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Author Archives: Dwayne Russell
Labor Market Effects of the Minimum Wage in South Korea
by Alec Ashforth
This paper analyzes survey data from businesses regarding individual worker earnings, hours, and characteristics from 1971 to 1998 in order to estimate the labor market effects of the minimum wage in South Korea. Since the minimum wage was only implemented in manufacturing, construction, and mining industries, we are able to compare earnings and hours of workers in these industries with workers in other industries using both a difference-in-differences and a synthetic control approach. Additionally, we test to see if the minimum wage had heterogeneous effects based on an individual worker’s gender, level of education, experience, and payment period.
Advisors: Professor Arnaud Maurel, Professor Kent Kimbrough | JEL Codes: J31, J38, O15
Forecasting the Effects of Battery Recycling on the Global Cobalt Market
by Elena Cavallero
This paper addresses existing concerns around a potential cobalt supply shortage driven by lithium-ion battery demand. Using econometric simultaneous equations, historical global cobalt supply and demand are estimated using data from 1981 to 2018. Based on the results of a Three-Stage Least Square estimation model of global supply and demand, this study forecasts global cobalt price and quantity in 2030. Additionally, a parametrization of battery recycling is added to study the effects of cobalt recovery on future market equilibrium. The results indicate that: 1) world GDP is a key determining driver of cobalt demand, 2) conflicts in the Democratic Republic of Congo, the world’s largest cobalt supplier, negatively impact global production, and 3) recycling lithium-ion batteries will increase global cobalt quantity supplied by 23% and decrease price by 60% in 2030 under the EU Green Deal regulations
Advisors: Professor Brian Murray, Professor Grace Kim | JEL Codes: C30, Q31, Q55
Bang for Your (Green) Buck: The Effects of ESG Risk on US M&A Performance
by Richard Chen
Mergers & Acquisitions (M&A) is a fundamental corporate activity that has not received much attention from an environmental, social, and governance (ESG) perspective. In this paper, I analyze how buyer and target ESG risks affect US M&A performance in both the short and long run as measured by deal valuations and changes in buyer operating metrics, respectively. I utilize a sample of 341 transactions from 2007-2020 with a cumulative value over $3 trillion from Capital IQ where both the buyer and target have available ESG data provided by RepRisk. Utilizing OLS, my results suggest that higher ESG risk causes buyers to pay more and targets to receive less. In the long run, buyer ESG risk is an important determinant of performance. When examining the components of ESG, governance is the most consistently significant, followed by social, then environmental – though it becomes more significant in the long run. Additionally, all three components appear to have some non-linear impacts on M&A performance.
Advisors: Professor Connel Fullenkamp, Professor Grace Kim | JEL Codes: G34, G14, M14
The Impact of Conflict on Economic Activity: Night Lights and the Bosnian Civil War
by Stephanie Dodd
The tendency of violent conflict to suppress economic activity is well documented in the civil war economic literature. However, differential consequences resulting from distinct characteristics of conflicts have not been rigorously studied. Utilizing new conflict data on the 1992-1995 Bosnian civil war from Becker, Devine, Dogo, and Margolin (2018) and DSMP-OLS night light data as a proxy for economic activity, this paper investigates the disparate economic impacts that different types of conflict have on Bosnia’s municipalities.
This investigation first uses data from other Yugoslavian countries to impute pre-war night light values for conflict-affected Bosnian municipalities. Next, a spatial autocorrelation model with fixed effects is used to determine if and how the occurrence of different types of violence vary in their implications for economic activity. This analysis finds that the five types of warfare identified in the context of the Bosnian Civil war have different impacts on night lights and economic activity.
Advisors: Professor Charles Becker, Professor Grace Kim | JEL Codes: F52, H56, O52
Predicting the Work Task Replacement Effects of the Adoption of Machine Learning Technology
by Shreya Hurli
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.
Advisors: Professor David Berger, Professor Michelle Connolly | JEL Codes: J23, J24, O33
Closing the Digital Divide: Evaluation of FCC’s Connect America Fund
by Yurong Jiang
Abstract
The still-unfolding IT revolution is a key driver for the remarkable performance of the U.S. economy since the 1990s. Getting on the rising tide requires a high-speed internet connection. The COVID-19 pandemic has further intensified the existing digital divide by driving most essential activities online. 18 million Americans that lack high-speed broadband connection are falling behind. The Connect America Fund is the largest on-going federal support for broadband buildout to unserved areas. This paper provides the first econometric assessment of the Connect America Fund between 2014 and 2018 using county-level data. It does not find robust evidence in support of the program. While subsidy recipient counties do not see substantial improvement in terms of the number of high-speed providers, the elasticity of the equilibrium subscription rate to total subsidies is near zero. Solely tackling the supply side shortfall is clearly not sufficient to produce a desirable outcome in the broadband market. As billions of taxpayer’s money is expected in the next decade, it is necessary to address the sluggish demand to make sure the newly deployed infrastructure is not standing idle.
Advisors: Professor Michelle Connolly | JEL Codes: H43, H32, H45
The Effects of Pharmaceutical Price Regulation on Probability of Patenting in OECD Countries
by Rachel Korn
The introduction of parallel trade mechanisms allowing for the free trade of pharmaceutical goods in the European Economic Area represents a significant departure from the standard monopolistic competition pricing structure in the pharmaceutical market, in which firms have a great deal of control over pricing. Another mechanism, external reference pricing, also contributes to undermining traditional price structures by imposing a price ceiling on drugs. As these methods of regulating pricing in the healthcare market are receiving growing interest in countries such as the United States, it is prudent to consider their effects. It is apparent that parallel trade and external reference pricing decrease average drug costs, but little has been said about their effects on drug availability. Using global patent data from the European Patent Office PATSTAT database as a proxy for drug availability, I investigate how parallel trade and external reference pricing affect the decision of firms to file a pharmaceutical patent in a given country. I accomplish this through a logistic regression model with a difference-in-differences approach to estimate the probability of patenting a pharmaceutical in an OECD country, given that a patent has previously been approved in the United States. I find that the presence of parallel trade in a country significantly decreases the probability of patenting and increases patent lag time while external reference pricing unexpectedly increases the probability of patenting and decreases patent lag time. These findings demonstrate the complexity in attempting to create policy to regulate rising pharmaceutical prices, as doing so may increase affordability of existing drugs in a country while decreasing availability of new ones.
Advisors: Professor Michelle Connolly | JEL Codes: I1, I11, I19
Analysis of Brain Diagnoses and the Incidence of Chronic Traumatic Encephalopathy (CTE)
by Arjun Lakhanpal
Chronic traumatic encephalopathy (CTE) has become a significant area of scientific inquiry in relation to various sports with contact exposure, specifically boxing and professional football, resulting from many individuals who participated in these sports being diagnosed with CTE neuropathology after death. This paper contributes to the CTE literature by analyzing the various predictors of the progression of neurodegenerative disorders, including CTE, that are associated with a history of head impact exposure. In addition, it analyzes how manner of death shifts depending on an individual’s clinical brain diagnosis, which is a decision based upon the clinical record and case review of a patient.
Through data from the NIH NeuroBioBank, the VA-BU-CLF Brain Bank, and data self-collected from living individuals with symptoms associated with CTE, this paper explores an analysis of various brain diagnoses through a large control population and small subset of athletes and veterans. Logistic regression models are established to analyze explanatory variables of clinical brain diagnosis, manner of death, and CTE presence and severity.
These logistic regression models confirm previous research surrounding the potential racial influence present in Black populations with schizophrenia related diagnoses and illustrate the degree to which neurodegenerative disorders, specifically Parkinson’s Disease, are influenced by increased age. Specific to CTE, the analysis conducted through the sample population illustrates the influence of an extra year of football played at the professional level, while counteracting existing literature regarding the association between position and CTE.
Advisors: Professor Jason Luck, Professor Michelle Connolly | JEL Codes: I10, Z20, J15
Myocardial Infarction, Health Behavior, and the Grossman Model
by Emma Mehlhop
This paper contributes an empirical test of Michael Grossman’s model of the demand for health and a novel application of the model to myocardial infarction (MI) incidence. Using data from the University of Michigan’s Health and Retirement Study (HRS), I test Grossman’s assumptions regarding the effects of hourly wage, sex, educational attainment, and age on health demand along with the effects of new variables describing health behaviors, whether or not a respondent is insured, and whether or not they are allowed sufficient paid sick leave. I use logistic regression to estimate health demand schedules using five different health demand indicators: exercise, doctor visits, drinking, smoking, and high BMI. I apply the Cox Proportional Hazard model to examine two equations for the marginal product of health investment both in terms of propensity to prevent death and to prevent MI, one of the leading causes of mortality in the United States. This study considers the effects of the aforementioned health demand indicators, among other factors, on the marginal product of health investment for the prevention of death compared to the prevention of MI. Additionally, there is significant evidence of a negative effect of health insurance on likelihood of exercising regularly, implying some effect of moral hazard on the health demand schedule.
Advisors: Professor Charles Becker, Professor Grace Kim, Professor Frank Sloan | JEL Codes: I1, I10, I12
