Insights from kernel conditional-probability estimates into female labour force participation decision in the UK

Abstract

The female labour force participation decision in the UK is a well-researched topic in empirical economics and econometrics. In this paper, using data from the UK Labour Force Survey in 2007, we contribute to the rich body of the literature by estimating a model using the kernel mixed-data-type conditional-probability (KMDTCP) estimator. We extend the analysis by comparing the predicted probabilities of the KMDTCP estimator with the estimates of a multinomial logit (MNL) model. Additionally, kernel smoothing specification tests are applied in order to compare different functional forms for the independent variables in the MNL model against the KMDTCP estimates. Our results demonstrate that the difference between the predictions from the KMDTCP estimator and the MNL is reduced when the functional form of continuous independent variables in the MNL model is relaxed to a semi-parametric form using dummy variables and interaction terms. The properties that are captured by the KMDTCP estimator show clearly that the propensity to work decreases with the increase in the number of dependent children. Furthermore, our analyses show that females older than 40 years of age have a high propensity to work irrespective of the number of dependent children.