Prediction of transition probability from unemployment to employment in Argentina (2003-2019)
Keywords:
Gender; Employment; Inequality; Machine Learning.Abstract
Despite their growing participation in the labor market, women who decide to go out and look for a job face greater difficulties in obtaining it. The participation of women in the labor force is considerably lower, even if entering the labor market the possibility of actually finding a job is also less than the chance that men have of doing so (CIPPEC, 2019). Being able to predict the probability of occupational insertion of men and women, and inquire about the factors that influence this probability, is essential in order to understand gender gaps in the labor market, helping to improve the design and implementation of public policies with a gender perspective, with the final goal to achieve equality of opportunities. In this framework, the present work will seek to predict the probability of transition from unemployment to the employment in Argentina from 2003 to 2019, using the Permanent Household Survey, based on traditional prediction techniques and Machine Learning, with the objective to find the most robust model that achieves the highest level of accuracy.References
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