Tag: Women

Lessons From the FROGEE Conference “The Playing Field in Academia: Why Are Women Still Underrepresented?”

Image of dark university area with two men representing women underrepresented in academia

Despite an increase in women’s representation since the beginning of the 20th century, women remain underrepresented in academia and other high-skilled professions. Academia has been prone to gender disparities both within and across fields as well as across academic ranks. In an endeavour to examine and address the underrepresentation of women in the academic profession, the Centre of Economic Analysis (CenEA), together with the Stockholm Institute of Transition Economics (SITE) and other partners of the Forum for Research on Gender Economics (FROGEE) at the FREE Network, organized the two-day conference “The playing field in academia: Why are women still underrepresented?”, in Warsaw June 21-22, 2023. This brief offers insights from the presentations and panel discussions held at the conference.

To date, there are few, if any, high-skilled professions exhibiting gender balance, and academia is no exception. Consequently, this imbalance has been subject to increased multidisciplinary research attention, exploring its origins and potential remedies. However, attaining a comprehensive understanding of gender disparities remains a challenge. For instance, much remains to be learnt about their long-run dynamics, a subject addressed by Carlo Schwarz, in one of the conference’s keynote lectures.

A Century of Progress

Carlo Schwarz (in joint work with Alessandro Iaria and Fabian Waldinger, 2022) trace the evolution of gender gaps in academia across a variety of domains at the global level throughout the 20th Century. Facilitated by an unprecedentedly large database of nearly 500,000 academics, spanning 130 countries and supplemented by publication and citation data, the authors specifically examine gender imbalances in recruitment, publishing, citation patterns, and promotions.

They find that in 1900 women constituted roughly 1 percent of all hires in academia (226 women, with only 113 hired as full professors). By 1969 the share of female academics had risen to about 6.6 percent, and by the year 2000 it had grown to approximately 17 percent. These rates varied across disciplines, institutions, and countries. For instance, teaching-centric disciplines such as pedagogy and linguistics, exhibited higher representation relative to research-oriented ones.

The research subsequently reveals a hump-shaped evolution of the gender gap in academic output – starting small before peaking at 45 percentage points fewer publications by women in 1969, thereafter declining to 20 percentage points. These publication disparities were also found to share a U-shaped relationship with the share of women in academia, indicating the interconnectedness of gender gaps.

The authors also address gender gaps in citations, identified by the use of a novel machine learning approach, forecasting a paper’s citations had it been written by a man. The results indicate a progressive reduction in the citation gap during the 20th century, decreasing from 27 percentage points (pre-WW1) to 14 percentage points (interwar) and eventually to 8 percentage points (post-WW2) fewer citations of papers by female relative to male academics. These gender gaps in academic output reiterated current evidence from Mexico, presented at the conference by Diana Terrazas-Santamaria, showing that women are associated with lower citation rates. Terrazas-Santamaria attribute the low rates to gender differences in both the number of publications and duration of academic careers.

The work by Iaria, Schwarz and Waldinger (2022) further showcase the gender disparities in career advancement in academia, which similarly decreased over the years.  At the point of the greatest gender disparity, women required an approximately 6 percentage points better publication record to have the same promotion probabilities as their male counterparts.

The Leaky, Dry Pipeline

In the conference’s second keynote, Sarah Smith highlighted how academia, much like other professional occupations, exhibits a leaky pipeline. This is a phenomenon characterized by a declining representation of women as they ascend through the academic hierarchy. When examining specific fields, Smith’s results indicate that the gender disparities in economics much more closely align with those observed in STEM fields (science, technology, engineering, and mathematics) than other social science disciplines. Furthermore, the economics’ field illustrate a significant lack of diversity among its new entrants. This phenomenon, referred to as the dry pipeline, generates future cohort implications, as they result in less demographically representative cohorts from which future professors can be recruited (see Stewart et al., 2009).

The cross-disciplinary comparison of the dry pipeline addressed in the keynote, contest the mathematical rigor of economics as a barrier to entry, as mathematics itself demonstrated higher women representation at A-level and undergraduate levels. In a following discussion panel, which focused on ensuring a fair start in academia (comprised of Yaroslava Babych, Alessandra Casarico, Federica Braccioli and Marta Gmurek, and moderated by Maria Perrotta Berlin), the panellists acknowledged that deeply engrained social expectations, gender trained behaviours and a lack of awareness constitute some of the persistent hindrances to the (early) involvement of women in specific fields, and the academic profession in general.

Additional factors influencing the gender balance in recruitment and promotion are gendered references, and the presence or absence of shared research interests between candidates and recruitment panels. These themes were extensively investigated in the work presented by Alessandra Casarico on the conference’s opening day. Specifically, results from collaborative work with Audinga Baltrunaite and Lucia Rizzica, highlight that grindstone words (e.g., “determined”, “hardworking”, etc.) are frequently used in recommendation letters to describe female candidates, while standout words (e.g., “excellent”, “strongest” etc.) typify male candidates’ references. Compared to their male counterparts, women are also shown to be more inclined to accentuate personality traits when serving as referees. This added to a broader literature demonstrating that female candidates’ recommendation letters frequently exhibit brevity, raise doubts, carry a weak tone, and emphasize candidates’ interpersonal skills and personality traits rather than their ability. Moreover, separate results from Casarico’s work (with Piera Bello and Debora Nozza) illustrate that research similarity between the recruiting committee and the candidate predict the likelihood of recruitment. The authors argue that the relationship is indicative of a bias against women if – as shown by the authors – women are less likely to be the candidates with the highest similarity.

In her presentation, Anne Sophie Lassen offered a different factor that may contribute to the attrition in the pipeline: the influence of parenthood on academic careers. Results from her work (with Ria Ivandić) indicate that while parenthood does not significantly influence graduation rates, it extends doctoral studies by an average of 7 months for women. Moreover, Lassen highlighted a declining trend of remaining in academia after becoming a parent, particularly pronounced among women.

More Areas of Imbalance

The remaining conference presentations and panel discussions explored additional domains of gender imbalances within academia. Iga Magda showcased evidence from her joint work with Jacek Bieliński, Marzena Feldy and Anna Knapińska of gender differences in remuneration during the early stages of an academic career, substantiating a gap within a year of graduation. These disparities endure throughout respondents’ careers and are contingent on the field of study – largest among engineering and technology graduates and lowest among those from the humanities and arts fields. Furthermore, it was observed that productivity plays a negligible role in the identified pay gaps, as its impact is similar for both genders.

The panel composed of Eleni Chatzichritou, Marta Łazarowicz-Kowalik, Jesper Roine and Joanna Wolszczak-Derlacz, and moderated by Michał Myck, deliberated on exposed disparities in the application for, and the success rates in attaining research funding in Poland and Europe – as seen in the National Science Centre (NCN) and the European Research Council research grants, respectively. The discussion highlighted how quantitative measures used in the allocation of research funding are riddled with subjective criteria that often benefit male academics. They also recognized how quests to allocate funds to the most successful candidate inadvertently penalize women with career breaks.

Another panel including Lev Lvovskiy, Carlo Schwarz, Sarah Smith, Marieke Bos and Joanna Tyrowicz, and moderated by Pamela Campa, lauded the growing objective data shedding light on gender inequalities in academia. The panellists discussed current challenges in identifying and quantifying aspects of gender disparities. For instance, currently used proxies do not allow to capture more subtle disparities, like microaggressions faced by female academics from students – emphasizing the need for more individual level survey data.

The panels were further enriched by personal anecdotes and filled with retrospective advice shared by both early career and established academics. To contextualize the above, a few cases from the FREE Network countries follow.

Evidence From Within the FREE Network

Yaroslava Babych shared insights concerning women in higher education in Georgia and other countries of the South Caucasus. Preliminary findings of her study confirm the presence of gender inequality in academia, evident in disparities in access to higher education as well as gender segregation across both fields and countries. Notably, women comprise a majority of the graduates in bachelor’s and master’s of art programs, whereas higher research-level programs such as doctors of science, and top echelons of the academic hierarchy remain predominantly male. Moreover, female academic output is found to be lower than that of male counterparts.

Lev Lvovskiy discussed the case of Belarus, highlighting the influence of the Soviet legacy. A significant factor linked to this legacy is exploiting university enrolment to circumvent compulsory conscription of men, allowing male university admissions to serve a secondary purpose beyond acquiring knowledge. This increases the perceived opportunity cost of enrolling a woman. Lvovskiy further documented the academic trajectories of Belarusians, revealing a majority of women at college and doctoral levels, but being underrepresented among doctoral graduates. The results further indicate significant cross-disciplinary gender disparities, with humanities having close to 80 percent women representation and engineering and information and technology (IT) fields having less than 30 percent women representation.

Monika Oczkowska provided evidence of gender disparities in Poland. Findings from the country reveal an overrepresentation of women graduates from bachelor through doctoral levels, and relative parity at post-doctoral level, but lower proportions at habilitation, associate professor, and professor levels. These general results confirm the higher detail findings presented by Karolina Goraus-Tanska on the first day of the conference. Results from Goraus-Tanska’s work (with Jacek Lewkowicz and Krzysztof Szczygielski) suggest that the drop-off among female academics from habilitation levels is not attributed to higher output expectations for women, but rather stems from the impact of parenthood.

Oczkowska further demonstrated that female academics in Poland are characterized by fewer international collaborations and lower levels of international output. Polish female academics were also showcased to engage in more international mobility during their doctoral studies relative to men, with the converse holding true after obtaining a doctoral degree. A potential explanation for this mobility decline among female academics, could be the increased burden of familial responsibilities at the post-doctoral and higher levels. Moreover, fewer women were reported to have applied for NCN grants and were underrepresented among the beneficiaries of these calls. Lastly, female academics in Poland record significantly lower total project costs relative to their male counterparts.

‘Plugging’ the Leak

In light of the aforementioned, what measures can be taken to address the gender imbalances in academia? As summarized by Sarah Smith, early initiatives have involved tracking women representation (e.g., in admissions, progression, hiring, etc.) within departments and/or institutions to identify where in the pipeline their progress is impeded. Attempted initiatives include formulation of seminar guidelines to overcome unfair experiences, as well as using gender-blind recruiting and objective hiring criteria to equalize hiring opportunities. Some other efforts, such as diverse recruitment panels have been unsuccessfully adopted, as they seem to embolden hostile male recruiters and load female panellists with unrewarded administration tasks. Conversely, mentoring has helped women build networks, publish more, and advance professionally. Awareness raising campaigns have reduced disparities in teaching evaluations and remain vital in addressing the dry pipeline and both transparent workload allocation and rewarding of administrative tasks have been shown to reduce promotion gaps in academia. In addition to the above, initiatives such as fostering gender-neutral networking opportunities, collaborations and a more diverse faculty were also deliberated during the conference.

Concluding Remarks

The conference advanced dialogue on societal and structural constraints to gender equality in academia and provided a platform to exchange ideas on how the shared objective of a more inclusive and equitable academic environment can be achieved. While the challenges remain abundant, and the costs associated not always negligible, it remains crucial to assess achievements, such as those resulting from mentoring and awareness intervention initiatives and recognize that further opportunities to enhance equity within the profession exist.

Additional Material

Seminar Programme 21.06.2023

Seminar Participants – short bios

Conference Programme 22.06.2023

Conference Participants – short bios

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Women Entrepreneurs in Belarus: Characteristics, Barriers and Drivers

20180513 Women Entrepreneurs Image 01

This policy brief summarizes the results of the research on aspects of female entrepreneurship in Belarus. The aim of this work was to shed a light on what the features of female-owned business in Belarus are and whether there are any differences in the motives and barriers it faces compared with male-owned companies. Results show that female-owned companies are smaller in size, less likely to grow fast and less effective in the monetization and promotion of their innovative products and ideas. This is partly due to differences in social roles, motives, decision-making process and macroeconomic factors.

Women’s entrepreneurship is not just a question of gender equality but one of the sources for the sustainable economic development of the country. The presence of women among decision makers is beneficial for companies’ performance, effectiveness and innovativeness, and impacts the growth of profitability of the company (Akulava, 2016; Noland et al., 2016).

Little is known about the state of women’s engagement in economic governance in Belarus. According to the 5th wave of the BEEPS survey conducted by the World Bank, female top managers operate in around 32.7% of Belarus’ firms and 43.6% of firms have women among their owners (The World Bank, 2013). At the same time EBRD research shows that, on average, for every 10 men taking loans for the development of their own enterprise, only one woman did. Furthermore, the probability of loan rejection is 55% higher for women than for men in Belarus (these average numbers were presented by EBRD representatives during the conference “Business Territory: Women’s View”, Minsk, 2017). Unfortunately there is no information on the size and purpose of the loans, but potentially this may be a sign of discrimination and constraints on women’s economic activity.

We tried to expand the understanding of the role of women in Belarus’ private sector and to uncover individual, social, economic and cultural barriers that affect economic behavior and career choices of women, as well as introduce new drivers for female entrepreneurship in Belarus.

For this purpose we conducted interviews in 3 focus groups with the involvement of women entrepreneurs and also ran a survey that covered 407 owners and top decision-makers in the small and medium enterprises (SMEs).

The data analysis showed that around 30% of businesses belong to women (Table 1). Women tend to choose to operate in wholesale/retail trade, manufacturing, and medical/social services. Trade is the most popular with 28.9% of female-owned companies being part of this industry, while manufacturing stays second (10.1%). Trade also attracts the largest share of the male-owned companies (29.6%), next go manufacturing (23.9%) and construction (18.9%).

Table 1. Sectoral distribution by gender of the owner


Female-owned Male-owned
Share in total sample (%) 30.3 69.7
Sectoral distribution
Trade 29.0 29.6
Manufacturing 10.1 23.9
Construction 7.3 18.9
Medical and social services 8.7 1.3
Hotel and catering 8.7 2.5
Transport 7.3 10.1
Other 29.0 13.8

Innovative behavior changes slightly depending on the gender of the owner (33.3% of female- and 38.9% of male-owned companies have implemented innovations during the last 3 years). The measure of implemented innovative activities includes information on whether the company introduced any radical or incremental innovation (product/service/novelty in business processes/new strategy) during the last three years.An average female-owned firm grows much slower than male-owned business (Table 2). The annual sales gain and the sales gain over the last 3 years are 4 times and 2 times smaller respectively. The average number of employees is also smaller among female-owned companies (10 vs. 17 employees). On average, the owner of the male-owned firm has almost 15 years of relevant working and 13 years of managing experience. Similar characteristics for female owners are 12.8 and 9.7 respectively.

However, the realization of the implemented innovations as well as their relevance look more successful among the male-owned businesses. According to the answers in the survey, the profit share due to implemented innovations equals 28.8% among male-owned businesses and just 16.4% among female-owned. Thus, the major part of return is generated by the established business model and not the novelty.

Table 2. Business characteristics by gender of the owner

Female-owned Male-owned
Sales growth 1yr (%) 7.6 27.1
Sales growth 3yr  (%) 18.4 36.1
Size of the company (employees) 10.6 17.3
Age of the company (years) 8.8 10.2
Relevant experience of the owner (years) 13 14.7
Managing experience of the owner  (years) 9.7 12.8
Owners with a higher education (%) 91.3 86.2
Implemented innovation  (%) 33.3 38.9
Profit share of implemented innovations  (%) 16.4 28.8


One of the potential reasons for differences in characteristics and performance indicators between genders is self-selection, meaning that women are choosing less productive sectors in order to have more flexibility in balancing various social roles they play. In order to check for this, we compare the characteristics mentioned above in three different sectors (manufacturing, wholesale/retail trade and medical/social services) (Table 2a). The male-owned companies form the majority in the manufacturing sector, while medical/social services industry is mostly presented by female-owned business. Finally, the wholesale/retail trade sector is located somewhere in between and is well presented by both female- and male-companies.

Table 2a. Business characteristics by gender of the owner in manufacturing, wholesale/retail  trade and medical/social services

Wholesale/Retail Trade Manufacturing Medical and social services
Female-owned Male-owned Female-owned Male-owned Female-owned Male-owned
Sales growth 1yr (%) 9.8 31 2 26.2 10 n/a
Sales growth 3yr  (%) 16.4 37.9 5.6 42.3 17.5 n/a
Size of the company (employees) 5.9 14 23.7 19.8 13 8.5
Age of the company (years) 8.8 7.8 16.1 9.2 12.6 16
Relevant experience of the owner (years) 13 13.8 15.3 14.8 15.2 16
Managing experience of the owner  (years) 9.8 11.2 12.3 13.3 10.3 22
Owners with a higher education (%) 85 83 100 89.5 100 50
Implemented innovation  (%) 35 34.1 57.1 57.9 16.7 50
Profit share of implemented innovations  (%) 2.5 25 30 34.1 n/a n/a

There are differences in size and age of the businesses subject to the industry of the businesses. However, controlling for industry does not reveal any significant changes in the picture in terms of companies’ performance and effectiveness. Male-owned firms are still growing faster and are more successful in promoting implemented innovations Thus, this is likely not an issue of self-selection but of the way male and female owners operate their businesses.

The analysis revealed a number of internal and external barriers creating obstacles for doing business that breaks down into the following categories: social roles, educational patterns, decision-making process and general macroeconomic factors.

Women’s social roles in Belarus

Women in Belarus are mainly at the wheel of domestic responsibilities, which are rarely shared with male partners. According to the survey results, 40% of female and just 9% of male entrepreneurs are responsible for at least 75% of family duties (Table 3). 37% of female and only 0.74% of male owners said that they are in charge for taking care of kids. The same is true for the responsibility to stay at home when kids are sick (32.6% vs. 1.28).

Table 3. Distribution of domestic responsibilities by gender of the owner

Women Men
Family duties
less than 25% 10.91 37.5
around 50% 49.10 53.5
more than 75% 40.00 9.00
taking care of kids 36.96 0.74
staying at home, when kids are sick 32.61 1.48

At the same time, participants of the focus groups admitted that particularly childbirth motivated them to start their own business with flexible working hours and the possibility to work from home, which is generally not possible in corporate business in Belarus. Thus balancing between family and business becomes challenging, impacting career decisions. That motive also appeared in the survey where on average 13% of female and 2.5% of male owners started businesses in order to combine work with parenting. This trend does not change much if we control for industry.


There is no significant gender difference in the educational level of business owners. According to the survey data, 91.3% of female and 86.2% of male owners have a university degree or higher. However, the established social role models of Belarusian women influence both their career and educational choices. Usually girls tend to choose education in arts and humanities, law or economics, rarely going to technical universities. Lack of technical background further prevents their access into hi-tech profitable industries.

Business and economic environment

During the interviews, women stated that “Both men and women businesses face generally the same obstacles in starting up, operational management and strategic development. But in an unfriendly environment – mostly men survive”. Similar messages were obtained from the survey, with almost no significant difference in the estimation of barriers was revealed. The main external barriers mentioned were government control (32.2% of female and 29.3% of male owners), administrative burden (44.1% vs. 41.1%) and tax system (33.5% and 30.5%) (Table 4). Almost all barriers were equally mentioned by the respondents except for corruption. Corruption is the only obstacle that differs between men and women, pointed out by 50% of women, while just 12% of men considered it a problem. We interpret it as women being more risk-averse and less likely do bold and dangerous actions in business like bribing. That corresponds to the literature, which finds women more risk-averse than men (Castillo and Freer, 2018; Croson and Gneezy, 2009).

Table 4. Main obstacles and motives for doing business by gender of the owner

Women Men
Main barriers
Government control 32.2 29.3
Administrative burden and legal system 44.1 41.1
Tax system 33.5 30.5
Corruption 49.7 11.8
Human capital 16.1 17.1
Unfair competition 28.5 26.9
Motivation to start-up business
Sudden business opportunity 47.8 42.8
Willingness to earn more 29 34.6
No chance to continue the previous activity 14.5 13.2
Improvement of state’s attitude to entrepreneurs 13 13.2
Possibility to combine work and parenting 13 2.5


The statistical evidence showed that female-owned businesses are smaller in size and grow more slowly compared with male-owned competitors. There are no signs of gender differences in entrepreneurial innovativeness. However, the monetization of implemented innovations is more successful among male-owned companies.

Altogether, the barriers of female entrepreneurship in Belarus are associated with the huge burden of household duties and childcare; hindered access to technical and business education; lack of managerial experience and industry knowledge. The existing exogenous barriers, excessive control, contradictory regulations and unfriendly entrepreneurial ecosystems are seen as additional constraints and contribute to the quality and dynamics of female business.

The obtained results confirm the necessity for adding a gender perspective to SME’s policy support in Belarus as well as for taking it into account when estimating the potential effects of business support programs and policies.

Further research of women entrepreneurship, collection of reliable statistics, comparison of the results with other transition countries are vital. These will give an encouragement to new gender specific initiatives and will contribute to economic growth and innovative perspectives of Belarus.


  • Akulava, M. (2016a). Gender and Innovativeness of the Enterprise: the Case of Transition Countries. Working Paper No. 31.
  • Castillo, M. and M. Freer. (2018). Revealed differences. Journal of Economic Behavior & Organization, 145: 202-217.
  • Croson, R. and U. Gneezy. (2009). Gender Differences in Preferences. Journal of Economic Literature, 47(2): 448-474.
  • Noland, M., Moran, T. and B. R. Kotschwar. (2016). Is gender diversity profitable? Evidence from a global survey. Peterson Institute for International Economics. Working Paper No. 16-3.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Highly Educated Women No Longer Have Fewer Kids

This policy brief summarizes evidence that the cross-sectional relationship between fertility and women’s education in the U.S. has recently become U-shaped. The number of hours women work has concurrently increased with their education. The theory that the authors advance is that raising children and home-making require parents’ time, which could be substituted by services such as childcare and housekeeping. By substituting their own time for market services to raise children and run their households, highly educated women are able to have more children and work longer hours. The authors find that the change in the relative cost of childcare accounts for the emergence of this new pattern.

In 2012, the European Commission published a special report on “women in decision making positions”, suggesting legislation to achieve balanced representation of women and men on company boards. Some countries such as Norway, France, Italy, Belgium and the Netherlands have already taken legal measures in that direction. Trends in women’s education give hope that such goals may be achieved as women are increasingly occupying more prestigious and demanding careers. Indeed, in today’s world, women have surpassed men in higher education in most developed countries (Goldin et al 2006; Hazan and Zoabi 2015a).

What are the consequences of this important development for fertility? Historically, highly educated women have had fewer kids than less educated women (see, for example, Jones and Tertilt 2008). This relationship is deep rooted in the economic and sociological literature to the extent that many theories have already been proposed to explain this relationship. Leading explanations rely on the difficulty to combine children and career (Mincer, 1963; Galor and Weil, 1996) and the quantity-quality tradeoff (Becker and Lewis, 1973; Galor and Weil, 2000; Hazan and Zoabi 2006). The shift in women’s education coupled with more demanding careers for women means that if the cross-sectional relationship between women’s education and fertility is stable over time, then future fertility rates will continue to decline from their already historically low levels.

In Hazan and Zoabi (2015b) we find, however, that the cross-sectional relationship between women’s education and fertility has changed from monotonically declining until the 1990s to a U-shaped pattern during the 2000s. This change is due to a substantial increase in fertility among women with advanced degrees who increased their fertility by 0.7 children, or by more than 50%. This is illustrated in Figure 1, which plots the cross-sectional relationship between fertility and women’s education in 1980 and during the period 2001-2011.

Figure 1: Fertility Rates by Women’s Education, 1980 and the 2000s.


What can explain the rise of fertility among highly educated women during the period that saw the largest increase in the labor supply of highly educated women? We argue that the rise in college premium increased the demand for child-care and housekeeping services by highly educated women and a rise in the supply for such services by low educated women. This ‘marketization’ weakened the tradeoff between career and family life and enabled highly educated women to pursue demanding career without giving up on their desired family size.

To establish the relationship between the rise in the college premium and fertility of highly educated women, we use data from the March CPS for the period 1983-2012. We estimate the average hourly wage in the “child day-care services” industry and allow it to vary by state and year. In addition, we compute the hourly wage of all women in the 25-50 year-old age group who reported a positive salary income. Figure 2 presents the fitted values of the average of this variable for each of our five educational groups. The figure shows that childcare has become relatively more expensive for women with less than a college degree but relatively cheaper for women with a college or an advanced degree.

Figure 2: Linear Prediction of the Log of the Ratio of Average Wage in the Childcare Industry to Average Wage in the Five Educational Groups 1983–2012


To utilize the micro data we estimate regression models where the dependent variable is a binary variable that takes the value of one if a woman, living in a specific state in a specific year gave birth during that year and zero otherwise. Our main explanatory variable is the labor cost in the child daycare industry divided by the own wage of that woman. We show that there is a highly statistically significant and economically large negative correlation between this measure of childcare cost and the probability of giving a birth. In our empirical analysis we find that this change in the relative cost can account for about one-third of the increase in the fertility of highly educated women. We use a battery of tests to show that this correlation is not driven by selection of women into the labor market, by the endogeneity of wages, or by specific years over the last three decades.

Figure 3: 2000s Actual and Hypothetical Fertility under the 1980s prices of Childcare


Figure 3 uses the estimates from the regression models described above and shows a hypothetical fertility for 2001-2011 under the 1983-1985 relative childcare cost. The figure shows that the hypothetical fertility curve is obtained by a clockwise rotation of the actual fertility curve around the group of women that has some college education.

Direct evidence on paid childcare services is consistent with this view. Figure 4 shows the average weekly paid childcare hours by all women aged 25-50 in 1990 and 2008. The figure has two salient features. First, in each of these years, paid childcare is increasing with women’s education. Secondly, between 1990 and 2008, paid childcare hours have stagnated for women with up to some college education but have sharply increased for highly educated women.

Figure 4: Paid Childcare Weekly Hours for Women aged 25-50.


We then rule out potentially other explanations. What if the increase in labor supply stems from women who did not give birth during that year? To address this concern we shows that the cross-sectional relationship between education and usual hours worked for the sub-sample of women age 15-50 who gave birth during the reference period exhibit the same positive correlation. Another concern might be that it is in fact the spouses who respond to a birth by lowering their labor supply enabling their wives to work more. Find that men who are married to highly educated women work more than men who are married to women with lower levels of education. Interestingly, fathers to newborns work more than husbands who do not have a newborn at home, regardless of the education of their wives. More importantly, usual hours worked by fathers to newborns monotonically increased with their wives’ education. Thus, the spouses of highly educated women are not the ones substituting in childcare for their working wives.

Another concern our model may raise is that marriage rates differ across different educational groups. If married women have higher fertility rates and if more educated women have higher marriage rates, more educated women’s higher fertility rates may not be caused by the availability of relatively cheaper childcare and housekeeping services, but rather simply by their higher marriage rates. We find that the fraction of women with advanced degrees who are currently married is somewhat lower than that of women with college degree.

Figure 5: Number of birth per 1,000 White Women in the US in Age Groups 35-39, 40-44 and 45-49: Women with Advanced Degrees (2001-2011) and Historical Rates.


A final potential explanation might be related to recent advancements in Assisted Reproductive Technology (ART) that enable women to combine long years in school without scarifying parenthood. We address this possibility in three ways. First, we show that historical levels of fertility rates among women above age 35 were higher than the levels during the 2000s (see Figure 5). This stands in contrast to the argument that highly educated women were not able to have higher fertility rates in the past due to medical reasons. Secondly, we note that ART accounts for less than 1% of births occurred during the 2000s. Finally, fifteen U.S. states have infertility insurance laws that provide coverage to infertile individuals. We compare fertility patterns by women’s education in these states to the rest of the country and find no difference in fertility rates during the 2000s between the two groups of states.

The results of this study have several implications. For public policy, it highlights potential benefits from pro-immigration policies. Unskilled immigrants can potentially have positive effect on fertility via an increase in the supply of cheap home production substitutes. For many developed countries that are facing aging and shrinking population this may be something to consider. It also has consequences for economic growth. Given the strong correlation between parents’ education and kids’ education, an increase in the relative representation of kids coming from highly educated families means that the next generation is going to be relatively more educated. These are good news for economic growth.


  • Gary S. Becker and Gregg H. Lewis. On the interaction between the quantity and quality of children. Journal of Political Economy, 81:S279–S288, 1973.
  • Oded Galor and David N. Weil. The gender gap, fertility, and growth. American Economic Review 86(3): 374–387, 1996.
  • Oded Galor and David N. Weil. Population, technology, and growth: From Malthusian stagnation to the demographic transition and beyond. American Economic Review 90(4): 806–828, 2000.
  • Claudia Goldin, Lawrence Katz, and Ilyana Kuziemko. The homecoming of American college women: A reversal of the college gender gap. Journal of Economic Perspectives 20(4): 133–156, 2006.
  • Moshe Hazan and Hosny Zoabi. Does longevity cause growth? A theoretical critique. Journal of Economic Growth, 11 (4), 363-376, 2006.
  • Moshe Hazan and Hosny Zoabi. Sons or Daughters? Endogenous Sex Preferences and the Reversal of the Gender Educational Gap. Journal of Demographic Economic, Vol 81, pp: 179-201, 2015a.
  • Moshe Hazan and Hosny Zoabi. Do highly educated women choose smaller families? Economic Journal, 125(587):1191–1226, 2015b.
  • Larry E. Jones and Michele Tertilt. An economic history of fertility in the u.s.: 1826-1960. In Peter Rupert, editor, Frontiers of Family Economics, pages 165 – 230. Emerald, 2008.
  • Jacob Mincer. Market prices, opportunity costs, and income effects. In Carl F. Christ, editor, Measurement in economics: Studies in mathematical economics and econometrics in memory of Yehuda Grunfeld. Stanford University Press, pages 67-82, 1963.