Welfare calculations in discrete choice settings: An exploratory analysis of error term correlation with finite populations

A difference in logsum terms (also known as inclusive values) is becoming a standard practice for anticipating the welfare impacts of transport policy when choice alternatives are discrete and behavior is (assumed to be) random-utility maximizing. However, this calculation is only an approximation when the population under study is finite. This paper examines the effect of error term correlations in such welfare analyses with finite samples, recognizing that individual preferences and unobserved attributes influencing choice are unlikely to change much, if at all, across scenarios or across alternatives. Such measures appear reasonably robust to deviations in assumptions of correlation. Nevertheless, we identify cases when the synthetic population samples need to be quite large for the average logsum to be realized. Another finding in these results is the substantial variation that emerges across synthetic populations, suggesting that policies that appear welfare-improving (when evaluated with average welfare formulations) may well be welfare-reducing (or vice versa) for a wide variety of actual, finite populations.