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Category Archives: 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.

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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.

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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.

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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.

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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 seasonlong salaries if the free agents were to sign.

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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.

Honors Thesis

Data Set

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.

Honors Thesis

Data Set

Advisor: Connel Fullenkamp | JEL Codes: C38, C5, G33, G34 | Tagged: Bankruptcy, Corporate, Discriminant Analysis, Distress, Machine Learning

Geo-Spatial Modeling of Online Ad Distributions

By Mitchel Gorecki

The purpose of this document is to demonstrate how spatial models can be integrated into purchasing decisions for real-time bidding on advertising exchanges to improve ad selection and performance. Historical data makes it very apparent that some neighborhoods are much more interested in some ads than others. Similarly, some neighborhoods are also much more interested in some online domains than others, meaning viewing habits across domains are not equal. Basic data analysis shows that neighborhoods behave in predictable ways that can be exploited using observed performance information. This paper demonstrates how it is possible to use spatially correlated information to better optimize advertising resources.

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Advisor: Charles Becker | JEL Codes: C3, C33, C53, M37 | Tagged: Ad Distribution, Advertising, Online, Real Time Bidding, Spatial

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.

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JEL Codes: C0, C3, C03, C32, C53, C58 | Tagged: Beta, GARCH, GARCHX, High-Frequency Data, Realized Varience

Identifying Supply and Demand Elasticities of Iron Ore

By Zhirui zhu

This paper utilizes instrumental variables and joint estimation to construct efficiently identified estimates of supply and demand equations for the world iron ore market under the assumption of perfect competition. With annual data spanning 1960-2010, I found an upward sloping supply curve and a downward sloping demand curve. Both of the supply and demand curves are efficiently identified using a 3SLS model. The instruments chosen are strong and credible. Point estimation of the long-run price elasticities of supply and demand are 0.45 and -0.24 respectively, indicating inelastic supply and demand market dynamics. Back-tests and forecasts were done with Monte Carlo simulations. The results indicate that 1) the predicted prices are consistent with the historical prices, 2) world GDP growth rate is the determining factor in the forecasting of iron ore prices.

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Advisor: Gale Boyd | JEL Codes: C30, Q31 | Tagged: Demand, Iron Ore, Supply, Simulation, Simultaneous Equation

Questions?

Undergraduate Program Assistant
Jennifer Becker
dus_asst@econ.duke.edu

Director of the Honors Program
Michelle P. Connolly
michelle.connolly@duke.edu