John J Conlon

I'm a postdoctoral fellow in the economics department at Stanford for the 2023-2024 academic year. I'll then join Carnegie Mellon's Department of Social and Decision Sciences as an Assistant Professor of Economics in 2024.

My research interests are primarily in behavioral, experimental, and labor economics. You can find my CV here and learn about my research below.

Working Papers

Using both large-scale nationally representative data and surveys administered among undergraduates at the Ohio State University, we document that US freshmen hold systematic misperceptions about the relationship between college majors and occupations. Students stereotype fields of study, greatly exaggerating the likelihood that majors lead to their distinctive jobs (e.g., counselor for psychology, journalist for journalism). In a field experiment, we find that reducing stereotyping has significant effects on students' intentions about what to study as well as the classes and majors they actually choose. Finally, we present a model of belief formation in which stereotyping arises as a product of associative recall. The model makes additional predictions—which new survey evidence broadly confirms—both about average beliefs and how heterogeneity in beliefs should systematically depend on the careers and majors of people students know personally.

I show experimentally that information persuades not only by shifting beliefs but also by redirecting attention. Participants repeatedly choose whether to "purchase" multi-attribute "goods." Randomly telling participants about the value of a desirable attribute—even when that information is already known and transparently redundant—greatly increases attention to the attribute it describes and distracts from other attributes. It also boosts average demand for the good, implying that inattention takes the form of neglect rather than shrinkage toward a prior. These forces combine to produce paradoxical responses to correcting beliefs: reducing overoptimism about an attribute nonetheless boosts demand for the good.


Learning in the Household.  With Malavika Mani, Gautam Rao, Matthew Ridley, and Frank Schilbach.

Do spouses pool useful information and learn from each other when they have incentives to do so? In an experiment with married couples in India, we vary whether individuals discover information themselves or must instead learn via a discussion about what their spouse discovered. Women treat their own and their husband’s information the same. In contrast, men respond half as much to information discovered by their wife, even when it is perfectly communicated. When paired with strangers, both men and women heavily discount their partner’s information relative to their own. We thus provide evidence of a gender difference in social learning (only) in the household.

Labor Market Search With Imperfect Information and Learning.  With Laura Pilossoph, Matthew Wiswall, and Basit Zafar. NBER Working Paper No. 24988.

We investigate the role of information frictions in the US labor market using a new nationally representative panel dataset on individuals' labor market expectations and realizations. We find that expectations about future job offers are, on average, highly predictive of actual outcomes. Despite their predictive power, however, deviations of ex post realizations from ex ante expectations are often sizable. The panel aspect of the data allows us to study how individuals update their labor market expectations in response to such shocks. We find a strong response: an individual who receives a job offer one dollar above her expectation subsequently adjusts her expectations upward by $0.47. The updating patterns we document are, on the whole, inconsistent with Bayesian updating. We embed the empirical evidence on expectations and learning into a model of search on- and off- the job with learning, and show that it is far better able to fit the data on reservation wages relative to a model that assumes complete information. The estimated model indicates that workers would have lower employment transition responses to changes in the value of unemployment through higher unemployment benefits than in a complete information model, suggesting that assuming workers have complete information can bias estimates of the predictions of government interventions. We use the framework to gauge the welfare costs of information frictions which arise because individuals make uninformed job acceptance decisions and find that the costs due to information frictions are sizable, but are largely mitigated by the presence of learning.

How People Use Statistics. With Pedro Bordalo, Nicola Gennaioli, Spencer Kwon, and Andrei Shleifer. Revise and Resubmit, Review of Economic Studies.

We document two new facts about the distributions of answers in famous statistical problems: they are i) multi-modal and ii) unstable with respect to irrelevant changes in the problem. We offer a model in which, when solving a problem, people represent each hypothesis by attending “bottom up” to its salient features while neglecting other, potentially more relevant, ones. Only the statistics associated with salient features are used, others are neglected. The model unifies biases in judgments about i.i.d. draws, such as the Gambler’s Fallacy and insensitivity to sample size, with biases in inference such as under- and overreaction and insensitivity to the weight of evidence. The model makes predictions about how changes in the salience of specific features should jointly shape the prevalence of these biases and measured attention to features, but also create entirely new biases. We test and confirm these predictions experimentally. Bottom-up attention to features emerges as a unifying framework for biases conventionally explained using a variety of stable heuristics or distortions of the Bayes rule.

Not Learning from Others.  With Malavika Mani, Gautam Rao, Matthew Ridley, and Frank Schilbach. Revise and Resubmit, Econometrica.

We provide evidence of a powerful barrier to social learning: people are much less sensitive to information others discover compared to equally-relevant information they discover themselves. In a series of incentivized lab experiments, we ask participants to guess the color composition of balls in an urn after drawing balls with replacement. Participants' guesses are substantially less sensitive to draws made by another player compared to draws made themselves. This result holds when others' signals must be learned through discussion, when they are perfectly communicated by the experimenter, and even when participants see their teammate drawing balls from the urn with their own eyes. We find a crucial role for  taking some action to generate one's `own' information, and rule out distrust, confusion, errors in probabilistic thinking, up-front inattention and imperfect recall as channels. 

Published Papers

Liquidity For Teachers: Evidence from Teach For America and LinkedIn.  With Lucas Coffman, Clayton Featherstone, Judd Kessler, and Jessica Mixon. Economics of Education Review, 2023.

There are teacher shortages in the U.S. and around the world. In a three-year field experiment with a large teacher placement program, Teach For America (TFA), Coffman, Conlon, Featherstone, and Kessler (2019) finds that providing upfront liquidity to prospective teachers in financial need dramatically increases the rate at which they start teaching through TFA. In this paper, we combine TFA administrative data, survey data, and publicly available data (e.g., LinkedIn profiles) to extend those results. We follow individuals for a few years post treatment and find that providing upfront liquidity not only increases the rate that financially constrained individuals join TFA but also increases the rate that they complete the full two years of teaching. Further, providing liquidity to those who need it increases their likelihood of being teachers at all—not just through TFA—through at least two years.


Memory and Probability.  With Pedro Bordalo, Nicola Gennaioli, Spencer Y. Kwon, and Andrei Shleifer. The Quarterly Journal of Economics. 2023.

People often estimate probabilities, such as the likelihood that an insurable risk will materialize or that an Irish person has red hair, by retrieving experiences from memory. We present a model of this process based on two established regularities of selective recall: similarity and interference. The model accounts for and reconciles a variety of conflicting empirical findings, such as overestimation of unlikely events when these are cued vs. neglect of non-cued ones, the availability heuristic, the representativeness heuristic, as well as over vs. underreaction to information in different situations. The model makes new predictions on how the content of a hypothesis (not just its objective probability) affects probability assessments by shaping the ease of recall. We experimentally evaluate these predictions and find strong experimental support. 

I test, in a field experiment at a flagship state university in the US, whether providing college students salary information can affect their choices of major and classes. I find that undergraduates are substantially misinformed about mean salaries by major. On average, students in my sample underestimate mean salaries, but there is also large heterogeneity in beliefs across individuals. I also find that providing information to correct these errors has a large impact on students' choices; students in the treatment group were nine percentage points (16%) more likely to major in a field about which they received information.

Liquidity affects Job Choice: Evidence from Teach for America. With Lucas C. Coffman, Clayton R. Featherstone, and Judd B. Kessler. The Quarterly Journal of Economics. 2019.

Can access to a few hundred dollars of liquidity affect the career choice of a recent college graduate? In a three-year field experiment with Teach For America (TFA), a prestigious teacher placement program, we randomly increase the financial packages offered to nearly 7,300 potential teachers who requested support for the transition into teaching. The first two years of the experiment reveal that while most applicants do not respond to a marginal $600 of grants or loans, those in the worst financial position respond by joining TFA at higher rates. We continue the experiment into the third year and self-replicate our results. For the highest need applicants, an extra $600 in loans, $600 in grants, and $1,200 in grants increase the likelihood of joining TFA by 12.2, 11.4, and 17.1 percentage points (or 20.0%, 18.7%, and 28.1%), respectively. Additional grant and loan dollars are equally effective, suggesting a liquidity mechanism. A follow-up survey bolsters the liquidity story and also shows that those pulled into teaching would have otherwise worked in private sector firms.

Blog Posts and Older Papers

Information Cascades with Informative Ratings: An Experimental Test. With Paul J. Healy and Yeochang Yoon.  Permanent working paper.

We study behavior in an information cascades setting where previous buyers of the product leave noisy but informative ratings of the product. Although this increases the amount of public information available, Yoon (2015, working paper) shows that ratings can actually increase the frequency of cascades in which buyers do not purchase even though the product is of high quality. This occurs because non-buyers do not leave ratings. Although we find some evidence roughly in line with the theory, those results are swamped by a strong tendency for subjects to purchase even when public information suggests they should not.

Political Polarization in Consumer Expectations. With Olivier Armantier and Wilbert van der Klaauw. Liberty Street Economics Blog Post. December 2017.

Introducing the SCE Labor Market Survey.  With Gizem Kosar, Giorgio Topa, and Basit Zafar.  Liberty Street Economics Blog Post.  August 2017. 

Measuring Americans' Expectations Following the 2016 Election.  With Basit Zafar.  Liberty Street Economics Blog Post.  January 2017.

A Spatial Analogue of Shoemaker's Global Freeze Argument.  The Philosophical Quarterly. 2016.

I argue that there could be observational evidence for absolute motion. My argument employs a spatial analogue of Shoemaker's classic ‘global freeze’ thought experiment, wherein he imagines a world whose inhabitants would seem to have strong evidence that a period of time without change had occurred. The inhabitants of my own imagined world, I claim, would have strong evidence that everything in the universe had moved the same distance in the same direction and, thus, strong evidence for absolute motion. This conclusion seems to pose a problem for spatial relationists insofar as they cannot account for absolute motion.