Determinants of labor productivity in the USA

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This study aims to examine whether the labor productivity of the US population directly depends on public or private insurance coverage of people, employment level, life expectancies, spending on the public health system as a percentage of GDP, and spending on the public health system in natural terms.
Empirical testing was carried out on the USА statistical data for 1987–2021 using a regression model with the fitting procedure backward stepwise selection (in Statgraphics software), and a multivariate adaptive regression spline MARS (using Salford Predictive Modeler software). The research hypothesis was confirmed for only two indicators: life expectancies and spending on the public health system in natural terms. Their impact on labor productivity appeared to be directly proportional. As an indicator, spending on the public health system has a greater impact on the change in productivity (0.0058%), whereas life expectancy has a lesser effect (0.0047%). The study showed that the MARS model provides more objective and accurate results compared to the regression model with the fitting procedure – backward stepwise selection. This conclusion is based on a comparison of real data modeled by both methods. The study proved that labor productivity in the USA grew yearly from 1987 to 2021 (the constant term in the MARS model’s regression equation is +0.48428). To calculate the specific values of labor productivity for each year, a model was developed depending on the optimal basic functions (automatically generated by the MARS model depending on the current values of life expectancies and spending on the public health system in natural terms).

Acknowledgment
This study is funded by Department of Applied Social Sciences of the Faculty of Organization and Management of the Silesian University of Technology for the year 2023 (grant number 13/020/BK_23/0081).

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    • Figure 1. MARS regression splines
    • Table 1. Characteristics of the regression model (5)
    • Table 2. Analysis of variance
    • Table 3. Evaluation of the basis functions
    • Table 4. Basis functions
    • Table 5. Statistical characteristics of the optimal MARS model
    • Table 6. Comparison table of real values of labor productivity and predicted values by regression (5) and MARS (6)
    • Table A1. Standardized indicators of the study
    • Conceptualization
      Aleksandra Kuzior, Vitaliia Koibichuk, Serhii Drozd, Dymytrii Grytsyshen, Tetiana Vasylieva
    • Funding acquisition
      Aleksandra Kuzior
    • Methodology
      Aleksandra Kuzior, Vitaliia Koibichuk, Serhii Drozd, Dymytrii Grytsyshen, Tetiana Vasylieva
    • Project administration
      Aleksandra Kuzior, Tetiana Vasylieva
    • Supervision
      Aleksandra Kuzior, Tetiana Vasylieva
    • Writing – original draft
      Aleksandra Kuzior, Vitaliia Koibichuk, Serhii Drozd, Dymytrii Grytsyshen, Tetiana Vasylieva
    • Writing – review & editing
      Aleksandra Kuzior, Vitaliia Koibichuk, Serhii Drozd, Dymytrii Grytsyshen, Tetiana Vasylieva
    • Data curation
      Vitaliia Koibichuk, Serhii Drozd
    • Investigation
      Vitaliia Koibichuk
    • Resources
      Vitaliia Koibichuk, Serhii Drozd
    • Software
      Vitaliia Koibichuk, Serhii Drozd
    • Validation
      Vitaliia Koibichuk, Dymytrii Grytsyshen, Tetiana Vasylieva
    • Formal Analysis
      Serhii Drozd, Tetiana Vasylieva
    • Visualization
      Serhii Drozd