Differing Roles of Leisure and Productivity in GDP - A Machine Learning based comparative analysis of Germany and USA

Achintya Ranjan; Uma Ranjan

Home/Archive/arXiv

arXiv · 2606.01234

Differing Roles of Leisure and Productivity in GDP - A Machine Learning based comparative analysis of Germany and USA

Achintya Ranjan·Uma Ranjan

View original source ↗Read source document ↗Export metadata ↓

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 in the relative contribution of working hours and productivity to the GDP.

Explore related subjects

econ.GN cs.CE cs.CV cs.GT cs.LG physics.soc-ph q-fin.EC

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Achintya Ranjan, Uma Ranjan. 2026-05-31. Differing Roles of Leisure and Productivity in GDP - A Machine Learning based comparative analysis of Germany and USA. https://arxiv.org/abs/2606.01234

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Access to Live AI Advice and Behavior Under Risk: An Incentivized Experiment

Generative AI has become an everyday advisor, and the systems people consult are live and interactive, not pre-scripted. We ask whether access to such a system changes behavior under risk. In an incentivized experiment (N = 158), participants made lottery choices with an optional decision aid presented as a conventional pre-written tool, a live one-shot AI, or a live interactive AI they could query, with information format held equivalent across conditions. Risk preferences are elicited via DOSE. We find no evidence that access to a live AI advisor changes risk aversion.

econ.GN↗

arXiv2026

Bricks or Cash? Externalities of Housing Upgrading in High-density Cities

We estimate housing externalities in a high-density city, exploiting the staggered rollout of Singapore's nationwide Main Upgrading Programme for public housing. Controlling for nonrandom neighborhood exposure, we find that upgrading raises treated buildings' prices by 11.5% upon completion and neighboring buildings' resale prices by about 2% within 500 meters, decaying to zero beyond. A model with distance-decaying externalities shows that in dense settings spillovers justify the distortions of in-kind provision; this advantage diminishes and reverses at lower densities. Administrative data on over 2 million residents show that upgrading disproportionately retains older incumbents, suggesting age-specific amenities as an underexplored externality channel.

econ.GN↗

arXiv2026

The Joneses Visit an Economics Lab

Existing literature offers persuasive evidence that individuals care about how their consumption compares to that of peers, and proposes a large variety of explanatory models. The present paper proposes a common framework for many of those models, and compares their ability to predict behavior in a laboratory experiment. We find evidence of Keeping up with the Joneses motivations but also find that conspicuous consumption is enhanced by Veblen motivations arising from peers' ability to observe one's own choice. Among the seven quasi-linear preference models we compare, our data are best explained by a model that contrasts envy and pride (upward vs downward comparisons) using a value function borrowed from Prospect Theory.

econ.GN↗