I'm a sixth-year economics PhD student at MIT, with interests in public, behavioral, and labor economics.Email: firstname.lastname@example.org
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.
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. We use an online experiment and an administrative survey to measure the role of information frictions and stigma as mechanisms. We find that raising the eligibility threshold reduces stigma without causing the new take-up; instead, information frictions explain the results. To interpret our findings, we develop a general model of the optimal eligibility threshold for welfare programs with incomplete take-up. The optimal threshold depends on the size of the inframarginal take-up response to changing the threshold, as well as how much information frictions and stigma contribute. Given our empirical results and certain modeling assumptions, the SNAP eligibility threshold is lower than optimal.
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.
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 that 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 the experts report, or if consumers overestimate the health harms from vaping.
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 1992–2018 are driven almost entirely by the bottom 10% 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.
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.
A checklist that I run through before launching any Qualtrics survey.
scfses: A Stata program to obtain quantiles of variables (point estimates and standard errors) in the Survey of Consumer Finances.