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World War II and the Industrialization of the American South

Published online by Cambridge University Press:  24 November 2017

Taylor Jaworski*
Affiliation:
Taylor Jaworski is Assistant Professor, Department of Economics, University of Colorado, Boulder, UCB 256 Boulder, CO 80309. E-mail: tjaworski@gmail.com.
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Abstract

When private incentives are insufficient, a big push by government may lead to industrialization. This article uses mobilization for WWII to test the big push hypothesis in the context of postwar industrialization in the American South. Specifically, I investigate the role of capital deepening at the county level using newly assembled data on the location and value of wartime investment. Despite a boom in manufacturing activity during the war, the evidence is not consistent with differential postwar growth in counties that received more investment. This does not rule out positive effects of mobilization on firms or sectors, but a decisive role for wartime capital deepening in the South's postwar industrial development should be viewed more skeptically.

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Articles
Copyright
Copyright © The Economic History Association 2017 
Figure 0

Figure 1 TRENDS IN AGGREGATE MANUFACTURING IN THE U.S. SOUTH

Notes: The panels show the share of establishments, employment, wages, and output and value added in the South in each year between 1880 and 1987. The South includes all counties in Alabama, Arkansas, Delaware, Florida, Georgia, Kentucky, and Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia, and the District of Columbia.Sources: Haines (2010).
Figure 1

Figure 2 WORLD WAR II INVESTMENT IN THE U.S. SOUTH

Notes: The figure shows the value of WWII investment.Sources: U.S. Civilian Production Administration (1946).
Figure 2

Table 1 DETERMINANTS OF WARTIME INVESTMENT

Figure 3

Figure 3 RELATIONSHIP BETWEEN WARTIME INVESTMENT AND PRE- AND POST-WAR MANUFACTURING

Notes: Each panel plots regression coefficients on log of wartime investment from an regression that includes state-year fixed effects and the log of the number establishments, employment, wage bill, and value-added as the dependent variable. Each panel shows coefficients and the 95 percent confidence interval in the solid and dashed black lines, respectively. The number of sample counties is 1,086.Sources: See text of the Data and Variables section.
Figure 4

Figure 4 IMPACT OF WARTIME INVESTMENT ON MANUFACTURING

Notes: Each panel plots regression coefficients on log of wartime investment from equation (1) with the log of the number establishments, employment, wage bill, and value-added as the dependent variable. The solid and dashed black lines show coefficients and the 95 percent confidence interval from a specification that includes state-year and county fixed effects as well as county-level controls (interacted with year fixed effects). The 95 percent confidence interval is based on standard errors clustered at the state level. The number of sample counties is 1,086.Sources: See text of the Data and Variables section.
Figure 5

Table 2 IMPACT OF WARTIME ON MANUFACTURING

Figure 6

Figure 5 IMPACT OF PUBLIC VERSUS PRIVATE WARTIME INVESTMENT

Notes: Each panel plots regression coefficients on log of wartime investment from public (solid black) and private (dashed gray) sources of financing from versions of equation (1) with the log of the number establishments, employment, wage bill, and value-added as the dependent variable. All specifications include state-year and county fixed effects as well as county-level controls (interacted with year fixed effects). The number of sample counties is 1,086.Sources: See text of the Data and Variables section.
Figure 7

Table 3 IMPACT OF WARTIME ON ESTABLISHMENTS BY SECTOR

Figure 8

Figure 6 CASE STUDY OF KANAWHA COUNTY (WEST VIRGINIA)

Notes: Each panel plots differences between counties in West Virginia with positive wartime investment (solid line) or Kanawha County (dashed line) relative to counties in West Virginia that received zero investment. The four panels show differences for the number establishments, employment, wage bill, and value-added.Sources: See text of the Data and Variables section.
Figure 9

Figure 7 CASE STUDY OF COBB COUNTY (GEORGIA)

Notes: Each panel plots differences between counties in Georgia with positive wartime investment (solid line) or Cobb County (dashed line) relative to counties in Georgia that received zero investment. The four panels show differences for the number establishments, employment, wage bill, and value-added.Sources: See text of the Data and Variables section.
Figure 10

Figure 8 IMPACT OF WARTIME PLANTS ON MANUFACTURING

Notes: Each panel plots regression coefficients on an indicator for a newly constructed plant due to WWII from equation (3) with the log of the number establishments, employment, wage bill, and value-added as the dependent variable. Each panel shows coefficients and the 95 percent confidence interval from specifications that include state-year and county fixed effects as well as county-level controls (interacted with year fixed effects) The 95 percent confidence interval is based on standard errors clustered at the state level. The number of sample counties is 1,086.Sources: See text of the Data and Variables section.
Figure 11

Table 4 IMPACT OF WARTIME ON NON-MANUFACTURING OUTCOMES

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