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Enriching administrative data using survey data and machine learning techniques

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  • Kunaschk, Max

Abstract

I propose an approach to enrich administrative data with information only available in survey data using machine learning techniques. To illustrate the approach, I replicate a prominent study that used survey data to analyze the federal minimum wage introduction in Germany. In contrast to the original study, I use the universe of German establishments rather than the limited number of establishments that participated in the survey. As the administrative data do not contain information on whether establishments were treated by the minimum wage, I use a random forest classifier, trained on survey data, to predict the treatment status of establishments. The results obtained using the administrative data are qualitatively similar to the results obtained using the survey data. Beyond replication of previous research, this approach broadens the research potential of administrative data, enabling researchers to explore more detailed research questions at scale.

Suggested Citation

  • Kunaschk, Max, 2024. "Enriching administrative data using survey data and machine learning techniques," Economics Letters, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:ecolet:v:243:y:2024:i:c:s0165176524004087
    DOI: 10.1016/j.econlet.2024.111924
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    References listed on IDEAS

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    1. Christian Dustmann & Attila Lindner & Uta Schönberg & Matthias Umkehrer & Philipp vom Berge, 2022. "Reallocation Effects of the Minimum Wage," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 137(1), pages 267-328.
    2. Doruk Cengiz & Arindrajit Dube & Attila Lindner & David Zentler-Munro, 2022. "Seeing beyond the Trees: Using Machine Learning to Estimate the Impact of Minimum Wages on Labor Market Outcomes," Journal of Labor Economics, University of Chicago Press, vol. 40(S1), pages 203-247.
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    More about this item

    Keywords

    Machine learning; Administrative data; Survey data; Minimum wage;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J38 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Public Policy

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