Recovering Ex Ante Returns and Preferences for Occupations Using Subjective Expectations Data

Abstract

We show that data on subjective expectations, especially on outcomes from counterfactual choices and choice probabilities, are a powerful tool in recovering ex ante treatment effects as well as preferences for different treatments. In this paper we focus on the choice of occupation, and use elicited beliefs from a sample of male undergraduates at Duke University. By asking individuals about potential earnings associated with counterfactual choices of college majors and occupations, we can recover the distribution of the ex ante monetary returns to particular occupations, and how these returns vary across majors. We then propose a model of occupational choice which allows us to link subjective data on earnings and choice probabilities with the non-pecuniary preferences for each occupation. We find large differences in expected earnings across occupations, and substantial heterogeneity across individuals in the corresponding ex ante returns. However, while sorting across occupations is partly driven by the ex ante monetary returns, non-monetary factors play a key role in this decision. Finally, our results point to the existence of sizable complementarities between college major and occupations, both in terms of earnings and non-monetary benefits.

Admissions Constraints and the Decision to Delay University*

“…Moreover, evidence of comparative advantage across fields of study implies that a student who might have above-average earnings in one field could earn below the average in a different field (Kirkebøen et al, 2016). Even if the wait to be admitted to a particular program does not lead to higher earnings net of any costs, other studies have pointed toward substantial non-monetary returns that vary across occupations, which are linked to different fields of study (Arcidiacono, 2004;Arcidiacono et al, 2014). Taken as a whole, this literature suggests that there might be incentives for individuals to delay if it means they can enter a preferred field of study.…”

Rationalizing Rational Expectations? Tests and Deviations

“…We focus on situations where the researcher has access to elicited beliefs about mean outcomes, as opposed to probabilistic expectations about the full distribution of outcomes. The type of subjective expectations data we consider in the paper has been collected in various contexts, and used in a number of prior studies (see, among others, Delavande, 2008;Zafar, 2011b;Arcidiacono, Hotz and Kang, 2012;Arcidiacono, Hotz, Maurel and Romano, 2014;Hoffman and Burks, 2020).…”

“…An alternative way of evaluating how critical the rational expectations hypothesis is for a given model would be to relax it and estimate it using instead elicited beliefs about future outcomes both on and off the agents' actual choice paths. However, the data requirements are formidable, and, as a consequence, this approach has only been pursued in a handful of studies (see, e.g., Arcidiacono et al, 2014;Stinebrickner and Stinebrickner, 2014a,b;Zafar, 2015, 2018). Our approach can be used much more broadly,…”

“…In particular, this test can be used in a data combination context, where individual realizations and subjective beliefs are observed in two different datasets that cannot be matched. Such situations are common in practice (see, e.g., Delavande, 2008;Arcidiacono, Hotz and Kang, 2012;Arcidiacono, Hotz, Maurel and Romano, 2014;Stinebrickner and Stinebrickner, 2014a;Kuchler and Zafar, 2019;Kapor, Neilson and Zimmerman, 2018). Besides, even in surveys for which an explicit aim is to measure subjective expectations, such as the Michigan Survey of Consumers or the Survey of Consumer Expectations of the New York Fed, expectations and realizations can typically only be matched for a subset of the respondents.…”

“…On the empirical side, we contribute to a rapidly growing literature on the use of subjective expectations data in economics (see, e.g., Manski, 2004;Delavande, 2008;Van der Klaauw and Wolpin, 2008;Van der Klaauw, 2012;Arcidiacono, Hotz, Maurel and Romano, 2014;de Paula, Shapira and Todd, 2014;Stinebrickner and Stinebrickner, 2014b;Wiswall and Zafar, 2015). In this paper, we show how to incorporate all of the relevant information from subjective beliefs combined with realized data to test for, and measure deviations from rational expectations.…”