Racial Segregation as a Social Determinant of Health: Evidence from the State of Georgia

Background: Despite decades of research, determining the causes of racial disparities in health remains a pernicious problem in the public health arena. Challenges include further refining definitions of health as well as expanding frameworks for social determinants of health to include relevant and related predictors. Racial segregation as a social determinant of health is understudied but of growing interest in the discourse on health disparities. This paper explores empirically the relationship between racial segregation and other predictors of social determinants of health and their collective impact on health outcomes defined in both objective and subjective terms. Methods: Ordinary least squares regression analysis was used to analyze health outcomes from the Robert Wood Johnson 2018 County Health Rankings for Georgia. At the county level we considered two distinct categories of health outcomes as the dependent variables, including objective measures of health status such as age-adjusted mortality and more subjective measures from the person’s perspective of quality of life such self-reported health. The independent variables representing racial segregation included the black-white segregation and non-white-white segregation indices. Results: Our findings are that racial segregation is not significantly associated with objective health outcome measures. Conversely and surprisingly, counties with higher levels of black-white and nonwhite-white segregation show better self-reported health. Control variables have the expected impact on health outcomes based on previous literature. Conclusions: While segregation does not suggest poorer health status, the findings of higher quality of life assessment is concerning as a person’s perspectives on their health predicts healthy behaviors and access to needed care. We suggest that racial segregation is an important addition to social determinants of health frameworks and models and worthy of continued multidisciplinary research on a national basis

Beyond Crowd Judgments: Data-driven Estimation of Market Value in Association Football

Association football is a popular sport, but it is also a big business. From a managerial perspective, the most important decisions that team managers make concern player transfers, so issues related to player valuation, especially the determination of transfer fees and market values, are of major concern. Market values can be understood as estimates of transfer fees—that is, prices that could be paid for a player on the football market—so they play an important role in transfer negotiations. These values have traditionally been estimated by football experts, but crowdsourcing has emerged as an increasingly popular approach to estimating market value. While researchers have found high correlations between crowdsourced market values and actual transfer fees, the process behind crowd judgments is not transparent, crowd estimates are not replicable, and they are updated infrequently because they require the participation of many users. Data analytics may thus provide a sound alternative or a complementary approach to crowd-based estimations of market value. Based on a unique data set that is comprised of 4217 players from the top five European leagues and a period of six playing seasons, we estimate players’ market values using multilevel regression analysis. The regression results suggest that data-driven estimates of market value can overcome several of the crowd’s practical limitations while producing comparably accurate numbers. Our results have important implications for football managers and scouts, as data analytics facilitates precise, objective, and reliable estimates of market value that can be updated at any time

English league transfer prices: is there a racial dimension? A re-examination with new data

Reilly and Witt (1995) examined the role of race in determining English football transfer prices. The aim of this article is to update their work with new and later data on productivity of football players. Using data from the 2001-2002 English league season, a transfer price equation is estimated to establish if equally productive black players command lower transfer prices. The evidence suggests they do not.

[Lake Champlain and Lake George, and the country between the Hudson and the lakes on the west, and Connecticut River on the east.

Scale ca. 1:640,000.Title from William Faden's Catalogue of a curious and valuable collection of original maps and plans.Manuscript, pen-and-ink and watercolor.Has watermarks.Imperfect: Right upper corner is missing.Base map from "A map of the country in which the army under Lt. General Burgoyne acted in the campaign of 1777, shewing the marches of the army & places of the principal actions. Drawn by Mr. Medcalfe & engraved by Wm. Faden" which was published in 1780 in Burgoyne's A state of the expedition from Canada, and in Faden's Atlas of the battles of the American Revolution. The manuscript lacks "the marches of the army & the places of the principal actions."LC Maps of North America, 1750-1789, 107

Monopsony and Teachers' Salaries in Georgia

Does monopsony power in the labor market for teachers affect teachers' salaries? Prior studies have found mixed evidence of monopsony effects in teacher labor markets. A major problem has been controlling for union wage effects, which potentially mask the wage-depressing effects of monopsony. We use data from the state of Georgia, one of the few states in the United States where no teacher bargaining takes place. We detect no evidence of lower average teacher salaries in less competitive labor markets. We also find limited evidence that salaries of beginning teachers may be about two percent lower in less competitive labor markets, but our findings are not robust with respect to our various measures of monopsony and labor market boundaries. We conclude that even in the absence of unions the effect of monopsony on teachers' salaries appears to be very small.

Racial residential segregation and COVID-19 vaccine uptake: an analysis of Georgia USA county-level data

Abstract Background Foundational literature demonstrates that racial residential segregation results in poorer health outcomes for Black people than white people due to a variety of social determinants of health. COVID-19 vaccine uptake is important for better health outcomes, regardless of race. The COVID-19 pandemic has elevated concerns about racial health disparities but with little discussion of racial residential segregation as a predictor of disparate health outcomes. This paper investigates the relationship between racial residential segregation and COVID-19 vaccine uptake using county level data from the State of Georgia (USA). Methods Using publicly available data, regression analysis is conducted for 138 of the 159 counties in Georgia USA, using a dissimilarity index that describes county level differences in racial residential segregation. The primary independent variable is Black-white differences in vaccine uptake at the county level. The analytic methods focus on a spatial analysis to support information for county level health departments as the basis for health policy and resource allocation. Results Constructing a variable of the difference in vaccination rates between Black and white residents we find that Black-white differences in COVID-19 vaccination are most notable in the 69 most segregated of the 159 counties in Georgia. A ten-point lower segregation index is associated with an improvement in the Black-white vaccination gap of 1.5 percentage points (95% CI -0.31, -0.00). Income inequality and access to health care resources, such as access to a primary care physician, also predict Black-white differences in vaccination rates at the county level. Suggested mapping approaches of publicly available data at a state county level, provides a resource for local policy makers to address future challenges for epidemic and pandemic situations. Conclusion County level and geospatial data analysis can inform policy makers addressing the impact of racial residential segregation on local health outcomes, even for pandemic and epidemic issues

An Additional Analysis of Estimation Techniques for the Degree of Financial Leverage

This study compares three different empirical proxies for the financial leverage component of a systematic risk‐composition model employed in prior financial research. We consider one static accounting measure and two elasticity‐based measures. We find that the traditional static accounting measure of financial leverage provides statistically different estimates of financial leverage when compared to estimates from elasticity‐based measures of the degree of financial leverage. The findings are important because the elasticity‐based models for the degree of financial leverage have clear theoretical links to market‐based models of systematic risk, while the static accounting measure of financial leverage does not. Practitioners and researchers should carefully consider why they are estimating financial leverage and choose the appropriate method for doing so given the goals and potential consequences for biased estimation