EXPLAINING DUTCH FERTILITY RATES IN A COMPARATIVE EUROPEAN PERSPECTIVE

ABSTRACT

This article contributes to the search for explanations of the cross-national variation in fertility rates by studying the influence of social policy, economic and cultural factors on fertility in the former EU15, while giving special attention to one case: The Netherlands. This country lacks good social policy for supporting parents to combine work and care and nevertheless has a relatively high fertility rate. This article addresses the backgrounds of that phenomenon, using comparative macro level data from several sources, such as ‘the child benefit package’, the European Values Studies, OECD and Eurostat. We argue that bivariate relationships cannot explain cross-national variation in fertility rates. Therefore, we analyse configurations of factors, which shows among others that high availability of part-time jobs for women constitutes only part of the explanation for the Dutch fertility level. Good economic prospects and high male wages in combination with relatively low female human capital make that Dutch families can afford children at their own costs when men work full-time and women work part-time. Therefore, the low level of social policy that supports parents in the indirect or direct costs of children does not result in low fertility rates.

Keywords:

• fertility rates
• family policy
• economy
• culture
• cross-national comparison
• The Netherlands

Notes

¹Measured by combining unemployment rates of four categories: men and women in the age groups 20–24 years and 25–34 years. The older groups are weighted double because of the double age range of this category.

²Formal child care implies both public and private provision in child care centres and in residential care homes. Also included is care by childminders based in their own homes and by carers who are not family members but live with the family in question.

³Sometimes a country with an extreme score is excluded from the K-means cluster analysis, and added to the highest or lowest group afterwards, to avoid clusters containing only one country. The K-means cluster analyses are conducted using SPSS.

⁴In Ragin's (1994) examples there are typically about four independent variables.

⁵For example, France does have long parental leave, but it is only paid for third and subsequent children (Bradshaw and Finch 2002). This makes it a pronatalistic policy measure, but not a policy that deserves a high score with regard to de-famialization, as parental leave is unpaid for the first two children.

⁶In 1999 the unemployment level for women aged 25–34 was 18 percent in Italy, 21 percent in Greece and 25 percent in Spain. For women aged 20–24 years the levels were above 30 percent (OECD 2002).

⁷The opposite however, is not true. The case of Sweden (medium TFR) shows that a beneficial social policy does not always go together with high fertility rates. In this country fertility rates appear to have been very sensitive to the economic recession during 1990s, leading to decreased employment and cutbacks in child benefits. Still, Swedish child benefits are generous by international standards (Hoem 2000).

⁸Besides the part-time fertility rate, the other economic conditions of Germany and Austria are also more or less similar to The Netherlands (no opposite scores), the only striking difference between these countries and The Netherlands are the high child benefit package of Austria and the high care ethos of Germany. These differences cannot explain why Germany and Austria have so much lower fertility rates. Other factors, not included in our analysis have to be responsible for these low fertility rates, such as low ideal family sizes in Germany and Austria (Goldstein et al. 2003).