Identification strategy

In my analysis I exploit the end of war as a natural experiment that caused an exogenous shock in socioeconomic conditions that induced a shock in the birth rate. The temporary nature of the shock allows for an identifi- cation of the cohorts born before and after the liberation. Furthermore, a larger effect on the birth rate is observed in the area in which the lib- eration caused larger changes in the socioeconomic environment. Hereby the liberation had a large effect on the birth rate in the north, whereas

37_{This figure ignores parental selection caused by the famine as children born in}

famine-exposed cities are left out of the analysis.

38_{At the same time, selection into the sample after the Birth Peak (e.g. those born in}

there was barely an effect on the birth rate in the south. The south serves as a natural control group, and controls for a shared macro-environment that may affect the long-run outcomes of the studied cohorts. I employ a difference-in-difference strategy in which I compare cohorts born before and after the liberation, where the latter are exposed to a better socioeconomic environment, in the north and south. The birth rate started to increase from March onward and remained fairly high up until September 1946 as is shown in Figure 2.12. Therefore, the Birth Peak cohort is defined as born between March and September 1946.39 _{The control groups contains}

children born between January 1944 and February 1946. As of the limited influence of the Birth Peak in the south, I can credibly estimate the effect of the fertility shock while controlling for common cohort-specific effects that may affect long-term child outcomes.

Equation 2.1 is estimated for different outcome variables yirt, where

subscripts refer to individual i born in region r, and month/year t. The labor market outcomes of interest are labor force participation, earnings and enrollment in any disability insurance scheme in 1999. The health out- comes of interest are indicators for mortality before age 65 and 70, and an indicator for whether the individual had any prescription drugs for diseases related to the individual’s lifestyle (i.e. mental health, cardiovascular, res- piratory, and diabetes) in 2006.40. Equation 2.1 contains an indicator for being born in the Birth Peak cohort (March-September 1946) (BPit), an

indicator for being born in the North of the Netherlands (N orthir), and

their interaction indicating that the child is born in the north during the

39_{Robustness checks with respect to the choice of this definition are provided in Sec-}

tion 2.5.4.

2.3. EMPIRICAL STRATEGY 37

Birth Peak and hence is considered a Child of the Birth Peak, henceforth CoBP (CoBPirt).41 An indicator for birth in the time period between the

liberation of south and north is added (LibSoutht). Linear and quadrat-

ic region-specific age trends are added to take into account region-specific age profiles in outcomes. For example, individuals in different regions may sort into different occupations, i.e. the agricultural south and more urban north. Vector Xicontains individual-specific controls, standard errors (irt)

are clustered by month of birth and region.42

yiprt = γ0 + γ1BPit+ γ2N orthir+ γ3(CoBPirt)

+γ4LibSoutht+ f (M oB, Y oB)ir+ Xiδ + irt

(2.1)

First, it is important to note that the studied cohorts were all subject to the same educational system, as there were no educational reforms up until 1968 (Dodde, 1983). Likewise, fertility after the war is not driven by the availability of medical care. However, a potential concern with the aforementioned identification strategy is that it is hard to eliminate the impact of other war events on fertility and parental selection. Although children exposed to the Hunger Winter are born from February 1945 to December 1945 (Scholte et al., 2015), i.e. before the Birth Peak, there might be responses to famine circumstances that might affect fertility and selection after the liberation. To exclude any potential confounding by the Hunger Winter all children born in famine-exposed cities (more than 40, 000 inhabitants in 1944)43 _{are excluded from the treatment group. Second, to}

41_{Hence this indicator represents BP}

it∗ N orthir.

42_{Equation 2.1 is estimated by OLS for the ease of interpretation, but results are}

robust to the use of binary probit models. Results are available on request.

exclude that the earlier liberation of the south is differentially affecting fertility across areas, an indicator for births within this time-interval is added to the specification. Section 2.5.4 shows that the results are robust to excluding all children who are conceived during the famine, and during the liberation of the south.

Equation 2.1 cannot distinguish between delayed fertility and unantici- pated conceptions, and rather gives the average effect of being born in this Birth Peak cohort. By using information on the marital status of the par- ents, a distinction is made between children who are conceived in-wedlock and those who are conceived out of wedlock. More precisely, to take in- to account potential premature births, children born within seven months after marriage are defined as in-wedlock conceptions. The difference-in- difference model is estimated separately for first births who are born in wedlock, where a distinction is made between in- and out-wedlock concep- tions. The first are more likely to be a product of delayed fertility, whereas the latter are more likely to reflect unanticipated conceptions.

Another worry is that it is hard to distinguish between the effect of cohort size and cohort composition, especially when studying the child’s outcomes in adulthood. Two tests are done to explore the effect of cohort size on the results. Firstly, a control variable for the size of the child’s birth cohort at the province level is added to the specification to account the potential effects of cohort size. The province level size of the birth cohort is a reasonable proxy for capturing cohort size considering the low residential mobility of the Dutch. Secondly, a stronger test is performed in which I examine the outcomes of younger siblings. Given that these younger siblings are born to the same mother, but in a different time, parental

2.3. EMPIRICAL STRATEGY 39

selection effects can be separated from cohort effects (see Section 2.6.1). The key assumption of a difference-in-difference strategy is that the trends in outcomes, for both treatment and control, would be the same in absence of treatment (Angrist and Pischke, 2008). This paper is about parental selection, and it is of primary interest to check that parental char- acteristics in north and south exhibit similar pre-trends. Figure 2.6 shows that the trends in maternal age at first birth are very similar in north and south prior to 1946, and Figure 2.8 shows a similar picture for age at second birth prior to 1945. Figures 2.A9 and 2.A10 show pre-trends at the child level for labor market and health outcomes. Pre-trends look very similar for the outcomes considered prior the last war years. I also test the common trend assumption for parent and child outcomes more formally. Following Autor (2003) I estimate Equation 2.2. The outcome of interest (y) of in- dividual i born in year t and region r is regressed on a set of region fixed effects (λr) and year fixed effects (δt), standard errors are clustered by birth

month/year and region. Indicators Dit represent interactions between the

treatment variable, i.e. born in the north, with birth year (ranging from 1941 (j = −5) to 1950 (j = 5) where 1940 is the reference category). The common trend assumption holds if parameter estimates for earlier years are not significantly different from zero. The results are reported in Table 2.A3 and 2.A4. I find no evidence for differences in pre-trends on the parental level and on child level in the years prior to the last war years.

yirt = λr+ δt+ +5

X

j=−5