Abstract
This study investigates female labor force participation in the Netherlands between 1985 and 2014 and proposes a new approach to address the age–period–cohort identification problem. The prime working age model assumes a constant effect of age on female labor force participation for women who are at least 45 years old but younger than 50. This model generates plausible predictions of the age-, period- and cohort profiles of female labor participation. Those predictions are very similar to the ones obtained by using the intrinsic estimator approach of Yang et al. (Sociol Methodol 34(1):75–110, 2004).
Highlights
Until the 1970s, the Netherlands had a very low labor force participation rate for women (32% in 1977) compared to other Western countries, such as Sweden (70%) and Denmark (65%) [see Euwals et al (2011)]
This finding is confirmed by Euwals et al (2011) who present a similar figure based on the Dutch Labor Force Survey 1992–2004.5 age and period effects cannot be disentangled in Fig. 1, this finding provides prima-facie evidence for the prime working age hypothesis which says that female labor force participation remains constant around these ages
Its positive value does not imply that younger cohorts work more than the older ones or that female labor force participation increases over time
Summary
Until the 1970s, the Netherlands had a very low labor force participation rate for women (32% in 1977) compared to other Western countries, such as Sweden (70%) and Denmark (65%) [see Euwals et al (2011)]. In terms of the model of De Ree and Alessie (2011) described above, Deaton and Paxson (1994) basically assume that the the linear time trend coefficient is equal to zero Often this assumption is justified by calling upon the importance of unanticipated business cycle effects. De Ree and Alessie (2011) show an example that reveals clearly that two different arbitrary assumptions lead to very different age profiles of life satisfaction Another strategy, proposed by Heckman and Robb (1985), is to come up with proxy variables for either period or cohort effects that describe the underlying processes causing these effects. Cohort and period variables, our regression models will include the following control variables: dummy variables indicating the nationality of the respondent (Dutch), marital status (Partner), the presence of children in the household (ChildHome), and education level
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