Home » Advisor (Page 2)

Category Archives: Advisor

Incentive Programs for Neglected Diseases

By Pranav Ganapathy   

We propose and evaluate an auction mechanism for the priority review voucher program. The 2007 voucher program rewards drug developers for regulatory approval of novel treatments for neglected tropical diseases. Previous papers have proposed auctioning vouchers for the priority review voucher program but have offered neither a mathematical model nor a framework. We present a mechanism design problem with one pharmaceutical company producing one drug for a neglected tropical disease. The mechanism that maximizes the regulator’s expected surplus is a take-it-or-leave-it offer, with three different offers based on low, intermediate, and high neglected disease burdens. We demonstrate how mechanism design can be applied to settings in which the buyer pays for public access to a product with regulatory speed. Finally, this paper may be useful to policymakers seeking to improve access to voucher drugs through modifications of the program.

View Thesis

Advisors: Professor David Ridley, Professor Giuseppe Lopomo, Professor Michelle Connolly| JEL Codes: I1, D44, D82

Comparing the Performance of Active and Passive Mutual Funds in Developing and Developed Countries

By Nalini Gupta  

This paper seeks to test the hypothesis that developing countries or informationally inefficient countries should see higher returns for active mutual funds on average than passive funds and the trend should be reversed in developed nations or informationally efficient economies. This analysis is done using a cross section of eight countries, four developed and four developing. Using a fund universe of 20 active and 20 passive funds per country and controls such as volatility, market return, financial market development and Human Development Index among others, we see that there is no clear systematically dominant strategy between active and passive investment universally. While developing countries are associated with lower returns, we do not find a significant difference between active and passive based on development classification. A key finding is that an increase in liquidity, acting as proxy for informational efficiency, leads to a co-movement of active and passive returns in each country. The paper also lends itself to further analysis regarding confounding factor such as noise trading and movement of foreign capital which impact the effect of increased liquidity on mutual fund returns.

View Thesis

View Data

Advisors: Professor Connel Fullenkamp, Professor Kent Kimbrough | JEL Codes: G1, G11, G14

Bridging the Persistence Gap: An Investigation of the Underrepresentation of Female and Minority Students in STEM Fields

By Aaditya Jain and Bailey Kaston

Prior literature on mismatch theory has concentrated primarily on minority students, whose lower average levels of pre-enrollment preparedness tend to discourage them from persisting in STEM fields as often as their non-minority counterparts at selective universities. Our study shifts the focus to the persistence gap between men and women, invoking social cognitive career theory to investigate how factors beyond preparedness – such as self-confidence – cause women to switch out of selective STEM programs at higher rates than men. Using the High School Longitudinal Study of 2009, we investigate the drivers of STEM persistence for all students and arrive at two main conclusions. First, higher levels of STEM preparedness are more beneficial to STEM persistence at selective universities, confirming mismatch theory in the sample. We then simulate the counterfactual scenario and find that 33% of students at selective schools would have been more likely to persist in STEM had they attended less selective schools, a figure that reaches 50% for underconfident female students. This observation ties to our second conclusion – that underconfidence in math relative to one’s true performance decreases the likelihood of STEM persistence for all students at selective universities, and that female students at selective schools are more likely to be underconfident than their male counterparts. Our findings suggest that the appropriate policy solution to reduce STEM attrition rates among women should then become a two-pronged approach: (1) more selective universities should better support the STEM self-confidence levels of female students, and (2) home environments should ideally cultivate that self-confidence long before women even reach college. In our final set of analyses, we thus explore the factors that drive math overconfidence in the first place, and conclude that both student and parental biases against female STEM ability are detrimental to the STEM self-confidence of female students.

View Thesis

View Data (Email for Access)

Advisors: Professor Peter Arcidiacono, Professor Michelle Connolly | JEL Codes: I2, I24, I26

Patrolling the Future: Unintended Consequences of Predictive Policing in Chicago

By Jenny Jiao   

In the past decade, police departments have increasingly adopted predictive policing programs in an effort to identify where crimes will occur and who will commit them. Yet, there have been few empirical analyses to date examining the efficacy of such initiatives in preventing crime. Using police and court data from the second-largest police department in the country, this paper seeks to evaluate the pilot version of Chicago’s Strategic Subject List, a person-based predictive policing program. Using a boundary discontinuity design, I find that individuals eligible for the Strategic Subject List were 2.07 times more likely to be found not guilty of all charges in court than similarly situated individuals in the control group. Taking into account crime category heterogeneity, I find evidence that individuals previously arrested for drug crimes drive this result. This research sheds light on the potential unintended consequences of person-based predictive policing.

View Thesis

Advisors: Professor Patrick Bayer, Professor Bocar Ba | JEL Codes: K4, K42, O33

User Loyalty and Willingness to Pay for a Music Streaming Subscription

By Nell Jones   

Music streaming has increased industry revenue and displaced piracy, but limited profits for artists. In this thesis, I examine user loyalty to streaming platforms, focusing on the asset specificity of features and estimating what users are willing to pay for each of these features. A structural equation model of survey data shows that feature satisfaction positively affects both asset specificity of and overall satisfaction with streaming platforms, strengthening user loyalty. Using conjoint analysis, I estimate that users are willing to pay at least $14.40 for platforms that offer algorithm, playlist and social features, and the ability to download music.

View Thesis

Advisors: Professor Michael Munger, Professor Grace Kim | JEL Codes: Z1, Z11, M21

Where You Live and Where You Move: A Cross-City Comparison of the Effects of Gentrification and How these Effects Are Tied to Racial History

By Divya Juneja   

This thesis compares the effects of gentrification on school and air quality in ten cities to see whether cities with larger amounts of white flight post-World War II exhibited worse gentrification effects on renters. I find that renters in high white flight cities more consistently experience school quality downgrades—likely attributed to moving from gentrifying neighborhoods to worse neighborhoods. High white flight meant widespread de-investment across neighborhoods which could have lowered the school quality experienced by displaced renters. Gentrification did not consistently affect air quality in any way related to white flight, meaning confounding variables could have influence.

View Thesis

Advisors: Professor Christopher Timmins, Professor Alison Hagy | JEL Codes: R2, R3, J11

The Impact of Medicare Nonpayment: A Quasi-Experimental Approach

By Audrey Kornkven   

In October 2008, a provision of the Deficit Reduction Act of 2005 known as Medicare “Nonpayment” went into effect, eliminating reimbursement for the marginal costs of  preventable hospital-acquired conditions in an effort to correct perverse incentives in hospitals and improve patient safety. This paper contributes to the existing debate surrounding Nonpayment’s efficacy by considering varying degrees of fiscal pressure among hospitals; potential impacts on healthcare utilization; and differences between Medicare and non-Medicare patient populations. It combines data on millions of hospital discharges in New York from 2006-2010 with hospital-, hospital referral region-, and county-level data to isolate the policy’s impact. Analysis exploits the quasi-experimental nature of Nonpayment via difference-in-differences with Mahalanobis matching and fuzzy regression discontinuity designs. In line with results from Lee et al. (2012), Schuller et al. (2013), and Vaz et al. (2015), this paper does not find evidence that Nonpayment reduced the likelihood that Medicare patients would develop a hospital-acquired condition, and concludes that the policy is not likely the success claimed by policymakers. Results also suggest that providers may select against unprofitable Medicare patients when possible, and are likely to vary in their responses to financial incentives. Specifically, private non-profit hospitals appear to have been most responsive to the policy. These findings have important implications for pay-for-performance initiatives in American healthcare.

View Thesis

View Data

Advisors: Professor Charles Becker, Professor Frank Sloan, Professor Grace Kim| JEL Codes: I1, I13, I18

Evolution of Wealth and Consumption in the Aftermath of a Major Natural Disaster

By Ralph Lawton   

Natural disasters can have catastrophic personal and economic effects, particularly in low-resource settings. Major natural disasters are becoming more frequent, so rigorous understanding of their effects on long-term economic wellbeing is fundamentally important in order to mitigate their impacts on exposed populations. In this paper, I investigate the effects of the 2004 Indian Ocean tsunami on real consumption and assets at the individual level. I also examine the heterogeneity of those impacts, and the related effects on inequality. Taking individual-specific heterogeneity into account with fixed effects, I find individuals living in heavily damaged areas experience major declines in real consumption and assets, and do not recover in the long term. These results are strikingly different than results that do not consider price effects, as well as previously published macroeconomic results. I also find significant heterogeneity by age, education-level, pre-tsunami socioeconomic status, and whether an individual went into a refugee camp. The tsunami resulted in large, long-term declines in asset inequality, and a temporary increase in consumption inequality that returns to near pre-tsunami levels in the long run.

View Thesis

Advisors: Professor Duncan Thomas, Professor Michelle Connolly | JEL Codes: D1, D15, H84

Forecasting Corporate Bankruptcy: Applying Feature Selection Techniques to the Pre- and Post-Global Financial Crisis Environments

By Parker Levi   

I investigate the use of feature selection techniques to forecast corporate bankruptcy in the years before, during and after the global financial crisis. Feature selection is the process of selecting a subset of relevant features for use in model construction. While other empirical bankruptcy studies apply similar techniques, I focus specifically on the effect of the 2007-2009 global financial crisis. I conclude that the set of bankruptcy predictors shifts from accounting variables before the financial crisis to market variables during and after the financial crisis for one-year-ahead forecasts. These findings provide insight into the development of stricter lending standards in the financial markets that occurred as a result of the crisis. My analysis applies the Least Absolute Shrinkage and Selection Operator (LASSO) method as a variable selection technique and Principal Components Analysis (PCA) as a dimensionality reduction technique. In comparing each of these methods, I conclude that LASSO outperforms PCA in terms of prediction accuracy and offers more interpretable results.

View Thesis

View Data (Email for Access)

Advisors: Professor Andrew Patton, Professor Michelle Connolly | JEL Codes: G1, G01, G33

Hedonic Pricing in the Sneaker Resale Market

By Kevin Ma and Matthew Treiber

This paper explores the secondary resale market for high-end and limited-edition sneakers, specifically analyzing the determinants that affect what value sneakers trade for in the secondary market. While it is common knowledge that the sneaker resale market is a thriving and active secondary market, there is little to no empirical research about what exactly causes such sneakers to sell for exorbitant prices in the resale market. The study utilizes a hedonic pricing approach to investigate the determinants of sneaker resale price. We use a dataset of sneaker resale transactions from the online marketplace StockX between the years of 2016 and 2020 as the basis for our research. After analyzing the results, we have determined that the amount of “hype” that surrounds a sneaker as well as supply scarcity are statistically significant factors when determining the resale price premium a particular sneaker commands in the secondary market. This work adds to the sparse literature on the sneaker resale industry and brings an econometrics-approach to determining the price a given pair of sneakers commands in the resale market.

View Thesis

View Data (Email for Access)

Advisors: Professor Kyle Jurado, Professor Michelle Connolly, Professor Grace Kim| JEL Codes: C2, C20, J19

Questions?

Undergraduate Program Assistant
Jennifer Becker
dus_asst@econ.duke.edu

Director of the Honors Program
Michelle P. Connolly
michelle.connolly@duke.edu