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Alex Chan 

Economist

Harvard University

Baker Library 447 - T/W/F

Littauer Center (Econ) 225 - M/R

Support Staff: Skye Tait (stait@hbs.edu

achan@hbs.edu 

I am an Assistant Professor at Harvard Business School 

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Economics of Discrimination and Diversity, Experimental and Behavioral Economics, Market Design, Health Economics, Labor Economics

Research Fields:

Research

Revise and Resubmit, American Economic Review

Discrimination against doctors is important but scantly studied. I report a field experiment which observes that customers discriminate against Black and Asian doctors when they choose healthcare providers, and that this can be substantially reduced by supplying information on physician quality. I evaluate customer preferences in the field with an online platform where cash-paying consumers can shop and book a provider for medical procedures based on a novel experimental paradigm. Actual paying customers evaluate doctor options they know to be hypothetical to be matched with a customized menu of real doctors, preserving incentives. Racial discrimination reduces patient willingness-to-pay for Black and Asian doctors by 12.7% and 8.7% of the average colonoscopy price respectively; customers are willing to travel 100-250 miles to see a white doctor instead of a Black doctor, and somewhere between 50-100 to 100-250 miles to see a white doctor instead of an Asian doctor. Providing signals of doctor quality reduces this willingness-to-pay racial gap by about 90%. Willingness-to-pay penalties on minority doctors are multiples of actual average racial quality differences and even the difference between doctors in highest and lowest quality levels. This field evidence shifts the focus beyond traditional taste-based and statistical discrimination to include behavioral mechanisms like biased beliefs and deniable prejudice. Discrimination against Black doctors are higher for non-college-graduate customers and residents in zipcodes that voted for the 2020 presidential candidate on the political right.  Actual booking behavior allows cross-validation of incentive compatibility of the stated preference elicitation. 

(with Alvin E. Roth)

Forthcoming, Journal of Political Economy

We conduct a lab experiment that shows current rules regulating transplant centers (TCs) and organ procurement organizations (OPOs) create perverse incentives that inefficiently reduce both organ recovery and beneficial transplantations. We model the decision environment with a 2-player multi-round game between an OPO and a TC. In the condition that simulates current rules, OPOs recover only highest-quality kidneys and forgo valuable recovery opportunities, and TCs decline some beneficial transplants and perform some unnecessary transplants. Alternative regulations that reward TCs and OPOs together for health outcomes in their entire patient pool lead to behaviors that increase organ recovery and appropriate transplants.

Work In-Progress

Opt in? Opt out?

Draft coming soon

2021, Stanford University Working Paper

Implementation of liver exchange using algorithm from this paper: Launching Liver Exchange and the First 3-Way Liver Paired Donation (2022, JAMA Surgery) ; see also Liver Exchange: A Pathway to Increase Access to Transplantation

Deliberate? An Experiment on Team Decision Making

(with Melisa Kurtis and Nathalie Romer)

Draft coming soon

Blood Ties

(with Annika Herr and Arndt Reichart)

Draft coming soon

Publications

2021, New England Journal of Medicine 385: 766-768

2020, JAMA 323(3): 278-279

(with Kevin Schulman)

2020, JAMA Health Forum 1(3): e200291

1st author (with Isabel Chien, Edward Moseley, Saad Salman, Sarah Kaminer Bourland, Daniela Lamas, Anne M Walling, James A Tulsky, Charlotta Lindvall)

2019, Palliative Medicine 33(2): 187-196

Teaching

BIOS 203: Market Design and Field Experiments for Health Policy and Medicine

Fall 2021, Stanford University, Primary Instructor

Instructor evaluation: 5.0 out of 5.0

HPM 206: Economic Analysis

Fall 2017, Harvard University, Head Teaching Assistant

Teaching evaluation: 4.7 out of 5.0

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