Does Manufacturing Still Matter?

Green, Gary Paul; Sánchez, L. Andrés

doi:10.1007/s11113-007-9043-8

Cited by 18 publications

(13 citation statements)

References 17 publications

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“…Some research has explored variation in industry effects between nonmetropolitan and metropolitan areas (Gibbs, Kusmin, and Cromartie ; Goe ), but most research focuses on variation in economic outcomes only (e.g., unemployment rates, income inequality, median earnings) (Lobao ; McCall ). The primary exception is Green and Sanchez (), who investigate industry's relationship to underemployment and find that places with a historical dependence on manufacturing benefit less from manufacturing than areas that have only recently shifted economic activity into the manufacturing sector. This research suggests potentially meaningful spatial differences in economic fortunes that are driven by trends in industries upon which places depend.…”

Section: Spatial Clustering In Poverty and Industry Trends In The Midmentioning

confidence: 99%

“…Despite a vast body of research on poverty in place, scholars do not fully understand why poverty is spatially clustered. One central idea points to industry, which is spatially clustered and relatively slow changing (Brady and Wallace 2001;Friedman and Lichter 1998;Green and Sanchez 2007;Kodras 1997;Lichter and McLaughlin 1995;Lobao and Schulman 1991;Tickamyer and Tickamyer 1988;Voss et al 2006;Weinberg 1987). However, few large-scale studies of spatial inequality have explicitly and simultaneously investigated how temporal trends in and the spatial clustering of industry impact poverty.…”

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confidence: 99%

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The Spatial Distribution of Poverty and the Long Reach of the Industrial Makeup of Places: New Evidence on Spatial and Temporal Regimes

et al. 2018

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A wealth of research identifies industrial structure as a central correlate of place‐level poverty and suggests that changes in and the clustering of industry contribute to the spatial clustering of poverty over time. However, few studies have investigated the spatial and temporal dimensions simultaneously, and none have effectively examined spatiotemporal interactions. Consequently, a core tenet of theory on poverty in place has not been adequately examined. To address this limitation, we explicitly test hypotheses about systematic variation in the poverty‐industry relationship over time and across space using a new method to quantify dynamic associations by simultaneously accounting for spatial and temporal autocorrelation and relationship heterogeneity. The Upper Midwest is our study site given dramatic regional changes in dominant industries (i.e., manufacturing, services, and agriculture) and poverty during the past several decades. We find that the specific character of the poverty‐industry relationship systematically varies along both the temporal and spatial dimensions: Industry is more protective in certain periods than in others according to sector trends, and is more protective in certain places than others conditional on sector dependence. Our approach yields a more precise and reliable understanding of the effect of the long reach of local industrial structure on the spatial clustering of poverty.

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Section: Spatial Clustering In Poverty and Industry Trends In The Midmentioning

confidence: 99%

mentioning

confidence: 99%

The Spatial Distribution of Poverty and the Long Reach of the Industrial Makeup of Places: New Evidence on Spatial and Temporal Regimes

et al. 2018

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“…The percentage of manufacturing is strongly related to income inequality at the .001 level of significance in the predicted negative in direction. Clearly, therefore, "manufacturing matters" (Cohen & Zysman, 1987;Green & Sanchez, 2007;Uchitelle, 2017) in moderating extreme inequality. Moreover, the four control variables also have strong predicted influences on inequality that are statistically significant at the .001 level.…”

Section: Data Methodology and Resultsmentioning

confidence: 99%

“…As we just saw, globalization had generally helped the U.S. economically during the industrial era. However, during the 1960s American producers started to move the labor-intensive parts of their operations offshore to take advantage of decreasing transportation costs and the much lower wage levels that prevailed in developing countries (Alderson, 1997(Alderson, , 1999Bluestone & Harrison 1982;Brady & Denniston, 2006;Brady & Wallace, 2001;Clark & Clark, 2016Duesterberg & Preeg, 2003;Green & Sanchez, 2007;Kollmeyer, 2009;Kollmeyer & Pichner, 2013;Reich, 2010;Ross & Trachter, 1990;Wood, 1994Wood, , 1995. This serious loss of manufacturing jobs was exacerbated by the impact of automation in reducing factory employment (Editorial Board, 2017; Gillespie, 2017; Krause, 2017).…”

Section: The Transformation Of Us Manufacturingmentioning

confidence: 99%

Manufacturing Matters: A Case Study of Alabama

2020

JESD

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Over the last fifty years, the United States has been marked by two important secular trends. One is a continuous increase in income inequality. Second, the nation has transitioned through three periods of manufacturing: (a) "The Industrial Period" during 1945-1979, (b) the "Deindustrialization of America" during 1980-2000, and (c) the "Reindustrialization of America" during 2000-present, as it began to shift to an economy largely dominated by service and knowledge jobs, with a major reduction in labor-intensive manufacturing. This study examines the relationship between manufacturing employment and income inequality in the state of Alabama to test the hypothesis that manufacturing plays a key role in moderating inequality. In particular, we consider whether manufacturing or other economic and demographic factors are determinants of the rising income inequality in Alabama. We control for other factors that are also likely to be related to rising income inequality. We employ empirical techniques that are best suited to address potential econometric problems in the empirical work. In general, our results indicate that manufacturing does indeed play a key role in moderating inequality. Other key findings are that more educated communities have less inequality, that rural areas have especially high inequality, that counties with a high proportion of African Americans face disproportionately high inequality, and that the poorest counties have the highest level of inequality

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“…Rural communities continue to concentrate much of their economic development effort on providing incentives, such as tax breaks and subsidies, to manufacturing firms. There is growing evidence, however, that manufacturing jobs in rural areas may not confer as many advantages for workers as they once did (Green & Sanchez, 2007). Competitive pressures from other low-wage regions have led to stagnant wages, reduced benefits, and fewer opportunities to advance oneself within the firm.…”

Section: Occupations and Industries In The Rural Usmentioning

confidence: 99%