Differing Roles of Leisure and Productivity in GDP - A Machine Learning based comparative analysis of Germany and USA
This paper uses machine learning to model a country's GDP based on working hours and productivity, demonstrating that the differing relative importance of these two factors between Germany and the USA reflects their distinct social structures.
Abstract
More Like ThisThe GDP of a country is modelled as the relative interaction between two agents - working hours, reflecting the social choice of a population, and Total Factor Productivity, reflecting the collective investment in productivity enhancers. It is shown that a Random Forest model can accu- rately predict the GDP from these two factors. The differences in the choices made by Germany and USA are analysed though Gini importance, SHAP plots and partial dependency. It is shown that the differences in the social structure of the countries are reflected in the relative contribution of working hours and productivity to the GDP.