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Measuring climate policy stringency: a shadow price approach

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Abstract

To assess the effect of environmental policy on production structures, trade structures, or foreign direct investment, a measure for the stringency of policy is necessary. Measures typically used in empirical studies share several disadvantages: they are not available on a sectoral basis to reflect concerns of industry competitiveness; they are not available for a wide range of countries to allow for international comparisons; or they are not broad enough to reflect the multidimensionality of environmental policy. This paper develops a thorough, internationally comparable, sector-specific measure of multidimensional climate policy stringency where a shadow price approach serves as a basis. The approach is applied to climate policy by determining sector-specific emission-relevant energy costs on the basis of the sectors’ usage of emission-relevant energy carriers and the carriers’ respective prices. The resulting shadow price estimates are heterogeneous and can be applied in future research to test for carbon leakage and pollution havens.

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Notes

  1. While Ederington et al. (2004) distinguish between a direct and an indirect effect, Copeland and Taylor (2004) similarly differentiate between a pollution haven effect and a pollution haven hypothesis.

  2. To thoroughly analyze issues such as carbon leakage, such a stringency measure constitutes the first step as it measures the impact of policy on specific sectors. But pollution haven and carbon leakage effects also depend on the intensity of competition in certain industries. Felbermayer et al. (2013) develop a measure for the intensity of competition based on trade costs, price elasticities, and (inter)national inter-sectoral linkages of value chains.

  3. For a detailed list of the included countries, please see Table 1.

  4. In general, the estimated sector-specific climate policy stringencies in this paper do not change this perception.

  5. An overview of the environmental and climate policy stringency measures can be found in Sect. 2.1.

  6. An extensive overview of environmental policy stringency approaches can be found in Brunel and Levinson (20013b).

  7. List and Co (2000) solve this problem by ranking states based on their weighted public and private sector pollution abatement expenditures in dollars.

  8. The interpretation of \(Z_{E}\) and \(p_{E}\) of van Soest et al. (2006) slightly differs from the one of Morrison Paul and MacDonald (2003), who differentiate between the observed price p and the effective/shadow price Z.

  9. For further discussion of advantages and disadvantages of the shadow price approach, see van Soest et al. (2006, pp. 1158-60).

  10. Looking at the US food processing sector, Morrison Paul and MacDonald (2003) found differences between the observed and the shadow price for capital and agricultural goods, but not for labor, energy, and two materials inputs. The alternative approach used by Morrison (1988) and Morrison and Schwartz (1996) nests the quantities of the input under consideration instead of their prices in the cost function.

  11. The literature on production functions provides several extensions of GL functions. For instance, Nakamura (1990) presents a non-homothetic extension of the GL cost function, which allows a broader modelling of scale effects and technological change. Data restrictions prevent us from modeling these aspects in more detail. A similar argument applies to Behrmann et al.’s (1992) extension of a CET–CES Leontief variable profit function, which allows expanded input and output substitution possibilities at the cost of introducing one additional parameter to be estimated.

  12. For now, additional sub-subscripts are for clarity reasons left out. In the final estimating model each coefficient is also classified with regard to its country, sector, and time specification.

  13. Other authors use the input–output specification to correct for potential heteroscedasticity, which this paper adjusts for by estimating robust standard errors (Morrison 1988; Morrison and Schwartz 1996; van Soest et al. 2006).

  14. The results are robust to alternative time periods, which may be of the same or different lengths.

  15. Given that WIOD does not offer sector-specific capital stock information for the whole time period under consideration, the capital stock data are constructed using the methodology explained in Appendix 1.

  16. Shadow prices have also been estimated using PPP units. This resulted in a tendency of higher shadow prices for poorer countries, which can be interpreted such that poorer economies spend a relatively higher share of income on emission-relevant energy use or, in other words, on costs resulting from climate policy regulation.

  17. Table 6 in Appendix 2 lists all included 33 sectors.

  18. ISIC refers to the International Standard Industrial Classification of All Economic Activities.

  19. The gross energy use includes the use of both energy carriers that are relevant for emissions and the ones that do not emit.

  20. If all observations are included—also the ones that are not in the base years—the seven energy sources on average make up 86 % of the total sectors’ energy use, which is relevant for emissions and, hence, for climate policy issues. As for some years data are not available for all seven energy carriers, the explained share in the base years only is even higher and, therefore, considered a reasonable estimate for the respective year’s average energy price. In the remaining years, the total average energy price is determined by the total energy PASCHE price index. The final shadow prices have been tested for robustness and do not reveal significant differences compared to estimates using alternative base years with similar explained shares of energy use.

  21. Table 7 in Appendix 3 summarizes the allocation of industry and household prices of the energy sources to the sectors.

  22. In addition, both the estimated wedge coefficients and the shadow prices are provided in an Excel dataset (Online resource 2).

  23. The rankings look the same based on the wedge coefficients.

  24. In this comparison, one needs to bear in mind that the OECD reports effective tax rates in 2012, whereas Table 3 shows average shadow prices for the time period 1995 to 2009. Given the fact that many countries increased the taxes over the last two decades at unequal rates, the estimated correlation is relatively high.

  25. There exist quite a number of examples for the differentiated treatment of sectors including the implementation of the European Union Emission Trading System and the distribution of the costs of the German Renewable Energy Act.

  26. Nevertheless, the dataset contains some missing data points.

  27. In other words, the measure is likely not to become another one that cannot be further extended due to the unavailability of data. Examples for this are the indices of Dasgupta et al. (2001) and Eliste and Fredriksson (2002).

  28. For instance, a negative estimated wedge may only indicate that the country is subsidizing the usage of the polluting input relative to the rest of the included countries.

  29. With regard to the cost function explained in Sect. 3, this paper follows the approach of van Soest et al. (2006), who take all cost information as exogenously given and, hence, cannot explain changes in explanatory variables of the cost function.

  30. The constructed capital stock measure has also been compared to the one reported in the Extended Penn World Tables 4.0. for all countries in WIOD and shows a very high correlation coefficient of 0.995.

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Acknowledgments

Financial support by the German Federal Ministry for Education and Research (BMBF) in the framework of the project “Climate Policy and the Growth Pattern of Nations” is gratefully acknowledged. The project was part of the BMBF priority funding “Economics of Climate Change.” An integral part of the call was to provide practically based knowledge for dealing with climate change and to relate the results to policy in order to improve the basis for decision-making. In this context, the authors participated in several conferences as well as forums initiated by the BMBF. The results of the paper were incorporated in the resulting accumulated policy recommendations, which were subsequently presented to stakeholders from politics, industry, and society. Concerning the refinement of the paper, we would like to thank two anonymous referees for their valuable comments. Moreover, we benefited from discussions with participants of the 5th WCERE 2014, the 71st Annual Congress of the IIPF 2015, and the Annual Conference of the German Economic Association 2015.

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    Wilhelm Althammer & Erik Hille

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Appendices

Appendix 1: Capital stock computation

The WIOD only offers sector-specific capital stock information until 2007. For this reason, the capital stock data are constructed by applying the perpetual inventory method to the PWT data and disaggregating the country-level estimates with the help of the information in WIOD on the shares of each sector in the total national capital stock.

The capital stock \(x_{K}\) is constructed using the perpetual inventory method explained in Caselli (2005), who computes the capital stock in time t as the sum of the real aggregate capital investments \(I_{t}\) in the respective year and the depreciated capital stock of the previous year:Footnote 30

$$\begin{aligned} x_{K_t } =I_t +\left( {1-\delta } \right) x_{K_{t-1} } \end{aligned}$$
(12)

Here, \(\delta \) refers to the depreciation rate. The initial capital stock in the year 1995 is determined following common practice by dividing the investments in 1995 by the sum of the depreciation rate and geometric mean growth rate g of the investments for the whole time period from 1995 to 2009:

$$\begin{aligned} x_{K_{1995} } =I_{1995} \left( {g+\delta } \right) ^{-1} \end{aligned}$$
(13)

As the PWT capital investments data are available in the country level only, a disaggregation scheme, which is derived from the WIOD sector-specific real fixed capital stock data, is used to disaggregate the capital stock estimates. The WIOD data are not used in the first place, because the WIOD offers no capital stock information for the years 2008 as well as 2009 and updating the WIOD data using prior growth rates seems to be problematic owing to expected negative consequences of the financial dept crisis that started in 2008. Therefore, the missing sector shares for 2008 and 2009 are replaced by the information given for the last available year in WIOD, namely 2007.

Appendix 2: Sector overview

See Table 6.

Table 6 Included sectors and the respective division-level ISIC Rev. 3.1
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Appendix 3: Distribution of industry and household prices

See Table 7.

Table 7 Distribution of industry and household prices of the seven energy carriers to the primary, secondary, and tertiary sector
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Althammer, W., Hille, E. Measuring climate policy stringency: a shadow price approach. Int Tax Public Finance 23, 607–639 (2016). https://doi.org/10.1007/s10797-016-9405-4

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