Differing Roles of Leisure and Productivity in GDP - A Machine Learning based comparative analysis of Germany and USA
arXiv:2606.01234v1 Announce Type: cross Abstract: The 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 i
The paper leverages recent advancements in machine learning techniques, particularly interpretable AI models like SHAP, to offer new insights into economic factors.
This research provides a data-driven comparative analysis of economic drivers in major economies, offering valuable insights for policymakers and investors on factors influencing GDP.
The use of interpretable AI to model complex economic interactions offers a more nuanced understanding of how policy choices and societal values impact national economic output.
- · Economic forecasters
- · Policymakers in Germany and USA
- · AI/ML researchers in economics
- · Traditional economic modeling approaches based solely on linear regressions
More sophisticated economic models will emerge that incorporate interpretable AI techniques to understand complex interactions between social choices and productivity.
Governments may begin to use such AI models to simulate policy interventions and predict their impact on GDP more accurately, influencing resource allocation.
The perceived differences in societal values affecting GDP might lead to renewed debates on economic policies aimed at optimizing leisure vs. productivity trade-offs.
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