How to design the optimal pro-natalist policy is an important open question for policymakers around the world. Our paper utilizes a large-scale natural experiment aimed to increase fertility in Russia. Motivated by a decade-long decrease in fertility and population, the Russian government introduced a sequence of sizable child subsidies (called Maternity Capitals) in 2007 and 2012. We find that the Maternity Capital resulted in a significant increase in fertility both in the short run and in the long run. The subsidy is conditional and can be used mainly to buy housing. We find that fertility grew faster in regions with a shortage of housing and with a higher ratio of subsidy to housing prices. We also find that the subsidy has a substantial general equilibrium effect. It affected the housing market and family stability. Finally, we show that this government intervention comes at substantial costs.
In all European and Northern American countries the fertility is below the replacement level (United Nations, 2017). Following this concern, most of the developed countries have implemented various large scale and expensive pro-natalist policies. Yet, the effectiveness of these policies is unclear, and the design of the optimal pro-natalist policy remains a challenge.
There are several important open research questions on the evaluation of these programs. The first is whether these programs can induce fertility in the short-run and/or in the long-run horizon. Indeed, very few of these expensive and large-scale policies are proved to be an effective tool to increase fertility (Adda et al, 2017). The next set of questions deals with further evaluation of the programs: What are the characteristics of families that are affected by this policy? How costly is the policy, i.e. how much is the government paying per one birth that is induced by the policy? Finally, what are the non-fertility related effects of these policies? While most of the studies that analyze the effect of pro-natalist policies concentrate on fertility and mothers’ labor market outcomes, these, usually large-scale, policies may have important general equilibrium and multiplier effects that may affect economies both in the short run and long run (Acemoglu, 2010).
In our paper we utilize a natural experiment aimed to increase fertility in Russia to address these questions.
Motivated by a decade-long decrease in fertility and depopulation, the Russian government introduced a sizable conditional child subsidy (called Maternity Capital). The program was implemented in two waves. The first wave, the Federal Maternity Capital program, was enacted in 2007. Starting from 2007, a family that already has at least one child, and gives birth to another, becomes eligible for a one-time subsidy. Its size is approximately 10,000 dollars, which exceeds the country’s average 18-month wage and exceeds the country’s minimum wage over a 10-year period. The recipients of the subsidy can use it only on three options: on housing, the child’s education, and the mother’s pension. Four years later, at the end of 2011, Russian regional governments introduced their own regional maternity programs that give additional – on the top of the federal subsidy – money to families with new-born children.
In our paper, we document that the Maternity Capital program results in a significant increase in fertility rates both in the short run (by 10%) and in the long run (by more than 20%). This effect can be seen from both within-country analysis and from comparing the long-term growth of fertility rates in Russia with Eastern and Central European countries that face similar economic conditions and had similar pre-reform fertility trends. Like Russia, Eastern European countries experienced a drop in fertility rates right after the collapse of the Soviet Union and had similar trends in fertility up until 2007. Our results show that while having similar trends in fertility before 2007, afterward Russia significantly surpassed all the countries from this comparison group.
Figure 1 illustrates the effect of the Maternity Capital on birth rates. The top two panels show monthly birth rates (simple counts and de-seasoned); the bottom panels show total fertility rates in Russia versus Eastern European countries, and versus the European Union and the US.
Figure 1. Total Fertility Rate, Russia, Eastern European countries, USA and EU.
Source: Sorvachev and Yakovlev (2019), and http://www.fertilitydata.org/.
The effects of the policy are not limited to fertility. This policy affects family stability: it results in a reduction in the share of single mothers and in the share of non-married mothers.
Also, the policy affects the housing market. Out of three options (education, housing and pension), 88% of families use Federal Maternity Capital money to buy housing. We find that the supply of new housing and housing prices increased significantly as a result of the program. Confirming a close connection between the housing market and fertility, we find that in regions where the subsidy has a higher value for the housing market, the program has a larger effect: the effect of maternity capital was stronger, both in the short run and long run, in regions with a shortage of housing, and in regions with a higher ratio of subsidy to price of apartments (i.e. those regions where the real price of subsidy as measured in square meters of housing is higher).
Figure 2 below shows the effect of Federal Maternity Capital on birth rates in different regions. It shows no effect on fertility in Moscow, small effect in Saint-Petersburg; whereas the sizable effect of maternity capital in other Russian regions.
Figure 2. Effect of Federal Maternity capital, by regions
Source: Sorvachev and Yakovlev (2019), and http://www.gks.ru/.
These results suggest that cost-benefit analysis of such policies should go beyond the short-run and long-run effects on fertility. Ignoring general equilibrium issues may result in substantial bias in the evaluation of both short-run and long-run costs and benefits of the program.
While there are many benefits of the program, we show that this government intervention comes at substantial costs: the government’s willingness to pay for an additional birth induced by the program equals approximately 50,000 dollars.
For more detailed evaluation of the results see Evgeny Yakovlev and Ilia Sorvachev, 2019, “Short-Run and Long-Run Effects of Sizable Child Subsidy: Evidence from Russia”, NES working Paper # 254, 2019.
- Acemoglu, Daron 2010 “Theory, General Equilibrium, Political Economy and Empirics in Development Economics”, Journal of Economic Perspectives, 24(3), pp. 17-32. 2010
- Adda, Jérôme, Christian Dustmann and Katrien Stevens 2017. “The Career Costs of Children”. Journal of Political Economy, 125, 2, 293-337.
- Ilia Sorvachev and Evgeny Yakovlev, 2019, “Short-Run and Long-Run Effects of Sizable Child Subsidy: Evidence from Russia”, NES working Paper #254 and LSE IGA Research Working Paper Series 8/2019
 Roughly, the WTP (US$50,000) exceeds nominal US$10,000 subsidy because the government pays for all (100%) families that give birth to a child to induce additional (20%) increase in fertility. See paper for more accurate elaboration.
This brief finds that whereas in the 1980s richer women had fewer children than women near the middle of income distribution in the US, it is no longer true today. It argues that the rise in inequality is the main driver for this change. Greater income inequality enables high-income families to outsource household production to lower-income people. Changes to minimum wage laws are thus likely to affect the fertility and career decisions of the rich.
“I have frequently been questioned, especially by women, of how I could reconcile family life with a scientific career. Well, it has not been easy.”
– Marie Curie, 1867-1934
Much has been made of women “leaning in” at work at a cost to their families. Indeed, this discussion has become more prevalent as women have surpassed men in higher education in most developed countries, and have entered prestigious careers en masse, a fact reinforced by public policy. For example, 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. One natural question to ask is, how high is the cost of a woman’s career to her family? This is a difficult, multifaceted, and even sexist question to ask.
High-income women have historically had fewer kids (Figure 1 for the year 1980). Social scientists’ leading explanations rely on the difficulty of combining children and a career. Under this view of the world, as more women focus on their careers, they have fewer children. On the other hand, the evidence shows that more educated (or wealthier) women produce more educated children. Given these two regularities, the majority of children are born to poorer mothers, and thus receive an inferior education. Moreover, this creates a feedback loop that depresses the average education through time making us question our ability to sustain a satisfactory average level of education.
Figure 1. Fertility rates by income deciles, 1980 and 2010
Notes: Calculated using Census and American Community Survey Data. The sample is restricted to white, non-Hispanic married women. Fertility rates are hybrid fertility rates, constructed by age-specific deciles. Deciles are constructed using total household income.
However, the negative relationship between family income and fertility ceases to hold after the 2000s. Figure 1 shows that for the year 2010, the cross-sectional relationship between income and fertility has flattened or even become a U-shape. Today, high-income women have higher fertility rates than those of women near the middle of income distribution. This is a result of a substantial increase in fertility among women in the 9th and 10th decile of family income: they increased their fertility by 0.66 & 0.84 children, respectively. The rise in fertility of high-skilled females was first documented in Hazan and Zoabi (2015), discussed in a previous FREE Policy Brief. The implications are profound; children are more likely to be born to wealthier or more educated mothers than in the past. This has a far-reaching impact on the future composition of the population.
How can we understand the change in fertility patterns over time? We argue that rising wage inequality played an important role. Data for the years 1980 and 2010 show that average real hourly wages, quoted in 2010 $ grew from $28 ($51) to $50 ($64) for women (men) in the 10th decile of the income distribution. This increase was accompanied by stagnant wages for women (men) in the 1st decile, precisely the people who are most likely to provide services that substitute for household chores (Figure 2). Thus, growing wage inequality over the past three decades created both a group of women who can afford to buy services that help them raise their children, and a group who is willing to supply these services cheaply. In a recent paper, we found that the increase in wage inequality from 1980 and 2010 can actually explain the rise in high income fertility (Bar et al. 2017). Moreover, this rise in inequality has resulted in a large increase in college attendance through the changing patterns of fertility. This is because more children are now born to highly educated mothers.
Figure 2. Wives’ Wage by Income Decile 1980 & 2010
Notes: Calculated using Census and American Community Survey Data. The sample is restricted to white, non-Hispanic married men. Deciles are constructed age-by-age, using total household income. Representative wages for each decile is the average of these decile-specific wages from ages 25 to 50.
Our new understanding of the interrelation between income inequality, the relative cost of home production substitutes, fertility pattern and educational choice induces us to rethink some typical economic debates. For instance, consider the minimum wage. The typical debate about the minimum wage is focused on how it affects lower wage individuals in terms of income and their ability to find work. However, if people who earn the minimum wage are disproportionately also those who help raise wealthier families’ children, or simply make running a household easier, then a higher minimum wage can make home production substitutes more expensive for high wage women, making it harder for them to afford both a family and a career. While indirect, this effect can be significant. Figure 3 shows the distribution of the real wage, relative to the minimum wage, both for the industries of the economy associated with home production substitutes and other sectors of the economy. The figure clearly shows that workers in industries associated with home production substitutes are concentrated around the minimum wage and thus are much more likely to earn wages that are close to the minimum wage.
Figure 3. The distribution of real wages, relative to the effective real minimum wage in each state and year, by sector of the economy
Notes: Data from Current Population Survey, 1980–2010, using all workers.
Interestingly, we calculate a change in the cost of home production substitutes following an increase of the Federal minimum wage from $7.25 to $15/hour, as suggested by Bernie Sanders during the 2016 presidential election. It turns out that this increase in the minimum wage would increase the cost of market services that substitute for household chores by about 21.1%. Indeed, the minimum wage has a strong impact on the average wages of workers producing home production substitutes. However, how does this increase affect the economy?
According to our theory, higher costs of home production substitutes would affect women’s choice of how to allocate their time between labor force participation and home production, including raising children. The higher cost of these substitutes induces women to buy less of them and spend more of their time producing home production goods. Indeed, we find that the increase in the minimum wage decreases fertility and increases mothers’ time at home, and more so for higher income households. The magnitudes are large. A 10th (5th) decile household decreases fertility by 12.8% (9.4%), while the mother spends 9.7% (2.5%) more time at home. Notice that these numbers are calculated under the assumption that women can adjust fertility. What about those who are “locked in” their fertility choice? We recalculate changes in mother’s time at home for these mothers using the model’s fertility in 2010 with the increased cost of market services that substitute for household chores. A 10th decile mother increases time at home by 25.9%, while a 5th decile mother increases it by 13.1%. These numbers are larger as the family has not had a chance to scale back fertility. The short run effect on labor supply is also very large. The average reduction in labor supply by women in the 9th and 10th deciles is 3.5%.
Whether an increase in the minimum wage is good or bad for the society is a big question. Not only does it lie beyond the scope of our theory, but also beyond the scope of social sciences. However, the one modest contribution we try to make is in observing that an increase in the minimum wage heightens the rivalry between a woman’s career and family. As such, it forces women to forgo one in order to opt for the other.
The sexist nature of our question lay in the implicit assumption that it is the mother’s responsibility to look after the children or home production in general, rather than the father’s. While once this was a nearly universal attitude, it is now increasingly common for fathers to take a more central role in childcare rather than leave everything to the mother. How does this change in gender roles affect our analysis? In modern times, both spouses’ careers are potentially affected by children, as both parents take a role in child care. Fathers are now facing the same tradeoffs as mothers did in the traditional gender role story: children vs. careers. As a result, marketization is more important than ever for career oriented parents.
Talk to a high wage family and no doubt that they’ll readily tell you how important their ability to purchase daycare, prepared food, or other help at home is to their success as parents. Perhaps parents don’t realize that the price of these goods are so intricately linked to inequality or the minimum wage, but the policy maker should bear in mind that these are key factors for career women and the family.
- Hazan and Zoabi (2015), “Do Highly Educated Women Have Smaller Families” The Economic Journal
- Bar, Hazan, Leukhina, Weiss, and Zoabi (In progress) “Is the Market Pronatalist? Inequality, Differential Fertility, and Growth Revisited”
This policy brief provides an overview of new evidence on the determinants of the mobility of scientists – high human capital workers who generate new ideas and expand the frontier of knowledge. New evidence from a large dataset of elite US life scientists shows that professional factors, including individual productivity and the quality of a scientist’s peer environment, matter for mobility. Strikingly, family structure also plays a significant role, with the likelihood of moving to decrease when a scientist’s children are in high school (14-17 years old). This suggests that even “star” scientists take into account more personal, family factors in their mobility decisions, likely due to the costs associated with disrupting their children’s social networks.
Workers often face an important decision during their career: should I move to a new city for a new job? Relocation is a complex decision that can involve numerous professional and personal factors, and some of these factors can constrain moves while others facilitate them. Relocating can, on one hand, mean significant transition costs in terms of uprooting one’s family and navigating a new city and workplace; on the other hand, it can open up new career opportunities and provide environments where one’s skills are put to better use.
Why should we care about whether and why workers move? Economic theory suggests that mobility is one channel through which worker productivity can be increased by improving the employer-employee “match”. Moreover, particularly for highly-skilled workers, the theory suggests that the mobility of workers can impact the productivity of their peers; if the human capital of the mobile worker “spills over” to their peers, then the peers left behind would experience a decline in productivity and those at the new destination would get a boost.
In light of this, understanding the mobility of scientists – high human capital workers who are generating new ideas and expanding the frontier of knowledge – is of particular importance when considering the potential role that mobility can play in increasing productivity and innovation, which are central to models of economic growth.
The Determinants of Mobility
While there is a growing literature trying to document how the mobility of scientists can impact their own productivity and the productivity of their peers (see e.g. Agrawal, McHale, & Oettl, 2014), a significant challenge is finding plausibly exogenous variation in both the timing and location choices of movers. In order to fully understand the impacts of mobility, we first need to know more about the determinant of mobility: why and when in their careers scientists move.
Several studies have examined the determinants of the mobility of scientists and inventors, but the literature has been hampered by lack of data that allow researchers to observe the relevant factors that may matter for mobility. These studies have tended to focus on professional factors for moves; especially how individual productivity measures, like a scientist’s number of publications, citations and patents, predict moves. Importantly, there has been less attention in this literature on the constraints to mobility, including more personal factors, like the role of children and family, and the quality of one’s peer environment.
The findings from these studies on the role of individual productivity for mobility is mixed with evidence pointing to a positive relationship (Zucker, Darby and Torero, 2002; Lenzi, 2009), and some evidence showing negative (Hoisl, 2007) and/or no effects (Crespi et al, 2007). One key professional factor that has remained underexplored in these studies is the quality of the peer environment, or how one’s colleagues can influence the choice of moving.
Moreover, very few studies have been able to examine non-professional factors such as the role of family and children. There is some evidence on family factors and inventor mobility from Sweden, where detailed data is widely available but within country mobility is relatively low (Ejermo and Ahlin, 2014). There is also some evidence from the sociology of science literature showing that children influence the scientific performance and mobility of scientists. Using data from the U.S. Census, Shauman and Xie (1996) find that children tend to constrain mobility; for women, children negatively impact mobility regardless of the age of the children, while for men, it is older, high school-age children that tend to constrain mobility. However, given that the study uses Census data, individual productivity measures are limited, which are needed to compare the effects for similarly accomplished scientists.
Evidence from Elite Life Scientists in the US
In Azoulay, Ganguli and Graff Zivin (2016) we examine the determinants of mobility of elite life scientists in the U.S. and for the first time, provide evidence on both professional and personal determinants of mobility. We use a unique panel dataset we compiled from the career histories of 10,004 elite life scientists to understand why and when scientists make decisions to move to new locations. We are able to observe the transitions scientists make between institutions, and focus on moves that are at least 50 miles apart (based on distance between the zip codes of the institutions) to increase the likelihood that a transition leads the scientists to change their place of residence.
The dataset includes individual productivity measures of publication counts and the U.S. National Institutes of Health (NIH) funding data. We also measure the quality of the peer environment at the scientists’ origin and destination locations using publication and funding counts of peers. We define peers as both collaborating and non-collaborating peers (those who are close in “idea space”), and those who are geographically close (less than 50 miles apart) and those who are distant (more than 50 miles apart). Finally, to examine the personal factors, for each scientist in our sample, we hand-collected information on their children, including each child’s year of birth.
Through regression analysis, we find that individual productivity is a positive predictor of moves, which is consistent with several other studies (Zucker, Darby and Torero, 2002; Coupé, Smeets & Warzynski, 2006; Lenzi, 2009; Ganguli, 2015). We also provide new evidence on additional professional factors that influence the propensity to move. For example, we find that obtaining recent NIH funding deters moves, perhaps as a result of transaction costs associated with transferring federally funded research between institutions. We also find that a scientists’ peer environment is a significant predictor of mobility, as scientists are less likely to move when the quality of the peer environment near their home institution is high and more likely to move when the quality of the peer environment at distant institutions is high.
Figure 1. Age of Children and Mobility: Age of Oldest Child
Figure 2. Age of Children and Mobility: Age of Youngest Child
Our most striking result is the role that family structure plays for mobility. We find a significant decrease in mobility when scientists’ children are of high school age. The likelihood of moving increases just before their oldest child enters high school, and again when their youngest child is beyond high school age. Figure 1 shows a notable spike in distant moves just before the oldest child in the household enters high school (11-14 years old), while Figure 2 shows a similar spike after the youngest child completes high school (18-20 years old). In both figures, the relationship between age of children and local (less than 50 miles) moves does not show a similar spike. This relationship between mobility and age of children persists in regressions that allow us to control for productivity measures and potential confounders.
This brief has discussed theory and evidence related to scientific mobility. New evidence from a large dataset of elite life scientists shows that while professional factors do matter for mobility, we also find that even “star” scientists take into account more personal, family factors in their mobility decisions, likely due to potential disruptions to the social networks of their children.
Given that there is still little evidence about what drives relocation decisions, it is important for further analysis of these issues, and our study raises several more questions for researchers to examine, many of which have important policy implications. For example, what is it about recent NIH grants that deter mobility – the terms of the grant contracts or costs of moving personnel and equipment? Regarding the family factors, we were unable to look at differences among female and male scientists, but an important question for further research is whether the age of children and other factors appear to affect women’s and men’s relocation decisions differently.
- Agrawal, A., McHale, J., & Oettl, A. (2014). Why stars matter, National Bureau of Economic Research Working Paper No. w20012.
- Ahlin L. and Ejermo O. (2015). The patent productivity effects of mobility for a panel of Swedish inventors, DRUID, 2015.
- Azoulay, P., Ganguli, I. and Graff Zivin, J.S. (2016). The Mobility of Elite Life Scientists: Professional and Personal Determinants, National Bureau of Economic Research Working Paper No. w21995.
- Coupé, T., Smeets, V., & Warzynski, F. (2006). Incentives, sorting and productivity along the career: Evidence from a sample of top economists. Journal of Law, Economics, and Organization, 22(1), 137-167.
- Ganguli, I. (2015). “Who Leaves and Who Stays? Evidence on Immigrant Selection from the Collapse of Soviet Science” in Aldo Geuna (ed), Global Mobility of Research Scientists: The Economics of Who Goes Where and Why, Elsevier.
- Hoisl, K. (2007). Tracing mobile inventors—the causality between inventor mobility and inventor productivity. Research Policy, 36(5), 619-636.
- Lenzi, C. (2009). Patterns and determinants of skilled workers’ mobility: evidence from a survey of Italian inventors. Economics of Innovation and New Technology, 18(2), 161-179.
- Shauman, K. A., & Xie, Y. (1996). Geographic mobility of scientists: Sex differences and family constraints. Demography, 33(4), 455-468.
- Zucker, L. G., Darby, M. R., & Torero, M. (2002). Labor Mobility from Academe to Commerce. Journal of Labor Economics, 20(3), 629-660.
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.