio ofwomen in managerial positions of local governments is constantly lower in the top 5 regions between 2002and 2014. Out of the seven metropolitan cities, the three with the lowest female representation are theregions with the highest sex ratios at birth: Ulsan (5.8 percent), followed by Busan (8.4 percent), and Daegu(8.9 percent).

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3 Data and Empirical Strategy

3.1 Data

We use the 1991–1994 sex ratio at birth of each person’s birthplace as a proxy for parental

gender norms. Sex ratios at birth are drawn from the Annual Report on Live Births and

Deaths Statistics published by the Korean Statistical Office. The data is compiled and

released annually based on the vital registrations of all Koreans. We sum up the number of

male and female births between 1991 and 1994 in each region to calculate region-specific sex

ratio at birth.

The primary dataset we use for time use information is the Korean Labor Income Panel

Survey (KLIPS). Using the sampling frame of the 1995 Census, the KLIPS is a longitudinal

survey of labor market activities of a nationally representative sample of households and

individuals residing in urban areas population. The first wave in 1998 surveyed 5,000 house-

holds and approximately 13,000 household members aged 15 and over. Follow-up surveys

are conducted annually, and 17 waves (1998-2014) have been completed so far.

In addition to the main survey, the KLIPS conducts topical surveys on various issues. In

wave 17, the time use survey was added. Respondents were asked to record what they did

during the previous day in 30-minute intervals. Activities belong to one of seven categories:

1) sleeping, 2) personal care, 3) work-related activities, 4) household-related activities, 5)

leisure, 6) social activities, and 7) others.7 We focus on time spent on “household-related

activities” (henceforth, housework), which consist of child care, family care (caregiving to

family members other than child), and housekeeping (food preparation, washing clothes,

indoor cleaning, grocery shopping, and visiting the bank or public office).

Although the classifications of time use in the KLIPS are less detailed than those in the

Korean Time Use Survey, the KLIPS has two important advantages. First, unlike many time

use surveys (including the American Time Use Survey), time use records in the KLIPS are

collected from not only the household head but all respondents between ages 15 and 74. This

allows us to directly examine within-household time allocation. Second, the KLIPS contains

rich information on demographic characteristics of all household members. Information on

birthplace of spouses, which is not available in the Korean Time Use Survey, is particularly

important because it is used to construct location-specific proxies of the spouse’s parental

gender norms.

We obtain time use records, individual-, and household-level characteristics from wave 17

and information on birthplace from previous waves of the KLIPS.8 We then merge this data

7There are 17 activities in total. As a result, each individual reports 48 (1440/30) time records.8For the initial sample of the KLIPS, information on birthplace was surveyed in the first wave (1998).

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with the sex ratio at birth statistics by one’s birthplace. Although the Annual Report on

Live Births and Deaths Statistics provides county-level data since 1991, the KLIPS identifies

one’s birthplace up to the province level and thus, we use province-level sex ratios at birth

throughout the paper. There are 16 provinces in South Korea.9

The estimating sample comprises of native-born, dual-earner married couples, younger

than or equal to age 55, who responded to the 2014 KLIPS time use survey. We exclude the

few individuals who report their birthplace to be “North Korea” or “foreign country” because

we cannot construct comparable measures of sex ratio at birth for them.10 A married couple

is defined as dual-earner if both the husband and wife are currently working for pay.11

We do not include all married households, because the time allocation decision in single-

earner (male-breadwinner) versus dual-earner households is subject to significantly different

time constraints.12 Dual-earner couples better fit the purpose of the paper in that we are

motivated by the gender gap in labor market outcomes. The age restriction is imposed to

minimize potential bias from selective retirement.13 Such criteria produce a final sample of

936 couples.

*** Table 1 here ***

Table 1 provides the summary statistics of the sample. On average, the men and women in

our sample are in their forties, are high school graduates, have 1.6 children in the household,

and work more than 40 hours per week. Mean housework time is 4.7 hours per week for

working men and 24.6 hours for working women, very similar to those reported in the 2014

Korean Time Use Survey (4.8 hours and 22.5 hours, respectively).14 The average 1991–1994

sex ratio at birth in province of birth is 115.5 boys per 100 girls. 43 percent of these couples

share the same province of birth.

For new household members added to the sample in later years (including the new sample added in 2009),the information was collected in the year of the first interview.

9Provinces are smaller than states and larger than cities.10Only 0.63 percent of the whole sample correspond to these cases.11Both wage or salary workers and the self-employed are included in the dual-earner sample. We exclude

unpaid family workers, because the distinction between work-related and household-related activities wouldnot be as straightforward for this group.

1250.6 percent of all married couples in the 2014 KLIPS are dual-earners.13We do not need a minimum age restriction because no men in our sample are under age 25, and only

one woman is.14We used the same dual-earner and housework time definitions as in the KLIPS sample. The Korean

Time Use Survey is a nationally representative time-use study collected by the Korean Statistical Office.Household members older than age 10 in 12,000 households are surveyed (N=27,000).

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3.2 Empirical Strategy

To investigate the effect of parental gender norms on a married couple’s housework time

allocation, we estimate the following system of equations:

yw = β0 + β1SRBm + β2SRBw + θX + εw (1)

ym = γ0 + γ1SRBm + γ2SRBw + θX + εm (2)

where subscript w represents the woman (wife) and m indicates the man (husband). The

dependent variable y is the number of hours per week an individual spends on housework.15

We jointly estimate the two equations using the Seemingly Unrelated Regression (SUR)

model, which allows the two error terms εw and εm to be arbitrarily correlated.16 This spec-

ification accounts for the fact household production is a public good for household members,

and thus housework time is probably not independently determined by the husband and the

wife. If the couple’s housework time are substitutes (complements), they would be negatively

(positively) correlated with each other. We also present separate regression results with total

housework time (yw + ym) as the dependent variable.

Although the Tobit is often used when the dependent variable is constrained to be non-

negative, we estimate the model via OLS. As discussed in Stewart (2013) and Foster and

Kalenkoski (2013), the Tobit may produce biased results in time-use analyses where the

window of observation is shorter than the period over which time allocation decisions are

made. Even if ym is observed to be zero, for instance, it may not be a “true zero” in that

we only observe time use of one day (the survey date). Considering that nearly 65 percent

of male respondents report zero hours of housework in our sample, we thus prefer the OLS

to the Tobit. Results are robust to alternative specifications including the Tobit, however,

as we show in Section 4.3.

The key covariates are SRBm and SRBw, the 1991–1994 sex ratio at birth in the province

of birth (POB SRB) of the husband and wife, respectively. Other control variables (X)

include individual-level characteristics such as years of schooling, age, job type (wage worker

or self-employed), log of earnings, and type of survey date (weekday or weekend), household-

level characteristics such as the number of children aged five and under and six to eighteen,

the number of adults in the household, and the log of non-labor family income.17 We consider

15Responses are in minutes during the previous day, but we convert to weekly hours via (minutes/60) ∗ 7for ease of interpretation.

16 Time-use studies that use the SUR framework include Kalenkoski et al. (2005), Kimmel and Connelly(2007), and Gimenez-Nadal and Molina (2013).

17Non-labor income is defined as the sum of income derived from dividends, interest, rent, transfer payment,and other sources of income. The couple’s working hours are not included in the baseline model becauseworking hours may be simultaneously determined with housework time. Adding working hours does not

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children by age group, because younger children demand more time from their parents.

Regional controls indicating Seoul, other metropolitan areas, and non-metropolitan areas

are also included to incorporate differences in the extent of at current residence. Standard

errors are clustered at the province of birth level.

The coefficients on SRBm and SRBw thus capture the impact of own and spouse’s POB

SRB on housework time, controlling for various individual- and household-level character-

istics. By comparing the significance and magnitude of these estimates, we would be able

to infer not only whether parental gender norms affect housework time allocation among

dual-earners, but also whether their effects are symmetrical for the husband and wife.

There are two potential concerns to our empirical framework. First, as in other studies

that analyze spousal outcomes, the model is not free from selection in the marriage market.

A woman who chose to marry a man from a region with strong son preference may differ

from a woman who chose to marry a man from a region with weak son preference, and such

factors could be related to how intra-household time allocation decisions are made. Thus,

β1, for instance, should not be interpreted as the causal effect of husband’s POB SRB on

the wife’s housework time. We control for various observables to alleviate the concern, and

discuss in detail how selection in the marriage market can affect the interpretation of our

results in Section 5.

Second, although restricting the sample to dual-earners has the advantage of studying

households with similar time constraints, an individual’s decision to work after marriage is

also a choice variable. Whether the secondary earner, usually the wife, works or not would

depend on characteristics such as her wage, wealth, educational attainment, number and

age of children, and gender attitudes. Our estimates of β and γ would not be biased if

selection in these dimensions are similar across POB SRB; but otherwise, it may be biased.

If labor force participation is lower among married women from higher-SRB provinces such

that they have particularly strong labor market attachment, POB SRB effects would be

underestimated. On the other hand, there could be an upward bias if these women choose

to work because they need additional income, and if poorer individuals are more likely to

have traditional gender attitudes. To address these concerns, we control for the couple’s

education level and non-labor family income in all our analyses and explore differences in

(potential) earnings. It is also reassuring to find that our results hold even when we extend

the sample to all married households.18

change results (see Section 4.3).18Results available upon request.

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4 Results

4.1 Descriptive Patterns

Figure 3 depicts the distribution of housework time by sex ratio at birth in the province of

birth (POB SRB) among dual-earner couples. The top panel illustrates the wife’s housework

time and the bottom panel the husband’s. We plot the mean housework time by each province

of birth, weighted by the number of observations. There are 16 provinces in total, and POB

SRB ranges from 109.3 to 124.8.


A number of observations can be drawn from the figure. First, there is a large gender

difference in the absolute level of housework time throughout the POB SRB spectrum.

Women spend roughly 20 hours per week more on household activities than men. Second,

there is a clear positive relationship between the wife’s housework time and POB SRB,

particularly the husband’s POB SRB. That is, the wife spends more time on housework

when her husband (or herself) was born in a province with skewed sex ratio at birth. Third,

there does not seem to be any systematic relationship between the husband’s housework

time and the couple’s POB SRB. The fitted line is nearly flat.

4.2 Main Results

To formally examine the relationship between parental gender norms and housework time of

dual-earner couples, Table 2 presents the regression results of equations (1) and (2). Note

again that the wife’s and husband’s time are jointly estimated using SUR and that the total

time is separately estimated via OLS. We show results including only the wife’s POB SRB,

the husband’s POB SRB, and both their POB SRB in separate columns.

Columns 1 and 3 show that the wife’s housework time incre-ases significantly with hus-

band’s POB SRB even when we control for demographic characteristics. To translate the

magnitude into real terms, the difference in the 1991–1994 sex ratio at birth between Jeon-

buk (109) and Gyeong-buk (125), for example, implies nearly 6 more hours of housework per

week for the wife. Wife’s own POB SRB has no significant effect on her housework time,

however, and its coefficient is close to zero once we control for various observables (cols. 2

and 3). The husband’s housework time is unaffected by neither the wife’s nor his own POB

SRB, confirming the pattern in Figure 3 (cols. 4–6).


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As a result, the sum of couple’s housework time simply mirrors the results from the wife’s:

it is larger when the husband’s POB SRB is higher (cols. 7–9). This is interesting because

it means that the wife’s and husband’s housework time are not substitutes. The correlation

between the residuals of equations (1) and (2) shown at the bottom of the table is positive,

and the Breusch-Pagan test of independence strongly rejects the null hypothesis that the

correlation is equal to zero. The wife does more housework when the husband is from a

province with higher SRB, but the reason is not because he does less housework than other

men; there is more household production in total. For example, they eat at home more often

(as to eating out) compared to families from lower-SRB provinces.19

Other covariates show expected signs. Housework time increases when there are more

children in the household, particularly those under age 5, because housework includes child-

care activities. Husband’s educational attainment is positively associated with the couple’s

housework time, consistent with findings in the literature that better educated parents spend

more time on childcare (e.g., Guryan et al., 2008; Dotti Sani and Treas, 2016). Wife’s educa-

tion is not as strongly correlated with her housework time, probably because housekeeping

(not childcare) takes up the lion’s share of women’s household work (see Table 1). Both

the husband’s and wife’s labor income are negatively correlated with their own housework

time; the higher the opportunity cost of one’s time, the less time one spends on non-market

activities. Non-labor family income is not statistically significant.


To investigate which specific household activity is most strongly influenced by parental

gender norms, we run regressions for each subcategory of housework, namely, child care,

family care, and housekeeping, in Table 3. Because time allocation across these activities

may be correlated with one another, in the sense that more time in one activity reduces

time available for another, we employ the SUR model as before but with three simultaneous

equations rather than two. The coefficient on the key variable of interest, husband’s POB

SRB, is positive for all three activities, but is statistically significant only for housekeeping.

The positive relationship between husband’s POB SRB and the wife’s housework time is

thus mainly driven by housekeeping activities—cooking, cleaning, and doing the laundry.

Although the cross-equation correlations reveal a strong trade-off between each pair of sub-

activities, results are similar when we restrict the sample to couples who have at least one

child under age 18.20

19We cannot conduct a direct analysis of relevant household expenditures. Although the KLIPS has aquestion about household-level living expenses such as food, only dining out on special occasions is classifiedseparately.

20An analogous analysis with husband’s housework time as the dependent variable reveals null effects of

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4.3 Robustness Checks

4.3.1 Alternative specifications

We conduct a series of tests to check the robustness of our findings. In Table 4 panel A,

instead of actual sex ratios at birth of each province, we use an indicator variable “top 5”

that equals 1 if the birthplace is ranked one of the top five in terms of the 1991–1994 sex

ratio at birth, and 0 otherwise.21 Results indicate that the wife spends about 3 hours per

week more on housework if her husband was born in one of the top five SRB provinces.

Again, husband’s housework time is not affected by the couple’s POB SRB rank.


In panel B, we apply the Tobit model as in several prior time-use studies (refer to footnote

16). Wife’s and husband’s time are jointly estimated, and total housework time is regressed

separately. Results are very similar to what we find using OLS. Only the husband’s POB

SRB has a significant effect on housework time, not the wife’s POB SRB. The magnitude of

the coefficient on husband’s POB SRB is preserved at about 0.4.

In panels C and D, we run the same baseline regressions but with different sample re-

strictions. In panel C, we exclude outliers—respondents whose housework time belongs to

the top 1 percent percentile within each group of survey date (weekday or weekend). For

example, we exclude couples in which the husband (wife) reports to have spent more than

24.5 (63) hours on housework during the week or more than 63 (108.5) hours during the

weekend. In panel D, we extend the sample to include all dual-earner couples under the age

of 65 (instead of 55). Again, we find similar results as in Table 2.

4.3.2 Alternative sex ratios at birth

Sex ratio at birth at one’s birthplace may be capturing not parental gender norms but the

current or childhood environment if many respondents continue to live in their birthplace.22

Sex ratio at birth at current or childhood residence may be associated with the region’s

labor market opportunities for women, the availability of market substitutes for household

production, or peer effects, which can affect intra-household time allocation decisions in ways

other than parental gender norms.

POB SRB on all three sub-activities.21Among the 16 provinces, the highest sex ratio at birth was recorded in Daegu, followed by Gyeong-buk,

Ulsan, Gyeong-nam, and Busan. A total of 34.6 percent of men and 35.0 percent of women in the samplewere born in these regions. Results are similar when we use the top 3, 4, or 6.

2243 percent of individuals in our sample report to be living in the same province as his or her birthplace.

12


Thus, in Table 6, we present estimation results from two different exercises. First, we

exploit within-province variation of POB SRB by including current province fixed effects

(cols. 1, 3, 5). Second, we control for the SRB in the province where the respondent lived at

age 14 (cols. 2, 4, 6). Because the majority of individuals lived in the same province from

birth to age 14, we focus on movers by interacting the age 14 SRB with a dummy that equals

1 if the individual moved between birth and age 14, and 0 otherwise.23 In both cases, we

observe the same pattern as before. The coefficient on husband’s POB SRB is positive and

statistically significant for the wife’s and total housework time whereas the wife’s POB SRB

has no explanatory power. The age 14 SRB interaction term is statistically insignificant,

indicating that where one lived during adolescence has no additional effect on housework

time.

4.3.3 Working hours

Although our sample consists of dual-earner couples, there may be substantial variation

within and across couples on the intensive margin of labor supply. For example, women

married to more traditional men may spend less time on market work and more time on

household work compared to women married to less traditional men (Fernandez et al., 2004;

Hwang, 2016a). In this case, the relationship between parental gender norms and housework

time will weaken substantially once we control for the wife’s actual working hours.


In Table 5, we investigate whether the effect of husband’s POB SRB remains even when

we control for the couple’s working hours. We consider both the actual working hours on

the survey date and the usual working hours reported in the KLIPS main survey. Cols. 1–4

present negative correlations between one’s working hours and housework hours, as expected.

Husband’s POB SRB, however, remains positive and statistically significant whether we

include actual or usual working hours. This implies that the effect of husband’s parental

gender norms on housework time allocation is not fully mediated via wife’s labor supply.

Women married to men from higher POB SRB work more in general (considering both

market and non-market work), other things equal.

2318 percent of respondents moved before age 14.

13

4.3.4 Intra-household bargaining

Another concern with POB SRB is that it may be correlated with the husband’s relative

bargaining power within the household, not parental gender norms at large. For example,

men from higher-SRB regions may be more likely to “marry down” in terms of socioeconomic

status, and this in turn could influence how resources are divided within the family.24 Thus,

in Table 7, we control for proxies for intra-household bargaining power, specifically, age

difference, ratio of years of schooling, and ratio of earnings between the husband and wife.

To address the potential “threat” of divorce and remarriage, we also calculate the current

region’s sex ratio among adults ages 20–44, both for the whole population and for the non-

married.


Among relative age, schooling, and income, only age has a significant effect on housework

time. If the age difference between the husband and wife is larger, the wife spends less and

the husband spends more time on housework. We are reluctant to interpret each of these

measures separately, however, as they are likely to be correlated. Sex ratios among adults

in the current region are negatively associated with the wife’s and total housework time:

more males than females in the (re-)marriage market reduces the wife’s housework time,

as predicted by bargaining models. Regardless of these controls, however, the sign and

magnitude of the coefficients on husband’s and wife’s POB SRB do not change.

All in all, the effect of POB SRB on housework time seems to be very consistent across

alternative specifications. When husband’s POB SRB is higher, the wife spends more time

on housework, even when we use different estimation models, or include regional fixed effects,

the couple’s working hours, current region’s sex ratio at birth, or intra-household bargaining

proxies. Sex ratio at birth in one’s birthplace is not confounded by any of these factors,

and results suggest that there is indeed a strong link between the husband’s parental gender

norms and his wife’s burden of the second shift.

5 Discussion

5.1 Potential Mechanisms

Section 4 reveals that the total and the wife’s housework time in dual-earner households

increase with the sex ratio at birth of the husband’s hometown. It remains a black box,

24See Lundberg and Pollak (1996) and Chiappori and Donni (2011) for a survey of the literature onnon-unitary models.

14

however, how or why parental gender norms (of the husband, in particular) translate into

differences in the allocation of household labor. In this section, we explore three poten-

tial explanations—human capital accumulation, preference formation, and selection in the

marriage market. See Figure 4 for a visual representation of the mechanisms.


5.1.1 Human capital channel

According to Becker (1991), the gains from specialization would lead to each member of

the household focusing on the market or the household sector, whichever one has greater

comparative advantage in. Given that the husband’s wage is usually higher than the wife’s,

theory predicts that the wife allocates more of her time to non-market work than her husband,

and such division of labor would be efficient for the household as a whole.

Within the Beckerian framework, how can we understand the relationship between parental

gender norms and the division of labor at home? Parents’ gender norms may affect children’s

human capital accumulation, and consequently, their comparative advantage relative to their

(future) spouse. When parents have strong son preference, not only are girls less likely to

be born, but they are treated differently from boys once they are born.25 For example, girls

are assumed to help out with household chores, whereas boys stay away from the kitchen.

Men from regions with higher sex ratio at birth would then have had fewer opportunities

to perform domestic work whereas women from these regions would be familiar with these

tasks.

Thus, if housework requires non-trivial learning, regional differences in gender-specific

human capital accumulation could explain our findings.26 Compared to women married to

men from low-SRB provinces, wives of men from high-SRB provinces would be relatively

more productive than their husbands in household work. Although parents’ initial decision

to enhance different types of human capital by child gender is neither efficient nor desirable,

once such gender-specific human capital is accumulated, specialization between the spouses

may be (ex post) efficient.

25There are studies on gender differential parental investments in many countries, including Korea. Seefor example, Baker and Milligan (2013), Barcellos et al. (2014), Pabilonia and Ward-Batts (2007), and Choiand Hwang (2015).

26Whether household work requires much learning is another question. With advanced household tech-nologies and appliances (electric washers, vacuum cleaners, microwaves, etc.), performing domestic tasks hasbecome trivial compared to the past.

15

5.1.2 Preference channel

Exposure to certain gender roles during childhood may affect one’s own preferences toward

household production. A man with traditional parents could grow up to prefer home goods

to market goods, and prefer the wife to produce them rather than outsourcing or producing

them himself. Because he is accustomed to his mother’s home-cooked meals, for instance,

he may expect the same from his wife regardless of her ability. The same reasoning could be

applied to women. A woman brought up in a more traditional household could be used to

cooking and readily assume the husband to be the primary breadwinner of the household,

following her parent’s example.27

The preference channel need not apply symmetrically to males and females, however. In-

tergenerational transmission of gender identity norms is possibly weaker for women. Because

women’s opportunities in tertiary education and the labor market changed dramatically over

the past several decades, daughters may not identify themselves with their mothers’ genera-

tion.28 In this case, a man from a high-SRB region may want his wife to produce home goods

whereas a woman from the same region has no interest in producing them. In this case, sim-

ilar to discussions in Akerlof and Kranton (2000) and Bertrand et al. (2015), the gender gap

in housework time would exist to assuage the husband’s unease with non-traditional gender

roles rather than to maximize household joint production.

5.1.3 Selection in the marriage market

Due to differences in human capital or preference regarding household production by parental

gender norms, selection in the marriage market could occur if individuals are aware of how

they would affect intra-household time allocation after marriage. Both positive and negative

sorting are possible. If both men and women are influenced by parental gender norms such

that those from traditional households grow up to prefer traditional gender roles, “mating of

likes” would take place. Men and women with similar parental gender norms would marry,

and no compensation would be necessary for men’s lack of housework contribution. Even

among dual-earner couples, women who chose to marry men from high-SRB provinces would

willingly assume more household duties.

Alternatively, there could be negative sorting if men who do not do housework—either

because of (lack of) human capital or own preference—are considered less attractive as

27Olivetti et al. (2016) studies how young women’s labor market decisions are influenced by the workingstatus of their mothers and their childhood friends’ mothers using the National Longitudinal Survey ofAdolescent Health.

28For example, Hwang (2016a) shows that a mother’s labor force participation and college education hasan impact on son’s but not daughter’s gender attitudes, using the Japanese General Social Survey.

16

husbands. These traditional men would then have to compensate their wives in other ways

such that conditional on marriage, men from provinces with higher sex ratios at birth have

higher qualities.

It is worth pointing out that selection is not mutually exclusive with the human capital

or preference channels. Human capital accumulation and preference formation are potential

mechanisms through which one’s skill or taste regarding household production—let us call

them housework “type,” for convenience—is determined. Selection in the marriage market

may or may not occur with regards to these types, however, if individuals are not fully aware

of their own or their partner’s housework type before cohabiting. Or even if they are aware,

they may have imperfect information and underestimate the net costs involved with living

with a certain housework type. Finding evidence on selection by housework type in the

marriage market, therefore, does not necessarily have direct implications for either of the

two alternative mechanisms.

The reason we discuss selection, nonetheless, is because selection has important efficiency

and welfare consequences (see Figure 4). With selection, the resulting couple’s housework

time allocation would be efficient because spouses (implicitly) agreed upon the division of

labor upon marriage. The wife, in particular, would not suffer a utility loss from doing more

housework because either she would be compensated or she shares the same identity norms

as her husband. Without selection, however, the differences in housework time allocation we

find in this paper may well be inefficient.

5.2 Suggestive Evidence on Underlying Mechanisms

5.2.1 Income

Although the alternative explanations are difficult to distinguish with observational data, we

run additional analyses in an attempt to better understand our main findings. We begin by

examining whether there is any evidence for selection in the marriage market by housework

“type,” as aforementioned. Specifically, we test whether there is a difference in earnings

among married men and women by their POB SRB. Income is of key interest, among various

characteristics that matter in the marriage market because it determines the opportunity

cost of housework time. With selection in the marriage market, we expect men from higher

SRB provinces to have higher earnings, other things equal. Similarly, we expect the wives of

men from higher SRB provinces to have lower labor market productivity, other things equal.

To test this hypothesis, we extend the sample to all married men and women under age

55 and regress their (potential) earnings on POB SRB. We do not use our baseline dual-

earner sample here for at least two important reasons. First, a large fraction of married

17

women are not employed. More than 51 percent of married women in the 2014 KLIPS data

report zero (or missing) income. Second, we are not able to distinguish whether a woman is

currently not participating in the labor market because of her low labor market productivity

or a traditional husband (in-laws) who discourages her from working.29

We impute income for the non-employed in two different ways, following prior studies.

In the “Nearest” procedure, we search the KLIPS backwards up to 2012 to recover earnings

observations from the nearest wave. In the “Median” procedure, we assign each individual

to a demographic group defined based on age (25–35, 36–45, 46–55), education (less than

college, some college, college graduate and higher) and location of residence. We then use

the median earning of each group when the 2012–2014 income data is missing (cf., Olivetti

and Petrongolo, 2008; Bertrand et al., 2015).


Table 8 presents the relationship between POB SRB and earnings using actual obser-

vations in the 2014 data, nearest observations in 2012–2014, and median earnings. All

three procedures show no significant relationship between POB SRB and earnings, for both

married men (cols. 1–3) and women (cols. 4–6). Thus, the extra amount of household pro-

duction performed by wives of men from provinces with high SRB cannot be justified by

income differences. Other covariates in the standard earnings equation, such as schooling

and experience have expected signs: more education and experience increase earnings.

5.2.2 Household appliances

We investigate whether the use of time-saving household appliances differs by parental gender

norms. According to Section 4, not only the share of wife’s housework time but also the

total housework time increase when the husband is from a province with higher SRB. Do

these households engage in more time-consuming activities by being less likely to adopt new

household appliances? If the human capital channel is dominant, we would expect households

with individuals who are less efficient at domestic tasks (men with high POB SRB) to be

more likely to outsource housework or exploit technology, other things equal.

We combine two datasets, the 1985 housing census and population census, to investigate

if POB SRB is associated with the probability of having an electric washer in 1985 when the

appliance was first introduced to Korean households. The housing census includes questions

on whether a household possesses major home appliances, such as a refrigerator, an electric

29Results are similar when we restrict the sample to dual-earners who have actual earnings information,however, which we can also infer from Section 4 in that the coefficients on POB SRBs are stable even whenwe control for the couple’s earnings and working hours.

18

washer, and an air conditioner. Electric washers are credited as one of the most time-saving

household appliances to have been invented (Greenwood et al., 2005). Slightly less than a

quarter of the entire households owned a washer by then. The population census of the

same year provides information on the county of birth for each individual. We match the 2

percent micro samples of these two sources and restrict the sample to married persons aged

55 or younger in 1985. In addition to age and years of schooling, we include dummies for city

size, occupation, and household size to control for family income and degree of urbanization.

Unfortunately, the data does not have information on spouse’s birthplace.


Table 9 indicates that a one-point increase in POB SRB is associated with a 0.34 to

0.55 percentage point decrease in the probability of possessing an electric washer for married

males. The difference in the 1991–1994 sex ratio at birth between Jeon-buk and Gyeong-buk

(16 points) translates into a gap of approximately 5.4 to 8.8 percentage points, approximately

23 to 38 percent of the sample mean. Coefficients on POB SRB for married women are also

negative and similar in magnitude.

While the evidence may not extrapolate to recent settings, it is interesting to note that

there is a negative association between an individual’s POB SRB and the probability of

owning a washer. In 1985, married men and women from regions with stronger son preference

were less likely to take advantage of a major time-saving household appliance. Even if similar

domestic tasks are performed across households, the time necessary to perform such tasks

could differ substantially by these choices and they reflect how the members regard household

production.

5.2.3 Gender attitudes

One explanation for the variation in housework time by husband’s parental gender norms

is that traditional women choose to marry traditional men. To explore the possibility that

our results are driven by the “mating of likes,” we examine whether the POB SRB effect

disappears once we consider the couple’s own attitudes towards gender roles at home.

The KLIPS has gender attitude questions in the time use section of the survey. Most

relevant for our purpose, individuals are asked whether they stongly agree, somewhat agree,

somewhat disagree, or strongly disagree with the following statements: “The husband’s job

is to earn money and the wife’s job is to look after the home and family” and “Dual-earner

couples should equally divide housework.”30 To test whether parental gender norms matter

30Other questions include “Mother’s labor-market work has a negative effect on preschool child,” “Hus-

19

even among those who claim to have “modern” gender attitudes, we construct an indicator

variable that equals 1 if the respondent “strongly disagrees (agrees)” with the first (second)

statement, and 0 otherwise.31


Table 10 shows the estimation results including gender attitude responses. If the husband

has modern gender attitudes, housework time is shorter by about 3 hours per week for the

wife and by about 4.5 hours in total. Wife’s gender attitudes are only weakly correlated

with housework time, in comparison. Whether we control for the response to the first or

second question, however, the coefficients on husband’s and wife’s POB SRB are stable. A

difference in the husband’s POB SRB by the gap between Jeon-buk and Gyeong-buk would

again translate into nearly 6 more hours per week of housework for the wife.

As we mention in Section 2, POB SRB is not a proxy for one’s own current gender

attitudes but parental gender norms. An individual’s personal opinion about gender roles can

be influenced by a variety of factors such as education, experience, peers, and neighborhood.

The sex ratio at birth in the province of birth, on the other hand, measures the prevalence

of son preference among the people in one’s hometown. It represents the gender norms one’s

parents followed. The significant effect of POB SRB on housework time allocation suggests

that the gender roles one’s parents performed has a lingering effect on one’s own actions

after marriage, apart from the gender attitudes one claims to have.

Another way to see how individuals actually think about household work is to use the

question in the KLIPS “How much housework do you think you do?” The question asks indi-

viduals to make a subjective assessment of one’s own contribution to household production.

Possible responses are (I do) lots of, enough, or little housework.32 The advantage of this

question is that the response would be related to not only the individual’s actual hours of

household work but also what the individual considers as fair (or ideal) amount of house-

work. If the husband believes that the wife is responsible for domestic work, for example,

he would respond that he is doing lots of (or enough) housework even if he is spending only

a few hours per week on household activities. Because we can directly link the response to

this question to the actual housework time recorded in the time use section of the survey,

we are able to test whether POB SRB matters even after controlling for actual housework

time.

band’s and wife’s incomes should be managed separately,” and “House where a couple live together shouldbe co-owned.”

3150.8 (57.4) percent of men’s responses and 49.9 (50.9) percent of women’s responses to these two questionsfall in the “somewhat disagree (agree)” categories.

32“Don’t know” and missing are excluded from the analysis.

20


Table 11 shows the results of regressing an indicator variable that equals 1 if the re-

spondent answered “I do lots of (or enough) housework,” and zero otherwise on the couple’s

POB SRB, baseline controls, and actual housework time. As expected, actual housework

time strongly predicts subjective assessment: both husband and wife are more likely to re-

spond that they do lots of (or enough) housework when they spend more time on housework,

and less likely to respond so when their spouse spends more time on housework. What is

interesting, however, is the effect of husband’s POB SRB on subjective assessment. Con-

trolling for actual housework time of himself and his wife, men with higher POB SRB are

more likely to respond that they are doing lots of (or enough) housework. The result implies

that men from higher-SRB provinces are less likely to think of household production as their

domain.

6 Conclusion

This paper shows that parental gender norms, as measured by sex ratio at birth in one’s

province of birth (POB SRB), affect the allocation of housework time even among work-

ing spouses. The relationship is not gender-symmetrical in two aspects: only the wife’s

housework time is affected, and only the husband’s POB SRB matters. That is, the wife’s

housework time increases with her husband’s POB SRB but not her own, and the husband’s

housework time is unaffected by the couple’s POB SRB. The result also implies that the

wife’s and husband’s housework time are not substitutes. When the husband is from a

more traditional region, it is not that the wife does more because the husband does less

than other men, but housework time increases in total. These findings are robust to various

specifications, including those controlling for the couple’s actual earnings and working hours.

We explore three alternative explanations to understand why parental gender norms

affect housework time allocation of dual-earner couples: (1) parental gender norms affect

children’s gender-specific human capital accumulation in household work, (2) parental gender

norms shape children’s preferences for household production, or (3) there is selection in the

marriage market by housework skill or preference. Conducting additional analyses, we do

not find evidence of there being selection in the marriage market such that the wife’s extra

housework time is compensated by her husband’s income. We also find that POB SRB and

the probability of owning an electric washer in 1985 is negatively correlated. The effect of

POB SRB does not disappear when we control for the couple’s own gender attitudes, either.

We do find, however, that men with higher POB SRB are more likely to respond that they

are doing lots of (or enough) housework, conditional on their actual housework time.

21

Although far from conclusive, differences in preference for how much housework should

be done by whom seems to be an important channel between parental gender norms and

intra-household housework time allocation. Men from regions with higher sex ratios at birth

are more likely to consider domestic tasks to be the wife’s job even when she is also working

for pay. Unless there are significant POB differences in gender-specific human capital or

women’s own preference for housework, the resulting housework time allocation would be

inefficient for the household. The cost of traditional gender norms at home spills over to the

labor market as well, because women end up with less time and energy compared to men of

similar ability.

22

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25

Table 1: Descriptive Statistics

Men Women

Mean SD Mean SD

Individual level variablesSex ratio at birth in province of birth 115.5 (4.57) 115.5 (4.65)Education: less than college 0.40 (0.49) 0.47 (0.50)Education: some college 0.20 (0.40) 0.22 (0.42)Education: college graduate + 0.40 (0.49) 0.31 (0.46)Age 44.02 (6.83) 41.51 (6.53)Wage worker 0.80 (0.40) 0.85 (0.35)Monthly earnings (10,000 KRW) 331.9 (216.0) 191.1 (137.5)

Time use variables (hours per week)Housework time 4.75 (9.29) 24.59 (18.34)

Housekeeping time 2.06 (5.47) 17.99 (11.43)Childcare time 2.42 (6.71) 6.05 (13.50)Family care time 0.27 (2.05) 0.56 (4.75)

Actual working hours 55.23 (27.95) 44.58 (27.10)Survey on weekday 0.82 (0.38) 0.78 (0.41)

Household level variablesRatio of schooling (Wife’s schooling/Husband’s schooling) 0.98 (0.15)Age difference (Husband’s age−Wife’s age) 2.50 (2.70)Relative earnings (Wife’s earnings/Total earnings) 0.36 (0.14)Number of children under age 5 0.25 (0.52)Number of children age 5-18 0.98 (0.91)Total number of children 1.56 (0.85)Number of adults in household 2.39 (0.77)Ln(non-labor family income) 3.16 (2.90)Live in Seoul 0.14 (0.34)Live in metro cities (other than Seoul) 0.29 (0.45)Live in small cities 0.58 (0.49)

Number of couples 936

Notes. Mean and standard deviations (in parentheses) of the dual-earner couple sample. Information on timeuse is drawn from the 2014 KLIPS and province of birth is collected from the 1998-2014 KLIPS. 10,000 KRW isworth approximately 10 USD.

26

Tab

le2:

Eff

ects

ofP

aren

tal

Gen

der

Nor

ms

onH

ouse

wor

kT

ime

Dep

endent

vari

able

:H

ouse

work

tim

e(h

ours

per

week)

Wif

eH

usb

and

Tot

al

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Hu

sban

d’s

PO

BS

RB

0.31

4**

0.33

6**

0.07

20.

069

0.38

5**

0.40

5**

(0.1

41)

(0.1

42)

(0.0

51)

(0.0

64)

(0.1

58)

(0.1

40)

Wif

e’s

PO

BS

RB

0.13

1-0

.042

0.04

00.

004

0.17

1-0

.037

(0.1

31)

(0.1

33)

(0.0

45)

(0.0

71)

(0.1

55)

(0.1

66)

No.

ofch

ild

ren

un

der

age

511

.632

***

11.6

21**

*11

.621

***

4.24

6***

4.24

7***

4.24

7***

15.8

77**

*15

.867

***

15.8

67**

*(2

.091

)(1

.483

)(2

.095

)(0

.815

)(0

.721

)(0

.823

)(2

.340

)(1

.740

)(2

.351

)N

o.of

chil

dre

nag

e5-

182.

908*

**2.

886*

**2.

903*

**0.

591*

**0.

588*

0.59

2***

3.50

0***

3.47

4***

3.49

5***

(0.5

11)

(0.7

01)

(0.5

02)

(0.2

05)

(0.3

16)

(0.2

05)

(0.5

65)

(0.7

22)

(0.5

54)

No.

ofad

ult

sin

hou

seh

old

0.24

80.

133

0.25

0-0

.017

-0.0

41-0

.017

0.23

10.

091

0.23

3(0

.594

)(0

.442

)(0

.596

)(0

.151

)(0

.277

)(0

.152

)(0

.610

)(0

.643

)(0

.614

)L

n(n

on-l

abor

fam

ily

inco

me)

-0.0

65-0

.025

-0.0

640.

066

0.07

40.

066

0.00

00.

048

0.00

2(0

.216

)(0

.142

)(0

.217

)(0

.094

)(0

.112

)(0

.095

)(0

.253

)(0

.172

)(0

.255

)H

usb

and

:so

me

coll

ege

2.88

4*3.

081*

2.90

5*0.

546

0.58

00.

544

3.43

03.

661*

3.44

9(1

.631

)(1

.798

)(1

.630

)(0

.912

)(0

.618

)(0

.920

)(2

.218

)(1

.822

)(2

.225

)H

usb

and

:co

lleg

egr

adu

ate

+3.

079*

3.12

1**

3.07

8*2.

120*

**2.

129*

*2.

120*

**5.

199*

*5.

250*

*5.

198*

*(1

.740

)(1

.462

)(1

.737

)(0

.801

)(0

.841

)(0

.801

)(2

.091

)(1

.930

)(2

.090

)W

ife:

som

eco

lleg

e-0

.631

-0.7

34-0

.622

0.99

40.

970

0.99

30.

363

0.23

60.

371

(1.1

86)

(1.2

47)

(1.1

94)

(0.9

18)

(0.7

41)

(0.9

18)

(1.7

53)

(1.7

83)

(1.7

58)

Wif

e:co

lleg

egr

adu

ate

+-0

.451

-0.4

71-0

.432

0.48

80.

478

0.48

60.

037

0.00

70.

054

(0.9

10)

(1.9

43)

(0.9

19)

(0.9

30)

(0.8

13)

(0.9

46)

(1.4

68)

(2.2

36)

(1.4

83)

Hu

sban

d’s

ln(e

arn

ings

)1.

068

1.20

4*1.

078

-1.4

15**

*-1

.390

***

-1.4

16**

*-0

.347

-0.1

86-0

.337

(0.7

21)

(0.7

00)

(0.7

37)

(0.3

97)

(0.4

81)

(0.3

98)

(1.0

09)

(0.8

94)

(1.0

21)

Wif

e’s

ln(e

arn

ings

)-5

.062

***

-5.1

33**

*-5

.060

***

0.47

30.

458

0.47

3-4

.589

***

-4.6

75**

*-4

.587

***

(1.0

14)

(0.6

69)

(1.0

10)

(0.3

85)

(0.3

33)

(0.3

86)

(1.2

71)

(0.7

33)

(1.2

71)

Con

trol

sY

esY

esY

esY

esY

esY

esY

esY

esY

esN

936

936

936

936

936

936

936

936

936

Dep

end

ent

vari

able

mea

n24

.59

24.5

924

.59

4.75

4.75

4.75

29.3

429

.34

29.3

4C

orre

lati

onof

resi

du

als

0.22

90.

232

0.22

9B

reu

sch

-Pag

ante

stof

ind

epen

den

ce:χ2(1

)49

.43

50.2

149

.45

p-va

lue

[0.0

00]

[0.0

00]

[0.0

00]

Notes.

Dat

aar

efr

omth

e20

14K

LIP

S.C

olum

ns

(1)

to(6

)ar

ees

tim

ated

usi

ng

SU

R.C

olum

ns

(7)

to(9

)ar

ees

tim

ate

dusi

ng

OL

S.C

ontr

olva

riab

les

incl

ude

bot

hth

ehusb

and’s

and

wif

e’s

age,

age

squar

ed,

dum

mie

sfo

red

uca

tion

(hig

hsc

hool

,so

me

colleg

e,co

lleg

eor

mor

e),

log

earn

ings

,lo

gnon

-lab

orfa

mily

inco

me,

dum

mie

sfo

rjo

bty

pe

(wag

ew

orke

ror

self

-em

plo

yed),

surv

eyday

(wee

kor

wee

kend),

num

ber

of

childre

nunder

age

5,

num

ber

of

childre

nage

d6–

18,

num

ber

ofad

ult

sin

hou

sehol

d,

and

dum

mie

sfo

rurb

anan

dru

ral

class

ifica

tion

(Seo

ul,

met

roci

ties

,an

dsm

all

citi

es).

Sta

ndar

der

rors

are

clust

ered

by

the

husb

and’s

PO

Bex

cept

inco

lum

ns

(2),

(5)

and

(8)

wher

ew

ecl

ust

erby

the

wif

e’s

PO

B.

*p<

0.10

,**

p<

0.05

,**

*p<

0.01

27

Tab

le3:

Eff

ects

ofP

aren

tal

Gen

der

Nor

ms

onW

ife’

sT

ime

Sp

ent

onH

ouse

keep

ing,

Child

Car

e,an

dF

amily

Car

e

Dep

endent

vari

able

:T

ime

(hours

per

week)

Hou

seke

epin

gC

hild

care

Fam

ily

care

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Husb

and’s

PO

BSR

B0.

165*

0.20

0**

0.08

90.

098

0.06

00.

038

(0.0

96)

(0.0

95)

(0.0

78)

(0.1

00)

(0.0

45)

(0.0

32)

Wif

e’s

PO

BSR

B0.

037

-0.0

660.

033

-0.0

170.

061*

*0.

042

(0.0

82)

(0.0

96)

(0.0

76)

(0.1

06)

(0.0

27)

(0.0

30)

Con

trol

sY

esY

esY

esY

esY

esY

esY

esY

esY

esN

936

936

936

936

936

936

936

936

936

Dep

enden

tva

riab

lem

ean

17.9

917

.99

17.9

96.

056.

056.

050.

560.

560.

56C

orre

lati

onof

resi

dual

sρ:

Hou

seke

epin

g/C

hild

care

-0.0

33-0

.031

-0.0

34ρ:

Hou

seke

epin

g/F

amily

care

-0.0

27-0

.024

-0.0

27ρ:

Child

care

/Fam

ily

care

-0.1

45-0

.144

-0.1

45B

reusc

h-P

agan

test

ofin

dep

enden

ce:χ2(3

)21

.36

20.7

221

.30

p-va

lue

[0.0

001]

[0.0

001]

[0.0

001]

Notes.

SU

Res

tim

atio

nu

sin

gth

e20

14

KL

IPS

.C

ontr

ols

are

as

inT

ab

le2.

Sta

nd

ard

erro

rsare

clu

ster

edby

the

hu

sban

d’s

PO

Bex

cep

tin

colu

mn

s(2

),(5

)an

d(8

)w

her

ew

ecl

ust

erby

the

wif

e’s

PO

B.

*p<

0.1

0,

**p<

0.0

5,

***p<

0.01

28

Table 4: Effects of Parental Gender Norms on Housework Time, Using Alternative Specifications

Dependent variable: Housework time (hours per week)

Wife Husband Total(1) (2) (3)

A. High & low POB SRBHusband’s POB SRB: High5 3.142** 0.820 3.962**

(1.372) (0.709) (1.557)Wife’s POB SRB: High5 -0.910 -0.207 -1.116

(1.085) (0.896) (1.636)N 936 936 936Dependent variable mean 24.59 4.75 29.34

B. TobitHusband’s POB SRB 0.367*** 0.206 0.434***

(0.134) (0.172) (0.165)Wife’s POB SRB -0.030 -0.058 -0.028


C. Sub-sample, excluding outliersHusband’s POB SRB 0.264** 0.033 0.300**

(0.129) (0.029) (0.133)Wife’s POB SRB -0.073 -0.029 -0.102


D. Extended sample, age ≤ 65Husband’s POB SRB 0.284** 0.055 0.339**

(0.138) (0.074) (0.061)Wife’s POB SRB -0.095 -0.022 -0.117

(0.117) (0.055) (0.136)N 1,170 1,170 1,170Dependent variable mean 23.06 4.27 27.33Controls Yes Yes Yes

Notes. Data are from the 2014 KLIPS. In panel A, High5 is a dummy that equals 1 if the birthplace is ranked oneof the top five in terms of the 1991–1994 sex ratio at birth, and 0 otherwise. Wife’s and husband’s time are estimatedusing SUR. Total time is estimated using OLS. In panel B, we jointly estimate wife’s and husband’s time using asystem of correlated Tobit models. Total time is solely estimated using the Tobit model. In panel C, the sample isrestricted to respondents whose housework time is within 99% percentile in each group of survey date (weekday orweekend). In panel D, the sample is extended to include couples with husband’s age under 65 (instead of 55). Allspecifications include controls as in Table 2. Standard errors are clustered by the husband’s POB. * p < 0.10, **p < 0.05, *** p < 0.01

29

Table 5: Effects of Parental Gender Norms on Housework Time, Controlling for Working Hours


Wife Husband Total

(1) (2) (3) (4) (5) (6)

Husband’s POB SRB 0.276* 0.330** 0.054 0.069 0.330** 0.399**(0.144) (0.149) (0.069) (0.069) (0.145) (0.142)

Wife’s POB SRB -0.020 -0.023 0.014 0.002 -0.006 -0.021(0.146) (0.139) (0.066) (0.067) (0.178) (0.170)

Wife’s actual working hours -0.347*** 0.019 -0.328***(0.046) (0.017) (0.048)

Husband’s actual working hours 0.061* -0.086*** -0.025(0.031) (0.010) (0.033)

Wife’s usual working hours -0.163*** 0.063*** -0.099(0.056) (0.023) (0.064)

Husband’s usual working hours 0.012 -0.055** -0.043(0.039) (0.024) (0.052)

Controls Yes Yes Yes Yes Yes YesN 936 936 936 936 936 936Dependent variable mean 24.59 24.59 4.75 4.75 29.34 29.34

Notes. Data are from the 2014 KLIPS. Wife’s and husband’s time are estimated using SUR. Total time is estimatedusing OLS. Actual working hours refer to the time spent on working during the previous day, reported in the time usesection of the survey (converted from minutes per day to hours per week). Usual working hours refer to the averageworking hours per week, reported in the main survey. Controls are as in the Table 2. Standard errors are clustered bythe husband’s POB. * p < 0.10, ** p < 0.05, *** p < 0.01

30

Table 6: Effects of Parental Gender Norms on Housework Time, Using Current and Age 14 SexRatios at Birth


Wife Husband Total

(1) (2) (3) (4) (5) (6)

Husband’s POB SRB 0.324** 0.326** 0.090 0.069 0.414** 0.395**(0.139) (0.148) (0.096) (0.064) (0.193) (0.141)

Wife’s POB SRB -0.064 -0.030 0.029 0.001 -0.035 -0.029(0.139) (0.138) (0.079) (0.070) (0.167) (0.167)

Husband: Moved*Age 14 SRB 0.006 0.001 0.007(0.011) (0.007) (0.013)

Wife: Move*Age 14 SRB -0.008 0.002 -0.006(0.011) (0.007) (0.016)

Regional FE Yes No Yes No Yes NoControls Yes Yes Yes Yes Yes YesN 936 933 936 933 936 933Dependent variable mean 24.59 24.61 4.75 4.76 29.34 29.37

Notes. Data are from the 2014 KLIPS. Wife’s and husband’s time are estimated using SUR. Total time is estimatedusing OLS. In columns (1), (3), and (5), we include regional dummies that indicate the current province of residence.Moved is a dummy variable that equals 1 if the individual moved to another region before age 14, and 0 otherwise.Age 14 SRB is the 1991–1994 sex ratio at birth of the province where the respondent lived at age 14. Controls are asin Table 2 except for the dummies for urban and rural classification (Seoul, metro cities, and small cities) in columns(1), (3), and (5). Standard errors are clustered by the husband’s POB. * p < 0.10, ** p < 0.05, *** p < 0.01

31

Table 7: Effects of Parental Gender Norms on Housework Time, Controlling for Intra-Household Bargaining


Wife Husband Total

(1) (2) (3) (4) (5) (6)

Husband’s POB SRB 0.367*** 0.382*** 0.068 0.065 0.438*** 0.450***(0.131) (0.131) (0.067) (0.068) (0.115) (0.114)

Wife’s POB SRB -0.003 0.008 -0.013 -0.015 -0.021 -0.013(0.118) (0.121) (0.067) (0.070) (0.148) (0.157)

Husband’s age-Wife’s age -0.367*** -0.369*** 0.201** 0.199** -0.282 -0.287(0.134) (0.136) (0.098) (0.098) (0.199) (0.202)

Wife’s schooling/Husband’s schooling -0.983 -0.800 -0.205 -0.182 -1.242 -1.034(3.451) (3.401) (2.851) (2.831) (5.288) (5.229)

Wife’s earnings/Total earnings -16.043 -16.826 1.754 1.780 -13.869 -14.624(14.178) (14.202) (4.641) (4.690) (15.273) (15.392)

Current region’s sex ratio of age 20–44 -0.500*** -0.044 -0.549**(0.173) (0.073) (0.211)

Current region’s sex ratio of non-married age 20–44 -0.132*** -0.001 -0.134*(0.049) (0.023) (0.064)


Notes. Data are from the 2014 KLIPS. Husband’s age-Wife’s age is the age difference between the husband and wife. Wife’s school-ing/Husband’s schooling is the ratio of years of schooling. Wife’s earnings/Total earnings is the share of household labor income earned bythe wife. Current region’s sex ratio of age 20–44 is the sex ratio of adults between ages 20 and 44 in the province of residence. Currentregion’s sex ratio of non-married age 20–44 is the sex ratio of non-married adults between ages 20 and 44. Controls are as in Table 2 exceptthe dummies for urban and rural classification (Seoul, metro cities, and small cities) and spouse’s age. Standard errors are clustered by thehusband’s POB. * p < 0.10, ** p < 0.05, *** p < 0.01

32

Table 8: Selection in the Marriage Market by Earnings?, All Married Couples

Dependent variable: Ln(earnings)

Men Women

Actual Nearest Median Actual Nearest Median(1) (2) (3) (4) (5) (6)

Husband’s POB SRB 0.004 0.005 0.004 -0.005 -0.006 -0.004(0.003) (0.003) (0.003) (0.006) (0.005) (0.003)

Wife’s POB SRB 0.000 0.001 0.000 0.003 -0.001 0.000(0.002) (0.002) (0.002) (0.007) (0.006) (0.004)

Husband’s years of schooling 0.057*** 0.057*** 0.057***(0.004) (0.005) (0.005)

Husband’s experience 0.047*** 0.047*** 0.046***(0.007) (0.007) (0.006)

Husband’s experience squared -0.001*** -0.001*** -0.001***(0.000) (0.000) (0.000)

Wife’s years of schooling 0.085*** 0.087*** 0.086***(0.009) (0.009) (0.007)

Wife’s experience -0.010 -0.015 -0.007(0.012) (0.011) (0.007)

Wife’s experience squared 0.000 0.000* 0.000(0.000) (0.000) (0.000)

Metro cities -0.006 -0.004 0.003 -0.027 -0.009 -0.041*(0.036) (0.036) (0.034) (0.047) (0.042) (0.022)

Small cities -0.036 -0.029 -0.025 -0.065 -0.059 -0.078***(0.026) (0.021) (0.024) (0.047) (0.036) (0.022)

Constant 3.857*** 3.749*** 3.836*** 4.344*** 4.887*** 4.446***(0.320) (0.344) (0.305) (0.467) (0.425) (0.290)

N 2,040 2,095 2,115 1,164 1,375 2,267Dependent variable mean 5.75 5.75 5.75 5.06 5.02 5.08

Notes. Data are from the 2014 KLIPS. Earnings are estimated using OLS. The sample includes married men andwomen aged 55 and younger whose spouse’s information is non-missing. The dependent variable in columns (1) and(4) is the respondent’s actual earnings in 2014. Only those who report positive income are included. In columns (2)and (5) we use the nearest income observation in the 2012–2014 waves for respondents who are currently not working.In columns (3) and (6), we use predicted median earnings by assigning each man (woman) into a demographic groupdefined based on age (25–35, 36–45, 46–55), education (less than college, some college, college and more), and location ofresidence ((i) Seoul, (ii) metro cities, and five regional clusters: (iii) Gyeonggi/Gangwon, (iv) Chungcheong, (v) Jeolla,(vi) Gyeongsang and (vi) Jeju). We use the median earning of each group for respondents who have missing incomedata in the 2012–2014 waves. Standard errors are clustered by the husband’s POB. * p < 0.10, ** p < 0.05, *** p < 0.01

33

Tab

le9:

PO

BSR

Ban

dth

eP

robab

ilit

yof

Hav

ing

Was

her

in19

85,

All

Mar

ried

Men

and

Wom

en

Dep

endent

vari

able

=1

ifhave

wash

er

Men

Wom

en

(1)

(2)

(3)

(4)

(5)

(6)

PO

BSR

B-0

.003

4***

-0.0

041*

**-0

.005

5***

-0.0

037*

**-0

.004

7***

-0.0

057*

**(0

.000

2)(0

.000

2)(0

.000

2)(0

.000

2)(0

.000

2)(0

.000

2)

Age

,sc

hool

ing,

occ

upat

ion

No

Yes

Yes

No

Yes

Yes

Hou

sehol

dsi

ze,

city

size

No

No

Yes

No

No

Yes

N13

2,31

313

2,31

313

2,31

314

3,64

514

3,64

514

3,64

5D

epen

den

tva

riab

lem

ean

0.23

30.

233

0.23

30.

221

0.22

10.

221

Notes.

Dat

aar

efr

omth

e2

per

cent

mic

rosa

mp

leof

the

1985

cen

sus.

Occ

up

ati

on

incl

ud

esd

um

mie

sfo

rn

ot

work

ing,

manu

al,

serv

ice,

cler

ical

,an

dp

rofe

ssio

nal

.C

ity

size

incl

ud

esdu

mm

ies

for

rura

lare

as

and

small

citi

es,

five

met

roci

ties

,an

dS

eou

l.R

obu

stst

and

ard

erro

rsin

par

enth

eses

.***p<

0.01,

**p<

0.05,

*p<

0.1.

34

Table 10: Effects of Parental Gender Norms on Housework Time, Controlling for Gender Attitudes


Wife Husband Total

(1) (2) (3) (4) (5) (6)

Husband’s POB SRB 0.300** 0.326** 0.053 0.061 0.353** 0.387**(0.138) (0.144) (0.064) (0.064) (0.137) (0.141)

Wife’s POB SRB 0.005 -0.034 0.028 0.014 0.033 -0.020(0.132) (0.130) (0.069) (0.073) (0.160) (0.163)

Modern husband: ideal family -3.204*** -1.290 -4.494**(1.111) (0.943) (1.597)

Modern wife: ideal family 1.496 1.766 3.261*(1.055) (1.124) (1.803)

Modern husband: housework division -2.921* -0.229 -3.149(1.670) (1.326) (2.337)

Modern wife: housework division 1.068 0.689 1.757(1.269) (0.654) (1.476)


Notes. Data are from the 2014 KLIPS. Respondents are asked if they agree or disagree with the following statements: “Thehusband’s job is to earn money and the wife’s job is to look after the home and family” (ideal family) and “Dual-earnercouples should equally divide housework.” (housework division). Possible responses are (1) Strongly agree, (2) Somewhatagree, (3) Somewhat disagree, and (4) Strongly disagree. Modern is a dummy that equals 1 if the response is “stronglydisagree” with the first statement or “strongly agree” with the second statement, and 0 otherwise. Controls are as in Table2. Standard errors are clustered by the husband’s POB. * p < 0.10, ** p < 0.05, *** p < 0.01

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Table 11: Subjective Assessment of One’s Contribution to Housework

Dependent variable= 1 if I do “lots of” or “enough” housework

Husband Wife

(1) (2) (3) (4)

Husband’s POB SRB 0.006* 0.007* 0.003 0.002(0.003) (0.003) (0.003) (0.003)

Wife’s POB SRB 0.001 0.000 -0.003 -0.003(0.004) (0.004) (0.004) (0.004)

Husband’s housework time 0.015*** -0.002(0.002) (0.001)

Wife’s housework time -0.003*** 0.002**(0.001) (0.001)

Controls Yes Yes Yes YesN 924 924 924 924Dependent variable mean 0.43 0.43 0.90 0.90

Notes. Data are from the 2014 KLIPS. The dependent variable equals 1 if the respondent responds “lots of” or“enough” to the statement “How much housework do you think you do?”, and 0 otherwise. Other possible responsesare “little” and “don’t know.” We exclude “don’t know” and missing. Husband’s (Wife’s) housework time is thehusband’s (wife’s) actual time spent on housework reported in the time use section of the survey. Controls are as inTable 2. Standard errors are clustered by the husband’s POB. * p < 0.10, ** p < 0.05, *** p < 0.01

36

106

108

110

112

114

116

Sex

rat

ios

at b

irth

(num

ber

of b

oys

per

100

girls

)

1980 1985 1990 1995 2000 2005 2010 2015

Figure 1: Sex Ratio at Birth in South Korea, 1980-2015Notes. Data are from Vital Statistics of Korea: Births and Deaths available via Korean Statistical Informa-tion Service (http://www.kosis.kr).

37

105 - 108108 - 110110 - 112112 - 114114 - 117117 - 120120 - 135

1988

1996 2000

1992

Figure 2: Sex Ratio at Birth by Province in Korea, 1988-2000Notes. Data are from the Annual Reports on Live Births and Deaths Statistics, 1988-2000.

38

010

2030

40W

ife's

hou

sew

ork

time

110 115 120 125Husband's POB SRB

010

2030

40W

ife's

hou

sew

ork

time

110 115 120 125Wife's POB SRB

010

2030

40H

usba

nd's

hou

sew

ork

time

110 115 120 125Husband's POB SRB

010

2030

40H

usba

nd's

hou

sew

ork

time

110 115 120 125Wife's POB SRB

Figure 3: Housework Time of Dual-Earner Couples by POB SRBNotes. Housework time and province of birth are from the KLIPS. Sex ratios at birth are from the AnnualReports on Live Births and Deaths Statistics. The y-axis is the number of hours per week an individualspends on housework. The x-axis is the 1991–1994 sex ratio at birth in one’s province of birth (POB SRB).The figure is weighted by the number of observations in each POB.

39

Housework

time

Marriage

market

Housework

“type”POB SRB

Parental

gender norms

Human capital

Selection Efficient

No selection Efficient, ex post

Preference

Selection Efficient

No selection Inefficient

Figure 4: Potential Mechanisms

40

gender norms and housework time allocation among dual

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