The Gender-Specific Effect of Working Hours on
Family Happiness in South Korea
Robert Rudolf and Seo-Young Cho
Georg-August-Universität Göttingen
New Directions in Welfare Congress, Paris, July 8th 2011
Stylized Facts
South Korea: long working hours, high female education,
traditional gender roles, low female labor force participation
– gradually decreasing working hours over the last two decades;
adoption of 5-day working week in 2004
 Still 2nd longest working hours in the OECD in 2009!
– Korea among the lowest female labor force participation rates in
the OECD (61.5% among those aged 20-54 (JP 71%; GER 81%))
– Korea with lowest time spent on „unpaid work“ in OECD, also
lowest time spent on childcare (OECD, 2011)
– Korea ranks very low on male housework participation (17%)
– High female education often „to increase likelihood to find a welleducated husband“ (Lee, 1998): women 50.5% of all college
graduates in 2005
– Part-time work in most cases only available in low-skilled jobs
– Before marriage education and labor force participation of women
positively related, after marriage negatively (Lee et al., 2008)
Introduction
• Paper objectives:
– Extend happiness literature‘s spatial coverage to East Asia
– Estimation of the effect of overall working hours reduction on
family happiness
– Evidence from a society with very strong traditional gender roles
– Use of latest ordered logit fixed-effects estimators
• Findings:
– Reduction in working hours makes Korean families happier
– Part-time jobs still dispreferred („아르바이트“, so-called „Arbeit“)
– Strong gender-specific effects:
• Husbands derive much higher utility from working than wives even
after controlling for income
• Wives most happy when housewife or working 31-40h and when their
husband works full time
• Husbands most happy when working full time without overtime hours
(31-50h)
Presentation Outline
I. Background
II. Data and Methodology
III. Satisfaction Regression Results
a) Life Satisfaction
b) Hours and Job Satisfaction
IV. Family Division of Labor
V. Concluding Remarks
I.
Background (Theory)
Employment as a means of social inclusion and self-fulfillment:
– Positive welfare gains even after controlling for income (Clark and
Oswald 1994; Winkelmann and Winkelmann 1998)
 Positive incentive for female engagement in labor market
Akerloff and Kranton‘s gender identity hypothesis (2000):
 individual behavior largely determined by our various identities,
particularly by our gender, and related expected behaviors
 Negative incentive for female engagement in labor market in
societies with very traditional gender roles
Time constraint (housework + market work + … = 24h/day)
Labor market constraints (part-time options, childcare, etc.)
I.
Background (Empirical Evidence)
Booth and Van Ours (2008, The Economic Journal): BHPS, GB
Findings: controlling for family income, women prefer working to notworking; their life satisfaction peaks at 30-40 hours; yet their hours and
job satisfaction is highest below 30 hours; men most happy with fulltime work
Booth and Van Ours (2009, Economica): HILDA Survey, Australia
Findings: women indifferent between not-working and part-time job,
working more than 35 hours decreases their satisfaction; men most
happy when working full-time between 35 and 50 hours
 Part-time jobs in GB and Australia: solution for the pursuit of both
expected gender identity as the main family care-taker and selffulfillment via market work
II. Data and Methodology
Data used
– 11 waves of the Korean Labor and Income Panel Study (KLIPS)
from 1998 to 2008
– Nationally representative longitudinal study of urban Korean
households modelled after the US‘s NLS and PSID
– 1998 started with 5,000 households and 13,783 individuales aged 15
or older (76.5% maintained throughout all waves)
– Broad information on education, employment, demographic, and
socio-economic variables
II. Data and Methodology
Sample restrictions
– Married and co-residing couples living with children
– Women aged 20-54 (prime years of motherhood)
– Men aged 20-64 (husbands often older than wives and a high
percentage still working with 64)
– Unbalanced panel, thus minimum requirement that an individual is
present in at least two waves
– Resulting sample: 25,461 person-year observations for women and
25,214 person-year observations for men
II. Data and Methodology
• Life satisfaction:
– “Overall, how satisfied or dissatisfied are you with your life?”
• Job satisfaction (only from wave 3 onwards):
– “Overall, how satisfied or dissatisfied are you with your main job?”
• Hours Satisfaction:
– “How satisfied or dissatisfied are you with regard to your main job
on the following aspects?”  “Working Hours”
• Respondents are asked to choose among:
5 (very satisfied)
4 (satisfied)
3 (neither satisfied nor dissatisfied)
2 (dissatisfied)
1 (very dissatisfied)
II. Data and Methodology
Table 1: Distribution of Satisfaction Measures by Gender (in %)
Wifes
Life
Hours
Job
satisfaction satisfaction satisfaction
1 (very dissatisfied)
1.5
2.7
1.0
2
11.4
23.0
13.8
3
54.8
43.5
58.6
4
31.5
29.5
25.9
5 (very satisfied)
.8
1.3
.8
Total
100
100
100
Mean
3.19
3.04
3.12
N
25,461
11,411
9,610
Husbands
Life
Hours
Job
satisfaction satisfaction satisfaction
1.4
3.0
1.0
11.1
23.7
15.1
54.4
45.4
56.9
32.1
26.9
26.3
.9
1.1
.8
100
100
100
3.20
2.99
3.11
25,214
Hours and job satisfaction only for individuals with non-missing and non-zero working hours.
21,509
18,267
II. Data and Methodology
0
10
20
30
Average Weekly Working Hours by Sex, 1998-2008
0
10
20
30
40
50
Working wives
60
70
80
90 100 110 120 130
Working husbands
II. Data and Methodology
2.8
52
3
54
3.2
56
3.4
58
Trends of Weekly Working Hours and Satisfaction, 1998-2008
50
2.6
1998
2000
2002
2004
2006
Year
Working hours
Hours satisfaction
Life satisfaction
Job satisfaction
2008
II. Data and Methodology
Table 2: Average Satisfaction by Working Hours
Hours 0
Hours 1-30
Hours 31-40
Hours 41-50
Hours 51-60
Hours 60+
Life
satisfaction
Wifes
Hours
Job
satisfaction satisfaction
3.23 (13390)
3.13 (1636)
3.27 (1751)
3.27 (3388)
3.09 (2370)
2.98 (2881)
3.31 (1546)
3.43 (1676)
3.34 (3152)
2.88 (2250)
2.43 (2787)
3.05 (1291)
3.28 (1508)
3.27 (2789)
3.05 (1949)
2.90 (2073)
%
wageempl.
57.2
71.0
80.1
65.7
31.2
Life
satisfaction
Husbands
Hours
Job
satisfaction satisfaction
2.68 (2137)
2.99 (1192)
3.34 (2328)
3.36 (7322)
3.24 (6111)
3.14 (6053)
2.92 (1131)
3.37 (2231)
3.32 (6814)
2.93 (5660)
2.52 (5673)
2.77 (912)
3.26 (2054)
3.25 (5934)
3.07 (4894)
2.96 (4473)
%
wageempl.
47.0
72.7
76.8
67.0
50.1
II. Data and Methodology
Fixed-effects ordered logit estimation
 Ordered logit inconsistent if unobservables correlated with covariates
(e.g. personality trait with occupation status)
 Thus fixed effects models usually estimated in subjective well-being
literature:
 Psychology/sociology: cardinal interpretation of satisfaction
scores, linear FE
 Economics: ordinal approaches, no standard work horse yet
 Recent advances: Fixed-effects ordered logit estimators (e.g.
Ferrer-i-Carbonell and Frijters, 2004; Baetschmann, Staub and
Winkelmann, 2011)
III. Results
Table 3: Life Satisfaction Regressions
Wife
Family
Log per-capita income
Log of regional per-capita income
Own house
N of old females
N of old males
N of sons age 0-14
N of daughters age 0-14
N of sons age 15-30 (econ. dep.)
N of daughters age 15-30 (ec. dep.)
Own working hours
Hours 1-30
Hours 31-40
Hours 41-50
Hours 51-60
Hours 60+
Partner's working hours
Hours 1-30
Hours 31-40
Hours 41-50
Hours 51-60
Hours 60+
Log likelihood
Observations
Individuals
Clusters
Husband
BUC
FF
(6)
(7)
Ologit
(1)
BUC
(2)
FF
(3)
FE-OLS
(4)
Ologit
(5)
FE-OLS
(8)
.414***
-.809***
.521***
-.006
-.012
-.026
.024
.037
-.049
.183***
-.700***
.293***
.062
-.496***
-.145**
-.111*
-.002
-.127**
.189***
-.810***
.283***
.035
-.461***
-.121**
-.040
-.014
-.069
.056***
-.203***
.076***
.014
-.140***
-.036**
-.031**
-.002
-.037**
.401***
-.437**
.506***
.010
-.086
.005
.021
.033
.009
.195***
-.585***
.308***
-.010
-.469**
-.047
-.137**
-0.039
-.098
.202***
-.425*
.326***
-.101
-.465***
-.025
-.107*
-.055
-.091
.059***
-.149***
.085***
.002
-.120***
-.013
-.032**
-.010
-.028*
-.218***
-.063
-.083*
-.288***
-.349***
-.117
-.085
-.087
-.117*
-.065
-.058
-.055
-.092
-.128*
-.020
-.028
-.018
-.019
-.029*
-.011
.619***
1.18***
1.25***
1.02***
.958***
.606***
.871***
.947***
.843***
.910***
.574***
.854***
.918***
.786***
.856***
.176***
.259***
.283***
.255***
.274***
.395***
.863***
.924***
.703***
.684***
-23,622
25,153
4,024
.455***
.678***
.748***
.647***
.711***
-12,220
33,779
13,634
3,227
.509***
.673***
.730***
.657***
.730***
-9,163
22,349
3,121
-
.151***
.203***
.220***
.197***
.216***
25,153
4,024
-
-.199***
-.059
-.084*
-.215***
-.368***
-23,261
24,919
3,998
-.130*
-.021
-.050
-.002
-.166**
-12,198
34,162
13,542
3,226
-.095
.003
-.020
-.012
-.162**
-8,999
22,160
3,096
-
-.037*
-.005
-.010
.003
-.042**
24,919
3,998
-
Notes: All specifications include control variables for household head and spouse as well as dummies for year of survey. Pooled cross-sectional orderered logit specifications in (1) and (5)
include additionally age, age2, years of schooling, and dummies for province of residence. These specifications were also corrected for clustering of observations. ***/**/* indicate a parameter
estimate is significant at the 1%/5%/10% level respectively. Data: KLIPS 1998-2008. Reference category for working hours: 0 hours per week (not working).
Table 4: Hours and Job Satisfaction Regressions
Wife
Ordered
logit
Dep. Variable: Hours Satisfaction
(1)
Job type (1=wage; 0=non-wage)
Own working hours
Hours 31-40
Hours 41-50
Hours 51-60
Hours 60+
Partner's working hours
Hours 0
Hours 31-40
Hours 41-50
Hours 51-60
Hours 60+
Log likelihood
Observations
Individuals
Clusters
.091
BUC
FF
FE-OLS
(2)
(3)
(4)
Ordered
logit
(5)
.527***
.045
.034
-.201***
Husband
BUC
FF
(6)
(7)
FE-OLS
(8)
.213*** -.360*** -.106***
.001
-.025
-.036
-.004
-.269*** -.246** -.233** -.076***
-1.11*** -.941*** -.982*** -.319***
-2.06*** -1.62*** -1.58*** -.579***
.826*** .491*** .579*** .199***
.644*** .346*** .418*** .155***
-.206*** -.238*** -.274*** -.086***
-1.13*** -.911*** -.949*** -.359***
-.077
.232**
.189**
.122
.033
-11,535
11,128
2,539
.168***
.124
.056
.080
-.041
-22,618
21,182
3,791
.075
.180
.156
.090
.058
-6,201
19,693
4,035
1,875
.005
.170
.225*
.066
.049
-3,816
9,587
1,709
-
-.002
.059
.063*
.030
.032
11,128
2,539
-
.101
.160*
.036
.073
.002
-13,828
45,511
12,281
3,222
.068
.104
-.002
.067
-.011
-7,931
19,219
2,917
-
.024
.050*
.022
.038
.026
21,182
3,791
-
Table 4 cont’d
Dep. Variable: Job
Satisfaction
Job type (1=wage;
0=non-wage)
Own working hours
Hours 31-40
Hours 41-50
Hours 51-60
Hours 60+
Partner's working hours
Hours 0
Hours 31-40
Hours 41-50
Hours 51-60
Hours 60+
Log likelihood
Observations
Individuals
Clusters
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
.101
-.111
-.092
-.008
-.037
-.068
-.215**
-.049**
.255***
.262***
.095
-.140
.086
.062
-.074
-.009
.212*
.144
.035
.027
.054*
.046*
.024
-.008
.897***
.861***
.559***
.424***
.314***
.266***
.136
.149
.612***
.544***
.336***
.229**
.163***
.161***
.111***
.084***
.195
.481***
.296***
.264**
.191*
-8,427
9,439
2,346
-.041
.236*
.063
.118
.147
-6,137
22,616
2,853
1,824
.077
.378**
.229
.221
.228
-2,797
7,256
1,377
-
-.001
.080**
.045
.036
.048
9,439
2,346
-
.213***
-.063
-.053
-.068
-.089
-16,814
18,097
3,563
.152*
.0001
0.00001
-.077
.056
-13,529
48,285
9,544
3,113
-.007
-.102
-.076
-.075
-.100
-5,983
14,980
2,415
-
.021
-.013
-.010
-.006
-.002
18,097
3,563
-
Hours and job satisfaction regressions include the following control variables: four dummies for number and composition of children, logs of household and regional percapita income (no regional income in job satisfaction regressions), 10 occupation dummies, 16 industry dummies, a dummy for wage employment as well as dummies for
year of survey. Pooled cross-sectional orderered logit specifications in (1), (5), (9) and (13) include additionally age, age2, years of schooling, and dummies for province of
residence. Pooled specifications were also corrected for clustering of observations. ***/**/* indicate a parameter estimate is significant at the 1%/5%/10% level respectively.
Data: KLIPS 1998-2008. Reference category for own working hours: 1 to 30 hours per week.
Robustness checks
(1) Different reference groups in life satisfaction regressions
(2) Earnings instead of household income (Interesting finding: Women
value men’s earnings higher than their own)
(3) Separate working hours dummies for wage vs. non-wage employed
(wage-employed slightly happier)
(4) Subjective health as additional control (only available from wave 6,
problematic because endogenous)
 No changes in main results
IV. Gender-specific time-use patterns
1
.9
.8
.7
.6
.5
.4
.3
.2
.1
0
0-.3
.3-.4
.4-.5
.5-.6
.6-.7
.7-.8
Men's share of market work
Young men (age 20 to 39)
.8-.9
.9-1
Older men (age 40 to 64)
Table 5: Reported increased activities after reduction of working hours (in %)
Women
1st choice
Men
2nd choice 3rd choice
1st choice
2nd choice 3rd choice
Income-earning activities
3.4
1.6
1.5
2.0
1.5
0.7
Household work
55.2
11.7
8.4
9.3
6.2
8.5
Self-development
7.8
9.8
9.4
16.7
11.1
12.3
Rest (sleep, etc.)
13.4
38.7
21.3
28.0
21.9
16.6
Watch TV
0.8
6.6
17.3
2.9
15.0
16.6
Travel/tour
11.9
12.5
17.3
15.4
15.4
17.1
Sports/excercise
3.4
10.2
10.4
20.5
17.3
11.6
0
0
0.5
0.9
2.5
3.7
Social/group activities
1.9
5.5
8.4
2.5
6.8
9.9
Civil/volunteer activities
0.4
1.6
2.0
0.4
0.9
1.8
Religious activities
1.1
1.2
3.5
0.6
0.9
0.7
Other
0.8
0.8
0
0.9
0.4
0.4
Total
100
100
100
100
100
100
N
268
256
202
689
675
543
Games
Source: KLIPS, 2004-2008. Statistics are pooled over time.
V. Concluding remarks
– Reduction of working hours made Korean families happier
– Still strong gender-specific effects of working hours on happiness
due to strong traditional gender roles
– Men derive much higher beyond-income utility from working than
women
– Controlling for family income, women are most happy when being
housewives or working 31-40 hours; men when working 31-50 hours
– Part-time jobs no alternative in Korea yet due to low quality nature
– Results support gender-identity hypothesis
– Results robust to different fixed-effects estimators and changes in
model specification
V. Concluding remarks
– Policy Implications:
• Further hours of work reductions
• Equality of chances at the work place
• Encouraging change in gender identities
• Flexible job and child care solutions
• Create part-time jobs in high-skilled sector
Thank you for your attention.
II. Data and Methodology
Fixed-effects ordered logit estimation
 Ferrer-i-Carbonell and Frijters (FF-estimator): individual fixed effects
ui and individual-specific thresholds λik are introduced into the model;
this allows reformulation of the ordinal logit as a binomial logit
 Baetschmann, Staub and Winkelmann (2011) show that the FFestimator is slightly downward biased since cutoff points are chosen
endogenously
 They suggest an own estimator: BUC-estimator
 BUC-estimator performs best in Monte-Carlo simulations
II. Data and Methodology
Fixed-effects ordered logit estimation
We chose to apply BUC-estimator, FF-estimator, and linear FE
Why FF still necessary?



FF-estimator converges to the true value as N ↑, T ↑, and K ↓ as in the case of
our sample
Comparability of results with Booth and Van Ours, who use FF-estimator (2008,
2009)
BUC performance needs further validations under different circumstances (e.g.
extreme distributions, unbalanced panel)
Why linear FE additionally?


Linear FE shown to produce similar results in satisfaction regressions (Ferrer-iCarbonell and Frijters, 2004)
Straightforward interpretation