The aim of this paper is to assess the importance of using micro-level data in the econometric analysis of consumer demand. To do this we utilize a time series of repeated cross sections covering some 4,000 households in each of 15 years. Employing a number of different aggregation procedures, we conclude that aggregate data alone are unlikely to produce reliable estimates of structural price and income coefficients. However, once certain "aggregation factors" as well as trend and seasonal components are included, an aggregate model is not necessarily outperformed across all demand equations in terms of forecasting ability.

This paper establishes that new inputs increase technical efficiency levels for U.S. manufacturing. Over the period 1950–1998, intermediate inputs exhibited higher rates of efficiency growth than labor and capital. Efficiency-adjusted productivity growth annually averaged 0.4 percentage points above measured growth. The gap between efficiency-adjusted and measured productivity growth arises from aggregating inputs using observed, and not efficiency-adjusted, cost share weights in the calculation of measured growth. Specifically, the decline in efficiency-adjusted material cost shares, compared to the measured shares, coupled with the comparatively high material input growth rate, was the main source of the productivity gap.