Welfare Calculations in Discrete Choice Settings: the Role of Error Term Correlation

A difference in logsum terms (also known as inclusive values) is becoming standard practice for anticipating the welfare impacts of transport policy when choice alternatives are discrete and behavior is (assumed to be) random-utility maximizing. This paper examines the effect of error term correlations in such analyses, recognizing that individual preferences and unobserved attributes influencing choice are unlikely to change much, if at all, across scenarios. Such measures appear reasonably robust to deviations in assumptions of correlation. What is most striking 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 populations. Another finding is that the synthetic population samples need to be very large for the mean to be realized (in finite populations).