Gabe Carroll and Jamie Morgenstern win the Social Choice and Welfare Prize

Congratulations to Jamie Morgenstern and Gabe Carroll. Their joint prize is a sign of how economics and computer science are advancing both separately and together.

JAMIE MORGENSTERN AND  GABRIEL CARROLL RECEIVE THE ELEVENTH SOCIAL CHOICE AND WELFARE PRIZE

"A jury composed of Pietro Ortovela (Princeton University), Ariel Procaccia (Harvard University), Szilvia Papai (Concordia Universtiy),  Arunava Sen (President of the Society for Social Choice and Welfare) and Marc Fleurbaey (Chair, President-elect of the Society for Social Choice and Welfare)  has chosen to award the eleventh Social Choice and Welfare Prize jointly to Gabriel Carroll (University of Toronto) and  Jamie Morgenstern (University of Washington).

"The purpose of the Social Choice and Welfare Prize is to honour young scholars of excellent accomplishment in the area of social choice theory and welfare economics. The laureate should be 40 years or less as of January of the year when the International Meeting of the Society for Social Choice and Welfare is scheduled to take place. During this meeting, the prize winner(s) will give a plenary lecture. For more information about the prize, please click here.

"The SCW prize medal "La Pensée" ("The Thought") is due to Raymond Delamarre (1890-1986), a rather well-known French sculptor associated with what has been called "Art Deco" (Chrysler Building and Empire State Building in New York, the architects Mallet-Stevens or Le Corbusier in France). He is in particular famous for his work at the entrance of the Suez Canal. A web site: www.atelier-raymond-delamarre.fr.

PAST LAUREATES :

2020 : PIETRO ORTOLEVA and  ARIEL PROCACCIA 

2018 : GEORGY EGOROV and DEBASIS MISHRA

2016 : FUHITO KOJIMA  and PARAG PATHAK

2014 : VINCENT CONITZER and TIM ROUGHGARDEN

2012 : LARS EHLERS and ADAM MEIROWITZ

2010 : FRANZ DIETRICH and CHRISTIAN LIST

2008 : TAYFUN SOMNEZ

2006 : JOHN DUGGAN

2004 : FRANCOIS MANIQUET

2002 : MATTHEW JACKSON

The Tenth Social Choice and Welfare Prize Lectures

Jamie Morgenstern

University of Washington

Gabriel Caroll

University of Toronto

NYC plugs a school choice leak (of random numbers)

 Some time ago, Esther Duflo likened market design to plumbing. I think she had in mind construction plumbing, making sure the pipes are all tight. But there's also maintenance (and home repair) plumbing, which involves plugging new leaks.  Parag Pathak alerts me to such an issue in New York City's school choice system.

The NY Post has the story:

Parents uncover major glitch in NYC school lottery system  By Susan Edelman 

"A Manhattan mom discovered an embarrassing glitch in the city Department of Education lottery system used to match students with middle and high schools.

"When NYC students filled out their online applications for 2022-23, each kid automatically received a long string of random numbers from 0 to 9 mixed with lower-case letters from a to f. 

"The random numbers are used to determine the order in which students are matched to programs.

"Lottery numbers starting with 0 are most likely to land students in a school at the top of their list – 8th graders can rank up to 12 preferred high schools. 

...

"But as one 8th-grader’s mom figured out, if students canceled and re-started their applications – as the DOE permitted – they received a different lottery number each time. The loophole allowed users to potentially game the system by simply re-applying until a favorable lottery number popped up.

"Parent leaders alerted the DOE’s Chief Enrollment Officer, Sarah Kleinhandler, who was unaware of the snafu and promised to look into it. She did.

...

"The DOE said it was able to identify 163 students who received new lottery numbers – less than 1 percent of applicants. They included 121 students out of 71,000 high-school applicants, and 42 students out of 58,000 middle school applicants, a spokesman said.

"Students who received new lottery numbers after restarting their applications will get their first lottery numbers back, a spokeswoman told The Post."

**********

Speaking of home repairs, here's an earlier post about some self inflicted problems:

Tuesday, May 12, 2020

Confusion in NYC high school wait lists

Market design at the Econometric Society summer school in Dynamic Structural Econometrics

 Here's an announcement that came by email from the Econometric Society (which reflects the continued evolution of market design):

ECONOMETRIC SOCIETY SUMMER SCHOOL in DYNAMIC STRUCTURAL ECONOMETRICS

Theme: Market Design

Massachusetts Institute of Technology

August 15–20, 2022

APPLY AT http://dseconf.org

DEADLINE: FRIDAY APRIL 1

We are pleased to announce the next event in the sequence of Econometric Society summer schools in Dynamic Structural Econometrics.

The primary focus of DSE summer schools is to provide early-stage PhD students with the tools to carry out research in the rapidly developing area of empirical market design with a strong emphasis on closely integrating economic and econometric theory in empirical work. The school covers the theoretical and methodological foundations in market design, the state-of-the-art methods for estimating models that are fundamental in this literature and providing an overview of open questions. We will use a variety of empirical applications to illustrate how these tools and methods are combined to address important applied questions.

The 2022 Econometric Society DSE summer school consists of 4 days of lectures held in conjunction with the Dynamic Structural Econometrics Conference 2022 (August 19-20). The conference brings together top junior and senior researchers from the field to discuss recent advances in theoretical and applied work. The summer school and the conference are hosted by the Massachusetts Institute of Technology.

Lecturers of the summer school and invited conference speakers include:

• Nikhil Agarwal (Massachusetts Institute of Technology)

• Fedor Iskhakov (Australian National University)

• Irene Lo (Stanford University)

• Victoria Marone (University of Texas at Austin)

• Robert A. Miller (Carnegie Mellon University)

• Whitney Newey (Massachusetts Institute of Technology)

• Ariel Pakes (Harvard University)

• Parag Pathak (Massachusetts Institute of Technology)

• John Rust (Georgetown University)

• Bertel Schjerning (University of Copenhagen)

• Paulo Somaini (Stanford Graduate School of Business)

• Daniel Waldinger (New York University)

Interested students and presenters are invited to submit their applications via the website http://dseconf.org. Application deadline is April 1, 2022.

School choice using deferred acceptance algorithms increases competition for selective schools, by Terrier, Pathak and Ren

 Here's a working paper from the LSE which concludes that making it safe for parents to truthfully report their preferences increases the competition for selective schools (called grammar schools, which prioritize students based on admission tests), with the unintended consequence of disadvantaging poorer families in England. The paper contains a good description of past and present school assignment regimes in England.

From immediate acceptance to deferred acceptance: effects on school admissions and achievement in England by Camille Terrier Parag A. Pathak and Kevin Ren,  Centre for Economic Performance Discussion Paper No.1815, November 2021

"Abstract: Countries and cities around the world increasingly rely on centralized systems to assign students to schools. Two algorithms, deferred acceptance (DA) and immediate acceptance (IA), are widespread. The latter is often criticized for harming disadvantaged families who fail to get access to popular schools. This paper investigates the effect of the national ban of the IA mechanism in England in 2008. Before the ban, 49 English local authorities used DA and 16 used IA. All IA local authorities switched to DA afterwards, giving rise to a cross-market difference-in-differences research design. Our results show that the elimination of IA reduces measures of school quality for low-SES students more than high-SES students. After the ban, low-SES students attend schools with lower value-added and more disadvantaged and low-achieving peers. This effect is primarily driven by a decrease in low-SES admissions at selective schools. Our findings point to an unintended consequence of the IA to DA transition: by encouraging high-SES parents to report their preferences truthfully, DA increases competition for top schools, which crowds out low-SES students."

And here are the paper's concluding sentences:

" In England, selective schools pick students based on test scores, which favors high-SES parents. After the transition to DA, high-SES parents enroll at these schools at higher rates. Selective admissions are widespread throughout education, so our results provide an important caution to equity rationales for DA over IA in settings where selective schools have large market share."

Market design course for health policy and medical students, at Stanford, taught by Alex Chan and Kurt Sweat

 Starting tomorrow, a short course in market design:

BIOS 203, Fall 2021: Market Design and Field Experiments for Health Policy and Medicine 

Primary Instructor: Alex Chan chanalex@stanford.edu | Office Hours: By appointment

Secondary Instructor: Kurt Sweat kurtsw@stanford.edu | Office Hours: By appointment

Description. Market design is an emerging field in economics, engineering and computer science about how to organize systems to allocate scarce resources. In this course, we study (1) the theory and practice of market design in healthcare and medicine, and (2) methods to evaluate the impact of such designs. Students will be provided with the necessary tools to diagnose the problems in markets and allocation mechanisms that render them inefficient, and subsequently develop a working toolbox to remedy failed markets and finetune new market and policy designs.

With a practical orientation in mind, we will learn how to construct rules for allocating resources or to structure successful marketplaces through successive examples in healthcare and medicine: medical residency matching, kidney exchange, allocation of scarce medical resources like COVID vaccine and tests, medical equipment procurement, online marketplace for doctors, and, if time permits, reward system for biopharmaceutical innovation. Guest lectures by practicing market designers and C-suite healthcare executives (CEO, CFO) would feature in the course as well.

An important goal of the class is to introduce you to the critical ingredients to a successful design: a solid understanding of institutions, grasps of economic theory, and well-designed experiments and implementation. In the final sessions, students will also learn how to design and deploy one of the most powerful tools in practical market design: A/B testing or randomized field experiments. These techniques are widely used by tech companies like UBER, Amazon, eBay, and others to improve their marketplaces.

At the end of the course, students should have acquired the necessary knowledge to become an avid consumer and user, and potentially a producer, of the market design and field experimental literature (recognized by 4 recent Nobel Prizes in Economics: 2007/2012/2019/2020).

Time & Location.

● Tue, Thu 6:30 PM - 8:00 PM (beginning November 16, 2021) at Encina Commons Room 119

Course Webpage. ● https://canvas.stanford.edu/courses/145148

Schedule and Readings

(* required readings, others are optional)

Session 1. Market design and Marketplaces – November 16

1. * Roth, A. E. (2007). The art of designing markets. harvard business review, 85(10), 118.

2. Kominers, S. D., Teytelboym, A., & Crawford, V. P. (2017). An invitation to market design. Oxford Review of Economic Policy, 33(4), 541-571.

3. Roth, A. E. (2002). The economist as engineer: Game theory, experimentation, and computation as tools for design economics. Econometrica, 70(4), 1341-1378

Session 2. Matching Markets: Medical Residents and the NRMP – November 18

1. * Chapter 1 in Gura, E. Y., & Maschler, M. (2008). Insights into game theory: an alternative mathematical experience. Cambridge University Press.

2. * Fisher, C. E. (2009). Manipulation and the Match. JAMA, 302(12), 1266-1267.

3. * National Resident Matching Program. (2021). Feasibility of an Early Match NRMP Position Statement

4. Roth, A. E., & Peranson, E. (1997). The effects of the change in the NRMP matching algorithm. JAMA, 278(9), 729-732.

5. Gale, D., & Shapley, L. S. (1962). College admissions and the stability of marriage. The American Mathematical Monthly, 69(1), 9-15.

Session 3. Kidney Exchange and Organ Allocation – November 30

1. * Wallis, C. B., Samy, K. P., Roth, A. E., & Rees, M. A. (2011). Kidney paired donation. Nephrology Dialysis Transplantation, 26(7), 2091-2099.

2. * Chapter 3 in Roth, A. E. (2015). Who gets what—and why: The new economics of matchmaking and market design. Houghton Mifflin Harcourt.

3. Gentry, S. E., Montgomery, R. A., & Segev, D. L. (2011). Kidney paired donation: fundamentals, limitations, and expansions. American journal of kidney diseases, 57(1), 144-151.

4. Salman, S., Gurev, S., Arsalan, M., Dar, F., & Chan, A. Liver  Exchange: A Pathway to Increase Access to Transplantation.

5. Sweat, K. R. Redesigning waitlists with manipulable priority: improving the heart transplant waitlist.

6. Agarwal, N., Ashlagi, I., Somaini, P., & Waldinger, D. (2018). Dynamic incentives in waitlist mechanisms. AEA Papers & Proceedings, 108, 341-347.

Session 4. 1 st Half: Repugnance as a Constraint on Markets – December 2

1. * Roth, A. E. (2007). Repugnance as a Constraint on Markets. Journal of Economic perspectives, 21(3), 37-58.

2. * Minerva, F., Savulescu, J., & Singer, P. (2019). The ethics of the Global Kidney Exchange programme. The Lancet, 394(10210), 1775-1778.

3. Chapter 11 in Roth, A. E. (2015). Who gets what—and why: The new economics of matchmaking and market design. Houghton Mifflin Harcourt.

2 nd Half: Market Design and Allocation during COVID-19 – December 2

1. * Emanuel, E. J., Persad, G., Upshur, R., Thome, B., Parker, M., Glickman, A., ... & Phillips, J. P. (2020). New England Journal of Medicine. Fair allocation of scarce medical resources in the time of Covid-19.

2. Piscitello, G. M., Kapania, E. M., Miller, W. D., Rojas, J. C., Siegler, M., & Parker, W. F. (2020). Variation in ventilator allocation guidelines by US state during the coronavirus disease 2019 pandemic: a systematic review. JAMA network open, 3(6), e201

3. Schmidt, H., Pathak, P., Sönmez, T., & Ünver, M. U. (2020). Covid-19: how to prioritize worse-off populations in allocating safe and effective vaccines. British Medical Journal, 371.

4. Schmidt, H., Pathak, P. A., Williams, M. A., Sonmez, T., Ünver, M. U., & Gostin, L. O. (2020). Rationing safe and effective COVID-19 vaccines: allocating to states proportionate to population may undermine commitments to mitigating health disparities. Ava

5. Neimark, J. (2020). What is the best strategy to deploy a COVID-19 vaccine. Smithsonian Magazine.

Session 5. 1 st Half: Auction Design and Procurement in Medicine – December 7

1. * The Committee for the Prize in Economic Sciences in Memory of Alfred Nobel. (2020). Improvements to auction theory and inventions of new auction formats. Scientific Background on the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 20

2. * Song, Z., Cutler, D. M., & Chernew, M. E. (2012). Potential consequences of reforming Medicare into a competitive bidding system. Jama, 308(5), 459-460.

3. Newman, D., Barrette, E., & McGraves-Lloyd, K. (2017). Medicare competitive bidding program realized price savings for durable medical equipment purchases. Health Affairs, 36(8), 1367-1375.

4. Cramton, P., Ellermeyer, S., & Katzman, B. (2015). Designed to fail: The Medicare auction for durable medical equipment. Economic Inquiry, 53(1), 469-485.

5. Ji, Y. (2019). The Impact of Competitive Bidding in Health Care: The Case of Medicare Durable Medical Equipment.

6. Thaler, R. H. (1988). Anomalies: The winner's curse. Journal of economic perspectives, 2(1), 191-202.

7. Chapter 2 in Haeringer, G. (2018). Market design: auctions and matching. MIT Press.

2 nd Half: (GUEST LECTURE) Ralph Weber, CEO, MediBid Inc. on “The Online Marketplace for Medicine” – December 7

Session 6. A/B Testing and Field Experiments to Test Designs – December 9

1. * Chapters 1, 4 in List, John. (2021). A Course in Experimental Economics (unpublished textbook, access on course website)

2. * Gallo, A. (2017). A refresher on A/B testing. Harvard Business Review, 2-6.

3. Chan, A. (2021). Customer Discrimination and Quality Signals – A Field Experiment with Healthcare Shoppers.

4. Kessler, J. B., Low, C., & Sullivan, C. D. (2019). Incentivized resume rating: Eliciting employer preferences without deception. American Economic Review, 109(11), 3713-44.

5. Chapters 3, 5, 6, 7, 8 in List, John. (2021). A Course in Experimental Economics (unpublished textbook, access on course website)

6. The Committee for the Prize in Economic Sciences in Memory of Alfred Nobel. (2019). Understanding development and poverty alleviation. Scientific Background on the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2019.

Bonus Session (optional). (GUEST LECTURE) Donald Lung, CFO, Antengene on “Designing Markets to Access Biopharmaceutical Intellectual Property Across Regulatory Regimes – the Case of China” – Date TBD

Bonus Session (optional). (GUEST LECTURE) TBD – Date TBD

NBER Market Design Working Group Meeting, Fall 2021

Market Design Working Group Meeting, Fall 2021

DATE October 21-23, 2021 (Times in EDT)

ORGANIZERS Michael Ostrovsky and Parag A. Pathak

NBER conferences are by invitation. All participants are expected to comply with the NBER's Conference Code of Conduct.

Thursday, October 21

12:00 pm

Randomized FIFO Mechanisms 

Francisco Castro, UCLA

Hongyao Ma, Columbia University

Hamid Nazerzadeh, University of Southern California

Chiwei Yan, University of Washington

12:45 pm

The Value of Excess Supply in Spatial Matching Markets 

Mohammad Akbarpour, Stanford University

Yeganeh Alimohammadi, Stanford University

Shengwu Li, Harvard University

Amin Saberi, Stanford University

1:30 pm

Break

2:00 pm

Choices and Outcomes in Assignment Mechanisms: The Allocation of Deceased Donor Kidneys 

Nikhil Agarwal, Massachusetts Institute of Technology and NBER

Charles Hodgson, Yale University

Paulo J. Somaini, Stanford University and NBER

2:45 pm

Price Discovery in Waiting Lists: A Connection to Stochastic Gradient Descent 

Itai Ashlagi, Stanford University

Jacob D. Leshno, University of Chicago

Amin Saberi, Stanford University

Pengyu Qian, Columbia Business School

3:30 pm

Adjourn

Friday, October 22

12:00 pm

Linear Pricing Mechanisms without Convexity 

Paul Milgrom, Stanford University

Mitchell L. Watt, Stanford University

12:45 pm

The Equilibrium Existence Duality: Equilibrium with Indivisibilities and Income Effects 

Elizabeth C. Baldwin, University of Oxford

Omer Edhan, University of Manchester

Ravi Jagadeesan, Stanford University

Paul D. Klemperer, University of Oxford

Alexander Teytelboym, University of Oxford

1:30 pm

Break

2:00 pm

Flow Trading 

Eric Budish, University of Chicago and NBER

Peter Cramton, University of Cologne

Albert "Pete" Kyle, University of Maryland

Jeongmin (Mina) Lee, Washington University in St. Louis

David Malec, University of Maryland

2:45 pm

Designing a Combinatorial Financial Options Market 

Xintong Wang, Harvard University

David Pennock, Microsoft Research

Nikhil Devanur, Amazon

David M. Rothschild, Microsoft Research

Biaoshuai Tao, Shanghai Jiao Tong University

Michael Wellman, University of Michigan

3:30 pm

Adjourn

Saturday, October 23

12:00 pm

Choice Screen Auctions 

Michael Ostrovsky, Stanford University and NBER

12:45 pm

Mechanism Design meets Priority Design: Redesigning the US Army's Branching Process 

Kyle Greenberg, United States Military Academy at West Point

Parag A. Pathak, Massachusetts Institute of Technology and NBER

Tayfun Sönmez, Boston College

1:30 pm

Break

2:00 pm

Decentralizing Centralized Matching Markets: Implications from Early Offers in University Admissions  

Julien Grenet, Paris School of Economics

Yinghua He, Rice University

Dorothea Kübler, WZB

2:45 pm

Mechanism and Priority Design for Distributional Objectives: An Application to Decrease Inequalities in Access to Experienced Teachers 

Julien Combe, CREST - Ecole polytechnique

Umut M. Dur, North Carolina State University

Olivier Tercieux, Paris School of Economics

Camille Terrier, University of Lausanne

M. Utku Ünver, Boston College

3:30 pm

Adjourn

Matching theory in the September issue of Games and Economic Behavior

 The September 2021 issue of Games and Economic Behavior (Volume 129, Pages 1-590) has five papers on matching theory.

In the order in which they appear:

An improved bound to manipulation in large stable matches  by Gustavo Saraiva

https://doi.org/10.1016/j.geb.2021.05.005Get rights and content

Abstract: This paper builds on Kojima and Pathak (2009)'s result of vanishing manipulability in large stable mechanisms. We show that convergence toward truth-telling in stable mechanisms can be achieved much faster if colleges' preferences are independently drawn from an uniform distribution. Another novelty from our results is that they can be applied to competitive environments in which virtually all vacancies end up being filled. So this paper adds evidence to the fact that, though stable matching mechanisms are not entirely strategy-proof, in practice, when the number of participants in the market is sufficiently large, they can be treated as being effectively strategy-proof.

*************

How lotteries in school choice help to level the playing field by Christian Basteck, Bettina Klaus, Dorothea Kübler

https://doi.org/10.1016/j.geb.2021.05.010Get rights and content

Abstract: School authorities in the UK and the US advocate the use of lotteries to desegregate schools. We study a school choice mechanism employed in Berlin where a lottery quota is embedded in the immediate acceptance (IA) mechanism, and compare it to the deferred acceptance mechanism (DA) with a lottery quota. In both mechanisms, some seats are allocated based on academic achievement (e.g., grades), while seats in the lottery quota are allocated randomly. We find that, in theory, a lottery quota strengthens truth-telling in DA by eliminating non-truth-telling equilibria. Furthermore, the equilibrium outcome is stable for DA with a lottery but not for IA with a lottery. These predictions are borne out in the experiment. Moreover, the lottery quota leads to more diverse school populations in the experiment, as predicted. Students with the lowest grades profit more from the introduction of the lottery under IA than under DA.

**********

Substitutes and stability for many-to-many matching with contracts  by Keisuke Bando, Toshiyuki Hirai, Jun Zhang

https://doi.org/10.1016/j.geb.2021.07.002Get rights and content

Abstract:We examine the roles of (slightly weakened versions of) the observable substitutability condition and the observable substitutability across doctors condition of Hatfield et al. (2021) in many-to-many matching with contracts. We modify the standard cumulative offer algorithm to find stable outcomes and prove new results on the existence of stable outcomes. It is remarkable that size monotonicity at the offer-proposing side is essential for the existence result under observable substitutability across doctors.

*************

Slot-specific priorities with capacity transfers  by Michelle Avataneo and BertanTurhan

https://doi.org/10.1016/j.geb.2021.07.005

Abstract: In many real-world matching applications, there are restrictions for institutions either on priorities of their slots or on the transferability of unfilled slots over others (or both). Motivated by the need in such real-life matching problems, this paper formulates a family of practical choice rules, slot-specific priorities with capacity transfers (SSPwCT). These rules invoke both slot-specific priorities structure and transferability of vacant slots. We show that the cumulative offer mechanism (COM) is stable, strategy-proof and respects improvements with regards to SSPwCT choice rules. Transferring the capacity of one more unfilled slot, while all else is constant, leads to strategy-proof Pareto improvement of the COM. Following Kominers' (2020) formulation, we also provide comparative static results for expansion of branch capacity and addition of new contracts in the SSPwCT framework. Our results have implications for resource allocation problems with diversity considerations.

**************

Stability in sequential matching with incomplete information by Fanqi Shi

https://doi.org/10.1016/j.geb.2021.07.001Get rights and content

Abstract: I study a two-period matching model where one side of the market (e.g. workers) have an option to invest and delay matching in the first period. Investment increases each agent's matching surplus in the second period, by a magnitude of the worker's investment ability in the match pair. Assuming each worker's investment ability is her private information that unfolds in the second period, I define a notion of sequential stability, and show that the set of sequentially stable outcomes is a superset of the complete information stable outcomes. Moreover, with transferable utility, as long as the cost of delay coincides on the same side of the market, efficient investment occurs in any sequentially stable outcome. When every agent shares the same cost of delay, efficient investment also occurs in any sequentially stable outcome with non-transferable utility. My analysis suggests that efficient investment is a robust prediction in sequential matching markets.

A lottery for antibody treatment, with slots reserved for vulnerable patients

 It's always good to see a collaboration between physicians and economists on allocating scarce resources, and here's a case report of allocating monoclonal antibodies in Boston (with some resemblance to school choice), forthcoming in the journal CHEST.

A novel approach to equitable distribution of scarce therapeutics: institutional experience implementing a reserve system for allocation of Covid-19 monoclonal antibodies  Emily Rubin, MD JD MSHP, Scott L. Dryden-Peterson, MD, Sarah P. Hammond, MD, Inga Lennes, MD MBA MPH, Alyssa R. Letourneau, MD MPH, Parag Pathak, PhD, Tayfun Sonmez, PhD, M. Utku Ünver, PhD.

DOI: https://doi.org/10.1016/j.chest.2021.08.003, To appear in: CHEST

"Background. In fall 2020, the Food and Drug Administration issued emergency use authorization for monoclonal antibody therapies (mAbs) for outpatients with Covid-19.  The Commonwealth of Massachusetts issued guidance outlining the use of a reserve system with a lottery for allocation of mAbs in the event of scarcity that would prioritize socially vulnerable patients for 20% of the infusion slots. The Mass General Brigham (“MGB”) health system subsequently implemented such a reserve system.

"Research Question. Can a reserve system be successfully deployed in a large health system in a way that promotes equitable access to mAb therapy among socially vulnerable patients with Covid-19?

...

"Results: Notwithstanding multiple operational challenges, the reserve system for allocation of mAb therapy worked as intended to enhance the number of socially vulnerable patients who were offered and received mAb therapy. A significantly higher proportion of patients offered mAb therapy were socially vulnerable (27.0%) than would have been the case if the infusion appointments had been allocated using a pure lottery system without a vulnerable reserve (19.8%) and a significantly higher proportion of patient who received infusions were socially vulnerable (25.3%) than would have been the case if the infusion appointments had been allocated using a pure lottery system (17.6%)

...

"The reserve for vulnerable patients was a “soft” reserve, meaning that if there were not enough patients in either the high SVI or high incidence town categories to fill the vulnerable slots, those slots were allocated to patients who were next in line by overall lottery number. This was done in order to avoid unused capacity for a therapy that is time sensitive and requires significant infrastructure to provide. Once the lottery had been run, dedicated, primarily multilingual clinicians who had been trained to discuss the therapies with patients called patients to verify eligibility and engage in a shared-decision making conversation to determine whether the patient would like to receive an infusion.

Early experience with running the lottery prior to patient engagement revealed that a large number of patients declined the therapy once offered, were deemed ineligible once contacted, or wished to discuss the therapy with a trusted clinician. The process subsequently was changed to allow clinicians to enter referrals for their own patients once they established patient interest (“manual referrals”). 

...

"All of the 274 patients who were guaranteed slots and 206 of 368 patients on the wait list were called, for a total of 480 patients called. The number of wait list patients called on a given day was a function of both how many of the guaranteed slots were not filled and how much capacity there was in the system to make phone calls on any given day. Of those patients who were called, 132 (27.5%) declined, 33 (6.9%) were deemed ineligible by virtue of being asymptomatic, 19 (4.0%) were deemed ineligible by virtue of having severe symptoms, 11 (2.3%) had been or were planning to be infused elsewhere, 61 (12.7%) could not be reached, and 191 were infused (39.8% of those called and 9.7% of total referred patients).

...

"Had we operated a pure lottery with no reserve for socially vulnerable patients, and all other factors had remained constant, 19.8% of patients offered therapy (88) would have been in the top SVI quartile as opposed to 27.0% (120) in our actual population, and 17.6% of infused patients (32) would have been in the top SVI quartile as opposed to 25.3% (46) in our actual population.

...

"The system we describe is to our knowledge the first instance of a reserve system being used to allocate scarce resources at the individual level during a pandemic.

"A reserve system with lottery for tiebreaking within categories can be straightforward to operate if there are few or no steps between the assignment of lottery spots and the distribution of the good. This could be true, for example, of allocation of antiviral medications to inpatients with Covid-19. In the case of monoclonal antibody therapies, there were multiple factors that could and often did interrupt the trajectory between allocation and distribution. These included the complexity of administering infusion therapy, the time sensitive nature of the therapy, the relative paucity of evidence for the therapy at the time the mAb program started, and the dynamic nature of Covid-19. The conversations with patients about a therapy that held promise but did not yet have strong evidence to support its efficacy and had not been formally FDA approved were often challenging and time consuming. Many patients identified for allocation were difficult or impossible to reach. Others declined therapy once it was offered and discussed, or had become either too well or too sick to be candidates for the therapy once they were reached.

...

"Notwithstanding significant challenges, the reserve system implemented in our health system for allocation of mAb therapy worked as intended to enhance the number of socially vulnerable patients who were offered the therapy. A significantly higher proportion of socially vulnerable patients were offered mAb therapy than would have been if the infusion appointments had been allocated using a pure lottery system without a vulnerable reserve. The intended enhancement of the pool of vulnerable patients who actually received monoclonal antibody therapy was counterbalanced to some extent by the disproportionate number of vulnerable patients who declined therapy, but even fewer socially vulnerable patients would have received the therapy if the lottery system had not included a vulnerable reserve. 

Market design, redesigned (in startups and university labs)

Market design is evolving, and new ways of organizing it are being explored. 

In my post yesterday, I talked about the early work on school choice that Atila Abdulkadiroglu, Parag Pathak, Tayfun Sonmez and I did under the auspices of Boston schools Superintendent Tom Payzant. The market design by economists in Boston, as with the earlier successful effort in New York City, was conducted as part of our research work as professors.  Not a penny changed hands, and we all felt good about that.

But if there was a flaw in that working arrangement, it was that no contracts were signed, and so as staff turnover took place in school districts, and the individuals we had dealt with departed, the district's institutional memory eroded, and they didn't always remember to turn to us when difficulties arose that we could have helped them with. Partly to address that, and to have at least one person able to devote time to approaching school districts, Parag and Atila and I supported Neil Dorosin in founding the non-profit  Institute for Innovation in Public School Choice, which during its lifetime helped school choice in a number of American cities, including Denver, New Orleans, and Washington D.C.

Parag and Atila went on to be founding members of MIT's School Effectiveness and Inequality Intiative, which just this week was "relaunched" with a different team as MIT Blueprint Labs, which aims to build on MIT's strengths not just in school choice but in a much wider area of market design and policy analysis, and to be a lab with a large staff and extensive fundraising:

Launch announcement of MIT Blueprint Labs

Featuring

Professor Parag Pathak Faculty Director MIT SEII / Blueprint Labs Research spotlight: K-12 education

Professor Joshua Angrist Faculty Director MIT SEII / Blueprint Labs Research spotlight: Higher education and the workforce

Professor Nikhil Agarwal Faculty Director, Health Care MIT SEII / Blueprint Labs Research spotlight: Health care

Eryn Heying Executive Director MIT SEII / Blueprint Labs

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Update: and here's the Blueprint Labs new (announced Aug. 11) website: https://blueprintlabs.mit.edu/

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In a related development, Parag has cofounded a new for-profit Ed-tech startup called Avela, that plans to spread the technologies he's helped pioneer.  A for-profit firm has some funding, employment and investing opportunities that aren't available to non-profits or university labs, let alone to teams of professors organized informally. And as in the Blueprint Lab, they hope that the tools they will develop will be readily applicable to quite a broad range of matching markets and market designs.

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These various efforts look to me like design experiments themselves, in the search for sustainable ways of making market design a permanent part of not only the research that economists do, but of the practical effects we hope to foster.

Observing all this from the West Coast, and over several decades, I can't help noticing that these institutional changes have been accompanied by team changes, and shifting collaborations among market designers.  

There are also a growing number of different kinds of economists (and computer scientists, operations researchers and businesses) involved in designing and assessing markets, and market design has not only changed markets, but changed the way economists work, in many small and large ways.  Econometricians and development economists have led the way in organizing large labs, and market design may be heading in that direction as well. Big and small tech firms have also started to think of market design as among their core competencies, and as a discipline they should be hiring.

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Here in California, I'd be remiss if I didn't mention that my colleague Paul Milgrom has for a long time engaged in auction design through his for-profit company Auctionomics.

And Susan Athey is the faculty director of a big lab at Stanford using different technologies in other areas of market design:  the Golub Capital Social Impact Lab, which describes itself this way:

"We use digital technology and social science research to improve the effectiveness of leading social sector organizations.

"Based out of Stanford GSB, the lab is a research initiative of affiliated academics and staff, as well as researchers and students, who are passionate about conducting research that guides and improves the process of innovation.

"Research Approach

We collaborate with a wide range of organizations, from large firms to smaller startups, for-profits to nonprofits, and NGOs to governments, to conduct research. Then, we apply and disseminate our insights to achieve social impact at large scale."