Externalities of Overhead Power Lines on Residential Housing Values
by Jake Park-Walters
AbstractÂ
Overhead electricity transmission lines (OHLs) create negative externalities on nearby housing values largely from perceived factors including aesthetics, safety, and health. Studies have been performed outside of the US to determine the specific value impact of power lines by proximity. It is not, however, well researched within the United States–specifically in suburban and urban areas. To assess the value loss from overhead power lines, this study examines housing transactions in North Carolina from 1997 to 2020 with a particular emphasis upon cities and townships. With GIS software, proximity variables are calculated such that a difference-indifference regression can estimate the impact of distance to OHL on transaction values. This is important for local policy regarding whether municipalities may want to invest into burying power lines as a means of improving local property values. The results attempt to illustrate how burying high impact lines (HILs) can generate high public benefit relative to cost through marginal value of public funds (MVPF) calculations. These HILs may be chosen based on a variety of factors including proximity to dense, high value housing to maximize value improvement by burial.
Professor David Berger, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL Codes: L94, H76, D04
Keywords: Electric Utilities, Policy Evaluation, Local Government Expenditure
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Heterogeneity in Mortgage Refinancing
By Julia Wu
Abstract
Many households who would benefit from and are eligible to refinance their mortgages fail to do so. A recent literature has demonstrated a significant degree of heterogeneity in the propensity to refinance across various dimensions, yet much heterogeneity is left unexplained. In this paper, I use a clustering regression to characterize heterogeneity in mortgage refinancing by estimating the distribution of propensities to refinance. A key novelty to my approach is that I do so without relying on borrower characteristics, allowing me to recover the full degree of heterogeneity, rather than simply the extent to which the propensity to refinance varies with a given observable. I then explore the role of both observed and unobserved heterogeneity in group placement by regressing group estimates on a set of demographic characteristics. As a complement to my analysis, I provide evidence from a novel dataset of detailed information on borrower perspectives on mortgage refinancing to paint a more nuanced picture of how household characteristics and behavioral mechanisms play into the decision to refinance. I find a significant degree of heterogeneity in both the average and marginal propensity to refinance across households. While observables such as education, race and income do significantly correlate with group heterogeneity, it is clear that much heterogeneity may still be attributed to the presence of unobservable characteristics.
Professor David Berger, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL codes: D9, E52, G21
Predicting the Work Task Replacement Effects of the Adoption of Machine Learning Technology
by Shreya Hurli
Abstract
This paper develops a methodology to attempt to predict which tasks in the workforce will be resistant to the replacement of labor by machine learning technology in the near future given current technology and technology adoption trends. Tasks are individual activities completed as parts of a job. Prior research in the field suggests that characteristics of tasks (non-roteness, creativity, analysis/cognitive work) that make them harder for machine learning technology to complete are good predictors of whether those tasks will be resistant to replacement in the workforce. This study utilizes O*NET (Occupational Information Network) task description and education data from October 2015 to August 2020 and Bureau of Labor Statistics salary data to use task characteristics to predict tasks’ resistance to replacement. Normalized scores, average salaries, and average worker education levels are calculated to quantify the relative presence or absence of non-roteness, creativity, and cognitive work in a task. This paper then uses the calculated scores, salary, and education data, as well as a number of interaction terms as inputs to a support vector machine (SVM) model to predict which tasks will be resistant to decline in their shares of workplace tasks weighted by the jobs under which the tasks fall. Using task characteristics, the SVM predicts that just approximately 39% of tasks are likely to be resistant to replacement. These tasks tend to be highly non-deterministic (very non-rote, analytical/cognitive, and/or creative) in nature.
Professor David Berger, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL Codes: J23, J24, O33
