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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.
Advisors: Professor Connel Fullenkamp, Professor Kent Kimbrough | JEL Codes: G1, G11, G14
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.
Advisors: Professor Patrick Bayer, Professor Bocar Ba | JEL Codes: K4, K42, O33
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.
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.
Advisors: Professor Christopher Timmins, Professor Alison Hagy | JEL Codes: R2, R3, J11
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.
Advisors: Professor Charles Becker, Professor Frank Sloan, Professor Grace Kim| JEL Codes: I1, I13, I18
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.
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.
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Advisors: Professor Andrew Patton, Professor Michelle Connolly | JEL Codes: G1, G01, G33
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.
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Advisors: Professor Kyle Jurado, Professor Michelle Connolly, Professor Grace Kim| JEL Codes: C2, C20, J19
By Kedest Mathewos
Given that productivity is a key component of long-term economic growth and that China has become an important source of external financing in Africa, this study aims to investigate the impact of Chinese foreign direct investment and government-to-government loans on productivity. Using a panel of the top fourteen African recipients of Chinese financing during the period 2003-2017, this study employs a two-stage regression process. The first relies on the use of a revised version of the Solow Model that accounts for human capital, natural resource accumulation and country-specific heterogeneity, to generate values of total factor productivity. The second examines the impact of Chinese financing on this generated measure of productivity. After taking into account significant confounding variables such as institutional quality, trade openness and manufacturing value-added, this study finds that Chinese foreign direct investment (FDI) has a significant negative impact on productivity while Chinese government loans are positively associated with productivity. However, consistent with the literature, the impact of Chinese FDI depends on the country’s absorptive capacity – proxied here by the level of human capital accumulation. Therefore, as African countries seek to boost productivity levels, they should continue to attract Chinese government loans while enhancing their FDI absorptive capacity.
Advisors: Professor Lori Leachman, Professor Grace Kim, Professor Kent Kimbrough| JEL Codes: O4, O47, F21