Literaturnachweis - Detailanzeige
Autor/in | Klose, Sophie-Charlotte |
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Titel | Machine Learning in Labor Economics: Clustering, Prediction, and Variable Selection in the Analysis of Female Employment. Gefälligkeitsübersetzung: Maschinelles Lernen in der Arbeitsökonomie: Clustering, Vorhersage und Variablenauswahl bei der Analyse der Frauenerwerbstätigkeit. |
Quelle | Duisburg (2021), 174 S.
PDF als Volltext Duisburg; Univ., Diss., 2021. |
Sprache | englisch |
Dokumenttyp | online; Monographie |
DOI | 10.17185/duepublico/74821 |
Schlagwörter | Clusteranalyse; Mutter; Frau; Künstliche Intelligenz; Elterngeld; Lernen; Fruchtbarkeit; Datengewinnung; Reformpolitik; Erwerbstätigkeit; Arbeitsmarkt; Arbeitsökonomie; Berufsbiografie; Erwerbsbeteiligung; Frauenerwerbstätigkeit; Auswirkung; Prognose; Quote; Verfahren; Ausländer |
Abstract | "In three separate chapters, this dissertation (develops and) demonstrates the effective use of various ML tools to tackle different empirical purposes in the analysis of female employment. Chapter 2 deals with data-driven classification in the analysis of maternal employment. The chapter focuses on detecting latent group structures in the effect of motherhood on employment and examines how the introduction of a generous parental benefit reform impacts the different cluster groups. Chapter 3 turns to the prediction aspect of ML in labor economics and analyzes in a data-driven way how far childbirth can be predicted from a rich set of predictor variables derived from female employment and wage histories. Chapter 4 introduces ML tools for controlled variable selection to economists. More specifically the chapter extends a recently proposed approach for datadriven variable selection in high-dimensional linear models to the non-linear case and exemplifies its usefulness with an application towards the labor market. All three chapters share in common the sparsity principle (e.g., Hastie et al. [2015]), which assumes that the DGP can be modeled accurately by a small number of predictors, even though the actual number of variables at hand is large. Sparsity can be motivated on economic grounds in situations where a researcher believes that the underlying DGP is parsimonious but is unsure about the identity of the relevant variables. In empirical research, it allows the effective use of a large set of covariates while at the same time maintaining the spirit of parsimonious modeling in economics.; Cumulative Dissertation Containing Three Essays: (1) Identifying Latent Structures in Maternal Employment: Evidence on the German Parental Benefit Reform (2) Predicting the Incidence of Having a First Child based on Employment Records { A Machine Learning Approach (joint work with Marie Paul) (3) A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics (joint work with Johannes Lederer)" (Text excerpt, IAB-Doku). |
Erfasst von | Institut für Arbeitsmarkt- und Berufsforschung, Nürnberg |
Update | 2021/4 |