Measuring the Value That Profit Buys: A Profit-Efficiency Approach to Macroeconomic Performance

Abstract This paper develops macroeconomic profit efficiency: the condition where aggregate profit is sufficient to elicit investment and innovation, but no higher. It reframes profit as a necessary incentive cost rather than a monotonic indicator of welfare. The central claim is that welfare is non-monotonic in profit extraction, with an interior optimum (P*). Below P*, the economy is under-incentivised; above it, additional profit reflects systemic rent extracted through market power, financialisation, and cost externalisation. The paper integrates these insights into the Macroeconomic Profit Efficiency Index (MPEI), defined as National Retained Value per unit of Profit Impost, scaled by a Participation Floor coefficient. The framework is illustrated through a worked US healthcare application, supported by an accompanying reproducibility package, and a lighter Japanese consumer-electronics contrast still under development. In the US healthcare illustration, observed performance is estimated at 41.9 per cent of the framework's floor-corrected benchmark, showing how high profit impost can coexist with weak retained-value performance. These illustrations show how the framework can be applied without treating the companion manuscripts as independently published evidence. The MPEI serves as a diagnostic framework to distinguish productive profit from extractive rent in economic analysis and policy stewardship. Keywords: Macroeconomic Profit Efficiency; rents; financialisation; welfare measurement; institutional stewardship JEL classifications: B52; L12; D02 AI Usage Disclosure: Generative AI tools, including Anthropic Claude and OpenAI ChatGPT, were used as drafting aids for language editing, structural review, code assistance, figure-formatting support and consistency checking. The conceptual framework, source selection, source verification, empirical interpretation, final calculations, conclusions and responsibility for the work are the author's. AI tools were not used as authors, and the author has reviewed the manuscript, references, numerical claims and accompanying reproducibility materials before submission.