用机器学习分析德美两国工作时长与生产率对GDP的影响差异。
Differing Roles of Leisure and Productivity in GDP - A Machine Learning based comparative analysis of Germany and USA
- 构建随机森林模型，以工作时长和生产率预测GDP。
- 德国更依赖生产率提升，美国更依赖工作时长增加。
- 适合关注经济结构差异与机器学习应用的研究者。
一国的GDP被建模为两个因素之间的相对互动：反映社会选择的工作时长，以及反映集体投资于生产率提升的全要素生产率。研究表明，随机森林模型能够准确预测GDP。通过基尼重要性、SHAP图和部分依赖图，分析了德国与美国在社会选择上的差异。结果显示，两国的社会结构差异体现在工作时长与生产率对GDP贡献的相对权重上。德国更依赖生产率增长，而美国则更多依赖工作时长的增加。
原文摘要 · Abstract (English)
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 in the relative contribution of working hours and productivity to the GDP.
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