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 Classification: L11; I11; C3
The Effect of Marriage on the Wages of Americans: Gender and Generational Differences
By William Song and Theresa Tong
A substantial body of literature on the wage effects of marriage finds that married American men earn anywhere from 10% to 40% higher wages than unmarried men on average, while married American women earn up to 7% less than unmarried women, even after controlling for traits such as background, education, and number of children. Because this literature focuses heavily on men born in a single time period, we study both men and women in two different generational cohorts of Americans (Baby Boomers and Millennials) from the National Longitudinal Surveys of Youth to examine how the wage effects of marriage differ between genders and across time. Using a fixed effects approach, we find that Millennial women—but not Baby Boomer women—experience an increase in wages after marriage, and we replicate the finding from the literature that men experience an increase in wages after marriage as well. However, after controlling for wage trajectory-based selection into marriage by using a modified fixed effects approach that allows wage trajectories to vary by individual, we find that the wage effects of marriage are no longer statistically significant for any group in our data, suggesting that the wage differences between married and unmarried individuals found in previous studies are primarily a result of selection.
Advisors: Professor Marjorie McElroy, Professor Michelle Connolly | JEL Codes: C33; D13; J12; J13; J22; J30
ICT Behavior at the Periphery: Exploring the Social Effect of the Digital Divide through Interest in Video Streaming
By Erik W. Hanson and Justin C. LoTurco
We investigate the factors that influence changes in consumer behavior with regard to video streaming. We focus our analysis on the effect of bandwidth impairment to explore a potential consequence of the digital divide. To measure the change in relative popularity of video streaming services, we use Google Trends data as a proxy. We then investigate whether broadband speed improvements in rural vs. urban regions affect the proxy differently. We find that increasing the broadband speeds in rural regions appears to stimulate greater interest in video streaming than equivalent speed increases in urban regions.
Advisors: Professor Michelle Connolly, Professor Grace Kim | JEL Codes: C33; J11; L96
Evaluating Economic Impacts of Electrification in Zambia
By Aashna Aggarwal
Energy poverty is prevalent in Zambia. It is one of the world’s least electrified nations with 69% of its citizens living in darkness, without access to grid electricity. Zambian government has a goal to achieve universal electricity access in urban areas and increase rural electrification to 51% by 2030. With its main goal to improve the quality of life and wellbeing of Zambians. Electrification is expected to have positive impacts on health, education and employment play an important role to achieve wellbeing, however, previous studies and analysis of renewable energy programs have found different, context-dependent results. To evaluate the impacts of electrification in Zambia I have used the Living Conditions Monitoring Survey (LCMS) of 2015 and applied two different estimation techniques: non-linear regressions and propensity score matching. My study finds that firewood consumption significantly decreases with assess to electricity and education has positive outcomes on grade attainment. I negligible effects on wage earning employment outcomes respiratory health outcomes. Based on these results I conclude that access to grid electrification does have certain positive impacts but empirical evidence is not as strong as the theoretical claims.
Advisors: Dr. Robyn Meeks and Dr. Grace Kim | JEL Codes: C31; C78; O13; Q40
BIDDING FOR PARKING: The Impact of University Affiliation on Predicting Bid Values in Dutch Auctions of On-Campus Parking Permits
By Grant Kelly
Parking is often underpriced and expanding its capacity is expensive; universities need a better way of reducing congestion outside of building costly parking garages. Demand based pricing mechanisms, such as auctions, offer a possible solution to the problem by promising to reduce parking at peak times. However, faculty, students, and staff at universities have systematically different parking needs, leading to different parking valuations. In this study, I determine the impact university affiliation has on predicting bid values cast in three Dutch Auctions of on-campus parking permits sold at Chapman University in Fall 2010. Using clustering techniques crosschecked with university demographic information to detect affiliation groups, I ran a log-linear regression, finding that university affiliation had a larger effect on bid amount than on lot location and fraction of auction duration. Generally, faculty were predicted to have higher bids whereas students were predicted to have lower bids.
Advisor: Alison Hagy, Allan Collard-Wexler, Kent Kimbrough | JEL Codes: C38, C57, D44, R4, R49 | Tagged: Auctions, Parking, University Parking, Bidder Affiliation, Dutch Auction, Clustering
Determining NBA Free Agent Salary from Player Performance
By Joshua Rosen
NBA teams have the opportunity each offseason to sign free agents to alter their rosters. Using only regular season per game statistics, I examine the best method of calculating a player’s appropriate salary value based upon his contribution to a team’s regular season win percentage. I first determine which statistics most accurately predict team regular season win percentage, and then use regression analysis to predict the values of these metrics for individual players. Finally, relying upon predicted statistics, I assign salary values to free agents for their upcoming season on specific teams. My results advise teams to rely heavily on Player Impact Estimate (“PIE”) when predicting their teams’ win percentage, and to seek players whose appropriate salaries would be significantly more than their actual season–long salaries if the free agents were to sign.
Advisor: Kent Kimbrough, Peter Arcidiacon | JEL Codes: C30, Z2, Z22 | Tagged: Free Agents, Salaries, NBA
Conditional Beta Model for Asset Pricing By Sector in the U.S. Equity Markets
By Yuci Zhang
In nance, the beta of an investment is a measure of the risk arising from exposure to general market movements as opposed to idiosyncratic factors. Therefore, reliable estimates of stock portfolio betas are essential for many areas in modern nance, including asset pricing, performance evaluation, and risk management. In this paper, we investigate Static and Dynamic Conditional Correlation (DCC) models for estimating betas by testing them in two asset pricing context, the Capital Asset Pricing Model (CAPM) and Fama-French Three Factor Model. Model precision is evaluated by utilizing the betas to predict out-of-sample portfolio returns within the aforementioned asset-pricing framework. Our findings indicate that DCC-GARCH does consistently have an advantage over the Static model, although with a few exceptions in certain scenarios.
Advisor: Andrew Patton, Michelle Connolly | JEL Codes: C32, C51, G1, G12, G17 | Tagged: Beta, Asset Pricing, Dynamic Correlation, Equity, U.S. Markets
Corporate Financial Distress and Bankruptcy Prediction in North American Construction Industry
By Gang Li
This paper seeks to explore the application of Altman’s bankruptcy prediction model in the construction industry by measuring its percentage accuracy on a dataset consisting of 108 bankrupt & non-bankrupt firms selected across the timeline of 1985-2013. Another main goal this paper is to explore the predictive power of an expanded variable set tailored to the construction industry and compare the results. Specifically, this measuring process is done using machine learning algorithm based on scikit-learn library that transforms a raw .csv file into clean vectorized dataset. The algorithm provides various classifiers to cross-validate the training set, which produces mixed statistics that favors neither variable set but provides insight into the reliability of the non-linear classifiers.
Advisor: Connel Fullenkamp | JEL Codes: C38, C5, G33, G34 | Tagged: Bankruptcy, Corporate, Discriminant Analysis, Distress, Machine Learning
Forecasting Beta Using Conditional Heteroskedastic Models
By Andrew Bentley
Conventional measurements of equity return volatility rely on the asset’s previous day closing price to infer the current level of volatility and fail to incorporate information concerning intraday influntuctuations. Realized measures of volatility, such as the realized variance, are able to integrate intraday information by utilizing high-frequency data to form a very accurate measure of the asset’s return volatility. These measures can be used in parallel with the traditional definition of the Capital Asset Pricing Model (CAPM) beta to better predict the time-varying systematic risk of an asset. In this analysis, realized measures were added to the General Autoregressive Conditional Heteroskedastic (GARCH) framework to form a predictive model of beta that can quickly respond to rapid changes in the level of volatility. The ndings suggest that this predictive beta is better able to explain the stylized characteristics of beta and is a more accurate forecast of the realized beta than the GARCH model or the benchmark Autoregressive Moving-Average (ARMA) model used as a comparison.
JEL Codes: C0, C3, C03, C32, C53, C58 | Tagged: Beta, GARCH, GARCHX, High-Frequency Data, Realized Varience