Revisiting California Proposition 209: Changes in Science Persistence Rates and Overall Graduation Rates
by Anh-Huy Nguyen
Abstract
California Proposition 209 outlawed race-based affirmative action in the University of California (UC) system in 1998. However, the UC system subsequently shifted towards race-blind affirmative action by also reweighing factors other than race in the admissions process. To evaluate the hypothetical changes in the science persistence rate and graduation rate of all applicants if racial preferences had been removed entirely, I estimate baseline and counterfactual admissions models using data from between 1995-1997. Using a general equilibrium framework to fix the total number of admits and enrollees, I find that the removal of racial preferences leads to a cascade of minority enrollees into less selective campuses and a surge of non-minority enrollees into more selective campuses. The improved matching between students and campuses results in higher science persistence rates and graduation rates across the pool of all applicants. In particular, the gains are driven by minority students who were admitted under racial preferences, because the gains from better matching across UC campuses outweigh the losses from potentially being pushed outside the UC system. Non-minority students who are originally rejected under racial preferences also benefit, as some are induced into the system in the counterfactual, where they are more likely to graduate. I also investigate claims that applicants may have strategically gamed during the admissions process by misrepresenting their interest in the sciences in order to maximize their admissions probability. While there exist incentives to apply in different majors across the campuses, I find evidence that applicants often fail to game optimally, suggesting that they may not be fully informed of their relative admissions probabilities in the sciences and non-sciences.
Professor Peter Arcidiacono, Faculty Advisor
JEL Codes: I23, I28, J24, H75
Subprime’s Long shadow: Understanding subprime lending’s role in the St. Louis vacancy crisis
by Glen David Morgenstern
Abstract
Using loan-level data, this analysis attempts to connect the events of the subprime home loan boom to the current vacancy crisis in St. Louis, Missouri. Borrowers in Black areas in the north of St. Louis City and St. Louis County received subprime home loans at higher rates during the subprime boom period of 2003-2007 than those in White areas, with differences in balloon loans especially stark. Specifically, borrowers in Black neighborhoods received subprime loans more frequently than those with equal FICO scores in White neighborhoods. As a result of these differential loan terms, North City and inner ring “First Suburb” areas saw more foreclosure and borrower payment delinquency, which in turn were highly associated with home vacancy, controlling for other risk factors. However, foreclosure was no longer a significant predictor of home vacancy after controlling for demographic factors and FICO score, indicating that the unequal loan terms may have driven much of the increase in home vacancy in the St. Louis area since the Great Recession.
Professor Charles Becker, Faculty Advisor
JEL Codes: R1; R3; R11; R31; J1; J15
The Effect of Sustainability Reporting on ESG Ratings
by Arthur Luetkemeyer
Abstract
Over the past decade the concept of Environmental, Social, and Governance (ESG) investing has emerged to aid investors to maximize return on investments while simultaneously supporting environmentally and socially friendly methods of production and operation. In this paper I investigate the effect of the quality of sustainability reporting on ESG ratings. I utilize a sample of 100 chemical companies with ESG ratings and sustainability disclosure indexes over a 14-year time period (2007-2020) to analyze the short- and long run effects of sustainability reporting on ESG ratings. Using OLS my regression results suggest that better overall ESG disclosure as well as individual E, S, and G disclosure leads to worse ESG ratings in both the short run and the long run.
Professor Christopher Timmins, Faculty Advisor
Professor Grace Kim, Faculty Advisor
JEL Codes: M14, M40
Economic Effects of the War in Donbas: Nightlights and the Ukrainian fight for freedom
Paper available to internal Duke affiliates only upon request.
Professor Charles Becker, Faculty Advisor
Professor Grace Kim, Faculty Advisor
JEL Codes: F51; H56; O52; N44
Withdrawal: The Difficulty of Transitioning to a Cashless Economy
by Praneeth Kandula
Abstract
In 2021, modern payment methods such as mobile pay have increased nearly fivefold since their introduction in 2015. This shift to an increasingly cashless, digital economy has been marked by inequitable financial and technological divides. Historically, Black and Latino adults have had less access to financial systems and are less likely to own traditional computers and home broadband. Without rectifying these issues, a cashless, digital economy only serves to widen divides. Using data from the Diary of Consumer Payment, this study descriptively examines the use of cash and alternative payment methods by different racial and ethnic groups from 2015 through 2020. I also extend this effort to address the effects of COVID-19. I find that racial differences not only exist but also the gap between Black and Latino adults and White adults grows between 2015 and 2019. Still, this paper finds that in 2020 the likelihood to employ cash for a transaction falls for Black adults but not for Latino adults. COVID-19 has been a critical driver of change, forcing both consumers and corporations to shift to a more digital-centric economy. While there have been positive shifts for Black adults, policy ensuring that all racial groups have access to the necessary financial and digital networks will be critical in establishing an equitable economy moving forward.
Professor Lisa A. Gennetian, Faculty Advisor
Professor Michelle P. Connolly, Faculty Advisor
JEL Codes: D1 D31 G20 I24 J11
Financial Inclusion and Women’s Economic Empowerment in India
by Nehal Jain
Abstract
On August 14th, 2014 India’s Prime Minister Narendra Modi implemented the largest ever financial inclusion scheme to date known as Pradhan Mantri Jan Dhan Yojana (PMJDY). The program aimed to bank all of India’s unbanked population. Prior to the program, India had one of the highest rates of unbanked citizens. The program also included measures that prioritized women’s access to these financial institutions given the gender gap in financial inclusivity. This paper aims both to understand the effectiveness of PMJDY on granting women equal access as men to financial institutions and whether financial inclusion results in increased economic empowerment, I find that PMJDY was successful in increasing access to bank accounts and separately, that access to bank accounts economically empowers women.
Professor Pengpeng Xiao, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL Codes: J1; G28; I31
Bayesian Non-Parametric Risk Metric
by Kiwan Hyun
Abstract
This thesis constructs completely non-parametric Risk Metric models through Dirichlet process in order to account for both the parametric uncertainty and model uncertainty that a Risk Metric may bring.
Value at Risk (VaR), along with its integrated form Continuous Value at Risk (CVaR) / Expected Shortfall (ES), is one of the most frequently used risk metrics in finance. VaR is a quantile value of forecasted return of a portfolio—linear and non-linear. [Siu, et. al., 2006] According to the Basel 95% and 99% VaR are recommended to be posted by the financial institutions for portfolios and assets; 97.5% CVaR/ES value needs to be set aside when making an investment for “capital buffer”. [Obrenovic & Akhunjonov, 2016] Therefore, an accurate estimation of risk is critical for VaR models and CVaR/ES models.
The traditional approach of a normal approximation to VaR and CVaR/ES has been discredited—especially for daily returns—and even blamed by some for causing the 2008 Financial Crisis [Nocera, 2009] Many advancements have been made to the VaR model including Bayesian inference to the normal model [Siu, et. al., 2006], Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) VaR model [Bollerslev, 1986], and Conditional Autoregressive Value at Risk (CAViaR) model [Engle & Manganelli, 2004]. When tested against 6 years (Jan, 2001 – Jan, 2005) of daily returns data of 10 different market indexes, the Bayesian CAViaR model has shown to be the most accurate in predicting daily 95% and 99% VaR. [Gerlach, et. al., 2011]
However, there were certain years for certain indexes where the 99% Bayesian CAViaR VaR did not perform well, especially for years that had multiple > 5% daily drops. Moreover, the Bayesian CAViaR models—though are almost non-parametric—follow a Skewed-Laplace distribution. To even account for the uncertainty of the likelihood model, this thesis constructed daily 97.5% VaRs for 7 different country indexes for 7 years (Jan, 2012 – Dec, 2019) using the completely non-parametric Dirichlet Process.
The Dirichlet Process 97.5% VaR outperformed all Bayesian Normal, Bayesian GARCH, and Bayesian CAViaR models of years when CAViaR models underperformed. The model may be inefficient for normal years since it is overly conservative. Nevertheless, the non-parametric model still seems to be significantly more accurate during fluctuant years.
Professor Kyle Jurado, Faculty Advisor,
Professor Simon Mak, Faculty Advisor
No JEL Codes on file at this time.
Does Responsiveness to Mortality Risk Vary by Age? Evidence from Pandemic Health Outcomes and Movement Patterns
by Ryan Jones Hastings
Abstract
When choosing whether to visit venues like stores and restaurants during the COVID-19 pandemic, individuals faced trade-offs between movement and mortality risk. This paper analyzes age-specific responsiveness to infection-related mortality risk in the Philadelphia metropolitan area from March through December 2020. First, we develop a theoretical model that characterizes potential sources of heterogeneity in the decisions of individuals choosing how much to move. Next, we use data on the health outcomes of COVID-19 patients to estimate fatality
rates for different demographic groups. Finally, we use a panel of cell phone data tracking visits to venues before and during the pandemic along with a revealed preference approach to estimate an empirical model that relates age to movement decisions. Our results suggest that older people’s movements are less sensitive to mortality risk. Under weak assumptions, this implies that older people have a lower willingness to pay for marginal reductions in the probability of death. This finding has implications for the cost-benefit analysis of policies that mitigate adverse health outcomes, such as pandemic movement restrictions and pollution remediation, and for the value of statistical life (VSL) literature more broadly.
Professor Christopher Timmins, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL Codes: D81; I12; J17; R2.
Long-term Benefits of Breastfeeding: Impact on Education in Indonesia
by Natalie Gulrajani
Abstract
Healthy breastfeeding behaviors have been shown to produce many long-term health benefits including improved cognition. This study uses data from the Indonesian Family Life Survey (IFLS) to assess the longitudinal impact of exclusive breastfeeding duration and early life breastfeeding practices on education. Though a positive correlation was found between breastfeeding duration and years of schooling in naïve regressions, the significance and magnitude of this effect decreased when household fixed effects were added. A stronger correlation was found between early life breastfeeding and schooling, with income-stratified results demonstrating that poorer households are potentially subject to greater benefits.
Professor Erica Field, Faculty Advisor
JEL Codes: I0; I12; I21
Generic Entry and The Effect on Prices in the Prescription Drug Market
by Sahana Giridharan
Abstract
Drug firms have utilized a variety of strategies that contribute to rising drug prices in the U.S. for the last few years. Strategic entry timing and number of indications a drug is approved might be two factors that contribute to this rise in prices. While there have been some studies uncovering a positive relationship between generic entry and branded prices, there has been little research done on the effects of generic entry on generic prices thus far. This work can impact policy aimed at decreasing generic drug prices and increasing competition in the generic drug market. Oncology and inflammatory bowel disease (IBD) are two disease areas that have a high price burden to patients in the U.S. today, hence using Medicare Part B Average Sales Price (ASP) data, I analyze the effect of entry timing on the price of 24 drugs in these two indication areas. Using the Drugs@FDA Database, I collect data on the FDA approval date of a drug, and on the indications a drug is approved for. Utilizing OLS, my results suggest that later entry times lead to lower drug prices, with a 1 year increase in entry time resulting in a 6.99% increase in prices. Results also suggest that an increase of 1 in the number of indications a drug is approved for leads to a 49.79% decrease in drug price. This could suggest that having existing generic competitors in the pharmaceutical market decreases generic prices, and that number of indications is a strong indicator of drug price. If the current work is confirmed by future studies similar to this studying entry time and price in the generic pharmaceutical market, it is possible that future drug policy should focus on promoting competition within the pharmaceutical market to lower generic prices.
Professor Frank Sloan, Faculty Advisor
Professor Grace Kim, Faculty Advisor
Professor Kent Kimbrough, Faculty Advisor
JEL Codes: L11; I11; C3
