Showing posts sorted by date for query Pathak. Sort by relevance Show all posts
Showing posts sorted by date for query Pathak. Sort by relevance Show all posts

Wednesday, November 20, 2024

School choice, school quality, and student performance

 School choice, which allows children to move away from neighborhood schools, doesn't by itself improve school quality, although it may allow under-performing schools to become smaller, which may make them easier to fix, or to close.

Here are two  recent assessments of two different transportation options available to Boston public school students. Both conclude that school quality matters.

The first is an op-ed in the Boston Globe saying that school choice among Boston public schools has led to too much transportation and not enough innovation. (One of the authors, Parag Pathak, played a critical role in designing  Boston's current school choice system.)

Boston needs to reexamine school assignment system
Rather than investing in high-cost travel to send students to schools across the city, Boston should consider redirecting those funds toward improving schools close to home.
By Joshua Angrist, Parag Pathak and Amanda Schmidt 

"Boston’s school assignment system has changed considerably since the 1970s. Busing today is voluntary: Students can choose to attend schools far from where they live as well as a range of neighborhood schools. This choice allows historically disadvantaged students to attend schools with more peers of different backgrounds, an option that many choose. Roughly three-quarters of students opted to enroll in non-neighborhood schools in the 2000s and 2010s. A recent study by our organization, MIT Blueprint Labs, shows that today’s assignment system works in the sense of facilitating integration.

However, the costs of the current system are high. Among the 100 US school districts with the highest enrollment, Boston maintains the greatest per-student transportation costs in the country. As of 2021, the city spent over $2,000 per student on travel, equivalent to 8 percent of per-pupil school spending.

Furthermore, the educational gains afforded by district-wide choice are less clear than the integration gains. Our research, which uses credible, randomized methods designed by Blueprint Labs to gauge the causal effect of enrollment at different types of schools, paints a nuanced picture of the benefits of travel to non-neighborhood schools. Black and Hispanic students who travel to a non-neighborhood school have more white and Asian peers than they otherwise would. But travel does not impact learning as measured by MCAS scores, high school graduation rates, or college enrollment. We argue that this is because in the current BPS choice system — unlike the separate and unequal system of 1974 — the schools students travel to are no better than those nearby.

...

"The vast sums that now go to cross-neighborhood transportation might be better spent. The city might instead invest in programs with proven educational benefits. Saga Education’s effective high-dosage tutoring program, for example, cost just $1,800 per student in 2023. This spending may do more to close racial achievement gaps than non-neighborhood assignment.

"Some might counter that choice is intrinsically valuable and that neighborhood schools are likely to be more segregated than the schools that many historically disadvantaged families choose today. These undeniable benefits must be weighed, however, against alternative uses of the money that flows to busing. Boston schools have improved greatly since 1974: Dropout rates for all students have declined, and gaps by race, while still present, have narrowed. School assignment plans originating in 1974 may therefore be less useful today. It’s time to consider changing transportation policy in light of these changes in the city’s education landscape."

####### 

Here's a paper, about a different transportation program available to some* Boston students, which takes them out of Boston to schools in neighboring towns and cities in the metropolitan area.  Moving to those suburban schools apparently improves student performance more than moving from one Boston school to another.

 Busing to Opportunity? The Impacts of the METCO Voluntary School Desegregation Program on Urban Students of Color  by Elizabeth Setren, NBER Working Paper 32864, DOI 10.3386/w32864, August 2024

Abstract: School assignment policies are a key lever to increase access to high performing schools and to promote racial and socioeconomic integration. For over 50 years, the Metropolitan Council for Educational Opportunity (METCO) has bussed students of color from Boston, Massachusetts to relatively wealthier and predominantly White suburbs. Using a combination of digitized historical records and administrative data, I analyze the short and long run effects of attending a high-performing suburban school for applicants to the METCO program. I compare those with and without offers to enroll in suburban schools. I use a two-stage least squares approach that utilizes the waitlist assignment priorities and controls for a rich set of characteristics from birth records and application data. Attending a suburban school boosts 10th grade Math and English test scores by 0.13 and 0.21 standard deviations respectively. The program reduces dropout rates by 75 percent and increases on-time high school graduation by 13 percentage points. The suburban schools increase four-year college aspirations by 17 percentage points and enrollment by 21 percentage points. Participation results in a 12 percentage point increase in four-year college graduation rates. Enrollment increases average earnings at age 35 by $16,250. Evidence of tracking to lower performing classes in the suburban schools suggests these effects could be larger with access to more advanced coursework. Effects are strongest for students whose parents did not graduate college."

*"The program is very popular: 50 percent of Black youth in Boston applied and 20 percent of Latinx youth in the past 20 years"

...

"After demonstrating the comparability of students with and without offers, I estimate the impact of receiving an offer to the program and the impact of participating in the program. Offers to enroll in suburban districts serve as instrumental variables and all models control for approximate waitlist position using age at the time of application, gender, and race controls. Therefore the estimates compare the outcomes of those who enroll in METCO to applicants with similar demographics, who applied at similar times, but did not enroll because they were not selected from the waitlist."

Thursday, October 17, 2024

NBER market design workshop at Stanford, tomorrow and Saturday

 Tomorrow and Saturday, market design at Stanford...

NBER Market Design Working Group Meeting, Fall 2024   DATE October 18-19, 2024

LOCATION SIEPR, Stanford University, Koret-Taube Conference Center, 366 Galvez Street

ORGANIZERS Michael Ostrovsky and Parag A. Pathak

  Format: 35 minute presentation, followed by 10 minute discussion.

Friday, October 18

9:00 am Continental Breakfast

9:30 am Designing Dynamic Reassignment Mechanisms: Evidence from GP Allocation, Ingrid Huitfeldt, University of Oslo, Victoria Marone, University of Texas at Austin and NBER, Daniel C. Waldinger, New York University and NBER

10:15 am Dynamic Matching with Post-allocation Service and its Application to Refugee Resettlement, Kirk C. Bansak, University of California, Berkeley, Soonbong Lee, Yale University, Vahideh Manshadi, Yale University, Rad Niazadeh, University of Chicago, Elisabeth Paulson, Harvard University

11:00 am  Break

11:30 am  Social Learning in Lung Transplant Decisions, Laura Doval, Columbia University, Federico Echenique, University of California, Berkeley, Wanying Huang, Monash University, Yi Xin, California Institute of Technology

12:15 pm  Endogenous Priority in Centralized Matching Markets: The Design of the Heart Transplant Waitlist, Kurt R. Sweat, Johns Hopkins University

1:00 pm Lunch

2:00 pm  Mechanism Reform: An Application to Child Welfare, E. Jason Baron, Duke University and NBER, Richard Lombardo, Harvard University, Joseph P. Ryan, University of Michigan, Jeongsoo Suh, Duke University, Quitze Valenzuela-Stookey, University of California, Berkeley

2:45 pm  The Competitive Core of Combinatorial Exchange, Simon Jantschgi, University of Oxford, Thanh T. Nguyen, Purdue University, Alexander Teytelboym, University of Oxford

3:30 pm  Break

4:00 pm  "Bid Shopping" in Procurement Auctions with Subcontracting(slides), Raymond Deneckere, University of Wisconsin-Madison, Daniel Quint, University of Wisconsin-Madison

4:45 pm Ads in Conversations: Market Thickness and Match Quality, Martino Banchio, Bocconi University, Aranyak Mehta, Google Research, Andres Perlroth, Google Research

5:30 pm Adjourn

7:00 pm  Group Dinner  Il Fornaio Palo Alto, 520 Cowper Street, Palo Alto, CA

Saturday, October 19

8:30 am Continental Breakfast

9:00 am Dynamic Auctions with Budget-Constrained Bidders: Evidence from the Online Advertising Market, Shunto J. Kobayashi, Boston University, Miguel Alcobendas, Yahoo Research

9:45 am The Welfare Effects of Sponsored Product Advertising, Chuan Yu, Harvard University

10:30 am Break

11:00 am An Empirical Analysis of the Interconnection Queue, Sarah Johnston, University of Wisconsin-Madison, Yifei Liu, University of Wisconsin-Madison, Chenyu Yang, University of Maryland

11:45 am  Iterative Network Pricing for Ridesharing Platforms Chenkai Yu, Columbia University, Hongyao Ma, Columbia University

12:30 pm  Lunch and Adjourn

Thursday, January 4, 2024

Topics in Market Design: Econ 287/365: Winter quarter, Itai Ashlagi

Itai Ashlagi will be teaching Econ 287 this quarter, on topics in market design.  It's highly recommended.

He writes that the syllabus below is very tentative, and will depend in part on how many of the enrolled students took Econ 285 (Ostrovsky and Roth) in the Fall (back in 2023:-)

Topics in Market Design 2024, Itai Ashlagi

Market design is a field that links the rules of the of the marketplace to understand frictions, externalities and more generally economic outcomes. The course will provide theoretical foundations on assignment and matching mechanisms as well as mechanism design. There will be emphasis on theories at the intersection of economics, CS and operations as well as applications that arise in labor markets, organ allocation, platforms.

The class will further expose students to timely market design challenges and will we will host a few guest lectures. The class offers an opportunity to begin a research project. Students will reading critique papers, present papers and write a final paper.

Lectures: Monday 10:30am-1:20pm Shriram 052

Course requirements: (i) reading and writing critiques about papers, (ii), presenting papers in class, and (iii) a term paper.

Instructor: Itai Ashlagi. iashlagi@stanford.edu

Some potential papers for presenting:

Equity and Efficiency in Dynamic Matching: Extreme Waitlist Policies, Nikzad and Strack.

Eliminating Waste in Cadaveric Organ Allocation, Shi and Yin

Pick-an-object mechanisms, Bo and Hakimov

Monopoly without a monopolist, Huberman, Leshno and Moallemi

The College Portfolio Problem, Ali and Shorrer

Equal Pay for Similar Work, Passaro, Kojima, and Pakzad-Hurson

Auctions with Withdrawal Rights: A Foundation for Uniform Price, Haberman and Jagadessan.

Optimal matchmaking strategy in two-sided marketplaces, Shi

Practical algorithms and experimentally validated incentives for equilibrium-based fair division (ACEEI),

Budish, Gao, Othman, Rubinstein

Congestion pricing, carpooling, and commuter welfare, Ostrovsky and Schwarz

Artificial intelligence and auction design, Banchio and Skrzypacz

Selling to a no-regret buyer, Braverman et al.

Dynamic matching in overloaded waiting lists, Leshno

The regulation of queue size by levying tolls, Naor

Optimal search for the best alternative, Weitzman

Whether or not to open Pandora’s box, Doval

Descending price optimally coordinates search, Kleinberg, Waggoner, Weyl

Market Failure in Kidney Exchange? Nikhil Agarwal, Itai Ashlagi, Eduardo Azevedo, Clayton Featherstone and Omer Karaduman

Choice Screen Auctions, Michael Ostrovsky

Incentive Compatibility of Large Centralized Matching Markets, Lee

Tentative schedule:

Week 1: Two-sided matching, stability and large markets.

Week 2: One-sided matching, duality, optimization and constraints.

Week 3: Multi-item auctions, auction design, revenue equivalence, optimal auctions, interdependent

valuations.

Week 4: Congestion, dynamic matching.

Week 5: Waitlists, search and learning.

Week 6: Foundations of mechanism design.

Week 7: Robustness in implementation

Weeks 8-10: Projects

We will host several guest lectures. Presentations of papers will take place throughout the course.

Background references

1. List of (mostly applied) papers are given in a separate document.

2. Books

Roth, Alvin E.and Marilda A. Oliveira Sotomayor, Two-sided matching: A study in game-theoretic modeling and analysis. No. 18. Cambridge University Press, 1992.

Vijay Krishna, Auction Theory, 2010.

Tilman Borgers, An Introduction to Mechanism Design by Tilman Borgers.

Milgrom, Paul, Putting Auction Theory to Work, 2004.

3. Papers

(a) Introduction

Roth, Alvin E. The Economist as Engineer: Game Theory, Experimentation, and Computation as Tools for Design Economics. Econometrica, 70(4), 2002. 1341-1378.

Klemperer, Paul, What Really Matters in Auction Design?, Journal of Economic Perspectives, 16(1): 169-189, 2002.

Weitzman, Martin, Is the Price System or Rationing More Effective in Getting a Commodity to Those Who Need it Most?, The Bell Journal of Economics, 8, 517-524, 1977.

(b) Stable matching and assignment

Gale, David and Lloyd Shapley, College Admissions and the Stability of Marriage, American Mathematical Monthly, 69: 9-15,1962.

Roth and Sotomayor, Chapters 2-5.

Hylland, Aanund, and Richard Zeckhauser. The efficient allocation of individuals to positions, The Journal of Political Economy, 293-314,1979.

Roth, Alvin E., The Evolution of the Labor Market for Medical Interns and Residents: A Case Study in Game Theory. Journal of Political Economy, 92: 991-1016, 1984.

Kojimam, Fuhito and Parag A. Pathak. Incentives and stability in large two-sided matching markets. American Economic Review, 99:608-627, 2009

Abdulkadiroglu, Atila and Tayfun Sonmez. School choice: A mechanism design approach. American Economic Review, 93:729-747, 2003.

Abdulkadiroglu, Atila , Parag A. Pathak, and Alvin E. Roth. The New York City high school match. American Economic Review, 95:364-367, 2005.

Ashlagi, Itai, Yash Kanoria, and Jacob D. Leshno. Unbalanced random matching markets: The stark effect of competition, Journal of Political Economy,

Ashlagi, Itai and Peng Shi. Optimal allocation without money: An engineering approach. Management Science, 2015.

Peng Shi and Nick Arnosti. Design of Lotteries and Waitlists for Affordable Housing Allocation, Management Science, 2019.

Peng Shi, Assortment Planning in School Choice, 2019.

Ashlagi, Itai, and Afshin Nikzad. What matters in tie-breaking rules? how competition guides design, 2015.

(c) Auctions and revenue equivalence

Myerson, Roger Auction Design, Mathematics of Operations Research, 1981.

Milgrom, Paul. Putting Auction Theory to Work. Chapter 2-3.

W. Vickrey, Counterspeculation, auctions, and competitive sealed tenders, The Journal of Finance, 16(1) 8–37, 1961.

R. Myerson, Optimal auction design, Mathematics of Operations research, 1981.

J. Bulow and J. Roberts, The simple economics of optimal auctions, Journal of Political Economy, 1989.

J. Bulow and P. Klemperer, Auctions vs negotiations, American Economic Review, 1996.

P.R. McAfee and J. McMillan, Auctions and bidding, Journal of Economic Literature 1987.

P. Milgrom and R. Weber, A theory of auctions and competitive bidding, Econometrica, 1982.

Roth, A. E. and A. Ockenfels, Late-Minute Bidding and the Rules for Ending Second-Price Auctions: Evidence from eBay and Amazon.” American Economic Review, 92(4): 1093-1103, 2002.

(d) Mechanism design

Vickrey, William (1961): Counterspeculation, Auctions and Competitive Sealed Tenders. Journal of Finance, 16(1): 8-37.

Ausubel, Larry and Paul Milgrom, The Lovely but Lonely Vickrey Auction. in Cramton et. al Combinatorial Auctions, 2005.

J.C. Rochet, A necessary and sufficient condition for rationalizability in a quasi-linear context”, 1987.

K. Roberts, The characterization of implementable choice rules”, 1979.

F. Gul and E. Stacchetti, Walrasian equilibrium with gross substitutes, Journal of Economic Theory, 1999.

I. Ashlagi, M. Braverman, A,. Hassidim and D. Monderer, Monotonicity and implementability, Econometrica, 2011.

(e) Dynamic mechanism design and dynamic pricing

G. Gallego and G. Van Ryzin, Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Management science, 40(8), 999-1020, 1994.

S. Board and A. Skrzypacz, Revenue management with forward-looking buyers, Unpublished manuscript, Stanford University,2010.

A. Gershkov, B. Moldovanu, P. Strack, Revenue Maximizing Mechanisms with Strategic Customers and Unknown, Markovian Demand

D. Bergemann and J. Valimaki, The dynamic pivot mechanism, Econometrica, 2010.

A. Gershkov and B. Moldovanu, Dynamic Revenue Maximization with Heterogeneous Objects: A Mechanism Design Approach, 168-198, 2009.

F. Gul, H. Sonnenschein, R. Wilson, Foundations of dynamic monopoly and the Coase conjecture, J. of Economic Theory, 1986.

D. Besanko and W. L. Whinston, Optimal price skimming by a monopolist facing rational consumers, Management Science, 1990.

(f) Dynamic matching

Itai Ashlagi and Alvin E. Roth. New challenges in multihospital kidney exchange. American Economic Review, 102:354-359, 2012

Nikhil Agarwal, Itai Ashlagi, Eduardo Azevedo, Clayton Featherston and Omer Karaduman. Market Failure in Kidney Exchange, 2018.

Anderson, R., Ashlagi, I., Gamarnik, D. and Kanoria, Efficient Dynamic Barter Exchange, Operations Research, 2015.

Mohammad Akbarpour, Shengwu Li, and Shayan Oveis Gharan. Dynamic matching market design. JPE, 2019.

Baccara, Mariagiovanna, SangMok Lee, and Leeat Yariv, Optimal dynamic matching, 2015.

Jacob Leshno, Dynamic Matching in Overloaded Waiting Lists, 2017.


Monday, November 13, 2023

More on Realtor's contracts and practices, and the recent court decision

 Here are some further articles with some details about the recent court decision that real estate contracts are anticompetitive. Both are from the Washington Post:

Jury awards $1.8B in realty case that could shake up brokerage commissions. A Kansas City jury unanimously found that the National Association of Realtors and other organizations conspired to artificially inflate home sale commissions   By Julian Mark 

"The plaintiffs pointed to an NAR rule that required sellers to make a nonnegotiable commission offer before listing homes on the property database, the Multiple Listing Service, or MLS, which feeds widely used real estate sites including Zillow. That commission hovers around 5 to 6 percent of the sale price and is paid by the home seller to the sellers’ agent and the buyers’ agent. If sellers do not agree to the commission terms, they go virtually unseen in the market, Ketchmark said.

"The rule has stifled competition and has resulted in higher prices, the plaintiffs alleged. They argued that if the rule were not in place, buyers would pay commissions to their own agents while buyers’ agents would have to compete by offering lower rates. The lawsuit pointed to countries whose total real estate commissions average 1 to 3 percent, such as the United Kingdom, Singapore, the Netherlands, Australia and Belgium.

########

Real estate industry trembles over commissions on home sales. After jurors recently found that there was a scheme to inflate commissions, experts say changes could shake up the business  By Julian Mark

"The judge overseeing the case has the power to issue an injunction that could break up the century-old “bundled” or “cooperative” commissions system, in which sellers’ and buyers’ agents split a commission that typically ranges between 5 and 6 percent of the home sale price. 

...

"The cooperative compensation structure was established in 1913, when National Association of Real Estate Exchanges, the precursor to NAR, said its member agents should share commissions with agents that produced buyers, according to a 2015 study by economists Panle Jia Barwick and Maisy Wong. The commissions rate hit 5 percent in 1940 and has remained virtually unchanged ever since, according to the study.

"Commissions work differently in countries such as the United Kingdom, where sellers pay typically less than 2 percent, and buyers pay their own agents, according to the study."

######

And here's the cited paper:

Barwick, Panle Jia, Parag A. Pathak, and Maisy Wong. 2017. "Conflicts of Interest and Steering in Residential Brokerage." American Economic Journal: Applied Economics9 (3): 191-222.

Abstract: This paper documents uniformity in real estate commission rates offered to buyers' agents using 653,475 residential listings in eastern Massachusetts from 1998–2011. Properties listed with lower commission rates experience less favorable transaction outcomes: they are 5 percent less likely to sell and take 12 percent longer to sell. These adverse outcomes reflect decreased willingness of buyers' agents to intermediate low commission properties (steering), rather than heterogeneous seller preferences or reduced effort of listing agents. Offices with large market shares purchase a disproportionately small fraction of low commission properties. The negative outcomes for low commissions provide empirical support for regulatory concerns over steering.

Thursday, September 7, 2023

Navigating NYC school choice: advice for families

 Each year a new cohort of families has to navigate school choice in New York City.  The city offers lots of resources for gathering information.  One advantage of employing methods that make it safe to reveal true preference orders is that at least one aspect of the process is straightforward. (Of course, constructing a list of 12 schools out of the many available isn't easy.)

The NY Times offers a guide, which is full of information on how to go about gathering information with which to form preferences over schools:

Applying to N.Y.C. Public Schools Can Feel Daunting. Here’s What to Know. What matters when choosing a school? How should you compare options? And what’s the best strategy for getting your first choice?  By Troy Closson, Sept. 5, 2023,

"What’s the best strategy when applying?

"You should rank schools and programs in order of your true preference. There is no better approach. Students are considered for a lower choice only if a higher ranked school does not have space.

"Admissions experts suggest creating a complete list of 12 schools with a balance of programs, priorities and demand per seat, which you can find on MySchools. Apply by the deadline; there is also no benefit to applying earlier"


HT: Parag Pathak

*******

Another resource:

Abdulkadiroglu, Atila , Parag A. Pathak, and Alvin E. Roth, "Strategy-proofness versus Efficiency in Matching with Indifferences: Redesigning the NYC High School Match,'' American Economic Review, 99, 5, Dec. 2009, pp1954-1978. 

Monday, September 4, 2023

Covid medication: allocation, information, hesitancy, and uptake: what are some things we have learned?

 I've posted before about how informational advertising about vaccine availability and safety seems to have had a positive effect on vaccination rates among disadvantaged populations. There was particular concern in the U.S. at one point that Black people were less likely to receive vaccines and other medications than other Americans.

Today's post collects several papers about the effect of randomly allocating invitations for temporarily scarce Covid medications, while giving members of disadvantaged groups a higher probability of receiving an invitation.  Included will be an editorial warning us that we shouldn't be satisfied to judge the outcome of a market design by its intended outcome ("Moving Beyond Intent and Realizing Health Equity").

There are market design lessons in these last few years of Covid experience that I hope will help make the responses to future pandemics more effective. Not least of these is that the allocation of public health  and medical resources turns out to be quite different from  the allocation of other kinds of resources, in many important ways that reflect the broader economic and social environments in which different kinds of allocation takes place.

###

Here's a paper in the most recent issue of JAMA Health Forum, by a team that includes both medical professionals and market designers.

Weighted Lottery to Equitably Allocate Scarce Supply of COVID-19 Monoclonal Antibody , by Erin K. McCreary, PharmD1; Utibe R. Essien, MD, MPH2,3; Chung-Chou H. Chang, PhD4,5; Rachel A. Butler, MHA, MPH6; Parag Pathak, PhD7; Tayfun Sönmez, PhD8; M. Utku Ünver, PhD8; Ashley Steiner, BS9; Maddie Chrisman, PT, DPT10; Derek C. Angus, MD, MPH11; Douglas B. White, MD, MAS11, JAMA Health Forum. 2023;4(9):e232774. Sept. 1, doi:10.1001/jamahealthforum.2023.2774 

"Objective  To describe the development and use of a weighted lottery to allocate a scarce supply of tixagevimab with cilgavimab as preexposure prophylaxis to COVID-19 for immunocompromised individuals and examine whether this promoted equitable allocation to disadvantaged populations.

"Design, Setting, and Participants  This quality improvement study analyzed a weighted lottery process from December 8, 2021, to February 23, 2022, that assigned twice the odds of drug allocation of 450 tixagevimab with cilgavimab doses to individuals residing in highly disadvantaged neighborhoods according to the US Area Deprivation Index (ADI) in a 35-hospital system in Pennsylvania, New York, and Maryland. In all, 10 834 individuals were eligible for the lottery. Weighted lottery results were compared with 10 000 simulated unweighted lotteries in the same cohort performed after drug allocation occurred.

"Main Outcomes:  Proportion of individuals from disadvantaged neighborhoods and Black individuals who were allocated and received tixagevimab with cilgavimab.

"Results:  Of the 10 834 eligible individuals, 1800 (16.6%) were from disadvantaged neighborhoods and 767 (7.1%) were Black. Mean (SD) age was 62.9 (18.8) years, and 5471 (50.5%) were women. A higher proportion of individuals from disadvantaged neighborhoods was allocated the drug in the ADI-weighted lottery compared with the unweighted lottery (29.1% vs 16.6%; P < .001). The proportion of Black individuals allocated the drug was greater in the weighted lottery (9.1% vs 7.1%; P < .001). Among the 450 individuals allocated tixagevimab with cilgavimab in the ADI-weighted lottery, similar proportions of individuals from disadvantaged neighborhoods accepted the allocation and received the drug compared with those from other neighborhoods (27.5% vs 27.9%; P = .93). However, Black individuals allocated the drug were less likely to receive it compared with White individuals (3 of 41 [7.3%] vs 118 of 402 [29.4%]; P = .003).

...

"Conclusions and Relevance:  The findings of this quality improvement study suggest an ADI-weighted lottery process to allocate scarce resources is feasible in a large health system and resulted in more drug allocation to and receipt of drug by individuals who reside in disadvantaged neighborhoods. Although the ADI-weighted lottery also resulted in more drug allocation to Black individuals compared with an unweighted process, they were less likely to accept allocation and receive it compared with White individuals. Further strategies are needed to ensure that Black individuals receive scarce medications allocated."

...

"The lottery was repeated over several weeks, but we chose to examine only the first assignment. The interpretation of later rounds is problematic because eventually all individuals were offered tixagevimab with cilgavimab. By focusing on the first draw, we can specifically evaluate whether the intent of the lottery was met."

##############

Closely related reports:

White, D.B., McCreary, E.K., Chang, C.C.H., Schmidhofer, M., Bariola, J.R., Jonassaint, N.N., Persad, G., Truog, R.D., Pathak, P., Sonmez, T. and Unver, M.U., 2022. A multicenter weighted lottery to equitably allocate scarce COVID-19 therapeutics. American Journal of Respiratory and Critical Care Medicine, 206(4), pp.503-506.

Rubin, E., Dryden-Peterson, S.L., Hammond, S.P., Lennes, I., Letourneau, A.R., Pathak, P., Sonmez, T. and Ünver, M.U., 2021. A novel approach to equitable distribution of scarce therapeutics: institutional experience implementing a reserve system for allocation of COVID-19 monoclonal antibodies. Chest, 160(6), pp.2324-2331.*

White, D.B. and Angus, D.C., 2020. A proposed lottery system to allocate scarce COVID-19 medications: promoting fairness and generating knowledge. Jama, 324(4), pp.329-330.

###########

And here's an editorial in the same issue of JAMA Health Forum as the most recent article, pointing out that less-disadvantaged patients among those living in census blocks identified as disadvantaged (in particular  commercially insured and White patients) were much more likely to receive the treatment:

Moving Beyond Intent and Realizing Health Equity, by Atheendar S. Venkataramani, MD, PhD, Invited Commentary, September 1, 2023, JAMA Health Forum. 2023;4(9):e232525. doi:10.1001/jamahealthforum.2023.2525

"In a study published in this issue of JAMA Health Forum, McCreary and colleagues3 report on a landmark effort at the University of Pittsburgh Medical Center (UPMC) to distribute equitably a scarce monoclonal antibody resource, tixagevimab with cilgavimab, for COVID-19 preexposure prophylaxis in immunocompromised individuals. In December 2021, UPMC received an allotment of 450 doses of tixagevimab with cilgavimab from the Pennsylvania Department of Health to cover a large health system with 35 hospitals and 800 outpatient facilities through February 2022. In an ex ante effort to mitigate health disparities and respond to guidance from the Commonwealth of Pennsylvania to allocate scarce resources in a manner that accounts for multiple ethical objectives, UPMC convened an advisory group of clinicians, community stakeholders, and experts in community outreach.

...

"The lottery was constructed using the Area Deprivation Index (ADI) to ensure that patients in highly disadvantaged neighborhoods had an equal opportunity to access tixagevimab with cilgavimab. Patients living in neighborhoods with ADIs above a specific cutoff that has been shown to best target less affluent, rural, and Black patients received 2 entries in the lottery, compared with 1 entry for patients in more advantaged neighborhoods. In their study, McCreary and colleagues3 found that this process resulted in equitable access: similar proportions of individuals in more advantaged and more disadvantaged neighborhoods (about 28% in each group) received tixagevimab with cilgavimab during the study period, although Black patients who were allocated the drug in the lottery were significantly less likely to receive it compared with White patients (7.3% vs 29.4%).

...

"Having identified its patient population, UPMC required only patient addresses as well as publicly available data on ADIs to implement the lottery intervention. The ADIs are defined at the census block group level, which include about 1000 residents on average. Thus, UPMC was able to achieve equitable opportunity to access tixagevimab with cilgavimab across small localities with very different socioeconomic profiles.

...

On the other hand, higher-resolution data that specifically measure the types of intersecting, reinforcing, and cumulative disadvantages faced by historically marginalized groups5 may be needed to achieve equitable outcomes across other dimensions, such as race and ethnicity. Within census blocks, patients assigned the same ADI levels but who may have faced relatively fewer structural barriers compared with Black patients or patients receiving Medicaid—namely, commercially insured and White patients—were more likely to access tixagevimab with cilgavimab conditional on being allocated to receive it in the lottery

...

"The lower rates of drug receipt among Black patients also underscores the importance of complementary investments and operational decisions to address additional structural barriers to accessing medical technology.

...

"The study by McCreary and colleagues3 represents the type of courageous and rigorous work that is needed to chart a path forward in determining how best to bridge the access gap for leading-edge medical technology. Future work would benefit from the same type of clarity demonstrated in this study by including clear definitions for how equity should be operationalized, attempting to address fragmentation between clinical services and services that address social drivers of health, aligning incentives, and addressing historical barriers that have made it difficult to achieve health equity."

##########

*Earlier:

Saturday, August 14, 2021


Tuesday, July 25, 2023

Incentives in matching markets: Counting and comparing manipulating agents by Bonkoungou and Nesterov

 Here's a paper that caught my eye in the current issue of Theoretical Economics, Volume 18, Issue 3 (July 2023)

Incentives in matching markets: Counting and comparing manipulating agents by Somouaoga Bonkoungou and Alexander Nesterov

Abstract: Manipulability is a threat to the successful design of centralized matching markets. However, in many applications some manipulation is inevitable and the designer wants to compare manipulable mechanisms to select the best among them.  We count the number of agents with an incentive to manipulate and rank mechanisms by their level of manipulability. This ranking sheds a new light on practical design decisions such as the design of the entry-level medical labor market in the United States, and school admissions systems in New York, Chicago, Denver, and many cities in Ghana and the United Kingdom.

"First, we consider the college admissions problem where both students and schools are strategic agents (Gale and Shapley (1962)) and schools can misreport their preferences as well as their capacities. We show that when all manipulations (by students as well as by schools) are considered, the student-proposing Gale–Shapley (GS) mechanism has the smallest number of manipulating agents among all stable matching mechanisms (Theorem 1). Dubins and Freedman (1981) and Roth (1982) show that this mechanism is not manipulable by students. This result was one of the main arguments in favor of its choice for the NRMP. However, it also has the largest number of manipulating schools among all stable mechanisms (Pathak and Sönmez (2013)). Our result still supports its choice when all strategic agents are considered. What is more, it is still the best choice even when schools can only misreport their capacities, but not their preferences. All these conclusions carry over to the general model where, in addition, students face ranking constraints: although the student-proposing GS mechanism is now manipulable by students, it is still the least manipulable mechanism.

"Second, we consider the school choice problem (Abdulkadiroglu and Sönmez ˘ (2003)) where students are the only strategic agents and also face ranking constraints. Historically, many school choice systems have used the constrained immediate acceptance (Boston) mechanism, but over time shifted toward the constrained student proposing GS mechanisms and relaxing the constraint. We demonstrate that the number of manipulating students (Theorem 2) weakly decreased as a result of these changes."


Thursday, July 20, 2023

Pitfalls of digital scholarship: Machiavelli and Matching

 One of the alluring features of the digitization of texts is that they can be searched, their citations can be examined and cross-referenced, and facts about texts, and the literatures that they comprise, can be detected.  But of course,  digital searches can also lead you astray.

Something like that may have happened in this study of business ethics. (Relax, this isn't a blog post about questionable ethics in science.)  

Maity, M., Roy, N., Majumder, D. et al. Revisiting the Received Image of Machiavelli in Business Ethics Through a Close Reading of The Prince and Discourses. J Bus Ethics (2023). https://doi.org/10.1007/s10551-023-05481-2

The authors of the above paper searched in journals related to business and economics, for papers  about Niccolò Machiavelli, the 16th century author of The Prince, whose name has entered into the language to describe the kind of advice he gave: Machiavellian.

Looking at the most highly cited papers, and their network of co-citations (i.e. citations of each other) they find three clusters in the Machiavelli literature. They note that two of the clusters include many citations from one to the other, but that the third cluster (in green) is not connected to the other two.  The third cluster they label "matching problems in markets." (In fairness, the authors of the paper note this separation, and concentrate their analysis on the first two clusters.)



Here are the papers in the clusters. The papers in cluster 3 will be familiar to many readers of this blog.


Here in larger font is cluster 3, of papers on "Matching problems in markets": Abdulkadiroǧlu et al. (2003), Abdulkadiroǧlu and Sönmez (2003), Dubins and Freedman, (1981), Gale and Shapley (1962), Gale and Sotomayor (1985a), Gale and Sotomayor (1985b), Kojima and Pathak (2009), Roth (1982, 1984a, 1984b, 1985, 2002), Roth and Sotomayor (1990), Roth and Peranson (1999).

This cluster indeed contains well cited papers that cite one another. Yet I'm pretty sure that none of them cite Machiavelli, nor would most readers think that they connect to The Prince.

This latter cluster was almost surely included because of the titles of two of the included papers, neither of which in fact cites Machiavelli. (His name made it into the titles in a sort of jokey way, having to do with the fact that players in matching games may sometimes profit from behaving unstraightforwardly.) They are:

Dubins, Lester E., and David A. Freedman. "Machiavelli and the Gale-Shapley algorithm." The American Mathematical Monthly 88, no. 7 (1981): 485-494.

and

Gale, David, and Marilda Sotomayor. "Ms. Machiavelli and the stable matching problem." The American Mathematical Monthly 92, no. 4 (1985): 261-268.


But Machiavelli might be proud to be included in an economic literature on incentives.

Wednesday, May 10, 2023

New Directions in Market Design, NBER conference May 11-12, 2023 in Washington DC (and on YouTube)

 I'm on my way to this conference, celebrating a quarter of a century of practical market design by economists.

New Directions in Market Design, NBER conference May 11-12, 2023 (US Eastern Time)

LOCATION Convene, 600 14th St NW in Washington, DC. and livestreamed on YouTube 

ORGANIZERS Irene Y. Lo, 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.

Supported by Schmidt Futures

 Thursday, May 11

8:30 am Continental Breakfast

9:00 am Opening Talk: Alvin Roth, Stanford University and NBER ("Market Design and Maintenance") 

9:30 am Break

9:45 am Electricity and Renewable Energy Market Design

Overview: Mar Reguant, Northwestern University and NBER

Viewpoint 1: Martin Bichler, Technical University of Munich

Viewpoint 2: Richard O’Neill, Federal Energy Regulatory Commission

11:05 am Market Design for the Environment

Overview: Estelle Cantillon, ULB

Viewpoint 1: Rachel Glennerster, University of Chicago and NBER

Viewpoint 2: Nathan Keohane, Environmental Defense Fund

12:25 pm Lunch discussions

2:00 pm Market Design in Healthcare

Overview: Benjamin Handel, University of California at Berkeley and NBER

Viewpoint 1: Mark Miller, Arnold Ventures

Viewpoint 2: Fanyin Zheng, Columbia University

3:20 pm Market Design for Organ Transplantation

Overview: Tayfun Sonmez, Boston College

Viewpoint 1: Nikhil Agarwal, Massachusetts Institute of Technology and NBER

Viewpoint 2: Jennifer Erickson, Organize

4:40 pm Break

5:00 pm Market Design for Education

Overview: Parag Pathak, Massachusetts Institute of Technology and NBER

Viewpoint 1: Derek Neal, University of Chicago and NBER

Viewpoint 2: Irene Lo, Stanford University

6:20 pm Adjourn

6:45 pm Group Dinner - JW Marriott

Friday, May 12

8:00 am Continental Breakfast

8:30 am Market Design for Public Housing

Overview: Nathan Hendren, Harvard University and NBER

Viewpoint 1: Winnie van Dijk, Harvard University and NBER

Viewpoint 2: Mary Cunningham, Urban Institute

9:50 am Market Design in Transportation

Overview: Michael Ostrovsky, Stanford University and NBER

Viewpoint 1: David Shmoys, Cornell University

Viewpoint 2: Wai Yan Leong, Singapore Land Transport Authority

11:10 am Break

11:30 am Market Design in Financial Markets

Overview: Haoxiang Zhu, Massachusetts Institute of Technology and NBER

Viewpoint 1: Eric Budish, University of Chicago and NBER

Viewpoint 2: Scott Mixon, CFTC

12:50 pm

Lunch discussions

2:20 pm Market Design Tools in the Regulation of Online Marketplaces

Overview: Susan Athey, Stanford University and NBER

Viewpoint 1: Preston McAfee, Google

Viewpoint 2: Michael Schwarz, Microsoft

3:40 pm Artificial Intelligence and Market Design

Overview: Kevin Leyton-Brown, University of British Columbia

Viewpoint 1: Hal Varian, Google

Viewpoint 2: Nikhil Devanur, Amazon

5:00 pm Break

5:20 pm Closing Talk: Paul Milgrom, Stanford University

5:50 pm Adjourn

6:30 pm Group Dinner - JW Marriott

Tuesday, April 4, 2023

The Robert Rosenthal Memorial Lecture for 2023 at BU, by Parag Pathak

 Parag Pathak gave this year's Robert Rosenthal Memorial Lecture at Boston University. The title of his talk is “Still Worth the Trip? The Evolution of School Busing in Boston” 

(The video below may undergo some further editing, but right now it starts with introductions at minute 3.) 


You can also find the Rosenthal lectures from previous years at the link.

(I had the honor of giving the 2007 lecture... Bob Rosenthal and I are academic siblings, we were both advised by Bob Wilson.)

Saturday, December 17, 2022

Economics of pandemic vaccination in Oxford Review of Economic Policy

Vaccine development and distribution during the Covid pandemic has had some notable successes and some significant shortcomings. 

Here's the latest issue of the Oxford Review of Economic Policy, which has collected articles by economists concerning some of those successes and failures and their lessons for future pandemics.

Volume 38, Issue 4, Winter 2022

Economics of Pandemic Vaccination

ARTICLES

Oxford Review of Economic Policy, Volume 38, Issue 4, Winter 2022, Pages 719–741, https://doi.org/10.1093/oxrep/grac036
Oxford Review of Economic Policy, Volume 38, Issue 4, Winter 2022, Pages 742–770, https://doi.org/10.1093/oxrep/grac037
Oxford Review of Economic Policy, Volume 38, Issue 4, Winter 2022, Pages 771–796, https://doi.org/10.1093/oxrep/grac026
Oxford Review of Economic Policy, Volume 38, Issue 4, Winter 2022, Pages 797–817, https://doi.org/10.1093/oxrep/grac029
Oxford Review of Economic Policy, Volume 38, Issue 4, Winter 2022, Pages 818–832, https://doi.org/10.1093/oxrep/grac028
Oxford Review of Economic Policy, Volume 38, Issue 4, Winter 2022, Pages 833–850, https://doi.org/10.1093/oxrep/grac032
Oxford Review of Economic Policy, Volume 38, Issue 4, Winter 2022, Pages 851–875, https://doi.org/10.1093/oxrep/grac035
Oxford Review of Economic Policy, Volume 38, Issue 4, Winter 2022, Pages 876–886, https://doi.org/10.1093/oxrep/grac031
Oxford Review of Economic Policy, Volume 38, Issue 4, Winter 2022, Pages 887–911, https://doi.org/10.1093/oxrep/grac033
Oxford Review of Economic Policy, Volume 38, Issue 4, Winter 2022, Pages 912–923, https://doi.org/10.1093/oxrep/grac027
Oxford Review of Economic Policy, Volume 38, Issue 4, Winter 2022, Pages 924–940, https://doi.org/10.1093/oxrep/grac034
Oxford Review of Economic Policy, Volume 38, Issue 4, Winter 2022, Pages 941–974, https://doi.org/10.1093/oxrep/grac038