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Populism in Place: The Economic Geography of the Globalization Backlash

Published online by Cambridge University Press:  09 February 2021

Abstract

A populist backlash to globalization has ushered in nationalist governments and challenged core features of the Liberal International Order. Although startling in scope and urgency, the populist wave has been developing in declining regions of wealthy countries for some time. Trade, offshoring, and automation have steadily reduced the number of available jobs and the wages of industrial workers since at least the 1970s. The decline in manufacturing employment initiated the deterioration of social and economic conditions in affected communities, exacerbating inequalities between depressed rural areas and small cities and towns, on the one hand, and thriving cities, on the other. The global financial crisis of 2008 catalyzed these divisions, as communities already in decline suffered deeper and longer economic downturns than metropolitan areas, where superstar knowledge, technology, and service-oriented firms agglomerate. We document many of these trends across the United States and Europe, and demonstrate that populist support is strongest in communities that experienced long-term economic and social decline. Institutional differences in labor markets and electoral rules across developed democracies may explain some of the variation in populists’ electoral success. Renewed support for the Liberal International Order may require a rejuvenation of distressed communities and a reduction of stark regional inequalities.

Information

Type
Internal Challenges to the Liberal International Order: Economic
Copyright
Copyright © The IO Foundation 2021
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Figure 1. Trump's (2016) two-party vote share compared with Romney's (2012) two-party vote share

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Figure 2. Share of manufacturing in total employment, 1970 through 2012Note: The data are from the US Bureau of Labor Statistics, International Labor Comparisons Program.

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Figure 3. The decline of US manufacturing communitiesNotes: The dots represent US counties. Manufacturing employment data come from the US Bureau of Economic Analysis. Labor force participation is estimated as total employment data (from the US Bureau of Labor Statistics) divided by population (from National Bureau of Economic Research [NBER] and the US Census). Median household income statistics are from the US Census Small Area Income and Poverty Estimates Program.

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Figure 4. The urban concentration of tradable services in EuropeNotes: The map shows the number of workers in tradable services relative to the number of workers in manufacturing. Tradable services are defined as information and communication services along with professional, scientific, and technical services.Source: Eurostat data from 2017.

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Figure 5. The urban concentration of tradable services employment in the United StatesNote: The map shows employment in business services as a share of total employment. The 2015 county-level labor shares come from the US Bureau of Labor Statistics.

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Figure 6. Correlates of voting for Trump in 2016 compared with voting for Romney in 2012Notes: The y-axis is the difference between Trump's two-party vote share in 2016 and Romney's two-party vote share in 2012, measured at the county level. The top panel includes all counties; the lower panel includes Michigan, Wisconsin, Indiana, Illinois, Ohio, and Pennsylvania.26 Changes in population and median income are from the US Census. The mortality risk data are from the US Centers for Disease Control.

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Figure 7. Deindustrialization and support for Trump in the 2016 US presidential election (county-level regression estimates)coef = .047, (robust) se = .015, t = 3.19Notes: This partial regression plot (also known as an added variable plot) demonstrates the relationship between Trump support and the decline in manufacturing employment shares (1970 through 2015), after controlling for demographic and economic variables. Declines are computed such that positive values indicate a smaller share of workers employed in the manufacturing sector in 2015 compared with in 1970. All variables are measured at the county-level. The full model estimates appear in Appendix Table A1, column 1.

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Figure 8. Deindustrializing counties and unemployment following the 2008–2009 financial crisisNote: County-level correlation between the decline in manufacturing employment share between 1970 and 2015, and the average rate of unemployment from 2010 through 2015.

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