We present a unified treatment of how welfare responds to changes in budget sets or technologies with taste shocks and nonhomothetic preferences. We propose a welfare metric that ranks production possibility frontiers that differs from one that ranks budget sets and characterize it using a general equilibrium generalization of Hicksian demand. This extends Hulten’s theorem, the basis for constructing aggregate quantity indices, to environments with nonhomothetic and unstable preferences. We illustrate our results using both long- and short-run applications. In the long run, we show that if structural transformation is caused by income effects or changes in tastes, rather than substitution effects, then Baumol’s cost disease is twice as important for our preferred measure of welfare. In the short run, we show that standard chain-weighted deflators understate welfare-relevant inflation for current tastes. Finally, using the COVID-19 recession, we illustrate that chain-weighted real consumption and real GDP are unreliable metrics for measuring welfare or production when there are taste shocks.

The money metric utility function is an essential tool for calculating welfare-relevant growth and inflation. We show how to recover it from repeated cross-sectional data without making parametric assumptions about preferences. We do this by solving the following recursive problem. Given compensated demand, we construct money metric utility by integration. Given money metric utility, we construct compensated demand by matching households over time whose money metric utility value is the same. We illustrate our method using household consumption survey data from the United Kingdom from 1974 to 2017 and find that real consumption calculated using official aggregate inflation statistics overstates money metric utility in 1974 pounds for the poorest households by around 0.5% a year and understates it by around a third of a percentage point per year for the richest households. We extend our method to allow for missing or mismeasured prices, assuming preferences are separable between goods with well-measured prices and the rest. We discuss how our results change if the prices of some service sectors are mismeasured.

This paper develops a general theory of aggregation in inefficient economies. We provide nonparametric formulas for aggregating microeconomic shocks in economies with distortions such as taxes, markups, frictions to resource reallocation, financial frictions, and nominal rigidities. We allow for arbitrary elasticities of substitution, returns to scale, factor mobility, and input-output network linkages. We show how to separately measure changes in technical and allocative efficiency. We also show how to compute the social cost of distortions. We pursue applications focusing on firm-level markups in the United States. We find that improvement in allocative efficiency, due to the reallocation over time of market share to high-markup firms, accounts for about half of aggregate TFP growth over the period 1997–2015. We also find that eliminating the misallocation resulting from the large and dispersed markups estimated in the data would raise aggregate TFP by about 15%, increasing the economy-wide cost of monopoly distortions by two orders of magnitude compared with the famous 0.1% estimate by Harberger (1954). These exact numbers should be interpreted with care because the data are imperfect and require substantial imputation.