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The Impact of Environmental Policy Stringency on Industrial R&D Conditional on Pollution Intensity and Relocation Costs

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Abstract

Stringent environmental regulations may encourage industrial innovation, as technological advancements lower the cost of pollution abatement (Popp et al. in Handbook of the economics of innovation, vol II. Academic Press, Burlington, pp 873–938, 2010). The pollution-havens hypothesis, on the other hand, indicates that, rather than innovating, dirty industries may relocate to countries with less stringent environmental regulations (Copeland and Taylor in J Econ Lit 42(1):7–71, 2004). Thus, more stringent environmental regulations may increase or decrease innovative activities. This paper examines empirically the impact of environmental regulations on R&D intensities and R&D expenditures in 21 manufacturing industries in 28 OECD countries from 2000 to 2007. I consider pollution intensity and the relative ease of relocation (immobility) as industry characteristics that determine the optimal industry response to increased environmental policy stringency. I find that more pollution intensive industries innovate less as regulatory environments become more restrictive relative to less pollution intensive industries. At the same time, more immobile industries innovate more than more mobile industries as environmental regulations become more stringent, illustrating innovation as an alternative to relocation.

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Notes

  1. Carbon costs, including indirect costs and abatement, are used as a part of the criteria for determining which sectors are at risk for relocation and are thus entitled free emissions allocations by the European Commission under Europe’s carbon market, the EU ETS (European Commission 2014).

  2. Eskeland and Harrison (2003) report a statistically significant correlation of 0.80 between toxic pollution emission intensities and abatement costs among U.S. industries. Cole and Elliott (2005) report high abatement operating costs for the four most pollution intensive industries in my sample.

  3. See Table A1 in the online appendix.

  4. This is similar to the use of U.S. industry characteristics in Maskus et al. (2012) and Rajan and Zingales (1998).

  5. A comparison with data from Cole et al. (2005) is provided in the online appendix in Table A2.

  6. For example, Mani and Wheeler (1998), Hettige et al. (1992), and Eskeland and Harrison (2003) use U.S. pollution intensities to rank or classify industries in cross-country analyses.

  7. The OECD’s STAN Database for Structural Analysis presents annual data on R&D expenditures by industry but does not distinguish between financing sources. I rely on the country fixed effects to absorb any unobserved heterogeneity among countries regarding R&D subsidies and the government financed share of R&D.

  8. Patents allow for the identification of environmental technologies (Popp 2005). This alternative measure can be problematic in industry specific studies as the industry code is not recorded by patenting offices (Jaffe and Palmer 1997). Although concordance tables attempt to link patent descriptions to industry codes, using patent data in industry specific studies introduces the potential for misclassification (Jaffe and Palmer 1997; Johnstone et al. 2012).

  9. The EPA’s National Emissions Inventory (NEI) data is available for 2002, 2005, and 2008. The 2002 inventory is selected because it is closest to the beginning of the data coverage period of 2000 to 2007. Using the 2005 or 2008 NEI data provides similar results. Table A3 in the online appendix compares total pollution intensity rankings from 2002, 2005, and 2008 NEI data.

  10. Industries are converted from U.S. SIC 1987 to two-digit ISIC using Jon Haveman’s industry concordances (2012).

  11. \(\mathrm {PM}\) includes \(\mathrm {PM}_{2.5}\) and \(\mathrm {PM}_{10}\). See U.S. Environmental Protection Agency (2012) for more information.

  12. Examples include the previously mentioned works by Kellenberg (2009), Wagner and Timmins (2009), and Johnstone et al. (2012).

  13. See the WEF Global Competitiveness Report 2007–2008 for more information on survey implementation, data collection, and aggregation techniques.

  14. Another proxy for environmental policy stringency is the U.S. Pollution Abatement Cost and Expenditures (PACE) survey, which provides data on the costs of compliance with environmental regulations at the industry level. This measure is limited to the U.S. and does not estimate capital and operating expenditures that would have occurred in the absence of environmental policies (Jaffe et al. 1995). Johnstone et al. (2012) find a negative correlation between the PACE and WEF measures.

  15. Clustering the standard errors in this particular dataset is not necessarily straightforward due to the fact that the dataset is non-nested and there are few clusters in each (country and industry) dimension. To be conservative regarding statistical inference, I cluster at the country-industry and industry level using the multi-way clustering method from Cameron et al. (2011) applied via the STATA command “cgmreg.”

  16. Many of the country, industry, and time dummies are also jointly significant.

  17. I thank two anonymous referees for pointing out these important points.

  18. Using a log transformation normalizes the right-skewed R&D expenditure data.

  19. With robust standard errors, \(\beta _1\) is significant at the 10 % level.

  20. Performing this analysis using each individual pollutant provides very similar results.

  21. To account for R&D intensity observations that are left censored at zero, I estimated a series of Tobit models. The interaction term coefficients remain significant in all cases but one. These results are provided in Table A4 in the online appendix.

  22. A similar institutional measure that relates to corruption yields nearly identical results.

  23. A composite stringency measure that includes both market and non-market approaches to environmental policies constructed by the OECD provides similar results. These results are not included due to differences in country availability (Botta and Koźluk 2014).

  24. \(\beta _1\) is significant at the one percent level with robust standard errors.

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Authors and Affiliations

  1. St. Lawrence University, 23 Romoda Drive, Canton, NY, 13617, USA

    Sahar Milani

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  1. Sahar Milani
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Correspondence to Sahar Milani.

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I thank Rebecca Neumann, Matthew McGinty, Itziar Lazkano, and two anonymous referees for their comments. All errors are my own.

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Milani, S. The Impact of Environmental Policy Stringency on Industrial R&D Conditional on Pollution Intensity and Relocation Costs. Environ Resource Econ 68, 595–620 (2017). https://doi.org/10.1007/s10640-016-0034-2

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