We examine how strongly fertility trends respond to family policies in OECD countries. In the light of the recent fertility rebound observed in several OECD countries, we empirically test the impact of different family policy instruments on fertility, using macro panel data from 18 OECD countries that spans the years 1982–2007. Our results confirm that each instrument of the family policy package (paid leave, childcare services and financial transfers) has a positive influence on average, suggesting that the combination of these forms of support for working parents during their children’s early years is likely to facilitate parents’ choice to have children. Policy levers do not all have the same weight, however: in-cash benefits covering childhood after the year of childbirth and the provision of childcare services for children under age three have a larger potential influence on fertility than leave entitlements and benefits granted around childbirth. Moreover, we find that the influence of each policy measure varies across different family policy contexts. Our findings are robust after controlling for birth postponement, endogeneity, time-lagged fertility reactions and for different aspects of national contexts, such as female labour market participation, unemployment, labour market protection and the proportion of children born out of marriage.
Nous examinons dans quelle mesure les tendances de la fécondité réagissent aux politiques familiales dans les pays de l’OCDE. En relation avec la ré-augmentation des taux de fécondité observés dans plusieurs pays de l’OCDE, nous testons l’influence de différentes mesures de politiques familiales sur la fécondité, sur un panel de 18 pays pour la période allant de 1982 à 2007. Nos résultats confirment que chaque mesure de cet ensemble (congé rémunéré, services d’accueil de la petite enfance et transferts financiers) ont en moyenne une influence positive sur la fécondité, suggérant que la combinaison de ces formes d’aides aux parents qui travaillent avec de jeunes enfants est susceptible de faciliter le choix d’avoir des enfants. Les différents instruments politiques n’ont toutefois pas le même poids : les prestations financières versées au-delà de la naissance et l’offre de service d’accueil pour les enfants de moins de trois ans ont une influence potentielle plus grande que les droits au congé et les aides financières associées à une naissance. De plus, l’effet de chaque mesure varie selon le contexte global constitué par les politiques familiales. Nos résultats sont robustes à différentes procédures testées pour contrôler les effets de recul de l’âge moyen à la naissance des enfants, traiter les problèmes d’endogénéité ou de décalage dans le temps de la réponse des taux de fécondité aux évolutions des politiques. Les variations de taux d’emploi des femmes, de taux de chômage ou de niveau de protection des marchés du travail sont aussi prises en compte.
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Family policies might influence fertility not only because they affect the economic determinants of fertility, but also because they impact and reflect the institutional and normative setting of a country and a society. However, a detailed discussion of cultural norms and institutional determinants beyond economic factors is outside the scope of this paper.
Denmark, Netherlands, Spain, Norway, Sweden, Portugal, France, New Zealand, Belgium, United States, Italy, Japan, Australia, United Kingdom, Ireland, Finland, Germany, Austria.
The amount spent per child is calculated on the basis of the total number of children under age 20. Since the age limit of children for which a family can receive family benefits varies across countries, it has been set at age 20 to obtain a comparable population basis. Moreover, the levels of family and child benefits are likely to be higher in richer countries, i.e. countries with higher GDP per capita. For this reason, the generosity of support can be more usefully measured by comparing the relative effort made by countries to support families with children, which is given by the proportion of income per capita that countries devote to child benefit. It is also likely that fertility will respond to changes in this relative-to-average income measure over time.
Expenditures per child are calculated on the basis of the total number of children under age three, whether or not they are enroled in childcare. A more accurate measure would be to consider only those children covered by childcare services, but time series on the number of children enroled in childcare services are not available.
Overall, two types of leave schemes can be distinguished. First, countries which were pioneers in introducing parental leave entitlements provide comparatively long periods of leave (up to 3 years) with flat-rate payments, which make a return to the labour market difficult, especially for low-qualified women. Second, countries where leave entitlements were introduced later and/or reformed recently offer shorter periods of leave, often combined with earnings-related payments and special incentives for fathers to take up parental leave (Nordic countries, Germany). This second type of leave scheme promotes a combination of work and family life for both parents and encourages mothers to participate in the labour market before and after childbirth. Overall, a polarization between countries can be observed between the two leave schemes over time. Only Germany has radically changed its leave policy scheme from the first to the second type, resulting in a drastic reduction in the number of paid leave weeks from 2007 on (a period not covered in the present study).
Preliminary checks of data properties were done to verify that regression results are affected by potential non-stationarity or cross section dependence of data series (see Luci and Thévenon 2012 for details). Stationarity tests for individual country time series as well as panel unit root tests were, therefore, carried out. The results show that nonstationarity of fertility and policy variables in levels cannot be ruled out. However, the assumption of stationarity of first difference variables is not rejected in most cases by individual country and panel unit root tests. This suggests that System GMM estimations, which includes first differences as instruments might be an accurate way to control for non-stationarity of data series (Blundell and Bond 1998). Then, a Pesaran (2004) test of cross section dependence provides strong evidence for the presence of cross section correlation within the sample. The two-way fixed effects transformation eliminates cross section dependence in the data if policy parameters and the influence of the unobserved common factor(s) are identical across countries.
However, adjTFR only corresponds to a pure quantum measure of fertility on the assumption of uniform postponement of all stages, i.e. an absence of cohort effects (Kohler and Philipov 2001). Consequently, adjTFR only controls imperfectly for tempo effects.
The addition of control variables certainly causes multicollinearity problems. A correlation between exogenous variables implies that interpreting the estimated coefficients becomes difficult, as we cannot ascribe the change in the endogenous variable to a certain determinant. However, we are primarily interested in the sign and significance of the estimated coefficient of our five policy variables and not in quantifying the estimated impact of our control variables on fertility. As we consider the economic context, women’s emancipation and societal norms as important factors for fertility, we prefer to reduce the risk of an omitted variable bias (OVB) by putting up with multicollinearity. At the same time, we abstain from introducing further control variables (one might think, for example, of access to and costs of housing and health care as other important determinants of fertility) to not further increase the problem of multicollinearity (and endogeneity) as well as to not further reduce the number of observations.
Another approach to investigate heterogeneity consists in running estimations for each category of countries separately. However, the small sample size of each category leads to insignificant parameters which prevent us from showing the results. In this context, a more convincing approach is the one described above with dummies for types of welfare states replacing country dummies (and not complementing them in order to avoid over-specifications). Country-specific linear time trends are also dropped to avoid over-specification.
We also add the log of GDP per capita (measured at purchasing power parity in constant 2005 US $) and its squared term to the five policy variables. This procedure allows controlling for a convex impact of economic development on fertility, as suggested by Luci and Thévenon (2010). GDP per capita turns out to have a convex but insignificant impact on TFR, as family policies seem to capture most of the fertility variations (results available on request).
Kurkowiak (2012) shows that price levels indexes for household final consumption are comparatively higher in Norway, Denmark, Sweden and Finland than in most other European countries.
The list of key contributions could easily be extended if our aim was to survey the literature, which is beyond the scope of the present paper. In general, the evidence suggests that while family benefits do significantly reduce the direct and indirect costs of children, their effect on fertility per se is limited. Furthermore, while family benefits have an effect on the timing of births, their effect on the final fertility choices of individuals is contested (Thévenon and Gauthier 2011).
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This research was funded by the European Commission within the project “Reproductive decision-making in a macro-micro perspective” (REPRO) in the Seventh Framework Programme under the Socio-economic Sciences and Humanities theme (Grant Agreement: SSH-CT-2008-217173) (http://www.oeaw.ac.at/vid/repro/). The paper has benefited greatly from comments from two anonymous referees and by many colleagues from the REPRO group, INED (Institut National d’Etudes Démographiques) and University Paris 1 Panthéon-Sorbonne.
We compare the Fixed Effects model to a Between Effects (BE) and a Random Effects (RE) model. Results of the Between Effects model are presented in column 1 in Table 6. Results of the Random Effects model are available on request. We use a Hausman (1978) test to invalidate the hypothesis that the unobserved country effects are not correlated with the error term in the RE model. The test suggests that the fixed effect specification is better than a random effects specification for controlling for unobserved country heterogeneity. The BE estimation obtains insignificant coefficients for all policy variables. The insignificance along with the high R² and the relatively low adjusted R² indicate that unobserved country-specific effects explain most of the fertility variance in the Between Effects model. We, therefore, consider the BE model to be inappropriate for our empirical analysis and conclude that the country and time fixed effects estimation with country-specific time trends (column 2 of Table 2) is best suited to capture the impact of family policies on fertility. This means that variations of policies over time within a country are most important to explain fertility variations in comparison to between-country and overall variations.
Subsequently, we control our fixed effects model for endogeneity. Therefore, we introduce time-lagged exogenous variables, i.e. we instrument childcare expenditure and childcare coverage with its time-lagged levels, which also takes into account time-lagged adaptations of fertility to changes in a country’s childcare context. One-year as well as 5-year lags are applied and results are presented in columns 2 and 3 of Table 6 (a discussion of the methodology is presented in the appendix). The results confirm a significant impact of spending on cash benefits; spending per birth and childcare enrolment when controlling for potential endogeneity. The estimated coefficient of childcare enrolment is higher for the model with 5-year lags than for the model with 1-year lags and the FE model presented in table, suggesting a considerable time-delayed response of fertility to changes in the supply of childcare facilities. This time delay seems to exceed 1 year, which is rather intuitive as fertility changes take at least 9 months to be realised.
The last column in Table 6 presents results of a System GMM estimation, which not only controls for endogeneity (along with OVB and non-stationarity), but also for dynamics of adjustment (by introducing a lagged endogenous variable among the regressors). Accounting for these dynamics is important as the impact of family policies on fertility is likely to depend on the countries’ initial fertility level, as assumed, for example, by Gauthier and Hatzius (1997) and D’Addio and Mira d’Ercole (2005). In order to significantly reduce the number of instruments, which is necessary to avoid an over-identification of the model, the GMM estimation is based on data containing observations for every 5 years (1985–2005). The System GMM results confirm a positive impact of spending on cash benefits and childcare enrolment for fertility.
Between Effects Estimation (Column 1)
The between effects estimator is based on time averages of each variable for each country and, therefore, focusses on between-country variation, i.e. the BE estimator allows answering the question if and how far policy differences between countries explain differences in fertility between countries. Estimation with a mean group estimators (MG) also capture the heterogeneous influence of policies on fertility trends across countries (Pesaran and Smith 1995). However, since our panel is relatively short and especially unbalanced, the standard errors obtained with this procedure are quite high and probably overestimated (Coakley et al. 2001). T statistics might be affected, while the pooled and fixed effects estimators have an efficiency advantage over the mean group estimator in small T samples. For this reason, we do not report the results of MG estimation. They are available on request.
2SLS Estimations (Columns 2 and 3)
The use of lagged exogenous variables lessens the risk of obtaining biased and inconsistent estimators due to reverse causality between the endogenous and the exogenous variables. For example, TFR observed in 2007 cannot impact childcare expenditure in 2006. At the same time, it is likely that variations in fertility resulting from changes in childcare expenditure appear time-lagged. Of course, the use of time-lagged variables represents only a ‘second best’ option for controlling for endogeneity, as this procedure cannot completely rule out a potential estimation bias caused by reverse causality. The best option would be to substitute each family policy variable by a proper instrumental variable that is highly correlated with the family policy variable but not correlated with fertility. As variables which meet these requirements are not available, we put up with lagged observations as instruments for current policy observations. At the same time, the use of lagged exogenous variables allows us to account for possible time delays in fertility responses to policy changes. We, therefore, estimate our models with 1-year lags as well as with 5-year lags to see how far the timing of policy implementation corresponds to the timing of fertility change.
System GMM Estimation (Column 4)
Besides capturing the dynamics of adjustment (lagged TFR as exogenous variable), the System GMM estimation helps to control for endogeneity and omitted variable bias, and limits the risk of spurious regressions due to non-stationarity (Blundell and Bond 1998). To do so, the System GMM estimator combines a set of first-differenced equations with equations in levels as a ‘system’, and uses different instruments for each estimated equation simultaneously. This involves the use of lagged levels of the exogenous variables as instruments for the difference equation, and the use of lagged first differences of the exogenous variables as instruments for the levels equation. The use of lagged exogenous variables is useful to limit inconsistencies raised by possible endogeneity, while difference variables control for omitted (time constant) variables as well as for non-stationarity. Our analysis of time properties of the data (Appendix) suggests that all time series are difference stationary, implying that System GMM controls for non-stationarity by the integration of first-differenced equations. The controls are imperfect; however, as lagged levels are likely to be poor instruments for differences, and differences are likely to be weak instruments for levels. Moreover, the use of so many instruments produces a risk of model over-identification. In order to reduce the number of instruments, we apply the System GMM estimator to reduced data which contain only observations of every 5 years (1985–2005), highlighting long-term variations. Column 4 shows that lagged levels of fertility capture most of the fertility variations, i.e. the control for dynamics of adjustment lessens the informative value of the model intending to capture the impact of family policies on fertility. Moreover, the relatively small p values of the Sargan tests (not significantly higher than 0.05) suggest that our model still risks being over-identified. Hence, we prefer to continue robustness checks (Table 3) with the Fixed Effects specification.
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Luci-Greulich, A., Thévenon, O. The Impact of Family Policies on Fertility Trends in Developed Countries. Eur J Population 29, 387–416 (2013). https://doi.org/10.1007/s10680-013-9295-4
- Family policies
- Demographic economics
- Female employment
- Politiques familiales
- Économie démographique
- Emploi des femmes