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Category Archives: 2015

A computational model of food choice: Utility optimization through external cuing and heuristic search

By Lucie Yang

The field of economics tends to view decision-making through a lens of assumed rationality and utility maximization. Unfortunately, choices in reality tend to be more complicated than perfect conscious value assignment. One such type of decision-making is food choice, which incorporates not only many inherent values (health, taste, price, energy), but also exists in a world of many external influences (marketing, social pressure). The details of the space in which choices are made can be highly influential, disrupting the typical top-down attentional decision-making assumed with a homo economicus. This paper seeks to utilize a behavioral experiment, eye-tracking, and a novel computational model (the drift diffusion model) in an effort to explore how humans make food decisions. The drift diffusion model links the metrics, reaction time, gaze fixations, and eye movement path length and frequency to the probability of subsequently choosing each item. The model takes into account not only the intrinsic attractiveness of each item, but also the context surrounding them, creating group distributions as well as individual distributions for parameters of the decision process. This paper aims to look at various aspects of food decisions: how do personal internal states, visual salience, and external cues effect how one weights the multiple value characteristics of food.

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Advisor: Kent Kimbrough, Philip Sadowski | JEL Codes: D8, D80, D87 | Tagged: Decision-Making, Drift Diffusion Model, Food Consumption, Neuroeconomics

Multiples Valuation and Abnormal Returns

By Joon Sang Yoon

I investigate whether three commonly used valuation multiples—the Price-to-Earnings Ratio, the EV-to-EBITDA multiple, and the EV-to-Sales multiple—can be used to identify mispriced securities. I find that multiples are successful in identifying mispricing in both the equal and value weighted portfolios relative to the One-Factor CAPM. I further find, after controlling for size and value effects, that the bulk of the abnormal returns are concentrated in smaller firms. Moreover, the Sales multiple seems to outperform the other two multiples in the equal weighted design. In the value weighted design, however, the P/E ratio outperforms the others.

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Advisor: Per Olsson | JEL Codes: G12, G14, M4 | Tagged: Equity Valuation, Long-run Abnormal Returns, Market Efficiency, Multiples Valuation

Competition from Incumbent Firms During Mergers: Estimating the Effect of Low-Cost Carriers on Post-Merger Prices

By Jonathan Gao

In an evaluation of a merger, the type of existing competitors in the market should play a role in constraining market power following the merger. In the airline industry, heterogeneity between low-cost carriers (LCCs) and legacy carriers suggest that the types of airline competitors could affect the price effects of a merger. This paper investigates the pro-competitive effects that existing, non-merging airline carriers have on prices when an airline merger occurs. Using data in the years around the 2008 merger between Delta and Northwest Airlines, the results show that average price levels of Delta and Northwest dropped after the merger, with larger price decreases on routes with LCC competitors. There is evidence that incumbent LCC competitors have a larger influence than legacy competitors in restricting post-merger prices and market power, confirming that the type of competitors matters in assessing the level of competition in a market. This paper also shows that much of the cost efficiencies from the merger were concentrated on routes with a hub of Delta or Northwest.

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Advisor: James Roberts | JEL Codes: L0, L11, L13 | Tagged: Airline Competition, Airline Merger, Market Structure

The Effect of Minority History on Racial Disparities in the Mortgage Market: A Case Study of Durham and New Haven

By Jisoo Yoon

In the aftermath of the housing market crash, the concentration of subprime mortgage loans in minority neighborhoods is a current and long-standing issue. This study investigates the presence of racial disparities in mortgage markets by examining two cities with contrasting histories of African American and Hispanic establishment: Durham, North Carolina and New Haven, Connecticut. This study examines data by the Home Mortgage Disclosure Act (HMDA), and distills the effect of minority legacy on the perception of racial risk by using econometric instruments to separate the behavior of national lenders and local lenders. The econometric methods allow national lenders to reflect objective risk measures and neighborhood race dynamics, while local lenders reflect subjective attitudes towards certain races. With its longer history of African American presence, Durham shows a positive attitude towards Black borrowers at the local level, while New Haven shows a more favorable attitude towards its Hispanic residents. Nonetheless, racial legacy also materializes as a negative factor in the form of increased residential segregation and spillover effects. Furthermore, a temporal variation analysis of pre- and post-mortgage market reform data affirms the disappearance of racial bias and continued presence of spillover risk in Durham.

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Advisor: Christopher Timmins | JEL Codes: C01, G21, J15, R21, R23, R31 | Tagged: Econometrics, Mortgages, Economics of Minorities, Races, Census, Migration, Population, Neighborhood Characteristics, Housing Supply and Market

Security Without Equity? The Effect of Secure Communities on Racial Profiling by Police

By Jack Willoughby

Anecdotal and circumstantial evidence suggest that the implementation of Secure Communities, a federal program that allows police officers to more easily identify illegal immigrants, has increased racial bias by police. The goal of this analysis is to empirically evaluate the effect of Secure Communities on racial bias by police using motor vehicle stop and search data from the North Carolina State Bureau of Investigation. This objective differs from most previous research, which has largely attempted to quantify racial profiling for a moment in time rather than looking at how an event influences racial profiling. I examine the effects of Secure Communities on police treatment of Hispanics vs. whites with an expanded difference-in-difference approach that looks at outcomes in
motor vehicle search success rate, search rate conditional on a police stop, stop rate, and police action conditional on stop. Statistical analyses yield no evidence that the ratification of Secure Communities increased racial profiling against Hispanics by police. This finding is at odds with the anecdotal and circumstantial evidence that has led many to believe that the ratification of Secure Communities led to a widespread increase in racial profiling by police, a discrepancy that should caution policy makers about making decisions driven by stories and summary statistics.

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Advisor: Frank Sloan | JEL Codes: J15, K14, K37, K42 | Tagged: Racial Policing, Bias, Immigration Law, Secure Communities

Optimizing the Electricity Bill Creating a two-part electricity tariffs to induce a targeted level of rooftop solar adoption while meeting utility operating expenses

By Hoel Weisner

Renewable energy technologies are a much needed, clean alternative to the conventional fossil fuel electricity power plants of the last century. The market for installing solar panels on rooftops is a highly promising avenue for expanding the use of these technologies, but its profitability depends significantly on the electricity prices offered by electric utilities. Investing in solar panels offset a percentage of the electricity purchased from the utility. This paper models the investment decision of electricity consumers and looks at what the optimal per unit price of electricity should be in order to make building solar panels a profitable decision for a target share of households. The model shows how this optimal rate decreases at lower prices of investing, when the share of utility-purchased electricity offset by the panels increases, and when the target level of solar adoption decreases. Finally, it looks at how this per unit rate impacts the utility’s decision to set a fixed monthly charge for electricity in order to recover all of its operating expenses.

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Advisor: Leslie Marx | JEL Codes: L94, Q42, Q48 | Tagged: Electricity Price, Renewable Energy, Solar Electricity

Understanding the Argentine Peso’s Devaluation in 2014 —Analysis on Argentina’s Fiscal Sustainability from 1993 to 2013

By Feng Pan

This research analyzes the fiscal sustainability of Argentina from 1993 to 2013. Specifically, it explains the peso devaluation in early 2014 and suggests that it is primarily due to the fundamental problems in Argentina’s economy. This paper highlights Argentina’s inability to enhance its fiscal conditions and suggests possible future economic developments in Argentina. This paper concludes that there is high
chance of hyperinflation, debt default, and the eventual dissolution of the managed exchange rate regime in Argentina in the future.

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Advisor: Alison Hagy, Craig Burnside | JEL Codes: E43, E44, E52, E58, E62, F31 | Tagged: Argentine Peso, Exchange Rate, Fiscal Sustainability

Shale Gas Development and Housing Value in the United Kingdom: Impact of the 13th Onshore Licensing, 2008

By Esther Lho

While shale gas is a prospective energy source, it is known to bring environmental deficits to the drilling neighborhood. Because of such concerns, property values fluctuate upon the possibility of shale gas fracturing. This paper examines the change in housing prices before and after the release of the 13th onshore oil and gas licensing round, which took place in 2008 when shale gas was increasingly being considered as the alternative to ease the United Kingdom’s dependency on coal. Results suggest that the 2008 licensing has caused a 3% decrease in housing price growth rate for the licensed areas.

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Advisor: Christopher Timmins | JEL Codes: Q42, Q5, Q51 | Tagged: Consumer Expectation, Fracturing, Hedonic Price, Housing Prices, Property valuation, Shale Gas, United Kingdom

Dealing with Data: An Empirical Analysis of Bayesian Black-Litterman Model Extensions

By Daniel Roeder

Portfolio Optimization is a common financial econometric application that draws on various types of statistical methods. The goal of portfolio optimization is to determine the ideal allocation of assets to a given set of possible investments. Many optimization models use classical statistical methods, which do not fully account for estimation risk in historical returns or the stochastic nature of future returns. By using a fully Bayesian analysis, however, this analysis is able to account for these aspects and also incorporate a complete information set as a basis for the investment decision. The information set is made up of the market equilibrium, an investor/expert’s personal views, and the historical data on the assets in question. All of these inputs are quantified and Bayesian methods are used to combine them into a succinct portfolio optimization model. For the empirical analysis, the model is tested using monthly return data on stock indices from Australia, Canada, France, Germany, Japan, the U.K.
and the U.S.

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Advisor: Andrew Patton | JEL Codes: C1, C11, C58, G11 | Tagged: Bayesian Analysis Global Markets Mean-Variance Portfolio Optimization

Adoption Subsidy, Foster Care Payment, and Foster Care Adoption: A Two-stage Least Squares Approach

By Chun Sun Bak

This paper examines the effect of the change in the magnitude of monthly governmental adoption subsidy on the adoption rate for foster children in foster family structures. In order to account for omitted variable bias attached to the amount of subsidy that a child receives, I construct an instrumental variable that takes advantage of the fact that each state has different policies on: (1) the base age from which a child is eligible for special needs; and (2) the amount of increased adoption subsidy that a child receives, on average, if he or she is eligible for special needs adoption. Using the data from the Adoption and Foster Care Analysis and Reporting System (AFCARS) during the years 2001 to 2012, I find that a dollar increase in the amount of adoption subsidy, holding the amount of foster care payment constant, is expected to increase a foster child’s probability of adoption by 0.255%. Although the positive sign of the coefficient is intuitive, and although it is statistically significant at all levels, its magnitude is unrealistically high, suggesting that there may be a problem in the instrument itself or in the accuracy and selection of the data.

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Advisor: Alison Hagy, Allan Collard-Wexler

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