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

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.

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

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.

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

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."

Tom Payzant, Boston schools superintendent who reformed school choice, dies at 80

 Tom Payzant played a critical role in transforming Boston's school choice from an immediate acceptance algorithm that exposed students and families to complex strategic risk when navigating the system, to a deferred acceptance algorithm that simplified their participation. As Superintendent of Boston Public Schools, Tom came to understand those issues well, and acted on them.

Here's his obit in the Boston Globe.

Thomas Payzant, whose education vision lifted Boston’s schools, dies at 80, By Bryan Marquard

and here's the statement from Boston Public Schools:

SUPERINTENDENT'S STATEMENT ON THE PASSING OF TOM PAYZANT

Here's a pic I took of Atila Abdulkadiroglu, Parag Pathak, and Tayfun Sonmez when we met with Payzant and his colleagues at Boston Public School headquarters, during the years we worked with BPS, starting around 2003.

Atila Abdulkadiroglu, Parag Pathak and Tayfun Sonmez at Boston Public School headquarters

Here's a paper that came out of those meetings, describing the deliberations that ultimately led BPS to adopt a deferred acceptance algorithm design for it's school choice system.

• Abdulkadiroglu, Atila, Parag A. Pathak, Alvin E. Roth, and Tayfun Sonmez, The Boston Public School Match," American Economic Review, Papers and Proceedings, 95,2, May, 2005, 368-371.

Over the course of those years, I was privileged to watch Parag evolve from a super smart young grad student to being a leader in the design of school choice.

I'll post tomorrow about some of Parag's latest efforts to bring the work associated with the design and evaluation of school choice, and market design more generally, into the world of startup companies and big university labs.

The Economics of Covid: The 31st Jerusalem Advanced School in Economic Theory (June 28-30)

This year's event doesn't involve travel. (Next year in Jerusalem!)

The 31st Jerusalem Advanced School in Economic Theory: The Economics of COVID-19 (Online event)

Mon, 28/06/2021 to Wed, 30/06/2021

 

General Director: Eric Maskin (Harvard University)

Co-director: Elchanan Ben-Porath (The Hebrew University)

 

List of lecturers and topics:

Glenn Ellison, MIT: Epidemiological models
Ben Golub, Northwestern University: Supply Chains
Michael Kremer, University of Chicago: Vaccines
Eric Maskin, Harvard University: Mechanism Design for Pandemics
Emily Oster, Brown University: Schools

Parag Pathak, MIT: Priority Schemes 

Redesigning the US Army's Branching Process, by Kyle Greenberg, Parag A. Pathak & Tayfun Sönmez,

 Here's a new NBER working paper that marks a significant step forward in matching soldiers to positions.

Mechanism Design meets Priority Design: Redesigning the US Army's Branching Process by Kyle Greenberg, Parag A. Pathak & Tayfun Sönmez, NBER WORKING PAPER 28911 DOI 10.3386/w28911,  June 2021

Army cadets obtain occupations through a centralized process. Three objectives – increasing retention, aligning talent, and enhancing trust – have guided reforms to this process since 2006. West Point’s mechanism for the Class of 2020 exacerbated challenges implementing Army policy aims. We formulate these desiderata as axioms and study their implications theoretically and with administrative data. We show that the Army’s objectives not only determine an allocation mechanism, but also a specific priority policy, a uniqueness result that integrates mechanism and priority design. These results led to a re-design of the mechanism, now adopted at both West Point and ROTC.

One of the unusual features of this paper is that the first author is both an economist and an Army officer, working in West Point's Office of Economic and Manpower Analysis:

"MAJ Greenberg is an Assistant Professor of Economics in the Department of Social Sciences and is OEMA’s Director of Long-Term Research. His primary areas of research are labor economics and public finance, with a focus on veteran employment, disability compensation, and military labor markets. Currently a Major in the U.S. Army, Kyle served tours in Iraq and Germany prior to teaching at the United States Military Academy. He earned a BS in Mathematics from the United States Military Academy in 2005 and a Ph.D. in Economics from the Massachusetts Institute of Technology in 2015."

*********

Here's a related earlier post, in which Major Greenberg discusses some of the design issues still facing the Army's assignment systems.

Monday, December 7, 2020

Officer assignment in the U.S. Army

The importance of very early education, by Gray-Lobe, Pathak, and Walters

 There's more to education than exam scores.  Here's a recent paper on the effects of early preschool education on long term educational outcomes.

The Long-Term Effects of Universal Preschool in Boston  by Guthrie Gray-Lobe, Parag Pathak, and Christopher Walters, SEII Discussion Paper #2021.05  ay 2021

ABSTRACT: We use admissions lotteries to estimate the effects of large-scale public preschool in Boston on college-going, college preparation, standardized test scores, and behavioral outcomes. Preschool enrollment boosts college attendance, as well as SAT test-taking and high school graduation. Preschool also decreases several disciplinary measures including juvenile incarceration, but has no detectable impact on state achievement test scores. An analysis of subgroups shows that effects on college enrollment, SAT-taking, and disciplinary outcomes are larger for boys than for girls. Our findings illustrate possibilities for large-scale modern, public preschool and highlight the importance of measuring long-term and non-test score outcomes in evaluating the effectiveness of education programs

Selective NYC high schools aren't as hard to get into as is sometimes reported: Sam Abrams in the Columbia Journalism Review

 In the Columbia Journalism Review, Sam Abrams explains how data from NYC's deferred acceptance algorithm for assigning students to schools is often misunderstood in the press, when it comes to reporting on how selective the schools are.

Getting Education Data Right: The Case of High School Admissions  By Samuel E. Abrams

"The trouble with the story about high school admissions begins with official data. The admissions numbers in the annual high school directories published by New York City’s Department of Education are indeed alarming. Eight consecutive schools in the 2019 directory, for example, exhibited daunting odds: Bard High School Early College, 30 applicants per seat; Baruch College Campus High School, 44; Beacon High School, 19; Business of Sports School (BOSS), 13; Central Park East High School, 37; Chelsea Career and Technical Education High School, 14; City College Academy of the Arts, 22; and The Clinton School, 21. These odds translate into acceptance rates ranging from 2.3 percent, in the case of Baruch, to 7.7 percent, in the case of BOSS. 

"But these students are not applicants in the conventional sense. They are students who rank a school by order of preference as one of up to 12 with which they would like to match. This process—introduced in 2004 and derived from the National Resident Matching Program for doctors introduced in 1952—employs an algorithm allowing only one match. Accordingly, if every eighth-grader in New York City exercised his or her right to list 12 schools, each school, on average, could in turn accept only one of 12 students, or 8.3 percent of applicants.

...

"I began encountering this reporting problem in 2005, when the Times published an article on then-Mayor Michael Bloomberg’s plans to create several new high schools to address the surplus demand for seats in exam and screened schools. The Times reported that Beacon had 6,000 applicants for 250 seats the previous year, meaning an acceptance rate of 4.2 percent.

"As a teacher at Beacon at the time, I knew the admissions process from the inside and emailed a correction to the paper: 6,000 students ranked Beacon as one of up to 12 schools in which they were interested; about 1,800 students submitted the requisite portfolio of their best work and visited the school for the mandated interview; and approximately 500 offers were made to fill 250 seats. This meant an acceptance rate of about 28 percent if all 1,800 applicants ranked Beacon first, which is highly improbable, given that approximately 50 percent of applicants to Beacon today who fulfill application requirements rank the school first. But that correction went nowhere, and I resigned myself to explaining the numbers to anxious parents fretting that their children had no chance of getting into Beacon given what they had read in the Times.

...

"Following the 2017 article about 10 of the city’s high schools being more selective than Yale, I wrote a letter to the Times. As that letter went unacknowledged and as the newspaper did not run another letter to elucidate the process, I published a critique on the Web site of a research center I run at Teachers College, Columbia University. That critique led to an article published by Chalkbeat and another by Phi Delta Kappan, which interviewed Alvin Roth, a professor at Stanford who shared the Nobel Prize in economics in 2012 for work decades earlier on market design and who, with two other economists, Atila Abdulkadiroglu and Parag Pathak, developed the algorithm used by the DOE. Roth explained that the Times had indeed greatly exaggerated the number of applicants because the algorithm pulled students from the applicant pool once they were matched. “If I applied to you as my seventh choice, and I got accepted by my first choice, I wasn’t rejected by you,” Roth said. “You never saw me.”

"With a matching algorithm, the closest one can truly get to an acceptance rate is a match rate through adding the number of students who matched with a particular school to the number of students who matched with a school they ranked lower than that school and then dividing the number of matches by that sum.

...

"What is nevertheless certain is that the algorithm developed by Roth with Abdulkadiroglu and Pathak has significantly streamlined the enrollment process in New York. The three economists developed the algorithm, they wrote in a 2005 article published in the American Economic Review, to “relieve the congestion of the previous offer/acceptance/wait-list process” that conferred “some students multiple offers” and “multiple students … no offers.:

Exploding offers of admission to Notre Dame Law School

Notre Dame Law School has apparently sent out more acceptance letters than it has positions, and the offers will expire automatically once sufficiently many students have accepted them by making a binding deposit.  Read on and see that there was also a threat to students who had been offered financial aid.  (I wonder if this will work out the way Notre Dame wants, or if enough law students are rich enough to make more than one deposit...)

 The blog "Above the Law' has the story:

Chaos Reigns: Notre Dame Law School Tells Non-Wealthy Students ‘Thanks, But No Thanks’ By Kyle McEntee and Sydney Montgomery

"Notre Dame makes application decisions on a rolling basis instead of a pre-selected date. Once an applicant is admitted, the school requires two deposits to confirm enrollment. Law schools have used this process (more or less) without incident for decades.

"Most law schools ask applicants to deposit by a certain date, traditionally mid-April to early May. Notre Dame’s first deadline was April 15 and required a $600 non-refundable deposit. Notre Dame’s offer letter, however, increased the pressure with an unusual warning. The school informed applicants that they had until the deadline or “when we reach our maximum number of deposits.”

...

"For the applicants who received a scholarship offer, pressure mounted with a second warning.

"While most law schools frown on double-depositing (holding seats at more than one law school), Notre Dame warned scholarship recipients that they may lose their scholarship offer if the applicant also deposits at another school.

...

"In other words, if you want to come to our school at the price we’re offering, you’d better send us a non-refundable deposit now."

HT: Paul Kominers, Parag Pathak

********

I recall that decades ago, a certain midwestern Economics department (just) once made more offers of graduate fellowships than it had, with the fellowship offer expiring when enough acceptances had been received. No binding deposits were involved in that episode, however.

Randomized control trials plus preferences: a market design for experiments by Yusuke Narita in PNAS

 Random assignment of patients to experimental treatments is intended to allow statisticians to cleanly measure the effect of the treatments. But if there is evidence that some patients might profit more from some treatment than others, fully random assignment may not maximize health outcomes. And if patients have preferences (e.g. for the risk of receiving a problematic kidney for transplant versus the risk of waiting for a better one), then fully random assignment may not maximize welfare.  Yusuke Narita thinks about how to design RCTs that elicit patient preferences and take account of prior's about outcomes, while still allowing the necessary statistical tests to determine treatment effects.

Incorporating ethics and welfare into randomized experiments  by Yusuke Narita

PNAS January 5, 2021 118 (1) e2008740118; https://doi.org/10.1073/pnas.2008740118

Edited by Parag Pathak, Massachusetts Institute of Technology, Cambridge, MA, and accepted by Editorial Board Member Paul R. Milgrom September 30, 2020 

"Abstract: Randomized controlled trials (RCTs) enroll hundreds of millions of subjects and involve many human lives. To improve subjects’ welfare, I propose a design of RCTs that I call Experiment-as-Market (EXAM). EXAM produces a welfare-maximizing allocation of treatment-assignment probabilities, is almost incentive-compatible for preference elicitation, and unbiasedly estimates any causal effect estimable with standard RCTs. I quantify these properties by applying EXAM to a water-cleaning experiment in Kenya. In this empirical setting, compared to standard RCTs, EXAM improves subjects’ predicted well-being while reaching similar treatment-effect estimates with similar precision.

...

"RCTs involve large numbers of participants. Between 2007 and 2017, over 360 million patients and 22 million individuals participated in registered clinical trials and social RCTs, respectively. Moreover, these experiments often randomize high-stakes treatments. For instance, in a glioblastoma therapy trial (1), the 5-y death rate of glioblastoma patients was 97% in the control group, but only 88% in the treatment group. In expectation, therefore, the lives of up to 9% of the study’s 573 participants depended on who received treatments. Social RCTs also often randomize critical treatments such as basic income, high-wage job offers, and HIV testing.

"RCTs, thus, influence the fate of many people around the world, raising a widely recognized ethical concern with the randomness of RCT treatment assignment: “How can a physician committed to doing what he thinks is best for each patient tell a woman with breast cancer that he is choosing her treatment by something like a coin toss? How can he give up the option to make changes in treatment according to the patient’s responses?”

...

"I propose an experimental design that I call Experiment-as-Market (EXAM). I choose this name because EXAM is an experiment based on an imaginary centralized market and its competitive equilibrium (12, 13). EXAM first endows each subject with a common artificial budget and lets her use the budget to purchase the most preferred (highest WTP) bundle of treatment-assignment probabilities given their prices. The prices are personalized so that each treatment is cheaper for subjects with better predicted effects of the treatment. EXAM computes its treatment-assignment probabilities as what subjects demand at market-clearing prices, where subjects’ aggregate demand for each treatment is balanced with its supply or capacity (assumed to be exogenously given). EXAM, finally, requires every subject to be assigned to every treatment with a positive probability.

"This virtual-market construction gives EXAM nice welfare and incentive properties. EXAM is Pareto optimal, in that no other design makes every subject better off in terms of expected predicted effects of and WTP for the assigned treatment. EXAM also allows the experimenter to elicit WTP in an asymptotically incentive-compatible way. That is, when the experimenter asks subjects to self-report their WTP for each treatment to be used by EXAM, every subject’s optimal choice is to report her true WTP, at least for large experiments.

"Importantly, EXAM also allows the experimenter to estimate the same treatment effects as standard RCTs do. Intuitively, this is because EXAM is an experiment stratified on observable predicted effects and WTP, in which the experimenter observes each subject’s assignment probabilities (propensity scores). As a result, EXAM’s treatment assignment is random (independent from anything else), conditional on the observables. The conditionally independent treatment assignment allows the experimenter to unbiasedly estimate the average treatment effects (ATEs) conditional on observables. By integrating such conditional effects, EXAM can unbiasedly estimate the (unconditional) ATE and other effects, as is the case with any stratified experiment (14)."

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somewhat related post:

Sunday, July 12, 2020

Incorporating patient preferences into transplant decisions can improve welfare--Genie, Nicolo and Pasini in J Health Economics

A hard (theoretical) look at school choice, in the AER by Chris Avery and Parag Pathak

 What are some of the difficulties that might hamper school choice from achieving educational equality (or at least substantially reducing inequality)?  Here's a model by Chris Avery and Parag Pathak.  The theoretical intuitions of top experts in college and school assignments are the sort of thing that can keep you awake at night.  In a sentence, if school choice narrows the quality gap between the best and worst municipal schools, it may also narrow the gap in housing prices, and higher housing prices at the low end may drive poorer families to move to other school districts, just as lower quality at the high end drives richer families to suburbs with excellent schools. ("White flight" has been the subject of many papers, so the issue being raised here is that an improvement at the low end of school quality may also raise prices of less expensive housing and drive out poorer residents.)

The Distributional Consequences of Public School Choice  by Christopher Avery and Parag A. Pathak AMERICAN ECONOMIC REVIEW, VOL. 111, NO. 1, JANUARY 2021, (pp. 129-52)

"Abstract: School choice systems aspire to delink residential location and school assignments by allowing children to apply to schools outside of their neighborhood. However, choice programs also affect incentives to live in certain neighborhoods, and this feedback may undermine the goals of choice. We investigate this possibility by developing a model of public school and residential choice. School choice narrows the range between the highest and lowest quality schools compared to neighborhood assignment rules, and these changes in school quality are capitalized into equilibrium housing prices. This compressed distribution generates an ends-against-the-middle trade-off with school choice compared to neighborhood assignment. Paradoxically, even when choice results in improvement in the lowest-performing schools, the lowest type residents need not benefit."

"Our analysis contributes to a recent literature on school choice mechanisms, which has focused on the best way to assign pupils to schools given their residential location in a centralized assignment scheme. In particular, research has examined the best way to fine-tune socioeconomic or income-based criteria in choice systems. Cities have now experimented with complex school choice tie-breakers in an effort to achieve a stable balance (Kahlenberg 2003). 17 By incorporating feedback between residential and school choices, our model suggests that analysis of school assignment that does not account for possible residential resorting may lead to an incomplete understanding about the distributional consequences of school choice.

"A common rationale for school choice is to improve the quality of school options for disadvantaged students. But, our analysis shows that feedback from residential choice can undercut this approach, for if a school choice plan succeeds in narrowing the range between the lowest and highest quality schools, that change should compress the distribution of house prices in that town, thereby providing incentives for the lowest and highest types to exit from the town’s public schools. This intuition extends to the idealized case of a symmetric model of many towns and partisans, where each town adopts school choice and all schools within a given town have the same quality. Although there is an equilibrium in this idealized model where schools in all towns have the same quality, this equilibrium would likely be unstable, and instead we would expect to observe an equilibrium with differentiation of school qualities and housing prices across towns. That is, the within-town diversity observed in equilibrium under neighborhood assignment could be replicated in cross-town diversity under school choice.

A broader implication of our model is that systemic changes beyond the details of the school assignment system may be necessary to reduce inequalities in educational opportunities."

Top trading cycles (and recollections of New Orleans), in AER:Insights, by Abdulkadiroğlu, Che, Pathak, Roth and Tercieux

A decade ago I was part of the team that designed the new school choice system for the New Orleans Recovery School District.  On the District side, the effort was led by Gabriela (Gaby) Fighetti. The design team was organized by the (then) Institute for Innovation in Public School Choice (IIPSC), led by Neil Dorosin. The heavy lifting on the design was done by Atila Abdulkadiroğlu and Parag Pathak.  Until the district expanded and developed more complex requirements for expressing priorities (and we had to switch to a deferred acceptance algorithm) the design was based on a top trading cycles (TTC) mechanism. It was the first time I know of that TTC was adopted and deployed in a widely used market design. It came to be called OneApp (since it replaced the old system of applications to each school with one application followed by the matching algorithm).

Some of the data from that system make their way into this new (primarily theory) paper, about some of the distinctive virtues of top trading cycles. The paper itself is a merged effort between the New Orleans design team, and work on TTC initiated separately by various combinations of Che, Tercieux and Abdulkadiroğlu.

Efficiency, Justified Envy, and Incentives in Priority-Based Matching†

By Atila Abdulkadiroğlu, Yeon-Koo Che, Parag A. Pathak, Alvin E. Roth and Olivier Tercieux, 

American Economic Review: Insights, December, 2020, 2, (4), 425–442.

Abstract: Top Trading cycles (TTC) is Pareto efficient and strategy-proof in priority-based matching, but so are other mechanisms including serial dictatorship. We show that TTC minimizes justified envy among all Pareto-efficient and strategy-proof mechanisms in one-to-one matching. In many-to-one matching, TTC admits less justified envy than serial dictatorship in an average sense. Empirical evidence from New Orleans OneApp and Boston Public Schools shows that TTC has significantly less justified envy than serial dictatorship. 

The first footnote of the paper suggests something of it's long history, and says in part:

"This paper supersedes “The Role of Priorities in Assigning Indivisible Objects: A Characterization of Top Trading Cycles,” cited by others as Abdulkadiroglu, Atila, and ˇ Yeon-Koo Che (2010) or Abdulkadiroglu, Atila, ˇ Yeon-Koo Che, and Olivier Tercieux (2010), and “Minimizing Justified Envy in School Choice: The Design of New Orleans’ OneApp” (2017) by Abdulkadiroglu, Atila, ˇ Yeon-Koo Che, Parag A. Pathak, Alvin E. Roth, and Olivier Tercieux. Roth is a member of the scientific advisory board of the Institute for Innovation in Public School Choice (IIPSC). IIPSC was involved in designing OneApp in New Orleans. Abdulkadiroglu, Pathak, and Roth also advised Boston Public Schools and New York City’s Department of Education on designing their student assignment systems, discussed herein. This article does not represent the views of the New Orleans Recovery School District or any other school district."

And here's a paragraph that offers a different kind of historical context:

"In 2011–2012, the New Orleans Recovery School District pioneered a unified enrollment process called OneApp, integrating admissions to all types of schools under a single offer system. Officials identified three major priority groups: sibling, applying from a closing school, and geography. The discussion about mechanism centered on the trade-off between efficiency and eliminating justified envy, and eventually TTC was selected based on the desire for “as many students as possible to get into their top choice school” (New Orleans Recovery School District 2012a). Vanacore (2011) and Vanacore (2012) provide additional details."

In conclusion:

"In the field, there is growing momentum for DA over TTC (see Abdulkadiroglu 2013 and Pathak 2017). This trend may be driven by a first-mover advantage of DA and its use in other contexts. New York City and Boston adopted DA in 2003 and 2005, and DA is widely used in residency matching (Roth and Peranson 1999). In 2013, New Orleans also switched from TTC to DA. One of the most important reasons for this switch involved challenges in explaining how TTC handles priorities.  Under DA, officials could explain that an applicant did not obtain an assignment at a higher ranked seat because another applicant with higher priority was assigned to that seat. At the time of the change, a clear explanation of how TTC reflects priorities was not available.

"It remains to be seen whether TTC will be used in the field again. But policymakers cannot ignore efficiency, which TTC delivers but DA does not. For this reason, TTC should remain a serious policy option. Our formal results may make it easier to explain how TTC incorporates priorities. It’s possible that TTC would have been chosen in some settings with knowledge of this result, and at the very least, advocates now have a new argument in its favor."

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Some long ago posts on school choice in New Orleans:

Saturday, November 19, 2011

School choice in New Orleans

Thursday, February 2, 2012

School choice design in New Orleans Recovery School District

Tuesday, February 7, 2012

New Orleans launches its new school choice process

Tuesday, April 17, 2012 

School Choice in New Orleans with top trading cycles

Friday, May 11, 2012

New Orleans school choice goes live

Thursday, September 27, 2012

New Orleans School Choice: bringing one application process to all schools

Friday, February 15, 2013

School choice in New Orleans, evolving

Sunday, May 12, 2013

Looking back at the first year of New Orleans' One App school choice system

Tuesday, October 1, 2013

New Orleans School Choice in the WSJ

Wednesday, February 11, 2015

School Choice Index from Brookings

Tuesday, August 18, 2015

A look back at school choice in New Orleans

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†Code for: Efficiency, Justified Envy, and Incentives in Priority-Based Matching

Principal Investigator(s):  r Principal Investigator(s) Atila Abdulkadiroglu, Duke University; Yeon-Koo Che, Columbia University; Parag Pathak, MIT; Alvin Roth, Stanford University; Olivier Tercieux, Paris School of Economics