Martin Huber
martin.huber@unifr.ch
+41 26 300 8274
https://orcid.org/0000-0002-8590-9402
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Ordentliche_r Professor_in,
Departement für Volkswirtschaftslehre
PER 21 bu. F421
Bd de Pérolles 90
1700 Fribourg
Statistik; datenbasierte Kausalanalyse; maschinelles Lernen (machine learning); Politikevaluation in der Arbeitsmarkt-, Gesundheits- und Bildungsökonomie; semi- und nichtparametrische Mikroökonometrie.
Biografie
Professor für Angewandte Ökonometrie und Politikevaluation. Ph.D. in Economics and Finance (2010) und anschliessend Assistenzprofessor an der Universität St.Gallen (bis 2014). Forschungsaufenthalte an der Harvard Universität (2011/2012) sowie and der Universität Sydney (2014 und 2019).
Forschungsinteressen: Daten-basierte Politikevaluation in der Arbeitsmarkt-, Gesundheits- und Bildungsökonomik; Weiterentwicklung von statistischen/ökonometrischen Methoden zur Messung von kausalen Effekten; Machinelles Lernen (machine learning) zur Vorhersage und Kausalanalyse.
Forschung und Publikationen
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Publikationen
70 Publikationen
Double Machine Learning for Sample Selection Models
Journal of Business & Economic Statistics (2023) | ArtikelThe Finite Sample Performance of Instrumental Variable-Based Estimators of the Local Average Treatment Effect When Controlling for Covariates
Computational Economics (2023) | ArtikelHow causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign
PLoS ONE (2023) | ArtikelWhen does gender occupational segregation start? An experimental evaluation of the effects of gender and parental occupation in the apprenticeship labor market
Economics of Education Review (2023) | ArtikelFlagging cartel participants with deep learning based on convolutional neural networks
International Journal of Industrial Organization (2023) | ArtikelHow residence permits affect the labor market attachment of foreign workers: Evidence from a migration lottery in Liechtenstein
European Economic Review (2023) | ArtikelEvaluating (weighted) dynamic treatment effects by double machine learning
Econometrics Journal (2022) | ArtikelDirect and Indirect Effects based on Changes-in-Changes
Journal of Business and Economic Statistics (2022) | ArtikelTransnational machine learning with screens for flagging bid-rigging cartels
Journal of the Royal Statistical Society. Series A: Statistics in Society (2022) | ArtikelBusiness analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets
Transportation Research Part B: Methodological (2022) | Artikel -
Working papers
7 Publikationen
Doubly Robust Estimation of Direct and Indirect Quantile Treatment Effects with Machine Learning
Hsu, Y.-C. and Huber, M. and Yen, Y.-M. , arXiv (2023) | SonstigesTreatment Effect Analysis for Pairs with Endogenous Treatment Takeup
Kormos, M. and Lieli, R.P. and Huber, M. , arXiv (2023) | SonstigesFrom Homemakers to Breadwinners? How Mandatory Kindergarten Affects Maternal Labour Market Outcomes
Gangl, S. and Huber, M. , SSRN (2022) | SonstigesTesting the identification of causal effects in observational data
Huber, M. and Kueck, J. , arXiv (2022) | SonstigesDetecting Grouped Local Average Treatment Effects and Selecting True Instruments
Apfel, N. and Farbmacher, H. and Groh, R. and Huber, M. and Langen, H. , arXiv (2022) | SonstigesA Wild Bootstrap Algorithm for Propensity Score Matching Estimators
Martin Huber, Hugo Bodory, Lorenzo Camponovo, Micheal Lechner, (2016) | Working PaperHow war affects political attitudes: Evidence from eastern Ukraine
Martin Huber, Svitlana Tyahlo, (2016) | Working Paper -
Buchbeiträge
2 Publikationen
An introduction to flexible methods for policy evaluation , in Handbook of Research Methods and Applications in Empirical Microeconomics
Huber, M. (2021) | BuchkapitelCorruption among Ukrainian businesses: Do firm size, industry and region matter?
Denisova-Schmidt, E. and Huber, M. and Prytula, Y. (State Capture, Political Risks and International Business: Cases from Black Sea Region Countries, 2016) | Buch -
Forschungsprojekte
Gender Occupational Segregation in the Swiss Apprenticeship Market: the Role of Employers in an Experimental Evaluation
Status: AbgeschlossenBeginn 01.03.2018 Ende 28.02.2019 Finanzierung SNF Projektblatt öffnen In this project, we seek to answer the question of whether chances of employment are identical for girls and boys applying for an apprenticeship position in Switzerland as measured by employers’ responses to applica-tions from equally qualified males and females. Differential performance in the labor market according to gender is well documented in the academic and popular press and a permanent fixture of everyday life. The causes – endogenous choice of women and families – or the result of (statistic, taste, or implicit) discrimination, or both – are far more difficult to pin down. This study aims to bring additional light to this important question by examining the earliest systematic labor market experience by individuals in developed economies: the process of applying for an apprenticeship position and the role employers might play in fostering occupational gender segregation. In order to identify whether employers take gender into consideration when evaluating employment applications, a correspondence test will be conducted. The study will select occupations that are commonly perceived as being male (or female), as well as other occupations that are viewed as more gender neutral, and will compare the success rate of both genders across those. The statistical comparison of success rates in invitations for interviews of males and females across these different occupation types will allow us to address the question of whether potential differential treatment is stereotypical in nature or otherwise systematic.