Lower conference - Volos, Grece. 10/11 september 2007

How much does it cost to stay at home?Career interruptions and the gender

wage gap in France.

Dominique MEURS, Ariane PAILHE and Sophie PONTHIEUX

August 2007

Motivation• Main gender difference in human capital : difference in work

experience. Why? Children! a part of mothers takes time out of the labour market ==> less work experience, depreciation of skills, signal of weak motivationFamily gap literature

• France : high rate of female participation thanks childcare facilities. So this question was not an issue until recently

• Change in the parental leave allowance (1994) : employment breaks after birth is more frequent and longer.= => recent interest in the implication of career interruption on the gender wage gap.

Objectives

• Which impact of various type of career breaks in the estimation of returns to total experience?

• Which effect of the number of children on the earnings?

• Which penalty of the career interruption for women?

Data set• Enquête Familles et Employeur - Ined

2005

• Individuals aged 20-49

• Respondents were asked about their activity history since the age of 18

• Individual variables, incl. total number of children (# number of children at home)

• Limitation : cross section, no data panel

Sample descriptive statistics

• Original sample : 9547 observations,

• Here, sample limited to paid workers and potential workers : 7562 individuals

• Excluded : students, retired or self employed

• Wage earners (minimum : 10 hours a week) : 6049 individuals (3034 males and 3015 females)

Basic statistics

Men Women Total 0 child 1 child 2 children 3 child. & + Total 0 child 1 child 2 children 3 child. & +

Weakly hours 39.6 39.1 39.7 40.0 40.0 34.1 36.2 34.7 33.1 32.0 Full-time (%) 97 96 96 97 98 73 86 79 66 58 Hourly wage (€) 40.9 36.8 41.0 43.4 44.2 36.4 35.4 36.5 37.2 35.9 Wf/Wh 89% 96% 89% 86% 81%

N 3034 946 607 966 515 3015 758 700 1081 476 Source: EFE 2005.

Measure of experience• Potential experience : number of years since

the end of initial education

EXPP (Potential experience) +Tenure (ANCI)

• Actual ExperienceEXPV (Actual experience) +Tenure (ANCI)

• Breaks in workUnemployment (NBCHO), Out of the labour market

(NBINAT)

Potential and actual work experience

0.0

1.0

2.0

3.0

4

0 10 20 30 40Years

potential exp males actual exp malespotential exp females actual exp females

Impact of the measure of experience…

3 specifications

• Lwh = a EDUC + b EXPPV + c ANCI + e (1)

• Lwh = a EDUC + b EXPV + c ANCI + e (2)

• Lwh = (2) + d NBCHO + g NBINAT (3)

ResultsTable 4 - Returns to experience

Women Men Specification (1) (2) (3) (1) (2) (3)

EXPPV 0.007 0.020 (2.98)** (8.09)**

EXPV 0.011 0.014 0.022 0.023 (4.12)** (5.29)** (7.87)** (8.34)**

ANCI 0.025 0.027 0.027 0.025 0.026 0.026 (10.97)** (12.08)** (11.92)** (9.92)** (10.61)** (10.27)**

NBCHO -0.042 -0.078 (5.21)** (6.03)**

NBINAT -0.008 0.011 (2.30)* (1.48)

Observations 3015 3015 3015 3034 3034 3034 R-squared 0.35 0.37 0.38 0.33 0.33 0.34

Absolute value of t statistic in parentheses - * significant at 5%; ** significant at 1%.

And the impact of the children

• Lwh = a EDUC + b EXPV + c ANCI +  NBENFT + e (2a)

• Lwh = a EDUC + b EXPV + c ANCI + d NBCHO + g NBINAT + f NBENFT +e (3a)

• Lwh = (3a) + t LAMBDA (3c)

Results

Table 5 – Experience and children-I

Women Men Specification (2a) (3a) (3c) (2a) (3a) (3b)

EXPV 0.011 0.014 0.014 0.019 0.020 0.019 (3.98)** (5.04)** (5.01)** (6.63)** (7.20)** (6.64)**

ANCI 0.027 0.025 0.025 0.023 0.023 0.020 (11.49)** (10.54)** (10.17)** (9.05)** (8.83)** (7.51)**

NBCHO -0.043 -0.044 -0.076 -0.035 (5.33)** (5.20)** (5.89)** (2.12)*

NBINAT -0.012 -0.013 0.009 0.007 (3.24)** (3.21)** (1.14) (0.85)

NBENFT 0.005 0.019 0.017 0.028 0.027 0.024 (0.96) (3.12)** (2.60)** (5.07)** (4.85)** (4.27)**

LAMBDA 0.015 -0.195 (0.43) (3.87)** Observations 3015 3015 3015 3034 3034 3034 R-squared 0.37 0.38 0.38 0.34 0.35 0.35

Absolute value of t statistic in parentheses - * significant at 5%; ** significant at 1%.

Complete specification

• Lwh = a EDUC + b EXPV + c ANCI + h JOBset + controls + e (2b)

• Lwh = a EDUC + b EXPV + c ANCI + d NBCHO + g NBINAT + h JOBset + controls + e (3b)

• Lwh = (3b) + t LAMBDA + e (4)

• JOBset• Controls

ResultsTable 6 – Experience and children-II

Women Men Specification (2b) (3b) (4) (2b) (3b) (4)

EXPV 0.009 0.011 0.012 0.013 0.014 0.013 (3.82)** (4.60)** (4.63)** (4.93)** (5.47)** (5.07)**

ANCI 0.021 0.019 0.020 0.016 0.016 0.014 (9.83)** (9.16)** (8.92)** (6.77)** (6.65)** (5.78)**

NBCHO -0.030 -0.031 -0.061 -0.035 (4.07)** (4.06)** (5.13)** (2.28)*

NBINAT -0.007 -0.008 0.006 0.005 (2.12)* (2.21)* (0.86) (0.68)

NBENFT 0.002 0.011 0.009 0.023 0.022 0.020 (0.51) (2.02)* (1.54) (4.47)** (4.34)** (3.90)**

LAMBDA 0.020 -0.125 (0.63) (2.68)** Observations 3015 3015 3015 3034 3034 3034 R-squared 0.50 0.50 0.50 0.46 0.46 0.46

Absolute value of t statistic in parentheses - * significant at 5%; ** significant at 1%.

Impact of children over the wage distribution

• quantilic regressions (QR)

0

0,005

0,01

0,015

0,02

0,025

0,03

0,035

10 20 30 40 50 60 70 80 90

WOMEN

MEN

Returns to children at different deciles

The gender wage gap and the interruption gap

• the gender wage gap for salaried aged 39 & + is composed of 2 parts :

1 2 1 1 2 2 1 2ˆ ˆ ˆ ˆ ˆ' ( ) ' ( ) ( )g g g g g g g gW W X X X X

• Each part is decomposed using Oaxaca-Neuman methodology

Wm – Wf = Wm – Wf1 + k (W f1 – Wf2)

0.17 0.10 0.608 * 0.12

Decomposition of the “interruption” wage gap between women aged 39-49

Model: (4a) (4b) (4)

Raw differential (Lwh) 0,12 0,12 0,12 Nobs 1347 1347 1347

a. Components of the total gap % % %

Explained 0,15 128,4 0,14 124,1 0,10 85,5

Unexplained -0,03 -28,4 -0,02 -24,1 -0,01 -5,6

Selection - - - - 0,03 20,1 Total 0,12 100,0 0,12 100,0 0,12 100,0

b. Composition of the explained part of the gap % % %

Education 0,01 8,1 0,01 5,6 0,006 6,5

Experience, tenure, unemployment 0,06 -0,04 0,032

Interruptions 0,07 0,05 0,028

Total Human Capital 0,13 85,2 0,11 74,3 0,066 59,6

Number of children -0,01 -6,7 -0,01 -4,2 -0,008 -8,5

Other 0,03 21,5 0,04 29,9 0,042 42,5

Total Explained 0,15 100 0,14 100 0,100 100

Decomposition of the gender wage gap in the population aged 39-49

Model: (4a) (4b) (4)

Raw differential (Lwh) 0,12 0,12 0,12 Nobs 1347 1347 1347

a. Components of the total gap % % %

Explained 0,15 128,4 0,14 124,1 0,10 85,5

Unexplained -0,03 -28,4 -0,02 -24,1 -0,01 -5,6

Selection - - - - 0,03 20,1 Total 0,12 100,0 0,12 100,0 0,12 100,0

b. Composition of the explained part of the gap % % %

Education 0,01 8,1 0,01 5,6 0,006 6,5

Experience, tenure, unemployment 0,06 -0,04 0,032

Interruptions 0,07 0,05 0,028

Total Human Capital 0,13 85,2 0,11 74,3 0,066 59,6

Number of children -0,01 -6,7 -0,01 -4,2 -0,008 -8,5

Other 0,03 21,5 0,04 29,9 0,042 42,5

Total Explained 0,15 100 0,14 100 0,100 100

Concluding and provisional remarks

• No difference in actual experience returns once taken into account job characteristics and time spent out of the labour market

• No direct negative effect of the number of children on mothers’ earnings

• For aged 39-49, differences in earnings between women are explained, mainly by differences in the total experience; differences in earnings between men and women without break in participation are not explained by observable characteristics

• To be done : exploit more individual biographies