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Industries, Occupations, and Trade Policy Preferences

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Abstract

The nature of one’s work, not just who one works for, is central to political and economic life. Yet models of trade policy preferences mostly ignore occupation, focusing exclusively on industries (perhaps because industries are the usual organizing dimension of economic policymaking). This article proposes new measures of how much risk trade imposes on different workers based on how diversified their occupation is across industries, thus considering both industry and occupation. Having a job specific to any sector appears to encourage protectionism, regardless of that sector’s comparative advantage, supporting the idea that public opinion may treat trade policy as insurance.

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Notes

  1. To be precise, they favor protection for their own sector; self-interest may still lead to demands for free trade for other parts of the economy.

  2. Risk aversion enhances, but is not necessary for, the effect.

  3. Asset specificity is not the only possible motivator for the costs discussed here. If imperfect labor markets mean that involuntary job loss will be of nonzero length or if layoffs have negative psychological or financial costs, people could also want trade protection as insurance. Yet skills and assets being linked to particular occupations suggest that a particularly important and salient dimension of risk will be tied to occupational status.

  4. Nickelsburg and Timmons (2012), however, have recently questioned how much skill specificity really affects demands for insurance.

  5. The Iversen–Soskice measure does have separate industry-based subcategories for two careers—“production and operations department managers” and “general managers.” It is unclear why these skills would be less portable across industries than are others, but their subdivision does acknowledge that industrial boundaries may play a role in skill specificity.

  6. In an interesting variation, Mayda and Rodrik (2005) actually infer industry from occupation.

  7. The minimum value, achieved when an occupation is equally represented in every industry, is one divided by the number of industries in the classification scheme.

  8. Calculations of occupational concentration based on one set of industrial and occupational classification schemes may not be strictly comparable with another, especially since category borders are inherently arbitrary. However, comparability issues bedevil other measurement schemes, too: Iversen and Soskice (2001, p. 881) measure specificity of a skill by summing employment totals for the related occupation across countries, thereby assuming that workers are perfectly mobile across national boundaries.

  9. Data corroborates this conceptual relationship: in the measures used below, the correlation between the two measures of occupational specificity is 0.89.

  10. Comparable data is not available for other years of the survey: the NES did not collect occupation data in 2002 and used a different industrial coding scheme before 2000.

  11. Question V043262e gives the industry and V043262n the occupation.

  12. Both measures of occupational specificity skew to the right; the usual logarithmic transformation largely corrects the problem.

  13. These dependent variables, respectively survey items V043152 and V043150, are ordinal, so the analyses discussed—unreported for brevity—use ordered-logit regressions. The results are robust to other regression technologies, such as ordered probit or ordinary least squares.

  14. This lessens if unemployment correlates across spouses, as is likely if both work in the same industry. But as long as the correlation is not perfect, spouses serve this insurance function.

  15. Including preexisiting sectoral tariffs in the model neither produces a significant coefficient nor affects other coefficients. This echoes the results of Scheve and Slaughter (2001).

  16. Other authors (e.g., Scheve and Slaughter 2001) use a more precise measure looking not just at the comparative-advantage position but also at the intensity of that position. The reliability problems noted in the text are likely to be even more acute for this measure (as indeed they seemed to be in Scheve and Slaughter’s results) than with the indicator variable. Both produce similar empirical results in the tables below.

  17. Similarly, meaningful tests of the returns model would require better data about industries. An interaction effect—sector trade position times occupational concentration or dependence—does not produce significant coefficients when included in the model, but this is to be expected given the scant sample size.

  18. The skew is strong enough that the mean level of concentration (0.27) is over twice the median (0.12). The “occupational dependence” measure has a similar distribution, with a mean of 0.27 and a median of 0.11.

  19. The range for the logged variables is about 3.2 for occupational concentration and 4.6 for occupational dependence.

  20. That is, an ideologically moderate white 47-year-old married male with 14 years of education and an income between $45,000 and $49,000 who works in a non-traded sector.

  21. This in effect imposes an extreme case of adverse selection on the insurance model.

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Acknowledgments

An earlier version of this paper was presented at Harvard University’s 2003–04 Political Economy Research Workshop; I thank the attendees, especially Michael Hiscox, Torben Iversen, and Kenneth Shepsle, for their excellent feedback. Gratitude is also due to Mark Peffley, Sonal S. Pandya, Jon Hurwitz, Jeffry Frieden, and the anonymous reviewers for additional helpful comments and to Yong Ouk Cho for research assistance.

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  1. Iowa State University, 503 Ross Hall, Ames, IA, 50011-1204, USA

    R. Urbatsch

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Urbatsch, R. Industries, Occupations, and Trade Policy Preferences. Polit Behav 35, 605–620 (2013). https://doi.org/10.1007/s11109-012-9206-0

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