Targeting In-Kind Transfers through Market Design: A Revealed Preference Analysis of Public Housing Allocation

Public housing benefits are rationed through wait lists. Theoretical work on public housing allocation has debated how much choice applicants should have over units, identifying a possible trade-off between efficiency and redistribution. This paper empirically establishes the existence and economic importance of this trade-off using wait list data from Cambridge, Massachusetts. I estimate a model of public housing preferences in a setting where heterogeneous apartments are rationed through waiting time. Eliminating choice would improve targeting but reduce tenant welfare by more than 30 percent. Such a change is only justified on targeting grounds by a strong social preference for redistribution. (JEL D47, H75, I38, R38)

Revealed Preference Analysis for Mode Choice in Graduate Level Engineering Students of Kathmandu Valley

Proper planning which is the key element in ensuring infrastructure efficiency, relies on demand analysis. Among the various trips under the domain of demand analysis educational trips occupy a significant part and hence the knowledge about patterns and attitudes of these trips is important to policymakers and infrastructure planners. The current study analyzes the mode choice of graduate-level engineering students in Kathmandu valley where the current transportation system is facing multiple problems thus requiring a proper planning intervention. This study reveals the educational mode preference among the students of engineering colleges. The reveal preference survey was conducted at the various engineering colleges in Kathmandu valley. The study concludes that the travel distance, number of siblings, and vehicle ownership effects the selection of personal modes of transport and distance effects the selection of public transportation, walking option being base criteria for both the cases. It is recommended that existing walking conditions should be improved within the educational zones while public transport which are preferred options for long journeys be designed considering movement between zones.

A revealed preference analysis to develop composite scores approximating lung allocation policy in the U.S

Background The patient ranking process for donor lung allocation in the United States is carried out by a classification-based, computerized algorithm, known as the match system. Experts have suggested that a continuous, points-based allocation framework would better serve waiting list candidates by removing hard boundaries and increasing transparency into the relative importance of factors used to prioritize candidates. We applied discrete choice modeling to match run data to determine the feasibility of approximating current lung allocation policy by one or more composite scores. Our study aimed to demystify the points-based approach to organ allocation policy; quantify the relative importance of factors used in current policy; and provide a viable policy option that adapts the current, classification-based system to the continuous allocation framework. Methods Rank ordered logistic regression models were estimated using 6466 match runs for 5913 adult donors and 534 match runs for 488 pediatric donors from 2018. Four primary attributes are used to rank candidates and were included in the models: (1) medical priority, (2) candidate age, (3) candidate’s transplant center proximity to the donor hospital, and (4) blood type compatibility with the donor. Results Two composite scores were developed, one for adult and one for pediatric donor allocation. Candidate rankings based on the composite scores were highly correlated with current policy rankings (Kendall’s Tau ~ 0.80, Spearman correlation > 90%), indicating both scores strongly reflect current policy. In both models, candidates are ranked higher if they have higher medical priority, are registered at a transplant center closer to the donor hospital, or have an identical blood type to the donor. Proximity was the most important attribute. Under a points-based scoring system, candidates in further away zones are sometimes ranked higher than more proximal candidates compared to current policy. Conclusions Revealed preference analysis of lung allocation match runs produced composite scores that capture the essence of current policy while removing rigid boundaries of the current classification-based system. A carefully crafted, continuous version of lung allocation policy has the potential to make better use of the limited supply of donor lungs in a manner consistent with the priorities of the transplant community.

A Revealed Preference Analysis to Develop Composite Scores Approximating Lung Allocation Policy in the U.S.

Background: The patient ranking process for donor lung allocation in the United States is carried out by a classification-based, computerized algorithm, known as the match system. Experts have suggested that a continuous, points-based allocation framework would better serve waiting list candidates by removing hard boundaries and increasing transparency into the relative importance of factors used to prioritize candidates. We applied discrete choice modeling to match run data to determine the feasibility of approximating current lung allocation policy by one or more composite scores. Our study aimed to demystify the points-based approach to organ allocation policy; quantify the relative importance of factors used in current policy; and provide a viable policy option that adapts the current, classification-based system to the continuous allocation framework.Methods: Rank ordered logistic regression models were estimated using 6,466 match runs for 5,913 adult donors and 534 match runs for 488 pediatric donors from 2018. Four primary attributes are used to rank candidates and were included in the models: (1) medical priority, (2) candidate age, (3) candidate’s transplant center proximity to the donor hospital, and (4) blood type compatibility with the donor.Results: Two composite scores were developed, one for adult and one for pediatric donor allocation. Candidate rankings based on the composite scores were highly correlated with current policy rankings (Kendall’s Tau ~0.80, Spearman correlation >90%), indicating both scores strongly reflect current policy. In both models, candidates are ranked higher if they have higher medical priority, are registered at a transplant center closer to the donor hospital, or have an identical blood type to the donor. Proximity was the most important attribute. Under a points-based scoring system, candidates in further away zones are sometimes ranked higher than more proximal candidates compared to current policy. Conclusions: Revealed preference analysis of lung allocation match runs produced composite scores that capture the essence of current policy while removing rigid boundaries of the current classification-based system. A carefully crafted, continuous version of lung allocation policy has the potential to make better use of the limited supply of donor lungs in a manner consistent with the priorities of the transplant community.

A Revealed Preference Analysis to Develop Composite Scores Approximating Lung Allocation Policy in the U.S.

Background: The patient ranking process for donor lung allocation in the United States is carried out by a classification-based, computerized algorithm, known as the match system. Experts have suggested that a continuous, points-based allocation framework would better serve waiting list candidates by removing hard boundaries and increasing transparency into the relative importance of factors used to prioritize candidates. We applied discrete choice modeling to match run data to determine the feasibility of approximating current lung allocation policy by one or more composite scores. Our study aimed to demystify the points-based approach to organ allocation policy; quantify the relative importance of factors used in current policy; and provide a viable policy option that adapts the current, classification-based system to the continuous allocation framework.Methods: Rank ordered logistic regression models were estimated using 6,466 match runs for 5,913 adult donors and 534 match runs for 488 pediatric donors from 2018. Four primary attributes are used to rank candidates and were included in the models: (1) medical priority, (2) candidate age, (3) candidate’s transplant center proximity to the donor hospital, and (4) blood type compatibility with the donor.Results: Two composite scores were developed, one for adult and one for pediatric donor allocation. Candidate rankings based on the composite scores were highly correlated with current policy rankings (Kendall’s Tau ~0.80, Spearman correlation >90%), indicating both scores strongly reflect current policy. In both models, candidates are ranked higher if they have higher medical priority, are registered at a transplant center closer to the donor hospital, or have an identical blood type to the donor. Proximity was the most important attribute. Under a points-based scoring system, candidates in further away zones are sometimes ranked higher than more proximal candidates compared to current policy. Conclusions: Revealed preference analysis of lung allocation match runs produced composite scores that capture the essence of current policy while removing rigid boundaries of the current classification-based system. A carefully crafted, continuous version of lung allocation policy has the potential to make better use of the limited supply of donor lungs in a manner consistent with the priorities of the transplant community.

Revealed Preference Analysis with Normal Goods: Application to Cost-of-Living Indices

We present a revealed preference methodology for nonparametric demand analysis under the assumption of normal goods. Our methodology is flexible in that it allows for imposing normality on any subset of goods. We show the usefulness of our methodology for empirical welfare analysis through cost-of-living indices. An illustration to US consumption data drawn from the Panel Study of Income Dynamics (PSID) demonstrates that mild normality assumptions can substantially strengthen the empirical analysis. It obtains considerably tighter bounds on cost-of-living indices and a significantly more informative classification of better-off and worse-off individuals after the 2008 financial crisis. (JEL D11, D12, E31, G01)

Analisis Karakteristik Penggunaan Moda Akses Bandara Internasional Yogyakarta

The new Yogyakarta International Airport began operations with an airport area of 645.63 hectares with a capacity of 14 million passengers a year. Access to the Yogyakarta international airport is distributed to 4 routes to the airport at Wates national road, Karangnongko road, the Southern Cross Road, Daendels road which is using the railroad mode. This study uses revealed preference analysis which is the approach by conveying a fact choice statement to be given an assessment by the respondent. The sample collection is assumed by the peak passenger of the Adisucipto airport, because the Yogyakarta International Airport is recently opened. It is expected to be able to represent demand predictions at the Yogyakarta International airport in the future. The results of the questionnaire were then processed by using statistical analysis to determine the factors that influence the selection of transportation modes to and from the airport. In research, the factors that influence mode choice are travel costs, travel time, travel distance, and generalized costs. The results illustrate that prospective air transport users are more dominant in choosing travel cost attributes compared to other attributes that influence. With the coefficient of determination is 0.528 and the results of data analysis for the selection of mode of transportation using private vehicles is 57% and public transportation is 43%..