Tale of Two Cities An Econometric Analysis of East & West Coast Fine Art Galleries

By Daniella Victoria Paretti

Abstract
In a 2021 report published alongside Art Basel and UBS, renowned cultural economist Dr.
Clare McAndrew posited that the value of art sales in 2020 amounted to an impressive $50 billion
(although this actually marks an over 10-year low). It is no secret that the global art markets are
extremely lucrative, attracting the interest of industry magnates and business tycoons alike.
Though it is important to note that art markets are historically quite distinct from their normal good
counterparts — the sector is laden with issues regarding transparency, high barriers to entry, and
hiding of wealth. Amidst the COVID-19 pandemic, however, the tides began to turn; online
platforms for museums, auction houses, and galleries were employed more than ever before,
effectively modernizing the antiquated industry and expanding its reach to new consumers. How
has this trend of digitalization changed and improved art markets? More specifically, how can data
analytics and other technological resources serve the interests of private galleries? Using sales data
from a parent gallery with multiple locations across the United States (each displaying similar
works/artists), I have conducted a number of qualitative and statistical analyses to identify key
differences between the West and East coast locations. In short, the gallery on the West coast sold
more works and at a lower average cost than its counterpart, providing key insights into this local
market’s consumer base. Beyond this, factors like size, medium, and artist gender were found to
have statistically significant effects on the ultimate sale price and turnover rate of works. My
findings suggest that means of data analytics should be utilized by all actors in the art markets to
optimize their approach to business, as well as understand their consumers better than ever before.

Professor Michelle Connolly, Faculty Advisor
Professor Hans Van Miegroet, Faculty Advisor

JEL classification: Z11, C10, J11, O33

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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

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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 classification: R1; R3; R11; R31; J1; J15

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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, Thesis Advisor
Professor Grace Kim, Faculty Advisor

JEL classification: M14, M40

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Economic Effects of the War in Donbas: Nightlights and the Ukrainian fight for freedom

By Riad Kanj

Abstract
The conflict in Eastern Ukraine began in 2014, and it has now turned into a full-scale
invasion. The separatist areas of Donetsk and Luhansk have remained isolated for the last eight
years while fighting between rebels and the Ukrainian government has continued at a low but
regular level since then. Previous studies analyze the impact of the war in Donbas on the
economic situation in the region, such as the industry and GRP growth. However, this research
uses data solely from the initial part of the conflict (2014-2016) and does not take into account the
severity of the fighting. By using both the DMSP-OLS and VIIRS data as an approximation of
economic activity in addition to the Uppsala Conflict Data Program (UCDP) casualty numbers,
the analysis explores the effects of violent conflict on economic activity over a longer period of
the Donbas war.
This paper uses both yearly and monthly satellite data in analyzing the general progression
of the conflict in addition to the monthly progression. Furthermore, nightlight data of Ukrainian
municipalities outside of Donbas are used in computing the Donbas region’s nightlight data across
several years. The UCDP data for civilian and battle-related casualties are used separately to show
the causal effects of the different fighting severities. A Two-Stage Least Squares regression is
used to see the effects of battle severity on economic outcomes.

Professor Charles Becker, Faculty Advisor
Professor Grace Kim, Faculty Advisor

JEL classification: F51; H56; O52; N44

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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 Classification: D1 D31 G20 I24 J11

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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.

Pengpeng Xiao, Faculty Advisor
Michelle Connolly, Faculty Advisor

JEL classification: J1; G28; I31

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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, Ph.D., Faculty Advisor,
Assistant Professor Simon Mak, Ph.D., Faculty Advisor

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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 classification: D81; I12; J17; R2.

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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 classification: I0; I12; I21

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Questions?

Undergraduate Program Assistant
Matthew Eggleston
dus_asst@econ.duke.edu

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