The Career Cost of Children

Career Cost ofChildren

Motivation

Data andDescriptiveStatistics

Model

EstimationResults

Evaluating theCareer Cost ofChildren

Conclusion

The Career Cost of Children

Jerome Adda (Bocconi and IGIER)Christian Dustmann (UCL)

Katrien Stevens (Univ of Sydney)

November 13, 2014

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Motivation


Model

EstimationResults


Conclusion

Motivation

• In almost all developed countries, women earn still lessthan men, despite significant improvements over the lastdecades.

• Women are often under-represented in leading positions,and their careers develop at a slower pace than those ofmen.

• Women bear still the main burden of caring for children.

• Assessing the career cost of children, and how they arecreated, is key for re-distributional public policy

• There are many policies that compensate women forlabour market behavior that is linked to fertility.

• But complex issue with many interdependencies

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Motivation


Model

EstimationResults


Conclusion

Motivation







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Motivation


Model

EstimationResults


Conclusion

Motivation







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Motivation


Model

EstimationResults


Conclusion

Motivation







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Motivation


Model

EstimationResults


Conclusion

Key Research Questions

• Is female disadvantage related to fertility? What is theoverall career cost of children?

• How does these costs decompose into:• labor supply responses?• skill depreciation due to career interruptions?• initial choice of occupation, as fertility expectations may

induce selection into jobs with different skill depreciation,entry wages and human capital accumulation?

• unobserved heterogeneity?

• To what extent does fertility explain wage differentialsbetween men and women?

• What is the impact of policies aimed at increasing fertilityon completed fertility and womens career paths?

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Motivation


Model

EstimationResults


Conclusion








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Motivation


Model

EstimationResults


Conclusion








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Motivation


Model

EstimationResults


Conclusion








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Motivation


Model

EstimationResults


Conclusion

Contribution

• We develop and estimate a dynamic life-cycle model ofcareer and fertility which allows for:

• labor supply decisions and human capital accumulation;• consumption - savings decisions• occupational choice;• the choice of number and spacing of births .

• Our estimation method allows us to combine differentdata sets, including extensive administrative data,describing the life-cycle of women for many birth cohortsin different regions.

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Motivation


Model

EstimationResults


Conclusion

Contribution

• We develop and estimate a dynamic life-cycle model ofcareer and fertility which allows for:

• labor supply decisions and human capital accumulation;• consumption - savings decisions• occupational choice;• the choice of number and spacing of births .

• Our estimation method allows us to combine differentdata sets, including extensive administrative data,describing the life-cycle of women for many birth cohortsin different regions.

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Motivation


Model

EstimationResults


Conclusion

Summary of Results

• We evaluate the career costs of children to be 33% of thenet present value of earnings at age 15.

• The main part of this cost (75%) is due to labor supplydecisions, rather than reduced wages.

• Skill depreciations represents about 30% of the wage cost.

• Occupation sorting represents about 25% of the wage cost.

• Fertility accounts for more than half of the observedmale-female wage gap.

• Conditional cash transfers at birth have a marked short-runeffect, but little long-run effects on natality. Most of theeffect is due to changes in the age at birth of mothers.

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Motivation


Model

EstimationResults


Conclusion

Related Literature

• Fertility Decisions:• Fertility choices as static models of neo-classical optimal

behavior (Becker (1960), Willis 1973, Becker and Lewis1973)

• Dynamic models of fertility (Ward and Butz 1980, Wolpin1984, Moffitt 1984, Blackburn, Bloom and Neumark 1990,Leung 1991, Hotz and Miller 1993, Walker 1995, Heckmanand Willis 1974, Rosenzweig and Schultz 1985, 1989, Hotzand Miller 1988, Newman 1988).

• Female labor supply (Mincer and Polachek (1974),Heckman and Macurdy 1980, Moffitt 1984, Eckstein andWolpin 1989, VD Klaauw 1996, )

• Discrimination and Occupational Choice:

• Few papers consider jointly fertility and labor supply.

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Motivation


Model

EstimationResults


Conclusion

Related Literature

• Fertility Decisions:




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Motivation


Model

EstimationResults


Conclusion

Related Literature



• Discrimination and Occupational Choice:• Discriminatory tastes on the side of the firm are a cause

for segregation. (Becker (1971), Bergmann (1974))• Segregation of males and females into different

occupations is the result of optimising behavior (Polachek(1981), Weiss and Gronau (1981)).

• Male-Female wage gap: Blau and Kahn 1992.


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Motivation


Model

EstimationResults


Conclusion

Related Literature




• Few papers consider jointly fertility and labor supply.• Keane and Wolpin (2007): welfare participation and

fertility, Gayle and Miller (2006): fertility and labordecisions.

• Equilibrium search models: Aiyagari et al (2000), Caucuttet al (2002)

• Empirical analysis of MBA careers: Bertrand, Goldin andKatz (2010).

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Motivation


Model

EstimationResults


Conclusion

Plan of Talk

1 Motivation

2 Data and Descriptive Statistics

3 ModelDynamic SpecificationEstimation Method

4 Estimation ResultsFit of the ModelEstimated Parameters

5 Evaluating the Career Cost of ChildrenCareer Cost of ChildrenGender Wage GapShort & Long run Effects of Pro-natalist Policies

6 Conclusion

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Motivation


Model

EstimationResults


Conclusion

The Sample

• We focus on many birth cohorts, born between 1955 and1975 in Germany and followed between 1975-2001.

• We study the career of young girls from the age of 15,who chose a vocational education:

• Early tracking system in Germany locks girls intooccupation at an early age (15) when fertility choices arenot fully formed.

• Large fraction of a birth cohort follow this vocationalsystem (65% of cohort).

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Motivation


Model

EstimationResults


Conclusion

Apprenticeship System in Germany

LowerSecondary

School37%

IntermediateSecondary

School40%

HigherSecondary

School23%

Primary School

?

Age

Apprenticeship

Apprenticeship

University

6

10

15

16

18

-100% of Age Group

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Motivation


Model

EstimationResults


Conclusion

Data Set

• IAB: 2% sample from German Social Security Records,1975-2000;

• Exact information on first (and subsequent) occupationalchoice for individuals

• Complete work histories: Information on wages,interruptions, education, PT/FT, etc.

• Over 5 million person-time observations.

• German Socio-Economic Panel, 1984-2005;• Information on fertility, marital status, education,

occupation, savings etc.

• EVS (Einkommens- und Verbrauchsstichprobe),1984-2005;

• Information on savings rate and family characteristics.

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Motivation


Model

EstimationResults


Conclusion

Definition of Occupation

• We group occupations by their task contents. We considerthree types of jobs:

• Routine (handling machines, constructing, copying,archiving, purchasing...)

• Manual (repairing, driving, nursing...)• Abstract (Education, panning, notarizing, law, research,

sales, managing )

• We use data from the German Qualification and CareerSurvey 1985/86.

• Task intensity indicators are constructed for each individualobservation and are then aggregated at 2-digit job levels.

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Motivation


Model

EstimationResults


Conclusion

Definition of Occupation (Extract)

Description Proportion Category

Secretaries/office clerks 25.80% AbstractSales person/shop assistant 12.30% RoutineConsultation hour assistant 7.65% ManualNurse 6.01% ManualBank specialists/professionals 5.35% AbstractHairdresser 3.92% ManualStenographer 3.27% AbstractWholesale and retail sales people 3.02% AbstractAccountant, tax advisor 1.50% AbstractDesign draftsman 1.38% AbstractInsurance specialists 0.98% AbstractSewer 0.94% RoutineBookkeeper 0.90% AbstractCook 0.87% Routine

These jobs represent 73% of all occupations in our sample.

More Desc Stat

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Motivation


Model

EstimationResults


Conclusion

Annual Occupational ChoiceTransition

Occupation Occupation in Year t + 1in Year t Routine Abstract Manual

Routine 98.3 1.2 0.5Abstract 0.6 99.2 0.2Manual 0.7 0.5 98.8

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Motivation


Model

EstimationResults


Conclusion

Wage - Experience Profile, byOccupation

3.4

3.6

3.8

44.2

4.4

Avera

ge log d

aily

wage

2 4 6 8 10 12 14 16 18 20 22work experience (in yrs)

Routine

Abstract

Manual

Based on IABS sample

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Motivation


Model

EstimationResults


Conclusion

Labor Supply By Occupation

Routine Abstract Manual All

Total work experience 11.55 12.81 12.14 12.34after 15yrs (3.273) (2.624) (2.880) (2.909)

FT work experience 10.32 11.92 10.86 11.29after 15yrs (3.907) (3.348) (3.570) (3.617)

PT work experience 1.229 0.889 1.274 1.056after 15yrs (2.187) (1.828) (2.125) (1.997)

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Motivation


Model

EstimationResults


Conclusion

Changes in Wages FollowingCareer Interruptions, by

Occupation-.6

-.4-.2

0ch

ange

in lo

g wa

ge

0 2 4 6duration of interruption (yrs)

RoutineAbstractManual

Based on IAB data, for experience levels 5-20yrs16 / 59


Motivation


Model

EstimationResults


Conclusion

Timing of First Birth, byOccupation

20

40

60

80

100

Pro

port

ion o

f child

less w

om

en (

%)

22 24 26 28 30 32 34 36 38 40age

routine abstract

manual

Source: GSOEP sample, by last occupation worked in at each age

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Motivation


Model

EstimationResults


Conclusion

Fertility at Age 38, by Occupation

020

40

60

80

Pro

port

ion (

%)

routine abstract manual

Source: GSOEP; last occupation by age 38

No children

1 child

2 or more

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Motivation


Model

EstimationResults


Conclusion

Savings rate and fertility

.1.1

1.1

2.1

3.1

4Sa

ving

s R

ate

0 5 10 15 20Time Since Birth

First birth Second birth

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Motivation


Model

EstimationResults


Conclusion

The Model

• Takes individuals from adolescence to the end of theirlives, between age 15 and 80. We focus on:

• occupational choices;• labor supply decisions;• consumption - savings choices;• marital status;• fertility choices (number of children, spacing of births).

• Initial occupation choice depends -inter alia- on region andtime effects.

• The time period is a six month interval.

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Motivation


Model

EstimationResults


Conclusion

Ex ante heterogeneity

• Agents differ in four characteristics, ex ante:• labor market productivity,• taste for leisure,• taste for children,• potential infertility.

• To simplify, we assume:• labor market productivity and taste for leisure are perfectly

correlated.• potential infertility is orthogonal to the other

characteristics.

• We denote the characteristics of each individual i by avector fi .

• Modeled as a mass point distribution (Heckman andSinger 1984).

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Motivation


Model

EstimationResults


Conclusion

Utility function• Utility is derived over:

• consumption, number of children, labor supply, interacted with:• age of youngest child, ageKit .• occupation, oit• presence of a husband, hit .

uit =(cit/c)(1−γC ) − 1

1− γCνL(lit , nit ; fi )

+νN(nit ; fi )νNN(lit , ageKit , oit ; hit)

• children are more enjoyable if a husband is present.• labor supply is more costly in some occupations if children are

present and if children are young to take into account the childraising value of occupations.

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Motivation


Model

EstimationResults


Conclusion

Budget Constraint• Denote:

• Assets : Ait

• Gross income of household: GIit . Consists of labor earnings ofwomen and spouse if present, transfers (UI, maternity benefits) oralimony if children are present but the father have left thehousehold.

• Household consumption: cTotit• Cost of having children and working: pW

Ait+1 = (1 + r)Ait + net(GIit , hit , nit)− cTotit

• The function net() converts gross into net income, and dependson household size and the presence of children.

• Individual consumption is a scaled version of householdconsumption:

cit =cTotit

S(hit , nit)

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Motivation


Model

EstimationResults


Conclusion

Occupation and labor supply

• occupation is characterized by several features:• particular wage path (level and growth);• different speed with which human capital depreciates

through intermittency (atrophy);• different arrival rates of offers when out of work;• different ”child raising value”

• Occupation and hours of work offers arrive at random witha probability φ0(oit , lit).

• Conditional on receiving an offer, the individual receives aparticular occupation / hours of work offer with probabilityφ1(oit+1, lit+1|oit , lit).

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Motivation


Model

EstimationResults


Conclusion

Human capital

• We denote human capital by xit .

• For each period, the agent accumulates or looses skills:

xit = xit−1 + ρ(xit−1, oit−1, lit)

Similar specification as in Polacheck and Kim (1994).• ρ(xit−1, oit−1, lit) = 1 if full time work.• ρ(xit−1, oit−1, lit) = 1/2 if part time work.• ρ(xit−1, oit−1, lit) < 0 if not working:

ρ(xit−1, oit−1, lit) = ρ1(oit) + ρ2(oit)Ixit∈[5,7yrs]

+ρ3(i)Ixit>7yrs

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Motivation


Model

EstimationResults


Conclusion

Wages

• Wages depend on experience, occupation and unobserved ability:

lnwit = αi + α2(oit) + α3(oit)xit + α4(oit)x2it + uwit

• Occupational specific wage paths. Allow for differences inintercept and slope.

• If married, the husband provides a wage

wHit = αH

0 + αHA1age

Mit + αH

A2ageM2it + αH

X1xit

+αHX2x

2it +

∑io

αHo,io Ioit=io + uHit

• We allow for assortative mating in a simple way, as women inbetter occupation and with higher labor market attachment willbe matched with higher earning men.

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Motivation


Model

EstimationResults


Conclusion

Marriage and divorce

• In each period, there is a probability of marriage andseparation:

P(hit = 1|hit−1 = 0; ageMit , xit , fi ) = λM0 + λM1 (ageMit )

+λM3 xit + λM4 fi

P(hit = 0|hit−1 = 1; ageMit , nit) = λD0 + λD1 (ageMit )

+λD3 nit

• Allows for assortative mating. Women with higher desiredfertility are more likely to marry and marry faster. Womenwith better labor market attachment may marryfaster/later.

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Motivation


Model

EstimationResults


Conclusion

Conception

• If a woman decides to conceive a child, a child is born inthe next period with a probability π(ageMit ).

• We draw on medical evidence showing that the probabilityof conception depends on the age of the mother anddeclines with age. Declines from about 90% at age 20 to0% at age 50.

• A child can be conceived out of wedlock.

• No conception errors in the model, but we haveunobserved shocks to the utility of conceiving.

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Motivation


Model

EstimationResults


Conclusion

State Space

• State space:

Ωit =lit−1, oit−1, hit−1, age

Mit , xit , nit , age

Kit ,

Ait ,Υit , uwit , u

Hit , fi

• lit−1: labor supply;• oit−1: occupation;• hit−1: marital status;• ageMit : Age of mother;• xit : labor market experience;• nit : number of children;• AgeKit : age of youngest child;• Ait : assets;• Υit : vector of taste shocks;• uWit , uHit : shocks to income of women and spouse;• fi : vector of unobserved characteristics.

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Motivation


Model

EstimationResults


Conclusion

Dynamic Specification

• The agent maximises consumption cit , choice ofconception, bit , occupation, oit and labor supply, lit :

Vt(Ωit) = maxcit ,bit ,oit ,lit

u(cit , oit , nit ; hit , ageKit , fi )

+βEtVt+1(Ωit+1)

• Choices are constrained by the budget constraint, andlabor market frictions.

• In retirement, no choice of occupation and labor supply.

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Motivation


Model

EstimationResults


Conclusion

Labor Market Transitions

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8/-95:%19-/#$

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Formal Description

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Motivation


Model

EstimationResults


Conclusion

Initial Choice of Occupation

• At age t0 = 15, the individual decides on an occupation.

oit0 = arg maxo

[ cost(o,Regioni ,Timeit0) + ηio︸︷︷︸“Cost” specific to regionand time at age 15

+ β6Vt0+6(Ωit+6)︸︷︷︸Value of all future deci-sions about fertility, LSand occupation

]

• The cost varies with region and time: diff-in-diff identification.• We remove aggregate time and region effects.

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Motivation


Model

EstimationResults


Conclusion

Estimation Method andIdentification

• Model is estimated using Simulated Method of Moments.• We solve the model (backward iterations).• We simulate life-cycle behavior (employment, wages,

fertility).• We construct a series of moments which we match to

moments from the IAB and the GSOEP.

• We use multiple birth cohorts of young women fromdifferent regions.

• We control for fixed regional differences and for aggregatetrends for all observed moments.

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Motivation


Model

EstimationResults


Conclusion

Moments Used in EstimationMoments Data Set

Utility of leisure * utility of children

Proportion of full time work, by age IABProportion of part time work, by age IABProportion of full time work, by age and initial occupation IABProportion of part time work, by age and initial occupation IAB

φ, Utility of occupation * children

Annual transition rate between occupation IABAnnual transition rate between full time, part time and no work, by occupation IAB

δ, φ0, utility of leisure

Average work experience, by age IAB

Wage function & skill depreciation

Average wage by age and by initial occupation IABOLS regression of log wage on experience, by occupation IABOLS regression of log wage for interrupted spells on duration, IABOLS regression of log wage on age, number of children and occupation GSOEP

Saving rates

Savings rate by age and number of children EVSSavings rate by age, marital status and age of child, if no or one child EVSSavings rate by age, marital status and age of child, if one or two children EVSSavings rate, by occupation and age EVS

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Motivation


Model

EstimationResults


Conclusion

Moments Used in Estimation,cont.

Moments Data Set

Trade-off between children and career interruption, by occupation.

Proportion with no children, by age GSOEPProportion with one child, by age GSOEPProportion with two children or more, by age GSOEPCentiles of age at first birth GSOEPCentiles of age at second birth GSOEPNumber of children at age 40 GSOEPAverage age at first birth, by current occupation GSOEPAverage age at second birth, by current occupation GSOEP

Utility of children * husband presence

Proportion of childbirth within marriage GSOEP

Opportunity cost of children & occupation

IV regression of fertility on age and initial occupation (instrumented) GSOEP

Unobserved heterogeneity in wages

Variance of residual of log wage on occupation, age, hours work GSOEPProportion of woman with log wage residual < 1 std dev GSOEP

Unobserved heterogeneity in wages and fertility

Mean of residual of # of children on age — wage residual<0 GSOEPMean of residual of # of children on age — wage residual>0 GSOEP

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Motivation


Model

EstimationResults


Conclusion

Model Fit

• The model contains 73 parameters to describe six broadoutcomes: wages, hours of work, occupational choice,number of children, spacing of each birth and savings.

• We have a total of 742 moments.

• For over 50% of them, we cannot reject the equality ofobserved and simulated moments at 5% level.

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Motivation


Model

EstimationResults


Conclusion

Model Fit: Occupational Choice:

Occupation Observed Simulated

All Periods

Routine 24.5 (0.12) 24.9Abstract 51.4 (0.15) 47.4Manual 24.1 (0.11) 27.8

At age 15

Routine 25.8 (0.93) 27.1Abstract 45.8 (0.88) 45.9Manual 28.4 (0.81) 27

Note: Data source: IAB. Proportion for allages based on 248,023 observations. Propor-tion at age 15 based on 27,979 observations.Simulated moments based on 10,000 replica-tions.

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Motivation


Model

EstimationResults


Conclusion

Model Fit: Labor Supply

20 25 30 35 400

0.2

0.4

0.6

0.8

1Work Full Time

Age

Pro

po

rtio

n

Observed

Predicted

20 25 30 35 400

0.2

0.4

0.6

0.8

1Work Part Time

Age

Pro

po

rtio

n

Observed

Predicted

20 25 30 35 400

0.2

0.4

0.6

0.8

1Out of Labor Force

Age

Pro

po

rtio

n

Observed

Predicted

20 25 30 35 400

0.2

0.4

0.6

0.8

1Unemployed

Age

Pro

po

rtio

n

Observed

Predicted

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Motivation


Model

EstimationResults


Conclusion

Model Fit: Wage by Age

Age Routine Abstract ManualObserved Simulated Observed Simulated Observed Simulated

20 40.8 (0.09) 40.8 47.3 (0.08) 50.8 39.5 (0.09) 41.625 50 (0.08) 49.5 60.5 (0.07) 61.4 51.4 (0.07) 51.430 50.8 (0.09) 50.4 66.1 (0.07) 65.4 52.3 (0.08) 51.435 47.6 ( 0.1) 46.2 60.9 (0.09) 60.9 48.8 ( 0.1) 48.340 47.8 ( 0.2) 46.1 58.8 ( 0.1) 59.4 48.9 ( 0.2) 45.5

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Motivation


Model

EstimationResults


Conclusion

Model Fit:Number of Children by Age

Age No Children One Child Two or moreObserved Simulated Observed Simulated Observed Simulated

20 0.981 (0.008) 1 0.0178 (0.007) 0 0.0009 (1e+03) 025 0.65 ( 0.02) 0.522 0.255 ( 0.01) 0.293 0.0946 (0.009) 0.18530 0.315 ( 0.03) 0.197 0.305 ( 0.01) 0.47 0.38 ( 0.02) 0.33335 0.16 ( 0.02) 0.0813 0.266 ( 0.02) 0.561 0.574 ( 0.04) 0.35840 0.14 ( 0.03) 0.0515 0.259 ( 0.03) 0.587 0.601 ( 0.05) 0.361

Note: Data source: GSOEP. Simulated moments based on 10,000 replications.

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Motivation


Model

EstimationResults


Conclusion

Model Fit: Spacing of Births

Decile Age at First Birth Age at Second BirthObserved Simulated Observed Simulated

10 21.1 (0.26) 21 23 (0.33) 22.520 22.6 (0.26) 22 24.9 (0.36) 2330 24.2 (0.26) 23 26.1 (0.26) 23.540 25.2 ( 0.2) 23.5 27.1 (0.28) 2450 26.4 (0.18) 24.5 28.3 (0.38) 24.560 27.4 (0.15) 26 29.5 (0.31) 25.570 28.5 (0.31) 27 30.6 (0.26) 26.580 30.3 (0.23) 28.5 31.8 (0.36) 27.590 32.3 (0.36) 31 33.5 (0.43) 29.5100 39.7 (0.36) 40 41.2 (0.43) 40

Note: Data source: GSOEP. Simulated moments based on 10,000 replica-tions.

More Tables on Fit

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Motivation


Model

EstimationResults


Conclusion

Estimated Parameters: Wages

Parameter Routine Abstract Manual

Wage Equation

Log Wage Constant 3.24 (0.0067) 3.5 (0.006) 3.15 (0.0087)Human Capital 0.0992 (4e-05) 0.089 (4.7e-05) 0.138 (0.00012)Human Capital Square -0.00347 (6.3e-06) -0.00191 (4.8e-06) -0.00551 (8.6e-06)

Average Return to Human Capital

At 5 Years of Experience 6.5% (0.0074) 6.99% (0.0068) 8.29% (0.014)At 10 Years of Experience 3% (0.013) 5.08% (0.011) 2.78% (0.021)

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Motivation


Model

EstimationResults


Conclusion

Estimated Parameters: Atrophy

Parameter Routine Abstract Manual

Percentage of Wage lost per year of Interruption

Experience ≤ 4 -0.22% (2e-06) -0.75% (0.0006) -0.14%(3e-06)Experience ∈ [5, 8[ -0.53% (0.002) -0.99% (0.007) -0.42%(0.05)Experience >8 years -0.64% (0.04) -2.4% (0.02) -1.5%(0.05)

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Motivation


Model

EstimationResults


Conclusion

Estimated Parameters:Unobserved Heterogeneity

Parameter Type 1 Type 2 Type 3 Type 4

Proportion in sample 0.02 0.185 0.36 0.436(0.000149) (0.000691) (0.138) (0.0007)

Log wage intercept 0 0 0.13 0.13- - (0.0028) (0.0028)

utility of leisure 0 0 -0.257 -0.257- - (0.0024) (0.0024)

Utility of one child 0.424 0.166 0.424 0.166(0.006) (0.005) (0.006) (0.005)

Utility of two children 1.35 -0.977 1.35 -0.977(0.00048) (0.0095) (0.00048) (0.0095)

Total fertility 1.9 0.963 1.9 0.964Prop in routine occupation 0.324 0.227 0.338 0.233Prop in abstract occupation 0.393 0.513 0.379 0.505Prop in manual occupation 0.283 0.26 0.283 0.262

Corr(Ability, desired fertility) 0.3

Note: Asymptotic standard errors in parenthesis. Proportions in given oc-cupation are calculated at the start of the career.

More Tables on Estimated Parameters

44 / 59


Motivation


Model

EstimationResults


Conclusion

Evaluating the Career Cost ofChildren

• Children impose different types of costs• Sorting into child friendly occupations• Lower labor market attachment• Depreciation of skills out of work

• Difficult to evaluate them using reduced form methods.Dynamic selection issues, multiple outcomes, short-termand long-term effects.

• We solve these issues by using our model and bysimulating different scenarios, with and without children.

45 / 59


Motivation


Model

EstimationResults


Conclusion

Occupation at age 15Effect of no fertility

Routine Abstract Manual−8

−6

−4

−2

0

2

4

6

% D

evia

tio

n F

rom

Ba

se

lin

e

46 / 59


Motivation


Model

EstimationResults


Conclusion

Labor SupplyEffect of no fertility

20 25 30 35 40 45 50 550

0.05

0.1

0.15

0.2

0.25

0.3

Age

Pro

po

rtio

n W

ork

ing

Devia

tio

n f

rom

Base

line

47 / 59


Motivation


Model

EstimationResults


Conclusion

Part-Time WorkEffect of no fertility

20 25 30 35 40 45 50 55

−0.25

−0.2

−0.15

−0.1

−0.05

0

Age

Pro

po

rtio

n W

ork

ing P

art

Tim

e

Devia

tio

n f

rom

Base

line

48 / 59


Motivation


Model

EstimationResults


Conclusion

WagesEffect of no fertility

20 25 30 35 40 45 50 550

0.05

0.1

0.15

0.2

0.25

Age

Lo

g W

ag

e D

iffe

ren

ce

49 / 59


Motivation


Model

EstimationResults


Conclusion

Selection into WorkRatio of High / Low Ability Women, Conditional

on Working

20 25 30 35 40 45 50 550.2

0.22

0.24

0.26

0.28

0.3

0.32

0.34

0.36

0.38

0.4

Age

Ra

tio

Lo

w t

o H

igh

Ab

ility

Fertility

No Fertility

Note: The unconditional ratio of High / Low Ability = 0.26

50 / 59


Motivation


Model

EstimationResults


Conclusion

The Career Cost of Children

• We calculate the net present value of all earnings at age15.

• We sum all wages, unemployment benefits and maternitybenefits:

NPV Si =

T∑t=0

βt(wSit IworkS

it+ bSU,it IUnemplSit

+ bSM,it IMat.LeaveSit

)• We compare two scenarios: with fertility (baseline) and

without:1− NPVNF/NPV F

51 / 59


Motivation


Model

EstimationResults


Conclusion

The Career Cost of ChildrenNet Present Value of Earnings (at Age 15)

Average First OccupationRoutine Abstract Manual

Total Cost -32.6% -26.6% -37% -27.1 %Oaxaca Decomposition of Total Cost

Labor Supply Contribution -25% -21% -29% -21 %Wage Contribution -7.3% -5.7% -8.3% -6.5 %

Oaxaca Decomposition of Wage Contributions to Total CostContribution of Atrophy -2.1% -0.56% -2.7% -1.8 %Contribution of Other Factors -5.3% -5.2% -5.5% -4.7 %Contribution of Occupation -1.8% -1.2% -2.6% -0.35 %Contribution of Other Factors -5.5% -4.5% -5.7% -6.1 %

• 3/4 of the cost is due to labor supply.• Atrophy represents about 30% of the wage cost.• Occupation sorting represents about 25% of the wage cost.

52 / 59


Motivation


Model

EstimationResults


Conclusion

Male-Female Wage Gap

15 20 25 30 35 40 45 503.4

3.6

3.8

4

4.2

4.4

4.6

4.8

Age

Log W

age

Observed Males

Observed Females

• Log daily wages of men and women, with similareducation.

53 / 59


Motivation


Model

EstimationResults


Conclusion

Fertility and the Male-FemaleWage Gap

15 20 25 30 35 403.4

3.6

3.8

4

4.2

4.4

4.6

4.8

Age

Lo

g W

ag

e

Observed(Males)

Observed & Predicted(Females)

Predicted FemalesNo Fertility

• We simulate a counterfactual economy where womenknow they are infertile.

• A substantial part of the wage gap is explained by fertility.

54 / 59


Motivation


Model

EstimationResults


Conclusion

What are the Effects ofPro-Natalist Policies?

• Many countries encourage natality by providing childpremium or child benefits.

• Empirical literature has found positive and significanteffects of these policies (Milligan 2005, Cohen et al 2008,Laroque and Salanie 2008)

• However, what are the long-term effects of these policies:• on fertility?• on human capital accumulation and gender wage gap?

• We simulate the introduction of a cash transfer at birth ofe6000.

55 / 59


Motivation


Model

EstimationResults


Conclusion

Effect of Increased Child Benefitson Number of Children, compared

to Baseline

2 4 6 8 10 12 14 16 18 200

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Year

%In

cre

ase in N

um

ber

of

Childre

n B

orn

56 / 59


Motivation


Model

EstimationResults


Conclusion

Effect of Increased Child Benefits

Age at start of policy15 25 35

Change, no child (in %) -0.1% -0.1% 0%Change, one child (in %) 0.01% 0.01% 0%Change, two children (in %) 0.002% 0% 0%Change, age at first birth (in years) -0.2 -0.1 0Change, age at second birth (in years) -0.03 0 0

Change, number of years working -0.1 -0.1 -0.01Change, number of years working PT 0.005 -0.03Change, human capital (in %) -0.4% -0.3% -0.05%Change, assets at age 60 (in %) -2.5% -2.1% -0.75%

Change, proportion routine 0.3% 0% 0%Change, proportion manual 0.01% 0% 0%Note: Based on a benefit increase of 1,220 euros. Simulationperformed over 8,000 individuals.

57 / 59


Motivation


Model

EstimationResults


Conclusion

Effect of Child Premium on Assets

20 25 30 35 40 45 50 55 60 65−20

−15

−10

−5

0

5

10

Age

Assets

in %

devia

tion fro

m n

o p

olicy

15

20

25

Age at Start

of Policy

58 / 59


Motivation


Model

EstimationResults


Conclusion

Conclusion

• This paper revisits the occupational segregation literature(Mincer and Polachek 1974) to explain labor marketdifferences between men and women.

• We develop a joint model of fertility, career, savings andoccupational choice.

• We use extensive data on life-cycle and rely on exogenousvariation that determines occupational choice at labormarket entry to identify our model.

• Based on the estimated model, we can perform a numberof counterfactual policy experiments:

• We find that fertility explains an important part of the gapin wages.

• Evidence on the role of occupational characteristics onfertility patterns.

• Long-run effects of pro-natalist policies are much lowerthan the short-term effects.

59 / 59


Descriptive Statistics: IAB SampleN mean sd min max

age at LM entry 72,430 17.4 1.53 15 21year of LM entry 72,430 1984 4.89 1976 1996birth cohort 72,430 1967 4.53 1955 1975age at end apprenticeship 72,430 19.6 1.69 16 26age at last observation 72,430 32.8 5.25 16 46year at last observation 72,430 2000 3.71 1977 2001work spellsa 3,387,748gross daily earnings (in Euro)a,b 2,654,637 54.2 21.6 1 137censored earningsa (% of earnings obs) 6,205 0.23%PT work spellsa (% of work spells) 2,664,789 14.3%nonwork spells 1,646,214unemployment spells (% of nonwork spells) 205,715 12.5%out of LF spells (% of nonwork spells) 1,440,499 87.5%occupation of apprenticeship: 72,409

(1) Routine 18,073 24.7%(2) Abstract 32,421 44.8%(3) Manual 21,915 30.3%

a work spells after apprenticeship

b daily earnings in IAB data are censored from above (if above the ’upper earnings limit’);

censored daily earnings are included in earnings observations, with reported earnings=limit

c of all observations after apprenticeship

Back


Descriptive Statistics: GSOEPSample

N mean sd min max

age observed 16,144 31.1 7.52 17 51year observed 16,144 1995 6.33 1984 2006age at first observation 1,432 23.8 5.96 17 50age at last observation 1,432 34.3 8.99 17 51birth cohort 1,432 1965 5.33 1955 1975# years observed 1,432 11.3 7.59 1 23work spells c 9,703PT work spells (% of work spells) 3,095 31.9%monthly earnings (in Euro)a 8,880 1450 666 31.7 7117age mother when first child 810 26.0 4.39 18 40age mother when second child 523 28.7 4.06 19 42total fertility (age 39): # children 502

0 78 15.6%1 124 24.7%2 216 43.0%≥3 84 16.7%

a work spells after apprenticeship

censored daily earnings are included in earnings observations, with reported earnings=limit

c of all observations after apprenticeship

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Initial Choice of Occupation: FirstStage Results

(1) (2)yrs after LM entry p-value p-value

0 0.0013 0.0115 0.018 0.12010 0.123 0.932

Region dummies yes yesTime dummies yes yesRegional trend no yes

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Effect of Occupational Choice onFertility

Outcome Variable: Number of Children

OLS IVAbstract -0.2257*** (0.0147) -0.3412*** (0.0975)Manual -0.0841*** (0.0176) -0.6373*** (0.1121)age 0.1288*** (0.0092) 0.1264*** (0.0095)age square -0.0007*** (0.0002) -0.0007*** (0.0002)R-squared 0.322 0.282Observations 18175 18175

*

p<0.05, ** p<0.01, *** p<0.001

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Model Fit: Hours of Work by Age

Age Full Time Part Time Unemployed OLFObserved Simulated Observed Simulated Observed Simulated Observed Simulated

20 0.769 (0.001) 0.853 0.0401 (0.0008) 0.0176 0.0836 (0.0006) 0.13 0.107 (0.001) 025 0.615 (0.001) 0.555 0.0588 (0.0007) 0.0528 0.0639 (0.0005) 0.0886 0.263 (0.001) 0.30430 0.375 (0.001) 0.381 0.109 (0.0007) 0.113 0.0588 (0.0005) 0.0655 0.457 (0.001) 0.44135 0.26 (0.001) 0.282 0.181 (0.0008) 0.169 0.0536 (0.0006) 0.0603 0.506 (0.001) 0.48940 0.254 (0.002) 0.303 0.245 ( 0.001) 0.224 0.0492 (0.0008) 0.0716 0.452 (0.002) 0.402

Note: Data source: IAB. Observed moments based on 81,343 observations.Simulated moments based on 10,000 replications.

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Model Fit: Log Wage Regressionfor Interrupted Spells

Observed Simulated.

Duration of interruption -0.0062 (0.003) -0.0033Experience 5-8 years -0.047 ( 0.01) 0.00032Experience >8 years -0.068 ( 0.02) 0.028Abstract 0.026 ( 0.01) -0.0021Manual 0.045 ( 0.01) 0.00049Abstract, Exp 5-8 years -0.085 ( 0.02) -0.051Manual, Exp 5-8 years -0.083 ( 0.02) -0.054Abstract, Exp > 8 years -0.096 ( 0.02) -0.041Manual, Exp > 8 years -0.12 ( 0.02) -0.031Part Time to Full Time 0.37 ( 0.01) 0.6Full Time to Part Time -0.41 (0.006) -0.59Duration, Exp [5-8] years -0.019 (0.004) -0.0056Duration, Exp >8 years -0.03 (0.004) -0.017Constant -0.026 ( 0.02) 0.00093

Note: Data source: IAB: Regression done respectively on 6003, 7236, 11601and 7430 observations. Simulated moments based on 10,000 replications.

Back


State Space

• State space:

Ω =(ageM ,X ,O, λ,N, ageK ,H

)• ageM : Age of mother.• X : labor market experience.• O: current occupation.• λ: Amount of hours of work supplied (No work / Part time

/Full Time).• ageK : age of youngest child.• H: marital status, 0: single, 1: married.

Back to Model


Estimated Parameters: Utility

Parameter Estimate

Discount factor

Annual discount factor 0.958 (0.00021)

Utility of consumption

CRRA utility 1.98 (0.00344)Consumption scale (c) 101 (0.427)Weight of children in consumption equivalence scale 0.392 (0.00768)

Utility of work

Utility of out of labor force 0.164 (0.0014)Utility of PT work 0.102 (0.001)Utility of Unemployment -1.55 (0.03)Utility of Occupation if Children, Routine 0 (-)Utility of Occupation if Children, Abstract -0.0338 (0.00054)Utility of Occupation if Children, Manual -0.0135 (0.0012)Utility of Unemployement if children -1.69 (0.18)Utility of no work if one child 2.66 (0.0024)Utility of no work if two or more children 1.42 (0.0038)Utility of no work if age child ≤ 3 1.24 (0.0093)Utility of no work if age child ∈]3, 6] 0.301 (0.0048)Utility of no work if age child ∈]6, 10] 1.33 (0.0021)Utility of PT work and children, Routine 0.553 (0.0038)Utility of PT work and children, Abstract 0.0903 (0.0028)Utility of PT work and children, Manual 0.551 (0.0087)Utility of PT work and age child ≤ 3 0.18 (0.011)Utility of PT work and age child ∈]3, 6] 0.195 (0.019)Utility of PT work and age child ∈]6, 10] 2.36 (0.0052)

Utility of Children

Utility of one child, high fertility 0.424 (0.006)Utility of two children, high fertility 1.35 (0.005)Utility of one child, low fertility 0.166 (0.00048)Utility of two children, low fertility -0.977 (0.0095)Utility of children & not married -0.901 (0.0516)

Back to Estimated Parameters


Probability of Occupation andHours of Work Offers

Routine Abstract ManualPrevious status PT FT PT FT PT FT

Routine job PT 0.95 0.0051 0.0051 0.043 0.00023 0.00023( 0.0012) ( 0.0005) ( 0.0005) (0.00068) (2.3e-05) (2.3e-05)

Routine job FT 0.0051 0.95 0.0051 0.00023 0.043 0.00023(9.4e-05) ( 0.0007) (9.4e-05) (5.6e-06) (0.00068) (5.6e-06)

Abstract job PT 0.0051 0.0051 0.95 0.00023 0.00023 0.043(0.00035) (0.00035) (0.00097) (1.6e-05) (1.6e-05) (0.00068)

Abstract job FT 0.039 0.00021 0.00021 0.95 0.0051 0.0051(0.00024) (2.1e-05) (2.1e-05) ( 0.001) ( 0.0005) ( 0.0005)

Manual job PT 0.00021 0.039 0.00021 0.0051 0.95 0.0051(4.1e-06) (0.00024) (4.1e-06) (9.5e-05) ( 0.0003) (9.5e-05)

Manual job FT 0.00021 0.00021 0.039 0.0051 0.0051 0.95(1.4e-05) (1.4e-05) (0.00024) (0.00035) (0.00035) (0.00073)

Note: Semi-annual offer rates.

Back to Estimated Parameters


Value of Work

Wi (Ω) = max[W Ci (Ω) + ηCi ,W

NCi (Ω) + ηNCi ]

• W Ci is the value of conceiving and working in occupation i .

• WNCi is the value of not conceiving and working in

occupation i .

• ηCi and ηNCi are two taste shocks.

Back to Model


Value of Not Conceiving

WNC (Ωi ) = u(w(X ,Oi , ε) + HwH ,Oi ,H,N, age

K)

+βδEU(Ω′i ) + β(1− δ)∑j

φi ,j

E max[W (Ω′ii ), W (Ω′ij), U(Ω′ii ), O(Ω

′ii )]

Ω′ij =

ageM + 1X + ρ(i ,X )

Occup′ = j |Occup = iN

IN>0(ageK + 1)H ′

Back to Model


Value of Conceiving

W C (Ωi ) = u(w(X ,Oi , ε) + HwH ,Oi ,H,N, age

K)

+π(ageM)βEM(Ω′i ,P)

+δ(1− π(ageM))βEU(Ω′i ) + (1− δ)(1− π(ageM))

β∑j

φi ,jE max[W (Ω′ii ), Wj(Ω′ij), U(Ω′ii ), O(Ω

′ii )]

Ω′i ,P =

ageM + 1X + ρ(i ,X )Occup = iN + 1

IN>0(ageK + 1)H ′

Ω′ij =

ageM + 1X + ρ(i ,X )

Occup′ = j |Occup = iN

IN>0(ageK + 1)H ′

Back to Model


Value of Unemployment

U(Ω) = max[UC (Ω) + ηCU ,UNC (Ω) + ηNCU ]

where:

UNC (Ωi ) = u(b + HwH ,N,H, age

K)

+β(1− φU)E max[U(Ω′ii ), O(Ω′ii )]

+βφU∑j

φi ,jE max[U(Ω′ii ), O(Ω′ii ), W (Ω′ij)]

UC (Ωi ) = u(b + HwH ,N,H, age

K)

+π(ageM)βEMU(Ω′i ,P)

+(1− φU)(1− π(ageM))βE max[U(Ω′i ,i ), O(Ω′i ,i )]

+φU(1− π(ageM))β∑j

φi ,jE max[U(Ω′i ,i ), O(Ω′i ,i ), W (Ω′i ,j)]

Back to Model


Value of being Out of the LaborForce

OC (Ωi ) = u(HwH ,N,H, age

K)

+π(ageM)βEMU(Ω′i ,P)

+(1− φO)(1− π(ageM))βEO(Ω′i ,i )

+φO(1− π(ageM))βE∑j

φij max[O(Ω′i ,i ), W (Ω′i ,j)]

ONC (Ωi ) = u(HwH ,N,H, age

K)

+(1− φO)βEO(Ω′i )

+φOβE∑j

φij max[O(Ω′i ,i ), W (Ω′i ,j)]

Back to Model


Value of Maternity Leave

M(Ωi ) = u(bM + HwH ,Oi ,H,N, age

K)

+ βu(bM + HwH ,Oi ,H,N, age

K)

+(1− φO)β2∑j

φi ,jE max[W (Ω′ii ), U(Ω′ii ), O(Ω

′ii )]

+φ0β2∑j

φi ,jE max[W (Ω′ii ), W (Ω′i ,j), U(Ω′ii ), O(Ω

′ii )]

with

Ω′j ,M =

ageM + TM

X + TMρ(i , j ,U)Occup = j

NTM

H ′

Back to Model

Documents

The Career Cost of Children