Employment Adequacy in Extractive Industries: An Analysis of Underemployment, 1974–1998

Abstract

Marginal employment stands as a major obstacle to the economic health of American families and communities. This is particularly true among those who work in extractive industries. We conceptualize marginal employment as underemployment, which goes beyond unemployment to include discouraged workers, involuntary part-time workers, and the working poor. Analyzing data from the March Current Population Survey from 1974 through 1998, we find that workers in extractive industries face far higher rates of underemployment than do those employed in other major industrial sectors. However, this aggregation masks great intracategory heterogeneity. The high rates of underemployment observed in extractive industries are largely a driven by the disadvantages of those employed in agriculture and forestry/fishing. In contrast, those employed in mining enjoy substantially lower rates of underemployment. The relative employment circumstances found in each industry are only partly explained by conventional predictors of underemployment.

Keywords:

• agriculture
• extractive industries
• fishing
• forestry
• mining
• underemployment

Support for this research was provided by the United States Department of Agriculture (NRICGP 98-35401-6157), by The Pennsylvania State University Agricultural Experiment Station Project 3501, and by the Population Research Institute, The Pennsylvania State University, which has core support from the National Institute on Child Health and Human Development (1 R24 HD1025). The authors thank W. J. Grigsby, A. E. Luloff, and Richard C. Stedman, as well as the editor and anonymous reviewers of Society and Natural Resources for comments on previous drafts of this manuscript. The authors alone are responsible for any substantive or analytic errors.

Notes

Note. Sources: March Current Population Surveys, selected years 1974–1998. FIRE, finance, insurance, real estate.

^aMajor industry ns do not sum to the total n due to those in the labor force according to the LUF, but without an industry in the reference week.

Note. Sources: March Current Population Surveys, selected years 1974–1998. FF, forestry and fishing.

^a Percentage of agricultural, forestry and fishing, and mining workers underemployed divided by the percentage underemployed in the total U.S. labor force.

^b Percentage of agricultural, forestry and fishing, and mining workers underemployed divided by the percentage underemployed in all extractive industries.

Note. Source: March Current Population Surveys, selected years 1974–1998. Cell entries are unstandardized logistic regression coefficients; n = 625,856 Ref., reference.

^a Coefficient not significant at p < .05.

Note. Source: March Current Population Surveys, selected years 1974–1998. Cell entries are unstandardized logistic regression coefficients. Agriculture n = 14,536. Forestry and fishing n = 2,263. Mining n = 5,664. Ref., reference.

^a Coefficient not significant at p < .05.

^b Coefficient for age squared has been multiplied by 100.

1 For an in-depth discussion of the LUF see Clogg (1979).

2 Across the models in Table 3, those with “no industry” face the highest odds of being underemployed. Most of these individuals would not be counted as in the labor force by standard definitions, but are included under the LUF. These individuals represent a substantial share of the “discouraged” by definition. We restrict our discussion in this article to differences in underemployment between industries.

3 One anonymous reviewer was concerned that by dichotomizing workers into those who are adequately employed and underemployed, we are somehow invoking labels of “acceptable” and “unacceptable” employment. We are sensitive to the impact that labeling has in society. However, it is not our intention to cast aspersion on those who find themselves underemployed (much as we reject the blaming of poverty on its victims). The reviewer also worried that our finding of a higher prevalence of underemployment in extractive industries (that proportionately more jobs in extraction are “bad jobs,” using the “good job/bad job” distinction) may lead to policies that would see such jobs eliminated. It is our hope that these findings would be used to argue for greater protections for workers in extraction, not the elimination of their jobs.