Linn Nordin Spring semester 2018 Master’s thesis, 15 ECTS Master’s program in Economics

Firm Competitiveness and the EU ETS An empirical study using Swedish firm-level data

Linn Nordin

Firm Competitiveness and the EU ETS

- An empirical study using Swedish firm-level data -

Linn Nordin, 2018

Abstract

This paper sets out to study the effect of the EU ETS on economic and financial

indicators through which the impacts on firm competitiveness may be observed.

Examining labor productivity, relative profit before tax, turnover, and value added after

the introduction of the EU ETS offers a method for evaluating the effects of the

program on economic performance. The study is based on a unique dataset of Swedish

firms covering the years 2003-2008. To assess the causal impact of the EU ETS on firm

performance the propensity score matching method is combined with the difference-in-

difference approach. The findings suggest a positive and significant impact on labor

productivity, turnover and value added, while a negative and significant effect can be

observed for relative profit before tax.

Keywords: cap-and-trade, economic performance, EU ETS, difference-in-difference,

firm competitiveness, firm-level data, propensity score matching

Acknowledgements I would like to thank my supervisor, Jūratė Jaraitė-Kažukauskė, for her inspiration,

support, and expertise. In addition, I would like to thank Tommy Lundgren for

providing the dataset used in this paper and the attendants of the thesis seminar for their

valuable feedback and comments.

Contents

1 Introduction 1

2 Background 2

2.1 The EU ETS ............................................................................................... 2

2.2 The EU ETS in Sweden ............................................................................. 4

3 Literature Review 4

4 Empirical Methodology 6

5 Data 9

6 Results 11

6.1 Propensity Score and Matching ............................................................... 11

6.2 Estimation of Average Treatment Effects based on Propensity Scores .. 13

6.3 Testing the Common-Trends Assumption ............................................... 15

7 Discussion and Concluding Remarks 15

References 18

Appendix A: A Placebo DID Matching Test 20

1

1 Introduction

The EU Emissions Trading System (EU ETS) is the world’s first and so far largest

emissions trading scheme for greenhouse gases (GHG). The EU ETS is an essential

instrument of the European climate policy, aiming to reduce GHG emissions cost-

effectively (Convery et al., 2008). Since the introduction of the EU ETS, the program

has received much attention worldwide because of its achievements. The program has

not just succeeded in reducing CO2 emissions cost-effectively, but it has also managed

to create a multinational trading system for the right to pollute (Hanley et al., 2007).

Although the cost-effectiveness of the EU ETS is widely acknowledged, the program

has led to a debate among industrial emitters and policymakers regarding the policy’s

potential adverse effects on the competitiveness of EU firms in the global market. The

discussions concern whether emission-intensive firms in the EU will relocate to regions

with no or less carbon restrictions. A relocation of EU firms could increase the risks of

losing jobs and decreasing market shares relative to companies outside the EU (Chan et

al., 2013; Marin et al., 2017).

Emissions trading systems have as a policy instrument the possibility to achieve

significant emissions reduction efforts. Hopefully, can a trading scheme, like the EU

ETS, inspire countries outside the program to future cost-effective mitigation efforts. To

gain an understanding of how participation in the EU ETS affects economic

performance is therefore of interest to policymakers that aim to develop an emissions

trading program. Many empirical studies have investigated the effects of the EU ETS on

firm performance. Still, these studies have reached various conclusions regarding the

impact of the policy on economic performance (e.g., Abrell et al., 2001; Anger &

Oberndorfer, 2008; Chan et al., 2013; Jaraitė & Di Maria, 2016; Marin et al., 2017; Yu,

2011).

This paper aims to study the effect of the EU ETS on economic and financial indicators

through which the impacts on firm competitiveness may be observed. The study is

based on a panel of Swedish firms covering the years 2003-2008, focusing on firms

whose primary activity is in mining and quarrying and manufacturing. Comparisons of

labor productivity, relative profit before tax, turnover, and value added after the

2

introduction of the EU ETS offers a method for evaluating the effects of the program on

firm performance. To evaluate the causal impact of the EU ETS on firm

competitiveness the propensity score matching method is combined with the difference-

in-difference (DID) approach. As discussed above, many empirical studies have

investigated the impacts of the EU ETS on firm performance, however, to the best of

our knowledge this study differs from the papers mentioned in one important way. This

study is one of the first to use a unique dataset including information on CO2 emissions

when evaluating the causal effect of the EU ETS on firm competitiveness. By using the

unique dataset enables us to provide better matches when applying the propensity score

matching method.

Contrarily to the concerns raised by industrial emitters and policymakers regarding the

negative impacts of environmental policy on economic performance, the results from

this study suggest a positive and significant effect on labor productivity, turnover, and

value added. Still, a negative and significant impact can be observed for relative profit

before tax.

The structure of the paper is as follows. Section 2, provides a background of the EU

ETS. Section 3 reviews earlier literature, while Section 4 contains the empirical

methodology. Section 5 describes the data. Section 6 presents and discusses the

empirical results. Section 7 includes a final discussion and concluding remarks.

2 Background

2.1 The EU ETS The European Emissions Trading Directive was adopted in 2003 and launched in 2005,

with the aim to establish a market for the right to pollute as well as a market price for

this entitlement while reducing GHG emissions cost-effectively (Convery et al., 2008).

Today the system operates in 28 EU countries plus Iceland, Liechtenstein, and Norway,

covering approximately 13,000 power stations and manufacturing plants. As from 2012,

the system also includes aviation (Swedish EPA, 2017).

3

The EU ETS is a cap-and-trade system aiming to reduce GHG emissions by 21 percent

below 2005 levels before 2020. The system is an essential instrument of European

climate change policy and works in the following way. First, each Member State (MS)

sets an annual cap limiting total CO2 emissions. The government then divides the cap

into individual allowances to emit one tonne of CO2 emissions and distributes them

between firms covered by the system. Allowances can be auctioned or allocated for free

(grandfathering). When the distribution of permits is complete, firms can start trading

emission allowances with each other. The participating firms are allowed to purchase

extra allowances if they emitted more, selling or saving allowances if they emitted less

(Convery et al., 2008).

The development of the EU ETS can be divided into different phases. The first trading

period (2005–2007) can be seen as a trial phase to gain experience as to achieve CO2

reductions. Industrial plants were given a free allocation of allowances under the

national allocation plants (NAPs) during the first and second (2008–2012) trading

periods, based on historical emissions and production forecasts (Convery et al., 2008).

Many MS, including Sweden, had a surplus of allowances during the first trading

period. As a consequence, the European Commission decided to reduce the number of

allowances by 6.5 percent in the second trading period. In the third phase (2013–2020)

an EU-wide cap on emissions was introduced, which will be reduced by 1.74 percent

each year. In this period, there will also be a progressive shift from free allocation of

allowances towards auctioning (European Commission, 2016).

In 2005 the average price of allowances was EUR 22 per tonne of CO2. In April 2006,

emissions data for 2005 was released, which showed that many MS had a surplus of

allowances. Because of this, the price fell sharply (almost zero by mid-2006) and

remained very low during the rest of the first period (Convery et al., 2008). During the

second trading period, the price of allowances was relatively more stable compared to

the first phase. However, the price fell from about EUR 30 per tonne in 2008 to less

than EUR 10 per tonne in 2012 (Marin et al., 2017). In April 2018, the price of

allowances was around EUR 13 per tonne of CO2 (Market Insider, 2018).

4

2.2 The EU ETS in Sweden

Since the oil crisis in the 1970s, the use of oil as energy supply has decreased in

Sweden, while the use of clean energy power (e.g., hydropower and nuclear power) has

increased. Between 1970 and 1998, the Swedish energy sector has successfully

succeeded in reducing CO2 emissions by approximately 40 percent (Yu, 2011).

The Swedish environmental policy is based on 16 environmental quality objectives (one

of the objectives is to reduce climate impact). Besides the environmental targets, the

Swedish climate strategy consists of instruments, follow-up and evaluation of the

development towards established targets. There are different instruments available for

policymakers aiming to combat climate change. To reduce CO2 emissions, Swedish

policymakers decided to introduce a CO2 tax in 1991 as a supplement to the energy tax.

CO2 taxes are levied on fuels that are used as motor fuels or for heating purposes. To

support the expansion of electricity production and effectively control emissions, the

Tradable Green Certificate (TGC) was introduced in the energy sector in 2003 and is

planned to be in effect until 2030 (Swedish EPA, 2012).

Sweden has been included in the EU ETS since the program launched in 2005. In 2012,

the trading scheme included 853 Swedish installations, corresponding to 264 firms1

(Coria & Jaraitė, 2018). Before the introduction of the EU ETS, Swedish firms needed

to pay the CO2 tax for the right to emit CO2 emissions. However, after the program

launched in 2005, the sectors covered by the EU ETS both needed to buy EU

allowances (EUA – unit price for one tonne of CO2) and pay the CO2 tax for the right to

emit CO2 emissions (Löfgren et al., 2013). Since 2008, Swedish ETS firms are entirely

or partially relieved of the energy tax and the CO2 tax for certain fuels (Swedish Tax

Agency, 2018).

3 Literature Review

Many empirical studies analyze the effects of the EU ETS on firm economic

performance and competitiveness. Earlier literature covering the subject differ in terms

1 Some of the firms covered by the EU ETS owned several installations.

5

of empirical strategy, sector and geographical extent and the selection of indicators they

evaluate.

Some academic studies cover a more extensive selection of European countries. Abrell

et al. (2011) assess the effect of the EU ETS at a firm level on profit margins, value

added, and employment during 2005-2008. Their results do not show any significant

impact on the participating firms’ profits, employment or added value during the first

trading period and the beginning of the second trading period. By using a panel of 5,873

firms in ten European countries during 2001–2009, Chan et al. (2013) analyze the

impact of the EU ETS on material costs, employment, and revenues in the power,

cement, iron and steel sectors. The authors find no significant effect of the EU ETS on

any of the three variables in cement and iron and steel sectors, but a significant positive

impact on material costs and revenues in the power sector. Additionally, their findings

do not substantiate any concerns over carbon leakage, job losses, and industry

competitiveness. Based on an extensive panel of European firms, Marin et al. (2017)

study the effect of the EU ETS on a broader set of indicators of economic performance

in its first and second trading periods. The authors conclude that the program did not

affect economic performance negatively and that firms reacted by passing-through

additional costs to the customers, resulting in a higher markup and an increase in

turnover.

Turning to an academic study that focuses solely on one specific European country,

Anger and Oberndorfer (2008) conduct a study on revenues and employment on

German firms during the first trading period of the EU ETS. The authors do not find

any support that the allowance allocation, within the first trading period, had a

significant effect on revenues and employment. By using firm-level data to analyze the

impact of participation in the EU ETS among Swedish power generating firms in 2005

and 2006, Yu (2011) do not find any significant effect on firms’ profitability in 2005.

However, the author concludes that the program had a significant negative impact in

2006. These findings can according to Yu be a result of the changes in the price of

allowances and potential investments in abatement. Additionally, the analysis suggests

that the EU ETS had a different effect on the profitability of under-cap and over cap-

firms in 2005, but not in 2006. Jaraitė and Di Maria (2016) use a dataset based on

Lithuanian firms between 2003 and 2010 to assess the impact of the EU ETS on

6

investments and profitability. Their findings suggest that the EU ETS led to some

additional investments in new capital equipment and that the firms do not seem to have

suffered from their participation during the early stages of the program. Still, according

to their results the firms might have become less profitable in 2007-2010.

4 Empirical Methodology

This paper aims to estimate the changes in firm competitiveness relative to what would

have occurred if the EU ETS had not been implemented. Propensity score matching

combined with the DID approach are applied to evaluate the causal effect of the EU

ETS on firm performance. The propensity score matching is a popular method used in

observational studies aiming to evaluate policy interventions. In an observational study,

the assignment of subjects to treatment and control groups is not random and as a

consequence, the estimation of the treatment effect might be biased due to the existence

of confounding factors. However, using propensity score matching is a way to adjust

the estimation of treatment effects controlling for the existence of confounding factors

given that the treated and control entities are as similar as possible (Becker & Ichino,

2002).

The propensity score, 𝑝(𝑋) , is defined by Rosenbaum and Rubin (1983) as the

conditional probability of being treated given pre-treatment characteristics:

𝑝 𝑋 ≡ 𝑃𝑟 𝐷 = 1 𝑋 = 𝐸 𝐷 𝑋 , (1)

where 𝐷 = 0,1 is an indicator variable that identifies a firm’s participation in the EU

ETS and 𝑋 is a multidimensional vector of pre-treatment characteristics. Based on the

propensity score, observations from the treated group are matched with observations

from the control group with similar characteristics (Becker & Ichino, 2002). Different

matching methods are available2, however, by following Jaraitė & Di Maria (2016) the

nearest neighbor (NN) and Kernel matching estimators are used in this paper.

By using the NN estimator, each treated unit is matched with the control unit that has

the closest propensity score. In this paper, the NN method is applied with replacement 2 See Caliendo and Kopeinig (2008) for a review about the properties of different matching estimators.

7

since each control unit can be the best match for more than one treated unit. After

matching each treated unit with a control unit the difference between the outcome of the

treated units and the outcome of the matched control units is estimated. By averaging

these differences the Average effect of Treatment on the Treated (ATT) is obtained.

With Kernel matching, each treated observation is matched with several control

observations with weights inversely proportional to the distance between treated and

control observations. The common support is applied to improve the quality of the

matches when using these methods, implying that the matching is restricted based on

the common range of the propensity scores (Becker & Ichino, 2002).

After each treated observation has been matched with observations from the control

group, the next step is to estimate the average treatment effect of the EU ETS on the

desired outcome variables. The ATT is estimated as:

𝐴𝑇𝑇 ≡ 𝐸 𝑌!"(1)− 𝑌!"(0) 𝐷! = 1 (2)

= 𝐸 𝐸 𝑌!"(1)− 𝑌!"(0) 𝐷! = 1,𝑝 𝑋!

= 𝐸 𝐸 𝑌!"(1) 𝐷! = 1,𝑝(𝑋!) − 𝐸 𝑌!"(0) 𝐷! = 0,𝑝 𝑋! 𝐷! = 1 ,

where 𝑖 = firm and 𝑡 = year; 𝑌!"(1) is the potential outcome conditional on participation;

𝑌!"(0) is the potential outcome conditional on non-participation. If firm 𝑖 participates in

the EU ETS it belongs to the treatment group, and 𝐷! is equal to 1. On the contrary, if

firm 𝑖 does not participate in the EU ETS the firm belongs to the control group, and 𝐷!

is equal to 0.

When estimating the propensity score matching combined with the DID approach, the

following two assumptions must hold. First, the assumption regarding the balancing of

pre-treatment variables must be satisfied. If the propensity score is defined as 𝑝(𝑋),

then:

𝐷 ⊥ 𝑋 𝑝(𝑋) (3)

This assumption implies that assignment to treatment is independent of the 𝑋

characteristics, given the same propensity score.

8

Second, the assumption concerning unconfoundedness given the propensity score must

be satisfied:

𝑌!,𝑌! ⊥ 𝐷 𝑝(𝑋) (4)

This assumption implies that the outcomes are independent of treatment, conditional on

the propensity score (Becker & Ichino, 2002).

For the estimate of the ATT to be unbiased it is essential that the treatment does not

affect the firms in the control group. One possible reason for this assumption to fail is

related to spillover effects through general equilibrium impacts. For example, could an

increase in the electricity price, induced by the EU ETS, influence other manufacturing

firms including non-ETS ones (Marin et al., 2017).

To apply the simple estimate of the ATT obtained by using the standard DID can lead to

biased estimators if the variables related to firm-level outcomes vary significantly

across the treatment and control groups. By using the observable differences across ETS

participants and non-participants, this potential bias can be reduced and the ATT can be

estimated by enforcing semi-parametric matching estimators. In the empirical

estimation, the following DID matching estimator (𝛼) is applied:

𝛼 = !!!

𝑌!! 1 − 𝑌!! 0 − 𝑤!" 𝑌!! 0 − 𝑌!! 0!∈!!!∈!! , (5)

where 𝑗 = participants and 𝑘 = non-participants; 𝑁! is the number of firms in the treated

group; 𝐼! is the set of firms that participate in the EU ETS; 𝐼! is the set of non-

participants; and 𝑤!" is the weight placed on firm 𝑘 when constructing the

counterfactual estimate for the treated facility 𝑗. In this paper, the NN and Kernel

matching estimators are used to define the weights 𝑤!". It is common that when the

observable characteristics of an untreated unit are close to the characteristics of a treated

unit, the untreated unit 𝑘 is weighted relatively more heavily in the construction of a

counterfactual estimate for unit 𝑗 (Jaraitė & Di Maria, 2016).

9

5 Data

In this study, data from 2003 to 2008 is used to analyze the effect of the first phase and

the beginning of the second phase of the EU ETS on firm competitiveness. The panel

dataset is collected and owned by Statistics Sweden (SCB)3.

The SCB has identified firms participating in the EU ETS and matched firms across

datasets by using firm-level identifiers. The dataset consists of firms, which are sorted

into sectors based on their NACE code4. Six NACE code industry dummies are

therefore created to identify the participating firms. The dataset consists of firms whose

primary activity is in mining and quarrying (NACE 10-14) and manufacturing (NACE

15-36). As discussed by Marin et al. (2017) firms within the manufacturing sector are

more exposed to international competition compared to firms in the power sector and

other non-tradable sectors. The level of competition might restrict firms’ ability to pass

through the additional cost imposed by the EU ETS to its customers, meaning that the

loss of international competitiveness of EU firms and job losses are of particular

concern when it comes to the manufacturing sector.

To evaluate the impact of the EU ETS on variables through which firm competitiveness

can be observed, the following measures will be examined: labor productivity (value

added divided by the total number of employees), relative profit before tax (profit

before tax divided by turnover), turnover, and value added. Besides the variables

mentioned, the dataset also includes the amount of fossil fuels (CO2 emissions) used by

the firms and the stock of tangible capital assets. The monetary variables are expressed

in SEK and have been deflated to 1990 prices using a producer price index (PPI)

specific to industries.

Table 1 presents the descriptive statistics for both participating and non-participating

firms. The dataset is summarized for the unbalanced sample running from 2003 to 2008.

It is evident that ETS firms, on average, emit more CO2 emissions, create higher added

value per employee, are more capital intensive, and have larger turnovers as well as

3 We would like to thank Tommy Lundgren for providing this data for us. 4 NACE is the Statistical Classification of Economic Activities in the European Community.

10

Tab

le 1

: Des

crip

tive

Stat

istic

s

Non

-ETS

firm

s

Std.

dev

.

6.67

5

395.

4

128.

8

7,71

3.8

77,4

77.8

20,1

91.4

0.10

4

0.33

1

0.48

5

0.25

9

0.39

7

0.40

7

Mea

n

0.78

8

142.

6

0.18

0

526.

4

8,91

7.0

2,60

9.8

0.01

1

0.12

5

0.37

9

0.07

3

0.19

6

0.20

9

Obs

.

14,6

33

351,

355

328,

244

351,

355

351,

355

351,

355

351,

355

351,

355

351,

355

351,

355

351,

355

351,

355

ETS

firm

s

Std.

dev

.

387.

4

583.

9

0.01

1

420,

095.

7

2,18

7,35

4

666,

721.

7

0.18

3

0.23

4

0.49

7

0.44

4

0.29

4

0.23

4

Mea

n

97.0

683.

2

0.00

1

174,

544.

7

1,19

9,70

3

321,

529.

9

0.03

5

0.05

8

0.44

5

0.27

0

0.09

6

0.05

8

Obs

.

636

690

662

690

690

690

690

690

690

690

690

690

All

obse

rvat

ions

Std.

dev

.

81.6

396.

5

128.

7

21,5

41.1

134,

625

38,4

16.6

0.10

4

0.33

1

0.48

5

0.26

0

0.39

6

0.40

6

Mea

n

4.79

8

143.

6

0.18

0

867.

5

11,3

04.8

3,23

4.9

0.01

1

0.12

5

0.37

9

0.07

3

0.19

5

0.20

9

Obs

.

15,2

69

352,

045

328,

906

352,

045

352,

045

352,

045

352,

045

352,

045

352,

045

352,

045

352,

045

352,

045

Mea

sure

men

t uni

t

Kilo

tonn

es

SEK

, tho

usan

ds

SEK

, tho

usan

ds

SEK

, tho

usan

ds

SEK

, tho

usan

ds

SEK

, tho

usan

ds

Dum

my

varia

ble,

1 if

N

AC

E 10

-14

Dum

my

varia

ble,

1 if

N

AC

E 15

-19

Dum

my

varia

ble,

1 if

N

AC

E 20

-22,

36

Dum

my

varia

ble,

1 if

N

AC

E 23

-26

Dum

my

varia

ble,

1 if

N

AC

E 27

-28

Dum

my

varia

ble,

1 if

N

AC

E 29

-35

Not

e: A

ll m

onet

ary

varia

bles

are

in re

al te

rms

Var

iabl

e

Foss

il fu

el C

O2 e

mis

sion

s

Labo

r pro

duct

ivity

Rel

ativ

e pr

ofit

befo

re ta

x

Tang

ible

cap

ital a

sset

s

Turn

over

Val

ue a

dded

Min

ing

and

quar

ryin

g

Food

, bev

erag

es, t

extil

es a

nd c

loth

ing

Woo

d, p

ulp

and

pape

r

Che

mic

als,

min

eral

pro

duct

s and

pl

astic

Met

al, m

etal

pro

duct

s

Mac

hine

ry a

nd e

quip

men

t, el

ectro

nics

11

value added. However, the firms included in the EU ETS are on average less profitable

compared to the firms not participating in the program.

6 Results

6.1 Propensity Score and Matching In this paper, the propensity score is the probability that a firm is regulated under the

EU ETS based on their observable characteristics. The propensity score is measured by

using unbalanced data from 2004 together with a probit model. The explanatory

variables include the amount of fossil fuels used by the firm, stock of tangible capital

assets, turnover, and six dummy identifiers for whether the firm belongs to one of the

six sectors created. By enforcing the common support option the final number of blocks

is eight. This ensures that the average propensity score of treated and controls are

similar in each of the blocks created. Additionally, the combination of X characteristics

discovered in the treated group can also be observed among firms in the control group.

The balancing property is satisfied (see Table 2).

Table 2: Distribution of Control and Treated Entities According to Their

Propensity Scores

Propensity score Non-ETS firms ETS firms Total

0.0011632 751 4 755

0.025 112 6 118

0.05 44 1 45

0.1 24 6 30

0.2 20 8 28

0.4 4 9 13

0.6 2 14 16

0.8 5 30 35

Total 962 78 1 040

Note: The final number of blocks is 8.

Table 3 presents the results of the propensity score estimations satisfying both the

common support condition and the balancing property. The probability of being

12

included in the EU ETS is positively and significantly related to the amount of fossil

fuels used by firms, tangible capital assets, and turnover. The six NACE code industry

dummies, on the contrary, do not correlate with the likelihood of being engaged in

emissions trading.

Table 3: Propensity score estimates

Variables

Fossil fuel CO2 emissions 0.739***

(0.079)

Tangible capital assets 0.146***

(0.055)

Turnover 0.130*

(0.076)

NACE 2

(food, beverages, textiles and clothing)

- 0.782

(0.708)

NACE 3

(wood, pulp and paper)

0.620

(0.676)

NACE 4

(chemicals, mineral products and plastic)

0.127

(0.667)

NACE 5

(metal, metal products)

- 0.269

(0.690)

NACE 6

(machinery and equipment, electronics)

- 0.343

(0.720)

Constant - 5.376***

(0.899)

Number of observations 2,067

LT 𝜒! (8) 426.42

Prob.> 𝜒! 0.0000

Pseudo 𝑅! 0.642

Notes: ***p < = 0.001; **p < = 0.05; *p < = 0.1. Coefficients with standard errors in parentheses. All

monetary variables are in natural logarithms.

13

6.2 Estimation of Average Treatment Effects based on Propensity Scores

This section presents the results of the Kernel and NN matching specifications

reflecting on the effects of the EU ETS. Because the EU ETS launched in 2005, the year

2004 is used as the pre-treatment year. Table 4 presents the results from both using the

outcome variables in 2004 compared with their counterparts in following years (2005,

2006, 2007 and 2008) as well as the year-on-year changes.

The first measure of economic performance to be examined is labor productivity.

According to the matching results in Table 4, the impact of the EU ETS on labor

productivity is positive and significant at low significance level in 2005, 2006, and

2008. By using a dataset based on an extensive panel of European firms, Marin et al.

(2017) find similar results for the second trading period. From their result, the authors

interpret the improvements in labor productivity as a result of capital deepening.

Next, the focus is on assessing the impact of the EU ETS on Swedish ETS firms relative

profit before tax. The matching results presented in Table 4 show that the ETS firms in

Sweden have become less profitable in the early stages of the program (2005, 2006, and

2007). However, the year-on-year changes show that the impact of the EU ETS on

relative profit before tax is positive and significant at a low significance level in 2006.

When analyzing Lithuanian firms, Jaraitė and Di Maria (2016) on the other hand find no

evidence that the ETS firms suffered from their participation in the beginning of the

first trading period. Given of the amount of over-allocation that occurred during the first

trading period, the authors find these results as no surprise. Still, according to their

results, the firms might have become less profitable in 2007-2010.

The matching results presented in Table 4 show that the ETS firms in Sweden have

increased their turnover for the years following the introduction of the program.

However, when analyzing the effect of the EU ETS on firm-level economic

performance, Marin et al. (2017) find that the European ETS firms have increased their

turnover only for the second trading period.

14

Tab

le 4

: Ave

rage

Eff

ects

of t

he E

U E

TS

Part

icip

atio

n

2008

Ker

nel

Out

com

e: C

hang

es c

ompa

red

to 2

004

339.

9**

(185

.9)

-0.0

03

(0.0

27)

493,

000*

* (2

83,0

00)

131,

000

(104

,000

)

Out

com

e: y

ear-

on-y

ear c

hang

es

386.

4 (4

43.5

)

0.00

0 (0

.000

)

68,8

21.6

(1

84,0

00)

12,2

48.9

(6

2,06

6.1)

Not

es: *

** p

< 0

.001

; **

p <

0.05

; * p

< 0

.10,

the

p-va

lues

are

est

imat

ed u

sing

one

-taile

d t-t

ests

. Coe

ffic

ient

s with

boo

tstra

pped

stan

dard

err

ors i

n pa

rent

hese

s. A

ll m

onet

ary

varia

bles

are

in re

al te

rms.

The

num

ber o

f ETS

firm

s in

the

treat

ed g

roup

is 7

8. T

he K

erne

l mat

chin

g is

bas

ed o

n 96

2 fir

ms i

n th

e co

ntro

l gro

up. T

he

num

ber o

f firm

s in

the

cont

rol g

roup

diff

ers b

etw

een

the

year

s whe

n us

ing

the

NN

mat

chin

g m

etho

d w

ith re

plac

emen

t5 .

NN

334.

3*

(226

.5)

-0.0

03

(0.6

92)

474,

000

(376

,000

)

90.6

32.5

(1

27,0

00)

292.

0 (7

70.1

)

-0.0

00

(0.0

01)

149,

000

(189

,000

)

5,23

7.6

(80,

379.

5)

2007

Ker

nel

-45.

1 (4

59.5

)

-0.0

03**

(0

.002

)

424,

000*

* (2

53,0

00)

119,

000*

(8

2,09

5.6)

-180

.5

(439

.4)

-0.0

00

(0.0

00)

26,2

35.7

(1

81,0

00)

14,8

81.9

(6

2,96

6.2)

NN

42.2

(5

95.5

)

-0.0

03*

(0.0

02)

325,

000

(843

,000

)

85,3

85.9

(1

08,0

00)

-182

.8

(897

.2)

-0.0

00

(0.0

01)

76,5

41.7

(2

06,0

00)

22,7

77.7

(6

5,57

0.4)

2006

Ker

nel

136.

2*

(90.

5)

-0.0

02

(0.0

02)

387,

000*

(2

69,0

00)

101,

000*

(6

3,89

4.0)

46.2

(8

9.5)

0.00

1*

(0.0

01)

-102

,000

(3

18,0

00)

-109

,000

(1

56,0

00)

NN

237.

0**

(125

.0)

-0.0

03*

(0.0

02)

236,

000

(339

,000

)

59,7

35.1

(9

6,79

8.6)

14.7

(1

35.4

)

0.00

0 (0

.001

)

-99,

200

(319

,000

)

-114

,000

(1

63,0

00)

2005

Ker

nel

89.8

(1

23.7

)

-0.0

03*

(0.0

02)

485,

000*

(3

56,0

00)

208,

000*

(1

50,0

00)

89.8

(1

20.1

)

-0.0

03*

(0.0

02)

485,

000*

(3

46,0

00)

208,

000*

(1

54,0

00)

NN

222.

0*

(151

.9)

-0.0

03**

(0

.002

)

331,

000

(412

,000

)

172,

000

(162

,000

)

222.

0*

(144

.3)

-0.0

03*

(0.0

02)

331,

000

(443

,000

)

172,

000

(148

,000

)

Year

Met

hod

Labo

r pro

duct

ivity

(0

00’ S

EK)

Rel

ativ

e pr

ofit

befo

re ta

x (0

00’ S

EK)

Turn

over

(0

00’ S

EK)

Val

ue a

dded

(0

00’ S

EK)

Labo

r pro

duct

ivity

(0

00’ S

EK)

Rel

ativ

e pr

ofit

befo

re ta

x (0

00’ S

EK)

Turn

over

(0

00’ S

EK)

Val

ue a

dded

(0

00’ S

EK)

5For labor productivity, turnover, and value added, the NN matching with replacement in 2005 and 2006 is based on 33 firms in the control group. In 2007 and 2008, the number of firms in the control group is 30. For relative profit before tax, the NN matching with replacement in 2005 and 2006 is based on 32 firms in the control group. In 2007, the number of firms in the control group is 27 while in 2008 the number of firms is 26.

15

The fourth measure of economic performance to be examined is value added. The

matching results presented in Table 4 show that the ETS firms in Sweden have

increased their value added in the early stages of the program (2005, 2006, and 2007).

Both Abrell et al. (2011) and Marin et al. (2017), however, fail to find any statistically

significant impact of the EU ETS on value added during the first and the second trading

period when using a sample of European firms.

6.3 Testing the Common-Trends Assumption One important assumption that needs to be satisfied when estimating the propensity

score matching combined with the DID approach is that the trends in the outcome

variables should be the same across the ETS and non-ETS firms over time. To test if

this assumption holds, a placebo DID matching test is applied using two years of pre-

treatment data (2003 and 2004). In this test, 2003 is treated as a pre-treatment year and

2004 as a treatment year. According to the results of the placebo DID matching test (see

Appendix A) there are no statistical differences in the outcome variables between the

treatment and control groups for 2003-2004. This result implies that the pre-ETS trends

in the outcome variables are the same for both the firms participating in the EU ETS

and the non-participating firms. Because there are no statistical differences in the

outcome variables before the introduction of the EU ETS, it will lend some support for

the assumption that they are the same after the EU ETS launched in 2005.

7 Discussion and Concluding Remarks

In this paper, we use a unique dataset of Swedish firms to analyze the effect of the EU

ETS on economic and financial indicators through which the impact on firm

competitiveness may be observed. Contrarily to the concerns raised by industrial

emitters and policymakers regarding the negative impacts of environmental policy on

economic performance, the results suggest a positive and significant effect on labor

productivity, turnover, and value added. Still, a negative and significant impact can be

observed for relative profit before tax. Given the data used in this paper, it is difficult to

identify the exact mechanisms behind these results.

16

Using the unbalanced dataset running from 2003-2008 to estimate the propensity score

and the average treatment effects can be considered as a limitation. However, the

balancing property is not satisfied when running the regressions with the balanced

dataset. This limitation could be related to the lack of observations.

Many of the conclusions made in Section 6.2 Estimation of average treatment effects

based on propensity scores are supported by low significance levels. This limitation

may occur since the findings are based on a small number of firms participating in the

EU ETS, which differ in terms of size, technologies, and level of allocation stringency.

Because of this, the effect of treatment differs across ETS firms, and the average effect

may appear insignificant.

As mentioned in Section 2.2 The EU ETS in Sweden, after the program launched in

2005, the sectors covered by the EU ETS both needed to buy EU allowances and pay

the CO2 tax for the right to emit CO2 emissions (Löfgren et al., 2013). It is possible that

this could affect the economic and financial indicators evaluated in this study. Still, both

the ETS and non-ETS firms were covered by the CO2 tax for many years, and therefore

it is likely that the effects found in this paper should be attributable to the EU ETS.

Another limitation related to the method used in this paper is the possible interaction

between the CO2 price and the electricity price. The concern is whether an increase in

the electricity price, induced by the EU ETS, will influence other manufacturing firms

including firms not participating in the emissions trading program. For the average

treatment effect to be unbiased, it is essential that the treatment does not affect the firms

in the control group. Still, it is possible that this requirement fails due to the potential

spillover effects through general equilibrium impacts (Marin et al., 2017). Since the oil

crisis in the 1970s, the use of oil as energy supply has decreased in Sweden, while the

use of clean energy power (e.g., hydropower and nuclear power) has increased (Yu,

2011). Given the amount of clean energy produced along with the surplus of allowances

Sweden had in the first trading period, Swedish firms had no reason to pass through the

CO2 price to the electricity price. Therefore it is assumable that the CO2 price does not

constitute the main component of the energy price.

17

To facilitate future development of cap-and-trade systems and to get a broader picture

of how the EU ETS affects firm competitiveness, the analysis can be extended in at

least two directions. As discussed in Section 2.1 The EU ETS, the first trading period

can be seen as a trial phase to gain experience. Because of this, it is essential to

remember that the study’s estimation results are viewed in consensus with the program

period analyzed. One suggestion for future research is therefore to include data from the

second and the third phase to get a broader picture of the program's impacts on firm

performance. Additionally, a more detailed assessment of the mechanisms analyzed in

the paper is needed. Further research needs to identify these mechanisms and quantify

their contribution to gain an understanding of the potential connection between the EU

ETS and firm competitiveness.

18

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20

Appendix A: A Placebo DID Matching Test

The propensity score is measured by using unbalanced data from 2003 together with a

probit model. The explanatory variables include the amount of fossil fuels used by the

firm, stock of tangible capital assets, turnover, and six dummy identifiers for whether

the firm belongs to one of the six sectors created. Table A1 presents the distribution of

control and treated entities according to their propensity scores satisfying both the

common support condition and the balancing property.

Table A1: Distribution of Control and Treated Entities According to Their

Propensity Scores

Propensity score Non-ETS firms ETS firms Total

0.0033818 428 9 437

0.1 22 4 26

0.2 14 2 16

0.4 9 11 20

0.6 3 8 11

0.8 1 36 37

Total 477 70 547

Note: The final number of blocks is 6.

Table A2 presents the results of the propensity score estimations satisfying both the

common support condition and the balancing property. The probability of being

included in the EU ETS is positively and significantly related to the amount of fossil

fuels used by firms. Tangible capital assets, turnover, and the six NACE code industry

dummies, on the contrary, do not correlate with the likelihood of being engaged in

emissions trading.

21

Table A2: Estimation of the Propensity Score

Variables

Tangible capital assets 0.092

(0.062)

Fossil fuel CO2 emissions 0.914***

(0.106)

Turnover 0.145*

(0.089)

NACE 2

(food, beverages, textiles and clothing)

- 0.329

(2.061)

NACE 3

(wood, pulp and paper)

1.457

(2.048)

NACE 4

(chemicals, mineral products and plastic)

0.674

(2.041)

NACE 5

(metal, metal products)

0.512

(2.051)

NACE 6

(machinery and equipment, electronics)

- 0.120

(2.097)

Constant - 6.079***

(2.206)

Number of observations 1,317

LT 𝜒! (8) 374.72

Prob.> 𝜒! 0.0000

Pseudo 𝑅! 0.685

Notes: ***p < = 0.001; **p < = 0.05; *p < = 0.1. Coefficients with standard errors in parentheses. All

monetary variables are in natural logarithms.

Table A3 presents the results of the placebo DID matching test, which shows that there

are no statistical differences in the outcome variables (namely, labor productivity,

relative profit before tax, turnover, and value added) between the treatment and control

groups for 2003-2004.

22

Table A3. Placebo DID matching test

Year

Method

2004

NN Kernel

Labor

productivity

(000’ SEK)

11.78

(99.03)

17.89

(102.83)

Relative profit

before tax

(000’ SEK)

0.000

(0.001)

0.000

(0.000)

Turnover

(000’ SEK)

220,000

(375,000)

211,000

(412,000)

Value added

(000’ SEK)

- 133,000

(142,000)

- 113,000

(154,000)

Notes: *** p < 0.001; ** p < 0.05; * p < 0.10, the p-values are estimated using one-tailed t-tests.

Coefficients with bootstrapped standard errors in parentheses. All monetary variables are in real terms.

There are 70 ETS firms in the treated group. NN matching with replacement is based on 22 firms in the

control group, while the Kernel matching is based on 477 firms in the control group.