An application of spatially autoregressive models to the study of US county mortality rates

Sparks, P. Johnelle; Sparks, Corey S.

doi:10.1002/psp.564

Cited by 44 publications

(38 citation statements)

References 42 publications

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“…Social scientists have found significant between-place differences in many indicators of wellbeing in the U.S., including employment (Smith and Glauber 2013), income (Peters 2013), education (Roscigno, Tomaskovic-Devey, and Crowley 2006), poverty (Cotter, Hermsen, and Vanneman 2007; Curtis, Voss, and Long 2012), program participation (Slack and Myers 2014), health and mortality (Burton et al 2013; Sparks and Sparks 2010), and residential segregation (Downey 2003). Many of these studies examined outcomes at the county level, but others explored employment, wage, educational, and health disparities at other geographic scales (e.g., states, regions, and (non)metropolitan areas) (Lochner et al 2001; Smith and Glauber 2013).…”

Section: Conceptual Frameworkmentioning

confidence: 99%

The Great Recession and America’s geography of unemployment

Thiede

Monnat

2016

DemRes

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BACKGROUNDThe Great Recession of [2007][2008][2009] was the most severe and lengthy economic crisis in the US since the Great Depression of the 1930s. The impacts on the population were multi-dimensional, but operated largely through local labor markets. OBJECTIVETo examine differences in recession-related changes in county unemployment rates and assess how population and place characteristics shaped these patterns. METHODSWe calculate and decompose Theil Indexes to describe recession-related changes in the distribution of unemployment rates between counties and states. We use exploratory spatial statistics to identify geographic clusters of counties that experienced similar changes in unemployment. We use spatial regression to evaluate associations between county-level recession impacts on unemployment and demographic composition, industrial structure, and state context. RESULTSThe recession was associated with increased inequality between county labor markets within states, but declining between-state differences. Counties that experienced disproportionate recession-related increases in unemployment were spatially clustered and characterized by large shares of historically disadvantaged racial and ethnic minority populations, low educational attainment, and heavy reliance on pro-cyclical industries. Associations between these sources of vulnerability were partially explained by unobserved state-level factors. CONCLUSIONSThe local consequences of macroeconomic trends are associated with county population characteristics, and the structural contexts and policy environments in which they are embedded. The recession placed upward pressure on within-state disparities in local labor market conditions. CONTRIBUTIONTo present new estimates of the recession's impact on local labor markets, quantify how heterogeneous impacts affected the distribution of unemployment prevalence, and identify county characteristics associated with disproportionately large recession-related increases in unemployment.

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Section: Conceptual Frameworkmentioning

confidence: 99%

The Great Recession and America’s geography of unemployment

Thiede

Monnat

2016

DemRes

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“…Fewer studies still consider the spatial structure underlying the data and spatial dependence which may bias statistical estimates and generate misleading conclusions (Cressie, 1993; Haining, 2003; Voss, Long, Hammer, & Friedman, 2006). Spatial analysis approaches that contend with spatial dependence have been uncommon in mortality research until recently (McLaughlin, Stokes, Smith, & Nonoyama, 2007; Sparks & Sparks, 2010; Yang, Jensen, & Haran, 2011; Yang, Teng, & Haran, 2009), and this work does not focus on income inequality.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Inequality-Mortality Relationship in the US with Bayesian Spatial Modeling

Yang

Jensen

2014

Popul Res Policy Rev

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While there is evidence to suggest that socioeconomic inequality within places is associated with mortality rates among people living within them, the empirical connection between the two remains unsettled as potential confounders associated with racial and social structure are overlooked. This study seeks to test this relationship, to determine whether it is due to differential levels of deprivation and social capital, and does so with intrinsically conditional autoregressive Bayesian spatial modeling that effectively addresses the bias introduced by spatial dependence. We find that deprivation and social capital partly but not completely account for why inequality is positively associated with mortality and that spatial modeling generates more accurate predictions than does the traditional approach. We advance the literature by unveiling the intervening roles of social capital and deprivation in the inequality-mortality relationship and offering new evidence that inequality matters in US county mortality rates.

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“…One of the dominant spatial autoregressive regression models is the spatial lag model (Ward and Gleditsch 2008) where a spatial lagged effect of the dependent variable is included into the analysis. It has been argued that the spatial lag model could capture the spatial homogeneity embedded in spatial data and has been widely used in previous research (Sparks and Sparks 2010; Yang et al 2011; Sparks et al 2012). To the best of our knowledge, Páez et al (2002) first demonstrated that a spatial lag effect could be fused into a Gaussian-based GWR and be estimated with the maximum likelihood (ML) approach.…”

Section: Methodsmentioning

confidence: 99%

When homogeneity meets heterogeneity: the geographically weighted regression with spatial lag approach to prenatal care utilisation

2014

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Using geographically weighted regression (GWR), a recent study by Shoff and colleagues (2012) investigated the place-specific risk factors for prenatal care utilization in the US and found that most of the relationships between late or not prenatal care and its determinants are spatially heterogeneous. However, the GWR approach may be subject to the confounding effect of spatial homogeneity. The goal of this study is to address this concern by including both spatial homogeneity and heterogeneity into the analysis. Specifically, we employ an analytic framework where a spatially lagged (SL) effect of the dependent variable is incorporated into the GWR model, which is called GWR-SL. Using this innovative framework, we found evidence to argue that spatial homogeneity is neglected in the study by Shoff et al. (2012) and the results are changed after considering the spatially lagged effect of prenatal care utilization. The GWR-SL approach allows us to gain a place-specific understanding of prenatal care utilization in US counties. In addition, we compared the GWR-SL results with the results of conventional approaches (i.e., OLS and spatial lag models) and found that GWR-SL is the preferred modeling approach. The new findings help us to better estimate how the predictors are associated with prenatal care utilization across space, and determine whether and how the level of prenatal care utilization in neighboring counties matters.

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An application of spatially autoregressive models to the study of US county mortality rates

Cited by 44 publications

References 42 publications

The Great Recession and America’s geography of unemployment

The Great Recession and America’s geography of unemployment

Exploring the Inequality-Mortality Relationship in the US with Bayesian Spatial Modeling

When homogeneity meets heterogeneity: the geographically weighted regression with spatial lag approach to prenatal care utilisation

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