Happy times: Identification from ordered response data - Shuo Liu; Nick Netzer (University of Zurich, Department of Economics, 2020)
Surveys are an important tool in economics and in the social sciences more broadly. However, methods used to analyse ordinal survey data (e.g., ordered probit) rely on strong and often unjustified distributional assumptions. In this paper, we propose using survey response times to solve that problem. Our main identifying assumption is that individual response time is decreasing in the distance between the value of the latent variable and an indecision threshold. This assumption is supported by a large body of evidence on chronometric effects in psychology and neuroscience. We provide conditions under which the expected value of the latent variable (e.g., average happiness) can be compared across groups, even without making distributional assumptions. By applying it to an online survey experiment, we show how our method can be implemented in practice and gives rise to new insights.
Keywords: C14; D60; D91; I31; 330; Surveys; ordinal data; response times; non-parametric identification