The career cost of children: career and fertility trade-offs

Women are often paid less than men, they are often underrepresented in leading positions, and their careers develop at a slower pace than those of men.  In this paper, we ask to what extent these differences can be explained by childbearing. To evaluate the career cost associated with having children, we consider women’s decisions regarding labor supply, occupation, fertility and savings throughout the life-cycle. We evaluate the life-cycle career cost of children to be equivalent to 35 percent of a woman’s total earnings. We further show that part of this cost arises well before children are born through selection into careers characterized by lower wages but also lower skill depreciation. Our results inform the debate on the gender pay-gap as well as policies that encourage fertility.

The costs of children for women’s careers and lifetime earnings may be substantial.

Despite improvements over the last decades, women still earn less than men in almost all developed countries.  Women are often underrepresented in leading positions and their careers develop at a slower pace (see e.g. Blau and Kahn 1996, Weichselbaumer and Winter-Ebmer 2005 and Goldin 2014). Having children may be one reason for these disadvantages, and the costs of children for women’s careers and lifetime earnings may be substantial.

Although there have been many studies of the link between fertility decisions and female labor supply, most have dealt with facets of the decision in isolation. For instance, early papers studied how fertility decisions may depend on household background variables in a static context (Becker 1960, Becker and Lewis 1973, and Willis 1973).  Later dynamic models of fertility assumed that labor supply decisions were exogenous.[1] A related literature on women’s labor supply behavior takes fertility decisions as exogenous[2].

But in studying the career costs of children, there are interdependencies between fertility and career choices to consider. Specifically, we argue that there are three interdependencies that may matter: First, children may require periods of care during which women may not be able to work. Second, during such interruptions, labor market skills cannot accumulate, and existing skills may even depreciate. Third, the anticipation of having children might even lead women to choose occupations where the career costs of children are low.

In Adda, Dustmann and Stevens (2017), we therefore study the career costs of children in a life-cycle model.  In our model, women make decisions about labor supply, occupation, whether to have children, and how much to save at each age.  Decisions have implications for the rest of the individual’s life, so women choose the path of these variables that maximizes their well-being over the course of their lives.

If a woman works, she accumulates skills that enhance her earnings potential in the future and allow for wage growth. If instead she does not work, her skills depreciate.

If a woman works, she accumulates skills that enhance her earnings potential in the future and allow for wage growth. If instead she does not work, her skills depreciate. Marriage and divorce are probabilistic events that depend on a woman’s characteristics.

To model the trade-off between career and fertility across groups, we have three types of occupations with different entry wages, rates of atrophy (skill depreciation) and wage growth.  “Routine” occupations require a skill set that is acquired early in the career but does not change much later on.  “Manual” occupations consist of jobs with tasks that are mostly manual but not routine, such as nurses. “Abstract” occupations involve mostly analytic or interactive tasks and likely require skills to be updated as the environment changes (e.g., new information technologies).  We allow skill atrophy rates to vary over the career cycle, and occupations to vary according to their amenity value with regards to combining work and children.[3]

Since, early in their career, women may face a trade-off between maternity and building assets, we allow for an inter-temporal budget constraint as well as risk aversion. Women can accumulate assets and save earned income to finance consumption in future periods.

Desired fertility may affect occupational choice even before any fertility decisions are made.  In our model, women differ in terms of desired fertility and earnings ability.  Some women can earn higher wages, which affects the opportunity cost of having children. So, women with different fertility plans or earnings ability can opt for different occupations to balance a potentially higher wage path with higher atrophy rates during work interruptions.

We estimate the model using longitudinal social security records and survey data sets from Germany over the period 1975-2006. We focus on women who undergo apprenticeship training: a form of post-secondary education that combines workplace training with formal school education over three years.  The program requires participants to choose a particular occupation at around the age of 16, before fertility decisions are taken. This system educates around 65 percent of each birth cohort and provides occupational skills in jobs ranging from banker and nurse to hairdresser

The data we use provide information about careers, fertility, marital and savings behavior, and family background for a sample of women from birth cohorts 1955-1975 who have gone through apprenticeship training after school completion.[4]



The key findings of our analysis are as follows.

  1. Different occupational choices lead to different costs of raising children. In abstract occupations, wage profiles are steeper, but skill atrophy rates are higher than in routine or manual occupations. Atrophy rates also vary over the career cycle, especially in abstract but also in manual occupations, with the highest atrophy rates around the time when women find it desirable to have children (in their mid to late 20s). This illustrates a tradeoff between fertility decisions and career choices. In addition, when considering the ease of combining work and childrearing, abstract jobs are the least desirable. Thus, fertility decisions are likely to be affected far more by career concerns in abstract (and to some extent in manual) jobs than in routine occupations. This induces women with a higher desired fertility to choose careers in routine occupations more often and to have children earlier.
  1. Desired fertility shapes career choices. Women who want many children are overrepresented in routine occupations, while the opposite applies to abstract occupations; this is true even if women are similarly productive on the labour market. Some costs of fertility are therefore incurred well before children are born. The correlation between desired fertility and ability is close to zero, suggesting further that it is not the combination of high ability and low taste for children that leads women in better paid careers to have fewer children. Rather, the choice of a steeper career path for these women induces considerable costs through the sacrifice of fertility.
  1. Households anticipate births by saving a larger fraction of earned income in the years prior to a birth. This allows them to smooth consumption once a child is born. The importance of savings in fertility decisions highlights the tension between earning high wages in an abstract job to build up assets faster towards a birth, and higher forgone earnings, skill depreciation and difficulties in combining work and children in those jobs. Moreover, savings are a potentially important mechanism through which policy interventions might affect fertility decisions.


Simulating women’s careers without children and the effects of a pro-natalist policy


Using our model, we quantify the life-cycle career costs associated with having children and analyze the long-run effects of policies that encourage fertility.

In a first experiment, we consider career and savings decisions when women cannot have children. We show that women are far more likely not only to work, but also to work full-time, which implies faster accumulation of skills and higher wages throughout the career. Overall, this would raise women’s earnings over the life-cycle by 35 percent (in discounted present value terms at age 15). About three quarter of this career cost of having children stems from lost earnings during interruptions from work and increased part-time work engagements after childbirth, while the remainder is due to wage responses, because of lost investments in skills, skill depreciation and the choice of occupation that has been conditioned on fertility expectations.

Figure 1(a) illustrates that, in the absence of children, 5% more women would choose to work in abstract jobs, rather than in routine and manual jobs. Figure 1(b) shows that without children, women would experience higher wages, from the start of their career, culminating in a gain of about 0.2 log points (or 25 percent) in their early forties.


Note: Panel (a) shows that under a no fertility scenario, women would be more likely to choose an “Abstract” occupation rather than a “Routine” or a “Manual” one at the beginning of their career. Panel (b) shows that wages would be higher without children, culminating in a log wage difference of 0.2 in the early forties. Panel (c) shows the extent to which fertility explains the gender wage gap (calculated as the daily log wage).


To study the wage differences between women and men over the life cycle and how these are affected by fertility decisions, we run simulations using a sample of comparable men who made similar educational choices. Figure 1(c) shows the wage profile by age for comparable men, and for women with and without fertility. The figure illustrates that fertility explains about one third of the gender wage-gap, especially for women in their mid-thirties.

In a second experiment, we study the changes in fertility and career decisions following a pro-natalist policy involving a cash transfer of 6000 Euros at birth.[5] This type of policy is common in many countries and is often implemented to encourage fertility. Countries with such policies include Australia, Austria, Canada, France, Germany and Israel.

Using our model, we can consider the long-run effects of the policy, distinguishing between the impact on total fertility, and the timing of births. Figure 2 shows the impact of the policy on the probability of giving birth, by age, comparing the behavior of women with and without the policy (policy vs. baseline). The difference in the probability is positive at first and then negative, showing that the policy induces women to have their children earlier, but it has little effect on the overall number of children per woman.


Note: The figure shows the effect of the policy (cash transfer of 6,000 euros at birth) by age on the probability of giving birth, comparing the policy to the baseline of no cash transfer. With a cash transfer, women opt for having children earlier, but the long-run effect of the policy does not change the final number of children per woman significantly.


One reason for fertility to be brought forward is that fewer assets need to be accumulated before a child is born.  Indeed, we show that a cohort that is at the start of its career when the policy is introduced, anticipates the policy, saves less in its early twenties and has children at an earlier age. With the policy, young women are also more likely to start working in routine and manual occupations and they engage more often in part-time work.

Our results highlight the difference in the shorter- and longer-run effect of transfer policies on choices other than fertility. However, they also suggest that the impact of these policies may be largest for cohorts that, due to their younger age, do not show immediate fertility responses.  For such cohorts, these policies may have consequences for career and savings decisions, aspects that are usually not investigated in the literature.

Discussion and Conclusion


We study the complex decisions determining fertility choices, how these interact with career decisions, and how they determine the career costs of children.  We do this within a rich model of individual behavior involving occupational choice, labor supply, fertility and savings decisions over the life course.

We consider occupational choice as an essential part of a woman’s career plan. We show that different occupations imply not only different opportunity costs for intermittency and different wage growth, but also diverge in the rate of skill depreciation during interruptions from work and the amenity “child raising value”.

Fertility plans affect career decisions even before the first child is born through the choice of the occupation for which training is acquired.

We document a career cost of having children that consists of a combination of occupational choice, lost earnings due to intermittency, lost investment into skills and atrophy of skills while out of work, and a reduction in work hours when in work. Importantly, fertility plans affect career decisions even before the first child is born through the choice of the occupation for which training is acquired.  This aspect is not only important for assessing policies aimed at influencing fertility behavior, but also for understanding better the behavior of women before children are born.

The wage gap between men and women has recently found renewed interest in the public debate.[6] In this discussion, one view is that employers bear a large part of the responsibility for the gender pay gap.[7] Following this line, in 2017 the British government introduced a requirement for all companies with more than 250 employees to report the gender pay gap, and asked them to take steps to close the pay difference.[8] While some responsibility for the gender pay gap may indeed lie with employers, our study suggests that reasons for the pay gap between men and women are complex, due to early career choices of women being based on the longer term expected trade-offs between fertility and lifetime earnings. The parameters that determine these choices, and that are ultimately contributing to wage differences between men and women, are only partly related to pay policies of single firms. They are influenced by a multitude of factors, some of them obvious (such as the cost of childcare), while others less immediate (such as access to credit, and the cost of debt). Many of the parameters that affect the trade-off between fertility and female career choices are shaped by government policies. Our framework helps to understand better the complexities of career decisions that underlie the gender wage gap, and the factors that influence them.

[1] Heckman and Willis (1976), Ward and Butz 1980, Rosenzweig and Schultz 1983 and 1985, Wolpin 1984, Cigno and Ermisch 1989, Blackburn, Bloom, and Neumark 1990, Heckman and Walker 1990, Hotz and Miller 1993, Leung 1994, Arroyo and Zhang 1997, and Altug and Miller 1998.

[2] Heckman and MaCurdy (1980), Blau and Robins (1988), Eckstein and Wolpin (1989), van der Klaauw (1996), Hyslop (1999), Attanasio, Low, and Sanchez-Marcos (2008), Keane and Sauer (2009), and Blundell et al. (2013); see Blundell and MaCurdy (1999) for a survey.

[3] Alternatively, Goldin (2014) stresses differences in the productive efficiency of individuals who work for different amounts of time as a source of the gender wage gap. In the same spirit, Adda et al (2017) allow for the fact that part-time work may result in less than half of a full-time wage.

[4] Data sources: IAB Employment Sample, German Socio-Economic Panel, Income and Expenditure Survey (EVS).

[5] This is equivalent to a transfer of 5240 GBP or 7410 USD. All monetary outcomes have been deflated into 1995 euros.

[6] See e.g.;

[7] See e.g.


Further reading

Adda, Jérôme and Christian Dustmann and Katrien Stevens. 2017. “The Career Costs of Children.”, Journal of Political Economy, 125, 2, 293-337.

Blau, Francine D. and Lawrence M. Kahn. 1996. “Wage Structure and Gender Earnings Differentials: An International Comparison.” Economica 63:S29–S62.

Goldin, Claudia. 2014. “A Grand Gender Convergence: Its Last Chapter.” American Economic Review 104 (4):1091–1119.

Polachek, S. (1981). “Occupational Self-Selection: A Human Capital Approach To Sex Differences In Occupational Structure.” Review of Economics and Statistics, 63(1), 60-69.

Weichselbaumer, D and R Winter-Ebmer. 2005. “A meta-analysis on the international gender wage gap.” Journal of Economic Surveys 19:479–511.