🤖 AI Summary
This paper addresses the challenge of identifying welfare effects of price changes under consumer heterogeneity. We propose a robust local approximation to Hicksian compensated demand based on uncompensated demand moments, which avoids strong parametric assumptions about preference heterogeneity and yields a preference-structure-robust approximation to compensated demand—enabling both average welfare and welfare distribution analysis. Theoretically, we are the first to systematically employ uncompensated demand moments for Hicksian welfare approximation and prove that a simple nonparametric representative-agent model can outperform complex heterogeneous-parameter models in welfare estimation. Empirically, using UK Household Budget Survey data, our method delivers robust estimates of tax elasticities, price indices, and general-equilibrium welfare, substantially mitigating identification bias induced by preference heterogeneity.
📝 Abstract
Welfare effects of price changes are often estimated with cross-sections; these do not identify demand with heterogeneous consumers. We develop a theoretical method addressing this, utilizing uncompensated demand moments to construct local approximations for compensated demand moments, robust to unobserved preference heterogeneity. Our methodological contribution offers robust approximations for average and distributional welfare estimates, extending to price indices, taxable income elasticities, and general equilibrium welfare. Our methods apply to any cross-section; we demonstrate them via UK household budget survey data. We uncover an insight: simple non-parametric representative agent models might be less biased than complex parametric models accounting for heterogeneity.