Wharton SPUR is a unique program that provides a select group of up to 10 highly motivated students an opportunity to design and perform in-depth research over a 10-week period in the summer under the guidance of some of Wharton’s preeminent faculty members. Additionally, the program forms a community of scholars in which students can hear research presentations by faculty members and doctoral students, learn about resources for research, and learn vicariously about other areas of inquiry through the sharing of research experiences.
PublicationExploring the Impacts of Salary Allocation on Team Performance(2017-08-24) Gao, JimmyStudy of salary has been an increasingly important area in professional sports literature. In particular, salary allocation can be a significant factor of team performance in NBA, given credit to the wisdom of team managers. This paper seeks to extend the scope of existing research on basketball by investigating on how salary allocation affected team performance and exploring other factors that lead to team success. Our findings indicate a moderate correlation between salary allocation and team performance, while average Player Efficiency Rating is a more crucial factor of team performance in comparison. PublicationAssessing Models using Monte Carlo Simulations(2017-01-01) Oh, SimonWe establish a framework for assessing the validity of a given model using Monte Carlo simulations and inferences based on sampling distributions. Using this framework, we show that geometric brownian motion alone cannot generate a majority of the patterns in the distribution of stock returns and wealth creation. Our paper represents an often overlooked departure from the traditional way of validating asset pricing models, in which implications are derived, parameters calibrated, and magnitudes compared to empirical data. Instead, we seek to leverage the power of large numbers by conducting numerous simulations and assessing the probability that they contain our realized stock market. PublicationRace and Higher Education: Is the LSAT systemic of racial differences in education attainment?(2017-01-01) Robbins, Mona ELaw school is the least diverse graduate school program, which translates to the lack of diversity among law professionals. Among America’s national law schools, Caucasians fill eighty-eight percent of the seats. This persistent trend over the years has led researchers to question what barriers of entry might exist that are limiting the diversity. One of the most significant barriers has shown to be the Law School Admissions Test. The LSAT is the highest weighing component on whether an applicant will be accepted or denied from law school. Trends have also revealed that underrepresented minorities statistically have much lower scores on the LSAT. This test score gap translates to the lack of diversity in America’s top law schools. This research paper attempts to answer the question of is the LSAT is systemic of racial differences in higher education attainment. In this research economic barriers will be analyzed for a greater idea on socioeconomic differences among students in law school. Following the analysis of the socioeconomic advantages and disadvantages among applicants, a analysis on the test score gap vs. the knowledge gap will be included. Finally, predictability of the LSAT to measure success on the law bar exam will be reevaluated. This reevaluation will comprehensively include data from test takers with subsequent attempts. This additional data will add a dimension to “success” because the ABA currently measures school’s success rate solely on first attempt takers. PublicationThe Impact of Carbon Pricing Policies in Reducing CO2 Emissions from Road Transportation: A Meta-Analysis of Empirical Studies(N/A, 2023-08-21) Didrik Wiig-AndersenTo achieve the 2030 climate goals outlined in the Paris Agreement, global carbon dioxide (CO2) emissions must be reduced. Prevailing literature and economic theory emphasize the role of carbon pricing policies as a fundamentally critical and cost-effective approach in mitigating these emissions. However, there is currently limited evidence substantiating this claim, thereby restricting governmental understanding and implementation of effective pricing policies. The paper performs a comprehensive literature review of empirical studies assessing the impact of pricing policies in reducing emissions from road transportation, a sector responsible for over a tenth of the global CO2 emissions. In particular, the paper aims to contribute by 1) presenting existing literature that has studied the influence of carbon pricing policies on emissions from road transportation; 2) exploring the types of carbon pricing policies governments have implemented in their effort to mitigate CO2 emissions; 3) detailing the quantified impacts of these policies; and 4) identifying the contributing factors driving effective policies and understanding the challenges that limit the impact of less effective ones through a discussion. The sample comprised eighteen studies—both peer-reviewed and gray literature—published between 2017 and 2023, analyzing eight different types of pricing policies across forty-six countries. First, the results suggest that there exists a positive relationship between the magnitude of the fee levied as part of the policy and the CO2 emissions reductions, regardless of the type of policy. Second, policies priced continuously and directly—encouraging reduction of every gram of CO2 per kilometer—were found to be more effective in reducing vehicle CO2 emission rates when compared to policies priced according to emission bands and thresholds. Third, the results show that registration and fuel taxes were seen to reduce the prevalence of high-emitting vehicles (>180gCO2/km) in the vehicle fleet, while similar to circulation taxes, promote the adoption of diesel vehicles. This study augments existing literature, reinforcing the established notion that combined policies offer the most potent reduction effects, given their ability to collectively address multiple factors influencing consumer decisions in vehicle selection (extensive margin) and vehicle usage (intensive margin). Further research is recommended to identify effective policy combinations. PublicationAn Exploration of Autonomous Quadrotor Trajectory Planning(N/A, 2023-09-15) Yijie LuIn this project, I explored a variety of algorithms used quadrotor trajectory planning and implemented each algorithm from scratch. Quadrotor trajectory planning is a pivotal part of autonomous quadrotor systems. These algorithms enable quadrotors, or drones, to autonomously navigate complex environments, performing a wide range of tasks. From surveillance and aerial photography to search and rescue operations, quadrotors are used extensively in applications requiring rapid and agile movement. Accurate trajectory planning ensures obstacle avoidance, stable flight, and efficient use of resources. PublicationAnalysis of Farmworker Wages from 1997-2016 and Effect of Yearly Rainfall on Farmworker Wages(2021-01-01) Figueroa, Fatima F PublicationAirbnb Economic Loss Due to COVID-19 in 50 Non-Metropolitan Cities: A Vector Autoregression Analysis(2020-01-01) Xu, Michelle LPrior to the COVID-19 pandemic, Airbnb achieved tremendous growth in rural America in fostering the democratization of accessible travel to areas without hospitality infrastructure. Due to COVID-19 lockdowns and restrictions, the travel industry sustained more significant supply and demand shocks than the overall economy. Using a vector autoregression analysis of Airbnb data in 50 selected non-metropolitan cities in the Midwest and South from July 2017 to June 2020, we first evaluated Airbnb revenue growth in 2019 and forecasted its continued growth in each city for the first half of 2020. This modeling of Airbnb growth enabled an estimate of Airbnb revenue loss in the first half of 2020 due to the disruption by the COVID-19 pandemic. Prior to the COVID-19 pandemic, these emerging Airbnb markets, characterized by small to medium sized cities, exhibited a high year-over-year revenue growth of 61% in 2019 and were forecasted to accelerate the growth in 2020 to 89%. Unfortunately, the pandemic halted the growth and led to a quite uniform 24% loss based on the forecasted revenue. There was little to no correlation between the forecasted economic loss percentages and state differences, city- specific characteristics used in this study, or known COVID-19 cases. This study has provided a comprehensive report of Airbnb growth across a variety of emerging markets both pre-COVID- 19 and post-COVID-19 and assessed the economic impact of the pandemic. PublicationCorporate Criminal Liability in India: A Pressing Issue(2022-01-01) Agrawal, PranaviA company is regarded as a distinct legal body from its owners. It may be characterized as a group of people working towards a single purpose, typically commercial. The degree to which a firm, as a distinct organization, is accountable for the actions of its workers is defined by Corporate Criminal Liability under Indian criminal law. The two main questions raised about corporate liability are whether a company can commit a crime, and whether it is legally accountable for the alleged criminal conduct. This paper analyzes the history of the concept of Corporate Criminal Liability around the world, its background and development in India, and how it has evolved over the years. Through case studies, this paper attempts to study how the concept applies to the Indian judicial system and lists the shortcomings that need to be addressed. In the end, the research suggests how Corporate Criminal Liability laws and their applications can be improved to hold corporations more accountable, and ultimately, safeguard the public. PublicationActive Simultaneous Localization and Mapping in Unstructured Environment with a Quadrotor(2022-09-15) He, SimingAn agent performing Simultaneous Localization and Mapping (SLAM) constructs a map of the environment while estimating its location at the same time. SLAM algorithms primarily focus on finding the best estimate of the location of the agent, and by extension, that of the landmarks in the environment, given observations from sensors. These algorithms do not typically address how an agent should explore an unknown environment to build a map efficiently. This ability for active exploration is important for autonomous robots to work in unknown, unstructured environments such as forests or caves. This paper proposes an active SLAM system that allows an agent to explore its surroundings, using visual-inertial data from an RGBD camera. We formalize this problem as taking actions that maximize the amount of information obtained from the scene. At each time step, a utility function that computes the incremental information gain is used to take actions. We conduct experiments using an Intel RealSense camera mounted on a custom-built quadrotor and show that we can explore indoor environments (while restricting the actions to choosing new viewpoints in SO(3) for practical reasons). PublicationEconometric Analysis of Labor Income and Job Seeking Disparities in the United States(2021-10-15) Wu, ShaolongIn the labor market of the United States, a wide range of socioeconomic and demographic factors impact workers’ income and decisions to seek new jobs, which are two critical metrics of labor income dynamics. Studies of income dynamics have historically been examining major demographic and situational factors such as marital and family matters of Americans from the 1960s to 2000s. However, Technological breakthroughs have drastically changed the landscape of the labor force and economy, and individuals face a more complex and diverse context. This paper recognizes the need to analyze the factors behind these two income dynamics metrics in the contemporary setting. This paper confirms that the wages and other types of income of the cohort (born in 1980s and 1990s) in the United States are explained by personal demographics, living habits, and family background conditions. This paper also finds a series of factors, such as region, marital status, and education, to be significant in determining whether individuals will seek a new full-time job over time. This paper limits itself to predictive and forensic analysis and leaves the question of job search motivations to authors of related areas.