Varying the Number of Signals in Matching Markets

Jagadeesan, Meena; Wei, Alexander

doi:10.1007/978-3-030-04612-5_16

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…Programs varied in the number of tokens they received and the proportion of signal applicants they chose to interview, with some interviewing only signaling applicants and some interviewing less than 10% (Figure 2). More specifically, programs received a median (IQR) of 352 (295-411) applications for a median of 42 (35-54) open interviews and were signaled to a median of 16 (8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26) times each. The median lowest ranked candidate to match on a program-by-program basis was ranked 9th (IQR, 6th-15th).…”

Section: Discussionmentioning

confidence: 99%

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Characterization of Applicant Preference Signals, Invitations for Interviews, and Inclusion on Match Lists for Residency Positions in Urology

et al. 2023

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ImportancePreference signals were to be implemented in over 15 specialties during the 2022-2023 residency match. Analyzing results from the implementation of signals during the American Urological Association (AUA) urology match may inform future behavior.ObjectiveTo characterize applicant and program signal usage and results in the Society of Academic Urology and AUA databases with respect to interview invites and rank list creation.Design, Setting, and ParticipantsThis cohort study involved all applicants and residencies in the 2021-2022 AUA match with data analysis conducted in April through July 2022.ExposuresFive signals indicating interest.Main Outcomes and MeasuresUsing verified match and survey data reported by applicants and programs, a logistic regression was performed on applicant factors associated with obtaining an interview—the main outcome (using inclusion on rank list as a proxy): age, gender, degree (MD or DO), dispersal of signal, US senior status, racial minority group status, Latino ethnicity, international medical graduate status, presence of a home program, AUA geographic section, and US Medical Licensing Examination Step 1 score. Applicant signal dispersal strategies were stratified by applicant and program competitiveness, as well as program behavior upon receipt of signal with respect to extending interviews and rank list ordering of applicants.ResultsA total of 2659 signals were sent by 553 candidates (mean [SD] age, 27.4 [2.9] years; 179 female [32.4%], 243 racial minority candidates [61.2%]) submitting rank lists for 364 positions at 143 programs. Programs received a median (IQR) of 352 (295-411) applications and were signaled to a median of 16 (8-26) times each. In a logistic regression estimating interview status, geographic proximity (OR, 3.25; 95% CI, 2.05-5.15; P = .001) and signal status (OR, 6.04; 95% CI, 3.50-10.40; P &lt; .001) were associated with receiving an interview. Using multiple imputation by chained equations to impute missing data and broadening the data set, male gender (OR, 0.64; 95% CI, 0.45-0.92; P = .04) and international medical graduate status (OR, 0.35; 95% CI, 0.15-0.81; P = .04) were negative variables, while MD degree (OR, 2.36; 95% CI, 1.27-4.36; P = .02) and US senior status (OR, 1.91; 95% CI, 1.13-3.23; P = .04) were positive variables.Conclusions and RelevanceThis study of the usage and trends of the newly added preference signals reported the most common strategies for signal dispersal; in an analysis of factors involved in obtaining an interview, geographic similarity between applicant and program and preference signal usage were associated with successful applications.

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Section: Discussionmentioning

confidence: 99%

“…Preference signals are not a novel innovation, and have been well described in existing economics and medical research. 8,13,14 In fact, several innovations have been explored for assuaging the burden of excessive interviewing in medical matching markets. Interview caps have been argued…”

Section: Discussionmentioning

confidence: 99%

Characterization of Applicant Preference Signals, Invitations for Interviews, and Inclusion on Match Lists for Residency Positions in Urology

et al. 2023

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Optimal Match Recommendations in Two-sided Marketplaces with Endogenous Prices

Shi

2024

Management Science

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Many two-sided marketplaces rely on match recommendations to help customers find suitable service providers at suitable prices. This paper develops a tractable methodology that a platform can use to optimize its match recommendation policy to maximize the total value generated by the platform while accounting for the endogeneity of transaction prices, which are set by the providers based on supply and demand and can depend on the platform’s match recommendation policy. Despite the complications of price endogeneity, an optimal match recommendation policy has a simple structure and can be computed efficiently. In particular, an optimal policy always recommends the providers who deliver the highest conversion rates. Moreover, an optimal policy can be encoded simply in terms of the frequency of recommending each provider to each customer segment, without the need to encode which subsets of providers are to be recommended together. On the other hand, if the platform were to optimize its match recommendations without accounting for price endogeneity, then the resultant policy would be more complex, and the market is likely to get stuck at a strictly suboptimal outcome, even if the platform were to continually reoptimize its match recommendations after prices re-equilibrate. This paper was accepted by Omar Besbes, revenue management and market analytics. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.02691 .

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Optimal Matchmaking Strategy in Two-Sided Marketplaces

Shi

2023

Management Science

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Online platforms that match customers with suitable service providers utilize a wide variety of matchmaking strategies; some create a searchable directory of one side of the market (i.e., Airbnb, Google Local Finder), some allow both sides of the market to search and initiate contact (i.e., Care.com, Upwork), and others implement centralized matching (i.e., Amazon Home Services, TaskRabbit). This paper compares these strategies in terms of their efficiency of matchmaking as proxied by the amount of communication needed to facilitate a good market outcome. The paper finds that the relative performance of these matchmaking strategies is driven by whether the preferences of agents on each side of the market are easy to describe. Here, “easy to describe” means that the preferences can be inferred with sufficient accuracy based on responses to standardized questionnaires. For markets with suitable characteristics, each of these matchmaking strategies can provide near-optimal performance guarantees according to an analysis based on information theory. The analysis provides prescriptive insights for online platforms. This paper was accepted by Omar Besbes, revenue management and market analytics.

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Varying the Number of Signals in Matching Markets

Cited by 7 publications

References 9 publications

Characterization of Applicant Preference Signals, Invitations for Interviews, and Inclusion on Match Lists for Residency Positions in Urology

Characterization of Applicant Preference Signals, Invitations for Interviews, and Inclusion on Match Lists for Residency Positions in Urology

Optimal Match Recommendations in Two-sided Marketplaces with Endogenous Prices

Optimal Matchmaking Strategy in Two-Sided Marketplaces

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