Why not Choose a Better Job?

Flexibility, Social Norms, and Gender Gaps in Japan

Kazuharu Yanagimoto

kazuharu.yanagimoto@cemfi.edu.es

CEMFI

June 7, 2023

Female Workers in Japan

Gap in Median Earnings of Full-time Workers in 2019

Fraction of Part-time in Female Workers in 2019

Large gap in earnings and high ratio of part-time jobs
Female participation is not low

Why is the gender wage gap large in Japan?

Why is the fraction of part-time workers large for women in Japan?

What Do I Do?

Document Female Employment in Japan

Large gender diff. in participation, occupations, working hours, and wage
Regular vs Non-regular job & Social norms on gender roles

Build a model

Choices on occupations and working hours
- Occupations differ in the way hours map into earnings (linear vs. convex)
Utility cost associated to social norms
- Wives earnings more than husbands

Model explains

All gender gaps in participation
33% in occupational choices, 74% in labor hours, and 34% in wage

Facts

Data

Japan Panel Study of Employment Dynamics (JPSED)

57,284 men and women older than 15 in Japan
Panel data 2015-2019
Earnings, working hours, housework, labor contracts
Use samples aged 25-59

Survey on Dual-Income Couples’ Household Economy and Attitudes

2200 couples, women (men) aged 35-49 (30-55), in the Greater Tokyo Area
One-year survey in 2014
Earnings, working hours, housework, types of contracts

Regular and Non-regular Jobs

In Japanese statistics, a definition is used: Regular and Non-regular jobs

Based on “how their occupations are classified in the company”
There is no precise definition, but typically,

	Regular	Non-Regular
Contract	Permanent	Temporary
Hours (week)	40/40+	Lower and Dispersed
Wage	High	Low

In JPSED,

92 % (91 %) of male (female) regular workers have permanent contracts
13 % (14 %) of male (female) non-regular workers have permanent contracts

Occupational Choices of Married Men and Women

Why Do Women Choose Non-regular Jobs?

Reasons for Choosing Non-regular Job, Women

Job Flexibility and Convex Earning

Goldin (2014) defines the two types of jobs by earning schedule

Linear jobs are lower wages and high flexibility
Non-linear (convex) jobs are high wage and low flexibility

These characteristics correspond to Regular and Non-regular jobs! Regression

Before Going to the Model…

Key Features

Job Flexibility (Regular vs. Non-regular)
Social Norm on Wife’s Earnings Cross-country Coparison

Gender Gaps

	Description	Gap	Men	Women
Partcipation	Participation rate	0.27	98%	70%
Ocuupation	Fraction of regular workers	0.59	89%	32%
Labor Hours	Mean of log weekly working hours	0.49	44.2h	20.3h
Wage	Mean of log hourly wage	0.76	2958 JPY	1534 JPY
Data: married, 25-59 aged in JPSED2016-2020

Model

Households’ Problem

Economy consists of couples, including husbands \((g = m)\) and wives \((g = f)\)
choose an occupation \(j_g\) from regular \(R\), non-regular \(NR\), not-working \(NW\)
Endowed one unit of time, and choose working hours \(h_m, h_f\), home hours \(T_m, T_f\), and leisure \(1 - h_m - T_m, 1 - h_f - T_f\)

\[ \max_{h_m, h_f, T_m, T_f, j_m, j_f} U = \log c + \gamma \log H(1 - h_m - T_m, 1 - h_f - T_f) - \delta \mathbb{1}\{e_m < e_f\} \]

subject to

\[\begin{aligned} c &= e(h_m, j_m) + e(h_f, j_f) \\ T &= T_m + T_f \end{aligned} \]

\(H(\cdot)\) : Joint leisure function
\(e(h, j)\) : Earning
\(T\) : Home hours requirement
\(\delta\) : Utility cost

Productivity

Each husband and wife is endowed job specific productivity:

\[ \begin{pmatrix}a_{m, R} \\ a_{f, R} \\ a_{m, NR} \\ a_{f, NR}\end{pmatrix} \sim \log\mathcal{N}\left(\begin{pmatrix}0 \\ 0 \\ \mu_{NR} \\ \mu_{NR}\end{pmatrix}, \begin{pmatrix} \sigma^2 & \rho_{mf}\sigma^2 & \rho_{R, NR}\sigma^2 & \rho_{R, NR}\rho_{mf} \sigma^2 \\ \cdot & \sigma^2 & \rho_{R, NR}\rho_{mf} \sigma^2 & \rho_{R, NR} \sigma^2 \\ \cdot & \cdot & \sigma^2 & \rho_{mf} \sigma^2 \\ \cdot & \cdot & \cdot & \sigma^2 \end{pmatrix}\right) \]

\(\mu_{NR} < 0 \Rightarrow\) Non-regular workers earns less than regular worker
\(\rho_{mf} > 0 \Rightarrow\) Assortative Mating
\(\rho_{R, NR} > 0 \Rightarrow\) Regular and Non-regular abilities are linked

No Gender Difference in Productivity

Convex Wage Schedules

Regular Jobs

\[ e(h, R) = \begin{cases} a_R h^{1 + \theta} & h < \bar{h} \\ a_R \left(\bar{h}^{1 + \theta} + \lambda_{R} \bar{h}^{\theta}(h - \bar{h})\right) & h > \bar{h} \end{cases} \]

Non-regular Jobs

\[ e(h, NR) = \begin{cases} a_{NR} h & h \le \bar{h} \\ a_{NR} \left(\bar{h} + \lambda_{NR} (h - \bar{h})\right) & h > \bar{h} \end{cases} \]

Leisure Function

\[ H = \left(\nu(1 - h_m - T_m)^{\xi} + (1 - \nu)(1 - h_f - T_f)^{\xi}\right)^{1/\xi} \]

\(\nu\) : share parameter. Each household is endowed \(\nu \sim Beta(\alpha_{\nu}, \beta_{\nu})\)
\(\xi\) : complementarity. \(\xi < 0 \Rightarrow\) complement

Home Hours Requirement

\[ \begin{aligned} T &= T_m + T_f \\ \frac{1}{2}T & \sim Beta(\alpha_T, \beta_T) \end{aligned} \]

Households has a home hours requirement \(T \in [0, 2]\)
\(T\) does not increase the utility
captures the heterogeneity of home hours requirements (children)

Estimation

Calibration Strategy

15 Parameters

\[ \{\underbrace{\lambda_{R}, \lambda_{NR},\theta, }_{\text{production function}} \underbrace{\mu_{NR}, \sigma^2, \rho_{R, NR}, \rho_{mf},}_{\text{productivity}} \,\, \underbrace{\gamma, \xi, \alpha_{\nu}, \beta_{\nu},}_{\text{leisure}} \underbrace{\alpha_{T}, \beta_{T},}_{\text{home hours }} \underbrace{\alpha_{\delta}, \beta_{\delta}}_{\text{social norm}}\} \]

Method of Simulated Moments

Model produces occupations, working hours, and wages of household
Compute 15 moments (e.g. ratio of regular workers, mean of working hours, gender correlation of wage…)
Minimize the distance between moments from data and model

Estimation

Parmeter	Value	Target	Data	Model
λ_R	0.57	mean of h_f for regular workers	0.50	0.48
λ_NR	0.63	mean of h_f for NR workers	0.30	0.27
θ	2.96	share of regular workers, females	0.32	0.37
μ_NR	−3.15	share of NR workers, females	0.38	0.28
σ	1.03	s.d. of ln w_f for R workers	0.72	0.72
ρ_{R, NR}	0.14	mean diff. of ln w_{f, R} and ln w_{f, NR}	0.62	0.62
ρ_mf	0.01	corr. of log wages, R×R couples	0.49	0.50
γ	0.84	s.d. of h_f for regular workers	0.11	0.11
ξ	−8.29	s.d. of h_f for NR workers	0.14	0.15
α_ν	13.04	mean of T_m for regular workers	0.14	0.13
β_ν	1.15	mean of T_m for NR workers	0.13	0.14
α_T	1.59	mean of T_f for regular workers	0.28	0.21
β_T	3.57	mean of T_f for NR workers	0.32	0.37
α_δ	0.59	share of couples with e_m < e_f	0.07	0.08
β_δ	11.81	corr. of working hours, couples	0.19	0.18

Estimation

Parmeter	Value	Target	Data	Model
λ_R	0.57	mean of h_f for regular workers	0.50	0.48
λ_NR	0.63	mean of h_f for NR workers	0.30	0.27
θ	2.96	share of regular workers, females	0.32	0.37
μ_NR	−3.15	share of NR workers, females	0.38	0.28
σ	1.03	s.d. of ln w_f for R workers	0.72	0.72
ρ_{R, NR}	0.14	mean diff. of ln w_{f, R} and ln w_{f, NR}	0.62	0.62
ρ_mf	0.01	corr. of log wages, R×R couples	0.49	0.50
γ	0.84	s.d. of h_f for regular workers	0.11	0.11
ξ	−8.29	s.d. of h_f for NR workers	0.14	0.15
α_ν	13.04	mean of T_m for regular workers	0.14	0.13
β_ν	1.15	mean of T_m for NR workers	0.13	0.14
α_T	1.59	mean of T_f for regular workers	0.28	0.21
β_T	3.57	mean of T_f for NR workers	0.32	0.37
α_δ	0.59	share of couples with e_m < e_f	0.07	0.08
β_δ	11.81	corr. of working hours, couples	0.19	0.18

\(\xi < 0\)

Leisure by husband and wife is complement

Estimation

Parmeter	Value	Target	Data	Model
λ_R	0.57	mean of h_f for regular workers	0.50	0.48
λ_NR	0.63	mean of h_f for NR workers	0.30	0.27
θ	2.96	share of regular workers, females	0.32	0.37
μ_NR	−3.15	share of NR workers, females	0.38	0.28
σ	1.03	s.d. of ln w_f for R workers	0.72	0.72
ρ_{R, NR}	0.14	mean diff. of ln w_{f, R} and ln w_{f, NR}	0.62	0.62
ρ_mf	0.01	corr. of log wages, R×R couples	0.49	0.50
γ	0.84	s.d. of h_f for regular workers	0.11	0.11
ξ	−8.29	s.d. of h_f for NR workers	0.14	0.15
α_ν	13.04	mean of T_m for regular workers	0.14	0.13
β_ν	1.15	mean of T_m for NR workers	0.13	0.14
α_T	1.59	mean of T_f for regular workers	0.28	0.21
β_T	3.57	mean of T_f for NR workers	0.32	0.37
α_δ	0.59	share of couples with e_m < e_f	0.07	0.08
β_δ	11.81	corr. of working hours, couples	0.19	0.18

\(\xi < 0\)

Leisure by husband and wife is complement

\(\alpha_{\nu} =\) 13.04, \(\beta_{\nu} =\) 1.15

\(E[\nu] =\) 0.92 > 0.5
Husbands have a higher weight on joint leisure

Estimation

Parmeter	Value	Target	Data	Model
λ_R	0.57	mean of h_f for regular workers	0.50	0.48
λ_NR	0.63	mean of h_f for NR workers	0.30	0.27
θ	2.96	share of regular workers, females	0.32	0.37
μ_NR	−3.15	share of NR workers, females	0.38	0.28
σ	1.03	s.d. of ln w_f for R workers	0.72	0.72
ρ_{R, NR}	0.14	mean diff. of ln w_{f, R} and ln w_{f, NR}	0.62	0.62
ρ_mf	0.01	corr. of log wages, R×R couples	0.49	0.50
γ	0.84	s.d. of h_f for regular workers	0.11	0.11
ξ	−8.29	s.d. of h_f for NR workers	0.14	0.15
α_ν	13.04	mean of T_m for regular workers	0.14	0.13
β_ν	1.15	mean of T_m for NR workers	0.13	0.14
α_T	1.59	mean of T_f for regular workers	0.28	0.21
β_T	3.57	mean of T_f for NR workers	0.32	0.37
α_δ	0.59	share of couples with e_m < e_f	0.07	0.08
β_δ	11.81	corr. of working hours, couples	0.19	0.18

\(\xi < 0\)

Leisure by husband and wife is complement

\(\alpha_{\nu} =\) 13.04, \(\beta_{\nu} =\) 1.15

\(E[\nu] =\) 0.92 > 0.5
Husbands have a higher weight on joint leisure

\(\alpha_{T} =\) 1.59, \(\beta_{T} =\) 3.57

Home hours requirement is 49 hours per week

Occupational Choices (Not-Targeted)

Time Allocations (Not-Targeted)

Gender Gaps

	Data	Model	Pct.
Participation	0.27	0.27	99%
Occupation	0.59	0.19	33%
Labor Hours	0.49	0.36	74%
Wage	0.76	0.26	34%

Gender Gaps

	Data	Model	Pct.
Participation	0.27	0.27	99%
Occupation	0.59	0.19	33%
Labor Hours	0.49	0.36	74%
Wage	0.76	0.26	34%

Model explains

Almost all the gap in the participation rate

Gender Gaps

	Data	Model	Pct.
Participation	0.27	0.27	99%
Occupation	0.59	0.19	33%
Labor Hours	0.49	0.36	74%
Wage	0.76	0.26	34%

Model explains

Almost all the gap in the participation rate
Significant proportion of other gender gaps

Mechanism

Flexible Regular Job: Occupational Choices

Eliminating inflexibility encourages wives to have regular jobs

Mechanism

	Baseline	θ = 0.0	δ = 0.0
Participation	0.27	0.14	−0.04
Occupation	0.19	0.01	0.18
Labor Hours	0.36	0.64	0.17
Wage	0.26	−0.03	0.22

Mechanism

	Baseline	θ = 0.0	δ = 0.0
Participation	0.27	0.14	−0.04
Occupation	0.19	0.01	0.18
Labor Hours	0.36	0.64	0.17
Wage	0.26	−0.03	0.22

Job inflexibility \(\theta\)

The main element prevents women from having regular jobs
Wage gap comes from occupational differences

Mechanism

	Baseline	θ = 0.0	δ = 0.0
Participation	0.27	0.14	−0.04
Occupation	0.19	0.01	0.18
Labor Hours	0.36	0.64	0.17
Wage	0.26	−0.03	0.22

Job inflexibility \(\theta\)

The main element that prevents women from having regular jobs
Wage gap comes from occupational differences

Social Norms \(\delta\)

Explains intensive and extensive margin of male and female participation

Conclusion

Build a Model

Regular (inflexible, high wage) vs. Non-Regular (flexible, low wage)
Social Norms (wives’ higher earnings)

Model Explains the Gender Gaps

Almost all of participation rate
33% in occupational choices, 74% in labor hours, and 34% in wage

Mechanism

Job flexibility and social norm play an important role in gender gaps
Housework services could reduce the gaps under job inflexibility and social norm Appendix

Outsourcing of Housework

Outsourcing housework could increase women’s labor supply

Raz-Yurovich and Marx (2019), Halldén and Stenberg (2014)

Also discussed as the impact of low-skilled immigrants

Cortés and Tessada (2011), Barone and Mocetti (2011), Farré, González, and Ortega (2011)

However, those housework services are rarely used in Japan

Japan has a restrictive policy on immigration
2+ member households pay 7.3 EUR per YEAR on average

Baseline Model with Housework Service

\[\max_{h_m, h_f, j_m, j_f} U = \log c + \gamma \log H - \delta \mathbb{1}(e_m < e_f)\]

subject to

\[ \begin{aligned} c + pt &= e(h_m, j_m) + e(h_f, j_f)\\ H &= (\nu(1 - h_m - T_m)^\xi + (1 - \nu)(1 - h_f - T_f)^\xi)^{1/\xi} \\ T &= T_m + T_f + t \end{aligned} \]

\(t\): housework service
\(p\): price of housework service

Experiment

Fix parameters in the baseline model
Set price as the median wage of non-regular job \((p = \exp(\mu_{a_{NR}}))\)

Outsourcing \(T\): Home Hours

Workers use outside services to do most of the home work

Outsourcing \(T\): Gender Gaps

	Base	Outsourcing t	Pct.
Participation	0.27	−0.02	−7%
Occupation	0.19	0.03	15%
Labor Hours	0.36	0.06	17%
Wage	0.26	0.25	97%

Given social norms, housework services

Outsourcing \(T\): Gender Gaps

	Base	Outsourcing t	Pct.
Participation	0.27	−0.02	−7%
Occupation	0.19	0.03	15%
Labor Hours	0.36	0.06	17%
Wage	0.26	0.25	97%

Given social norms, housework services

Reduce gender gaps in participation, occ. choices, and labor hours

Outsourcing \(T\): Gender Gaps

	Base	Outsourcing t	Pct.
Participation	0.27	−0.02	−7%
Occupation	0.19	0.03	15%
Labor Hours	0.36	0.06	17%
Wage	0.26	0.25	97%

Given social norms, housework services

Reduce gender gaps in participation, occupational choices, and labor hours
Do not reduce wage gap

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Appendix

Related Literature

Home Hours and Gender Gaps

Erosa et al. (2022)
- Models couples’ decisions on occupations with different job flexibility
Cubas, Juhn, and Silos (2019)
- Women are penalized for the lack of work in the peak hours (8am-5pm)

Bertrand, Kamenica, and Pan (2015)
- A sharp gap in the wife’s earnings relative to the husband’s earnings

Gender Gaps in Japan

Kitao and Mikoshiba (2022)
- Role of fiscal policies on female labor force participation and occ. choices

Job Flexibility and Convex Earning

To see the convex and linear wage schedules, run

\[y_{it} = a_{i} + \lambda_t + \left(\sum_{h \in H, h \ne 40} \beta_h I_{ith}\right) + \gamma X_{it} + \varepsilon_{it}\]

\(y_{it}\) : yearly earnings of individual \(i\) at time \(t\)
\(a_{i}\) : individual fixed effect
\(\lambda_{t}\) : time fixed effect
\(X_{it}\) : age, age-square, educational attainment, industry
\(H = \{20\mbox{-}24, 25\mbox{-}29, \dots, 60\mbox{-}64\}\) : 5 hour bins for weekly working hours
\(I_{ith}\) : indicator if \(i\)’s working hours in the bin \(h \in H\) at time \(t\)

This is in the line of Bick, Blandin, and Rogerson (2022)

Earning Curves

Regular Jobs
- Convexity before 40 hours \(\Rightarrow\) Concentration at 40 hours
- After 40 hours, the slope is different from the below-40-hour
Non-regular Jobs
- Almost linear relationship

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Marriage Penalty

If there are social norms regarding wives earning more than husbands, after the marriage, women might choose: lower working hours or changing/quitting jobs

Using JPSED2016-2020, I see

Men and Women married at 2018
Change in market outcomes in 2017
Child Penalty as in Kleven et al. (2019)
Female earnings decline by 4600€ 1-year after the marriage

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Marriage Penalty

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Key Features

Job Flexibility (Regular vs. Non-regular)
Social Norm on Wife’s Earnings

Key Features

Job Flexibility (Regular vs. Non-regular)
Social Norm on Wife’s Earnings

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References

Barone, Guglielmo, and Sauro Mocetti. 2011. “With a Little Help from Abroad: The Effect of Low-Skilled Immigration on the Female Labour Supply.” Labour Economics 18 (5): 664–75. https://doi.org/10.1016/j.labeco.2011.01.010.

Bertrand, Marianne, Emir Kamenica, and Jessica Pan. 2015. “Gender Identity and Relative Income Within Households *.” The Quarterly Journal of Economics 130 (2): 571–614. https://doi.org/10.1093/qje/qjv001.

Bick, Alexander, Adam Blandin, and Richard Rogerson. 2022. “Hours and Wages*.” The Quarterly Journal of Economics, January, qjac005. https://doi.org/10.1093/qje/qjac005.

Cortés, Patricia, and José Tessada. 2011. “Low-Skilled Immigration and the Labor Supply of Highly Skilled Women.” American Economic Journal: Applied Economics 3 (3): 88–123. https://www.jstor.org/stable/41288640.

Cubas, German, Chinhui Juhn, and Pedro Silos. 2019. “Coordinated Work Schedules and the Gender Wage Gap,” December, w26548. https://doi.org/10.3386/w26548.

Erosa, Andrés, Luisa Fuster, Gueorgui Kambourov, and Richard Rogerson. 2022. “Hours, Occupations, and Gender Differences in Labor Market Outcomes.” American Economic Journal: Macroeconomics 14 (3): 543–90. https://doi.org/10.1257/mac.20200318.

Farré, Lidia, Libertad González, and Francesc Ortega. 2011. “Immigration, Family Responsibilities and the Labor Supply of Skilled Native Women.” The B.E. Journal of Economic Analysis & Policy 11 (1). https://doi.org/10.2202/1935-1682.2875.

Goldin, Claudia. 2014. “A Grand Gender Convergence: Its Last Chapter.” American Economic Review 104 (4): 1091–1119. https://doi.org/10.1257/aer.104.4.1091.

Halldén, Karin, and Anders Stenberg. 2014. “The Relationship Between Hours of Domestic Services and Female Earnings: Panel Register Data Evidence from a Reform.” SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2534703.

Kitao, Sagiri, and Minamo Mikoshiba. 2022. “Why Women Work the Way They Do in Japan: Roles of Fiscal Policies.” SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4054049.

Kleven, Henrik, Camille Landais, Johanna Posch, Andreas Steinhauer, and Josef Zweimüller. 2019. “Child Penalties Across Countries: Evidence and Explanations.” AEA Papers and Proceedings 109 (May): 122–26. https://doi.org/10.1257/pandp.20191078.

Raz-Yurovich, Liat, and Ive Marx. 2019. “Outsourcing Housework and Highly Skilled Women’s Labour Force Participation of a Policy Intervention.” European Sociological Review 35 (2): 205–24. https://doi.org/10.1093/esr/jcz001.

Why not Choose a Better Job?

Female Workers in Japan

What Do I Do?

Document Female Employment in Japan

Build a model

Model explains

Facts

Data

Japan Panel Study of Employment Dynamics (JPSED)

Survey on Dual-Income Couples’ Household Economy and Attitudes

Regular and Non-regular Jobs

Occupational Choices of Married Men and Women

Why Do Women Choose Non-regular Jobs?

Job Flexibility and Convex Earning

Social Norms

Before Going to the Model…

Key Features

Gender Gaps

Model

Households’ Problem

Productivity

Convex Wage Schedules

Leisure Function

Home Hours Requirement

Estimation

Calibration Strategy

15 Parameters

Method of Simulated Moments

Estimation

Estimation

Estimation

Estimation

Occupational Choices (Not-Targeted)

Time Allocations (Not-Targeted)

Social Norms

Gender Gaps

Gender Gaps

Gender Gaps

Mechanism

Roles of Job Inflexibility & Social Norms

1. Inflexibility of Regular Job \(\theta\)

2. Social Norms \(\delta\)

Flexible Regular Job: Occupational Choices

No Social Norm: Occupational Choices

Mechanism

Mechanism

Mechanism

Conclusion

Build a Model

Model Explains the Gender Gaps

Mechanism

Outsourcing of Housework

Outsourcing of Housework

Baseline Model with Housework Service

Experiment

Outsourcing \(T\): Home Hours

Outsourcing \(T\): Gender Gaps

Outsourcing \(T\): Gender Gaps

Outsourcing \(T\): Gender Gaps

Appendix

Related Literature

Home Hours and Gender Gaps

Social Norms and Occupational Choices

Gender Gaps in Japan

Job Flexibility and Convex Earning

Earning Curves

Marriage Penalty

Marriage Penalty

Key Features

Key Features

References