Sunday, August 22, 2021

Disruptive marketplaces in HBR, by Clifford Maxwell and Scott Kominers

 Here's a Harvard Business Review article on markets that create new kinds of transactions.

What Makes an Online Marketplace Disruptive?  by Clifford Maxwell and Scott Duke Kominers

"Summary.   Platforms like Airbnb, eBay, and Angie’s List have changed how markets work. But while many are innovative and make life easier for consumers, which are truly disruptive? Hewing to Clay Christensen’s theory of disruption, platforms — which operate as online marketplaces — are disruptive when they create new consumers, producers, or both, functionally creating new transactions (and new kinds of transactions) that weren’t possible before. Specifically, there are four novel transaction types that can unlock disruptive potential: smaller supply units, bundles, new suppliers, and trust wrappers.

...

"For a marketplace to be disruptive, it must identify either new supply, new demand, or both — targeting individuals or businesses who were unable to profitably produce or consume goods and services in incumbent channels. And the most powerful disruptive marketplaces are often those that simultaneously connect nonconsumers with nonproducers."

A link in the article points to "what venture capital firm Andreessen Horowitz identified as the top 100 marketplace startups in 2020."

That report notes that "The biggest marketplace companies are big—really big. The top 4 companies (Airbnb, Doordash, Instacart, and Postmates) account for 76 percent of the list’s total observed GMV, even though there are 96 other marketplace companies on the list. Interestingly, three of the four are ways to get food delivered to your home. Similarly, almost all the companies on the list harness technology to interact with the offline world—using mobile apps to make food, healthcare, childcare, and fitness more convenient and accessible. "

Saturday, August 21, 2021

Introduction to NLDAC (the National Living Donor Assistance Center)

 I'm on the advisory board of the National Living Donor Assistance Center, which has recently gotten increased resources and mandate to financially support means-tested living kidney donors who have out of pocket expenses for travel, child care, and lost wages.  Nondirected donors are now also eligible for support. The idea is to remove financial disincentives for donation, and NLDAC aims to backstop other efforts, as a federally funded payer of last resort.

They are trying to spread the word, and have prepared a one minute video: Introduction to NLDAC

"Learn about support for people considering living organ donation. NLDAC helps eligible donors with travel, lost wages, and dependent care costs. Visit our website to learn more. Your transplant center can help you apply."

Friday, August 20, 2021

Preference Signaling and Worker-Firm Matching: Evidence from Interview Auctions, by Laschever and Weinstein

 Here's a recent working paper from the IZA Institute of Labor Economics concerned with the importance of signals of interest in labor market matching:

Preference Signaling and Worker-Firm Matching: Evidence from Interview Auctions, by Ron A. Laschever and Russell Weinstein,  IZA DP No. 14622

Abstract: "We study whether there are improvements in worker-firm matching when employers and applicants can credibly signal their interest in a match. Using a detailed résumé dataset of more than 400 applicants from one university over five years, we analyze a matching process in which firms fill some of their interview slots by invitation and the remainder are filled by an auction. Consistent with the predictions of a signaling model, we find the auction is valuable for less desirable firms trying to hire high desirability applicants. Second, we find evidence that is consistent with the auction benefiting overlooked applicants. Candidates who are less likely to be invited for an interview (e.g., non-U.S. citizens) are hired after having the opportunity to interview through the auction. Among hires, these candidates are more represented among auction winners than invited interviewees, and this difference is more pronounced at more desirable firms. Finally, counterfactual analysis shows the auction increases the number and quality of hires for less desirable firms, and total hires in the market

...

"Auctions for interview slots may address two important frictions in the matching process: uncertainty over applicant quality, and uncertainty over the likelihood that an applicant accepts an offer. Even if employers can successfully identify desirable applicants, there remains the challenge of identifying which candidates are truly interested in the job and would accept an o↵er with high probability. In recent years the cost of job applications has fallen as more postings and applications are online. This
further raises the potential that applicants will have a low likelihood of accepting an offer.
...
"Though not common, there are a few markets in which all applicants have an equal opportunity to credibly signal their preferences for an employer. One example is the American Economic Association (AEA) job signaling mechanism, which allows candidates to send a signal of interest to two departments. Importantly, there is no requirement that employers interview the applicants sending the signal. In contrast, in our setting an employer is compelled to meet with some signaling job seekers.

"A second example, and the focus of this paper, is the auction system used in the market for professional master’s degree students, most commonly MBA students, at many top-ranked programs. These programs allow employers to choose some percentage of the applicants they interview, but require the remainder of the interview slots are allocated through an auction. Typically, firms first invite applicants for interviews, before applicants have had the opportunity to signal. Next, there is an auction for the remaining interview slots, and thus auction participants are students who were not invited for an interview by the firm. Each student is provided with an equal allotment of “bid points,” and the auction winners are guaranteed interviews
with the firm."

Thursday, August 19, 2021

Radical content on YouTube, in PNAS

 Here's a paper in PNAS which finds that YouTube viewing of politically radical content reflects viewers' other web behavior, rather than being driven by the YouTube recommender system.

Examining the consumption of radical content on YouTube by Homa Hosseinmardi,  Amir Ghasemian,   Aaron Clauset,   Markus Mobius,   eDavid M. Rothschild, and   Duncan J. Watts. 

PNAS August 10, 2021 118 (32) e2101967118; https://doi.org/10.1073/pnas.2101967118

Abstract: Although it is under-studied relative to other social media platforms, YouTube is arguably the largest and most engaging online media consumption platform in the world. Recently, YouTube’s scale has fueled concerns that YouTube users are being radicalized via a combination of biased recommendations and ostensibly apolitical “anti-woke” channels, both of which have been claimed to direct attention to radical political content. Here we test this hypothesis using a representative panel of more than 300,000 Americans and their individual-level browsing behavior, on and off YouTube, from January 2016 through December 2019. Using a labeled set of political news channels, we find that news consumption on YouTube is dominated by mainstream and largely centrist sources. Consumers of far-right content, while more engaged than average, represent a small and stable percentage of news consumers. However, consumption of “anti-woke” content, defined in terms of its opposition to progressive intellectual and political agendas, grew steadily in popularity and is correlated with consumption of far-right content off-platform. We find no evidence that engagement with far-right content is caused by YouTube recommendations systematically, nor do we find clear evidence that anti-woke channels serve as a gateway to the far right. Rather, consumption of political content on YouTube appears to reflect individual preferences that extend across the web as a whole.


"Our data are drawn from Nielsen’s nationally representative desktop web panel, spanning January 2016 through December 2019 (SI Appendix, section B), which records individuals’ visits to specific URLs. We use the subset of N = 309,813 panelists who have at least one recorded YouTube pageview. Parsing the recorded URLs, we found a total of 21,385,962 watched-video pageviews (Table 1). We quantify the user’s attention by the duration of in-focus visit to each video in total minutes (32)."


Wednesday, August 18, 2021

1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO'21)

 Irene Lo writes:

As many of you may know, I've been part of launching a new ACM conference series and publication venue: the 1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO'21). Registration is now live, and I'd be delighted to see many of you there! See below for more details.

***

We are thrilled to announce that the registration for EAAMO ‘21 is now live! Please register for regular admission on Eventbrite by September 10, 2021.

Conference registration is $20 for ACM members, $15 for students, and $35 for non-ACM members. We also provide financial assistance and data grants in order to waive registration fees and provide data plans to facilitate virtual attendance. Please apply here before September 10, 2021.

A main goal of the conference is to bridge research and practice. Please nominate practitioners working with underserved and disadvantaged communities to join us at the conference (you can also nominate yourself if you are a practitioner). Invited practitioners will be included in facilitated discussions with researchers. 

For more information, please see below or visit our website and contact us at gc@eaamo.org with any questions.

***

 The inaugural Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO ‘21) will take place on October 5-9, 2021, virtually, on Zoom and Gather.town. EAAMO ‘21 will be sponsored by ACM SIGAI and SIGecom

 

The goal of this event is to highlight work where techniques from algorithms, optimization, and mechanism design, along with insights from the social sciences and humanistic studies, can improve access to opportunity for historically underserved and disadvantaged communities. 

 

The conference aims to foster a multi-disciplinary community, facilitating interactions between academia, industry, and the public and voluntary sectors. The program will feature keynote presentations from researchers and practitioners as well as contributed presentations in the research and policy & practice tracks. 

We are excited to host a series of keynote speakers from a variety of fields: Solomon Assefa (IBM Research), Dirk Bergemann (Yale University), Ellora Derenoncourt (University of California, Berkeley), Ashish Goel (Stanford University), Mary Gray (Microsoft Research), Krishna Gummadi (Max Planck Institute for Software Systems), Avinatan Hassidim (Bar Ilan University), Radhika Khosla (University of Oxford), Sylvia Ortega Salazar (National College of Vocational and Professional Training), and Trooper Sanders (Benefits Data Trust).

 

ACM EAAMO is part of the Mechanism Design for Social Good (MD4SG) initiative, and builds on the MD4SG technical workshop series and tutorials at conferences including ACM EC, ACM COMPASS, ACM FAccT, and WINE.


Tuesday, August 17, 2021

Stanford SITE seminar: Dynamic Games, Contracts, and Markets, Aug 18-20

 

Date
 - 
Location
Zoom
ORGANIZED BY
  • Simon Board, University of California, Los Angeles
  • Gonzalo Cisternas, Massachusetts Institute of Technology
  • Mira Frick, Yale University
  • George Georgiadis, Northwestern University
  • Andrzej Skrzypacz, Stanford GSB
  • Takuo Sugaya, Stanford GSB

The idea of this session is to bring together microeconomic theorists working on dynamic games and contracts with more applied theorists working in macro, finance, organizational economics, and other fields. First, this is a venue to discuss the latest questions and techniques facing researchers working in dynamic games and contracts. Second, we wish to foster interdisciplinary discussion between scholars working on parallel topics in different disciplines, in particular, helping raise awareness among theorists of the open questions in other fields.

This is a continuation of successful SITE annual sessions 2013-2020. In previous years, we attracted people from economics, finance, operations research, political economy, and other related fields, ranging from Ph.D. students to senior professors. We hope to have a similar number of attendees this year as in the past. Specific topics likely to be covered include repeated and stochastic games, dynamic optimal contracts, dynamic market pricing, reputation, search, and learning and experimentation.

In This Session

Wednesday, August 18, 2021

AUG 18
9:00 AM - 9:45 AM

Wealth Dynamics in Communities

Presented by: Daniel Barron (Northwestern University)
Co-author(s): Yingni Guo (Northwestern University) and Bryony Reich (Northwestern University)

This paper develops a model to explore how favor exchange in communities influences wealth dynamics. We identify a key obstacle to wealth accumulation: wealth crowds out favor exchange. Therefore, low-wealth households are forced to choose between growing their wealth and accessing favor exchange within their communities. The outcome is that some communities are left behind, with wealth disparities that persist and sometimes even grow worse. Using numerical simulations, we show that place-based policies encourage both favor exchange and wealth accumulation and so have the potential to especially benet such communities.

AUG 18
9:45 AM - 10:00 AM

Break

AUG 18
10:00 AM - 10:45 AM

Optimal Dynamic Allocation: Simplicity through Information Design

Presented by: Faidra Monachou (Stanford University)
Co-author(s): Itai Ashlagi (Stanford University) and Afshin Nikzad (University of Southern California)

We study dynamic nonmonetary markets where objects are allocated to unit-demand agents with private types. An agent’s value for an object is supermodular in her type and the quality of the object, and her payoff is quasilinear in her waiting cost. We analyze direct-revelation mechanisms that elicit agents’ types and assign them to objects over time. We identify the welfare-maximizing mechanism and show that it can be implemented by a first-come first-served wait-list with deferrals when the
marketmaker can design the information disclosed to agents about the objects. The optimal disclosure policy pools adjacent object types.

AUG 18
10:45 AM - 11:00 AM

Break

AUG 18
11:00 AM - 11:45 AM

Probabilistic Assortative Matching under Nash Bargaining

Presented by: Nicolas Bonneton (University of Mannheim)
Co-author(s): Christopher Sandmann (London School of Economics)

This paper re-visits the canonical random search and matching model with Nash bargaining. By introducing pair-specific production shocks, our framework generates meeting-contingent match outcomes that are random. We provide a robust characterization of probabilistic matching patterns for any non-stationary environment, generalizing results by Shimer and Smith (2000). We nd that, although their prediction of single-peaked preferences over meetings is robust, search frictions upset positive assortative matching across well-assorted pairs. As a second contribution, we show that the non-stationary random search matching model is a mean eld game, and admits a representation as a system of forward-backward stochastic differential equations. This representation affords a novel existence and uniqueness result, casting doubt on the robustness of multiple self-fulfilling equilibrium paths frequently reported in the literature.

Thursday, August 19, 2021

AUG 19
9:00 AM - 9:45 AM

Price Experimentation in Confidential Negotiations

Presented by: Jangwoo Lee (McCombs School of Business, University of Texas at Austin)

I develop a model in which a long-lived seller concurrently negotiates with multiple long-lived buyers over two periods. Within this framework, I consider two protocols: a public negotiation process and a confidential negotiation process. In the confidential negotiation process, buyers competitively engage in “price experimentation”: they sacrifice initial profits so that they can enjoy informational advantages over competitors later. Due to this channel, the seller benefits from (1) maintaining confidentiality over past offers and (2) reducing the number of buyers in the confidential negotiation process, even without any entry cost.

AUG 19
9:45 AM - 10:00 AM

Break

AUG 19
10:00 AM - 11:45 AM

Large-Sample Rankings of Information Structures in Games

Presented by: Mira Frick (Yale University)
Co-author(s): Ryota Iijima (Yale University) and Yuhta Ishii (Pennsylvania State University)

 

 

AUG 19
10:45 AM - 11:00 AM

Break

AUG 19
11:00 AM - 11:45 AM

The Cost of Optimally Acquired Information

Presented by: Alexander W. Bloedel (Stanford University)
Co-author(s): Weijie Zhong, (Stanford Graduate School of Business)

This paper develops a theory for the expected cost of optimally acquired information when information can be acquired sequentially and there is no explicit cost of delay. We study the “reduced-form” Indirect Cost functions for information generated by sequential minimization of a “primitive” Direct Cost function. The class of Indirect Costs is characterized by a recursive condition called Sequential Learning-Proofness. This condition is inconsistent with Prior Invariance: Indirect Costs must depend on the decision-maker’s prior beliefs. 

We show that Sequential Learning-Proofness provides partial optimality foundations for the Uniformly Posterior Separable (UPS) cost functions used in the rational inattention literature: a cost function is UPS if and only if it is an Indirect Cost that (i) satisfies a mild regularity condition or, equivalently, (ii) is generated (only) by Direct Costs for which the op timal sequential strategy involves observing only Gaussian diffusion signals. We characterize the unique UPS cost function that is generated by a Prior-Invariant Direct Cost; it exists only when there are exactly two states. 

We also propose two specific UPS cost functions based on additional optimality principles. We introduce and characterize Total Information as the unique Indirect Cost that is Process Invariant when information can be decomposed both sequentially and “simultaneously”: it is uniquely invariant to the “merging” and “splitting” of experiments. Under regularity conditions, Mutual Information is the unique Indirect Cost that is Compression-Invariant when as pects of the state space can be “freely ignored”: it is uniquely invariant to the “merging” and “splitting” of states. We argue that Total Information and Mutual Information represent the normatively ideal costs of, respectively, “producing” and “processing” information. 

 

Friday, August 20, 2021

AUG 20
9:00 AM - 9:45 AM

Optimal Feedback in Contests

Presented by: George Georgiadis (Northwestern University)
Co-author(s): Jeffrey Ely (Northwestern University), Sina Khorasani (UC San Diego), and Luis Rayo (Northwestern University)

We derive optimal contests for environments where output takes the form of breakthroughs and the principal has an informational advantage over the contestants. Whether or not the principal is able to provide real-time feedback to contestants, the optimal prize allocation is egalitarian: all agents who have succeeded in a pre-specified time interval share the prize equally. When providing feedback is feasible, the optimal contest takes a stark cyclical form: contestants are fully apprised of their own success, and at the end of each fixed-length cycle, they are informed about peer success as well.

AUG 20
9:45 AM - 10:00 AM

Break

AUG 20
10:00 AM - 10:45 AM

Dynamic Amnesty Programs

Presented by: Sam Kapon (New York University)

A regulator faces a stream of agents each engaged in crime with stochastic returns. The regulator designs an amnesty program, committing to a time path of penalty reductions for criminals who self-report before they are detected. In an optimal time path, the intertemporal variation in the returns from crime can generate intertemporal variation in the generosity of amnesty. I construct an optimal time path and show that it exhibits amnesty cycles. Amnesty becomes increasingly generous over time until it hits a bound, at which point the cycle resets. Agents engaged in high return crime self-report at the end of each cycle, while agents engaged in low return crime self-report always.

AUG 20
10:45 AM - 11:00 AM

Break

AUG 20
11:00 AM - 11:45 AM

Screening for Breakthroughs

Presented by: Ludvig Sinander (Northwestern University)
Co-author(s): Gregorio Curello (University of Bonn)

We identify a new and pervasive dynamic agency problem: that of incentivising the prompt disclosure of productive information. To study it, we introduce a model in which a technological breakthrough occurs at an uncertain time and is privately observed by an agent, and a principal must incentivise disclosure via her control of the agent’s utility. We uncover a striking deadline structure of optimal mechanisms: they have a simple deadline form in an important special case, and a graduated deadline structure in general. We apply our results to the design of unemployment insurance schemes.

 

Monday, August 16, 2021

Alain Enthoven on fragmented American health care

 Writing in Health Affairs, Alain Enthoven notes that most American workers insured through their jobs work for self-insured employers, i.e. employers who themselves pay for the health care of their covered lives. This means that many of them are relatively small buyers of health insurance, which leads them to deal with fee for service providers, rather than big health maintenance organizations, which might be a better model for a national health care system.

Employer Self-Funded Health Insurance Is Taking Us In The Wrong Direction by Alain C. Enthoven

"The 2020 Kaiser Family Foundation Survey of Employer Health Benefits reports that 67 percent of employed, insured workers are covered under self-insured, or self-funded, arrangements. Under these arrangements, the employer, not an external insurer, directly bears the financial risk for the cost of employee health care.

Self-funded arrangements have grown steadily as a share of the insurance market over the past 15 years and now include many employers with less than 200 employees. While this may be the most cost-effective decision for individual employers under the current regulatory framework, it has the effect of locking in uncoordinated, open-ended fee-for-service (FFS) and locking out comparatively economical Integrated Delivery Systems (IDS)."

Sunday, August 15, 2021

Fair algorithms for selecting citizen assemblies, in Nature

 Here's a paper that grapples with the problem that not every group in society is equally likely to accept an appointment for which they have been selected, which complicates the problem of selecting representative committees while also giving each potential member approximately the same chance of being selected.

Fair algorithms for selecting citizens’ assemblies. by Bailey Flanigan, Paul Gölz, Anupam Gupta, Brett Hennig & Ariel D. Procaccia, Nature (2021). https://doi.org/10.1038/s41586-021-03788-6

Abstract: Globally, there has been a recent surge in ‘citizens’ assemblies’1, which are a form of civic participation in which a panel of randomly selected constituents contributes to questions of policy. The random process for selecting this panel should satisfy two properties. First, it must produce a panel that is representative of the population. Second, in the spirit of democratic equality, individuals would ideally be selected to serve on this panel with equal probability2,3. However, in practice these desiderata are in tension owing to differential participation rates across subpopulations4,5. Here we apply ideas from fair division to develop selection algorithms that satisfy the two desiderata simultaneously to the greatest possible extent: our selection algorithms choose representative panels while selecting individuals with probabilities as close to equal as mathematically possible, for many metrics of ‘closeness to equality’. Our implementation of one such algorithm has already been used to select more than 40 citizens’ assemblies around the world. As we demonstrate using data from ten citizens’ assemblies, adopting our algorithm over a benchmark representing the previous state of the art leads to substantially fairer selection probabilities. By contributing a fairer, more principled and deployable algorithm, our work puts the practice of sortition on firmer foundations. Moreover, our work establishes citizens’ assemblies as a domain in which insights from the field of fair division can lead to high-impact applications.

...

"To our knowledge, all of the selection algorithms previously used in practice (Supplementary Information section 12) aim to satisfy one particular property, known as ‘descriptive representation’ (that the panel should reflect the composition of the population)16. Unfortunately, the pool from which the panel is chosen tends to be far from representative. Specifically, the pool tends to overrepresent groups with members who are on average more likely to accept an invitation to participate, such as the group ‘college graduates’.  

...

"Selection algorithms that pre-date this work focused only on satisfying quotas, leaving unaddressed a second property that is also central to sortition: that all individuals should have an equal chance of being chosen for the panel.

...

"Although it is generally impossible to achieve perfectly equal probabilities, the reasons to strive for equality also motivate a more gradual version of this goal: making probabilities as equal as possible, subject to the quotas. We refer to this goal as ‘maximal fairness’

...

"the algorithms in our framework (1) explicitly compute a maximally fair output distribution and then (2) sample from that distribution to select the final panel (Fig. 1). Crucially, the maximal fairness of the output distribution found in the first step makes our algorithms optimal. To see why, note that the behaviour of any selection algorithm on a given instance is described by some output distribution; thus, as our algorithm finds the fairest possible output distribution, it is always at least as fair as any other algorithm."



Saturday, August 14, 2021

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?

...

"ResultsNotwithstanding 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. 

Friday, August 13, 2021

Generalizing deferred acceptance in many to one matching with contracts, by Hatfield, Kominers and Westkamp in RESTUD

 Stability, Strategy-Proofness, and Cumulative Offer Mechanisms, by John William Hatfield, Scott Duke Kominers, Alexander Westkamp, The Review of Economic Studies, Volume 88, Issue 3, May 2021, Pages 1457–1502, https://doi.org/10.1093/restud/rdaa052

Abstract: We characterize when a stable and strategy-proof mechanism is guaranteed to exist in the setting of many-to-one matching with contracts. We introduce three novel conditions—observable substitutability, observable size monotonicity, and non-manipulability via contractual terms—and show that when these conditions are satisfied, the cumulative offer mechanism is the unique mechanism that is stable and strategy-proof (for workers). Moreover, we show that our three conditions are, in a sense, necessary: if the choice function of some firm fails any of our three conditions, we can construct unit-demand choice functions for the other firms such that no stable and strategy-proof mechanism exists. Thus, our results provide a rationale for the ubiquity of cumulative offer mechanisms in practice.


Thursday, August 12, 2021

Guns and public health: research funds available again

 Here's the story, from the Journal of the American Medical Association:

Gun Violence Researchers Are Making Up for 20 Years of Lost Time by Alicia Ault, JAMA. Published online August 4, 2021. doi:10.1001/jama.2021.11469

"By late July, the Gun Violence Archive reported 25 370 US firearm deaths in 2021, putting the year on track to surpass last year’s 43 559 deaths. US Centers for Disease Control and Prevention (CDC) data showed that 39 707 people lost their lives to gun violence in 2019. It was the third consecutive year in which US gun violence deaths approached 40 000 and the end of a decade in which the death rate from gun violence increased by 17%, from 10.1 to 11.9 deaths per 100 000 population. The rate has remained above 11 per 100 000 population since 2015.

"Although the CDC gathers firearm mortality data, its gun violence research had largely been dormant since 1996 when the Dickey Amendment prohibited the agency from using its injury prevention funding “to advocate or promote gun control.” The amendment technically didn’t prohibit gun violence research, but the chill was numbing.

"In 2019, however, Congress authorized $25 million in spending on gun violence research, to be split evenly between the CDC and the National Institutes of Health (NIH). Although the amount is nearly 10 times greater than the $2.6 million that the CDC was spending on gun violence prevention studies when the Dickey Amendment took effect, a leading expert said the field is still woefully underfunded.