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Dirty money: Is there a wage premium for working in a pollution intensive industry?

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Abstract

Within a compensating wage differential framework we investigate whether there is a wage premium for working in a pollution intensive industry. Our results for the economy as a whole suggest a small wage premium of approximately one quarter of one percent associated with the risk of working in a dirty job. This premium rises to over fifteen percent for those individuals who work in one of the five dirtiest industries. We also find evidence of a fatal risk wage premium, providing estimates of the value of a statistical life of between £12 million and £19 million (2000 prices).

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Notes

  1. A lively debate on the existence of wage compensation continues. See Dorman (1996) for a broader discussion of these assumptions and theoretical reasons for doubting their applicability. Most value of statistical life (VSL) studies centre on one basic premise: that the VSL should roughly correspond to the value that people place on their lives in private decisions. See Viscusi (1993), Dorman and Hagstrom (1998) and Viscusi and Aldy (2003) for a review of the existing literature and Mrozek and Taylor (1999) for the results of an often cited meta-analysis on the determinants of the value of life. Mrozek and Taylor (1999) offer a best practice estimate of VSL of $2 million (1998 prices). Note that their estimates are considerably less than the Environmental Protection Agency (EPA) VSL value of $6 million (1998 dollars). Burtraw et al. (1998), Hagler-Bailly (1995) and US EPA (1997) all show that the benefits far outweigh the abatement costs even if VSL figures were to be reduced by two thirds. See Viscusi (2007) for an overview of the regulation of health, safety and environmental risks.

  2. UK estimates of VSL have often been notably different to those estimated for the US (see Viscusi and Aldy 2003).

  3. There are a small number of studies that have examined the impact of environmental regulations on employment although they generally find little effect. For the US, studies by Morgenstern et al. (2002) and Berman and Bui (2001) find no evidence to suggest that regulations have adversely affected industrial employment with the former actually finding weak evidence that regulations may result in a small net increase in employment. Cole and Elliott (2007) find a similar result for the UK. However, studies by Henderson (1996), Kahn (1997) and Greenstone (2002), again for the US, indicate that industries located in counties with stringent regulations have experienced job losses, or at the very least, lower employment growth rates, relative to industries in less regulated counties.

  4. Ozone pollution stems mainly from emissions of Nox and VOCs with concentration levels depending on the amount and location of emitted pollutants, geographical characteristics, meteorological conditions, and atmospheric chemistry and transport. Ozone formation is complicated and non-linear; for example, under certain conditions an increase in Nox can reduce ozone concentrations. PM10 pollution stems mainly from direct emissions of particles, and from reactions of NOX and SO2 with other substances in the atmosphere. Potential emission sources are building and construction, diesel trucks and buses, forest fires, refuse burning and some manufacturing industries. See Cesar et al. (2002) for details.

  5. In a study for the US Viscusi (2003) reveals that blacks do face a higher fatality risk and nonfatal injury-risk but that the differences are not great. He also shows that black employees receive significant premia for nonfatal risks. The problem, however, is that although black employees undertake greater risk than whites, they also receive lower annual pay. Viscusi (2003) states that “…there must be fundamental differences in labor market opportunities for blacks and whites as well as the structure of their offers for risky jobs” (p. 254). For non-fatal injury both men and women earn a wage differential but this figure is three times larger for women. Leeth and Ruser (2003) also show that white women earn the highest wage compensation for non-fatal risk. Black, Hispanic and other minorities also receive higher pay for bearing nonfatal injury risk but the premia were smaller than for white women.

  6. A related issue is the controversy surrounding unobservable worker heterogeneity and VSL estimates. One recognised problem with all wage-risk studies is the issue of endogeneity, as raised by Garen (1988). It is possible that those workers with the greatest earnings capacity are likely to choose safer and less pollution intensive working environments (assuming safety and pollution-free working conditions are normal goods). After attempting to control for endogeneity issues, Garen (1988) finds generally larger VSL estimates. However, as Kostiuk (1990) points out, Garen’s (1988) methodology removes unobserved worker heterogeneity as an influence on the estimates. This is fine if the unobserved heterogeneity is the behaviour of workers in the face of risk alone. However, differing risk parameters across workers are a necessary condition for the market to generate compensating wage differentials unless we assume individuals’ indifference curves are identical. The Garen technique therefore removes too much. Nevertheless, we utilise the Garen methodology to control for the possible endogeneity of our fatal, nonfatal and pollution risk variables. The coefficient on nonfatal risk did not alter significantly and remained insignificant and had little impact on the pollution risk coefficient. We are unable to instrument for possible endogeneity between wages and pollution intensity as no suitable instrument is available. Results when fatal and nonfatal risk are instrumented are available from the authors upon request.

  7. Dickens and Katz (1987) find that roughly a quarter of individual level wage variation is explained by industry level wage premia casting doubt on the ability of unmeasured worker heterogeneity to account for industry wage differentials.

  8. The UK Environmental Accounts (EA) use a combination of 2, 3 and 4-digit SIC codes. See http://www.statistics.gov.uk/.

  9. In unreported results we also use a fourth exposure measure, namely pollution per worker. Results were broadly similar to those for the other exposure measures but are omitted for reasons of space. They are available upon request.

  10. The QLFS is a rotating panel that follows the same individuals for five consecutive quarters. It currently includes a representative sample of approximately 60,000 households made up of five “waves”, each of approximately 12,000 households. A systematic random sample design is used for the survey and it is therefore representative of the whole of Great Britain. All estimates based on the LFS are subject to sampling error. Our sample excludes the self-employed. Care is taken to ensure that individuals are not replicated.

  11. The difference between weekly pay and hourly pay is that the former includes usual hours of paid overtime.

  12. Bellman (1994) uses occupational risk variables for blue-collar workers for Germany and finds a significant positive effect for non-fatal occupational illness of male employees, controlling for schooling, experience and change of industry. However, for nonfatal injuries at work the coefficient was significant and negative. He concluded that for Germany there was no explicit evidence for the existence of compensating wage differentials, especially for non-fatal risk. In contrast, Grund (2000) finds evidence of compensating wage differentials for increased accidents for blue-collar workers in West Germany.

  13. The majority of studies are US based and merge industry-average risk measures (BLS at 2 or 3-digit) or the NIOSH’s National Traumatic Occupational Fatality project which reports fatalities by 1-digit industry. Seven of the eight studies summarised in Dorman and Hagstrom (1998) use these data sets.

  14. Import and export variables are not included as our sample has both tradable and non-tradable sectors.

  15. Appendix contains the sample means of all of the variables used in Table 1.

  16. None of these UK studies use our richer LFS dataset.

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Acknowledgment

Matthew Cole and Robert Elliott are grateful for funding from the Leverhulme Trust grant F/00094/AG. We would like to thank Steve McIntosh and participants at EAERE Kyoto, The Health and Safety Executive for useful comments. The usual disclaimer applies.

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Authors and Affiliations

  1. Department of Economics, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

    Matthew A. Cole & Robert J. R. Elliott

  2. Department of Economics, University of Sheffield, 9 Mappin Street, Sheffield, S10 2TN, UK

    Joanne K. Lindley

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  1. Matthew A. Cole
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  2. Robert J. R. Elliott
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  3. Joanne K. Lindley
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Correspondence to Robert J. R. Elliott.

Appendix

Appendix

Table 4 UK environment agency industry classification and SIC92 concordance
Full size table
Table 5 Pollutants groupings
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Table 6 Table of sample means
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Table 7 OLS (log) Wage equation for all pollutants, full specification (1995–2003)
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Cole, M.A., Elliott, R.J.R. & Lindley, J.K. Dirty money: Is there a wage premium for working in a pollution intensive industry?. J Risk Uncertain 39, 161–180 (2009). https://doi.org/10.1007/s11166-009-9077-x

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