Charlie Rafkin

Job Market Candidate

Research Fields

Public Economics, Behavioral Economics, Labor Economics

Contact Information

Phone 3125338205
Email Address crafkin@mit.edu

Job Market Paper

"Eviction as Bargaining Failure: Misperceptions and Hostility in the Rental Housing Market" (with Evan Soltas), Fall 2023. 

Abstract: Formal eviction from rental housing is widespread and costly, spurring interest in anti-eviction policies. The desirability of policy intervention depends on whether evictions come from efficient non-bargaining or inefficient bargaining failures. We test for two causes of bargaining failure — hostile social preferences and misperceptions — by conducting lab-in-the-field experiments in Memphis, Tennessee with 1,808 tenants and 373 landlords facing eviction. We find that 25–39% of relationships engage in dominated hostile behaviors in real-stakes Dictator Games. Both parties misperceive court or bargaining payoffs in ways that undermine bargaining. Motivated by the possibility of inefficient eviction, we evaluate an emergency rental assistance program in Memphis using administrative data. Event-study estimates suggest the program had small effects on eviction. Combining the event-study and lab-in-the-field results, we estimate a bargaining model and find that about 20% of evictions are inefficient, of which about 70% are caused by hostility. These forces affect eviction policy: Perverse selection on altruism partially explains the program’s small treatment effects.

Working Papers

“Self-Targeting in U.S. Transfer Programs” (with Adam Solomon and Evan Soltas), June 2023.

Abstract: Transfer receipt is voluntary and costly, generating “self-targeting” through selective take-up among the eligible. How does self-targeting select on need, and what are its policy implications? We show self-targeting is advantageous in eight U.S. transfers: On average, recipients have lower consumption and lifetime incomes than eligible nonrecipients with similar current incomes. Due to self-targeting, these transfers provide 50 to 75 percent more to the consumption-poorest and lifetime-poorest than would automatic transfers that are distributionally equivalent by income. Self-targeting makes automatic transfers undesirable: We estimate the social benefits of self-targeting are approximately six cents per transfer dollar, generally exceeding the social costs of ordeals.

“The Welfare Effects of Eligibility Expansions: Theory and Evidence from SNAP” (with Jenna Anders), November 2022. Conditionally accepted at American Economic Journal: Economic Policy.

Abstract: We study the U.S. rollout of eligibility expansions in the Supplemental Nutrition Assistance Program. Using administrative data from the U.S. Department of Agriculture, we show that expanding eligibility raises enrollment among the inframarginal (always-eligible) population. Using an online experiment and an administrative survey, we find evidence that information frictions, rather than stigma, drive the new take-up. To interpret our findings, we develop a general model of the optimal eligibility threshold for welfare programs with incomplete take-up. Given our empirical results and certain modeling assumptions, the SNAP eligibility threshold is lower than optimal.


“Intergenerational Mobility in India: New Measures and Estimates Across Time and Social Groups” (with Sam Asher and Paul Novosad). Forthcoming at American Economic Journal: Applied Economics.

Abstract: We study intergenerational mobility in India. We propose a new measure of upward mobility: the expected education rank of a child born to parents in the bottom half of the education distribution. This measure works well under data constraints common in developing countries and historical contexts. Intergenerational mobility in India has been constant and low since before liberalization. Among sons, we observe rising mobility for Scheduled Castes and declining mobility among Muslims. Daughters’ intergenerational mobility is lower than sons’, with less cross-group variation over time. A natural experiment suggests that affirmative action for Scheduled Castes has substantially improved their mobility.

“Optimal Regulation of E-cigarettes: Theory and Evidence” (with Hunt Allcott). NBER Working Paper #27000. American Economic Journal: Economic Policy, November 2022.

Abstract: We model optimal e-cigarette regulation and estimate key parameters. Using tax changes and scanner data, we estimate relatively elastic demand. A demographic shift-share identification strategy suggests limited substitution between e-cigarettes and cigarettes. We field a new survey of public health experts who report that vaping is more harmful than previously believed. In our model’s average Monte Carlo simulation, these results imply optimal e-cigarette taxes are higher than recent norms. However, e-cigarette subsidies may be optimal if vaping is a stronger substitute for smoking and is safer than our experts report, or if consumers overestimate the health harms from vaping.

“Mortality Change Among Less Educated Americans” (with Sam Asher and Paul Novosad). American Economic Journal: Applied Economics, October 2022. 

Abstract: Measurements of mortality change among less educated Americans can be biased because the least educated groups (e.g., dropouts) become smaller and more negatively selected over time. We show that mortality changes at constant education percentiles can be bounded with minimal assumptions. Middle-age mortality increases among non-Hispanic Whites from 1992 to 2018 are driven almost entirely by the bottom 10 percent of the education distribution. Drivers of mortality change differ substantially across groups. Deaths of despair explain most of the mortality change among young non-Hispanic Whites, but less among older Whites and non-Hispanic Blacks. Our bounds are applicable in many other contexts.

“When Guidance Changes: Government Stances and Public Beliefs” (with Advik Shreekumar and Pierre-Luc Vautrey). Journal of Public Economics, April 2021.

Abstract: Governments often make early recommendations about issues that remain uncertain. Do governments’ early positions affect how much people believe the latest recommendations? We investigate this question using an incentivized online experiment with 1900 US respondents in early April 2020. We present all participants with the latest CDC projection about coronavirus death counts. We randomize exposure to information that highlights how President Trump previously downplayed the coronavirus threat. When the President’s inconsistency is salient, participants are less likely to revise their prior beliefs about death counts from the projection. They also report lower trust in the government. These results align with a simple model of signal extraction from government communication, and have implications for the design of changing guidelines in other settings.