رامین خوچیانی

Understanding the impact of financialization on the economy is crucial for policymakers seeking to design strategies that enhance social welfare. This study examines the effect of financialization on economic welfare in Iran from 1990 to 2023, employing a threshold regression approach to account for nonlinear dynamics. The results reveal a threshold level of institutional quality at 57%. Across both, i.e., low and high institutional quality regimes, financialization exerts a negative and significant influence on economic welfare. However, once institutional quality surpasses the threshold, the adverse impact of financialization intensifies markedly. Findings highlight the paradoxical role of institutional quality, showing that greater financialization consistently undermines welfare in Iran, with stronger institutions amplifying rather than mitigating its negative effects. It means that in environments with higher institutional quality, advanced financial instruments and capital markets develop; however, access to financial development is usually asymmetrical. Consequently, wealthy individuals and large corporations benefit the most, while low-income households receive minimal benefits and may even suffer from asset inflation or consumer debt. Thus, strong institutions do not necessarily prioritize public welfare. Policymakers may regulate to develop financial markets in a way that prioritizes the financial sector’s profitability over social interests. This mechanism can lead to financial sector growth occurring faster than the real economy’s capacity, ultimately undermining welfare.

The recent surge of artificial-intelligence (AI) activity has sparked concern that large-scale model training, cloud inference, and data-centre expansion could accelerate national energy demand. We marshal a 21-year annual panel for the United States (2004–2024) that couples multiple AI proxies—technology-stock valuations and a ChatGPT-era dummy—with four aggregate energy series (fossil fuels, nuclear, renewables, total primary energy). A five-stage empirical protocol implemented in Python combines Engle–Granger cointegration testing, higher-order ADF stationarity checks, linear and nonlinear dependence diagnostics (Pearson, Dynamic Time Warping, mutual information), multicollinearity screening (variance-inflation factors), and out-of-sample forecasting with linear regression, decision trees, random forests, and support-vector machines augmented by SHAP explainability. Across all tests we find no evidence that AI developments imprint on national energy use: AI variables cointegrate only with one another, their short-run correlations with energy vanish once trends are removed, their mutual-information scores remain near zero, and their inclusion never improves predictive accuracy beyond a parsimonious macro model driven by GDP, inflation, and population. SHAP rankings confirm that AI features carry negligible explanatory weight relative to conventional fundamentals. We conclude that, to date, AI’s macro-level energy footprint is statistically invisible—any electricity it consumes is either too small or offset by efficiency gains within the wider economy. Policymakers should therefore continue to anchor long-range energy scenarios to established economic drivers while monitoring localised data-centre hotspots that national aggregates obscure.