Work-life balance and the genderdifferences in self-employment income
during the start
start-up
up stage in Japan
岡室 博之（一橋大学経済学研究科）
池内 健太（一橋大学経済学研究科）
健太
橋 学経済学 究
二次利用共同研究集会 報告資料
2011年11月11日 数理統計研究所

Outline
1. Introduction
2. Literature
3. Conceptual Framework and Hypotheses
4 Data and Empirical Models
4.
5. Estimation Results and Discussion
6. Conclusion

2

1 Introduction (1): Gender and childcare
1.
• Various reasons to switch from employment to selfemployment by starting up new businesses:– WomenW
tendt d tot selectl t self-employmentlfltb
because th
they seekk
flexible work to balance career and family.
• It is reasonable for female workers engaged in childcare toprefer self-employment that provides higher flexibility ofwork,, even though
g theyy expect
p lower income.
– Occupational choice model by Lucas (1978) and Evans andJovanovic (1989)– In fact, Kawaguchi (2008) finds that self-employed workers
are more satisfied with their work than employees.
31 Introduction (2): Institutional change
1.
• With regard to gender equality and childcare support, somemajor legal and institutional changes have been introduced inJapan since the 1990s:• Clear trend of household structure:– Households with both husbands and wives working
1
drasticallyincreased in the 1980s and have exceeded theother type
yp of households since 1997.
• However, few empirical studies have been conducted inJapan on the determinants of self-employment and selfemployment income, focusing on gender differences andconsidering these institutional changes.
4スライド 41

岡室 博之, 2011/11/04

1 Introduction (3): Aims of the study
1.
• To investigate the determinants of the self-employment
income during the start-up stage– BasedB d on microiddatat off JJapanese official
ffi i l statistics
t ti ti withith
three cohorts from 1992 to 2001
– Comparing the income levels• between male and female self-employed workers, and• between female workers in self-employment
self employment and paid
employment• Special attention paid to the effects of gender and pre-school
children• To check the changes in the 1990s
5

2 Literature (1): Self-employment
2.
Self employment income• Besides survival and growth, income has been regarded as animportant performance measure of start-up firms.
• Previous
P istudiest di mainly
i l address
ddth
the effects
ff t off owners’’ hhumancapital, especially formal education and job experience (e. g.
Robinson and Sexton 1994; Parker and van Praag 2006; Van
Praag et al. 2009).
• Empiricalpstudies on the effect of ggender and household
structure on self-employment income are relatively scarce,compared to rich literature on gender gap in wage and salaryamong employees (e.g. Blau and Kahn 1997; Hersch andStratton 1997; Stratton 2001; Blau and Kahn 2006).
6

2 Literature (2): Gender gap in income2.
• Hundley (2000) provides the first systematic analysis of thegender gap in self-employment income in comparison withthat in wage and salary income
income.
• In a similar line, Hundley (2001) shows that the number ofyoung children and housework hours are negatively
correlated with average hourly earnings of female selfemployed (but not males).
• In contrast to Hundley’s argument, Walker (2009)
demonstrates that housework effort of self-employed women,rather than their housework hours, contributes to lower
income compared to their male counterpart.
7

2 Literature (3): Contribution of the study2.
• To focus more directly on the effect of childcare than Hundley(2000)• To
T ttargett att th
the ffounders
d off new start-ups
t t• To compare the results between three different cohorts in the
period of 1987-2001
1987 2001• To extend the representative empirical research by Hundley(2000) to the Japanese labor market and to apply it to theJapanese context

8

3. Conceptual Framework and Hypotheses (1):
Incentives of female to become self-employed• Several studies suggest that women select self-employment
because they seek flexible work to balance career and family(e g Macpherson 1988; Lombard 2001).
(e.g.
2001)
• The income gap between male and female self-employedworkers may be attributed to their different commitments tohousehold and market work (Hundley , 2000).
• Burden of childcare on female workers ((around 2000))– M-form curve of the female labor force participation– 70% of female workers retire when the first child is born.
– 64% of mothers in paid employment take childcare leave.
– 86% of working mothers main players of childcare.
93. Conceptual Framework and Hypotheses (2):Hypothesis 1 and 2• Regarding the choice of self-employment, therefore, we
postulate the following hypotheses for the empirical analyses.
– H1:
H1 FemaleFl workers
k withith pre-schoolh l children
hild
are morelikely to start up their businesses than male workers withpre-schoolpreschool children
children.
– H2: Female workers with pre-school children are more
likelyy to start up
p their businesses than other female
workers.
• This is because female workers with pre-school children haveto devote themselves to childcare and thus are more likely toseek flexibility in work as compared to their male counterpart
andd ffemalel workers
k without
ith t pre-schoolh l children.
hild
10

3. Conceptual Framework and Hypotheses (3):Hypothesis 3 and 4• FFemalel self-employedlf
l d workers
k withi h pre-schoolh l children
hild
devote themselves more to childcare (resulting in lower
income)) than those without p
pre-school children.
– H3: Female self-employed workers with (more) pre-school
children tend to achieve lower income than female selfemployed workers without (with less) pre-school children.
• Female self-employed workers with pre-school children are
more family-oriented
f l
d and
d thush ddevote lless effort
ff to marketkwork than those in paid employment who are more careeroriented.
– H4: Female self-employed workers with pre-school
children tend to achieve lower income than female
workers with pre-school children in paid employment.
11

3. Conceptual Framework and Hypotheses (4):Hypothesis 5• Female workers have comparative advantage in childcare.
• They can adjust their working hours and intensity more
fl ibl to
flexiblyt their
th i needd ffor childcarehild
iin self-employment
lf
l
t th
than iin
paid employment.
• Thus, we can predict that the gender income difference inself-employment
self
employment increases with the number of pre-school
pre school
children to a larger extent than among employees:– H5: Male/female income difference among self-employed
self employedworkers is more sensitive to the number of pre-school
children than among employees.
124. Data and Empirical Models (1):Data
• Anonymous micro data of the Employment Status Survey– Japanese official statistical survey.
– Three cohorts: 1992, 1997, and 2002.
– Random sample of 1% of the entire population in Japan• Final dataset comprises both:– New self-employments (up to five years since start-up)– Employees (not previously self-employed)• Sample size: Approximately one million people
134. Data and Empirical Models (2):Empirical model• 1st stage: Employees decide whether to become selfemployed or continue to be employees (employment mode
choice).
choice)– Dependent variable: Self (dummy variable: 1 for new selfemployed 0 for employees)
employed,– Binary probit model• 2nd stage: The income level of workers is determined.
– Dependent variable: Income– Ordered probit model• Information on annual income is not available asabsolute values, but only as categories in the dataset. 14

4. Data and Empirical Models (3):Independent variables in the 1st stage••••

Fem : Dummy variable: 1 for females and 0 for males
Child : The number of pre-school children (under 6 years old)
Self : Dummy variable for the new self-employment
All possible interaction terms of Self with Fem and Child• Control variables:– Annual working days and hours (11 categories)– Individual characteristics– Regional characteristics– Current and previous job characteristics– Survey year (cohort) dummies
15

4. Data and Empirical Models (4):Rate of new self-employment (%)

3.0

22
2.2

20
2.0

2.0

1.3

Male

0.8

1.0

Female0.0
No pre-school children
6.0
50
5.0
4.0
3.0
2.0
1.0
0.0

1 or more pre-school children

Average income (million yen)
4.9

4.8
2.1

1.9

3.2

2.6
1.5

0.9

Male
FemaleNo pre-school 1 or more pre- No pre-school 1 or more prechildrenhildschoolh l children
hild
childrenhildschoolh l children
hild
Employee

New self-employed16

5. Estimation Results and Discussion (1):Estimation results of 1st stage model
Dependent
variable
Independent
variables(1) Self(binary probit)

Female (Fem)−0.19***[0.011]pre-school# of pchildren (Child)

0.032***[0.007]

Fem * Child0.122
0122***[0.012]

H1: (Partially)
H1(P i ll ) supported.
dFemale workers with 2 pre-school
children or more are more likely
to start up their businesses thanmale workers with 2 pre-school
childrenhildor more.
H2: Supported.
Supported
Female workers with pre-school
children are more likely to startup their businesses than otherfemale workers.

Notes: Standard errors are in brackets. ***p<0.01, **p<0.05 and *p<0.1. Coefficients17of other control variables are not displayed for simplification.

5. Estimation Results and Discussion (2):Estimation results of 2ndd stage model
Dependentdvariablebl
Independent variables( ) Income(2)( ) Income(3)(ordered probit) (ordered probit)

New self-employment (Self)−1.579***−1.267***

Female (Fem)−0.73***−0.704***# of pre-school children (Child)−0.039***−0.046***

Self * Fem

0.353***

0.509***

Self * Child

0.086***

0.057***

Fem * Child−0.02***

0.032***

S lf * FFem * Child
Self−0.249***0 249***−0.19***
0 19***-

Included

Weekly working hours

Notes: ***p<0.01, **p<0.05 and *p<0.1. Coefficients of other control variables
are not displayed for simplification.

18

5. Estimation Results and Discussion (3):Predicted income ratios with/without pre-school children• H3: Supported.
– Female self-employed workers with (more) pre-school children
tend to achieve lower income than female self-employedworkers without (with less) pre-school children.
120
100
80
(%) 6040
20
010098 96 94

10097
94

91

109
100103106

100
86
74

Male

FemaleEmployeeMale

63

No pre-school
pre school children
With 1 pre-school childWith 2 pre-school children
With 3 pre-school children

FemaleSelf-employedSelf
employed

Note: Predicted income of workers with no pre-school children = 100(%)

19

5. Estimation Results and Discussion (4):Predicted self-employed/employee income ratios• H4: Supported.
– Female self-employed workers with pre-school childrent d tto achieve
tend
hi
l
lower
incomeith ffemale
thanl workers
k withith
pre-school children in paid employment.
100
80
(%)

60
40

42.2 44.3 46.4

48.7

48.5 43.2

No pre-school children
38.3 33.8

20With 1 pre-school childWith 2 pre-school
pre school children
With 3 pre-school children

0
MaleFemaleNote: Predicted income of employees = 100(%)

20

5. Estimation Results and Discussion (5):Predicted female/male income ratios• H5: Supported.
– Male/female income difference among self-employedworkersk is
i more sensitive
iti tot theth numberb off pre-schoolh lchildren than among employees.
100
80
(%)

68.5 67.7 66.8 66

78.8

No pre-school children

66.1
55.1

60

45.8

40

With 1 pre-school childWith 2 p
pre-school children

20

With 3 pre-school children

0
Employee

Self-employed

Note: Predicted income of male workers = 100(%)

21

5. Estimation Results and Discussion (6):Structural change of the income model (2ndd stage) :Dependent variable = Income• No distinct differences across three cohorts.
Cohort

1992

1997

2002

New self-employment (Self)−1.121***−1.307***−1.312***

Female (Fem)−0.745***0 745***−0.705***0 705***−0.65***
0 65***# of pre-school children (Child)−0.065***−0.036***−0.032***

Self * Fem

0 458***
0.458

0 444***
0.444

0 616***
0.616

Self * Child

0.008

0.056

0.093**

Fem * Child

0.01

0.03***

0.066***

Self * Fem * Child−0.177***−0.153**−0.259***Weekly working hours

Included

Included

Included

Independentddvariablesi bl

Notes: Standard errors are in brackets. ***p<0.01, **p<0.05 and *p<0.1.
Coefficients of other control variables are not displayed for simplification.

22

6 Conclusion: Summary
6.
• Results are mostly consistent with the hypotheses:– Self-employment may provide working mothers with preschoolh l children
hild
with
ith flflexibility
ibilit iin workk andd opportunities
t iti off
achieving better work-life balance.
– No significant structural changes across three cohorts
cohorts.
• Limitations:
1 Most variables are available only as categories.
1.
categories
2. Panel data analysis is impossible.
3 Neglecting
3.
N l ti endogeneitydit off selecting
l ti self-employmentlflt
4. Ignoring the choice of retiring from the labor market
5 Difficulty5.
Diffi lt tot identify
id tif theth reason forf lowerlincomeiofffemale self-employed with pre-school children.
23

Major references
Blau, Francine D. and Kahn, Lawrence M. (2006), The US gender pay gap in the 1990s:
Slowing convergence. Industrial and Labor Relations Review 60, 45-66.
H
Hersch,h Joni,J i andd St
Stratton,tt
LLeslieli SS. (1997)(1997), H
Housework,
k fi
fixed
d effects,
ff t andd wages off
married workers. Journal of Human Resources 32, 285-307.
Hundley, Greg (2000), Male/female earnings differences in self-employment: The
effectsff
off marriage, children,
h ld
and
d the
h h
householdh ld ddivision off llabor,b Industrial
d
l and
d
Labor Relations Review 54, 95-114.
Hundley, Greg (2001), Why women earn less than men in self-employment, Journal ofLabor Research 22, 817-829.
Parker, Simon C. and van Praag, Mirjam (2006), Schooling, capital constraints, andentrepreneurial performance: The endogenous triangle, Journal of Business &Economic Statistics 24, 416-431.
Stratton, Leslie S. (2001). Why does housework lower women’s wages? Testing
hypotheses involving job effort and hours flexibility. Social Science Quarterly 82,
67-76.
Walker, John R. (2009), Earnings, effort, and work flexibility of self-employed womenand men: The case of St. Crois county, Wisconsin. Journal of Labor Research 30,
269-288.
24

Appendix (1): Control variables• V
Variablesi bl ffor iindividual
di id l characteristicsht i ti– UnivGrad : Dummy variable for university graduates– Age : Dummy variables for ten age groups– HeadHH : Dummy variable for the household head
– Marriage : Dummy variable for the married– Spouse : Dummy variable for the spouse of household head
• Characteristics of previous jobs of the new self-employments– TenureT(9 categories),i )– Employment status (5 types),– Firm size of (11 categories),
categories)– Industries (10 sectors).
– Dummy variable which takes 1 if the industries of current andprevious jobs is same and 0 otherwise
25Appendix (2):Structural change of the job mode choice model (1st stage) :Dependent variable : Self
• No significant differences across three cohorts.
Cohort

1992

1997

2002

Female (Fem)−0.157***−0.131***−0.275***# of prepre-school
school children (Child)

0 05***
0.05***

0 048***
0.048***−0.0070 007

Fem * Child

0.047**

0.132***

0.202***

University graduates dummy−0.211***0.211−0.229***0.229−0.177***0.177

Head of household dummy

0.33***

0.209***

0.264***

pof head of household dummyy
Spouse−0.052**−0.008−0.027

Marriage dummy−0.021

0.008−0.042***−0.068***−0.059***−0.078***

yes

yes

yes

I d
Independentdvariablesi bl

Metropolitan dummy
Age dummies
Notes: ***p<0.01, **p<0.05 and *p<0.1.

26