Economics researchers often assume that random variables are drawn from distributions that are members of scale or location-scale families of distributions. This article generalizes earlier results in the literature on the bias in least squares estimates of multiplicative error models, and uses those results to construct a test of the scale and location-scale hypotheses. A Monte Carlo simulation shows that the test is powerful in large samples. The empirical relevance of these findings is illustrated with estimates of a supply function for U.S. wheat production. Implications for applied economics research are discussed.