Energy Policy 36 (2008) 12–22

Viewpoint

Firm performance and employment in the EU emissions trading scheme:

An empirical assessment for Germany

Niels Anger1, 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 emissions

reduction 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 CO2

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 directive

linking 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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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—the

remaining 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). The

sectoral 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 the

national 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-called

Porter 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 on

production 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 the

updating of allocation rules. While emissions-based updating should be avoided as it creates perverse investment

incentives, 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, given

domestic 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 assess

the 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 of

environmental 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 from

the 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 empirical

contributions 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 approaches

tackling the question about performance—competitiveness—impacts of environmental regulation have their

idiosyncratic flaws: While innovations do not represent

an 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.

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

allowances than their respective emissions, while companies from other countries have received a large relative

amount 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 economic

variables 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 ETS

allocation 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

Austria

Belgium

Cyprus

Czech Republik

Denmark

Estonia

Finland

France

Germany

Greece

Hungary

Ireland

Italy

Latvia

Lithuania

Luxembourg

Netherlands

Poland

Portugal

Slovakia

Slovenia

Spain

Sweden

United Kingdom

TOTAL

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 competitiveness4

—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 use

of 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), IRLS

results 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 20042003, revenue differences 20032002,

revenues 2003, the number of employees 2003, and the

differences of the number of employees 20042003 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 20052004 419 139.43 3982.08 79,678.02 8361.04

Revenues 20042003 419 77.01 507.40 320.02 8539.71

Revenues 20032002 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 20052004 419 413.08 5367.39 73,336 22,660

No. employees 20042003 419 424.90 5328.04 3508 72,712

No. employees 20032002 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.

16 N. Anger, U. Oberndorfer / Energy Policy 36 (2008) 12–22
and (5) the IRLS results. From (2), (4), and (6),

insignificant explanatory variables (besides the allocation

factor) have been eliminated. For OLS and 2SLS, such

elimination 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 R2 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 by

differences 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 insignificant

explanatory variables and show a good fit of the econometric model, with an R2 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 allocation

factor is positive. However, the magnitude of the coefficient is small and is not significant at any conventional

level. 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 20052004 (Mio. Euro) No. employees 20052004

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

R2 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 parameter

estimates of the control variable set (cp. regression numbers), as well as results of regression equations from which insignificant control variables 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 of

about 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 different

econometric 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 EU

environmental 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 measures

of 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 and

Victoria Alexeeva-Talebi for helpful comments and suggestions. We would like to thank Ingrid Amann, Falk

Bra ̈uning, Thorsten Doherr, and Thomas Pfeiffer for

valuable 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

ARTICLE IN PRESS

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)

18 N. Anger, U. Oberndorfer / Energy Policy 36 (2008) 12–22
Table A2Correlation analysis for German firm sample

Allocationfactor

Revenues20052004

Revenues20042003

Revenues2005

No.employees20052004

No.employees20042003

No.employees2005

Mining Electricity Energy Business Pu

pa

Allocationfactor

1.00

Revenues20052004

0.01 1.00

Revenues20042003

0.03 0.36 1.00

Revenues2005

0.02 0.52 0.57 1.00

No.employees20052004

0.03 0.22 0.03 0.89 1.00

No.employees2004–2003

0.02 0.06 0.43 0.35 0.19 1.00

No.employees2005

0.01 0.90 0.56 0.79 0.02 0.02 1.00

Mining 0.03 0.02 0.01 0.04 0.03 0.03 0.02 1.00

Electricity 0.17 0.02 0.06 0.06 0.03 0.03 0.04 0.13 1.00

Energy 0.02 0.01 0.04 0.04 0.02 0.02 0.03 0.09 0.11 1.00

Business 0.03 0.03 0.03 0.04 0.01 0.01 0.02 0.07 0.09 0.06 1.00

Pulp &paper

0.05 0.01 0.04 0.06 0.03 0.03 0.04 0.05 0.13 0.09 0.08 

Coke &petrol.

0.04 0.01 0.02 0.01 0.01 0.01 0.02 0.02 0.05 0.04 0.03 y0

Othermanuf.

0.04 0.04 0.04 0.05 0.03 0.05 0.04 0.08 0.22 0.15 0.12 0

Note: 419 observations. Pearson’s correlation coefficients for the respective variable pairs are given. Lagged levels and differences of higher order for the revenue a

for brevity.
EU-wide databank EU Community Transaction Log. The

Community 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 9

million 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

20052004 (Mio.

Euro)

Revenues

20052004 (Mio.

Euro)

Revenues

20052004 (Mio.

Euro)

Revenues

20052004 (Mio.

Euro)

Revenues

20052004 (Mio.

Euro)

Revenues

20052004 (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

20042003 (Mio.

Euro)

0.03 (0.66) – 0.01 (1.25) – 0.22*** (0.00) 0.21*** (0.00)

Revenues

20032002 (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

R2 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.

20 N. Anger, U. Oberndorfer / Energy Policy 36 (2008) 12–22
non-metallic mineral products, basic metals and fabricated

metal products, machinery and equipment, electrical and

optical 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 conducted

by 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. The

matching results have been carefully checked for consistency reasons (Tables A1–A4).

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