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Does Economic Advancement ‘Cause’ a Re-increase in Fertility? An Empirical Analysis for OECD Countries (1960–2007)

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Abstract

In the light of the recent reversal of fertility trends in several highly developed countries, we investigate the impact of economic development and its components on fertility in OECD countries from 1960 to 2007. We find that the strong negative correlation between GDP per capita does no longer hold for high levels of per capita economic output; the relation and fertility instead seems to turn into positive from a certain threshold level of economic development on. Survival of an inverse J-shaped association between GDP per capita and fertility is found when controlling for birth postponement, omitted variable bias, non-stationarity and endogeneity. However, gaps between actual and predicted fertility rates show implicitly the importance of factors influencing fertility above and over per capita income. By decomposing GDP per capita into several components, we identify female employment as co-varying factor for the fertility rebound that can be observed in several highly developed countries. Pointing out to important differences with regard to the compatibility between childbearing and female employment, our results suggest that fertility increases are likely to be small if economic development is not accompanied by institutional changes that improve parents’ opportunities to combine work and family life.

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Notes

  1. Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, South Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Spain, Sweden, Switzerland, Turkey, United Kingdom, USA.

  2. OECD countries without: Australia, Belgium, Canada, France, Germany, Greece, Korea, Luxembourg, Mexico, New Zealand, Switzerland, Turkey.

  3. Other assumptions regarding this relationship are of course possible. For instance, one can anticipate the relationship between GDP per capita and fertility to change from a certain point in time onward, which may correspond to a certain situation of institutional background. Hence, we aim at controlling as much as possible for time-varying unobserved characteristics. However, this control is not perfect due to the correlation between trends of these characteristics and those of GDP per capita (thanks to anonymous reviewers for having drawn our attention on this issue).

  4. Country-specific influences of these averages are then estimated to approximate the incidence of unobserved factors which may vary across countries. This allows for more flexibility as the impact of the unobserved common factors can differ across country while the evolution of these factors may be nonlinear or even non-stationary.

  5. In particular, to be unbiased, the CCE estimator requires that the number of unobserved factors is not larger than K + 1 (K being the number of independent variables and equal to one here); or it requires that the factors loadings of independent and dependent variables (i.e. TFR and GDPpc) to be uncorrelated, which is not likely to be the case (Sarafidis and Wansbeek 2012).

  6. For the linear and the quadratic model, we use the natural logarithm of GDP per capita (lnGDPpc) which is standard in most macro-econometric works, as the logarithmic form reduces absolute increases in the levels of GDP per capita and therefore captures proportional rather than absolute differences in the distribution of GDP per capita levels.

  7. The log-likelihood function is actually maximised by using a two-stage grid-search algorithm that in the first stage varies the value of lnGDPpccrit from 9.5, 9.6, 10.0, 10.8, …, and in a second stage refine the search with a step size of 0.01 in the neighbourhood of the best-fitting first stage of lnGDPpccrit. The likelihood profile is available on request.

  8. More precisely, we use lagged variables of lnGDPpc as instruments for lnGDPpc and lagged variables of lnGDPpc² as instruments for lnGDPpc² We perform the IV-regression in two steps (two-stage least squares estimator) by using 1-year lags as well as 5-year lags. The use of lagged exogenous variables lessens the risk of obtaining biased and inconsistent estimators due to inverse causality between the endogenous and the exogenous variables. It is for example likely that variations of fertility that lead back to changes in the economic environment appear time-lagged. At the same time, it is less likely that TFR observed in 1984 impacts GDP per capita levels of the year 1980. However, the use of lagged exogenous variables does not completely rule out the problem of inverse causality between fertility and GDP per capita. TFR of 1984 may affect GDP per capita of 1980 due to birth postponement, for example. Women who delay childbirth from 1980 to 1984 in favour of labour market participation actually do influence GDP levels measured in 1980 by the fact that they have a child in 1984 only.

  9. Labour productivity = GDP/sum of working hours; avrg. working hrs. per worker = sum of working hours/active population; employment ratio = active population/total population.

  10. Ratio active population = active population (ages 25–54)/total population (ages 25–54).

  11. However, the goodness of fit of the exponential model cannot be directly compared to the goodness of fit of the linear and quadratic model, as the form of the endogenous variable of the exponential model (lnTFR) differs from the other two models (TFR). We therefore also test the linear and the quadratic model using lnTFR as endogenous variable while keeping GDP in its logarithmic form. For this specification (not presented here), R² of the linear model is 0.33, while R² of the quadratic model is 0.41. The goodness of fit of the quadratic model, using lnTFR as endogenous variable and GDP per capita and its squared form as exogenous variable is 0.34. The form of the quadratic model presented in column 3 of Table 1 is thus confirmed in having the highest goodness of fit.

  12. Fertility and economic outcomes may also follow a non-stationary evolution path, in which case there is some risk of getting spurious regression results. To deal with this issue and to test the robustness of our results, we also carry out First Difference and System GMM estimations, which take into account non-stationarity. The results are presented in Table 6 in the appendix.

  13. By contrast, the Common Correlated estimator shown in the Annex (Table 6) seems to provide a better control for these unobserved variables with a slightly lower correlation of residuals for which the assumption cross-section independence is no longer rejected (column 5). Here the model also foresees a switch to a positive impact of GDPpc on fertility rates, which is however predicted to happen at much lower level of income per capita (US$ 8929) once correlated factors are controlled for. In practice, for all countries which experienced an upturn in fertility rates, this upturn happened at much higher levels of GDP per head which makes this estimate very implausible (see Figs. 3, 4).

  14. Similar results are obtained when regressions are run separately on two sub- samples, one for lnGDPpc higher than 10 and one for lnGDPpc lower than 10 (results available on request).

  15. Note that the estimated GDP per capita-breakpoint varies largely between the different applied estimation models (see last rows of Tables 1, 6). The predicted minimum never falls outside the observed range of GDP per capita-values (max. GDP pc observed: 65 000 USD) and therefore the trend reversal is not a statistical artefact. However, for most of the models, the location of the predicted minimum is on a relatively high level of GDP per capita, especially for the FE estimation (GDP pc levels above 31 746 US$ are observed for 7 out of 30 countries), and the predicted fertility upswing is relatively slight (see Figs. 3, 4: inverse J-shaped pattern).

  16. Because data on mean age at birth are available for only a limited time period which also varies across countries, the panel becomes highly unbalanced, which makes it impossible to run tests of cross-section independence for the residuals.

  17. The rate of change of MAB is found to have a significantly negative impact on TFR, however, while the convex impact of lnGDPpc on TFR stays unchanged (results available on request). This indicates that the mean age of mothers at childbirth might have an ambiguous impact on TFR for our covered time period. Introducing MAB and its square as exogenous variables yields insignificant results for both MAB-coefficients (results available on request). However, column 3 shows that the impact of MA1B on TFR is actually convex.

  18. Granger Causality tests (Granger 1969) suggest that female employment Granger causes TFR, whereas TFR does not Granger cause female employment. However, Granger causality is not sufficient to imply true causality when the true relation involves three or more variables (Granger 1969). Nevertheless, GMM results taking into account endogeneity issues (and capturing both within- and between-country variation) confirm a significantly positive impact of female employment on TFR (Granger tests and GMM results available on request).

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Acknowledgments

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 by the two anonymous referees and by many colleagues from the REPRO group, INED (Institut National d’Etudes Démographiques) and University Paris 1 Panthéon- Sorbonne. We also thank the participants of the Population American Association (PAA) meeting in Dallas, USA, the European Society for Population Economics (ESPE) congress in Essen, Germany, and the European Association for Population Studies (EAPS) conference in Vienna, Austria for their comments and advice, in particular Francesco Billari, Mikko Myrskylä and Peter McDonald. Furthermore, we thank Hye-Won Kim, Wooseok Ok and Youngtae Cho for their discussion at the Seoul International Conference on Population Prospects and Policy Responses. Tomas Sobotka, who provided the data on the adjusted tempo fertility rates, Willem Adema, Jean-Bernard Chatelain, Laurent Toulemon and Herbert Smith are also gratefully acknowledged for careful suggestions.

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  1. Economics Department, Université Paris 1 Panthéon Sorbonne, Paris, France

    Angela Luci-Greulich

  2. Institut National d’Etudes Démographiques (INED), 133, Boulevard Davout, 75980, Paris Cedex 20, France

    Olivier Thévenon

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  1. Angela Luci-Greulich
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Correspondence to Olivier Thévenon.

Appendix

Appendix

The IV-estimation results are based on 5-year lags as instruments for the exogenous variables. The estimated coefficients based on 1- to 4-year lags do not differ much and thus are not presented in particular.

The Hausman (1978) test comparing the fixed effects to the RE model suggests that the difference of the estimation results of the fixed and the RE models is systematic. The fact that the p value is below 0.05 (0.0371) implies that the hypothesis that the unobserved country effects are not correlated with the error term in the RE model must be rejected. Hence, for our data the fixed effect specification is superior to a RE specification in controlling for unobserved country heterogeneity.

The estimated critical GDP per capital level which leads to a turn in the correlation between fertility and lnGDPpc is strikingly low for the First Difference Estimator. This is due to the fact that the first difference of the natural logarithm of GDP per capita approximates the year-to-year relative changes of GDP per capita. Hence, the First Difference Estimator estimates the impact of GDP per capita growth on fertility variations and therefore risks obtaining biased estimates due to an ‘underdevelopment’ effect. Low levels of GDP per capita are likely to go hand in hand with steeper increases (due to convergence mechanism) and thereby might be rather associated with fertility declines than with fertility increases, referred as a period of demographic transition. This is likely to bias the estimated critical level of GDP per capita. Furthermore, as the First Difference Estimator is not based on level variations, the estimated constant differ largely from the constants estimated by the other estimation methods presented in Tables 2 and 6 and makes it impossible to calculate the minimum level of TFR. Consequently, the First Difference Estimator confirms a convex impact of GDP per capita on TFR while controlling for non-stationarity, but does not permit clear statements about the exact turning point in the correlation between the two variables.

See Tables 4, 5, 6 and Fig. 5.

Table 4 Summary statistics
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Table 5 Granger causality
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Table 6 Robustness checks
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Box
figure 5

Generalised method of moments applied to the analysis of fertility trends

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Luci-Greulich, A., Thévenon, O. Does Economic Advancement ‘Cause’ a Re-increase in Fertility? An Empirical Analysis for OECD Countries (1960–2007). Eur J Population 30, 187–221 (2014). https://doi.org/10.1007/s10680-013-9309-2

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