Energy Policy 36 (2008) 12–22



## Viewpoint 

# Firm performance and employment in the EU emissions trading scheme: An empirical assessment for Germany

Niels Anger 1 

# , Ulrich Oberndorfer Centre for European Economic Research (ZEW), P.O. Box 103443, 68034 Mannheim, Germany

Received 26 June 2007; accepted 5 September 2007
Available online 18 October 2007



### Abstract

This paper empirically investigates the role of the EU Emissions Trading Scheme (EU ETS) for firm performance and employment in Germany. We provide an overview of relative allowance allocation within the EU ETS as well as an econometric analysis for a large sample of German firms covered by the scheme in order to assess the impacts of EU emissions regulation on both firm revenues and employment. The dataset indicates that the EU ETS was in an overall long position in 2005, although allowance allocation was very heterogeneous across member states. Our econometric analysis suggests that, within the first phase of the EU ETS, relative allowance allocation did not have a significant impact on firm performance and employment of regulated German firms. r 2007 Elsevier Ltd. All rights reserved.

Keywords: EU ETS; Competitiveness; Employment



### 1. Introduction

In 2005, the European Union Greenhouse Gas Emissions Trading Scheme (EU ETS) was launched ( European Union, 2003 ). The scheme represents a cornerstone of the efforts by EU member states to fulfil the emissionsreduction targets under the Kyoto Protocol. This agreement requires European countries to reduce their greenhouse gas emissions on average by 8% until 2012 compared with 1990 emissions levels ( UNFCCC, 1997 ). The EU ETS covers European producers in four sectors, namely energy (e.g. electric power, oil refinement), production and processing of ferrous metals, minerals (e.g. cement, glass), as well as pulp and paper. The ETS currently covers almost half (46%) of the total CO 2 

### emissions of EU countries. While in the scheme’s first phase (2005–2007) almost all emission allowances are grandfathered by means of National Allocation Plans (NAPs) of each member state and only up to 5% may be auctioned, in the second phase (2008–2012) the auctioning limit rises to 10%. Furthermore, the amending directivelinking the EU ETS with the Kyoto Protocol’s projectbased mechanisms enables EU companies to generate emissions reductions by means of the Clean Development Mechanism (CDM) or Joint Implementation (JI) ( European Union, 2004 ). Since its initiation, the EU ETS has been accompanied by discussions on potential losses in competitiveness in international markets of companies that are covered by the EU ETS legislation. 2 

### Against this background, this paper presents a first empirical assessment of the effects of the EU ETS on firm performance, i.e. competitiveness, and employment. Following Balassa (1962) , we define competitiveness as a firm’s ability ‘‘to sell on foreign and domestic markets’’ and approximate this ability by firms’ market revenues. We rely both on real-world data on allocated allowances and verified emissions for the first trading period from the EU Community Independent Transaction Log ( European Union, 2007 ) and on economic firm-level data from two comprehensive databases. 

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www.elsevier.com/locate/enpol

0301-4215/$ - see front matter r 2007 Elsevier Ltd. All rights reserved.
doi: 10.1016/j.enpol.2007.09.007

Corresponding author. Tel.: +49 621 1235 337; fax: +49 621 1235 226.
E-mail addresses: anger@zew.de (N. Anger)

, oberndorfer@zew.de (U. Oberndorfer)

.

1 Tel.: +49 621 1235 206; fax: +49 621 1235 226. 2 For a recent overview on model-based assessments of costs and competitiveness effects of the EU ETS, see Oberndorfer and Rennings (2007)

.

### Previous quantitative studies have assessed the efficiency aspects and competitiveness implications of the EU ETS predominantly in numerical modeling frameworks. Bo  ̈ hringer et al. (2005) show that the exclusive coverage of energy-intensive installations by EU ETS implies that—in the absence of the potential use of CDM and JI—theremaining industries have to be regulated by complementary abatement policies in order to meet the national Kyoto targets. Such a hybrid emission regulation can cause large inefficiencies within EU economies, but may also worsen the prospects of linking the EU ETS to emerging emissions trading schemes beyond Europe (see Anger, 2007 ). Unlike employment aspects of the EU ETS, competitiveness implications of the current European trading scheme have been analyzed in numerical model frameworks ( Kemfert et al., 2005 ; Klepper and Peterson, 2004 ; Peterson, 2006 ). Thesectoral competitiveness implications of allowance allocation under the EU ETS have been assessed both for the European electricity industry ( Neuhoff et al., 2006 ) and for the cement sector ( Demailly and Quirion, 2006 ). Enlarging the purely European perspective, Alexeeva-Talebi and Anger (2007) assess both the economy-wide and the sectoral competitiveness effects of linking the EU ETS internationally to emerging trading systems outside Europe (such as Japan, Canada or Australia) within an applied general equilibrium model framework.

The previous empirical literature on emissions regulation under the EU ETS is rather scant. Analyzing the verified emissions of the participating installations as well as the respective allowances allocated, Ellerman and Buchner (2006) conclude that ‘‘over-allocation occurred and that its magnitude may have been as much as 100 million EU
allowances’’. Kettner et al. (2007) present similar findings, suggesting that in the first EU ETS trading year the scheme was in a long position regarding emissions allowances. Moreover, to date there is no empirical contribution available assessing the competitiveness or employment impacts of emissions allocation under the EU ETS. Our paper aims at starting to fill this gap. In this respect, the contribution of this analysis is twofold: Relying on installation-level allocation data from the EU Community Transaction Log in 2005, we (i) descriptively assess the
relative allowance allocation under the EU ETS at thenational level and (ii) econometrically test for competitiveness and employment impacts of the EU ETS for a large sample of German companies.

The present article is structured as follows. Section 2 summarizes the empirical literature. Section 3 discusses the relative allowance allocation in Europe as well as the data underlying the empirical analysis for Germany. Section 4 presents the econometric assessment and Section 5
concludes.

2. Literature review

The necessity of environmental regulation is mainly based on the reasoning that there are social costs of negative externalities such as pollution. However, strict environmental regulation is often accused of harming the competitiveness of the affected sector or firm. Such adverse economic effects (and especially effects on competitiveness) of environmental regulation are challenged by the so-calledPorter hypothesis, suggesting that environmental regulation provides incentives for companies to innovate and that these innovations can stimulate economic growth and competitiveness of the regulated country ( Porter and van der Linde, 1995 ).

In the context of competitiveness and employment, an important characteristic of emissions trading schemes is the choice of the underlying allocation method. There are several studies dealing with this issue: Demailly and Quirion (2007) quantify the impact of the EU ETS onproduction and profitability as two dimensions of competitiveness for the iron and steel industry. They find that competitiveness losses for this sector are small but are significantly determined by pass-through rates and theupdating of allocation rules. While emissions-based updating should be avoided as it creates perverse investmentincentives, output-based updating has ambiguous competitiveness effects—softening production losses, but reducing the likely gains in earnings before interests, taxes, debt and
amortization. Bo  ̈ hringer and Lange (2005) investigate the trade-off between compensation and economic efficiency for output- and emissions-based allocation rules in an international emissions trading scheme. They find that the output-based rule not only induces substantially lower efficiency losses than the emissions-based rule, but also performs better in ameliorating adverse production and employment effects for energy-intensive industries. Fischer and Fox (2007) present a welfare analysis of alternative
emissions allocation rules within a domestic US emissions trading scheme, focusing on sectoral and international leakage as generated by restricted sectoral coverage of domestic ETS and unilateral action. They find that, givendomestic and international leakage, output-based allocation of emissions permits to the covered sectors is preferable to auctioned permits in welfare terms, even when allowing for pre-existing tax distortions. Moreover, grandfathered permits generate the highest welfare costs of emissions regulation.Our empirical literature review focuses on competitiveness, as the empirical literature on employment effects of environmental regulation is rather scant. One exception is Golombek and Raknerud (1997) , who empirically assessthe employment effects of imposing environmental standards on polluting firms. Using Norwegian data they find that, for two out of three manufacturing sectors, firms under strict environmental regulations had a higher tendency to increase employment and a lower tendency
to exit than firms under weak or no environmental regulation. Empirical analysis of the effects of environmental regulation on competitiveness or, more general, economic performance of firms or sectors is rather rare, too, as truly 

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N. Anger, U. Oberndorfer / Energy Policy 36 (2008) 12–22 13

### exogenous measures are often barely accessible. Pickman (1998)

, Brunnermeier and Cohen (2003) as well as Jaffe and Palmer (1997) use US Pollution and Abatement Costs and Expenditures (PACE) as a proxy of the stringency of environmental regulation in order to test for the innovation effects of US industries. However, such costs may depend on other factors such as the response to regulation, as well as the right measurement and exact self-report of firms and industries. Therefore, it is unclear whether compliance costs under- or overstate true regulation costs ( Brunnermeier and Cohen, 2003 ). Pickman (1998) as well as Brunnermeier and Cohen (2003) find evidence that those costs positively affect innovation, while the results of Jaffe and Palmer (1997) do not confirm such causal relationship. What is more, it is controversial if a positive effect ofenvironmental regulation on innovation (or even environmental innovation) would imply a positive competitiveness record of environmental regulation, as e.g. opportunity costs (e.g. other investment or conventional innovations that have not been realized due to the burden of regulation costs) are neglected in such a setting. Such problems do not arise in event studies on environmental regulation. Such studies measure the impact of environmental regulation on stock returns of firms (possibly) affected. They often only compute short-term financial market reactions, however. Furthermore, they hinge on the assumptions of efficient financial markets and of no anticipation of regulation by the market actors, which may often be very crucial for the interpretation of the results computed. Butler and McNertney (1991) consider the effect of elections, namely the 1982 state-wide gubernatorial elections in six US states. These states were
identified as those where the election results were uncertain and expected to affect environmental regulation for energy utilities. The study shows that in those states in which the victory of a democratic governor was most unpredictable significantly negative cumulative abnormal returns arise. Blacconiere and Northcut (1997) consider the impact of the US Superfund Amendments and Reauthorization Act (SARA) of 1986 on stock returns for corporations fromthe chemical industry, finding significant negative cumulative abnormal returns only for 17 out of 26 SARA-related events analyzed. Two more recent studies consider the
effect of the US Clean Air Act Amendments of 1990 on stock returns for energy utilities ( Diltz, 2002 ; Kahn and Knittel, 2003 ). Both studies cannot show sharp financial
market reactions. Oberndorfer and Ziegler (2006) find that the German phasing out of nuclear energy (similarly to Butler and McNertney (1991) , measured by the victory of the acting government with participation of the Green party in the 2002 German Federal Elections) had at least no general negative short- and mid-horizon effect on the economic performance of energy corporations. As far as the EU ETS is concerned, there are not yet empiricalcontributions available that measure competitiveness impacts of the introduction of and allocation inside of the
scheme. All in all, most of the existing studies find only weak evidence of an effect of environmental regulation on firm performance. Furthermore, all groups of approachestackling the question about performance—competitiveness—impacts of environmental regulation have their idiosyncratic flaws: While innovations do not representan ideal competitiveness indicator and the use of compliance costs as a proxy for regulation is not uncontroversial, most event studies only focus on short-term financial market reactions given environmental regulation.For the EU ETS, no empirical contribution on competitiveness impacts is available yet. Preliminary and descriptive evidence, however, suggests that the scheme is characterized by a relatively generous emissions cap compared with verified emissions.

3. Data and variables

In this section, we present the data basis underlying the emissions allocation within the EU ETS. This is done by firstly giving an overview over (relative) allocation at the national level for all EU ETS countries. In a second step, we present the data basis used for our empirical policy assessment of employment and competitiveness effects
associated with EU ETS relative allowance allocation in Germany.

3.1. EU ETS data

The 2005 allocation data were extracted from the EU Community Transaction Log ( European Union, 2007 ).
The allocation factor measures the allocation of EU
emissions allowances relative to the actual emissions of the respective entity and is calculated as the quotient of
allowances allocated to the verified emissions. The allocation factor thus shows the relationship between the amount of allocated allowances and actual emissions, i.e. an allocation factor larger than 1 suggests that an entity has received allowances that exceed its emissions, while an allocation factor smaller than 1 suggests that the respective entity either has to buy additional emissions allowances or abate some of its emissions in order to comply with EU ETS regulation. In this context, one problem may be that verified emissions do not stem from a pre-EU ETS period (this emissions data is actually not available) but from 2005, and are thus of ex-post nature. Therefore, relative allocation cannot be distinguished from actual early
abatement in 2005 and also the allocation factor has an ex-post character. 3 

### First evidence, however, suggests that abatement in 2005 remained relatively low, so that the allocation factor should be at least a very good indicator for relative allocation ( Ellerman and Buchner, 2006 ). 

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3 Moreover, note that the allocation factor is dependent on factors such as stochastic variations in weather, production, energy prices, or other variables affecting emissions. N. Anger, U. Oberndorfer / Energy Policy 36 (2008) 12–22 14

### Fig. 1 shows the allocation factor, aggregated at the national level and based on disaggregated installation level data from the EU Community Transaction Log, for all EU ETS countries. Our aggregated data rely on 10,276 installations, covering the entire set of identifiable EU ETS installations. It indicates that companies in some countries—e.g. Ireland and the UK—have received fewerallowances than their respective emissions, while companies from other countries have received a large relativeamount of emissions allowances given their actual emissions. Noteworthy in this respect is Lithuania, with its companies having received allowances for more than twice of their actual emissions. For the EU ETS level, the data extracted from the EU Community Transaction Log suggest that, in 2005, the scheme as a whole was in a long position. Furthermore, it already indicates that the relative allowance allocation enormously differs across single entities. This is in line with the findings of Kettner et al. (2007)

.

More specifically, Kettner et al. (2007) , consistently with our calculations, identified Lithuania as the country exhibiting the biggest ‘‘net long’’ position and Ireland and the UK as countries exhibiting the biggest ‘‘net short’’position within the scheme. Additionally to our calculations, however, they provide information on long and short positions at the sector level, which is not in the focus of our analysis.

3.2. German sample

In the framework of an empirical analysis for Germany, we want to assess the impact of relative allocation of EU emissions allowances on competitiveness and employment at the firm level. The econometric analysis can only be conducted within a case study for Germany, as economicvariables that could indicate the development of competitiveness and employment at the firm level up to 2005 were not available to us for all EU countries. Still, the econometric analysis may offer important insights into the economic effects of the EU ETS in Europe as a whole, as Germany is the most important country within the EU ETS, its companies representing about 24% of all allowances allocated. To our knowledge, our approach represents the first ex-post analysis of the economic impacts of the EU ETS. For the purpose of this empirical investigation, EU ETSallocation data stemming from the Community Transaction Log ( European Union, 2007 ) (i.e. the allocation factor, see above) were aggregated at the firm level for Germany. The relative emissions allocation data were subsequently
matched with economic data from the CREDITREFORM database. Sectoral (indicator) variables were generated according to the four-digit NACE industry codes that are
contained in the AMADEUS database. Our sector classification includes the business, electricity, energy, mining, coke & petroleum, pulp & paper, and (other) manufacturing sectors. For more detailed information on the economic, sectoral and emissions data employed in this analysis, please refer to Appendix A.

All in all, given our economic data, 419 German firms covered by the EU ETS could be analyzed in the empirical
framework. Table 1 gives first information on the 2005 data from the Community Transaction Log and the CREDITREFORM database. It shows that, on average, the companies included in the empirical analysis have been, in comparison with all German EU ETS participating firms, relatively highly allocated with EU emissions allowances. While for Germany as a whole the allocation factor is 1.04, for the sample analyzed it is 1.24. Furthermore, the economic data indicate that, in 2005, the decisive year of our analysis, the firms included have, on average, under circumstances of shrinking revenues, reduced their number of employees. More than a quarter of our sample firms stem from the manufacturing sector; mining and coke & petroleum firms, in contrast, are very infrequent here. In all, 153 firms could not be classified in our sectoral classification (for an overview over the sectoral distributions, see Table A1 ).

4. Econometric analysis for Germany

4.1. Estimation approach

An econometric analysis is the only means to empirically measure the impact of relative allocation of EU emissions allowances on competitiveness and employment. In the 

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Allocation factor

0 0.5

1 1.5

2 2.5

Aust r ia Bel

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m

a r k Esto n i a

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F

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Hungary Ireland I t al

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Latv

ia
Li

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embo u r g

Ne

t h erlands Poland

P

o r t u gal

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ak

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Spai n Sweden United Kingdom TO

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Fig. 1. Allocation factors at an aggregate national level for EU ETS countries ( source : European Union, 2007 ; own calculations). N. Anger, U. Oberndorfer / Energy Policy 36 (2008) 12–22 15

### following, we employ a regression analysis in order to test whether the relative allocation (as measured by the allocation factor) had an impact on competitiveness as measured by firm revenues—here: representing the ‘‘ability to sell’’ as one concept of competitiveness 4 

### —and employment of the German firm sample. The related correlations
are shown in Table A2 . As dependent variables we use the firm revenue change in 2005, i.e. revenue 2005 minus revenue 2004, as an indicator of their ability to sell, and firm employment change in 2005, i.e. number of employees 2005 minus number of employees 2004. As it is common for an analysis with cross-sectional firm data and a continuous dependent variable (in both cases), we use ordinary least squares (OLS) in a first step in order to compute our regression results. Still, as lined out in the previous sections, the explanatory variable of our special interest in this analysis, the allocation factor, may be endogenous in such setting. This is due to the fact that its calculation is based on (verified) emissions from 2005 given that historic emission data are not publicly available. However, if revenue and/or employment development in 2005 had an impact on the respective emissions, reverse causality would render our estimation results from OLS
biased and inconsistent. As the most common technical solution in such setting, additionally to OLS, we make useof the instrumental variable technique employing the socalled two-stage least squares estimator (2SLS). Doing this, in the regression equation of our interest (second stage), the possibly endogenous allocation factor is replaced by its fitted values from its (first stage) regression on exogenous variables (so-called instruments). As instruments for the allocation factor, firm data on revenues and employment in differences and levels are available besides sectoral variables that partly more strongly correlate with the allowance factor than the economic variables do. Furthermore, OLS results have been controlled for possibly outlier-driven results using so-called iteratively reweighted least squares (IRLS), the most common ‘‘robust’’ regression method.However, analogously to the OLS results, IRLS regressions may suffer from reverse causality problems. Given this fact (and the fact that our central results on relative allocation are consistent for all techniques applied), IRLSresults are only displayed in Appendix A, and interpretation focuses on OLS and 2SLS.

4.2. Estimation results

The central results of the regression analysis are shown
in Table 2 ( Tables A3 and A4 give more detailed results). In the regression analysis (1) using revenue changes between 2005 and 2004 as a dependent variable, we include, besides the sectoral indicator variables and a constant term, the allocation factor as the explanatory variable of our major interest, as well as revenue
differences 2004

2003, revenue differences 2003

2002, revenues 2003, the number of employees 2003, and the differences of the number of employees 2004

2003 as explanatory (control) variables. Using lagged levels and differences of revenues and employment as explanatory variables, we circumvent possible reverse causality or simultaneity (endogeneity) problems that can arise if the dependent variable has an influence on these explanatory
variables. 5 

### Regression (3) gives the respective 2SLS results, 

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Table 1 Descriptive statistics of German firm data

Variable No. obs. Mean Std. dev. Min Max

Allocation factor 419 1.24 0.57 0.26 5.95 Allowances allocated 419 603,961.5 4,795,754 272 9.02e+07 Verified emissions 419 579,399 4,862,883 50 9.12e+07
Revenues 2005

2004 419

139.43 3982.08

79,678.02 8361.04
Revenues 2004

2003 419 77.01 507.40

320.02 8539.71
Revenues 2003

2002 419

18.92 1917.70

21,191.99 18,451.61 Revenues 2005 419 972.32 4423.90 0.16 56,172.84 Revenues 2004 419 1111.75 7340.71 0.16 135,850.90 Revenues 2003 419 1034.74 7080.48 0.16 131,569.00 No. employees 2005

2004 419

413.08 5367.39

73,336 22,660 No. employees 2004

2003 419 424.90 5328.04

3508 72,712 No. employees 2003

2002 419

80.21 705.73

6899 3850 No. employees 2005 419 2705.22 20,010.84 1 384,723 No. employees 2004 419 3118.29 20,606.48 1 362,063 No. employees 2003 419 2693.39 19,104.49 1 365,571

Note : Revenue data are given in Mio. Euro and is measured in prices of 2000 (GDP market price deflator). Revenues give the value of annual sales of goods and services—including other types of revenue such as dividends, interest, and rent—of the respective company.

4 As our empirical assessment focuses on the EU ETS, we refer to within-EU competitiveness among EU firms here (as opposed to international competitiveness vis-a ` -vis non-EU regions). 5 In contrast to the allocation factor that we instrument in the 2SLS approach, these explanatory variables are incorporated in lagged form instead of being instrumented. Thereby we assume actual lagged relationships between the explanatory and independent variables. N. Anger, U. Oberndorfer / Energy Policy 36 (2008) 12–22 16

### and (5) the IRLS results. From (2), (4), and (6), insignificant explanatory variables (besides the allocation factor) have been eliminated. For OLS and 2SLS, suchelimination of insignificant explanatory variables is supported by an F -test. All in all, our results show a good fit of the econometric model, with an R 2 

### of 84%. According to
the results of the F -test, the null hypothesis of joint insignificance of all explanatory variables can be rejected at the 1% level for any equation. The results, i.e. the parameter estimates of significant variables as well as their significance levels, are relatively robust to the choice of estimation technique as well as to the elimination of insignificant explanatory variables. The main insight of this regression is that we do not find empirical evidence for a significant impact of the relative
allocation of EU emissions allowances on firm revenue development in 2005. From a theoretical emissions-market perspective, a higher relative (grandfathered) allowance allocation induces lower compliance costs of emissions regulation (see e.g. Bo  ̈ hringer et al., 2005 ). Thus, relative allowance allocation and the subsequent trading of emissions permits affect the cash flow of the regulated firms. Clearly, the impacts of environmental regulation on firm revenues, production and employment are more complex and depend on the allocation rule ( Demailly and Quirion, 2007 ). Our estimation results show a positive
coefficient of the allocation factor both in the OLS and 2SLS regressions, which, given large standard errors, does not significantly differ from 0 in all equations presented in
Table A3 . Our results thus suggest that companies that received a relatively high amount of allowances within the allocation process could not, consequently, increase their revenues compared to other German companies within the emissions trading scheme. Besides the sectoral indicator variables that show a highly significant impact on revenue development, which can, for example, be explained bydifferences in sectoral demand, most other control variables do not show significance at any conventional level. An exception to this is the coefficient of the number of employees in 2003 that enters with a negative sign in the equation (with an estimated coefficient of about

0.20), suggesting that firms with a larger working force were less successful in increasing their revenues in 2005. IRLS gives partly different results, but, as indicated above, does not show any significance for the estimated coefficient of the
allocation factor either.

In regression analysis (7) using the changes in the number of employees between 2005 and 2004 as a dependent variable, we include, besides the sectoral indicator variables and a constant term, the allocation factor as the explanatory variable of our major interest, as well as revenues 2004–2003, revenues 2003, the number of employees 2003, the number of employees 2004–2003, and the number of employees 2003–2002 as explanatory (control) variables. As in regression (1)–(6), the use of lagged levels and differences of revenues and employment as explanatory variables is due to the potential problem of endogeneity as well as of assumed lagged relationships (see footnote 4). Here as well, the results are robust to the elimination of insignificantexplanatory variables and show a good fit of the econometric model, with an R 2 

### even slightly higher than in regression (1)–(4) (up to 85%). According to the results of
the F -test, the null hypothesis of joint insignificance of all explanatory variables can be rejected at the 1% level for all approaches used. Regression (9) gives the respective 2SLS results, (11) the IRLS results. From (8), (10), and (12), insignificant explanatory variables (besides the allocation factor) have been eliminated (exclusion is supported by the
F

-test for the 2SLS and IRLS cases). Analogously to the revenue analysis, we did not receive empirical evidence for a significant impact of the relative allocation of EU emissions allowances on the change in (firm level) employment in 2005. For regression (7) (as well as (11) and (12)), the estimated coefficient of the allocationfactor is positive. However, the magnitude of the coefficient is small and is not significant at any conventionallevel. According to economic theory, stringent environmental regulation may induce employment losses if the output effect of regulation (i.e. lower production and employment levels) dominates the substitution effect (i.e. the shift to a higher labour intensity of production). Our estimation results suggest, however, that firms with a lower allocation factor within the trading scheme did not react with worker layoffs on a net basis. In regressions (8)–(10), the sign of the estimated allocation factor coefficient changes (for IRLS, again, it 

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Table 2 Selected regression results for German firm sample

Dep. var. Revenues 2005

2004 (Mio. Euro) No. employees 2005

2004

Regression number/ estimation technique (1) OLS (3) 2SLS (7) OLS (9) 2SLS

Allocation factor 122.14 (110.72) 50,538.58 (42,836.84) 30.23 (119.04)

44,067.80 (48,347.34)
No. obs. 419 419 419 419

R 2 0.84 0.84 0.85 0.85

F

-test ( p -val.) 0.00 0.00 0.00 0.00

Note : (White) robust std. errors in brackets. Results from regressions including the full set of control variables. Detailed results including paramete r estimates of the control variable set (cp. regression numbers), as well as results of regression equations from which insignificant control variable s have been eliminated and the IRLS estimations are given in Appendix A. N. Anger, U. Oberndorfer / Energy Policy 36 (2008) 12–22 17

### is positive in both regressions). However, the coefficients fail to show significance at any conventional level in all equations. In contrast to (1)–(6), the estimated coefficient of the number of employees 2004–2003 is—with a value ofabout 1—very high and negative. The coefficient, significant at the 1% level in each regression, suggests that the lay-off of workers in 2004 had a (similar) negative effect on the change of employment in 2005, i.e. the lay-off of one worker in 2004 resulted in the lay-off of an additional worker in 2005. This may be due to labour market rigidities as well as employment policies of the companies analyzed, provided that suspensions were relatively stable over time (2004–2005). Sectoral indicator variables have a highly significant impact on employment only using OLS. These results are therefore not very robust over the differenteconometric specifications, indicating that sectoral affiliation did not necessarily play a role in employment changes in 2005. Furthermore, the interpretation of the individual sectoral dummies is difficult, as the estimated parameters attribute the deviations of employment changes of the relative sector to those firms that formed part of sectors that were not explicitly modelled. Most other control variables fail to show significance at any conventional level.The effect of revenue as well as of employment development in 2004 on employment development in 2005 is extremely robust with regard to both point estimates and statistical significance. This undermines the findings of a positive relationship between 2004 revenue development and 2005 employment, as well as of a negative relationship between employment development in 2004 and 2005. At least as far as signs and significance of the estimated parameters are concerned, IRLS results resemble 2SLS.

5. Conclusions

This paper empirically investigates the role of the EU Emissions Trading Scheme (EU ETS) for competitiveness and employment at the firm level. We provide an overview over relative allowance allocation within the EU ETS, as well as an econometric analysis for a large sample of German ETS firms in order to assess the economic impacts
associated with emissions allocation under the EU ETS.

Our calculations suggest that the total EU ETS was generally long in 2005. The long position is very large in Lithuania, while other countries were short in emissions allowances. Regarding the competitiveness effects of EUenvironmental regulation, we conduct an econometric expost regression analysis for Germany, which, to our knowledge, is the first of its kind concerning the EU ETS. Following the competitiveness concept ‘‘ability to sell’’, as an empirical indicator for competitiveness we employ firm revenues. As a second economic indicator we use employment levels of the respective firms. Our econometric analysis provides evidence on the fact that
the allowance allocation within the EU ETS framework did not have a significant impact on revenues and employment of regulated German firms. Our results thus suggest that for regulated companies the competitiveness impacts of the emissions allocation within the first phase of the EU ETS were not pronounced. This finding could be due to the low overall burden of emissions regulation within the EU ETS. Some disclaimers apply to these results. First, it is definitely very early to conduct an ex-post analysis for the EU ETS. In this respect, it is possible that competitiveness effects of this regulation could occur after 2005. Consistent economic firm data at a European level for 2005 or later were not available to us, so that our—first, and, due to the small dataset, basic—econometric analysis could only be performed within a case study for Germany, the most important country within the EU ETS according to the verified emissions. Furthermore, ex-post analyses do not have to be restricted to revenues and employment, although these are definitely two factors of great interest in the context of environmental regulation. Other measuresof interest may be, e.g., innovation, profits, and international trade effects that could not be tackled within the analysis conducted here. All in all, future empirical research in many directions is needed to complement these first ex-post insights into the effects of regulation according
to the EU ETS.

Acknowledgments

The authors are grateful to two anonymous referees andVictoria Alexeeva-Talebi for helpful comments and suggestions. We would like to thank Ingrid Amann, Falk
Bra  ̈ uning, Thorsten Doherr, and Thomas Pfeiffer forvaluable data support. Funding by the European Commission under the framework contract B2/ENTR/05/091-FC is gratefully acknowledged.

Appendix A. Data on EU ETS allocation

Our analysis is based on data on approximately 12,000 installations being covered by the EU ETS legislation. Each installation has an Operator Holding Account in its national registry to which the allowances are submitted, and each member state of the European Union has an obligation to interlink the national registry with the 

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Table A1 Sectoral distribution of sample firms

Sector Frequency: no. sample firms (%)

Mining 9 (2) Electricity 55 (13) Energy 29 (7) Business 20 (5) Pulp & paper 43 (10) Coke & petroleum 8 (2) Other manufacturing 102 (24) Other 153 (37) Total 419 (100) N. Anger, U. Oberndorfer / Energy Policy 36 (2008) 12–22 18

# ARTICLE IN PRESSTable A2Correlation analysis for German firm sampleAllocationfactorRevenues20052004Revenues20042003Revenues2005No.employees20052004No.employees20042003No.employees2005Mining Electricity Energy Business Pulp &paperCoke &petroleumOthermanufacturingAllocationfactor1.00Revenues200520040.01 1.00Revenues200420030.030.36 1.00Revenues20050.020.52 0.57 1.00No.employees200520040.030.220.03 0.89 1.00No.employees2004–20030.02 0.06 0.43 0.35 0.19 1.00No.employees20050.010.90 0.56 0.79 0.02 0.02 1.00Mining 0.03 0.02 0.01 0.04 0.03 0.030.02 1.00Electricity 0.17 0.020.060.06 0.03 0.030.040.13 1.00Energy 0.02 0.010.040.04 0.02 0.020.030.090.11 1.00Business0.030.030.030.04 0.01 0.010.020.070.090.06 1.00Pulp &paper0.05 0.010.040.06 0.030.030.040.050.130.090.08 1.00Coke &petrol.0.040.010.020.01 0.010.010.020.020.050.040.03 y0.05 1.00Othermanuf.0.04 0.040.040.05 0.030.050.040.080.220.150.120.190.08 1.00Note: 419 observations. Pearson’s correlation coefficients for the respective variable pairs are given. Lagged levels and differences of higher order for the revenue and employment variables are omittedfor brevity. N. Anger, U. Oberndorfer / Energy Policy 36 (2008) 12–22 19

### EU-wide databank EU Community Transaction Log. TheCommunity Transaction Log’s web pages contain information on allowances that have been allocated in accordance with the final National Allocation Plans, verified emissions, surrendered allowances and compliance status for all installations in member states with registries. We assessed the emissions data from the Community Transaction Log in two steps:



#  data extraction from the Community Transaction Log

and data processing; and



#  aggregation of installation-level data on the sectoral and

national levels.

A.1. The AMADEUS database

Besides the emissions data from the Community Transaction Log, economic data are of great importance for assessing the competitiveness effects of the EU ETS. AMADEUS (Analyse Major Databases from European Sources) is a comprehensive, pan-European database containing economic and financial information on 9million public and private companies. AMADEUS combines data from over 30 specialist sources and provides data in a comparable format. It is created and produced by Bureau van Dijk. In this analysis, sectoral information for our German firm sample is based on the four-digit NACE (industry) codes of the firms provided by AMADEUS. According to this, we have created several indicator variables that are given the value 1 for a company that forms part of the respective industry, and 0 otherwise. The indicator variables are ‘‘electricity’’ (13% of the sample firms; NACE code between 4000 and 4020, ‘‘production and distribution of electricity’’), ‘‘energy’’ (7% of the sample firms; NACE code between 4020 and 4500, ‘‘manufacture of gas; distribution of gaseous fuels through mains’’, ‘‘steam and hot water supply’’, ‘‘collection, purification and distribution of water’’), ‘‘pulp & paper’’ (10% of the sample firms; NACE code between 2100 and 2200, industry subsection ‘‘manufacture of pulp, paper, and paper products’’), ‘‘mining’’ (10% of the sample firms; NACE code between 1000 and 1500, industry subsection ‘‘mining and quarrying’’), ‘‘coke & petroleum’’ (2% of the sample firms; NACE code between 2300 and 2400, ‘‘manufacture of coke, refined petroleum products and nuclear fuel’’), ‘‘other manufacturing’’ (24% of the sample firms; NACE code between 2600 and 3700, manufacture of 

# ARTICLE IN PRESS

Table A3 Regression results on 2005 revenue development for German firm sample

Dep. var. (1) OLS (2) OLS (3) 2SLS (4) 2SLS (5) IRLS (6) IRLS

Revenues
2005

2004 (Mio. Euro) Revenues
2005

2004 (Mio. Euro) Revenues
2005

2004 (Mio. Euro) Revenues
2005

2004 (Mio. Euro) Revenues
2005

2004 (Mio. Euro) Revenues
2005

2004 (Mio. Euro)

Allocation factor 122.14 (110.72) 83.72 (117.58) 50,538.58 (42,836.84) 1552.76 (2140.87)

0.33 (0.75)

0.36 (0.76)

Revenues
2004

2003 (Mio. Euro) 0.03 (0.66) – 0.01 (1.25) – 0.22*** (0.00) 0.21*** (0.00)

Revenues
2003

2002 (Mio. Euro) 0.01 (0.18) –

0.30 (0.84) –

0.02*** (0.00)

0.02*** (0.00)

Revenues 2003 (Mio. Euro) 0.05 (0.18) – 1.24 (1.72) – 0.03*** (0.00) 0.03*** (0.00)

No. employees
2003 0.18*** (0.07)

0.19*** (0.03)

0.66 (0.64)

0.19*** (0.03) 0.00*** (0.00) –

No. employees
2004–2003 0.15 (0.11) – 0.34** (0.14) – 0.00*** (0.00) –

Mining

225.89 (255.96) – 338.81 (6456.00) – 6.21** (2.99) 5.58* (2.95) Electricity

483.89*** (134.43)

471.20*** (112.97)

8980.94 (11737.64) – 7.61*** (1.39) 7.07*** (1.30) Energy

491.44*** (128.93)

488.11*** (105.70)

3335.58 (5964.47)

443.98** (212.50) 4.92*** (1.76) 4.17** (1.70)
Business

1083.73** (529.95)

1085.56* (598.66) 5823.88 (6566.77) – 1.28 (2.08) – Pulp & paper

482.81*** (116.57)

478.65*** (100.03) 2020.76 (3730.35)

293.81* (155.56) 0.39 (1.51) – Coke & petroleum

836.93* (479.15)

878.94* (469.98) 5482.63 (7324.90) – 7.61** (3.15) 8.07*** (3.15)
Other manufacturing 181.27 (192.49) – 423.98 (3146.08) – 1.38 (1.12) –

Constant term 436.91*** (152.38) 475.14*** (164.28)

61,101.61 (51,916.09)

1431.85 (2611.28)

3.17*** (1.18)

2.44** (1.05)

No. obs. 419 419 419 419 413 414

R 2 0.84 0.80 0.84 0.82 – –

F

-test ( p -val.) 0.00 0.00 0.00 0.00 0.00 0.00

F

-test on excl. exp. var. ( p -val.) – 0.50 – 0.29 – 0.00

Note : Std. errors in brackets (OLS, 2SLS: white robust std. errors). *, ** and *** show significance at the 10%, 5%, and 1% levels, respectively. N. Anger, U. Oberndorfer / Energy Policy 36 (2008) 12–22 20

### non-metallic mineral products, basic metals and fabricated metal products, machinery and equipment, electrical andoptical equipment, transport equipment, other manufacturing), and ‘‘business’’ (5% of the sample firms; NACE code between 7000 and 7500, section ‘‘real estate, renting, and business activities’’).

A.2. The CREDITREFORM database

This database is a financial and economic database that includes information of sales and employment of German firms. It is the most comprehensive database on German firms, containing a random sample of 20,000 solvent and 1000 insolvent firms in Germany. Given a consistent form identification number, it is coherent with the AMADEUS
database. From the CREDITREFORM database we use levels and differences from firm revenue and employment data between 2002 and 2005; from AMADEUS we use generated sectoral indicator variables (see above). Those
data have been matched with the allocation factor (allowances allocated divided by verified emissions) from the Community Transaction Log. This has been conductedby supplementing allocation data that have been aggregated at the firm level with AMADEUS and CREDITREFORM data. The main criteria for this database matching were the respective company names and addresses. Thematching results have been carefully checked for consistency reasons ( Tables A1–A4 ).

References

Alexeeva-Talebi, V., Anger, N., 2007. Developing supra-European Emissions Trading Schemes: an efficiency and international trade analysis. ZEW Discussion Paper No. 07-038, Mannheim. Anger, N., 2007. Emissions trading beyond Europe: linking schemes in a post-Kyoto world. Energy Economics doi:10.1016/j.eneco.
2007.08.002

. Balassa, B., 1962. Recent developments in the competitiveness of American industry and prospects for the future. In: Joint Economic Committee—Congress of the United States (Eds.), Factors Affecting the United States Balance of Payments. Washington, DC, pp. 27–54. Blacconiere, W.G., Northcut, W.D., 1997. Environmental information and market reactions to environmental legislation. Journal of Accounting, Auditing and Finance 12, 149–178. 

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Table A4 Regression results on 2005 employment development for German firm sample

Dep. var. (7) OLS (8) OLS (9) 2SLS (10) 2SLS (11) IRLS (12) IRLS

No. employees
2005

2004 No. employees
2005

2004 No. employees
2005

2004 No. employees
2005

2004 No. employees
2005

2004 No. employees
2005

2004

Allocation factor 30.23 (119.04)

12.00 (62.79)

44,067.80 (48347.34) 8126.44 (9355.91) 0.49 (2.04) 0.40 (2.21)

Revenues 2004

2003 (Mio. Euro) 4.58*** (0.71) 4.29*** (0.70) 4.43*** (0.93) 4.38*** (0.62) 0.02*** (0.01) 0.02*** (0.01) Revenues 2003 (Mio. Euro) 0.05 (0.28) –

0.71 (1.38) – 0.00** (0.00) – No. employees 2003

0.05 (0.11) – 0.22 (0.52) – 0.00*** (0.00)

0.02*** (0.00) No. employees 2004

2003

1.00*** (0.04)

1.01*** (0.04)

1.15*** (0.16)

1.05*** (0.05)

1.00*** (0.00)

1.01*** (0.00) No. employees 2003

2002

0.85 (0.80) –

1.40 (1.98) – 0.00 (0.00) – Mining 486.99 (316.47) 373.76* (193.43) 323.98 (5404.09) –

4.86 (8.01) – Electricity 562.16* (307.10) 431.50** (183.63) 8032.52 (11732.76) – 0.62 (3.76) –

Energy 576.89** (280.85) 460.36*** (178.56) 3036.61 (5619.92) – 9.57** (4.77) –

Business 555.29* (315.56) 394.59* (228.14)

4859.04 (6423.88) – 6.85 (5.62) –

Pulp & paper 565.95* (320.64) 432.77** (186.05) 1540.41 (3394.63) – 0.62 (4.07) –

Coke & petroleum 624.19** (273.35) 512.96*** (192.22) 6473.68 (7243.06) – 11.07 (8.56) –

Other manufacturing 285.82 (365.37) –

235.53 (2741.93) – 4.31 (3.02) –

Constant term

642.96* (388.52) 467.25** (198.56) 53,153.02 (58,410.43) 9668.14 (11,335.02) 3.32 (3.17) 4.37 (3.03)

No. obs. 419 419 419 419 415 416

R 2 0.85 0.83 0.85 0.35 – –

F

-test ( p -val.) 0.00 0.00 0.00 0.00 0.00 0.00

F

-test on excl. exp. var. ( p

val.) – 0.00 – 0.96 – 0.21

Note : Std. errors in brackets (OLS, 2SLS: white robust std. errors). *, ** and *** show significance at the 10%, 5%, and 1% levels, respectively. N. Anger, U. Oberndorfer / Energy Policy 36 (2008) 12–22 21Bo  ̈ hringer, C., Lange, A., 2005. Economic implications of alternative

allocation schemes for emission allowances. Scandinavian Journal of Economics 107, 563–581.
Bo

 ̈ hringer, C., Hoffmann, T., Lange, A., Lo  ̈ schel, A., Moslener, U., 2005. Assessing emissions regulation in Europe: an interactive simulation approach. Energy Journal 26, 1–22. Brunnermeier, S.B., Cohen, M.A., 2003. Determinants of environmental innovation in US manufacturing industries. Journal of Environmental Economics and Management 45, 278–293. Butler, M.R., McNertney, E.M., 1991. Election returns as a signal of changing regulatory climate. Energy Economics 13, 48–54. Demailly, D., Quirion, P., 2007. European Emission Trading Scheme and competitiveness: a case study on the iron and steel industry. Energy
Economics doi:10.1016/j.eneco.2007.01.020

. Demailly, D., Quirion, P., 2006. CO 2 abatement, competitiveness and leakage in the European cement industry under the EU ETS: grandfathering versus output-based allocation. Climate Policy 6,
93–113. Diltz, J.D., 2002. US Equity Markets and Environmental Policy: The case of electric utility investor behaviour during the passage of the Clean
Air Act Amendments of 1990. Environmental and Resource Economics 23, 379–401. Ellerman, D., Buchner, B., 2006. Over-allocation or abatement? A preliminary analysis of the EU ETS based on the 2005 emissions data. FEEM Nota di Lavoro 139.2006, Venice. European Union, 2003. Directive establishing a scheme for Greenhouse Gas Emission Allowance Trading within the Community and Amending Council Directive 96/61/EC. European Commission,
Brussels. Available at: / http://eur-lex.europa.eu/LexUriServ/site/en/ oj/2003/l_275/l_27520031025en00320046.pdf S

. European Union, 2004. Directive 2004/101/EC, amending Directive 2003/ 87/EC establishing a scheme for greenhouse gas emission allowance trading within the Community, in respect of the Kyoto Protocol’s project mechanisms. European Commission, Brussels. Available at: / http://europa.eu.int/eur-lex/lex/LexUriServ/site/en/oj/2004/l_338/ l_33820041113en00180023.pdf S

. European Union, 2007. Community Independent Transaction Log.
Available at: / http://ec.europa.eu/environment/climat/emission/citl_
en.htm S

. Fischer, C., Fox, A.K., 2007. When revenue recycling isn’t enough: permit allocation strategies to minimize intra- and international emissions leakage. Paper presented at the 15th Annual Conference of the European Association of Environmental and Resource Economists, Thessaloniki, Greece, June 28, 2007. Golombek, R., Raknerud, A., 1997. Do environmental standards harm manufacturing employment? Scandinavian Journal of Economics 99,
29–44. Jaffe, A.B., Palmer, K., 1997. Environmental regulation and innovation: a panel data study. Review of Economics and Statistics 79, 610–619. Kahn, S., Knittel, C.R., 2003. The impact of the Clean Air Act
Amendments of 1990 on electric utilities and coal mines: evidence from the stock market. Working Paper, Department of Finance and Economics, Boston University. Kemfert, C., Kohlhaas, M., Truong, T., Protsenko, A., 2005. The environmental and economic effects of European Emissions Trading. DIW Discussions Papers 533, Berlin. Kettner, K., Ko  ̈ ppl, A., Schleicher, S.P., Themius, G., 2007. Stringency and distribution in the EU Emissions Trading Scheme—the 2005 evidence. FEEM Nota di Lavoro 22.2007, Venice. Klepper, G., Peterson, S., 2004. The EU Emissions Trading Scheme, allowance prices, trade flows, competitiveness effects. Kiel Working Paper no. 1195, Kiel Institute for World Economics, Kiel. Neuhoff, K., Keats Martinez, K., Sato, M., 2006. Allocation, incentives and distortions: the impacts of the EU ETS emissions allowance allocations to the electricity sector. Climate Policy 6, 73–91. Oberndorfer, U., Rennings, K., 2007. Costs and competitiveness effects ofthe European Union Emissions Trading Scheme. European Environment 17, 1–17. Oberndorfer, U., Ziegler A., 2006. Environmentally oriented energy policy and stock returns: an empirical analysis. ZEW Discussion Paper No. 06-079, Mannheim.Peterson, S., 2006. The EU Emissions Trading Scheme and its competitiveness effects upon European business. Results from the CGE model DART. Mimeo, Kiel Institute for World Economics, Kiel. Pickman, H.A., 1998. The effect of environmental regulation on environmental innovation. Business Strategy and the Environment 7,
223–233. Porter, M., van der Linde, C., 1995. Green and competitive: ending the stalemate. Harvard Business Review September–October, 120–134. UNFCCC, 1997. Kyoto Protocol to the United Nations FrameworkConvention on Climate Change. United Nations Framework Convention on Climate Change, FCCC/CP/L.7/Add1, Kyoto. 

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