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Random Forest Regression Residuals and the Regression Gini Index

In: Advanced Statistics for Health Research

Author

Listed:
  • Richard J. Butler
  • Matthew J. Butler
  • Barbara L. Wilson

Abstract

In this chapter, we pull together some of the strands of research in the last few chapters of Advanced Statistics by merging CPS employment surveys with CDC data on state-specific, month-by-month COVID-19 cases to estimate wage-offer equations since January 2020 for female nurses until September 2021, which we compare to the wage-offer distributions for males across all occupations. We estimate OLS models, 2SLS models, and Random Forest Residuals Regression (RFRR) analysis in our female–male analyses. We find that the machine learning nature of RFRR provides the most feasible results in terms of linear fit and identification of the wage-offer functions for our sample. Relative to all males working, gender inequality against nurses declined over the period. RFRR indicates that female nurse wage inequality fell by 10%.

Suggested Citation

  • Richard J. Butler & Matthew J. Butler & Barbara L. Wilson, 2023. "Random Forest Regression Residuals and the Regression Gini Index," World Scientific Book Chapters, in: Advanced Statistics for Health Research, chapter 20, pages 333-369, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789811262876_0020
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