Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-01T11:23:35.881Z Has data issue: false hasContentIssue false
  • English
  • Français

The Boll Weevil Versus “King Cotton”

Published online by Cambridge University Press:  03 March 2009

Kent Osband
Kent Osband
Affiliation:
Department of Economics, University of California, Berkeley, California 94720 and Russian Research Center, Harvard University, Cambridge, Massachusetts 02138.
Get access Rights & Permissions [Opens in a new window]

Abstract

The boll weevil's impact on southern agriculture poses a dilemma. Micro-level evidence suggests the weevil triggered a transition out of cotton, but macro-level indicators fail to register much long-term impact. Econometric simulation of boll weevil impact—taking into account the low demand elasticity for southern cotton, differences between states in the timing and levels of infestation, and long-term supply and demand shifts independent of the weevil—shows that the two sets of evidence are not inconsistent.

Type
Articles
Information
The Journal of Economic History , Volume 45 , Issue 3 , September 1985 , pp. 627 - 643
Copyright
Copyright © The Economic History Association 1985

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

I wish to thank Richard Sutch, Albert Fishlow, Peter Lindert, Claudia Goldin, Peter Temin, and anonymous referees for many helpful criticisms of earlier versions of this paper.Google Scholar

1 Ransom, Roger and Sutch, Richard, One Kind of Freedom: The Economic Consequences of Emancipation (New York, 1977), chap. 9.Google Scholar

2 U.S. Bureau of the Census, Census of Agriculture: 1930 (Washington, D. C., 1932), vol. 3, part 2, State Table I (type of farm) andGoogle ScholarSensus of Agriculture: 1950 (Washington, D.C., 1952), vols. by state, State 12 (historical record of acreage and value of crops).Google Scholar

3 Higgs, Robert, “The Boll Weevil, The Cotton Economy, and Black Migration 1910–1930,” Agricultural History, 50 (04 1976), pp. 335–50.Google Scholar

4 Since what is eaten by the weevil need not be harvested, this assumption may exaggerate the marginal weevil impact. Replacing W with W to the k–th power, for k less than 1, equates a 1 percent weevil loss with a k percent price drop. In the model described in the next section, regressions were run for various values of k, but reductions in k consistently worsened the fit.Google Scholar

5 Schultz, Harry, The Theory and Measurement of Demand (Chicago, 1938), p. 316.Google Scholar

6 Wright, Gavin, The Political Economy of the Cotton South (New York, 1978), p. 100.Google Scholar

7 Nerlove, Marc, The Dynamics of Supply: Estimation of Farmers' Response to Price (Baltimore, 1958), p. 201.Google Scholar

8 DeCanio, Stephen, “Cotton ‘Overproduction’ in Late Nineteenth-Century Southern Agriculture” this JOURNAL, 33 (09 1973), pp. 608–33.Google Scholar

9 United States Department of Agriculture, Economic Research Service, “World Demand Prospects for Cotton in 1980, with Emphasis on Trade by Less Developed Countries”, Foreign Agricultural Economic Report Number 68 (Washington, D.C., 1971).Google Scholar

10 United States Department of Agriculture, Bureau of Agricultural Economics, Statistics on Cotton and Related Data, Statistical Bulletin 99 (Washington, D.C. 1951), table 52.Google Scholar

11 The rough equivalence between af/ad and the ratio of foreign to domestic production in those years suggests that U.S. and foreign cotton were very close substitutes.Google Scholar

12 From a theoretical standpoint, one would probably not expect γ, the growth rate of the cooton farmer pool, to be independent of the pattern of farmer discouragement. Independence could only hlod if new entrants share the same attitudes (in a probabilistic sense) as currently active cotton farmers. However, alternative specifications with γ dependent on expected shadow price S e did not generate statistically significant parameter estimates.Google Scholar

13 Because the 1909 starting date postdates some severe infestations in Texas and Louisiana and imposes rather arbitrary assumptions about price expectations in that year, the first two or three years of simulations provide less reliable comparisons than later years do.Google Scholar

14 Tables available from author upon request.Google Scholar

15 Differences in timing of equivalent flows out of cotton production may have influenced the proportion of discouraged farmers leaving the South entirely. So the findings must be slightly quanlified with respect to out-migration.Google Scholar

16 Another way in which harvest price may affect supply id though its influence on choice of harvest methods. With higher prices fields may be picked more closely.Google Scholar