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Economics Thesis Proposals December 2020

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Economics Thesis Proposals December 2020 ECONOMICS THESIS PROPOSALS Note: December 2020 1.PDF bookmarks are included (show left bookmark panel) 2. Zoom links for each day are embedded in underlined bold blue text TUESDAY 12/1/20 3:00 TO 5:00 Sean Gao The Effects of Medicaid Patient Populations on Hospital Reimbursement and Care Quality: Evidence from Washington Andrew Swenson Stackelberg Advantages in Land Allocation: How Timing Affects Tradeoffs between Agglomeration and Local Market Power Kevin Ma The Impact of CARES Act Benefits on Retail Trade Employment Trishala Roy Political Affiliations, Investment in Public Health, and Effects on Covid-19 Mortality Sirig Gurung Spillovers of India's Demonetization to the Nepali Economy through Migrants from Nepal to India Ahliaa Moore The Effects of FDI on the Socioeconomic Outcomes of Jamaican Citizens THURSDAY 12/3/20 3:00 TO 5:00 Angela Zhao Health, Race, and Place: The Effects of Segregation on Racial Disparities in Health Outcomes Arnav Parikh Assessing the Effectiveness of Affirmative Action In India using Fertility Rates Emily Kiernan The Venmo Effect: The Impact of Digital Payment Platforms on Consumer Willingness to Pay Rafael Gonzalez An Exploration of the Role of Social Connections in Job Placement Pedro Morais The Economics of Misinformation: Informational Signals and Citizen Responses in the COVID-19 Pandemic Claire Holleman School Based Health Centers and Their Impact on Student Achievement FRIDAY 12/4/20 9:00 TO 11:00 Erik March Implicit Discount Rates in Solar Investments: Implications for Environmental Policy Thai Nguyen Do Vietnam’s State-Owned Enterprises Improve their Performance after Equitization? Evidence from the Last Decade Dana Frishman Athletic Participation and Academic Outcomes in NYC Public High Schools Crystal Yujing Zhou Migrate for A Brighter Future? The Impact of Parental Migration Decisions on Children’s Education in China Seamus Lawton A Model of Grade Inflation as a Collective Action Problem Page 1 TUESDAY TUESDAY 12/1/20 3:00 TO 5:00 Sean Gao The Effects of Medicaid Patient Populations on Hospital Reimbursement and Care Quality: Evidence from Washington Andrew Swenson Stackelberg Advantages in Land Allocation: How Timing Affects Tradeoffs between Agglomeration and Local Market Power Kevin Ma The Impact of CARES Act Benefits on Retail Trade Employment Trishala Roy Political Affiliations, Investment in Public Health, and Effects on Covid-19 Mortality Sirig Gurung Spillovers of India's Demonetization to the Nepali Economy through Migrants from Nepal to India Ahliaa Moore The Effects of FDI on the Socioeconomic Outcomes of Jamaican Citizens Page 2 Economics Thesis Proposal Name: Sean Gao Title: The Effects of Medicaid Patient Populations on Hospital Reimbursement and Care Quality: Evidence from Washington Field: Health Economics, Industrial Organization Version: Version 3 on 11/17/2020 Advisor(s): Prof. Jun Ishii, Prof. Jessica Reyes Question Does a hospital’s Medicaid patient percentage have an effect on reimbursement rates from private insurers; do those reimbursement differences affect care quality? Area This topic lies in the intersection of health economics and industrial organization. Specifically, the question addresses the competition dynamics of the healthcare industry between hospitals and insurance companies, with the subsequent investigation of care quality and health outcomes falling in line with many studies in health economics. Motivation Medicaid is a public health insurance program that covers patients with limited income or resources. Bargaining determines private insurance reimbursement rates, but Medicaid rates are set by individual state governments. These rates are generally substantially lower than private insurer reimbursement rates; in many cases, hospitals are losing money on these reimbursements. Medicaid patient populations can affect hospital financials through their own lower reimbursement, but may also impact hospital-private insurer bargaining and subsequent private insurance reimbursement. Many states have or are in the process of expanding Medicaid coverage through the Affordable Care Act. As a result, Medicaid reimbursement concerns have become especially salient in recent years. To my knowledge, there is not much literature on the impact of this expansion or Medicaid patients on reimbursement rates and care quality. Theory Negotiations between hospitals and insurers generally center around the inclusion of a hospital in an insurer’s network. Hospitals in a network offer lower prices to patients insured by that insurer. Hospital-insurer bargaining has generally been modeled using a Nash bargaining framework. Let the hospital’s and insurer’s utility functions be given by h(x) and n(x), respectively. The Nash bargaining solution for the allocation of surplus is the pair of values, (x,y ), that maximizes (h(x)(− h d))(n(y)(− n e)), where (d,e ) are the hospital’s and insurer’s outside options, respectively. Using the framework of Baron and Berman (2014), let α [0,1 ] be the bargaining power of the hospital, and 1 − α be that of the insurer. The bargaining solution maximizes (h(x)(− h d))α (n(y)(− n e))1−α . Among others, Lewis and Pflum (2015) demonstrate that market share and power affect reimbursement through bargaining power and position. Hospitals have explicit and implicit capacity constraints; explicit capacity constraints include bed capacity, while implicit capacity constraints include reduced care quality or longer wait times. Medicaid patients treated at hospitals use resources and fill capacity. An increase in the Medicaid patient population at a hospital then reduces the “capacity” available to privately insured patients. This has two effects. Privately insured patients, if they face greater wait times or lower-quality care, may choose to go to hospitals that do not suffer from high admission. This reduced patient traffic decreases the attractiveness of the constrained hospital in an insurer’s Page 3 network. Hospitals with capacity concerns due to greater Medicaid patient populations thus may also see declines in their market shares in the market for privately insured healthcare; as market share is a determinant of bargaining outcomes, we would expect that this would result in lower reimbursement rates for those hospitals in negotiations with private insurers. Given that a hospital may face lowered private insurance reimbursement rates with increases in Medicaid patients, whose reimbursements are already low and sometimes below cost, hospitals may face financial difficulties. Accordingly, it is intuitive to expect cost-cutting measures in these hospitals. Decreases in care quality may occur; decreasing care quality for privately insured patients may further drive substitution away from these hospitals, so care quality changes may differ between privately insured and publicly insured patients, as the utilization of this hospital by the latter group may not be as valued. Methods Estimating the effects of increasing Medicaid patient populations requires exogenous variation in these populations. This variation must be uncorrelated with hospitals’ capacity decision-making. While Medicaid expansion in Washington state in 2014 may have impacted hospital capacity decision making, the over enrollment of new patients was exogenous. I use Washington hospitals from 2010-2017 as the basis for my proposed specification: Markupht = β1XHHIht + β2 rt + β3InsNumrt + β4 Med%ht + β5RivalMed%ht + β6SystemMed%ht + εhrt where Markup is the difference between average price and cost for a service, X is hospital ​ ​ characteristics, HHI and InsNum are measures of hospital and insurer market concentration, and ​ ​ ​ ​ Med% variables denote the percentage of Medicaid patients in the patient population for the ​ hospital, its rivals, and its hospital system. The observations are indexed by hospital h, region r, ​ ​ ​ and year t. I will also include time and system fixed effects. I may remove system Medicaid ​ ​ percentage and include it in a separate specification as system competition has a large impact on bargaining outcomes; in that case, I will define system rivals separately as well. An important consideration here is potential interactions between HHI and the Medicaid share variables. I am also exploring the inclusion of Med%-RivalMed% as an explanatory variable with a spline ​ ​ specification that separates positive and negative values for that variable, negative and positive shares relative to rivals may result in differing coefficients and effects on reimbursement. My proposed regression equations to investigate care quality is : Qualityit = γ1Xht + γ2InsT ypeit + γ3 P redMarkuphit + γ4 InsT ypeit × P redMarkuphit + εhit . Quality may include hospital readmission rate, use of imaging procedures, and risk-adjusted ​ mortality, InsType is the patient insurance, and PredMarkup is the predicted markup from the ​ ​ ​ ​ previous regression for a hospital. The observations are indexed by patient i, hospital h, and year ​ ​ ​ ​ t. There are factors that affect both predicted markup and care quality; hospital Medicaid ​ percentage may impact both. PredMarkup should only include the care effects of Medicaid ​ ​ patient percentage that occur through effects on markup; so, one possible plan is to use the PredMarkup values from the specification that uses Medicaid percentage differences between ​ the hospital and its rivals and include the hospital’s own Medicaid percentage as a control. Issues ● Bargaining is over surplus; is markup a suitable proxy for this allocation?
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