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.
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Sivasathivel Kandasamy
Independent Researcher
GDP per capita is the default lens through which governibng bodies track the economic prosperity and consequences of economic events , yet it is blind to two first-order determinants of lived prosperity: income/wealth distribution and inflation impact. Inequality-adjusted income measures are themselves not new but What is missing from the macroeconomic monitoring toolkit specifically is not a welfare concept but an operational monitoring trigger: a statistic minimal enough to compute annually from public data, transparent enough to audit without modelling assumptions, and normalised so that year-on-year, cross-country change ? the quantity a regulator needs to act on? is legible. We assemble such an instrument, the Gini- Adjusted GDP per Capita Index (GAGI): a reproducible, publicly computable formulation that rescales each country's GDP per capita by its inequality-adjustment factor (1-G) and its price level, normalised to a 2010 baseline. GAGI is a general-purpose welfare index, not inherently specific to AI automation, applicable wherever welfare-adjusted prosperity needs tracking. Applying GAGI to the G7 economies over 2010-2026, we show that welfare-adjusted prosperity has diverged persistently and increasingly from headline GDP growth, that the divergence widens sharply after 2022, temporally coincident with, though not, on this evidence alone, demonstrated to be caused by the after effects of COVID and the acceleration of generative-AI deployment. We argue that GAGI is a necessary complement to GDP-based monitoring: any macroeconomic monitoring instrument that tracks only aggregate output will systematically miss the distributional harm that automation can cause even while reported growth remains strong.