Using Post-Regularization Distribution Regression to Measure the Effects of a Minimum Wage on Hourly Wages, Hours Worked and Monthly Earnings

Martin Biewen and Pascal Erhardt ()
Additional contact information
Pascal Erhardt: University of Tübingen

No 16894, IZA Discussion Papers from IZA Network @ LISER

Abstract: We evaluate the distributional effects of a minimum wage introduction based on a data set with a moderate sample size but a large number of potential covariates. Therefore, the selection of relevant control variables at each distributional threshold is crucial to test hypotheses about the impact of the treatment. To this end, we use the post-double selection logistic distribution regression approach proposed by Belloni et al. (2018a), which allows for uniformly valid inference about the target coefficients of our low-dimensional treatment variables across the entire outcome distribution. Our empirical results show that the minimum wage crowded out hourly wages below the minimum threshold, benefitted monthly wages in the lower middle but not the lowest part of the distribution, and did not significantly affect the distribution of hours worked.

Keywords: wage structure; automatic specification search; double machine learning (search for similar items in EconPapers)
JEL-codes: C3 J31 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2024-03
New Economics Papers: this item is included in nep-big, nep-inv, nep-lma and nep-ltv
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://docs.iza.org/dp16894.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:iza:izadps:dp16894

Access Statistics for this paper

More papers in IZA Discussion Papers from IZA Network @ LISER Contact information at EDIRC.
Bibliographic data for series maintained by Mark Fallak ().