Mean scaling is a common assumption in the estimation of aggregate consumer elasticities—in particular, expenditure elasticities, but also (implicitly) compensated price elasticities. The assumption is that each household’s income changes in the same proportion as aggregate income. If...

While it is well known that demand elasticities calculated at the macro level will in general differ from those calculated at the micro level because of aggregation effects, there remain the questions of how large the effects are and how they vary with the degree of inequality in the income...

The paper explores the allocation of consumption expenditure by the older population among different categories of goods and services, and how expenditure patterns change with age within that population. Of particular interest is whether observed differences between pre-retirement and...

A theoretical model and a two-stage econometric estimation procedure are proposed for determining the parameters of industry-region-specific cost, input-demand, or other functions using grouped data. The model and estimation procedure are appropriate when only marginal totals or averages are...

A common assumption implicit in the literature on aggregation error in consumer demand theory is that micro relations are exact. Attention is thus focused solely on the errors introduced when one assumes that a micro relation holds at the macro level. We consider the Almost Ideal Demand System...

Policy analysis frequently requires estimates of aggregate (or mean) consumer elasticities. However, estimates are often made incorrectly, based on elasticity calculations at mean income. We provide in this paper an overall integrated analytical framework that encompasses these biases and...

We specify and estimate a model of the demand for electricity and natural gas in commercial buildings using data from the Commercial Building Energy Consumption Survey. Although not observed, declining rate schedules are approximated by a downward sloping function fitted to billing data for...