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;
Private Equity IPOs: Long-term Performance and Drivers of Success
by Ignacio Hidalgo Perea
Abstract
In this paper, I explore the impact Private Equity ownership has on portfolio companies post-exit. This thesis aims to add to the discussion of whether the proliferation of Private Equity in the United States is a positive development for the country. Using a proprietary dataset that compiles thousands of IPOs between the years 2000 and 2016, I look at whether there are significant differences in performance between IPOs that come from Private Equity firms and those that go public on their own. Specifically, I use empirical analysis with robust regression to estimate the effects of Private Equity ownership on four key measures of financial success: MCAP growth, Revenue Growth, EBITDA Margin, and EV / EBITDA multiple. By looking at the changes in these measures of performance across three different time windows: 3 years post-IPO, 6 years post-IPO, and 9 years post-IPO, this paper determines how Private Equity ownership affects company performance post-exit and whether those effects persist over time.
Professor Grace Kim, Faculty Advisor
JEL Codes: G23, G24
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, Faculty Advisor
Professor Grace Kim, Faculty Advisor
JEL Codes: M14, M40
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
Generic Entry and The Effect on Prices in the Prescription Drug Market
by Sahana Giridharan
Abstract
Drug firms have utilized a variety of strategies that contribute to rising drug prices in the U.S. for the last few years. Strategic entry timing and number of indications a drug is approved might be two factors that contribute to this rise in prices. While there have been some studies uncovering a positive relationship between generic entry and branded prices, there has been little research done on the effects of generic entry on generic prices thus far. This work can impact policy aimed at decreasing generic drug prices and increasing competition in the generic drug market. Oncology and inflammatory bowel disease (IBD) are two disease areas that have a high price burden to patients in the U.S. today, hence using Medicare Part B Average Sales Price (ASP) data, I analyze the effect of entry timing on the price of 24 drugs in these two indication areas. Using the Drugs@FDA Database, I collect data on the FDA approval date of a drug, and on the indications a drug is approved for. Utilizing OLS, my results suggest that later entry times lead to lower drug prices, with a 1 year increase in entry time resulting in a 6.99% increase in prices. Results also suggest that an increase of 1 in the number of indications a drug is approved for leads to a 49.79% decrease in drug price. This could suggest that having existing generic competitors in the pharmaceutical market decreases generic prices, and that number of indications is a strong indicator of drug price. If the current work is confirmed by future studies similar to this studying entry time and price in the generic pharmaceutical market, it is possible that future drug policy should focus on promoting competition within the pharmaceutical market to lower generic prices.
Professor Frank Sloan, Faculty Advisor
Professor Grace Kim, Faculty Advisor
Professor Kent Kimbrough, Faculty Advisor
JEL Codes: L11; I11; C3
Economic Situations and Social Distance: Taxation and Donation
by Alexander Brandt
Abstract:
This experimental study evaluated the effects of two common economic situations – taxation and donation – on the social distance between participants in the situations, an original effect of interest that is the opposite of prior research. This study employed a novel survey framework, in which subjects gave money to others in the economic situations and socially judged recipients of their money. Findings mostly did not support predictions that the economic situations would differently affect social distance, but the novel framework enabled an effective test of the effect of economic situations on social distance and is a major contribution to the field.
Professor Rachel E. Kranton, Faculty Advisor
Professor Scott A. Huettel, Faculty Advisor
Professor Grace Kim, Seminar Advisor
JEL Codes: C91; D64; D89; D90
Forecasting the Effects of Battery Recycling on the Global Cobalt Market
by Elena Cavallero
Abstract
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.
Professor Brian Murray, Faculty Advisor
Professor Grace Kim, Faculty Advisor
JEL Codes: C30, Q31, Q55
Bang for Your (Green) Buck: The Effects of ESG Risk on US M&A Performance
by Richard Chen
Abstract
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.
Professor Connel Fullenkamp, Faculty Advisor
Professor Grace Kim, Faculty Advisor
JEL Codes: G34, G14, M14
The Impact of Conflict on Economic Activity: Night Lights and the Bosnian Civil War
by Stephanie Dodd
Abstract
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.
Professor Charles Becker,Faculty Advisor
Professor Grace Kim, Faculty Advisor
JEL Codes: F52, H56, O52
Myocardial Infarction, Health Behavior, and the Grossman Model
by Emma Mehlhop
Abstract
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.
Professor Charles Becker,Faculty Advisor
Professor M. Kate Bundorf, Faculty Advisor
Professor Grace Kim, Faculty Advisor
Professor Frank Sloan, Faculty Advisor
JEL Codes: I1, I10, I12
