Female Labour Force Projections Using Microsimulation for Six EU Countries

Understanding low female labour force participation: Policy evaluation using microsimulation

“…In the Swedish education scenario, initial educational attainments are modified to reflect the same distribution of educational levels as in Sweden. Then, the share of highly educated individuals increases, in all countries, as predicted in the aggregate projections presented in Richardson et al (2018), at the expenses of the share of poorly educated individuals.…”

“…Thévenon (2011) performs a principal component analysis aimed at identifying 2. The microsimulation model is described in details in Richardson et al, (2018) as well as -more briefly -in the Appendix to the present work. It is developed using the JAS-mine simulation platform (Richiardi and Richardson, 2017) available open source from the JAS-mine website (see http://www.jas-mine.net/demo/labour-forceparticipation).…”

“…Fostering the higher participation of women is crucial to meet the Europe 2020 target of achieving an overall employment rate of at least 75% by 2020; this in turn would be crucial to reduce the risk of social exclusion and poverty for women and to achieve inclusive growth (European Comission, 2013). In this framework, Richardson et al (2018) present medium-and long-term projections of female participation and employment rates for a selected number of EU Member States (Sweden, Italy, Spain, Ireland, Hungary and Greece) and suggest that by 2020 none of these countries except Sweden will have reached the employment rate target (though Ireland will be close). In the present paper, we analyse the role played by some key drivers (demography, education, participation behaviour) and by some policy actions in shaping future female labour market participation in the same selection of countries, in order to obtain indications on which are the factors having the highest bearing in the unsatisfactory projected evolution of participation in those countries.…”

“…In the present paper, we analyse the role played by some key drivers (demography, education, participation behaviour) and by some policy actions in shaping future female labour market participation in the same selection of countries, in order to obtain indications on which are the factors having the highest bearing in the unsatisfactory projected evolution of participation in those countries. To be more specific, we explore the implications of different scenarios for the parameters of the microsimulation model introduced in Richardson et al (2018), and contrast these scenarios with the baseline projections for the selected EU Member States that were also presented in that paper.…”

Increasing the Impact of Dynamic Microsimulation Modelling

“…As outlined above, there are still many unanswered questions in relation to what are the best methodological choices to make in the use and development of microsimulation models. Despite Pudney and Sutherland's (1994) work on the use of confidence intervals in microsimulation models, their use is still low, with some exceptions (Creedy, Kalb, & Kew, 2007;Pudney, Hancock, & O'Donoghue, Dekkers Increasing the Impact of Dynamic Microsimulation Modelling Sutherland, 2006;Richardson, Pacelli, Poggi, & Richiardi, 2018). Also, the pitfalls of using confidence intervals in combination with alignment remain to be explored.…”