This research paper is a Master's Thesis by Paichen Li for Duke University's School Environment. The author sought to identify a link between evictions and utility spending in light of a nationwide eviction and housing affordablity crisis. The study territory was Southern California (Edison). The methods in this study are quantitative, using regression analysis.
"The main hypothesis of this study is energy burden is positively correlated eviction rate, even holding constant other explanations of eviction rates. This hypothesis is tested through three quantitative analysis models, using census tract level data over a 5-year period from 2012 to 2016 (refer to the data section for details). In each model, the same set of variables areincluded: energy burden, poverty rate, percentage of renter-occupied households, rent burden, percentage of multi-family housing, percentage of Hispanic/Latino population, percentage of African American population, percentage of Asian population, percentage of other minorities’ population. The percentage of white population and the year dummy variable for 2012 are left out for collinearity."
Though the author pointed out a number of limitation to the study, including the construction of the hypothesis, which inferred causality without the access to proper data, the study did show some evidence of the author's argument that higher energy burden = higher eviction rate. The author calls for an initiative "to have utilities consistently collect and maintain census tract/zip code level data on household energy use, energy spending, and utility disconnections from failure to pay bills" to help with recognition in policy circles of the connection between energy burden and eviction.
Paichen Li, "Correlational analysis of energy burden and eviction rate", contributed by Roya Haider, The Energy Rights Project, Platform for Experimental Collaborative Ethnography, last modified 16 May 2020, accessed 21 November 2024. https://energyrights.info/content/correlational-analysis-energy-burden-and-eviction-rate
Critical Commentary
This research paper is a Master's Thesis by Paichen Li for Duke University's School Environment. The author sought to identify a link between evictions and utility spending in light of a nationwide eviction and housing affordablity crisis. The study territory was Southern California (Edison). The methods in this study are quantitative, using regression analysis.
"The main hypothesis of this study is energy burden is positively correlated eviction rate, even holding constant other explanations of eviction rates. This hypothesis is tested through three quantitative analysis models, using census tract level data over a 5-year period from 2012 to 2016 (refer to the data section for details). In each model, the same set of variables areincluded: energy burden, poverty rate, percentage of renter-occupied households, rent burden, percentage of multi-family housing, percentage of Hispanic/Latino population, percentage of African American population, percentage of Asian population, percentage of other minorities’ population. The percentage of white population and the year dummy variable for 2012 are left out for collinearity."
Though the author pointed out a number of limitation to the study, including the construction of the hypothesis, which inferred causality without the access to proper data, the study did show some evidence of the author's argument that higher energy burden = higher eviction rate. The author calls for an initiative "to have utilities consistently collect and maintain census tract/zip code level data on household energy use, energy spending, and utility disconnections from failure to pay bills" to help with recognition in policy circles of the connection between energy burden and eviction.