Who Gets Wind? Investigating Economic Attributes of Iowa Counties Prior to Wind Turbine Development
by Karianna Klassen
Abstract
Iowa is a national leader in wind energy, producing nearly two-thirds of its electricity from wind turbines. However, the development of wind energy infrastructure across the state has been uneven—some counties host hundreds of turbines while others have none. This paper investigates whether county-level economic conditions influence the likelihood of wind turbine development. Using panel data from 1990 to 2023 and a two-way fixed effects regression framework, I examine the relationship between wind energy development and three economic indicators: farm income per capita, non-farm income per capita, and unemployment rate. I control for political affiliation, farming success, prior turbine presence, land availability, and demographic variables. Contrary to existing qualitative literature that suggests economic need drives local acceptance of wind projects, my analysis finds that these economic indicators are not statistically significant predictors of turbine development. One exception is political affiliation, which in some regressions indicates that a higher share of Democratic votes is associated with a lower probability of turbine development—contradicting national-level trends linking Democratic support with renewable energy expansion. All models have low between-county explanatory power (R² < 0.05), suggesting that factors not captured in county-level economic data—such as individual landowner decisions, developer strategies, or transmission infrastructure—may better explain wind energy siting patterns. These findings call for deeper investigation into localized, non-economic factors that shape renewable energy development, particularly as the push toward decarbonization accelerates.
Professor Jeffrey DeSimone, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL Codes: O13, R11, Q42,
Keywords: Wind Energy, Renewable Development, Agriculture
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
Illuminating the Economic Costs of Conflict: A Night Light Analysis of the Sri Lankan Civil War
by Nicholas Kiran Wijesekera
Abstract
This paper investigates the economic consequences of the Sri Lankan Civil War (1983-2009) by using event-based data on civilian and combatant fatalities in addition to night light imagery as a proxy for economic activity. By looking at regional economic activity across the island of Sri Lanka, this paper seeks to identify how violence led to declines or undershoots of economic activity in the areas in which it was most prevalent. The use of night light data gives a hyper-localized proxy measurement of this activity for each year of the war. The investigation finds that government and rebel deaths have strong, negative effects on economic activity, and that these effects spill over across time and space. Additionally, the manner in which civilian deaths occur is an important determinant of their subsequent economic impact. The paper offers new findings on the economic legacy of the Sri Lankan Civil War and extends existing work on the use of night light data to measure economic activity during conflict.
Professor Charles Becker, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL Codes: H56, N45, O53
Improving Institutional Performance: Foreign Aid Evaluation and Determinants of Foreign Aid Project Success Ratings
by Susan Sawyer O’Keefe
Abstract
In this paper, I use a regression model to predict project outcome ratings for international aid
projects by 12 multilateral and bilateral aid agencies taking place in 183 recipient countries. The
influential factors considered are project duration, project size, evaluation type, evaluation lag, donor
ratings, and country-level indicators of development. I find a significant relationship supporting
differences in project outcome ratings for projects evaluated by an independent evaluation agency, a
resource that some banks use to access project performance by an unbiased party. I also examine the
significance of other project-level factors and compare these to trends identified in past literature on
foreign aid project effectiveness.
Professor Michelle Connolly, Faculty Advisor
JEL Codes: H43, O22
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;
Short and Long-Term Impacts of a Large-Scale Natural Disaster on Individual Labor Outcomes: Evidence from the 2004 Indian Ocean Tsunami
by Tony Sun
Abstract
Natural disasters are often highly disruptive to the livelihoods of impacted populations. This paper investigates the effects of the 2004 Indian Ocean tsunami on male wages and labor supply from its immediate aftermath into the long run. Using fixed effects models that account for individual-specific heterogeneity, I find evidence of significant real wage declines for workers from heavily damaged areas that persist beyond the short-term. This long-term wage effect contrasts with previous literature, particularly in the context of relatively less severe disasters. Male workers also increased their hours-of-work following the tsunami, which suggests reliance on labor markets to smooth income losses and shifted their labor towards less disrupted industries. Additionally, I document the heterogeneity of tsunami impact on wages and hours-of-work by birth cohort and education, as well as by industry and sector of employment.
Professor Duncan Thomas, Faculty Advisor
Professor Michelle P. Connolly, Faculty Advisor
JEL Codes: J2; J21; J30; O10; Q54
A perfect storm: The effect of natural disasters on child health
by Cheyenne Danielle Quijano
Abstract
Typhoons and their accompanying flooding have destructive effects, including an increase in the risk of waterborne disease in children. Using a spatial regression discontinuity design, I explore the immediate to short-term effects of flooding as a result of Typhoon Labuyo on the incidence of diarrhea and acute respiratory infection in the Philippines by comparing children living in a flooded barangay (town) to children living just outside of the flooded area. I build on the existing literature by accounting for both incidence and intensity of the typhoon’s flooding in my model. I construct this new flooding measure using programming techniques and ArcGIS by manipulating data collected by the University of Maryland’s Global Flood Monitoring System. This data as well as health data from the 2013 Philippines National Demographic Health Surveys were collected the day after Typhoon Labuyo left the Philippines, providing a unique opportunity to explore the immediate impact of the typhoon on child health. Most of my results are insignificant, but subgroup analyses show that the effect of flooding on waterborne disease incidence is less impactful in the immediate term following a flood and more impactful in the medium-term. This is important, because understanding the detrimental health effects of flooding is of utmost importance, especially because climate change will only increase the frequency and intensity of natural disasters.
Professor Erica M. Field, Faculty Advisor
Professor Michelle P. Connolly, Faculty Advisor
JEL Codes: I150, O120, O130, Q540
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
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
