Skip to main content

Advertisement

SpringerLink
Log in

Assessing multi-dimensional performance: environmental and economic outcomes

  • Published:
Journal of Productivity Analysis Aims and scope Submit manuscript

Abstract

This study examines economic performance, environmental performance, and regulatory activity for plants in three industries: pulp and paper, oil, and steel. Stochastic frontier production function models show significant deviations from production efficiency. Older plants are less efficient in production, but perform no worse on emissions. Plants spending more on pollution abatement tend to do worse on both production efficiency and emissions. Stricter local regulatory pressure is associated with somewhat lower emissions, but has mixed effects on production efficiency. Positive correlations between SUR residuals for emissions and production efficiency suggest unmeasured plant-level characteristics that drive both economic and environmental performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Notes

  1. See Denison (1979), Gollop and Roberts (1983), Gray (1986, 1987), Boyd and McClelland (1999), Berman and Bui (2001), and Gray and Shadbegian (2002, 2003).

  2. Regulatory pressures can be related to plant closings, as shown by Deily and Gray (1991) for the steel industry in the early1980s, but there were fewer closings in our industries during the current period.

  3. Shadbegian and Gray (2005) used annual data on abatement costs for detailed inputs (not available for the 1990s) to make such adjustments to production function estimation.

  4. It is also possible to decompose TE into two parts (1) pure technical inefficiency (i.e., not producing on the isoquant) and (2) deviations from constant returns to scale (i.e., producing under diminishing or increasing returns). By assuming constant returns, Farrell excluded the second part from his calculations.

  5. This is an extension of the SF approach first proposed by Aigner et al. (1977) and Meeusen and van den Broeck (1977).

  6. In the two-stage estimation procedure, the first stage estimates the efficiency effects assuming they are independently distributed, while the second stage models them as a function of the plant’s characteristics (so the efficiencies for any two observations of the same plant would be correlated). Jointly estimating both stages is more efficient and removes this inconsistency in assumptions.

  7. Note that FRONTIER estimates inefficiency measures (1/TE), not efficiency measures.

  8. 4-digit SIC deflators discussed in Bartelsman and Gray (1996) were used to put nominal values into real terms.

  9. We categorize each plant’s technology based on information from their respective industry directories: Lockwood-Post Pulp and Paper Directory; Oil and Gas Journal; and Directory of Iron and Steel Plants.

  10. DIRTY TECH for oil refineries is mostly connected to air pollution, but for consistency we include it in the water pollution models as well.

  11. We would like to thank John Haltiwanger for providing the plant age information. In our analysis we used a single dummy to measure plant age (OLD = open before 1972) for two reasons: our sample includes some very old plants, likely to heavily influence any linear (or non-linear) age specification, and concern with environmental issues was not prominent before the 1970s.

  12. All variables below that are measured relative to plant capacity are measured in this way.

  13. For the TRI analysis we use total pollution abatement operating costs relative to plant size (PAOC).

  14. Regressions include a dummy variable for missing Compustat data, MISSFIRM, which cannot be reported due to Census Bureau disclosure rules.

  15. Particles of 2.5 μm or less in diameter.

  16. Relatively few of our emissions reports are based on actual monitored emissions; the majority of emission reports are based on calculated emissions or engineering estimates, based on the capacity of the production process and the design efficiency of the installed pollution abatement equipment.

  17. TRI chemicals are limited to those included in the ‘core chemical’ list for the 1988 TRI (found at http://www.epa.gov/triexplorer/list-chemical-core-88.htm).

  18. Gray and Shadbegian (2005) found some evidence that compliance with air pollution regulations by plants which are owned by larger firms is less sensitive to inspections and more sensitive to enforcement actions than those owned by smaller firms.

  19. We would like to thank Randy Becker, who created this dataset and graciously made it available to us for this project. The data is described in more detail in Becker (2005).

  20. A revised version of the PACE survey was done in 1999, but it was not longitudinally consistent with the pre-1999 PACE data so it is not used here (see Becker and Shadbegian (2005) for more information).

  21. Estimating small and insignificant contributions of capital to output is common in empirical results, as discussed in Griliches and Mairesse (1995).

  22. See Kodde and Palm (1986) for a table of critical values for this test.

  23. The estimated production function coefficients from that estimate are similar to those in the first part of the simultaneously estimated models in Tables 2–4; results available from authors.

References

  • Aigner D, Lowell CAK, Schmidt P (1977) Formulation and estimation of stochastic frontier production function models. J Econom 6:21–37

    Article  Google Scholar 

  • Aiken DV, Pasurka CA (2003) Adjusting the measurement of US manufacturing productivity for air pollution emissions control. Resour Energy Econ 25:329–351

    Article  Google Scholar 

  • Bartelsman EJ, Gray WB (1996) The NBER manufacturing productivity database. NBER Technical Working Paper 205

  • Battese GE, Coelli TJ (1995) A model for technical inefficiency effects in a stochastic frontier production function model for panel data. Empir Econ 20:325–332

    Article  Google Scholar 

  • Becker RA (2005) Air pollution abatement costs under the clean air act: evidence from the PACE survey. J Environ Econ Manage 50:144–169

    Article  Google Scholar 

  • Becker RA, Shadbegian RJ (2005) A change of PACE: A comparison of the 1994 and 1999 pollution abatement costs and expenditures survey. J Econ Soc Meas 30:63–95

    Google Scholar 

  • Berman E, Bui L (2001) Environmental regulation and productivity: evidence from oil refineries. Rev Econ Stat 83:498–510

    Article  Google Scholar 

  • Bernstein MA, Feldman SL, Schinnar AP (1990) Impact of pollution controls on the productivity of coal-fired power plants. Energy Econ 12:11–17

    Article  Google Scholar 

  • Boyd GA, McClelland JD (1999) the impact of environmental constraints on productivity improvement in integrated paper plants. J Environ Econ Manage 38:121–142

    Article  Google Scholar 

  • Coelli TJ, Prasada Rao DS, Battese GE (1998) An introduction to efficiency and productivity analysis. Kluwer Academic Publishers

  • Deily ME, Gray WB (1991) Enforcement of pollution regulations in a declining industry. J Environ Econ Manage 21:260–274

    Article  Google Scholar 

  • Denison EP (1979) Accounting for slower economic growth: the U.S. in the 1970s. The Brookings Institution, Washington

    Google Scholar 

  • Directory of iron and steel plants, Association for Iron and Steel Technology (various issues)

  • Farrell MJ (1957) The measurement of productivity. J R Stat Soc 120:253–290

    Article  Google Scholar 

  • Gray WB (1987) The cost of regulation: OSHA, EPA and the productivity slowdown. Am Econ Rev 77:998–1006

    Google Scholar 

  • Gray WB (1986) Productivity versus OSHA and EPA Regulations. UMI Research Press, Ann Arbor, MI

    Google Scholar 

  • Gray WB, Deily ME (1996) Compliance and enforcement: air pollution regulation in the U.S. steel industry. J Environ Econ Manage 31:96–111

    Article  Google Scholar 

  • Gray WB, Shadbegian RJ (2005) When and why do plants comply? Paper mills in the 1980s. Law and Policy 27:238–261

    Article  Google Scholar 

  • Gray WB, Shadbegian RJ (2003) Plant vintage, technology, and environmental regulation. J Environ Econ Manage 46:384–402

    Article  Google Scholar 

  • Gray WB, Shadbegian RJ (2002) Pollution abatement costs, regulation, and plant-level productivity. In: Gray WB (ed) Economic costs and consequences of environmental regulation. Ashgate Publishing

  • Gollop FM, Roberts MJ (1983) Environmental regulations and productivity growth: the case of fossil-fueled electric power generation. J Polit Econ 91:654–674

    Article  Google Scholar 

  • Griliches Z, Mairesse J (1995) Production functions: the search for identification. NBER Working Paper 5067

  • Harrington W (1988) Enforcement leverage when penalties are restricted. J Public Econ 37:29–53

    Article  Google Scholar 

  • Kodde DA, Palm FC (1986) Wald criteria for jointly testing equality and inequality restrictions. Econometrica 54:1243–1248

    Article  Google Scholar 

  • Laplante B, Rilstone P (1996) Environmental inspections and emissions of the pulp and paper industry in Quebec. J Environ Econ Manage 31:19–36

    Article  Google Scholar 

  • Lockwood-post pulp and paper directory, Miller-Freeman Publishing Company, various issues

  • Magat WA, Viscusi WK (1990) Effectiveness of the EPA’s regulatory enforcement: the case of industrial effluent standards. J Law Econ 33:331–360

    Article  Google Scholar 

  • McGuckin RH, Pascoe GA (1988) The longitudinal research database: status and research possibilities. Surv Curr Bus 68:30–37

    Google Scholar 

  • Meeusen W, van den Broeck J (1977) Efficiency estimation from Cobb–Douglas production functions with composed error. Int Econ Rev 18:435–444

    Article  Google Scholar 

  • Nadeau LW (1997) EPA Effectiveness at reducing the duration of plant-level noncompliance. J Environ Econ Manage 34:54–78

    Article  Google Scholar 

  • Oil and Gas Journal, PennWell Corporation (various issues)

  • Pitt MM, Lee LF (1981) The measurement and sources of technical inefficiency in the Indonesian weaving industry. J Devel Econ 9:43–64

    Article  Google Scholar 

  • Shadbegian RJ, Gray WB (2005) Pollution abatement expenditures and plant-level productivity: a production function approach. Ecol Econ 54:196–208

    Article  Google Scholar 

  • Shadbegian RJ, Gray WB (2003) What determines the environmental performance of paper mills? the roles of abatement spending, regulation, and efficiency. Topics Econ Anal Policy 3, http://www.bepress.com/bejeap/topics/vol3/iss1/art15

  • U.S. Bureau of the Census, pollution abatement costs and expenditures. U.S. Govt. Printing Office, Washington, DC, various issues

Download references

Acknowledgments

Financial support for the research from the National Science Foundation (Grant # SBR-9410059) and the Environmental Protection Agency (Grants # R-832155-01-0 and #R-826155-01-0) is gratefully acknowledged, as is access to Census data at the Boston Research Data Center, which is partially supported by the National Science Foundation (Grant #SES-0427889). Excellent research assistance was provided by Anna Belova and Bhramar Dey. The opinions and conclusions expressed are those of the author and not the Census Bureau, EPA, or NSF. All papers are screened to ensure that they do not disclose confidential information. Any remaining errors or omissions are the authors’.

Author information

Authors and Affiliations

  1. University of Massachusetts at Dartmouth, Dartmouth, MA, USA

    Ronald J. Shadbegian

  2. National Center for Environmental Economics, USEPA, Washington, DC, USA

    Ronald J. Shadbegian

  3. Economics Department, Clark University, 950 Main Street, Worcester, MA, 01610, USA

    Wayne B. Gray

  4. NBER, 1050 Massachusetts Ave., Cambridge, MA, USA

    Wayne B. Gray

Authors
  1. Ronald J. Shadbegian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wayne B. Gray
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wayne B. Gray.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shadbegian, R.J., Gray, W.B. Assessing multi-dimensional performance: environmental and economic outcomes. J Prod Anal 26, 213–234 (2006). https://doi.org/10.1007/s11123-006-0017-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11123-006-0017-3

Keywords

JEL Classifications

Search

Navigation