Fact or Fluff: Does Wording Used by Gene Editing Companies Affect Investor Behaviors?
by Thomas Freireich
Abstract
The writing style a startup uses to portray itself has an impact on investors’ perceptions of them, subsequently affecting their venture capital decisions. This funding is particularly important given the prominence of venture capital as a primary financial source for growing early-stage biotechnology companies. Currently, due to recent scientific advances, many of these startup companies are utilizing novel gene editing based approaches to cure a variety of previously untreated diseases. For the sake of those affected, it is essential that this sector of the biotechnology industry is managed properly early on so that developed treatments can eventually reach FDA approval. This paper is in part inspired by recent happenings revolving around the fraudulent biotech startup, Theranos. Elizabeth Holmes, Theranos’ founder, was renowned for making comments lauding the company’s product. It seemed to many that investors were lulled by the idea of what Holmes made Theranos to be, invested in the company based on false verbal promises instead of the reality of the scientific product. Occurrences like the demise of Theranos are detrimental to both investors and competing companies in need of venture funds in order to develop their treatments. Thus, this paper explores the impact of word-usage and writing style on venture capital investment in various gene editing based startups,hoping to elucidate whether investors are being swayed by word choice.
Professor Michelle Connolly, Faculty Advisor
JEL Codes: M1, M13, O3, O32
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
What Affects Post-Merger Innovation Outcomes? An Empirical Study of R&D Intensity in High Technology Transactions Among U.S. Firms
by Neha Karna
Abstract
High levels of global M&A activity have characterized the past decade, making the policy debate over the impact of mergers on innovation even more pertinent. Innovation is a significant driver of economic growth and therefore a negative effect of mergers on innovation outcomes may have detrimental consequences. Nevertheless, the existing literature demonstrates mixed results leaving it unclear whether the overall effect is positive or negative. This paper contributes to existing literature on the relationship between mergers and innovation and examines the effects of M&A on the subsequent innovative activity of acquiring firms that operate in high technology (high-tech) industries. I construct a sample of U.S.-based public-to-public deals from 2010-2019 involving high-tech acquiring firms. Using multivariable regression with robust considerations, I analyze factors that may explain post-merger R&D intensity defined as the merged entity’s R&D expenditure divided by its total assets one year after deal completion. I consider firm characteristics of the target and acquirer, including size, industry, and age, and industry competition. I find potential positive impact of relative target size on post-merger R&D intensity and significant interaction effects between relative target size and firm age, relative target size and industry relatedness, and target industry competition and industry relatedness. My results suggests that beyond the occurrence of a merger, specific deal characteristics may affect postmerger innovation outcomes.
Professor Grace Kim, Faculty Advisor
Professor Kent Kimbrough, Faculty Advisor
JEL Codes: G3; G34; L40; O31; O32;
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 Codes: Z11, C10, J11, O33
Predicting the Work Task Replacement Effects of the Adoption of Machine Learning Technology
by Shreya Hurli
Abstract
This paper develops a methodology to attempt to predict which tasks in the workforce will be resistant to the replacement of labor by machine learning technology in the near future given current technology and technology adoption trends. Tasks are individual activities completed as parts of a job. Prior research in the field suggests that characteristics of tasks (non-roteness, creativity, analysis/cognitive work) that make them harder for machine learning technology to complete are good predictors of whether those tasks will be resistant to replacement in the workforce. This study utilizes O*NET (Occupational Information Network) task description and education data from October 2015 to August 2020 and Bureau of Labor Statistics salary data to use task characteristics to predict tasks’ resistance to replacement. Normalized scores, average salaries, and average worker education levels are calculated to quantify the relative presence or absence of non-roteness, creativity, and cognitive work in a task. This paper then uses the calculated scores, salary, and education data, as well as a number of interaction terms as inputs to a support vector machine (SVM) model to predict which tasks will be resistant to decline in their shares of workplace tasks weighted by the jobs under which the tasks fall. Using task characteristics, the SVM predicts that just approximately 39% of tasks are likely to be resistant to replacement. These tasks tend to be highly non-deterministic (very non-rote, analytical/cognitive, and/or creative) in nature.
Professor David Berger, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL Codes: J23, J24, O33
The Effects of Health IT Innovation on Throughput Efficiency in the Emergency Department
by Michael Levin
Abstract
Overcrowding in United States hospitals’ emergency departments (EDs) has been identified as a significant barrier to receiving high-quality emergency care, resulting from many EDs struggling to properly triage, diagnose, and treat emergency patients in a timely and effective manner. Priority is now being placed on research that explores the effectiveness of possible solutions, such as heightened adoption of IT to advance operational workflow and care services related to diagnostics and information accessibility, with the goal of improving what is called throughput efficiency. However, high costs of technological process innovation as well as usability challenges still impede wide-spanning and rapid implementation of these disruptive solutions. This paper will contribute to the pursuit of better understanding the value of adopting health IT (HIT) to improve ED throughput efficiency.
Using hospital visit data, I investigate two ways in which ED throughput activity changes due to increased HIT sophistication. First, I use a probit model to estimate any statistically and economically significant decreases in the probability of ED mortality resulting from greater HIT sophistication. Second, my analysis turns to workflow efficiency, using a negative binomial regression model to estimate the impact of HIT sophistication on reducing ED waiting room times. The results show a negative and statistically significant (p < 0.01) association between the presence of HIT and the probability of mortality in the ED. However, the marginal impact of an increase in sophistication from basic HIT functionality to advanced HIT functionality was not meaningful. Finally, I do not find a statistically significant impact of HIT sophistication on expected waiting room time. Together, these findings suggest that although technological progress is trending in the right direction to ultimately have a wide-sweeping impact on ED throughput, more progress must be made in order for HIT to directly move the needle on confronting healthcare’s greatest challenges.
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Advisors: Michelle Connolly, Ryan McDevitt | JEL Codes: I1, I18, O33
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.
Advisors: Professor Patrick Bayer, Professor Bocar Ba | JEL Codes: K4, K42, O33
The Impact of Microfinance on Women’s Empowerment: Evidence from Rural Areas of Uganda
By Sonia Maria Hernandez
Microfinance is the practice of extending small collateral-free loans to underserved populations in developing areas with no access to credit. The Village Savings and Loan Association (VSLA) randomized access to microfinance treatment for women in rural areas of Uganda and tracked outcomes through surveys. This research determines the impact of microfinance by analyzing outcomes over five dimensions of women’s empowerment, including decision making power, community participation, business outcomes, emotional wellness, and beliefs about women. The strongest results showed that access to the VSLA program empowered women in terms of business outcomes and decision-making power. This leads to the conclusion that microfinance can more easily impact how a woman behaves within the household than change how a woman behaves within the community.
Advisors: Professor Kent Kimbrough, Professor Lori Leachman | JEL Codes: O1, O12, O35
Evaluating The Forward Citations-Patent Value Relationship: The Role Of Competition
By Neelesh T. Moorthy
I assess whether forward citations—how often patents are cited by subsequent patents—reliably capture patent quality. A high-quality invention might lack forward citations if there are no competing, patenting firms. This introduces measurement error in using citations to measure patent value. I test whether greater competition makes forward citations better measures of patent quality, with eight and twelve-year patent renewal rates serving as my benchmark measures of patent quality. Patent data come from the manufacturing survey in Cohen, Nelson, and Walsh (2000). I conduct logit regressions of patent renewal on forward citations and the number of competitors faced by surveyed manufacturing labs. While the regression results do not support the competition hypothesis, they confirm that forward citations positively predict renewal. They also lend insight into firms’ strategic renewal decisions.
Advisors: Wesley Cohen and Michelle Connolly | JEL Codes: O31, O34
Evaluation of the Impact of New Rules in FCC’s Spectrum Incentive Auction
By Elizabeth Lim, Akshaya Trivedi and Frances Mitchell
On March 29, 2016, the FCC initiated its first ever two-sided spectrum auction. The auction closed approximately one year later, having repurposed a total of 84 megahertz (MHz) of spectrum. The “Incentive Auction” included three primary components: (1) a reverse auction where broadcasters bid on the price at which they would voluntarily relinquish their current spectrum usage rights, (2) a forward ascending clock auction for flexible use wireless licenses which determined the winning bids for licenses within a given geographic region, and (3) an assignment phase, where winning bidders from the forward auction participated in single-bid, second price sealed auctions to determine the exact frequencies individual licenses would be assigned within that geographic region. The reverse auction and the forward auction together constituted a “stage.” To guarantee that sufficient MHz were cleared, the auction included a “final stage rule” which, if not met, triggered a clearing of the previous stage and the start of a new stage. This rule led to a total of four stages taking place in the Incentive Auction before the final assignment phase took place. Even at first glance, the Incentive Auction is unique among FCC spectrum auctions. Here we consider the estimated true valuation for these licenses based on market conditions. We further compare these results to more recent outcomes in previous FCC spectrum auctions for wireless services to determine if this novel auction mechanism
impacted auction outcomes.
Advisor: Michelle Connolly | JEL Codes: L5, O3, K2, D44, L96
