🤖 AI Summary
This paper addresses the inefficiency and fragility of conventional stated-preference experiments for probabilistic choices, where ex ante expected returns and willingness-to-pay (WTP) estimates rely on multiple choice rounds and strong parametric assumptions—leading to lengthy surveys and low feasibility for ex ante policy evaluation. We propose a nonparametric identification method requiring at most two probabilistic choices per respondent. It imposes no functional-form assumptions on utility and, for the first time, fully identifies both the population distribution of ex ante expected returns and WTP for structured preference objects (e.g., multidimensional job attributes). Theoretical foundations integrate nonparametric identification theory with structured discrete choice modeling. Applied to elite student employment preferences in Côte d’Ivoire, the method robustly identifies a significant upward effect of public-sector jobs on private-sector hiring costs—demonstrating both empirical validity and direct policy relevance.
📝 Abstract
One of the exciting developments in the stated preference literature is the use of probabilistic stated preference experiments to estimate semi-parametric population distributions of ex ante returns and willingness-to-pay (WTP) for a choice attribute. This relies on eliciting several choices per individual, and estimating separate demand functions, at the cost of possibly long survey instruments. This paper shows that the distributions of interest can be recovered from at most two stated choices, without requiring ad-hoc parametric assumptions. Hence, it allows for significantly shorter survey instruments. The paper also shows that eliciting probabilistic stated choices allows identifying much richer objects than we have done so far, and therefore, provides better tools for ex ante policy evaluation. Finally, it showcases the feasibility and relevance of the results by studying the preference of high ability students in Cote d'Ivoire for public sector jobs exploiting a unique survey on this population. Our analysis supports the claim that public sector jobs might significantly increase the cost of hiring elite students for the private sector.