On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Caroline Chuard who has a PhD from the University of Zurich. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.
Is there a strong health economics evidence base on family policies?
The literature on parental leave and family health is relatively young. This literature emphasises that the returns depend on several key features. First, the timing of measurement matters. Therefore, the eﬀects differ according to whether they are measured in the short- versus long-run. Second, the initial level of parental leave and the extent to which parental leave is increased are both key inﬂuencing factors. As such, an introduction is more beneﬁcial than an increase at an already generous level of parental leave. Third, the results depend on the targeted group.
But keep in mind that the effects of family policies on health outcomes are just one part of a large literature that studies the effect on other outcomes such as maternal labour market outcomes, fertility, and child cognitive and non-cognitive development (e.g. Ruhm (2000), Lalive and Zweimüller (2009), Baker and Milligan (2008), Dustmann and Schönberg (2012), Lalive et al. (2014), Carneiro et al. (2015), Dahl et al. (2016), Danzer and Lavy (2018), Butikofer et al. (2018) and many more which have recently been reviewed by Olivetti and Petrongolo (2017) and Rossin-Slater (2018)).
What policy changes were you able to evaluate in your research?
I exploit two types of family policy changes in two countries. On the one hand, I use three changes in parental leave duration in Austria and, on the other hand, I use cantonal variation in family allowances across Switzerland.
More specifically, Austria increased parental leave by 1 year to 2 years in July 1990. This was partially reversed again in July 1996, by exclusively reserving 6 months to fathers so that maternal leave was essentially reduced to 1.5 years. Finally, in July 2000, there was another large extension in paid parental leave by 1 year to 2.5 years. Enforcement of all these changes was very strict, changing from one day to another depending on giving birth in June or July. This sharp discontinuity allows me to employ a regression discontinuity design.
In the case of Switzerland, I analyse the impact of birth allowances (so-called baby bonuses) on fertility, newborn health and birth scheduling. I exploit a unique quasi-experimental setting of Switzerland’s family allowances system. In this system, cantons are free to choose whether they want to implement birth allowances and how much they want to pay. During the last 50 years, 11 cantons have introduced a baby bonus, all increase the amount paid thereafter, and two cantons even abolished the baby bonus after all. This gives rise to a lot of cantonal variation. Thus, I use a difference-in-differences setting where I can analyse both the introduction and the intensity of the treatment.
What were the key strengths of the data sets that you used?
For all my studies I rely on administrative data. Thus, I can use the universe of observations delivered with high quality, as both Austria and Switzerland have very reliable administrative data.
In the Austrian case, I can even combine several different data sets. Namely, I use the Austrian Social Security Database (ASSD), which covers the complete working history of every worker in Austria. The ASSD covers every birth of employed mothers and their actual duration of parental leave. I can link the ASSD to the Austrian Birth Register (ABR) recording newborn health outcomes and additional individual-level characteristics of the mother. Finally, for a part of Austria, I additionally merge the data to health outcomes recorded in the health insurance data. This data set records every outpatient doctor visit, prescribed medication, and hospital stays including diagnosis code.
All of this, together, gives a huge variety of different variables on an individual basis allowing me to study a broad set of outcomes (such as health outcomes next to the directly targeted labour market outcomes). Furthermore, the detailed level of information allows me to study the impact of labour market behaviour on two margins—the extensive margin of mothers who choose to work or not and the intensive margin of how much mothers choose to work. The richness of the data also makes it possible to analyse heterogeneous effects across mothers and by work environment.
Did the policies achieve what they were designed to achieve?
This is a little hard to tell from looking at my results only. For example, in Austria the initial increase of parental leave duration by 1 year was introduced so that fathers could take up to 6 months of the full duration. This policy reform was a result of parliamentary procedural requests which wanted to introduce paternal leave. Due to the ﬂat beneﬁt structure almost no fathers were taking up parental leave, which essentially resulted in an increase of maternal leave from 1 to 2 years and, ultimately, led to the second policy change by exclusively reserving 6 months out of the total 2 years for fathers.
However, what I want to mention here, note that I explicitly evaluated side effects. All three chapters of my dissertation highlight the importance of studying alternative and indirect outcome measures in addition to the direct measures targeted by policymakers.
For example, in the Swiss study, we only ﬁnd little fertility eﬀects, the directly targeted outcome measure of birth allowances, but a sizable and signiﬁcant reduction in the stillbirth rate as well as a positive impact on birth weight. A policymaker, who would now only study fertility, would argue that birth allowances were expensive to implement with little to no result, which, however, does not capture the full story.
Is there heterogeneity in how family policy reforms affect families?
The answer depends on the person affected and the studied outcome. For example, the Austrian parental leave duration reform affects maternal work behaviour during pregnancy regardless of the mother’s socioeconomic background and the industry. This change in prenatal maternal work status doesn’t affect newborn health at all.
However, when I study the same reforms with respect to maternal health, there is substantial heterogeneity. The initial increase in leave length is especially good for low-wage and unmarried mothers. Reducing leave duration harms mothers with unhealthy babies, proxied by a preterm birth or low birth weight baby. Substantially increasing leave duration is, though, especially bad for maternal health of those mothers who already suffered from mental diseases pre-birth. Also, for the paper on the Swiss baby bonus, we find a more beneficial impact in the decline of stillbirths for low socioeconomic status mothers.
Based on your research, how would you design parental leave policies?
With my research, I tried to give a more complete picture on the impact of family policies by taking into account health outcomes which have vastly been neglected so far. Nevertheless, for a policy recommendation it is crucial to take the findings from the previous literature into account.
Firstly, introducing parental leave has generally been shown to be very beneficial for the cognitive development of children (Carneiro et al., 2015). Secondly, these returns are, however, quickly declining (Butikofer et al., 2018). In combination with my findings of no impact of working during pregnancy on child health and a negative impact of too long parental leave policies for maternal health (Chuard, 2018), I would clearly put the focus on mandatory leave in the first months of a newborn’s life. While this might seem obvious for many European countries, this is still not the case in the US. And even Europe might face the risk on the other end of the parental leave duration scale. Many European countries tend to expand leave rather generously both pre- and post-natal, which seems from my research not necessary (always keep in mind, these policies are extremely expensive) and could potentially even be harmful in the long-run.