The Advantages of Comparative LISA Techniques in Spatial Inequality Research: Evidence from Poverty Change in the United States

Brooks, Matthew M.

doi:10.1007/s40980-019-00052-4

Cited by 19 publications

(14 citation statements)

References 70 publications

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“…In step with the recent volume presented by Ashwood and MacTavish [26] and the work of Walker et al [7], we propose rurality remains an under-explored dimension of both environmental and energy injustice. The spatial inequality faced between urban and rural areas in America has been long documented by researchers [73,74], with poverty rates consistently higher and economic development more stagnant in rural areas, relative to urban [73,75]. While many high-profile environmental justice studies have taken place in rural areas [30], the consideration of environmental and energy injustice between urban and rural areas remains underdeveloped [26,30].…”

Section: Ruralitymentioning

confidence: 99%

Burdened by renewable energy? A multi-scalar analysis of distributional justice and wind energy in the United States

Mueller

Brooks

2020

Energy Research & Social Science

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The transition towards renewable energy is likely to be uneven across social and spatial dimensions. To ensure this transition is equitable and just, energy injustice has become the key framework for analyzing and interpreting the distribution of energy infrastructure. Wind energy development has experienced a significant gap between broad public support for increased development but persistent localized opposition to proposed projects, indicating that wind represents a locally unwanted land use. We present the theoretical argument that although the negative impacts of wind energy infrastructure are less extreme than those posed by other, more toxic, unwanted land uses, their status as a locally unwanted land use will produce similar distributional injustices as have been found throughout the environmental injustice literature.

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Section: Ruralitymentioning

confidence: 99%

Burdened by renewable energy? A multi-scalar analysis of distributional justice and wind energy in the United States

Mueller

Brooks

2020

Energy Research & Social Science

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“…LISA values that are unexpectedly higher or lower than the global Moran's I can be mapped and represent five forms of spatial patterns: high value units next to other high value units (high-high), high-value units next to low-value units (high-low), low-value units next to high value units (low-high), low-value units next to other low-value units (low-low), and non-significant differences [61]. Previous research has applied univariate LISA statistics to identify clusters and outliers and other spatial patterns of social vulnerability [6,16,25,26,[62][63][64][65].…”

Section: Spatial and Statistical Analysismentioning

confidence: 99%

Social Vulnerability across the Great Lakes Basin: A County-Level Comparative and Spatial Analysis

Fergen

Bergstrom

2021

Sustainability

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Social vulnerability refers to how social positions affect the ability to access resources during a disaster or disturbance, but there is limited empirical examination of its spatial patterns in the Great Lakes Basin (GLB) region of North America. In this study, we map four themes of social vulnerability for the GLB by using the Center for Disease Control’s Social Vulnerability Index (CDC SVI) for every county in the basin and compare mean scores for each sub-basin to assess inter-basin differences. Additionally, we map LISA results to identify clusters of high and low social vulnerability along with the outliers across the region. Results show the spatial patterns depend on the social vulnerability theme selected, with some overlapping clusters of high vulnerability existing in Northern and Central Michigan, and clusters of low vulnerability in Eastern Wisconsin along with outliers across the basins. Differences in these patterns also indicate the existence of an urban–rural dimension to the variance in social vulnerabilities measured in this study. Understanding regional patterns of social vulnerability help identify the most vulnerable people, and this paper presents a framework for policymakers and researchers to address the unique social vulnerabilities across heterogeneous regions.

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“…We evaluate our hypotheses using Spatial lag of X (SLX) models with both period and unit fixed effects with cluster-robust standard errors (Cameron and Miller, 2015;Vega and Elhorst, 2015). It was necessary to use a spatial econometric model due to the permeable nature of county boundaries and the spatial clustering of social factors in the United States (Brooks, 2019;Chi and Zhu, 2019;Lobao et al, 2007a,b;Thiede et al, 2018). We use the SLX model, which is a local spillover specification including spatial lags of all independent variables, due to the recommendations of Vega and Elhorst (2015).…”

Section: Analytic Approachmentioning

confidence: 99%

Market Concentration and Natural Resource Development in Rural America

Mueller¹,

Shircliff²,

Steinbaum³

2021

Preprint

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Natural resource development, both extractive (oil, gas, mining, timber) and non-extractive (tourism, real estate, outdoor recreation), has been found to negatively impact economic prosperity in rural America. One mechanism recently proposed for why this occurs is high levels of labor market concentration, or oligopsony. Oligopsony occurs when there are few employers within a labor market and can lead to suppressed wages and a power imbalance between employers and workers. In this paper, we test the moderating effect of labor market concentration on the relationship between natural resource development and per capita income and poverty in rural America from 2010 to 2016. By comparing results between extractive and non-extractive development, as well as manufacturing, we show that labor market power attenuates the beneficial relationship observed at low levels of specialization in natural resources—particularly for extractive forms of development. Further, by finding no significant relationship between manufacturing specialization and economic prosperity, nor any moderating effect of labor market concentration in the case of manufacturing, we demonstrate that natural resource development and labor market concentration have a unique relationship with rural American economic prosperity.

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The Advantages of Comparative LISA Techniques in Spatial Inequality Research: Evidence from Poverty Change in the United States

Cited by 19 publications

References 70 publications

Burdened by renewable energy? A multi-scalar analysis of distributional justice and wind energy in the United States

Burdened by renewable energy? A multi-scalar analysis of distributional justice and wind energy in the United States

Social Vulnerability across the Great Lakes Basin: A County-Level Comparative and Spatial Analysis

Market Concentration and Natural Resource Development in Rural America

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