Abstract
Female Labor Force Participation (FLFP) is perhaps one of the most relevant theoretical issues within the scope of studies of both labor and behavioral economics. Many statistical models have been used for evaluating the relevance of explanatory variables. However, the decision to participate in the labor market can also be modeled as a binary classification problem. For this reason, in this paper, we compare four techniques to estimate the Female Labor Force Participation. Two of them, Probit and Logit, are from the statistical area, while Support Vector Machines (SVM) and Hamming Clustering (HC) are from the machine learning paradigm. The comparison, performed using data from the Venezuelan Household Survey for the second semester 1999, shows the advantages and disadvantages of the two methodological paradigms that could provide a basic motivation for combining the best of both approaches.
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Zambrano, O., Rocco S, C.M., Muselli, M. (2007). Estimating Female Labor Force Participation through Statistical and Machine Learning Methods: A Comparison. In: Chen, SH., Wang, P.P., Kuo, TW. (eds) Computational Intelligence in Economics and Finance. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72821-4_5
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DOI: https://doi.org/10.1007/978-3-540-72821-4_5
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