Identifying and Estimating Perceived Returns to Binary Investments

TL;DR

This paper introduces a method to estimate agents' perceived returns to binary investments using cross-sectional data with binary choices and prices, accounting for imperfect price knowledge and leveraging price variation known to agents.

Contribution

It proposes a novel identification strategy that relies on an identity relating profits, revenues, and costs, enabling consistent estimation without eliciting agent beliefs.

Findings

Method successfully estimates perceived returns in simulations.

Using price variation known to agents improves estimation accuracy.

Adjustments to standard binary choice estimators are effective for this purpose.

Abstract

I describe a method for estimating agents' perceived returns to investments that relies on cross-sectional data containing binary choices and prices, where prices may be imperfectly known to agents. This method identifies the scale of perceived returns by assuming agent knowledge of an identity that relates profits, revenues, and costs rather than by eliciting or assuming agent beliefs about structural parameters that are estimated by researchers. With this assumption, modest adjustments to standard binary choice estimators enable consistent estimation of perceived returns when using price instruments that are uncorrelated with unobserved determinants of agents' price misperceptions as well as other unobserved determinants of their perceived returns. I demonstrate the method, and the importance of using price variation that is known to agents, in a series of data simulations.