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Machine Labor

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  • Joshua D. Angrist
  • Brigham Frandsen

Abstract

The utility of machine learning (ML) for regression-based causal inference is illustrated by using lasso to select control variables for estimates of college characteristics’ wage effects. Post-double-selection lasso offers a path to data-driven sensitivity analysis. ML also seems useful for an instrumental variables (IV) first stage, since two-stage least squares (2SLS) bias reflects overfitting. While ML-based instrument selection can improve on 2SLS, split-sample IV and limited information maximum likelihood do better. Finally, we use ML to choose IV controls. Here, ML creates artificial exclusion restrictions, generating spurious findings. On balance, ML seems ill-suited to IV applications in labor economics.

Suggested Citation

  • Joshua D. Angrist & Brigham Frandsen, 2022. "Machine Labor," Journal of Labor Economics, University of Chicago Press, vol. 40(S1), pages 97-140.
    • Handle: RePEc:ucp:jlabec:doi:10.1086/717933
    DOI: 10.1086/717933
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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General
    • J08 - Labor and Demographic Economics - - General - - - Labor Economics Policies

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