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Impact of Medicare Advantage Supplemental Benefit Expansion on Startup Funding
by Judy Tianhong Zhong
Abstract
In 2018, the Center for Medicare and Medicaid Services (CMS) announced that they would expand the supplemental benefits that can be included in Medicare Advantage (MA) plans. The goal was to encourage insurers to innovate and test new benefit offerings that could improve health outcomes and reduce healthcare spending. A key player in this transformation is the MA vendor that provides supplemental benefit offerings to insurance plans, but this market is rather underdeveloped. To assess the implementation of this supplemental benefit expansion, this study examines the flow of funding into the emerging market of MA vendors. This paper uses a longitudinal approach and Crunchbase data on funding for 79,004 firms from 2014 to 2018 to determine whether there is a significant jump in funding toward MA vendors with supplemental benefit services following the policy change. The results show that both the average amount of funding per deal and the number of deals a MA vendor firm receives significantly increased following the expansion when compared with all other firms. This suggests that the policy may have been successful in promoting the development of the MA vendors market and the innovation of benefit offerings as more funding goes towards these companies.
Kate Bundorf, Faculty Advisor
David Ridley, Faculty Advisor
Michelle Connolly, Faculty Advisor
JEL Codes: I1; I11; I18
Technological Impacts on Return to Education in Brazil
by Yirui Zhao
Abstract
The wage return to education has been studied for a long time. Acemoglu and Autor (2010) connect the decrease of medium-level job opportunities in the U.S. with technological advances. Their theoretical model predicts that if technology replaces routine jobs, workers with medium-level skills will experience decreases in wages relative to both high-skill workers (who become more productive with the improved technology) and low-skill workers (who can less easily be replaced since their work is not routine). Moreover, their theoretical model predicts that if medium-skill workers are closer substitutes for low-skill workers than they are for high-skill workers, the relative return of high-skill workers to low-skill workers should increase. Using education as proxy of skill (Acemoglu & Autor, 2012), this paper checks if these three predictions about relative wage returns to education also hold in Brazil. This paper finds that the impact of technological change on the Brazilian formal labor market between 1986 and 2010 is consistent with predicted changes in the return to education for medium-skill workers relative to both low and high skill workers. The impact is consistent with predicted changes in the return to education for high-skill workers relative to low-skill workers when Lula’s presidency is considered in the model.
Michelle Connolly, Faculty Advisor
Rafael Dix-Carneiro, Faculty Advisor
Daniel Xu, Faculty Advisor
JEL Codes: J24; J31; O33
The Cost of Delay: Evidence from the Ethereum Transaction Fee Market
by Yinhong “William” Zhao
Abstract
Delaying a financial transaction can be costly, but the cost of delay is difficult to estimate in traditional finance. I exploit the unique data offering and market design of the Ethereum blockchain to estimate the cost of delaying financial transactions in decentralized finance (DeFi). I construct a dynamic auction model for the Ethereum transaction fee market that relates users’ optimal transaction fee bids to their delay cost functions and network conditions, and I structurally estimate the delay cost functions for different users and transaction types. The average cost of delaying a transaction by one minute is 8.78 US dollars, but the distribution of delay costs is highly skewed to the right. Delay costs are higher for complex transactions and users who trade more frequently. I estimate that welfare loss due to network delay on Ethereum was 14.03 million US dollars per day in July 2021, and I apply the delay cost estimates to evaluate the welfare losses under alternative transaction fee mechanisms.
Campbell Harvey, Faculty Advisor
Michelle Connolly, Faculty Advisor
JEL Codes: D44; G10; L17;
Determinants of Sustained Success in NFT Markets
by Emily Xu
Abstract
Non-Fungible Tokens (NFTs) took headlines by storm in 2021 and have since established their own marketplace. As public interest in the space wanes in 2022-2023, I characterize this emerging space and investigate factors that distinguish top-performing NFT projects within their respective market segments while controlling for external market and cryptocurrency exposures. Literature in this emerging space remains sparse and I contribute in the following ways: 1) My cross-sectional time series panel synthesizes the most recent data from February 2022 to March 2023, utilizing information from five platforms (NonFungible.com, Twitter, OpenSea, ArtIndex, YahooFinance). To the best of my knowledge, this is the first holistic dataset that combines time-varying secondary sales data, Twitter, and market data. 2) My analysis categorizes data points by both NFT market segments and secondary sales performance, allowing for finer comparison between top and low performers within their respective categories. I find that a change in Twitter followers and tweets over time is a statistically significant and positive predictor of secondary sales, indicating that top-performing NFT projects must consistently add value and market to investors in order to generate sustained secondary sales. Additionally, top-performers saturate the space at incredibly high speeds and grand scales. For instance, the median top-performing collectible project has a collection of 10,000 items, attracted at least 60,000 Twitter followers, and achieved over $42 million in total sales, all within 1-2 years of the “NFT hype.” This concludes that royalties generated from NFTs are not passive, requiring creators to be reactive and consistent in their efforts.
Professor Michelle Connolly, Faculty Advisor
Professor Connel Fullenkamp, Faculty Advisor
JEL Codes: M30, M31, M37
Illuminating the Economic Costs of Conflict: A Night Light Analysis of the Sri Lankan Civil War
by Nicholas Kiran Wijesekera
Abstract
This paper investigates the economic consequences of the Sri Lankan Civil War (1983-2009) by using event-based data on civilian and combatant fatalities in addition to night light imagery as a proxy for economic activity. By looking at regional economic activity across the island of Sri Lanka, this paper seeks to identify how violence led to declines or undershoots of economic activity in the areas in which it was most prevalent. The use of night light data gives a hyper-localized proxy measurement of this activity for each year of the war. The investigation finds that government and rebel deaths have strong, negative effects on economic activity, and that these effects spill over across time and space. Additionally, the manner in which civilian deaths occur is an important determinant of their subsequent economic impact. The paper offers new findings on the economic legacy of the Sri Lankan Civil War and extends existing work on the use of night light data to measure economic activity during conflict.
Professor Charles Becker, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL Codes: H56, N45, O53
The True Cost: An Aggregate Analysis of the Advanced Clean Cars II Policy
by Lauren Mackenzie Sizemore
Abstract
Global climate change, emphasis on the global, requires local solutions. Every entity plays a role, some more than others. Yet, when improvements in pollution or emissions in one region leads to more problems in another, how is the net cost or benefit to be deciphered for the environment, for the economy, and for humanity in general? Advanced Clean Cars II (ACC II), a proposed policy in California, United States, is a practical test of this question. For each model year beginning in 2026, the potential law gives a percentage of new vehicle sales that must be zero-emission vehicles (ZEVs) – cars that do not emit exhaust gas or other pollutants from the onboard source of power – or plug-in-hybrid electric vehicles (PHEVs). By 2035, ACC II would require all new vehicles purchased in California to be either a ZEV or a PHEV. With reduced tailpipe emissions, California expects to benefit from reduced smog, less carbon emissions, better air quality, a reduction in air-related health issues such as asthma, and increased sales from California-based electric vehicle companies such as Tesla and Rivian. Since air is a common resource, improving California’s quality also betters air globally. Yet emissions and pollution produced during the mining, production, and scrappage phases work in opposition to the decreased tailpipe emissions. By converting each type of pollutant into a per vehicle dollar cost, I paint a better picture of the global cost-benefit. The per vehicle cost is scaled based on the expected number of electric and conventional vehicles in California which is predicted under two scenarios: ACC II passes with full enforcement and the law is not passed. I forecast the number of electric vehicles likely bought in both instances using the Bass Model for New Product Growth of Consumer Durables (Bass 1969). I determine that a maximum of eighteen states, including California, could successfully implement ACC II and lower emissions given their 2021 electricity grid’s carbon intensity.
Professor Connel Fullenkamp, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL Codes: Q5, Q51, Q58
Student Effort and Parent Attitude on Education Attainment: Evidence from Multi-year Survey in Gansu, China
by Ridge Zhong-yuan Ren
Abstract
This paper explores whether student effort and parent attitudes have varying effects at different stages of a student’s life in terms of educational attainment and job outcomes. With survey data in Gansu, China, a largely rural province in Northwest China that lags behind the rest of China in education, this paper employs a multivariate regression model. This method allows me to measure the achievement or outcome of the child between each successive wave of surveys and estimate which factors held the strongest effect on the next wave. Student achievement in early waves is measured by the student’s score on assessments in math and Chinese, and the later outcome is measured by the student’s income and the highest level of education achieved. This paper finds that effort in Math and math achievement have a positive association with better education attainment and career outcomes later in life. In addition, I find that parental education levels also have a positive association with child outcomes.
Professor Pengpeng Xiao, Faculty Advisor
Professor Kent P. Kimbrough, Faculty Advisor
JEL Codes: I25, I26
Short Term Effectiveness of Chinese Stock Connect Program — a Study of the Pricing Dynamics of Cross-listed Stocks
by Kaiyu Ren
Abstract
This thesis examines the pricing dynamics of cross-listed stocks in the Chinese A-share and Hong Kong H-share markets. By identifying an announcement-implementation window, I offer a fresh perspective on the short-term price adjustment of cross-listed stocks around the launch of the first Stock Connect program. My findings reveal a significant increase of the A-H price ratio, but this price discrepancy appears to have been mitigated by the implementation of the Stock Connect program.Additionally, my observations suggest the existence of market inefficiencies, particularly among the groups of A-share stocks that are excluded from the Stock Connect program.
Professor Ronald Leven, Faculty Advisor
JEL Codes: G14; G18
Price Determinants and Depreciation of Used Cars Post-COVID-19
by Ayaan Sundeep Patel
Abstract
Throughout the COVID-19 pandemic, the price of used cars has fluctuated greatly due to numerous factors. Inflation and supply chain issues have been at the forefront of the news and have affected not only cars but most consumer goods. While the majority of society has seemingly progressed past COVID-19, its effects still linger in the used car market, as prices rose 4.6% from January 2023 to February 2023. Therefore, in an effort to study this phenomenon, I scraped data from autotrader.co.uk on February 23, 2023. This study aims to understand the effect of various factors, including mileage, age, and engine size, on various classes of used cars. The five classes being studied are compact cars, luxury sports sedans, luxury mid-size sedans, luxury full-size sedans, and luxury SUVs. A log-linear model is used to model the price determinants of the used cars. A linear model is incorporated to model the depreciation rate of the cars in the dataset. Lastly, this model is used to predict the three-year depreciation rate for each car model, which is then compared to the pre-COVID-19 three-year depreciation rate to see the inflated prices in the UK used car market.
Professor Michelle Connolly, Faculty Advisor
Professor Andrea Lanteri, Faculty Advisor
JEL Codes: D12, J11, L62
Improving Institutional Performance: Foreign Aid Evaluation and Determinants of Foreign Aid Project Success Ratings
by Susan Sawyer O’Keefe
Abstract
In this paper, I use a regression model to predict project outcome ratings for international aid projects by 12 multilateral and bilateral aid agencies taking place in 183 recipient countries. The influential factors considered are project duration, project size, evaluation type, evaluation lag, donor ratings, and country-level indicators of development. I find a significant relationship supporting differences in project outcome ratings for projects evaluated by an independent evaluation agency, a resource that some banks use to access project performance by an unbiased party. I also examine the significance of other project-level factors and compare these to trends identified in past literature on foreign aid project effectiveness.
Professor Michelle Connolly, Faculty Advisor
JEL Codes: H43, O22
