IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v243y2024ics0165176524004087.html
   My bibliography  Save this article

Enriching administrative data using survey data and machine learning techniques

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176524004087
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2024.111924?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecolet:v:243:y:2024:i:c:s0165176524004087. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.