Firm Strategies and Consumer Behavio r
under Ma rk et-Based Sustainabilit y P olicies
v orgelegt v on
Helene Blanche Naegele, M. A.
geb. in Berlin
v on der Fakult ¨
at VII – W irtschaft und Management
der T echnischen Univ ersit ¨
at Berlin
zur Erlangung des akademischen Grades
Doktor der W irtschaftswissenschaften
– Dr . rer . oec. –
genehmigte Dissertation
Promotionsausschuss:
V orsitzender: Prof. Dr . Georg Meran
Gutachter: Prof. Dr . Pio Baake
Dr . habil. Pauline Giv ord (Centre de r echerche en
´
economie et statistique (CREST), Paris)
Prof. Dr . Christian v on Hirschhausen
T ag der wissenschaftlichen A ussprache: 8. Dezember 2017
Berlin 2018

T o my par ents,
who made everything possible.

Abstract
This dissertation studies dif ferent approaches to sustainability r egulation. The author
ev aluates the ef fectiv eness of sev eral policy measures in achie ving their objectiv e and
discusses potential unintended side-ef fects. Both depend on strategic fir m beha vior and
consumer sensitivity .
Chapter II considers the impact of fuel taxes on the fuel ef ficiency of ne w automobiles.
The results rely on exhaustiv e consumer -le v el data of French automobile purchases; the
author estimates the parameters of automobile demand using a nested logit model ac-
counting for heterogeneity of consumer gr oups. The estimated parameters are used to
predict the impact of tw o hypothetical changes in fuel taxes. The results sho w that new
v ehicle purchases react v ery little to changes in fuel tax and the impact of the examined
fuel tax policies is economically small.
Chapter III studies the strategic interactions betw een tw o sustainability labels com-
peting for fir ms to of fer their products: it sho ws why the industry has an interest to
introduce its o wn label. The results rely on a model of oligopolistic firms offering se v-
eral horizontally (betw een fir ms) and v ertically (betw een label qualities) differentiated
products. These fir ms interact with tw o labeling organizations that pursue dif ferent ob-
jectiv es: a for -profit label and an industry standard maximizing joint firm profit. The
results sho w that the industr y benefits from introducing an industry standard that re-
duces competition b y segmenting the market; b y contrast, a social planner maximizes
consumer w elfare and total social w elfare b y maximizing the number of labeled goods.
Horizontal dif ferentiation pla ys a key r ole for the final market outcome.
Chapter IV measures the magnitude of fixed transaction costs in Eur opean emissions
trading. T ransaction costs are defined in a broad sense as monetary and non-monetar y
frictions from certificate trading of firms. The results rely on plant-le v el administrativ e
data from Eur opean emissions trading; the author estimates the distribution of fixed
transaction costs arising from the use of “normal” European certificates, on the one hand,
and inter national of fset certificates, on the other hand. The results sho w that for most
fir ms, the bulk of transaction costs stems from market participation in general rather than
from the use of international certificates. The magnitude of transaction costs is such that
a fifth of all fir ms does not participate in profitable of fset trading.
Chapter V studies whether European emissions trading has led to a displacement of
European carbon emissions to other parts of the w orld (“carbon leakage”), both via relo-
cation and via loss of market shares to foreign competitors. A literature surv ey re v eals
dif ferent approaches to identify carbon leakage empirically . This chapter ’s results rely
on a combination of sector -lev el trade data and plant-le v el data from European emissions
trading; using v arious w a ys of defining both outcome and stringency of environmental
i

ii
policy , the author finds no evidence of carbon leakage.
Keyw ords: automobile demand, carbon dioxide, carbon leakage, climate change,
consumer labels, demand estimation, emissions trading, EU ETS, exter nalities, fuel tax,
nested logit model, sustainability , transaction costs.

Zusammenfassung
Diese Dissertation untersucht unterschiedliche Ans ¨
atze zur Nachhaltigkeitsregulierung.
Die A utorin bew ertet die W irksamkeit v on Politikmaßnahmen so wie potenzielle unbeab-
sichtigte Nebenwirkungen. Beide h ¨
angen v om strategischen V erhalten v on Unternehmen
so wie der S ensitivit ¨
at der V erbraucher ab.
Kapitel II betrachtet die A uswirkung v on T reibstoffsteuern auf die Kraftstoffef fi zienz
v on neuen A utomobilen. Die Ergebnisse beruhen auf V erbraucherdaten ¨
uber franz ¨
osischen
A utomobilk ¨
aufe; die A utorin sch ¨
atzt die Parameter der A utomobilnachfrage mit einem
Nested Logit Modell, das die Heterogenit ¨
at v on V erbrauchergruppen ber ¨
ucksichtigt. Die
gesch ¨
atzten Parameter w erden v erw endet, um die A uswirkung v on zw ei hypothetischen
¨
Anderungen der T reibstof fsteuer zu berechnen. Die Er gebnisse zeigen, dass neue Fahr -
zeugk ¨
aufe sehr w enig auf ¨
Anderungen der T reibstof fsteuer reagieren und die A uswir -
kungen der untersuchten Steuerreformen wirtschaftlich v ernachl ¨
assigbar sind.
Kapitel III untersucht die strategischen Interaktionen zwischen zw ei Nachhaltigkeits-
labels im Kaf feemarkt: es zeigt, w arum die R ¨
ostereibranche ein Interesse daran hat, ihr
eigenes Label einzuf ¨
uhren. Die Er gebnisse beruhen auf einem Modell mit Fir men im
Oligopol, die mehrere horizontal (zwischen Firmen) und v ertikal (zwischen Labelqua-
lit ¨
aten) dif ferenzierte Produkte anbieten. Diese Unternehmen interagieren mit zw ei La-
belorganisationen, die unterschiedliche Ziele v erfolgen: ein profitorientierter Lizensier er
und ein Industriestandard, der den gemeinsamen Unter nehmensgewinn maximiert. Die
Ergebnisse zeigen, dass ein Industriestandar d immer v ersucht, den W ettbew erb durch
S egmentierung des Marktes zu reduzieren. Im Gegensatz dazu maximiert ein sozialer
Planer die gesamtgesellschaftliche W ohlfahrt durch die Maximierung der Zahl der gela-
belten Produkte. Die horizontale Dif ferenzierung spielt eine entscheidende Rolle f ¨
ur das
Marktergebnis.
Kapitel IV misst T ransaktionskosten im europ ¨
aischen Emissionshandel. T ransakti-
onskosten w erden hier im w eiten Sinne als monet ¨
are und nicht monet ¨
are A ufw ¨
ande
v on Fir men im Zertifikatshandel definiert. Die Ergebnisse beruhen auf administrativ en
Daten des europ ¨
aischen Emissionshandels; die A utorin sch ¨
atzt die V erteilung der fixen
T ransaktionskosten, die sich aus der V er w endung v on ”nor malen ¨
europ ¨
aischen Zertifika-
ten einerseits und inter nationalen Of fsetzertifikaten andererseits er geben. Die Ergebnisse
zeigen, dass f ¨
ur die meisten Unter nehmen der Großteil der T ransaktionskosten v on der
Marktbeteiligung im Allgemeinen und nicht v on der V er w endung inter nationaler Offset-
zertifikate stammt. Die Gr ¨
oßenordnung der T ransaktionskosten ist so, dass ein F ¨
unftel
aller Fir men nicht am gewinnbringenden Of fsethandel teilnimmt.
Kapitel V untersucht, ob der europ ¨
aische Emissionshandel zu einer V erschiebung der
europ ¨
aischen CO2-Emissionen in andere T eile der W elt gef ¨
uhrt hat (”carbon leakage”),
so w ohl durch V erlagerung der Pr oduktion als auch durch V erlust v on Marktanteilen an
ausl ¨
andische W ettbew erber . Eine Literaturrecherche zeigt v erschiedene Ans ¨
atze zur Iden-
iii

iv
tifizierung v on carbon leakage in den Daten. Die Ergebnisse dieses Kapitels beruhen auf
einer Kombination v on Handelsdaten und Daten aus dem europ ¨
aischen Emissionshan-
del; mit v erschiedenen Definitionen der Ergebnisv ariablen so wie der Emissionskosten,
findet die A utorin keine Hinw eise auf carbon leakage.
Schl ¨ usselw ¨ orter: A utomobilnachfrage, Emissionshandel, EU ETS, Exter nalit ¨
aten, Kli-
ma w andel, Kohlendioxid, Konsumentenlabels, Nachfragesch ¨
atzung, Nachhaltigkeit, Nes-
ted Logit Modell, T ransaktionskosten, T reibstof fsteuer .

Ackno wledgements
First and foremost, I w ould like to thank my supervisors: Pio Baake for giving me the
freedom to set my o wn research agenda, for shar pening my mind with seemingly inno-
cent questions, and for his company and the DIW’s best coffee; Christian v on Hirschhausen
for his passionate policy opinions, for his unconditional support ev en when my resear ch
agenda w as distant from his, for being an excellent coach and constantly encouraging me
to “start w orking”.
I further thank Pauline Giv ord for her a v ailability despite all other responsibilities,
for her shar p mind and for tra v eling to Berlin for my defense; Jana Friedrichsen for be-
lieving in a better w orld; Aleksandar Zaklan for his patience and infinite proof-reading;
Ber nd Fitzenberger for advice on quantile r egressions that so fe w people seem to grasp;
St ´
ephane Caprice and Dennis Rickert for their w ar m w elcome during my research sta y
at the T oulouse S chool of Economics; Sylv ain Chab ´
e-Ferret for his metaphysical thoughts
about standards for good science; T obias S chmidt for being an inspiring colleague ev en
though he ran a w a y too quickly; and Roland Rathelot for pushing me to pursue a disser-
tation.
I also thank my friends for supporting me along the w a y; in particular , Kathrin for
constantly challenging me; Frieda for shaping who I am; and Mathis for patiently putting
up with what mathematics look like in my head.
Many thanks to Johanna M ¨
ollerstr ¨
om and the entire department Competition and Con-
sumers . This dissertation w ould not ha v e been possible without the financial support
from the DIW Graduate Center and the Leibniz Gemeinschaft.
v

Contents
T itle page 1
Abstract iii
Zusammenfassung v
Contents vii
List of T ables xi
List of Figures xiii
I Introduction 1
1 Sustainability policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1 Pollution tax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 Cap-and-trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Priv ate v oluntary standards . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1 Nested logit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2 Binary quantile estimation . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3 T reatment ef fects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.1 Fuel taxes and automobile fuel ef ficiency . . . . . . . . . . . . . . . . 14
3.2 Competition betw een sustainability labels . . . . . . . . . . . . . . . 16
3.3 T ransaction costs in European emissions trading . . . . . . . . . . . 17
3.4 Carbon leakage and European emissions trading . . . . . . . . . . . 17
4 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
5 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
II How do fuel taxes impact new car purchases? An ev aluation using French
consumer -lev el data 23
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2 Choice model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
vii

viii Contents
3 Data and descriptiv e evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.1 New v ehicle registrations . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.2 T ypes of consumers: demographic groups . . . . . . . . . . . . . . . 29
3.3 Diesel and gasoline cars . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.4 Cost per kilometer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4 Econometric approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.1 Nested logit estimation . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.2 Endogenous v ariables and instruments . . . . . . . . . . . . . . . . . 36
5 Empirical results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
5.1 Aggregate elasticities to fuel price v ariation . . . . . . . . . . . . . . 38
5.2 T ax alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
5.3 Carbon tax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
5.4 Robustness checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
7 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
A Descriptiv e statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
B Details on the computation of the elasticities . . . . . . . . . . . . . . 50
C Complementary results for the main specification . . . . . . . . . . . 55
C.1 Ra w coefficients . . . . . . . . . . . . . . . . . . . . . . . . . 55
C.2 Demand for selected car models . . . . . . . . . . . . . . . . 56
D T esting for w eak instruments . . . . . . . . . . . . . . . . . . . . . . . 60
E Robustness checks: elasticities . . . . . . . . . . . . . . . . . . . . . . 62
III Competition betw een for -profit and industr y labels: the case of social labels
in the cof fee market 63
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
1.1 Related literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
1.2 Cof fee market and fairtrade . . . . . . . . . . . . . . . . . . . . . . . . 66
2 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
2.1 Consumer demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
2.2 Fir ms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
2.3 Labeling organizations . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
2.4 Game sequence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
3 Market equilibrium with licenser F only . . . . . . . . . . . . . . . . . . . . . 72
3.1 Consumer prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
3.2 Product line decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
3.3 License fee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
3.4 Label quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
3.5 Minimum quality requirement . . . . . . . . . . . . . . . . . . . . . . 76
4 Market entry of industr y standard I . . . . . . . . . . . . . . . . . . . . . . . 77
4.1 Consumer prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.2 Product line decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

Contents ix
4.3 License fee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.4 Label quality of incumbent licenser F . . . . . . . . . . . . . . . . . . 79
4.5 Label quality of new entrant I . . . . . . . . . . . . . . . . . . . . . . 81
4.6 Minimum quality requirement . . . . . . . . . . . . . . . . . . . . . . 85
5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
6 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
A Proof of Lemma 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
B Proof of Lemma 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
C Calculations for Proposition 2 . . . . . . . . . . . . . . . . . . . . . . 92
D Calculations for Proposition 3 . . . . . . . . . . . . . . . . . . . . . . 93
E Calculations for Proposition 4.(iii) . . . . . . . . . . . . . . . . . . . . 93
IV Offset credits in the EU ETS: a quantile estimation of firm-lev el transaction
costs 95
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
2.1 Institutional framew ork . . . . . . . . . . . . . . . . . . . . . . . . . . 99
2.2 Why are of fset certificates cheaper? . . . . . . . . . . . . . . . . . . . 100
2.3 Definition and interpretation of transaction costs . . . . . . . . . . . 101
3 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
3.1 Emissions trading with of fset credits: reference scenario . . . . . . . 103
3.2 T rading costs for both certificate markets . . . . . . . . . . . . . . . . 105
4 Data and empirical research design . . . . . . . . . . . . . . . . . . . . . . . 106
4.1 Emissions, allocation and of fset entitlement . . . . . . . . . . . . . . 106
4.2 Price spread and realized sa vings . . . . . . . . . . . . . . . . . . . . 107
4.3 Descriptiv e evidence for transaction costs . . . . . . . . . . . . . . . . 109
4.4 Econometric methodology . . . . . . . . . . . . . . . . . . . . . . . . 111
5 Estimation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
5.1 S ector-specific r esults . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
7 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
A National of fset entitlement rules . . . . . . . . . . . . . . . . . . . . . 122
B Of fset use as a fixed horizon problem . . . . . . . . . . . . . . . . . . 123
C Exogeneity of allocation status . . . . . . . . . . . . . . . . . . . . . . 124
D Parametric estimation results . . . . . . . . . . . . . . . . . . . . . . . 126
E Quantile regression fit . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
V Does the EU ETS cause carbon leakage in European manufactur ing? 129
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
2 T rade theor y and carbon leakage . . . . . . . . . . . . . . . . . . . . . . . . . 133
2.1 (Neo-)classical approach . . . . . . . . . . . . . . . . . . . . . . . . . . 133

x Contents
2.2 New trade appr oach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
3 Empirical Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
3.1 Measures of environmental stringency . . . . . . . . . . . . . . . . . 136
3.2 Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
3.3 Net flo ws . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
3.4 Bilateral flo ws . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
4 Data and descriptiv es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
4.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
4.2 Descriptiv e statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
5.1 Net trade flo ws . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
5.1.1 Main results . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
5.1.2 S ector heterogeneity . . . . . . . . . . . . . . . . . . . . . . . 147
5.2 Bilateral trade flo ws . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
6 Summary and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
7 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
A Potential bias from using normalized v ariables . . . . . . . . . . . . 157
B Additional regression results . . . . . . . . . . . . . . . . . . . . . . . 159
B.1 Net trade flo ws . . . . . . . . . . . . . . . . . . . . . . . . . 159
B.2 Bilateral trade flo ws . . . . . . . . . . . . . . . . . . . . . . . 160

List of T ables
T able Page
I.1 Ov er view b y chapter: topic, pre-publication and author ’s contribution . . . . . 15
II.1 Descriptiv e statistics: main characteristics of new car registrations 2003-2007 . 29
II.2 Descriptiv e statistics: a v erage mileage b y household characteristics . . . . . . . 30
II.3 Elasticities with respect to fuel prices: diesel share, a v erage fleet fuel con-
sumption (L/km) and CO 2 intensity (g/km) . . . . . . . . . . . . . . . . . . . . 39
II.4 Percentage impact of a carbon tax and a tax alignment on diesel share, a v erage
fleet fuel consumption (L/km) and CO 2 intensity (g/km) . . . . . . . . . . . . 40
II.5 Robustness checks: percentage impact of carbon tax and tax alignment on
diesel share, a v erage fleet fuel consumption (L/km) and CO 2 intensity (g/km) 41
II.6 Distribution of demographic groups among buy ers (%) . . . . . . . . . . . . . . 49
II.7 Descriptiv e statistics: car characteristics . . . . . . . . . . . . . . . . . . . . . . . 50
II.8 Estimates for the coefficient on cost per km β d . . . . . . . . . . . . . . . . . . . 55
II.9 Estimates for the coefficient σ 1 d (substitutability within model, betw een engine
types) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
II.10 Estimates for the coefficient σ 2 d (substitutability within segment, betw een mod-
els) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
II.11 Estimates for the coefficient on v ehicle price γ d . . . . . . . . . . . . . . . . . . 59
II.12 Demand elasticity for selected models with respect to fuel prices . . . . . . . . 59
II.13 Conditional F-v alues of the w eak instrument test – instruments for the price . . 60
II.14 Conditional F-v alues of the w eak instrument test – instruments for the market
shar e of the model within its segment s d j | s . . . . . . . . . . . . . . . . . . . . . . . . 60
II.15 Conditional F-v alues of the w eak instrument test – instruments for the market
shar e of a fuel-type within its model nest s d f | j . . . . . . . . . . . . . . . . . . . . . . 61
II.16 Robustness checks: elasticities with respect to fuel prices of diesel share, a v er-
age fleet fuel consumption (L/km) and CO 2 intensity (g/km) . . . . . . . . . . 62
III.1 Minimum quality requir ement q set b y the social planner as a function of
horizontal dif ferentiation µ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
IV .1 Ov erview of transaction cost estimates per fir m in the EU ETS in the literature 103
IV .2 Descriptiv e firm statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
xi

xii List of T ables
IV .3 Estimates fr om binar y quantile estimation and probit regr ession . . . . . . . . 114
IV .4 Of fset limits from National Allocation Plans . . . . . . . . . . . . . . . . . . . . 122
IV .5 Parametric mean estimates for transaction costs . . . . . . . . . . . . . . . . . . 127
IV .6 Specification test of binary regression quantile models (predicted and ob-
ser v ed probabilities) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
V .1 Descriptiv e statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
V .2 Regression results for EU net imports . . . . . . . . . . . . . . . . . . . . . . . . 147
V .3 Regressions of net import flo ws on environmental cost and its interaction with
transport costs and higher order terms of emission cost . . . . . . . . . . . . . . 148
V .4 Bilateral trade flows in logs of million U.S. dollars and embodied CO 2 emissions 151
V .5 Regression of net trade flo ws on emission cost with additional control for
scaling v ariables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
V .6 Summar y o v er view stating only the coef ficient of the ETS stringency v ariables
(for dif ferent specifications of net flo ws) . . . . . . . . . . . . . . . . . . . . . . . 159
V .7 Summar y o v er view stating only the coef ficient of the bilateral ETS stringency
variables (for dif ferent specifications of bilateral flo ws) . . . . . . . . . . . . . . . 160
V .8 Summar y o v er view stating only the coef ficient of the two separate ETS strin-
gency variables (for different specifications of bilateral flo ws) . . . . . . . . . . . 161

List of Figures
Figure Page
II.1 Nested decision-making structure of the car purchaser .............. 2 7
II.2 Ov er view of spatial v ariation in share of diesel cars and mileage ........ 3 0
II.3 Diesel fuel prices and market shares in Europe in 2012 .............. 3 2
II.4 Monthly fuel prices and cost per km ......................... 3 2
II.5 Monthly new registrations b y fuel-type ....................... 3 3
III.1 Nested decision-making structure of consumers .................. 6 8
III.2 Product line equilibrium as a function of license fee L 0 and horizontal differ -
entiation μ ........................................ 7 4
III.3 Equilibrium quality q F ∗
0 as a function of horizontal dif ferentiation μ ...... 7 6
III.4 Minimum quality requirement q as a function of horizontal differentiation μ .7 7
III.5 Reaction function of quality q F ∗
1 as a function of industry standard quality q I .8 0
III.6 Preferred product range equilibrium of licenser F as a function of quality q I
and horizontal dif ferentiation μ ............................ 8 1
III.7 Equilibrium qualities q I ∗ and q F ∗
1 as a function of horizontal dif ferentiation μ .8 4
III.8 License fee as a function of label quality q F
1 for μ = 0.4 and q I = 0.07 ...... 9 2
III.9 Joint firm profit Π as a function of industr y standard q I giv en licenser reaction
q F ∗
1 ( q I ) for μ = 0 . 4 ................................... 9 3
III.10 Comparing license fees from t = 0 and t = 1 .................... 9 4
IV .1 Stylized illustration of aggregate market equilibrium ............... 1 0 1
IV .2 Prices of Eur opean certificates (EUA) and offsets (CER) .............. 1 0 8
IV .3 Ratio of used of fset credits o v er o v erall offset entitlement ............. 1 0 9
IV .4 Relationship betw een of fset use, of fset entitlement and net allocation status . . 110
IV .5 Estimated transaction cost (in e ) - quantile plot .................. 1 1 5
IV .6 Estimated transaction costs (in e ) - distribution function and density ...... 1 1 5
IV .7 Observ ed frequencies and predicted pr obabilities of quantile method and probit 116
IV .8 S ector -specific quantile estimation results (in e ) .................. 1 1 7
IV .9 McCrary’s test for continuity ............................. 1 2 5
V .1 Stylized illustration of the pollution ha v en hypothesis .............. 1 3 4
xiii

xiv List of Figures
V .2 Correlation of the measures of environmental stringency (scatter and fitted
linear trend, sector -y ear a v erages acr oss countries, 2007 & 2011) . . . . . . . . . 144
V .3 Distribution of import taxes and transport costs (sector-a v erages 2004-2011) . . 145
V .4 Imports in v alue and imports in embodied carbon b y bilateral EU ETS treat-
ment status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

I
Intro duction
1

2 I Introduction
Un Chat, nomm ´ e Rodilardus,
Faisait de Rats telle d ´ econfitur e
Que l’on n’en voyait pr esque plus,
T ant il en avait mis dedans la s ´ epultur e.
Le peu qu’il en r estait, n’osant quitter son tr ou,
Ne tr ouvait ` a manger que le quart de son so ˆ u ;
Et Rodilard passait, chez la gent mis ´ erable,
Non pour un Chat, mais pour un Diable.
Or , un jour qu’au haut et au loin
Le Galand alla cher cher femme,
Pendant tout le sabbat qu’il fit avec sa dame,
Le demeurant des Rats tint chapitr e en un coin
Sur la n ´ ecessit ´ e pr ´ esente.
D ` es l’abord, leur Doyen, personne fort prudente,
Opina qu’il fallait, et plus t ˆ ot que plus tard,
Attacher un gr elot au cou de Rodilard ;
Qu’ainsi, quand il irait en guerr e,
De sa mar che avertis ils s’enfuiraient sous terr e ;
Qu’il n’y savait que ce moyen.
Chacun fut de l’avis de Monsieur le Doyen ;
Chose ne leur parut ` a tous plus salutair e.
La difficult ´ e fut d’attacher le gr elot.
L ’un dit : Je n’y vas point, je ne suis pas si sot ;
L ’autre : Je ne saurais. Si bien que sans rien faire
On se quitta.
Jean de la Fontaine (1668)
Old Rodilard, a certain cat,
Such havoc of the rats had made,
’T was difficult to find a rat
W ith natur e’ s debt unpaid.
The few that did r emain,
T o leave their holes afraid,
Fr om usual food abstain,
Not eating half their fill.
And wonder no one will
That one who made of rats his r evel,
W ith rats pass’d not for cat, but devil.
Now , on a day , this dr ead rat-eater ,
Who had a wife, went out to meet her;
And while he held his caterwauling,
The unkill’d rats, their chapter calling,
Discuss’d the point, in grave debate,
How they might shun impending fate.
Their dean, a prudent rat,
Thought best, and better soon than late,
T o bell the fatal cat;
That, when he took his hunting r ound,
The rats, well caution’d by the sound,
Might hide in safety under gr ound;
Indeed he knew no other means.
And all the r est
At once confess’d
Their minds wer e with the dean’ s.
No better plan, they all believed,
Could possibly have been conceived.
No doubt the thing would work right well,
If any one would hang the bell.
But, one by one, said every rat,
“I’m not so big a fool as that.”
The plan knock’d up in this r espect,
The council closed without effect.
T ranslation b y Elizur W right (1882)
Our society is facing great challenges. Among them, climate change has become
more imminent but da ys where it makes telle d ´ econfitur e 1 are still impending. Other
consequences of the lack of environmental sustainability can be easily felt toda y . While
w orking on my PhD at the DIW Graduate Center , I tra v eled to Beijing and coughed my
lungs out ev ery da y . Ther e w as so much air pollution in T eheran that I could not see the
base of the TV to w er while standing on top of it. I could barely breathe in Mexico City .
In Bogot ´
a, I tried to find a fairtrade far mer , knocking on many doors in v ain: it seems
the a v erage fairtrade far mer is too poor to ha v e a postal address. 2 In Amman, my friend
1 French for: total collapse
2 I ended up in front of the presidential palace because one farmer wrote do wn the only postal address

1. Sustainability policies 3
told me her broom closet w as originally included in order to house a modern-da y sla v e.
S omeone must start taking responsibility , without sa ying “Je ne suis pas si sot” 3 or
“Je ne saurais.” 4 S ome steps are clear , like the need to reduce pollution or to find a
gr elot , 5 while others much less so, like ho w to sustain comfortable living standards in the
meantime. W ith commitment and persev erance, I am convinced solutions can be found
that mo v e to w ard a mor e sustainable economic system: for this, w e need to e v aluate the
possible paths to sustainability , but not just the desirability of the outcome.
1 Sustainability policies
This dissertation assesses, empirically and theoretically , different market-based ap-
proaches to sustainability regulation. In particular , I ask whether the regulation alleviates
the problem it tar gets and at what cost. This section introduces the fundamental issues
behind sustainability regulation, giv es a historic o v er view of regulatory strategies, and
briefly presents the approaches studied in this dissertation. Section 2 then summarizes
the methods used in this dissertation and S ection 3 pro vides a summary of each chapter ’s
contributions; S ection 4 concludes.
Sustainability signifies the ability of a system to remain stable and productiv e indef-
initely . This concept contrasts with an economic system that is depleting economic and
natural resources and ma y collapse after depletion. Generally , three aspects of sustain-
ability of production systems are str essed: social w ell-being, environmental integrity , and
economic enterprise. Sustainability regulation often focuses on exter nalities as the main
challenge to environmental sustainability .
An exter nality arises whenev er one party’s actions af fect another party , without the
first party incurring any cost for this effect on the second: a side-ef fect of an (economic)
activity . A typical example is a production plant polluting a riv er , such that do wnstream
users suf fer from poor w ater quality without being responsible for , or benefiting fr om,
the polluting activity . In the context of air pollution, Crocker (1966, p. 62) describes
the exter nality phenomenon as the “div orce of emission costs from emission benefits.”
Useful concepts to think about negativ e exter nalities 6 include priv ate cost, damage, and
social cost. Priv ate cost refers to the polluting party’s cost, for example the cost of pro-
duction inputs such as labor and capital. Damage refers to the additional cost caused
to the party suf fering from the externality . Finally , social cost is the sum of damage and
priv ate cost.
T ypical economic analysis sho ws that a dif ference betw een mar ginal priv ate cost and
marginal social cost is harmful to social w elfare: a pr oducer not held responsible for the
he knew .
3 French for: “I am not so stupid [to sacrifice].”
4 French for: “I’m not able.”
5 French for: bell
6 As the bulk of environmental economics, this dissertation focuses on negativ e exter nalities; for
positiv e exter nalities, one has to replace damage and cost b y benefit.

4 I Introduction
polluted riv er w ater pollutes excessiv ely . Hence the concept of internalizing exter nalities
b y introducing a correction that aligns the polluting party’s priv ate marginal cost with
social marginal cost.
One of the major concer ns for environmental sustainability in 2017 is climate change
induced b y anthropogenic greenhouse gas emissions. The link betw een sustainability
and negativ e exter nalities is particularly salient here: the consequences of climate change
will be felt b y future generations, especially in poor countries 7 – surely not the same
agents emitting greenhouse gases toda y . W ithout regulatory inter v ention, emissions ex-
ceed w elfare-maximizing amounts when one includes future generations’ w elfare in cost-
benefit analysis. Estimates of marginal damage range fr om $14/tCO2e to $350/tCO2e
(V an den Bergh 2010); other methodologies yield estimates with smaller , but still large,
v ariance (Pindyck 2016). Exter nalities from greenhouse gases range from incr eases of
heat-related human mortality , o v er wildfires and floods, to declining w ater resources,
limited food security and destruction of terrestrial and marine ecosystems (IPCC 2014,
p. 14).
Moreo v er , climate change is a global exter nality: no matter where greenhouse gases
are emitted, their impact on the climate is the same (IPCC 2014). Regulating a global
exter nality problem is therefor e particularly dif ficult, as the efforts of some might be
undone b y fr ee-riding of others. Local pollution, like pollution of lake w ater , is easier to
address because one can rely on existing local institutions.
Although not usually seen as an exter nality , incr easingly inter national v alue chains
of consumer goods ha v e the unintended side-effect of increasing social inequality . Be it
textiles or commodity food products, such as cocoa and cof fee, the lack of social sus-
tainability materializes in po v erty of farmers, producers and emplo y ees. Prices of food
commodities are often so lo w and v olatile that pr oducers can barely economically sustain
their business. Sur prisingly , consumers commonly state that they are in principle willing
to pa y higher prices to reduce inequality . 8
In this context, what regulation can lead us to w ard mor e sustainability? Such regula-
tion has to contribute to mo ving to w ar d sustainable production processes (ef fectiv eness);
ideally , regulation should be shaped such that it minimizes cost and reduces unintended
side-ef fects (ef ficiency). This dissertation contributes to the search for ef fectiv e sustain-
ability policies.
The traditional approach to externalities is to forbid, in a top-do wn manner , the
har mful activity or to prescribe a less harmful alter nativ e, thereb y directly regulating
quantities of the exter nality . S o-called command-and-control policies prescribe emissions
standards or for ce adoption of best a v ailable technology . Economists ha v e r epeatedly
expressed the concern that command-and-control policies lead to inef ficiencies: the one-
7 IPCC (2014, p. 31): “Climate change is a threat to sustainable dev elopment[...]. Climate change
exacerbates other threats to social and natural systems, placing additional bur dens particularly on the
poor .”
8 In practice, the efforts ho w e v er often remain limited. For example, the Fairtrade initiativ e has v er y lo w
market shares; the highest Fairtrade market share is in the cof fee market, where it amounts to about 2% of
globally traded coffee in 2014 (Panhuysen and Pierr ot 2014).

1. Sustainability policies 5
technology-fits-all approach does not let any r oom for shifting the reduction to fir ms that
abate at the lo w est cost. In response, market-based approaches ha v e been put for w ar d.
“Market-based” policies in this context are defined as policies that regulate either quan-
tities or prices, but lea v e the other one of the tw o free to be deter mined b y a market
equilibrium. The follo wing subsections briefly presents Pigouvian taxes, emissions trad-
ing, and v oluntary priv ate standar ds as alternativ es to command-and-contr ol policies.
Note that the textbook ef ficiency of these policies depends on a set of assumptions, in
particular most models I allude to in this short introduction are static (not taking into
account dynamic considerations, in particular when facing inv estment decisions) and
ignore political economy considerations; in practice, their superiority to command-and-
control policies is less clear .
1.1 Pollution tax
The classical economic thinking about exter nalities is fundamentally shaped b y Pigou
(1920), 9 calling for market-based approaches using a price mechanism. Pigouvian taxes
on negativ e exter nalities – or subsidies for positiv e exter nalities – ha v e, for a long time,
been the economics textbook-solution to externality problems. The tax is a straightfor-
w ard for m of inter nalizing the exter nality: the regulator imposes a tax equal to marginal
damage to any emitter of a negativ e exter nality and giv es a subsidy equal to marginal
benefit to those causing a positiv e exter nality , thereb y aligning priv ate and social cost.
Once the polluter incurs the cost of his exter nalities, the market regulates the quantities:
higher production costs reduce the amount of externality b y reducing the quantity of
the polluting good and inciting more emission-ef ficient production pr ocesses. Chapter II
studies such a pollution tax.
Examples include fuel taxes, tobacco taxes, deposit-refund systems, and fat taxes.
While ideally the amount of the tax reflects the mar ginal exter nal damage to society , in
practice implementation is an “iterativ e t ˆ
atonnement type of planning game”(W eitzman
1974, p. 478) and abo v e mentioned examples can hardly be seen as pr ecise measures of
marginal damage.
The first adv antage of pollution taxes is that they are relativ ely simple to implement,
because public authorities and fir ms typically ha v e existing capacities for tax manage-
ment. Moreo v er , there is discussion about a “double dividend” of both go v ernment rev-
enue and exter nality reduction when re v enue-raising distorting taxes are substituted b y
more ef ficient externality taxes (see the critical discussion b y Fullerton and Metcalf 1997).
In case of technology-dependent exter nalities, such as CO 2 emissions, a tax has the dou-
ble ef fect of inducing immediate action as w ell as pushing for long ter m inv estments in
energy-ef ficiency .
On the negativ e side, in practice it is rather dif ficult to deter mine the marginal damage
of exter nalities, e.g. the cost of a ton of CO 2 emissions: when taxes are too lo w , damage
control is insuf ficient; when taxes are too high, resour ces are w asted in excessiv e damage
9 As cited in Baumol and Oates (1971), who coined the term “Pigouvian tax.”

6 I Introduction
control. Pindy ck (2016), for example, discusses the difficulty to measure the social cost
of carbon; his sur v ey r ev eals the lar ge v ariance of estimates ev en among experts.
In his seminal paper , W eitzman (1974) compares quotas (quantity regulation) to taxes
(prices). His starting point is the paradoxical observ ation that “the a v erage economist in
the W ester n marginalist tradition has at least a v ague preference to w ard indir ect control
b y prices, just as the typical non-economist leans to w ar d the direct regulation of quan-
tities[...]. Certainly a careful reading of economic theory yields little to support such a
univ ersal proposition”(W eitzman 1974, p. 477). T o W eitzman’s ey es, prices and quantities
are equally dif ficult to determine optimally: the social cost of a potential error of the pol-
icy maker v aries case b y case, and the choice betw een both policy tools should depend
on this relativ e “cost of a mistake.”
Moreo v er , there is some mixed evidence that consumers my opically under-inv est in
future economies from ener gy-ef ficiency (Allcott and W ozny 2013): many other factors
also impact the sensitivity of consumers. Thus, the impact of fuel taxes on inv estment in
automobile emission-ef ficiency is an empirical question, as ev aluated in Chapter II.
1.2 Cap-and-trade
Radically challenging conv entional wisdom, Coase (1960) pro v ocativ ely asks whether
the problem of externalities has been correctly laid out: before then, externalities w ere
thought of as unilaterally caused b y an emitter (the upstream firm polluting the riv er)
and af fecting an innocent party (the do wnstream r esidents eating poisoned fish). In
contrast, Coase insists on the r ecipr ocal nature of the problem: if my crop is set on fire b y
the nearb y railw a y , ma ybe my field is too close to the rails? Coase famously states that if
there w ere no costs to negotiation, granting a transferable o wnership right to one of the
parties could cost-ef ficiently solv e the exter nality problem, and more ef fectiv ely so than
a command-and-control appr oach or a tax; 10 only the redistributiv e question of initial
allocation remains.
Ho w e v er , after stating abov e-mentioned if -statement, Coase adds: “This is of course
a v er y unrealistic assumption.” (Coase 1960, p. 15) and continues on the importance of
taking into account economic considerations when making legal decisions, as these do
ha v e an impact on final resource use in presence of frictions. Ov er the course of his life, 11
Coase kept insisting on the importance of studying, “the importance of transaction costs,
the possibility of merger solutions, the costs associated with state action, and the need
for a comparativ e institutional approach” (Medema 2014, p. 111) and w as a co-founder
of Inter national S ociety for New Institutional Economics (ISNIE), while the economic
community enthusiastically w elcomed his negotiation result as a reason not to study
institutional economics. Ignoring this fundamental if leads to what Demsetz (1969) calls
a “nir v ana approach”: comparing reality with an optimal and, thus, utterly unrealistic,
unattainable w orld.
10 T ransferable ownership right in this context is equiv alent to full legal liability for either side.
11 Indeed, Coase started much earlier to insist, in another context, on the importance of the “cost of
using the price mechanism” (Coase 1937, p. 390).

1. Sustainability policies 7
Notwithstanding these considerations, the “Coase” theorem 12 w as fundamentally im-
portant for new er implementations of sustainability regulation, in particular for de v elop-
ing emissions trading schemes. The main for m of emissions trading schemes is cap-and-
trade: such a scheme fixes a maximum quantity (cap) of emissions and lets polluting
parties subsequently trade the units within this capped quantity . Thus, cap-and-trade
is a quantity regulation via the price mechanism; it allo ws firms to shift emissions be-
tw een regions, y ears, and sectors. Getting aggregate quantities “right” seems indeed
easier than specifying the right quantity for each sector or ev en firm, as in traditional
command-and-control quota policies. Crocker (1966, p. 81) underlines the “infor mation-
pro viding potential of a price system” to regulate air pollution. 13 Dales (1968) w as the
first to la y out the applicability of a cap-and-trade scheme as a solution to w ater pollu-
tion, follow ed b y a more general treatment b y Montgomer y (1972). Chapters IV and V
study the European cap-and-trade scheme.
As Professor Dominique Strauss-Kahn put it in my under graduate economics class,
“emissions trading is the solution [to the exter nality problem] that is at the same time
most cherished b y economists and most ignored b y policy makers.” 14 T oda y , this state-
ment is no longer entirely correct. Follo wing tentativ e pro visions in the US Clean Air
Act in 1977 and more decisiv e steps in the US Acid Rain Program in T itle IV of the 1990
Clean Air Act, emissions trading is no longer a niche idea. In particular , greenhouse gas
emissions no w fall under the international system of the Ky oto Protocol, the European
Emissions T rading System (EU ETS), the Califor nia Cap-and-T rade Program, Regional
Greenhouse Gas Initiativ e (RGGI, sev eral US states) and the A ustralian Clean Energy
Act. Currently China is w orking on its o wn national scheme. The US also has a trading
system for sulfur dioxide and nitrogen oxides.
The key argument in fa v or of cap-and-trade schemes is the cost-effectiv eness results
from Montgomery (1972): giv en a fixed quantity of emissions, o v erall abatement cost
is minimized as abatement is shifted to where abatement is cheapest. The other much-
discussed property of emissions trading deriv es directly fr om the “Coase” theorem: the
fact that final allocation of productiv e factors is independent of initial allocation, called
the independence pr operty . “This property is v ery important because it allo ws equity and
ef ficiency concer ns to be separated in a relativ ely straightforw ard manner ” (Hahn and
Sta vins 2011, p. 267). Based on this pr operty , emission certificates w ere distributed for
free during the first tw o phases of the EU ETS, leading to a massiv e redistribution of
funds across firms. Ho w ev er , the independence property fundamentally relies on the
absence of transaction costs. This condition is studied in Chapter IV.
One disadv antage of emissions trading is that it inv olv es the creation of an artificial
12 The “Coase” theorem w as first called so b y Geor ge Stigler , and might thus rather be called “Stigler
theorem.” I follo w a suggestion b y McCloske y (1998) to add the “quotation marks around the non-Coasean
‘Coase’ theorem.”
13 Crocker also points out the potential free-rider problem as no one can be excluded from using the
(scarce) resour ce “free air ”; he concludes that this problem makes totally decentralized solutions (without
quantity regulation b y the contr ol authority) undesirable.
14 Cited from the author ’s memor y of an undergraduate economics class at Sciences Po Paris in 2006.

8 I Introduction
market: to non-economists, the underlying logic often appears counterintuitiv e. As there
is no natural market and assets are intangible, the regulator has to incur administra-
tiv e costs for establishing a cap-and-trade scheme. Firms also ha v e to build capacities
for emissions trading, additionally to existing structures for tax management. Giv en
the complexity of aggregating national rules, European emissions trading “abounds in
loopholes” 15 is vulnerable to regulator y capture (Ga w el et al. 2014, p. 176). The most-
discussed disadv antage of emissions trading is one that is common to all unilateral reg-
ulation of global exter nalities: if envir onmental regulation makes it more expensiv e to
produce in one region, production might simply mo v e to another , unregulated region; be
it b y loss of market share of regulated firms or relocation of their production facilities. In
the US, the debate is structured around the “pollution ha v en hypothesis” and American
environmentalists ha v e called for the establishment of measures to correct the distortion
at the border in or der to pr otect regulated industries. In the EU, the issue is ter med
“carbon leakage” b y adv ersaries of envir onmental policy , who call for a w eakening of
environmental stringency . The potential for carbon leakage depends, among other fac-
tors, on the relativ e cost impact of environmental policy , trade barriers, and competition
intensit. The empirical importance of carbon leakage is ev aluated in Chapter V.
1.3 Pr iv ate v oluntar y standards
An alter nativ e that has ev olv ed in parallel to abo v e-mentioned public policy approaches
are priv ate v oluntary standards. In areas where consumers feel that public regulation
is not strong enough, firms ha v e v oluntarily committed to higher environmental, social,
or safety standards: o v er the last 100 y ears, v oluntar y implementation of higher stan-
dards is especially common in the food industry . The first mo v ements concentrated on
the environmental impact of food pr oduction as w ell as health ef fects from the use of
chemicals. V oluntar y self-regulation started mo ving into mainstream production with
the establishment of v oluntar y third-party v erified labels (ecolabels) in the 1970s, for ex-
ample in Ger many with the Bioland label. Follo wing increasing market shares of these
ecolabels, national gov ernments established official requirements and labels for ecological
food production that ef fectiv ely established minimum label standar ds. These labels are
still v oluntar y and complemented b y more demanding priv ate standards such as Deme-
ter (Ger many) or Nature&Progr `
es (France). Chapter III studies the interaction when
sev eral labels compete in one market.
The success of ecolabels spurred dev elopment of labels in other domains. Such labels
allo w firms to credibly commit to higher production standards than those prescribed
b y regulation regarding, for example, social sustainability , safety , absence of genetically
modified plants, and user friendliness – anything consumers ha v e come to perceiv e as
a negativ e exter nality . V oluntar y labels solv e the problem in a decentralized w a y: con-
sumers themselv es can take on the responsibility of ensuring that acceptable production
15 A V A T carousel fraud on Eur opean emissions allo w ances caused e 5 billion in damage for Eur opean
taxpa yers, see for example https://www.europol.europa.eu/newsro om/news/
further- investigations- vat- fraud- link ed- to- ca rb on- emissions- trading- system ; retriev ed on the 05/05/2017.

1. Sustainability policies 9
conditions exist. The need for third-party labels can be explained b y the fact that most of
certified points are not v erifiable before purchase and often not e v en after . Interestingly ,
third-party sustainability labels often certify characteristics of the pr oduction process,
rather than intrinsic characteristics of the good. Economics ha v e pr o vided relativ ely little
evidence on ho w such labels affect pr oduct markets in equilibrium.
Fairtrade labels are a particularly interesting case. When production chains ar e geo-
graphically dispersed, national go v er nments struggle to address externalities with stan-
dard policy-tools. An example for an inter national v alue chain and b y far the largest
fairtrade sector is the cof fee market: while almost all coffee is drunk in W ester n indus-
trialized countries, its production r emains largely in the Global South. Consequently ,
v oluntar y labels ha v e a natural adv antage o v er national regulators in such sectors.
Labels pursue dif ferent objectiv es, as they are backed b y differ ent labeling organi-
zations. S ome labels are established b y nongo v ernmental organizations (NGOs) or b y
a public regulator to tar get some dimension of social w elfare. Others are organized as
priv ate fir ms, maximizing their o wn profits fr om license fees. Finally , labels are often
established as industr y standards b y fir ms themselv es, maximizing industr y profits for
example in reaction to the establishment of a priv ate label (Fischer and L y on 2014). S ome
examples of ecolabel certifiers are Ecocert (for -profit) and the Eur opean Union Ecola-
bel (public); safety-labels for textiles include BlueSign (for -profit) and Oeko-T ex (NGO);
sustainability labels in the w ood industr y are Forest Ste w ardship Council (NGO), PEFC
(industry standard), and the Sustainable Forestry Initiativ e (industr y standard).
Adv ocates of priv ate v oluntary standards underline that each industr y kno ws its o wn
business field best and, thus, is best able to decide on appropriate regulation; be it b y
establishing its o wn standar d or b y deciding which NGO-backed or public standard to
of fer . Bringing forw ar d an invisible hand argument, they ar gue that fir ms ha v e an in-
terest to serv e consumer inter ests, pro vided the consumer is willing to pa y accordingly .
Putting all po w er into the consumer ’s hand, third-party labels ar e sometimes portra y ed
as an opportunity to “shop for a better w orld.” Moreo v er , when production is distributed
around the globe, no gov ernment can directly regulate production standar ds, environ-
mental protection, and labor conditions, so that public policy cannot be a substitute to
v oluntar y industr y self-regulation here.
Opponents claim that priv ate v oluntary standards often address the issues too super -
ficially and are mere “green-w ashing” marketing tools. The independence of third-party
labels is often not w ell established, leading to conflicts of interest and credibility issues.
A larger pr oblem is that if the go v ernment has the aforementioned difficulties in deter -
mining the correct le v el of damage control, it seems rather unlikely that a decentralized
mass of consumers can do a better job. When fir ms are competing in oligopoly , they can
establish priv ate v oluntary standards that serv e as coor dination tools in or der to segment
the market, as sho wn in Chapter III.

10 I Introduction
2 Methodology
The chapters of this dissertation use dif ferent modeling and estimations techniques that
are briefly introduced in this section.
2.1 Nested logit
Both Chapters II and III model consumers taking a discrete choice decision. Such a de-
cision is commonly modeled using the nested logit model. The nested logit is a general-
ized for m of the multinomial logit model, going back to the seminal article b y McFadden
(1978).
In the general model, the consumer i faces a limited number of K alter nativ es (prod-
ucts, most of the time), each of them pro vides him with utility
U ik ( p k , X k , ξ k , ϵ i k ; θ ) , (1)
where p k is the price of alternativ e k , while X k are the other observ ed and ξ k the unob-
ser v ed characteristics of this alter nativ e. ϵ i k is an error term and θ a v ector of parameters.
The consumer chooses option k if it giv es him the highest utility:
k = arg max
j U i j with j = 0, 1, ..., K . (2)
In the simple logit model, the consumer ’s utility is of the functional for m
U ik = α + β p k + γ X k + ξ k + ϵ i k (3)
where the residual ϵ i k is assumed to follo w an extreme v alue type I distribution. In-
tegrating o v er this distribution allo ws me to determine each consumer ’s probabilities
to choose a particular alter nativ e k , and giv en homogeneous consumer preferences this
probability equals the market share s k . For simplicity , I denote δ k = α + β p k + γ X k + ξ k
the deter ministic part of utility:
s k = exp ( δ k )
∑ K
j = 0 exp ( δ j ) (4)
Note that I need to fix the utility of one of the goods in order to normalize the equation
system. Usually , the product 0 is defined to be the outside good, i.e. no purchase, with
utility δ 0 = 0, so that I ha v e:
ln ( s k / s 0 ) = α + β p k + γ X k + ξ k (5)
which is the equation that allo ws me to estimate parameters α , β and γ , treating the
unobser v ed product characteristic ξ k as an error term.
This model implies the independence of irr elevant alternatives (IIA): the choice betw een
tw o alter nativ es is assumed independent of the existence of another third alternativ ely .

2. Methodology 11
This assumption is often considered unrealistic, famously illustrated b y the red bus/blue
bus problem in McFadden (1980).
The nested logit model attempts to alleviate this pr oblem b y adding a nested gr oup
structure o v er alternativ es, assuming IIA within nests but allo wing substitution to de-
pend on nest structure. T echnically , this dependence is achiev ed b y allo wing error ter ms
to be correlated within a nest. In the car market application of Chapter II, for example,
I assume that sports car driv ers substitute more easily to another sports car than to a
multi-v an
Consumer utility from good k ∈ T g is then defined as
U ik = δ k + ζ i g + ( 1 − σ ) ϵ i k (6)
where both ϵ i k and ζ i g + ( 1 − σ ) ϵ i k are assumed to follo w an extreme v alue type I dis-
tribution. σ then measures the strength of within-nest correlation; when σ = 0, the
nesting structure is irrele v ant and the nested logit collapses into a simple multinomial
logit model. By integrating, one can deriv e expressions for market shares s g for the
market share of nest g and s k | g for the market share of good k within nest g :
s k | g = exp ( δ k / ( 1 − σ ) )
D g
with D g = ∑
j ∈ T g
exp ( δ j / ( 1 − σ ) ) ; (7)
s g = D ( 1 − σ )
g
∑ G
h = 0 D ( 1 − σ )
h
; (8)
s k = s k | g × s g . (9)
When estimating nested logit models, it is important to account for the endogeneity
of the price p k and within market shares s k | g b y using instrumental v ariables.
2.2 Binar y quantile estimation
Chapter IV uses a more recently de v eloped w a y of modeling discrete choice: binar y
quantile estimation (Kordas 2006). This estimation technique uses binar y decisions b y
fir ms to infer infor mation about an underlying continuous distribution of transaction
costs.
Most econometric methods commonly used concentrate on the mean: certainly the
mean is not the only infor mativ e parameter , but it has conv enient statistical pr operties.
Koenker and Bassett Jr . (1978) famously challenged this practice b y intr oducing the com-
putationally more cumbersome quantile estimation method that allo ws the econometri-
cian to estimate conditional distributions.
While their original model applied to continuous outcomes, Kordas (2006) built on
the (smoothed) maximum score estimator (Manski 1975, Horo witz 1992) to establish a
methodology to estimate binary regression quantiles. The model assumes that there is a

12 I Introduction
latent continuous v ariable Y ∗ , of which only a binar y indicator Y is observ ed:
Y ∗
i = X ′
i β + ϵ i (10)
Y i = 1 { Y ∗
i > 0 } , (11)
where X i is a v ector of co v ariates for obser v ation i , ϵ i is a random error term and β is a set
of parameters of interest. If Y ∗ w as obser v able, one could estimate a quantile regr ession
follo wing Koenker and Bassett Jr . (1978) for each quantile τ ∈ ( 0, 1 ) :
Q Y ∗ | X ( τ ) : = F − 1
Y ∗ | X ( τ ) = X ′ β (12)
where Q Y ∗ | X ( · ) and F Y ∗ | X ( · ) are the conditional quantile and distribution functions of
Y ∗ . Giv en ho w ev er that Y ∗ is not obser v able in many settings, one can use the fact
that quantile estimates are robust to a monotone transformation of the outcome v ariable
(Koenker and Hallock 2001). An indicator function is a monotone transfor mation, so the
conditional quantile distribution of Y is giv en b y
Q Y | X ( τ ) = 1 { X ′ β ≥ 0 } (13)
Although I only obser v e a binar y outcome Y , I can dra w conclusions on the condi-
tional distribution F Y ∗ | X ( · ) of the latent continuous v ariable Y ∗ .
If the error term is assumed to be independent and identically distributed, follo wing
a nor mal distribution, the median quantile of equation (13) can be estimated with a
standard pr obit model. Ho w e v er , this assumption might not be appropriate in many
situations, e.g. if the error distribution is ske w ed. Binar y quantile estimation allo ws
the researcher to r emain agnostic about the distribution of the error term, making this
method particularly robust to outliers.
Just like the probit r egression relies on an assumption of a mean zer o error term, the
binary quantile regression assumes that the conditional median error is zero. In practice,
the binar y quantile estimator at the median ( τ = .5 ) maximizes the number of “correct
predictions.” The estimation of this model inv olv es optimization o v er a complex func-
tion. In Chapter IV, I use simulated annealing which has the adv antage of being more
robust to starting v alues, local optima, and discrete parts of the objectiv e function. Such
methods ha v e only recently become a v ailable with the dramatic increase of a v ailable
computing po w er . 16
2.3 T reatment effects
While the pre vious methods use a discrete choice approach, Chapter V is based on con-
tinuous outcomes in a treatment ef fect analysis frame w ork.
The standard w a y to think about the treatment ef fect is shaped b y Rubin’s (1974)
model for causal inference using counterfactual outcomes. The fundamental problem is
16 Nev ertheless, the main estimation of Chapter IV needs almost six hours to run.

3. Contr ibution 13
that the outcome for a particular individual usually is obser v ed with the treatment or
without, but rarely both. Nev ertheless, one w ould like to find the treatment effect τ ,
giv en b y:
τ = E ( Y | T = 1, X ) − E ( Y | T = 0, X ) (14)
where Y is some outcome, X a set of co v ariates and T = { 0, 1 } the treatment status.
When treatment is randomized across individuals and compliance is full, the estimate for
the a v erage treatment effect is simply giv en b y the difference betw een sample mean of
the treated and the sample mean of the untreated. In practice, situations with incomplete
compliance – when some assigned to the treatment do not follo w – or e v en obser v ational,
non-randomized data are more common; a lar ge literature dev eloped to addr ess these
problems, notably b y the w ork of Heckman, Angrist, and Imbens.
Chapter V uses such a framew ork in a some what unusual setting: obser v ations i
are in this case sectors and treatment can be defined as either binary or continuous,
relating this w ork to studies with multi-v alued treatment (also called treatment intensity
or dose-response). In the most simple v ersion, one assumes a constant unit treatment
ef fect Y j − Y j − 1 = β for all j and all sectors, so that the model can be estimated using
linear regression models. I further assume strong unconfoundedness, i.e. conditional on
a set of co v ariates, environmental policy stringency (the treatment) does not depend on
import intensity of a sector (the outcome). I thus estimate
Y i = α + β θ i + γ X i + ϵ i (15)
where X i is a set of co v ariates, ϵ i a random err or ter m and θ i a continuous treatment
measure. Alter nativ ely , I also test whether my results dif fer when I use a binary indicator
function ˜
θ i = 1 ( θ i > 0 ) . Ho w e v er , Angrist and Imbens (1995) remind us that collapsing
a multi-v alued treatment into a binar y treatment indicator – abo v e/belo w some cut-off –
generally bias the estimates.
3 Contr ibution of this dissertation
Each chapter concentrates on one of the abo v e-mentioned policies: Chapter II studies the
impact of fuel taxes on new automobile fuel ef ficiency; Chapter III studies the strategic
quality setting of sustainability labels; Chapter IV estimates transaction costs in European
emissions trading; and Chapter V searches for evidence of carbon leakage in Eur opean
emissions trading. T able I.1 pro vides an o v erview of chapter titles, co-authors and pre-
publications.
Chapters II, IV and V study policies that address environmental sustainability con-
cer ns, while Chapter III focuses on social sustainability . Chapter V additionally relates to
the economic sustainability of emissions trading. Chapters II, IV and V rely on economet-
ric analysis, while Chapter III dev elops a theoretical model. Chapters II and III present
a hypothetical ex ante ev aluation of a policy , while the last tw o chapters pro vide an ex
post assessment of particular aspects of emissions trading.

14 I Introduction
While all of the chapters belong to environmental economics, this dissertation is posi-
tioned at the crossr oads with sev eral other sub-fields of economics. Chapters II, III and IV
use approaches fr om industrial organization. Chapter V is based on literature from trade
and inter national economics, and Chapter IV relies on the concept of transaction costs
that is central to new institutional economics.
In practice, the impact of a policy depends v er y much on the agent’s sensitivity to
economic incentiv es, which Chapters II and V attempt to measure. The desirability of
v oluntar y approaches and industry self-regulation depends on strategic fir m interactions
as studied in Chapter III. Finally , the crucial if of the “Coase” theorem deter mines if
one can use a solution relying on negotiation or not, which Chapter IV studies for the
European emissions trading scheme. Note that these policies address the pr oblem from
v er y dif ferent angles, so that the unit of analysis is sometimes the consumer (Chapter II
and III), sometimes the fir m (Chapters III and IV) or e v en sector-le v el aggregates of firms
(Chapter V).
The follo wing subsections go more into detail on each chapter ’s contribution.
3.1 Fuel taxes and automobile fuel ef ficiency: the importance of consumer
elasticity
Follo wing ef forts to address emissions fr om priv ate r oad transports and to ensure po-
litical independence from oil-pr oducing countries, automobile fuels are among the most
hea vily taxed goods categories in Europe. The impact of fuel taxes depends on consumer
elasticity in tw o dimensions: in the short run, consumers can adjust their mileage driv en
with their current car (intensiv e margin); in the long run, consumers can inv est in more
ef ficient cars (extensiv e margin); additionally , there is potentially an interaction betw een
margins, as consumers who inv ested in a more ef ficient might react less in their mileage
or ev en driv e more, the so-called “rebound ef fect.”
Chapter II ev aluates the impact of a hypothetical fuel tax on the extensiv e margin, i.e.
on new car pur chases. The research relies on exhaustiv e consumer -lev el data of monthly
registration of new cars in France. I use infor mation on the car holder to account for
heterogeneous preferences acr oss purchasers and identify demand parameters thr ough
the large oil price fluctuations of this period. The results suggest that the sensitivity of
short-ter m demand with respect to fuel prices is generally lo w and, in particular , for
corporate purchases.
Using the estimated parameters of consumer demand to compute elasticities, Chap-
ter II estimates the ex ante impact of tw o dif ferent policies. First, a policy equalizing
diesel and gasoline taxes w ould reduce the share of diesel-engines in new car pur chases,
without substantially changing the a v erage fuel consumption or CO 2 emission lev els of
new cars. S econd, I suggest a rev enue-equiv alent carbon tax that w ould be at 51 e /ton
of CO 2 . Again, this policy has only small effects on a v erage fuel consumption or a v erage
CO 2 emission lev els of ne w cars.
As I refrain from taking any hypothesis on mileage and mileage elasticity of con-
sumers, I cannot identify whether consumers under-inv est in fuel ef ficiency relativ e to

3. Contr ibution 15
T able I.1: Ov er view b y chapter: topic, pre-publication and author ’s contribution
Ch. T itle Co-A uthors Pre-Publication Contribution
I Ov er view of market-based sus-
tainability policies
Single author (not published)
II How do fuel taxes impact ne w car
purchases? An ev aluation using
French consumer -le v el data
Pauline Giv ord,
C ´
eline Grislain-Letr ´
emy
DIW Discussion Paper
1428, 2014;
Revise&Resubmit at Ener gy
Economics;
Follo w-up pr oject of the
author ’s master thesis
A uthor w orked on the initial draft, in-
cluding in particular the literature re-
view . Final de v elopment of the model,
computational implementation, inter pre-
tation of results and writing w as collabo-
rativ e.
III Competition betw een for -profit
and industry labels: the case of so-
cial labels in the cof fee market
Pio Baake DIW Discussion Paper
1686, 2017;
under re vision
Research w as initiated b y the author , re-
sponsible for writing the manuscript. Es-
tablishment of the model w as collabora-
tiv e.
IV Of fset credits in the EU ETS: a
quantile estimation of fir m-lev el
transaction costs
Single author DIW Discussion Paper
1513, 2015;
Environmental and Resour ce
Economics, forthcoming.
The final publication is
a v ailable at Springer via
https://doi.org/10.1007/
s10640-017-0111-1.
The author is responsible for all parts of
the research.
V Does the EU ETS cause carbon
leakage in European manufactur -
ing?
Aleksandar Zaklan DIW Discussion Paper
1689, 2017;
under re vision
Research w as initiated b y the author , re-
sponsible for dev eloping the model, per -
for ming data analysis and interpreting
the results. W riting, as w ell as data col-
lection and management w ere collabora-
tiv e.

16 I Introduction
potential sa vings from more ef ficient cars: the rationality of consumer response is not
assessed here. Nev ertheless, in order to impact a v erage fuel efficiency b y a magnitude
not only statistically significant but also economically meaningful, fuel tax w ould ha v e
to be much higher , raising the question of political feasibility .
3.2 Competition betw een sustainability labels: ho w an industr y standard
strategically impacts product lines
Priv ate v oluntary standards (labels) of fer the consumer the possibility to enfor ce certain
points of production practices be y ond legal minimum lev els. From a firm perspectiv e, la-
bels are a tool to dif ferentiate pr oducts from their competitors and/or their o wn product
lines.
The cof fee market is characterized b y labels more than any other pr oduct category .
Cof fee is a product with an international v alue chain, as coffee farming is not possible
in most cof fee consuming countries, for example in Europe. Moreo v er , w orld coffee
prices ha v e dramatically fallen since the end of the Cold W ar (which marked the end of
the Inter national Cof fee Agreement) so that many cof fee farmers liv e at mere subsistence
lev el. Consumers are w orried about such inequality and see the coffee farmers’ po v erty as
a negativ e side-effect of mainstr eam conv entional coffee pr oduction. Different countries
sho w dif ferent market constellations of pr oduct line riv alr y: firms specialize in some
countries, while they compete on all market segments in others, similar to the results of
my model.
Chapter III examines a market with labels of dif ferent quality and objectiv es. I model
ho w an industry standard interacts with the fairtrade label, facing firms in duopoly that
decide which labels to of fer . The incumbent fairtrade label maximizes its own profit and
is challenged b y an industr y standard that maximizes joint fir m profits. Using a nested
logit, the result of this multi-stage game depends crucially on the (exogenous) degree
of horizontal dif ferentiation. The industr y label alw a ys w ants to segment the market, if
possible, and attempts to distort the number of labeled products do wnw ards. For high
lev els of horizontal dif ferentiation, the industry cannot coordinate on not competing on
all labels; for lo w lev els of horizontal differ entiation, the market is segmented; finally ,
for inter mediate lev els of horizontal dif ferentiation, the industry sets a strategically lo w
standard that in equilibrium r educes the o v erlap of pr oduct lines from dif ferent firms
and thereb y reduces competition. At all le v els of horizontal dif ferentiation, the industr y
benefits from the intr oduction of the industr y standar d.
A social planner w ould like to prev ent such a distortion, as this maximizes both con-
sumer utility and aggregate social w elfare. I explore whether there is scope for a policy
imposing a minimum label quality and find that a minimum label is only binding in
the cases with inter mediate horizontal dif ferentiation in which the industry standard
strategically reduces the number of a v ailable pr oducts. For v er y high or v er y lo w le v-
els of horizontal dif ferentiation, the industr y standard cannot strategically induce mor e
segmented product lines; in these cases, a minimum label quality is not binding.

3. Contr ibution 17
3.3 T ransaction costs in European emissions trading: the fundamental if in
the Coase theorem
As underlined b y , for example, Rose and Ste v ens (2001), the cost-ef fectiv eness from
cap-and-trade schemes stems largely fr om the transferability of pollution in time (bank-
ing/borro wing), betw een fir ms (trade), and across regions (linking of regional emissions
trading schemes). Many hopes ha v e in particular been put into the last point, trading be-
tw een geographical regions; ho w ev er , critiques argue that industrialized countries should
not hamper economic dev elopment of the Global South b y pa ying them for not produc-
ing. When putting into place the EU Emissions T rading System (EU ETS), the EU thus
introduced a possibility to substitute some foreign emission r eduction ef fort to domestic
ef fort via of fset certificates, but also strongly limited the o v erall amount of such of fset
certificates.
An of fset certificate is created from emission r eductions in unregulated regions, cer-
tified b y the responsible UN Envir onment Programme (UNEP). Of fset certificates ha v e
been cheaper than European certificate at all times, although the y are substitutes within
the EU ETS. Thus, fir ms had a strong incentiv e to use of fset certificates up to their firm-
specific quota. Ho w e v er , a considerable number of fir ms did not exhaust their offset
quota and, b y doing so, seemingly for w ent pr ofits.
While most literature on emissions trading ev aluates the ef ficiency of regulation in a
frictionless w orld, in practice fir ms incur costs when complying with regulation. T rans-
action costs of emissions trading include infor mation gathering, forecasting of allo w ance
prices, finding trading partners, bargaining, contracting, and managing price risk or , in-
stead, the costs of out-sourcing the whole trading process – costs that are contingent on
activ ely buying and selling certificates. In order to assess the rele v ance of emissions trade
related fixed transaction costs, Chapter IV examines the use of inter national offset cr edits
in the EU ETS. It establishes a model of fir m decision under fixed (quantity-inv ariant) en-
try costs and estimates the magnitude of trading costs rationalizing fir m beha vior using
semi-parametric binary quantile regressions.
The resulting cost estimates are sizable: the y pre v ent a fifth of all fir ms, esp ecially
small emitters, from using of fset certificates. Comparing binary quantile results with
probit estimates sho ws that high a v erage transaction costs result fr om a strongly right-
skew ed underlying distribution. For most fir ms, the bulk of transaction costs stems from
certificate trading in general, rather than additional participation in of fset trading.
3.4 Carbon leakage and European emissions trading: “much ado about
nothing”?
When the EU ETS w as introduced, carbon leakage w as one of the biggest concerns of pol-
icy makers, industr y representativ es, and academics. The fundamental dilemma is that
the regulator , on the one hand, w ants the social cost of emissions to be passed thr ough
to fir ms and consumers in order to align priv ate and social costs. On the other hand,
the policy maker is concer ned about the effect of the policy on pr oduct prices, because

18 I Introduction
this makes foreign products (pr oduced in non-regulated regions) mor e attractiv e. This
problem stems inherently fr om the unilateral nature of this ef fort to reduce a global ex-
ter nality . The debate is particularly salient in Europe, where the EU ETS co v ers emissions
of many traded sectors.
In a first step, Chapter V sur v e ys ho w carbon leakage and the pollution ha v en hypoth-
esis ha v e been identified in previous literature with particular attention to the definition
of outcome and policy treatment v ariables.
In a second step, Chapter V uses a panel of trade and input-output data from the
Global T rade Analysis Project (GT AP), in order to compute trade flo ws in v alue and in
embodied carbon, and combine it with administrativ e plant-lev el data from the EU ETS.
This allo ws me to account for dir ect and indirect (through electricity use) envir onmental
cost from cap-and-trade regulation. I consider both bilateral trade flows and net imports.
I do not find any evidence in fa v or of carbon leakage caused b y the EU ETS during its
first tw o trading phases.
4 Concluding remarks
The challenges to contain climate change and to reduce global po v erty are just some
aspects of a global search for sustainability . The relativ ely ne w consciousness about our
impact on future generation’s living conditions giv es us a responsibility to address these
problems quickly .
This dissertation assesses the ef fects and side-effects of certain appr oaches to sustain-
ability regulation. Chapter II finds that households react little to fuel-tax incentiv es in
their v ehicle choice and fir ms react ev en less. Chapter III finds that strategic interaction
betw een labels reduce the w elfare benefit of labels. Chapter IV attempts to put a price
tag on inertia and the burden of r egulator y complexity of European emissions trading.
Chapter V finally finds that alar ming scenarios of side-ef fects of European emissions
trading – production relocation and competitiv eness loss of Eur opean firms – ha v e not
materialized.
Economic systems are embedded in social structures. The isolated view of individual
policy measures easily allo ws the economist to compute optimal beha vior in stylized
models, but empirical e v aluation typically finds that agents r eact much less. A typical
example are the contrasting findings of ex ante models predicting dramatic le v els of
carbon leakage, and ex post econometric studies that fails to find any evidence of carbon
leakage (such as Chapter V). Ov erall, the surrounding social and organizational elements
increase the system’s inertia. The findings about transaction costs in this dissertation
underline the importance of keeping complexity at ba y .
Thus, it seems as if society needs to take much more drastic steps if change is to be
achiev ed quickly . More drastic r egulation necessarily implies higher costs and must be
backed b y a strong commitment to sustainability . Emissions trading is an example of
lacking commitment: policy makers both w ant to make emissions more costly for fir ms
and at the same time protect firms from these costs. S olutions to the carbon leakage

4. Concluding remarks 19
problem include bor der -carbon adjustments (carbon-based tarif fs) and output-based al-
location; while the for mer is v er y likely to be against WTO la ws, the latter unfortunately
undoes (part of) the incentiv e ef fect that is the raison d’ ˆ etre of emissions trading. The
point that has so far not attracted a great deal of attention is that carbon leakage is not
– at current price lev els – an actual problem (Chapter V): firms that remain in Europe
despite labor costs being 10 to 30 times higher than in emerging nations (e.g. Schr ¨
oder
2016) do not relocate for emissions costs of e 5/tCO2e.
More research is necessary in order to identify ho w sustainability policies can ha v e the
largest ef fect at the smallest cost. Ho w ev er , this search for ef ficiency should not conceal
the hard truth that change comes at some cost and that clear priorities and determination
are key to mo v e a w a y from toda y’s unsustainable practices.

20 I Introduction
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I I
Ho w do fuel taxes impact new ca r
purchases? An evaluation using F rench
consumer-level data
Quitte `
a pleurer , je pr ´
ef `
ere
pleurer dans une Jaguar .
Fran ¸ coise Sagan
on the rationality of automobile
purchases.
23

24 II Fuel T axes
1 Introduction
In France, road transport produces more than a thir d of total CO 2 emissions and much
higher shares of other greenhouse gases. 1 On the one hand, this problem might be
alleviated b y a shift to diesel-fueled cars, as diesel is more dense in ener gy and diesel-
engines particularly ef ficient in using it: typically , a diesel car produces less CO 2 per km
than a similarly-sized gasoline-fueled car . On the other hand, diesel cars also produce
medically hazardous fine particles (in particular black carbon) and nitrogen oxides (NO x ).
Thus, policy makers are facing both a global climate problem as w ell as a local health
issue; shifting to w ard more diesel-fueled cars might alle viate the global exter nality , but
increase local concer ns.
Facing the conundrum betw een global and local pollution, European policy makers
ha v e, for a long time, opted to support diesel, particularly in France (Hiv ert 2013). This
tax adv antage for diesel is fueling a renew ed debate, sparked b y episodes of smog. In De-
cember 2016, air quality in France dropped so lo w that the go v er nment hea vily restricted
driving and Paris authorities ha v e banned the oldest and most polluting v ehicles from
the city center , pledging “an end to diesel” in Paris b y 2020. As stressed, for instance
b y Ma y eres and Pr oost (2001), environmental benefits of diesel cars ha v e been o v eres-
timated: the environmental costs of diesel cars are much higher than those of gasoline
cars. While diesel cars emit more fine particles and NO x that har m human health, new
technology decreases the spread betw een CO 2 -emission-ef ficiency of diesel and gasoline
cars. 2 The production of diesel-models is also more CO 2 intensiv e because the y are hea v-
ier . Against this background, in 2015 the French go v er nment announced the progressiv e
reduction of the relativ e tax adv antages for diesel fuel. 3 This tax alignment adds to a
previous “carbon tax” passed in France in 2003 at a modest e 15 per tonne of CO 2 . Such
a tax is proportional to the amount of CO 2 emitted and is, thus, considered a more effi-
cient incentiv e aligning directly the priv ate cost to the consumer and the externality cost
to society .
Emissions from r oad transport depend hea vily on the v ehicle fleet in circulation, as
cars are durable goods, and regulation affecting the entry of new v ehicles impact emis-
sions for a long time. While mandatory standards (command-and-control regulation)
w ere the most prominent regulation until the 1990s, alter nativ e regulations ha v e been
tested since, in particular economic incentiv es such feebates or fuel taxes. 4,5 Fuel taxes
1 Se e http://www.citepa.o rg/en/air- and- climate/analysis- b y- sector/transports ; retrie v ed on 14/03/2015.
2 Mira v ete et al. (2015) go as far as to argue that diesel-friendly policy in Eur ope is essentially a
non-tariff trade barrier against American manufacturers.
3 The difference w as reduced from 14.9 cent in 2015 to 11.7 cent in 2016 and 9.4 cent in 2017. The path
to full equalization such as described in this chapter has y et to be defined; see
http://www.douane.gouv.fr/a rticles/a12285- ca rburants- gazole- sup er- e10- taux- de- taxe- pa r- region ; retriev ed
on 05/09/2017.
4 Besides recent scandals sho w that standards seem dif ficult to enfor ce ef fectiv ely .
5 Feebates, a system combining fees (for more polluting cars) and rebates (for less polluting cars) w ere
implemented in sev eral European countries in the 2010s. This mechanism is expected to shift consumer
expenses to w ar d less polluting goods, and to be self-financed as the fees should compensate the rebates.

1. Introduction 25
ha v e the adv antage of af fecting both the present and future emissions: car o wners are
immediately encouraged to driv e less with their current car when fuel prices rise, while
at the same time inv estment in fuel-efficient cars becomes more attractiv e. On this lat-
ter aspect, some pre vious results, based mostly on the US market, emphasize an “en-
ergy paradox,” meaning that consumers systematically underv alue future economies of
energy-ef ficiency (e.g. Allcott and W ozny 2014); others, like Sallee et al. (2016) or Busse
et al. (2013) find no evidence of such consumer my opia. Meta-studies (Helfand et al. 2011,
Greene 2010) find that the empirical e vidence about the energy paradox is inconclusiv e.
The ef fect of fuel taxes on carbon emissions depends on the extent to which car o wn-
ers react to fuel price, i.e. whether such taxes are able to change the composition of the
v ehicle fleet to w ard more fuel ef ficiency (greenhouse gases) and ho w the share of diesel
cars ev olv es (local pollution). This chapter estimates the short-ter m sensitivity of auto-
mobile purchases to changes in fuel prices in France. W e ev aluate the impact of tw o
(hypothetical) fuel tax policies on aggregate characteristics of the v ehicle fleet in circula-
tion, lea ving aside the question whether consumers adjust their mileage both to changing
fuel prices and to changing fuel ef ficiency of their car (potential rebound ef fect). 6 W e
contribute to the literature b y addressing the aggregate impact on the composition of the
v ehicle fleet in circulation, disregar ding whether a lo w sensitivity to fuel prices is due to
elastic mileage or to consumer my opia.
W e use French car registration data fr om 2003 to 2007, which includes exhaustiv e in-
for mation about both household and fir m automobile purchases. Our main focus lies on
the aggregate impact of fuel taxes on fuel consumption, CO 2 emission intensity and the
share of diesel purchases. Our dataset links technical car characteristics to infor mation on
the car holder . This enables us to define consumer types to account for heterogeneity in
preferences acr oss purchasers. In particular , w e can separate betw een priv ate consumers
and fir ms. While the latter represent more than one-thir d of purchases of ne w cars in
France (o v er our period), virtually no evidence exists so far on their responsiv eness to
changes in fuel prices.
As it is common in this literature, w e rely on a static discrete choice model assuming
that the decision to buy a specific car depends on sev eral car characteristics, including
the cost per kilometer . The nested logit specification enables us to model substitution
patter ns depending on car market segments and on fuel-type v ersions. Ov er these fiv e
Ho w ev er , D’Haultfœuille et al. (2014) sho w that the French experience has led to unexpected r esults. In
absence of previous empirical e vidence on consumers elasticities to car prices, the feebate system has
resulted in a sharp increase in car sales, but also in CO 2 intensity . This disappointing result is partly
explained b y a “rebound ef fect”: with a more fuel-efficient car , the cost per kilometer is low er , which ma y
induce more driving.
6 There are four components to the reaction of total emissions to fuel taxes: the direct mileage elasticity
to fuel prices, the elasticity of the new car ’s fuel ef ficiency to fuel prices (analyzed here), the elasticity of
mileage to this new fuel ef ficiency and the elasticity of car lifetime. Frondel and V ance (2014), for example,
examine the first point and find that the elasticity of mileage to fuel prices is not significantly differ ent for
diesel and gasoline driv ers. W e examine the second point. Small and V an Dender (2007) examine the third
point. Adda and Cooper (2000) w ork on the fourth point.

26 II Fuel T axes
y ears, monthly fuel prices v ary considerably . W e identify the impact of fuel cost in car
choice using time v ariation in fuel prices and cross-sectional dif ferences in fuel ef ficiency .
W e deduce the elasticity of demand for cars with respect to an increase in fuel taxes.
Our results suggest that short-term sensitivity of demand with respect to fuel prices
is generally lo w , but presents significant heterogeneity across pur chasers. The differ ence
betw een priv ate and corporate purchases is particularly salient: fir ms are much less re-
activ e than households. W e use our estimates to simulate the impact of tw o hypothetical
policies, the equalization of diesel and gasoline taxes and a “carbon tax.” Both policies
increase taxes relativ e to the status quo but they are calibrated to be re v enue-equiv alent
to each other . 7 Assuming that consumers react to changes in final consumer prices with-
out distinguishing betw een taxes or oil price changes, our results suggest that equalizing
diesel and gasoline taxes w ould reduce the market share of diesel cars (from 69% to 65%)
in the short-run without notably changing a v erage fleet fuel consumption or CO 2 inten-
sity . The carbon tax lea v es the diesel share almost constant and has a similarly small
impact on the other tw o outcomes. Ov erall, fuel taxes do not seem an ef fectiv e tool to
influence car choices.
This chapter is in line with the literature on the impact of fuel prices on the automo-
bile sector . Most papers focus on American data (Allcott and W ozny 2014, Busse et al.
2013, Klier and Linn 2010) and concentrate on the question of consumer rationality , as
re view ed in Greene (2010) and Helfand et al. (2011), while w e choose to take a policy
maker ’s perspectiv e and concentrate on the aggregate v ehicle fleet characteristics. Klier
and Linn (2013), who ev aluate the effect of fuel prices on ne w v ehicle fuel economy in
the eight largest Eur opean markets (including France), obser v e strong dif ferences be-
tw een European and American markets. Most of this existing literature relies on data
with little or no infor mation on consumers, while w e ha v e individual data matching cars
to consumers and can identify corporate purchases. Previous results for France suggest
that the elasticity of fuel demand to fuel prices in France is heterogeneous across demo-
graphic groups (Cler c and Marcus 2009), depending notably on w orking status. W e only
estimate short-run reactions, as w e take supply as giv en: list prices can be adjusted in
the medium-ter m and the set of a v ailable cars might change in the long-run.
The chapter is organized as follo ws. S ection 2 explains our assumptions on the de-
cision making process. S ection 3 presents the data and some descriptiv e statistics. The
model is presented in S ection 4. S ection 5 discusses results and robustness tests, and
S ection 6 concludes.
2 Choice model
T o model market shares of ne w v ehicles, w e rely on a standard discrete choice model
with dif ferentiated products. More specifically , w e assume that the purchaser buys one
product maximizing his utility that is a linear function of ne w v ehicle characteristics and
a v ehicle-specific unobser v ed effect. The individual v aluation of these v ehicles ma y v ary
7 As a consequence, our carbon tax scenario is more ambitious than the tax v oted in France.

2. Choice model 27
among individuals, like e.g. Allcott and W ozny (2014), tracing back to seminal w ork b y
McFadden (1978).
W e assume that the consumer decision can be modeled as a hierarchical choice, choos-
ing first a car segment (i.e. SUV , compact, etc; see list in T able II.1), then a model (com-
bination of nameplate and car body style) within this segment, and, finally , one of the
tw o fuel-type v ersions of this model. 8 While this structure is largely ad hoc, it seems em-
pirically v alidated b y our parameter estimates (see Appendix C on page 55). The nested
logit model yields heterogeneous substitution patterns betw een products that are more
or less similar; for instance a sporty BMW Z3 is more substitutable to a BMW Z4 than
to a bulky Renault Kangoo. W e also consider an outside option, which is not to buy any
new v ehicle. 9 This substitution pattern is represented in the tree diagram of Figure II.1.
purchaser i
no purchase segment 1
model 11
gas. diesel
model 1 j
gas. diesel
segment s
model s 1
gas. diesel
model s J s
gas. diesel
Figure II.1: Nested decision-making structure of the car purchaser
The individual utility of choosing the product with model (combination of nameplate
and car body style) j , fuel-type f and segment s , for purchaser i at month t is written:
u i j f t = α i + β i p k m
j f t + γ 1 i p j f t + γ 2 i X j f t + ξ i j f t + ϵ i j f t , (1)
where p j f t denotes the car price and X j f t repr esents the characteristics of new cars. p k m
j f t
is the cost at time t for the amount of fuel needed to driv e one km with the model j
of fuel-type f . 10 ξ i j f t measures the unobser v ed (to the econometrician) preference for
product j f . As such, it captures attributes like perceiv ed quality , design and reputation.
W e rely on a nested logit specification with tw o nesting lev els to reflect our decision
8 In order to clarify the v ocabulary , nameplate refers to the brand name of the car , for instance Corolla,
Prius. W ithin the same nameplate, there are usually sev eral models that are defined in this chapter b y the
intersection of a nameplate and a body style, i.e. Corolla sedan or Corolla station w agon. Each model
typically exists as tw o different pr oducts , i.e. in a diesel- and a gasoline-v ersion.
9 As w e consider monthly sales, the outside option’s market share is likely to be much larger than any
other option’s share. For the sake of comparison, o v er the period the number of new cars registered a
month ranges from 75,000 to 160,000 v ehicles, for around 37.5 millions of driv ers in France.
10 Another wa y to look at this w ould be to multiply the fuel consumption by the number of kilometers
expected b y the purchaser and using some sort of discounting; this is equiv alent to our presentation if β i is
defined to include this expected number of kilometers and discount factor of purchaser i .

28 II Fuel T axes
process of Figure II.1. This means w e assume the error term can be decomposed as:
ϵ i j f t = ν i s t + ( 1 − σ 2 i ) ( ν i j t + ( 1 − σ 1 i ) e i j f t ) , (2)
where ν i j t measures the individual preference for unobserv ed characteristics of model j
common to both fuel v ersions, for example design, while ν i s t is the consumer ’s o v erall
preference for segment s , for example status symbol v alue of SUVs. The remaining error
e i j f t is assumed to be independent and identically distributed according to an extreme
v alue distribution. There is a unique distribution for ν i s t and ν i j t such that ϵ i j f t follo ws an
extreme v alue distribution (Cardell 1997). This specification is standard in this literature
(see in particular Berry 1994).
The parameters σ 1 i and σ 2 i capture the correlation betw een individual preferences
for cars within nests, as defined abo v e. As sho wn b y McFadden (1978), the nested logit
model is consistent with random-utility maximization for v alues of σ 1 i and σ 2 i betw een
0 and 1. σ 1 i = 0 means that substitution ef fects are identical across and within model, 11
while a high σ 1 i , approaching 1, implies a high correlation betw een preferences for both
fuel-v ersions of the same model. σ 2 i = 0 implies that the purchaser is a priori indiffer ent
to substitute betw een models within and across segments (see for example V erbov en 1996
for a more complete discussion of these terms).
3 Data and descr iptiv e evidence
3.1 New v ehicle registrations
W e use the exhaustiv e dataset of all new cars register ed in France from January 2003
to No v ember 2007, pro vided b y the Association of French A utomobile Manufacturers
(CCF A, Comit ´ e des Constructeurs Fran ¸ cais d’Automobiles ), giving us o v er 7 million obser v ed
registrations. As a feebate scheme w as introduced in January 2008, which dramatically
changed the demand for fuel economy , w e only use data up to the date of its announce-
ment in No v ember 2007. 12
Our data includes all infor mation necessary for the registration of a new car , i.e. both
technical specifications of the car as w ell as demographic infor mation on the purchaser .
The CCF A has further linked this data to list prices of ne w cars as pro vided b y the car
manufacturers. 13
A product is defined b y brand, nameplate (Corolla, Kangoo, etc.), fuel-type (diesel or
gasoline), 14 CO 2 intensity class and body style (for instance city-car and sedan). 15 More-
11 “W ithin-model” substitution refers to the substitution betw een the gasoline-po w ered and the
diesel-po w ered v ersions of the same model.
12 S ee D’Haultfœuille et al. (2014) for an analysis of this policy and a description of this dataset.
13 List prices ma y dif fer from the actual selling prices, which are unobserv ed.
14 W e exclude electric and hybrid v ehicles as the y constitute a tiny share of the French market o v er the
examined period.
15 The definition seeks to be detailed enough to a v oid the aggregation of heterogeneous products. At the
same time, a too narro w definition yields many zer o monthly market shares, which ha v e to be dropped b y

3. Data and descr iptive e vidence 29
o v er , the dataset contains other characteristics like number of doors, horsepo w er , w eight,
cylinder capacity . Giv en the outlined structure of the decision pr ocess, w e exclude mod-
els a v ailable with only one fuel-type; this is only the case for exceptional cars which
represent o v erall 7% of sales. 16
T able II.1: Descriptiv e statistics: main characteristics of new car registrations 2003-2007
Products Sales-w eighted Products Sales-w eighted
By type of car-body By class of CO 2 (g/km)
City-car 3% 7% ≤ 100 0% 0%
Compact 14% 34% 101 to 120 4% 18%
S edan 33% 24% 121 to 140 9% 27%
Miniv an 13% 24% 141 to 160 14% 33%
Utilitarian 6% 4% 161 to 200 29% 21%
Sport 20% 3% 201 to 250 26% 6%
All-road/SUV 10% 5% > 250 18% 2%
By horsepower By type of fuel
≤ 60 14% 34% Gasoline 57% 32%
61 to 100 35% 60% Diesel 42% 74%
101 to 140 27% 10%
141 to 180 13% 2%
> 180 10% 1%
Number of products and observ ations 2, 148 7, 828, 903
Source: CCF A, authors’ calculations.
3.2 T ypes of consumers: demographic groups
Our administrativ e registration data match e v er y sale of a new car with information on
the new car o wner . W e can distinguish betw een priv ate buy ers and fir ms. Fuel price
elasticities are likely to be related to consumer characteristics such as income, w orking
status and area of residence. Most of the relev ant literature on fuel elasticity relies on
aggregate data, but as noted b y Bento et al. (2012), this omission might entail erroneous
findings about fuel economy v aluation.
In order to account for heter ogeneous prefer ences, w e split our sample into consumer
types based on demographic characteristics: w e differentiate three firm sectors and three
occupational types of priv ate consumers. W e further dif ferentiate types based on ge-
ography and income, resulting in 28 distinct consumer types. These categories aim at
capturing factors essential to v ehicle choice and fuel-price sensitivity: mileage and pref-
erence for diesel cars, as w ell as a comfort-price trade-off. The location additionally
captures the extent to which a buy er can substitute with other means of transports (bike,
definition: the logit model does not accommodate zero market shares, conceptually , and w e cannot take the
log of zero, practically . The definition used here is similar to Allcott and W ozny (2014) and somewhat more
detailed than those used in most of the literature (e.g. Goldberg 1995, and V erbov en 1996).
16 One of the robustness checks v erifies that this assumption is not crucial for the results, cf. S ection 5.4.

30 II Fuel T axes
Share of population going to work by car
below 55 %
55-70%
70-80%
over 85%
Share of diesel purchases
below 65 %
65-70
70-75%
75-80%
over 80%

Figure II.2: Ov er view of spatial v ariation in shar e of diesel cars and mileage
Source: CCF A (left graphic) and INSEE National T ransport and T ra v el Surv ey 2007 (right graphic), maps
generated b y the authors using R and GEOFLA base maps.
public transport, etc.). The groups are designed in a w a y to explain as much v ariation in
diesel share, annual mileage 17 and car price as possible.
T able II.2: A v erage mileage b y household characteristics (priv ate consumers only),
km/y ear
Not emplo y ed Emplo y ed
Income Lo w High Lo w High
Urban 10,850 10,950 14,950 15,600
Suburb./rural 10,750 14,300 16,250 18,850
Paris urban 9,750 14,050
Paris suburban 11,950 18,350
Source: INSEE National T ransport and T ra v el Sur v e y 2007, author ’s calculations.
For both priv ate consumers and fir ms, w e differentiate betw een types of residence
areas. Residence area (rural or urban) accounts for dif ferences in a v erage tra v el distance
and the a v ailability of means of transport other than the car . Residence area is deriv ed
from the postal code: w e sort areas of r esidence betw een urban Paris, the larger Paris
metropolitan region, 18 other urban areas and suburban/rural zones. Differ ent types of
17 Infor mation on annual mileage is a v ailable for households only and not b y age group, computed from
INSEE National T ransport and T ra v el Surv ey 2007, see T able II.2.
18 In the following, w e use the term “Paris” or “urban Paris” for Paris and its close and densely
populated suburbs (departments Paris (75), Hauts-de-S eine (92), S eine-Saint-Denis (93), V al-de-Mar ne (94)
and some adjoining municipalities) while “Paris metropolitan region” or “suburban Paris” describe the r est
of the ˆ
Ile-de-France region.

3. Data and descr iptive e vidence 31
residence areas ha v e considerably different a v erage tra v el times and distances (Baccaini
et al. 2007). The a v erage y early mileage is consistently smaller in the Paris region with
its dense public transportation netw ork than in other comparable areas.
Activity status is an additional important factor for priv ate owners, as emplo y ed con-
sumers ha v e larger mileage across all geographic areas, sho wn in T able II.2. Indeed, the
dif ference betw een a v erage y early mileage ranges from around 10,000 km/y ear for non-
activ e households living in urban Paris, to almost twice more for w orking households
living in w ealthy suburban areas. As sho wn in Cler c and Marcus (2009), French priv ate
consumer elasticity to fuel prices largely depends on whether the consumer uses their
car to go to w ork, as commuting represents the majority of kilometers driv en in France.
The Paris region is again special to this extent as reflected in Figure II.2, which sho ws
that this region has an exceptionally lo w share of people using their car to go to w ork.
W e consider the three groups: y oung emplo y ed under the age of 30, emplo y ed (o v er
30-y ear-old), and not emplo y ed, with the latter including retirees and unemplo y ed.
W e moreo v er split households accor ding to income. W e proxy the buy er income
b y the median ear nings of their age group at the precise municipality ( “commune” )o f
each consumer and define tw o groups corresponding to the upper and lo w er half of this
distribution. As group sizes are smaller in the Paris region, w e do not distinguish along
income dimensions for this region (see T able II.6 in the Appendix on page 49 for group
sizes).
Little is kno wn about the factors of heterogeneity in mileage for firms; thus, w e use
the same geographic partition as for households as it is partly related to infrastructure
facilities. W e also dif ferentiate with respect to the business sector that is a v ailable in
the data: industry and agriculture, rental, and trade/ser vices. Little is kno wn in the
literature about the factors influencing firm’s fuel-price sensitivity .
3.3 Diesel and gasoline cars
As sho wn b y Hiv ert (2013), the adv antage giv en to diesel cars in France is particularly
salient in inter national comparison. Figure II.3 illustrates this specific position of France
among European countries. Outside Europe, policies are much less fa v orable for diesel
and diesel-engines virtually do not exist: in both Japan and the US, diesel cars make up
about 2% of the o v erall v ehicle fleet in circulation (Cames and Helmers 2013).
W ithin the time frame of the data used in this chapter , from January 2003 through
No v ember 2007, gasoline and diesel prices became more v ariable, with a general upw ar d
trend, after some time of relativ e stability , as sho wn in Figure II.4. Fuel prices v aried
considerably betw een e 1.01 per liter and e 1.38 per liter of gasoline, and betw een e 0.75
and e 1.21 per liter of diesel; 19 this v ariation is about the same order of magnitude as the
19 Monthly fuel prices are obtained from the French Ministr y of Environment; w e use sales-w eighted
national a v erage prices a v ailable at
http://www.developp ement- durable.gouv.fr/Prix- de- vente- moy ens- des, 10724.html ; retriev ed on 06/12/2016.
For diesel prices w e use the price of car diesel oil ( “gazole” ), while for gasoline price w e use premium
unleaded gasoline ( “super sans plomb 95” ). All price indications in this chapter are deflated b y the French

32 II Fuel T axes
Figure II.3: Diesel fuel prices and market shares in Europe in 2012
Source: European A utomobile Manufacturer ’s Association (ACEA). Price adv antage of diesel is defined
as the price differential (including taxes) betw een diesel and super unleaded gasoline (95 RON) divided
b y the latter .
Figure II.4: Monthly consumer fuel prices (incl. taxes) and cost per km (resulting from
fuel prices ( e ) and fuel consumption (L/km) of new car pur chases)
Source: French Ministr y of Ecology and CCF A, authors’ calculations.
policies w e consider in this chapter .
Pre-tax prices for gasoline and diesel are highly correlated (correlation o v er 0.95) and
National Statistical Institute (INSEE) consumer price index, taking January 2008 as reference. Local prices
are a v ailable only since 2007 and cannot be used here. How ev er , the spatial v ariation is much low er than
the temporal v ariation: the relativ e standar d deviation is belo w 2 % for monthly fuel prices measured at the
local (French “d ´
epartement”) lev el in 2007, while it abo v e 10% for national monthly prices ov er the period
2003-2007.

3. Data and descr iptive e vidence 33




     


  
Figure II.5: Monthly new registrations b y fuel-type (in thousands, ra w and smoothed
series, studied period shaded in blue)
Source: CCF A, authors’ calculations.
their dif ference is small (betw een -3 and 9 cents), so that w e assume price v ariations of
both depend equally on oil prices. The final fuel tax rates result from the combination
of a fuel-type specific lump sum tax 20 and the proportional V A T of 19.6%. Ov er the
whole examined period, diesel fuel prices are significantly lo w er than gasoline prices
(Figure II.4) because of the lo w er TICPE tax on diesel fuel: in 2011, the consumption tax
on energy pr oducts reached e 0.61 per liter of gasoline, while it w as e 0.44 per liter of
diesel. Moreo v er , fir ms benefit from an 80% rebate on V A T for diesel only , meaning that
fir ms ha v e an ev en stronger incentiv e to inv est in diesel cars.
Diesel has a higher energy content so it pr oduces more CO 2 per liter than gasoline:
one liter of gasoline is transformed to 2.33 kg of CO 2 while one liter of diesel is trans-
for med to 2.63 kg of CO 2 . 21 Besides this important global greenhouse gas, diesel cars
also emit local pollutants like NO x , as w ell as fine particles (see e.g. Cames and Helmers
2013). As a consequence, the French go v ernment has decided to adjust diesel taxation.
In France until 2017, the number of diesel cars sold has consistently been higher than
the number of gasoline cars, and this difference has been increasing o v er the period under
study in this chapter (Figure II.5). The ov erall number of ne w registrations is str ongly
seasonal, but is virtually constant o v er the y ears. The details of the choice betw een diesel
and gasoline cars is amply discussed b y Rouw endal and de V ries (1999).
Bey ond fuel taxation, fir ms face an annual tax related both to the CO 2 class and to the
fuel-type. Prior to 2004, the amount of this tax depended on horsepo w er; since 2004, it
depends on CO 2 class, which is closely related to horse po w er but slightly less fa v orable
20 Consumption tax on energy products, “T axe int ´
erieure de consommation sur les produits
´
energ ´
etiques” (TICPE).
21 The differences in CO 2 intensity are due to the dif ferences in density of the fuel-types, see for example
Demirel (2012). The mass of CO 2 per liter of fuel that w eighs less than a kg might seem surprising; it
results of the association of carbon elements from the fuel and ambient oxy gen.

34 II Fuel T axes
to diesel cars. 22 As it ma y impact the preferences of fir ms to w ard one or other class, w e
use dummies for CO 2 classes in our estimations. This also accounts for marketing-based
preferences for CO 2 classes (Koo et al. 2012) be y ond direct v aluation of fuel cost sa vings.
3.4 Cost per kilometer
Our focus lies on the consumer sensitivity to fuel prices when buying a new v ehicle, via
the cost of driving. W e thus focus on the impact of the expected cost E ( p km
jft ) at time t for
the amount of fuel f needed to driv e one km with the car jf . By definition, it depends
on the car ’s fuel consumption φ jf in L/100km, its fuel-type f (diesel or gasoline) and the
expectations about fuel prices.
E ( p km
jft )= 1/ 100 × φ jf  1 f = diesel E t ( p D )+ 1 f = ga s E t ( p G )  ,
where p D and p G denote the fuel prices including tax for one liter of diesel and gasoline,
respectiv ely . φ jf denotes the car ’s fuel consumption, measured in L/100km, which is the
inv erse of fuel efficiency as typically used in the US, measured in miles per gallon (MPG).
Note that it is not equal to the total amount of fuel consumed, which results from the
product of fuel consumption and mileage.
As a car is a durable good, the decision to buy a giv en product jf at time t should take
into account the discounted utility of the future utilization of this car net of operating
cost. W e need therefore to take an assumption on ho w purchasers forecast future gasoline
prices: according to Anderson et al. (2013), consumer beliefs regar ding future fuel prices
are indistinguishable from a no change forecast, consistent also with a random w alk.
Ho w e v er , giv en that ne w cars are rarely sold “of f the rack,” it usually takes a few months
betw een purchase and the actual deliv ery and registration, which is our point of data
collection. Thus, in our estimates, w e do not use the contemporaneous fuel price but
rather a three months lag of fuel prices. Alter nativ e approaches in the literature include
using mo ving a v erages, which are for example consistent with a purchaser belief in mean-
rev ersion of fuel prices. In a model similar to ours, Klier and Linn (2013) use both current
fuel prices and mo ving a v erages, and find that this assumption has no significant impact
on parameter estimates, but standard err ors are lar ger with mo ving a v erages. 23
Across dif ferent cars in our data, the price of driving one kilometer , i.e. the product
of fuel price p f and fuel consumption φ in liters per 100 km, co v ers a wide range from
e 2.60 per 100 km up to e 30.9 per 100 km depending on the car .
22 The y early amount of the tax ranges from e 750 for the smaller cars to e 4,500 for the biggest ones in
2014.
23 In an earlier v ersion of this chapter , w e estimated the results using mo ving a v erages o v er 6 months
before purchase without finding significantly dif ferent results.

4. Econometr ic approach 35
4 Econometr ic approach
4.1 Nested logit estimation
W e take adv antage of the fact that our data matches consumers and products: w e assume
that systematic dif ferences in the v aluation of the different characteristics ar e captured b y
obser v ed purchaser types. W e thus use the 28 consumer types as specified in S ection 3.2
and estimate our model separately for each demographic group. Our approach is an
alter nativ e to tw o common w a ys to include demographic v ariation: random coefficient
models ` a la BLP (Berr y et al. 1995) and linear specifications as in Goldberg (1998). First,
random coef ficient models allo w prefer ences to be shaped b y aggregate distributions
of household demographics, which is useful when only aggregate data is a v ailable. 24
As rele v ant heter ogeneity is assumed to be obser v ed and captured b y the demographic
groups here, w e can refrain fr om using such complex models (see also Grigolon and V er-
bo v en 2014). S econd, our specification is more flexible than the solution of, for instance,
Goldberg (1995, 1998) who makes certain parameters linearly dependent on household
demographics b y including interactions of purchaser and product characteristics.
W e thus aggregate individual choices within each consumer type, in or der to reco v er
the market shares of each product j f (model j of fuel-type f ) up to an identifying nor-
malization. As usual in the literature, identification stems from the normalization of the
outside good’s v alue to zero. As an inter mediar y step, w e thus obtain a linear speci-
fication for the market share s d j f t of the product j f at time t among consumer type d
relativ ely to s d 0 t the market share of the outside good for that same demographic group:
ln ( s d j f t ) − ln ( s d 0 t ) = α d + β d p k m
j f t + γ 1 d p j f t + γ 2 d X j f t + σ 1 d ln ( s d f | j ) + σ 2 d ln ( s d j | s ) + ξ d j f t ,
(3)
where s d f | j = s d j f t
s d j t is the relativ e share of purchases of fuel-type f within purchases of
model j in each month t and s d j | s = s d jt
s ds t is the relativ e share of model j within the sales of
segment s .
Ho w e v er , these shares are defined o v er the entire potential market size, which in our
case – as in virtually all cases – is unkno wn. Indeed, this market size should contain
only those who consider buying a car in a giv en period (and ma ybe decide not to). As
detailed infor mation on this market size is unkno wn, using some approximation is a
standard pr ocedure in this literature (for instance the seminal papers b y McFadden 1978,
Goldberg 1995), using for example most recent estimates of the population size or the
number of people holding a driv er ’s license. This number dramatically o v erstates the
actual market with durable goods like cars, because in each giv en month only a small
fraction of consumers considers buying a car . Moreo v er , when a large portion of ne w
car registrations are made b y fir ms and not b y priv ate o wners, it is not clear whether
24 How ev er , this comes at the cost of high computational complexity . This complexity is also sho wn to
lead to numerical instability in some cases: Knittel and Metaxoglou (2014) find results often depend on
starting v alues and optimization algorithms.

36 II Fuel T axes
the number of driving license holders is relev ant. Huang and Rojas (2014) sho w both
theoretically and practically that coef ficients estimated using such a wrong market size
ma y be considerably biased.
T o a v oid this potential bias, w e follow a suggestion b y Huang and Rojas and refor mu-
late Equation (3): b y using quantities rather than market shares, the market size cancels
out on the left-hand side. W e are left with the log of the outside good’s quantity , which
w e can mo v e to the right-hand side and estimate it as part of the time-specific constant.
Giv en the highly seasonal fluctuations of the number of purchases in Figure II.5, w e al-
lo w this constant to v ar y with y ear and calendar month. The o v erall market size and the
outside good quantity are not necessary to compute the relativ e shares s d j | s and s d f | j . Our
main estimation equation is thus:
ln ( q d j f t ) = α d + β d p k m
j f t + γ 1 d p j f t + γ 2 d X j f t + σ 1 d ln ( s d f | j ) + σ 2 d ln ( s d j | s ) + y d + m d + ξ d j f t ,
(4)
where q d j f t stands for the number of sales of product j f . The characteristics of the new
car , namely horsepow er , CO 2 class, number of doors, fuel-type, car body (sedan, sport,
compact, etc.) and brand are controlled for . Y ear and calendar month dummies, y d and
m d , account for temporal trends as w ell as seasonality in aggregate ne w cars purchases.
The main parameter of interest is the parameter β d measuring sensitivity to fuel
prices. W e use the parameters of Equation (4) to compute the fuel price elasticity , which
takes into account both direct and indirect ef fects of an incr ease in fuel prices in the
market share of one specific car . This elasticity can be approximated b y: 25
η d s j f = ∂ s d s j f / s d s j f
∂ p e / p e ,
≈ ( 1 + t V AT ) p e ( β d
1 − σ 1 d
φ s j f d + ( β d
1 − σ 2 d
− β d
1 − σ 1 d ) ¯
φ s j d + β σ 2 d
1 − σ 2 d
¯
φ sd ) . (5)
Equation (4) is estimated using the generalized method of moments separately for
each demographic group, assuming these groups homogeneous enough to include only
buy ers with the same demand parameters.
4.2 Endogenous v ar iables and instr uments
Gas prices can be considered as exogenous in the French case, as France represents about
2% of w orld oil consumption and produces less than 0.1% of the w orld production. 26
French gas prices are defined b y the inter national energy market, on which France has
only a limited w eight (which ma y be not the case for the US, see Da vis and Kilian (2011)
for a discussion).
By contrast, the v ehicle price p j f t is endogenous, as it is the result of demand and sup-
ply which b y assumption v ary with the unobser v ed attractiv eness ξ d j f t . As it is usual in
the literature, w e use a set of instruments based on the characteristics of potential substi-
25 Details of elasticity computation are giv en in the Appendix B on page 50.
26 In 2009, see http://www.eia.gov/countries/country- data.cfm ; retriev ed on 14/03/2015.

5. Empir ical results 37
tutes aiming at capturing market density , and thus bey ond production cost, the v ariation
in mark-ups. More specifically , in a multi-product Bertrand competition frame w ork, one
can deriv e a set of instruments based on the sums of each characteristics of other models
produced b y the same firm in the same segment and those of competing firms (Berr y et al.
1995, henceforth “BLP”). This measure is computed twice; once o v er all products within
the same nest, and another time o v er all products in all other nests. Importantly , w e use
y early list prices and thus assume that purchase prices do not to v ar y with fuel prices.
In the short ter m, this is likely to be true, as list prices are set on a much longer horizon
than fuel prices; in the long ter m, list prices can ob viously adapt to fuel price v ariation.
Ar mstrong (2016) ar gues that in markets with a lar ge number of heterogeneous
goods, BLP instruments are no longer suf ficiently str ong. Thus, w e add cost-shifters,
such as the prices of ra w materials, that pro vide exogenous v ariations in market prices as
they ar e related to supply but not demand. Thus, w e use the price indices of iron (current
and lagged v alue) and indices of export prices of tires as instruments, both w eighted b y
the car ’s w eight. These cost shifters appear strongly correlated to v ehicle prices.
W ithin segment, the market share s d j | s is endogenous b y definition. As for the price,
w e use BLP-style instruments for this v ariable and further add the number J s of offer ed
goods per segment s .
Finally , w e instrument the within-model market share s d f | j b y the dif ference in char -
acteristics of gasoline and diesel v ersions, as w ell as the difference in costs shifters for
these tw o v ersions, capturing the relativ e attractiv eness of each v ersion.
As pointed out b y Bound et al. (1995), using many o v er -identifying restrictions as
w e do can lead to misleading results if the instruments are w eak. In case of only one
endogenous v ariable, it is no w common to test the strength of the instruments b y using
on the first-stage F-v alues, as proposed b y Stock and Y ogo (2005). As sho wn b y Sander-
son and W indmeijer (2016), this method can be extended to regressions with multiple
endogenous v ariables: for each endogenous v ariable, the rele v ant test statistic is then the
first-stage F-v alue conditional on the other tw o endogenous regr essors, that can be com-
pared to the v alues tabulated b y Stock and Y ogo (2005). W e compute these test statistics
for each of our three endogenous v ariables and for each demographic group. At a 5%
significance lev el, w e can reject for most regr essions a bias of the 2SLS regression r elativ e
to an OLS of more than 5%; in only tw o cases (out of eighty) w e can only reject biases
superior to 20% (cf. T ables II.13, II.14 and II.15 in the Appendix on page 60). One case is
problematic, as w e cannot reject that our instruments are too w eak to identify the within-
model parameter σ 1 d for the purchases b y car rental companies in the Paris suburban
area. This group is small and aggregate results ar e virtually identical if w e drop it. Thus,
w e are confident that our results are not biased b y w eak-instrument effects.
5 Empir ical results
Our aggregate outcomes of interest are: the share of diesel cars (local pollution), a v erage
fleet fuel consumption (inter national fuel dependency) and a v erage CO 2 intensity (global

38 II Fuel T axes
pollution).
The presentation of the empirical results pr oceeds in three steps: first, w e present the
aggregate elasticities of market shares, diesel share, fuel consumption, and CO 2 emission
intensity . 27 Then, these elasticities are used to compute ex ante estimates of the impact of
tw o policies, one equalizing tax on diesel and gasoline; the other taxing carbon directly .
The tw o policy scenarios are calibrated such that the y are re v enue-equiv alent for the
implementing go v ernment in absence of consumer reaction. The ra w coef ficients cannot
be interpreted directly , but w e discuss them in the Appendix C on page 55, where w e
also compute the demand elasticities for some popular car models.
5.1 Aggregate elasticities to fuel pr ice v ar iation
W e model the aggregate elasticities to a change in fuel prices (both gasoline and diesel)
through an international oil price shock. As diesel engines tend to be more efficient
with an a v erage fleet fuel consumption of 5.6L/100km v ersus 6.8L/100km for gasoline
engines (T able II.7 in the Appendix on page 50), an increase of fuel prices raises the share
of diesel cars among new pur chases π D (see elasticity η D in T able II.3). 28 Consequently ,
the a v erage fleet fuel consumption decreases as w ell as a v erage CO 2 intensity . Ho w e v er ,
all these ef fects ha v e a small magnitude.
These results can be compared to some pre vious estimates obtained in the literature.
Using aggregated data on se v eral European car markets, Klier and Linn (2011) estimate
that a 1$ increase in fuel prices per gallon w ould increase the a v erage miles-per -gallon
(MPG) ef ficiency in France b y 0.21, implying an a v erage fuel consumption elasticity η φ
of -0.017. 29 This v alue is similar to our estimate and much lo w er than the v alue the y
find for the US: there, 1$ decreases the a v erage MPG b y 1.03, implying an a v erage fuel
consumption elasticity of -0.042. Our estimate is smaller than the estimates b y Clerides
and Zachariadis (2008), who find a short ter m elasticity of a v erage fleet fuel consumption
to fuel prices equal to -0.08 for the EU, using aggregate data. Klier and Linn (2011)
also estimate that a hypothetical policy equalizing diesel and gasoline prices reduces the
diesel market share in France b y 1.4 percentage points only; much less than suggested
b y our estimate of around 4 percentage points.
5.2 T ax alignment
These estimates allo w us to simulate the impact of a policy that aligns diesel and gasoline
taxes. Lea ving gasoline taxes unchanged, this policy raises diesel taxes b y almost a third,
from 43 cent/liter to 60 cent/liter . Futher more, this policy abandons the V A T adv antage
for corporate diesel cars, increasing it to the standard rate of 19.6%.
27 S ee the Appendix B on page 50 for details on the computation of these elasticities.
28 π D is the market share of diesel cars among pur chased cars whereas the market shares s j , s s etc. are
defined on the whole market, including the outside good.
29 Brons et al. (2008) analyze more in detail the aggregate elasticity of fuel demand, resulting of the
elasticities of mileage, fuel consumption and car o wnership; their meta-study also finds this elasticity to be
empirically small.

5. Empir ical results 39
T able II.3: Elasticities with respect to fuel prices: diesel share, a v erage fleet fuel consump-
tion (L/km) and CO 2 intensity (g/km)
Diesel share Fuel cons. CO 2
η D η φ η CO 2
Households 0.026
( 0.003 )
∗∗∗ − 0.013
( 0.001 )
∗∗∗ − 0.015
( 0.001 )
∗∗∗
Firms 0.017
( 0.003 )
∗∗∗ − 0.004
( 0.001 )
∗∗∗ − 0.006
( 0.001 )
∗∗∗
T otal 0.029
( 0.003 )
∗∗∗ − 0.010
( 0.001 )
∗∗∗ − 0.012
( 0.001 )
∗∗∗
Source: CCF A, authors calculations. Estimates rely on the parame-
ters of Equation (4) estimated b y GMM separately for each type of
consumers. Standard err ors in parentheses are estimated b y bootstrap
(500 replications).
As expected, the induced v ariation in diesel share is negativ e and str ong: since taxes
only increase for diesel, they w ould push many purchasers to substitute for a gasoline-
fueled car . W e find that such a policy w ould reduce the aggregate share of diesel cars
in o v erall sales b y 5.9%, that is from 69% to 65% (T able II.4). This decrease in diesel
sales comes mostly from households who substitute much more easily a w a y fr om diesel
engines, rather than from firms (7.4% and 3.6% reduction, respectiv ely).
This result can be compared to the one in Klier and Linn (2011) who also e v aluate
a hypothetical policy of equalizing diesel and gasoline prices. At the European lev el,
their estimates suggest that the impact of such a policy on the market share of diesel
cars w ould be negligible (less than 1%). T w o elements explain this differ ence. First, our
analysis is focused on France, where the gap betw een gasoline and diesel taxes is the
highest of all countries they consider: the hypothetical policy change is strong which
is not the case for other countries. 30 S econd, as they emphasize, Klier and Linn (2011)
cannot distinguish in their data company cars from priv ately o wned cars. According to
our estimates, fir ms are much less sensitiv e to fuel prices (T able II.3).
Gasoline cars consume more liters of fuel per km but produce 13% less CO 2 per liter
of fuel – gasoline is a less energy-rich combustible. The effect of a demand shift to-
w ard gasoline cars on CO 2 is thus a priori ambiguous. According to our estimations,
substitutions betw een gasoline and diesel cars ha v e only a marginal impact on both fuel
consumption of the new v ehicle fleet and CO 2 intensity . It increases fuel consumption (T a-
ble II.4) and r educes the a v erage CO 2 intensity of newly purchased cars. Both effects ar e
significant but small: in spite of the large jump in diesel tax, a v erage fleet fuel consump-
tion increases only b y 0.44% and a v erage CO 2 intensity decreases b y 0.12%. The absolute
magnitudes of these changes are small: fuel consumption increases b y 26 mL/100km
from the a v erage of 6L/km and CO 2 intensity is reduced b y 180mg/km from the a v erage
of 152g/km.
30 Estimates detailed by countries ar e a v ailable in a previous w orking paper (Klier and Linn 2011). They
obtain that the diesel market share in France w ould decrease b y 1.4 percentage points. This reduction is
higher than the effect in most other countries the y examine.

40 II Fuel T axes
T able II.4: Percentage impact of a carbon tax and a tax alignment on diesel share, a v erage
fleet fuel consumption (L/km) and CO 2 intensity (g/km)
T ax alignment Carbon tax
Diesel
share
Fuel cons. CO 2 Diesel
share
Fuel cons. CO 2
Δ t D η D Δ t D η φ Δ t D η CO 2 Δ t c η D Δ t c η φ Δ t c η CO 2
Households − 7.43
( 0.36 )
∗∗∗ 0.50
( 0.03 )
∗∗∗ − 0.13
( 0.01 )
∗∗∗ 0.15
( 0.07 )
∗∗ − 0.43
( 0.02 )
∗∗∗ − 0.43
( 0.02 )
∗∗∗
Firms − 3.55
( 0.46 )
∗∗∗ 0.28
( 0.09 )
∗∗∗ − 0.11
( 0.06 )
∗ 0.65
( 0.12 )
∗∗∗ − 0.21
( 0.03 )
∗∗∗ − 0.15
( 0.03 )
∗∗∗
T otal − 5.94
( 0.32 )
∗∗∗ 0.44
( 0.04 )
∗∗∗ − 0.12
( 0.02 )
∗∗∗ 0.59
( 0.07 )
∗∗∗ − 0.37
( 0.02 )
∗∗∗ − 0.33
( 0.02 )
∗∗∗
Source: CCF A, authors calculations. Estimates rely on the parameters of Equation (4) estimated b y GMM
separately for each type of consumers. Instrumental v ariables for prices are the price indices of iron (current
and lagged v alue) and indices of export prices of tires, interacted with the car model’s w eight. Standar d
errors in parentheses are estimated b y bootstrap (500 replications).
5.3 Carbon tax
W e also predict the impact of a carbon tax, i.e. a tax increase that is proportional to the
carbon emissions of each fuel-type. The amounts are calibrated such that the go v ernment
rev enue is equal to the pre vious tax alignment policy , yielding a price of e 51 per tonne
of CO 2 . This results in an increase of 11.9 cent/liter of gasoline and 13.4 cent/liter of
diesel, representing around 9% of the a v erage end-user price. 31 A v ery similar but less
ambitious policy has been v oted in France in 2014, leading to a progressiv e increase in
fuel taxes up to e 30.5/tCO2 in 2017. 32
The impact Δ t c η D of this carbon tax policy on the share of diesel engines sold is
positiv e, but v er y small: it increases the diesel share b y 0.6% (T able II.4). This is the
result of tw o contrasting effects: on the one hand, the carbon tax is higher on diesel than
on gasoline, but on the other hand, diesel cars are more fuel-efficient. The incentiv e for
purchasers to buy more fuel-ef ficient cars seems to dominate. The carbon tax reduces
a v erage fleet fuel consumption as w ell as a v erage CO 2 intensity (T able II.4). The impacts
are significant but again v er y small. The fuel consumption decreases b y 0.37%, which is
ho w e v er only around 22 mL/100km from the a v erage of 6L/km; CO 2 emission intensity
shift b y 0.33% which is 500mg/km from the a v erage of 152g/km.
The impact of both policies on fuel consumption and CO 2 intensity is economically
small. The main dif ference is that lev eling out the diesel tax adv antage induces a no-
ticeable shift a w a y from diesel engines, thus reducing local pollution. Moreo v er , the
carbon tax achiev es a lar ger reduction in CO 2 intensity and further more reduces fuel
consumption, thus leading – on its modest lev el – to a lo w er dependency on foreign
petrol imports.
31 This scenario maintains the V A T rebate for diesel cars of corporate consumers.
32 S ee the w ebsite of the French ministry of environment:
https://www.ecologique- solidaire.gouv.fr/fiscalite- ca rb one ; retriev ed on 09/09/2017.

5. Empir ical results 41
T able II.5: Robustness checks: percentage impact of carbon tax and tax alignment on
diesel share, a v erage fleet fuel consumption (L/km) and CO 2 intensity (g/km)
T ax alignment Carbon tax
Diesel
share
Fuel cons. CO 2 Diesel
share
Fuel cons. CO 2
∆ t D η D ∆ t D η φ ∆ t D η CO 2 ∆ t c η D ∆ t c η φ ∆ t c η CO 2
Main specification - including degenerate nests (gas- or diesel-only models)
Households − 9.37
( 0.35 )
∗∗∗ 0.80
( 0.03 )
∗∗∗ 0.02
( 0.01 )
∗∗∗ 0.55
( 0.08 )
∗∗∗ − 0.50
( 0.02 )
∗∗∗ − 0.47
( 0.02 )
∗∗∗
Firms − 3.85
( 0.41 )
∗∗∗ 0.24
( 0.08 )
∗∗∗ − 0.19
( 0.05 )
∗∗∗ 0.70
( 0.11 )
∗∗∗ − 0.23
( 0.02 )
∗∗∗ − 0.17
( 0.03 )
∗∗∗
T otal − 7.15
( 0.32 )
∗∗∗ 0.62
( 0.04 )
∗∗∗ − 0.06
( 0.02 )
∗∗∗ 0.99
( 0.06 )
∗∗∗ − 0.44
( 0.02 )
∗∗∗ − 0.36
( 0.02 )
∗∗∗
Alternative specification - Nests (segment > model)
Households − 9.17
( 0.38 )
∗∗∗ 0.79
( 0.03 )
∗∗∗ 0.02
( 0.01 )
∗∗∗ 0.49
( 0.08 )
∗∗∗ − 0.48
( 0.03 )
∗∗∗ − 0.45
( 0.02 )
∗∗∗
Firms − 3.51
( 0.54 )
∗∗∗ 0.22
( 0.09 )
∗ ∗ − 0.17
( 0.06 )
∗∗∗ 0.59
( 0.14 )
∗∗∗ − 0.22
( 0.03 )
∗∗∗ − 0.16
( 0.03 )
∗∗∗
T otal − 6.84
( 0.40 )
∗∗∗ 0.59
( 0.05 )
∗∗∗ − 0.05
( 0.02 )
∗ ∗ 0.97
( 0.08 )
∗∗∗ − 0.42
( 0.02 )
∗∗∗ − 0.35
( 0.02 )
∗∗∗
Main specification - BLP-instruments only
Households − 8.67
( 0.39 )
∗∗∗ 0.60
( 0.04 )
∗∗∗ − 0.13
( 0.01 )
∗∗∗ 0.28
( 0.08 )
∗∗∗ − 0.51
( 0.02 )
∗∗∗ − 0.50
( 0.02 )
∗∗∗
Firms − 3.12
( 0.55 )
∗∗∗ 0.29
( 0.10 )
∗∗∗ − 0.06
( 0.06 ) 0.65
( 0.15 )
∗∗∗ − 0.16
( 0.03 )
∗∗∗ − 0.10
( 0.03 )
∗∗∗
T otal − 6.27
( 0.42 )
∗∗∗ 0.49
( 0.05 )
∗∗∗ − 0.10
( 0.02 )
∗∗∗ 0.84
( 0.07 )
∗∗∗ − 0.42
( 0.02 )
∗∗∗ − 0.36
( 0.02 )
∗∗∗
Main specification - without purchaser heter ogeneity
T otal − 7.45
( 0.77 )
∗∗∗ 0.45
( 0.07 )
∗∗∗ − 0.26
( 0.02 )
∗∗∗ 0.26
( 0.05 )
∗∗∗ − 0.61
( 0.06 )
∗∗∗ − 0.60
( 0.05 )
∗∗∗
Source: CCF A, authors calculations. Estimates rely on the parameters of Equation (4) estimated b y GMM sep-
arately for each type of consumers. Instrumental variables for prices ar e the price indices of iron (current and
lagged v alue) and indices of export prices of tires, interacted with the car model’s w eight. Standard errors in
parentheses are estimated b y bootstrap (500 replications).
5.4 Robustness checks
W e estimate sev eral alternativ e specifications to check that results are not driv en b y our
main specification choice, but also to emphasize the impact of individual hypothesis
underlying this main specification. On the whole, the estimated impact of our policy
scenarios remains at a similar or der of magnitude across specifications.
Our first test includes all models, i.e. including those that are a v ailable only with
either gasoline or diesel motor . In our main specification, w e drop these models as
they lead to “degenerate” nests at the end of the decision tree, where a model-branch
only includes one product. While the aggregate elasticities (T able II.16 in the Appendix
on page 62) appear similar to our main specification, the policy simulation sho ws that
this model slightly o v er -estimates the policy impact while leading broadly to the same
conclusions.
In the same spirit, our second test uses a more commonly used model accounting only
for tw o lev els: purchasers choose a segment and then a product within that segment. The
tw o fuel-type v ersions of a model then count as independent products, which is the same
as constraining all σ 1 d coef ficients to zero. The elasticities are similar to the previous test

42 II Fuel T axes
(T able II.5) and just slightly stronger than our main specification. Although the changes
are small, w e still reject this more constrained model as in our main estimation σ 1 d w as
significantly dif ferent from zer o for almost all demographic gr oups (T able II.9 in the
Appendix on page 57).
Our third test dr ops the cost-shifter instruments and includes only the BLP-style
instruments. Again, the elasticities are v er y similar and the policy impacts giv e the same
intuition, but o v erstate the impact of a carbon tax on the diesel share.
As a last test, w e estimate the model jointly for all demographic groups, which means
w e do not account for consumer heterogeneity . Bento et al. (2012) suggest that unac-
counted heterogeneity biases estimated elasticity do wnw ar ds, which w e do not find here
(T able II.16 in the Appendix on page 62). Quite the contrary , elasticities and estimated
policy impacts o v erstate the consumer reaction in our case (T able II.5).
Our main specification still seems most appropriate, but these alter nativ e specifica-
tions do not dramatically change the implications of this chapter .
6 Conclusion
This chapter estimates the short-ter m impact of fuel prices on new automobile pur chases
of both households and fir ms. These estimates allo w us to compute elasticities which
w e aggregate to estimate ex ante the impact of tw o tax reforms. Using a nested logit
specification, w e control for hedonic v aluation of a large range of car characteristics. W e
also account for taste heterogeneity betw een consumer gr oups, in particular betw een
priv ate and cor porate purchases.
Our aggregate outcomes of interest are: the share of diesel cars (local pollution), a v-
erage fleet fuel consumption (international fuel dependency), and a v erage CO 2 intensity
(global pollution). W e use our estimates to examine a (hypothetical) policy equalizing tax
lev els on gasoline and diesel. W e find that this policy decreases the share of diesel cars
in sales from 69% to 65%. As purchasers w ould substitute to (less ef ficient) gasoline cars,
the a v erage fuel consumption w ould rise in response to this policy , while at the same
time a v erage CO 2 intensity w ould slightly decr ease as gasoline cars emit less CO 2 per
liter of used fuel. The examined carbon tax – which implements a much higher carbon
price than the recently v oted French policy – is expected to slightly increase the shar e of
diesel cars among new pur chases. It decreases both fuel consumption and CO 2 intensity
significantly , but the o v erall amounts sta y lo w .
All in all, the estimated effects of these tw o tax policies are significant but economi-
cally small in the short-run, i.e. holding supply constant. This is ev en more notew orthy ,
as one might argue that our policy scenarios are some what o v erly ambitious and might
not be politically feasible. Ov erall, fuel taxes do not appear to be a strong policy tool for
influencing car choices in the short-run.
An important adv antage is pro vided b y our individual registration data, as w e can
account for purchaser heter ogeneity and our estimates are thus less prone to omitted
sorting bias. Indeed, purchaser types r eact differ ently to fuel tax changes. A large part

6. Conclusion 43
of aggregate market reaction comes fr om households, and particularly from urban and
non w orking consumers. T o our kno wledge, this important distinction betw een house-
hold and fir m purchases is not accounted for in earlier r elated literature, although fir m
purchases constitute about a thir d of the market in our sample. Cor porate purchases are
particular important for the diesel share, as fir ms buy a lot more diesel-po w ered cars and
are less likely to substitute a w a y from them.
A limitation of this chapter is that our simple demand model does not take into ac-
count long-run shifts on the supply side. While one can be confident that the monthly
fuel price v ariation used for identification in this article does not impact the characteris-
tics of a v ailable cars instantaneously , it is likely that producers react more to long-term
shifts: if fuel ef ficiency becomes more v aluable, they might in the medium-run adjust
their list prices and in the long-run adjust the products de v eloped and offered. For Klier
and Linn (2011) this means that these short-run results underestimate the true impact
on fuel ef ficiency and emissions, which w ould be enhanced b y the producer ’s reactions.
Ho w e v er , as sho wn b y V erbo v en (2002), producer price reaction should counteract pur -
chaser reaction to changes in dif ferential fuel taxation. Ho w ev er , one could argue like
Goldberg (1998) that a short-term consumer reaction as small as suggested b y our esti-
mates is unlikely to shift supply , so that the long-run ef fect should be small as w ell.
The aim of environmental policy is ultimately not to incr ease fuel ef ficiency , but to de-
crease CO 2 emissions which result fr om the interaction of fuel consumption and mileage.
Additional research is needed to clarify the impact of fuel ef ficiency on car mileage. Pre-
vious research suggests that r ebound ef fects might reduce any impact on fuel consump-
tion (see for example A ustin and Dinan 2005, Frondel et al. 2012), so that our (alr eady
small) estimated ef fects become ev en less economically and environmentally significant.
Nev ertheless, the change in the composition of the v ehicle fleet impacts fuel ef ficiency in
the long run as cars circulate on a v erage for 13 y ears in France (Bilot et al. 2013).
W e do not use any data on mileage nor assume anything on car lifetime and dis-
counting, so that w e remain agnostic on the actual pr ofit a consumer realizes with fuel
ef ficiency . As a consequence, w e cannot ev aluate w elfare ef fects of the policy such as
Bento et al. (2009) or Bureau (2011) or the rationality (or my opia) of consumers such as
re view ed in Greene (2010) and Helfand et al. (2011). T o our kno wledge, there is no study
that includes mileage elasticity to fuel prices and to fuel ef ficiency , as w ell as potentially
elastic lifetime, so that computations usually remain back-of-the-env elope sketches (e.g.
Grigolon et al. 2014, Allcott and W ozny 2014, Busse et al. 2013 33 ). Ne v ertheless, our esti-
mated consumer reactions are too small to fully account for the change in operating cost
if utilization does not change. In this light, it ma y seem sur prising that cor porate pur -
chases are e v en less reactiv e to fuel price changes than household purchases. Ho w ev er ,
similar results ha v e been obtained on the market for airline tickets. Fir ms can deduce
total fuel cost from taxes and ma y be able to pass costs through to consumers. These
33 These papers account for mileage at a detailed car- or consumer -lev el but assume zero elasticity; the y
can thus not account for w ell documented phenomena such as the “rebound effect” (Small and Dender
2007).

44 II Fuel T axes
factors ma y explain why they r eact less to fuel prices than households. Further research
is needed to clarify whether this is due to dif ferences in mileage or whether there are
beha vioral and organizational factors at pla y .

7. Bibliography 45
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8. Appendices 49
Appendices
A Descr iptiv e statistics
T able II.6: Distribution of demographic groups among buy ers (%)
Private consumers
Not emplo y ed Y oung emplo y ed ( < 30) Emplo y ed ( ≥ 30)
Income Lo w High Lo w High Lo w High T otal
Urban 150,214 82,692 389,903 192,957 679,981 646,949 2,142,696
5.0% 2.5% 8.7% 8.3% 1.7% 1.5% 27.6%
Suburban/rural 136,187 116,348 246,876 331,066 450,728 564,686 1,845,891
1.7% 1.5% 3.2% 4.2% 5.8% 7.2% 23.6%
Paris urban 40,298 186,758 486,700 713,756
0.5% 2.4% 6.2% 9.1%
Paris suburban 11,069 45,160 81,893 138,122
0.1% 0.6% 1.0% 1.8%
T otal 536,808 1,392,720 2,910,937 4,840,465
11.3% 27.3% 23.5% 62.1%
Firm purchases
Industry & Car rental & T rade &
agriculture repairing services T otal
Urban 307,871 1,261,364 374,754 1,567,383
3.9% 16.1% 4.8% 24.8%
Suburban/rural 113,947 66,416 137,182 383,855
1.5% 0.8% 1.8% 4.1%
Paris urban 203,606 313,880 172,532 565,762
2.6% 4.0% 2.2% 8.8%
Paris suburban 7,674 4,083 25,129 47,902
0.1% 0.1% 0.3% 0.5%
T otal 633,098 1,645,743 709,597 2,564,902
8.1% 21.0% 9.1% 38.2%
Source: CCF A, authors’ calculations.

50 II Fuel T axes
T able II.7: Descriptiv e statistics of car characteristics
Percentiles
Mean Coefficient
of
v ariation
(%)
25% Median 75%
Gasoline (N= 2,376,527)
Car price ( e ) 16,606 69.4 11,738 13,975 18,800
Cost of driving 100 km ( e ) 8.4 22.7 7.3 8.1 9.1
Horse po w er (kW) 70 48.8 54 60 80
Fuel consumption (L/100km) 6.8 21.7 6.0 6.5 7.4
CO 2 intensity (g/km) 159.3 21.7 139.0 152.0 172.0
Diesel (N= 5,452,376)
Car price ( e ) 22,968 41.0 16,783 21,875 26,236
Cost of driving 100 km ( e ) 5.7 27.1 4.8 5.4 6.3
Horse po w er (kW) 78 34.6 63 78 88
Fuel consumption (L/100km) 5.6 24.5 4.7 5.4 6.0
CO 2 intensity (g/km) 147.0 24.5 124.0 141.0 157.0
Note: The coefficient of v ariation, or unitized risk, is the ratio of the standard error to the mean.
Source: CCF A, authors’ calculations.
B Details on the computation of the elasticities
For the computation of elasticities it is useful to introduce a decomposition of the fuel
price per km
p km
j = φ j ( 1 + t VA T )  p e + 1 j = diesel ( t D )+ 1 j = ga s ( t G )  , (6)
where φ j denotes the characteristic fuel-consumption (inv erse of fuel-ef ficiency , thus in
liter of fuel per kilometer), t D and t G the consumption tax rates for energy products
for one liter of diesel and gasoline, respectiv ely , and t VA T the V A T rate. The fuel prices
excluding taxes for diesel and gasoline are v er y similar and strongly correlated as the y
are driv en b y oil prices; for the sake of simplicity , w e thus assume that the fuel prices per
liter excluding taxes are the same for diesel and gasoline, denoted p e .
The demand elasticity η sj f for a giv en pr oduct with respect to oil price p e exclusiv e
of tax at a giv en point in time can be computed using parameters corresponding to the
demand model. Fuel prices affect all pr oducts proportionally to their fuel consumption:
both the nominator and the denominator of the market shares are impacted. In order to
find this elasticity , let us dif ferentiate Equation (3) for the model j in segment s and of
fuel-type f , using the definition of the cost per kilometer: 34
∂ s sj f
s sj f
− ∂ s 0
s 0
= β∂ p e ( 1 + t VA T ) φ sj f + σ 1 ( ∂ s sj f
s sj f
− ∂ s j
s j
)+ σ 2 ( ∂ s j
s j
− ∂ s s
s s
) (7)
34 For the sake of readability , w e omit the index for demographic groups and do not state the ob vious
aggregation o v er these groups for all equations in this section.

8. Appendices 51
or slightly rearranged:
∂ s s j f − ∂ s 0
s 0
s s j f = β ∂ p e ( 1 + t V AT ) φ s j f s s j f + σ 1 ( ∂ s s j f − s s j f
∂ s j
s j
) + σ 2 s s j f ( ∂ s j
s j
− ∂ s s
s s
) . (8)
W e then aggregate this last equation o v er both fuel-type v ersions of the same model,
to obtain the change in the market share of one model j in one segment s :
∂ s j − ∂ s 0
s 0
s j = ∑
f ∈ j
( ∂ s s j f − ∂ s 0
s 0
s s j f )
= β ∂ p e ( 1 + t V A T ) ∑
f ∈ j
φ s j f s s j f
   
¯
φ j s j
+ σ 1 ( ∑
f ∈ j
∂ s f j s
   
∂ s j
− ∂ s j
s j ∑
f ∈ j
s s j f
   
∂ s j
)
+ σ 2 ( ∂ s j
s j
− ∂ s s
s s
) ∑
f ∈ j
s s j f
   
s j
W e define ¯
φ j as the sales-w eighted a v erage fuel consumption of both fuel-type v er -
sions of the same model j . Thus w e obtain that
( 1 − σ 2 ) ∂ s j
s j
= β ∂ p e ( 1 + t V A T ) ¯
φ j − σ 2
∂ s s
s s
+ ∂ s 0
s 0
. (9)
Aggregating further , w e can also reco v er the relativ e v ariation in the market shar e of
segment s ( ∂ s s
s s ) or of the outside good ( ∂ s 0
s 0 ) b y summing on respectiv ely all cars in the
same segment, and all new cars. For segment s , w e obtain that:
∂ s s
s s
= β ∂ p e ( 1 + t V A T ) ¯
φ s + ∂ s 0
s 0
,
while for the o v erall number of sold cars w e get:
∂ s 0
s 0
= − β ∂ p e ( 1 + t V A T ) ¯
φ ( 1 − s 0 ) .
Combining these expressions in 7 w e finally can compute the elasticity η s j f as:
η s j f = ∂ s s j f / s s j f
∂ p e / p e
= β ( 1 + t V AT ) p e ( ρ 1 φ s j f + ( ρ 2 − ρ 1 ) ¯
φ j − ( ρ 2 − 1 ) ¯
φ s ) − β ( 1 + t V AT ) p e ¯
φ ( 1 − s 0 )
≈ β ( 1 + t V AT ) p e ( ρ 1 ( φ s j f − ¯
φ j ) + ρ 2 ( ¯
φ j − ¯
φ s ) + ¯
φ s ) , (5)
where ρ i = 1
1 − σ i ∈ [ 1, + ∞ ] . The demand elasticity depends on the parameter β measuring

52 II Fuel T axes
sensitivity to fuel prices, the V A T rate t V AT , 35 as w ell as on the current price of fuel and
the car ’s fuel consumption φ s j f relativ e to the a v erage fuel economy of its substitutes
(within the same model ¯
φ j , within its segment ¯
φ s and among all sales ¯
φ ). The share of
the outside good s 0 is v ery close to 1, as a monthly frequency is high compared to v ehicle
lifetime: most people do not buy a car in any giv en month and monthly sales are small
compared to the market size. Thus, the second ter m inv olving ¯
φ ( 1 − s 0 ) is negligible.
The easier purchasers substitute betw een fuel-type v ersions of the same model, resp.
betw een models within a segment, the higher is σ 1 , resp. σ 2 , and, thus, the higher is
ρ 1 , resp. ρ 2 . Intuitiv ely speaking, a higher correlation of preference for similar pr oducts
(same nests) leads to a relativ ely higher w eight put onto the comparison with these
similar products.
Ob viously , diesel taxes affect cars dif ferently depending on their fuel-type. Using our
main model defined in Equation (4), the elasticity η t D
s j f of demand for a giv en car s j f with
respect to an increase in diesel tax (holding gasoline tax constant) can be computed as:
η t D
s j f = ∂ s s j f / s s j f
∂ t D / t D
= β ( 1 + t V AT ) t D ( ρ 1 ( 1 f = d i e s e l φ s j f + ( ρ 2 − ρ 1 ) π D
j ¯
φ j − ( ρ 2 − 1 ) π D
s ¯
φ s ) −
β ( 1 + t V AT ) t D ¯
φ D π D ( 1 − s 0 )
≈ β ( 1 + t V AT ) t D ( ρ 1 ( 1 f = d i e s e l φ s j f − π D
j ¯
φ j ) + ρ 2 ( π D
j ¯
φ j − π D
s ¯
φ s ) + π D
s ¯
φ s ) . (10)
where the indicator 1 f = di e s e l takes the v alue 1 if the v ehicle s j f is running on a diesel
engine, π D
s j is the share of diesel in sales of model j , π D
s is the share of diesel in sales
of segment s , and π D is the o v erall market share of new diesel cars (among pur chases).
¯
φ D is the mean fuel consumption of new diesel cars (sales-w eighted a v erage). Again,
( 1 − s 0 ) is v er y close to zero and this elasticity can be closely approximated b y the first
part of the equation.
Intuitiv ely , an increase in the diesel tax rate has a direct negativ e impact for all diesel
cars. Ho w ev er , this ef fect ma y be reduced if its substitutes are also impacted b y this
increase. The effect for gasoline cars of a diesel tax is expected to be positiv e.
On a more aggregate le v el, w e examine the impact of an increase in fuel prices on
the composition of the automobile fleet, with a particular focus on the amount of diesel
cars purchased. More specifically , w e e v aluate the elasticity of the share of diesel cars
among new pur chases π D . Assuming again that an inter national oil price shift equally
af fects both gasoline and diesel pre-tax prices, such a price shift w ould change the share
35 This is specific to the French for m of petrol tax: as the fuel-type specific taxes are of a lump-sum form,
they do not pla y a role here. The t V A T is the same for both fuel-types.

8. Appendices 53
of diesel cars b y η D . In the simple logit demand, this change can be computed as:
η D = ∂ π D / π D
∂ p e / p e
= ∑ s , j , f 1 f = di e s e l s s j f η s j f
∑ s , j , f 1 f = di e s e l s s j f
− ∂ ( 1 − s 0 )
∂ p e
p e
1 − s 0
= β ( 1 + t V AT ) p e ( ρ 1 ( ¯
φ D − ˜ ¯
φ j ) + ρ 2 ( ˜ ¯
φ j − ˜ ¯
φ s ) + ˜ ¯
φ s − ¯
φ ) ,
= β ( 1 + t V AT ) p e
π D ( 1 − s 0 ) ∑
s , j
s j ⎛
⎜
⎝ ρ 1 π D
j ( φ D
j − ¯
φ j )
   
S 1
+ ρ 2 ( π D
j − π D
s ) ¯
φ j
   
S 2
+ ( π D
s − π D ) ¯
φ s
   
S 3
⎞
⎟
⎠ , (11)
which inv olv es w eighted a v erages of fuel consumption, where the w eights are giv en b y
the share of diesel sales. 36 ˜ ¯
φ j = ∑ s , j
π D
j s j
π D ( 1 − s 0 ) ¯
φ j is the a v erage fuel consumption w eighted
b y the share of diesel per model, whereas ˜ ¯
φ s = ∑ s π D
s s s
π D ( 1 − s 0 ) ¯
φ s is the a v erage w eighted b y
the diesel share per segment. φ D
j is the fuel consumption of the diesel v ersion of model
j . π D
j , resp. π D
s , is the share of diesel among purchases of model j , resp. of segment s .
The interpretation of this equation is not straightfor w ar d. In the simplest logit case
( σ 1 = σ 2 = 0), η D = β ( 1 + t V AT ) p e ( ¯
φ D − ¯
φ ) . Naturally , η D depends on the a v erage fuel
consumption of diesel cars relativ e to the o v erall a v erage fuel consumption. ¯
φ D − ¯
φ is
alw a ys negativ e because diesel cars are mor e fuel-efficient. β is negativ e as w ell, so that
η D is positiv e: if fuel prices increase, purchasers substitute to more fuel-ef ficient diesel
cars and their share among purchases incr eases.
In a nested setup, the effect is less straightforw ar d, but w e still expect a positiv e
sign. Indeed, the first ter m S 1 in Equation (11) inv olv es the dif ference betw een diesel
fuel consumption and a v erage fuel consumption; again, this change is expected to be
negativ e as diesel engines tend to be more fuel-ef ficient. Ho w e v er , w e do not ha v e such
an unambiguous relation for the tw o other terms S 2 and S 3 . 37 Both ρ 1 and ρ 2 are positiv e
and larger than one. In practice ρ 2 is smaller than ρ 1 , so that η D is most strongly impacted
b y the first element of the parenthesis, which is likely to be positiv e.
Similarly , the elasticity of the share of diesel cars π D to a change in fuel taxes (holding
gasoline taxes constant) η t D
D ma y be written:
η t D
D = ∂ π D / π D
∂ t D / t D
= β ( 1 + t V AT ) p e ( ρ 1 ( ¯
φ D − ˜
π D
j ¯
φ j ) + ρ 2 ( ˜
π D
j ¯
φ j − ˜
π D
s ¯
φ s ) + ˜
π D
s ¯
φ s − ¯
φ ) . (12)
36 W ith any variable A w e denote ˜
A = ∑ s , j , f
s s j f
π D ( 1 − s 0 ) A s j f 1 f = di e s e l this v ariable w eighted by the shar e of
the diesel v ersion amongst all diesel cars (for example, ˜
φ s j f corresponds to the a v erage fuel consumption of
diesel cars ¯
φ D ).
37 The last ter m for example does not ha v e a w ell defined sign. For example in the case of only tw o
segments in proportion s 1 and ( 1 − s 1 ) , this ter m is proportional to s 1 ( 1 − s 1 ) ( π D
s 1 − π D
s 2 ) ( ¯
φ s 1 − ¯
φ s 2 ) . One
cannot exclude that this term is positiv e, for example if cars ha v e a much higher fuel consumption on
a v erage in the segment with the higher share of diesel cars.

54 II Fuel T axes
This elasticity η t D
D depends only on the fuel consumption of diesel cars and on their
relativ e share among pur chases: the lo w er their fuel consumption, the smaller the impact
of a diesel tax increase.
Finally , w e can also compute the elasticity η φ (respectiv ely η CO 2 ) of the a v erage fuel
consumption (respectiv ely of a v erage CO 2 intensity) of new cars with respect to fuel
prices p e and to fuel taxes.
η φ = ∂ ¯
φ / ¯
φ
∂ p e / p e
= β ( 1 + t V AT ) p e
( 1 − s 0 ) ¯
φ ∑
j , s , f ( φ s j f s s j f ( ρ 1 ( φ s j f − ¯
φ j ) + ρ 2 ( ¯
φ j − ¯
φ s ) + ¯
φ s − ¯
φ ) ) . (13)
For example, in the simple logit demand model, η φ simplifies to:
η φ = β ( 1 + t V AT ) p e ( φ 2 − φ 2
¯
φ ) , (14)
with φ 2 is the mean of squared fuel consumption of new v ehicles. The impact of an oil
price shock on a v erage fuel consumption depends thus on the ratio of the v ariance and
the mean of fuel consumption. Both the v ariance and the mean of φ are alw a ys positiv e,
so that η φ is alw a ys negativ e in the simple logit case: when fuel prices increase, w e
expect to find that a v erage fuel consumption is reduced. In the more realistic nested logit
demand model, the conclusion is less straightfor w ar d. Again, w e ha v e some intuition for
the first ter m of Equation (13) which is of first or der in the sum: it can be simplified
re written as β ρ 1 ∑ s , j π D
j ( 1 − π D
j ) s j ( φ D
j − φ G
j ) 2 and is thus expected to be negativ e.
The elasticity of a v erage fuel consumption η t D
φ (respectiv ely η t D
CO 2 ) to a change in diesel
tax (holding gasoline tax constant) can be written in case of a simple logit demand model:
η t D
φ = ∂ ¯
φ / ¯
φ
∂ t D / t D
= β t D ( 1 + t V AT ) β π D
¯
φ
   
< 0
⎛
⎜
⎝ φ 2
D − φ 2
D
   
> 0
+ ( 1 − π D ) ¯
φ D ( ¯
φ D − ¯
φ G )
   
< 0
⎞
⎟
⎠ . (15)
This elasticity depends on the fuel consumption of diesel cars and on their relativ e
share among purchases compar ed with the a v erage fuel consumption. The sign is not
clear -cut. An increase in the diesel tax can reduce the shar e of diesel cars, which are more
fuel-ef ficient. The higher the gap betw een the a v erage fuel consumption of gasoline and
diesel cars, the higher the increase in the a v erage fuel emissions of new cars. This effect
ma y be partially offset b y the dispersion in fuel emissions of diesel cars, as w e expect
that an increase in diesel prices has more impact on less fuel-ef ficient cars. Ov erall, w e
expect that a rise in diesel tax increases the a v erage fuel emissions of new cars if diesel
cars are much more fuel-ef ficient that gasoline cars and that the diesel shar e is not too
high.

8. Appendices 55
C Complementar y results for the main specification
C.1 Ra w coef ficients
T ables for estimated coef ficients are not directly interpretable. This is why the body
of this chapter concentrates on elasticities and counterfactual policy impacts. The coeffi-
cients β d measure each demographic group’s dir ect sensitivity to fuel prices. As expected,
β d is statistically significant for most demographic groups and is alw a ys negativ e when
significantly dif ferent from zer o: as fuel prices incr ease, the utility from any giv en car
decreases (T able II.8).
W e find substantial heterogeneity in the relativ e magnitude of β d across pur chaser
types. The heterogeneity in this sensitivity parameter depends on three main factors:
first, the flexibility of the consumer ’s car usage (if he can adjust his car mileage, the
fuel ef ficiency becomes less important for his purchasing decision); second, whether the
consumer buys fuel-ef ficient cars no matter what (there might not be much of a mar gin
to react on for some consumers); and finally , the consumer ’s income and preferences for
other characteristics of the car .
T able II.8: Estimates for the coef ficient on cost per km β d
Private consumers
Not emplo y ed Y oung professional Emplo y ed ( > 30)
Income Low High Lo w High Lo w High
Urban − 0.11
( 0.02 )
∗∗∗ − 0.08
( 0.02 )
∗∗∗ − 0.15
( 0.02 )
∗∗∗ − 0.13
( 0.02 )
∗∗∗ − 0.13
( 0.02 )
∗∗∗ − 0.14
( 0.01 )
∗∗∗
Suburb./rural − 0.08
( 0.02 )
∗∗∗ − 0.11
( 0.02 )
∗∗∗ − 0.10
( 0.02 )
∗∗∗ − 0.15
( 0.02 )
∗∗∗ − 0.10
( 0.02 )
∗∗∗ − 0.15
( 0.01 )
∗∗∗
Paris urban − 0.10
( 0.02 )
∗∗∗ − 0.09
( 0.02 )
∗∗∗ − 0.10
( 0.01 )
∗∗∗
Paris suburban − 0.03
( 0.02 ) − 0.08
( 0.02 )
∗∗∗ − 0.10
( 0.01 )
∗∗∗
Firm purchases
Agriculture & Car T rade &
S ector industry rental services
Suburban/rural − 0.01
( 0.01 ) − 0.03
( 0.04 ) − 0.06
( 0.01 )
∗∗∗
Urban − 0.09
( 0.02 )
∗∗∗ − 0.16
( 0.03 )
∗∗∗ − 0.10
( 0.01 )
∗∗∗
Paris urban − 0.07
( 0.02 )
∗∗∗ 0.08
( 0.02 )
∗∗∗ − 0.01
( 0.01 )
Paris suburban − 0.01
( 0.02 ) 0.01
( − ) − 0.04
( 0.02 )
Source: CCF A, authors’ calculations. Bootstrap standard err ors in parentheses. Equation (4) is estimated
b y GMM separately for each type of purchasers. Other controlling v ariables include horsepo w er , brand
fixed effects, segment fixed ef fects, class of CO 2 , month-y ear ef fects, and price. Instrumental v ariables for
prices are the price indices of iron (current and lagged v alue) and indices of export prices of tires (both
interacted with the car ’s w eight), BLP-style instruments and differences of characteristics betw een gasoline
and diesel v ersions. The estimation of car rental purchases in the Paris suburban area appears to ha v e a
problem of w eak instruments (see S ection 4.2) and does not conv erge for all bootstrap dra ws, so that w e
giv e no bootstrap error ter m for it.
Among priv ate consumers, the ef fect of fuel price increases is stronger for emplo y ed
consumers (T able II.8). W orking people ha v e to driv e more and tra v el distances cannot

56 II Fuel T axes
be easily reduced; they are thus expected to be the more responsiv e to fuel price changes.
This ef fect is less strong in the Paris region, wher e more public transport alter nativ es are
a v ailable.
Generally , fir ms react less strongly to fuel prices than priv ate consumers. Among
other factors this ma y be due to fir ms’ ability to pass through fuel costs to the consumer
and to smaller absolute fuel price v ariations when V A T refund is taken into account.
W ithin firms, w e see considerable heterogeneity (T able II.8). The most responsiv e fir ms
are in urban areas except Paris. In the Paris metropolitan region, sensitivity is particularly
lo w and almost ne v er significant.
Ho w e v er , because of the nested logit specification, the magnitude of the parameters
is not directly informativ e on the actual fuel prices elasticities. One has to consider
indirect ef fects due to the correlation (and thus higher potential substitution) betw een
gasoline and diesel v ersions of the same model captured b y σ 1 d , as w ell as substitution
within segment σ 2 d . The estimates for these parameters are as expected all betw een
0 and 1. σ 1 d is on a v erage 0.5 implying a relativ ely high correlation betw een the tw o
fuel-type v ersions of the same model (T able II.9, while σ 2 d is relativ ely lo w , on a v erage
0.2, implying a relativ ely lo w correlation within segments (T able II.10). If the purchaser
has a preference for a particular model, he substitutes easily betw een gas and diesel
v ersions when fuel prices change, rather than switching to a differ ent model and only
reluctantly switches segment. Intensity of substitution betw een the gasoline and diesel
v ersions of the same model appears to be higher in urban areas (including Paris urban
and metropolitan areas) than in rural ar eas. Indeed, while diesel cars yield sa vings in
running costs for long jour ne ys, this adv antage is not clear cut for city driving.
The signs of other v ariables’ coefficients are as expected; in particular , the v ehicle
price impacts utility negativ ely (T able II.11).
C.2 Demand for selected car models
For a giv en product, the demand elasticity to fuel prices depends on the car ’s fuel con-
sumption (relativ e to competing products) and on the pr eferences of the consumer types
that buy this car (T able II.12). For the sake of illustration, w e compute different elastic-
ities η j f implied b y the pre viously presented parameters for some selected cars, as w ell
as the shifts in demand ∆ t c η j f and ∆ t D η j f corresponding to the equalization of diesel and
gasoline taxes ( t D ) and the carbon tax ( t c ), respectiv ely .
An increase in fuel prices (both gasoline and diesel) reduces demand for all cars ( η j f <
0), but the magnitude v aries: T able II.12 giv es only a sample of the most popular cars
in our data, where the Peugeot 307 gasoline model had an elasticity with respect to fuel
price of -0.17, while the Citroen C3 gasoline model had an elasticity of -0.34. An increase
in diesel fuel tax strongly lo w ers the demand for diesel cars ( ∆ t D η j f < 0); for example
the sales of the A udi A6 with diesel engine w ould decrease b y 18.2% (T able II.12). At the
same time, such a policy has a small but significantly positiv e effect on the demand for
gasoline cars, reflecting a substitution ef fect.

8. Appendices 57
T able II.9: Estimates for the coef ficient σ 1 d (substitutability within model, betw een engine
types)
Private consumers
Not emplo y ed Y oung professional Emplo y ed ( > 30)
Income Low High Lo w High Lo w High
Urban 0.41
( 0.04 )
∗∗∗ 0.48
( 0.04 )
∗∗∗ 0.51
( 0.03 )
∗∗∗ 0.51
( 0.03 )
∗∗∗ 0.55
( 0.02 )
∗∗∗ 0.59
( 0.02 )
∗∗∗
Suburb./rural 0.45
( 0.04 )
∗∗∗ 0.41
( 0.03 )
∗∗∗ 0.38
( 0.03 )
∗∗∗ 0.41
( 0.03 )
∗∗∗ 0.55
( 0.02 )
∗∗∗ 0.52
( 0.02 )
∗∗∗
Paris urban 0.30
( 0.04 )
∗∗∗ 0.62
( 0.03 )
∗∗∗ 0.62
( 0.02 )
∗∗∗
Paris suburban 0.10
( 0.06 ) 0.34
( 0.04 )
∗∗∗ 0.57
( 0.03 )
∗∗∗
Firm purchases
Agriculture & Car T rade &
S ector industry rental services
Suburban/rural 0.29
( 0.03 )
∗∗∗ 0.26
( 0.08 )
∗∗∗ 0.24
( 0.03 )
∗∗∗
Urban 0.33
( 0.03 )
∗∗∗ 0.18
( 0.04 )
∗∗∗ 0.23
( 0.03 )
∗∗∗
Paris urban 0.17
( 0.04 )
∗∗∗ − 0.16
( 0.04 )
∗∗∗ 0.18
( 0.03 )
∗∗∗
Paris suburban 0.77
( 0.05 )
∗∗∗ 0.42
( − ) 0.60
( 0.05 )
∗∗∗
Source: CCF A, authors’ calculations. Bootstrap standard err ors in parentheses. Equation (4) is estimated
b y GMM separately for each type of purchasers. Other controlling v ariables include horsepo w er , brand
fixed effects, segment fixed ef fects, class of CO 2 , month-y ear ef fects, and price. Instrumental v ariables for
prices are the price indices of iron (current and lagged v alue) and indices of export prices of tires (both
interacted with the car ’s w eight), BLP-style instruments and differences of characteristics betw een gasoline
and diesel v ersions. The estimation of car rental purchases in the Paris suburban area appears to ha v e a
problem of w eak instruments (see S ection 4.2) and does not conv erge for all bootstrap dra ws, so that w e
giv e no bootstrap error ter m for it.

58 II Fuel T axes
T able II.10: Estimates for the coef ficient σ 2 d (substitutability within segment, betw een
models)
Private consumers
Not emplo y ed Y oung professional Emplo y ed ( > 30)
Income Low High Lo w High Lo w High
Urban 0.11
( 0.02 )
∗∗∗ 0.13
( 0.02 )
∗∗∗ 0.22
( 0.02 )
∗∗∗ 0.19
( 0.02 )
∗∗∗ 0.32
( 0.01 )
∗∗∗ 0.39
( 0.01 )
∗∗∗
Suburb./rural 0.14
( 0.02 )
∗∗∗ 0.16
( 0.02 )
∗∗∗ 0.23
( 0.01 )
∗∗∗ 0.21
( 0.01 )
∗∗∗ 0.28
( 0.02 )
∗∗∗ 0.34
( 0.01 )
∗∗∗
Paris urban 0.17
( 0.02 )
∗∗∗ 0.26
( 0.02 )
∗∗∗ 0.37
( 0.02 )
∗∗∗
Paris suburban 0.21
( 0.02 )
∗∗∗ 0.20
( 0.02 )
∗∗∗ 0.30
( 0.02 )
∗∗∗
Firm purchases
Agriculture & Car T rade &
S ector industry rental services
Suburban/rural 0.08
( 0.02 )
∗∗∗ 0.16
( 0.03 )
∗∗∗ 0.01
( 0.02 )
Urban 0.07
( 0.02 )
∗∗∗ 0.08
( 0.03 )
∗∗∗ 0.16
( 0.02 )
∗∗∗
Paris urban 0.12
( 0.03 )
∗∗∗ 0.10
( 0.02 )
∗∗∗ 0.24
( 0.02 )
∗∗∗
Paris suburban 0.28
( 0.03 )
∗∗∗ 0.22
( − ) 0.32
( 0.03 )
∗∗∗
Source: CCF A, authors’ calculations. Bootstrap standard err ors in parentheses. Equation (4) is estimated
b y GMM separately for each type of purchasers. Other controlling v ariables include horsepo w er , brand
fixed effects, segment fixed ef fects, class of CO 2 , month-y ear ef fects, and price. Instrumental v ariables for
prices are the price indices of iron (current and lagged v alue) and indices of export prices of tires (both
interacted with the car ’s w eight), BLP-style instruments and differences of characteristics betw een gasoline
and diesel v ersions. The estimation of car rental purchases in the Paris suburban area appears to ha v e a
problem of w eak instruments (see S ection 4.2) and does not conv erge for all bootstrap dra ws, so that w e
giv e no bootstrap error ter m for it.

8. Appendices 59
T able II.11: Estimates for the coef ficient on v ehicle price γ d
Private consumers
Not emplo y ed Y oung professional Emplo y ed ( > 30)
Income Low High Lo w High Lo w High
Urban − 0.63
( 0.05 )
∗∗∗ − 0.57
( 0.05 )
∗∗∗ − 0.30
( 0.04 )
∗∗∗ − 0.31
( 0.04 )
∗∗∗ − 0.21
( 0.03 )
∗∗∗ − 0.12
( 0.03 )
∗∗∗
Suburb./rural − 0.65
( 0.05 )
∗∗∗ − 0.66
( 0.05 )
∗∗∗ − 0.42
( 0.04 )
∗∗∗ − 0.30
( 0.04 )
∗∗∗ − 0.36
( 0.03 )
∗∗∗ − 0.15
( 0.03 )
∗∗∗
Paris urban − 0.36
( 0.05 )
∗∗∗ − 0.32
( 0.04 )
∗∗∗ − 0.21
( 0.03 )
∗∗∗
Paris suburban − 0.20
( 0.05 )
∗∗∗ − 0.25
( 0.04 )
∗∗∗ − 0.14
( 0.03 )
∗∗∗
Firm purchases
Agriculture & Car T rade &
S ector industry rental services
Suburban/rural − 0.22
( 0.03 )
∗∗∗ − 0.29
( 0.08 )
∗∗∗ − 0.10
( 0.03 )
∗∗∗
Urban − 0.01
( 0.03 ) 0.14
( 0.05 )
∗∗∗ − 0.00
( 0.03 )
Paris urban − 0.01
( 0.03 ) − 0.03
( 0.04 ) − 0.09
( 0.03 )
∗∗∗
Paris suburban − 0.14
( 0.03 )
∗∗∗ − 0.28
( − ) − 0.27
( 0.05 )
∗∗∗
Source: CCF A, authors’ calculations. Bootstrap standard err ors in parentheses. Equation (4) is estimated
b y GMM separately for each type of purchasers. Other controlling v ariables include horsepo w er , brand
fixed effects, segment fixed ef fects, class of CO 2 , month-y ear ef fects, and price. Instrumental v ariables for
prices are the price indices of iron (current and lagged v alue) and indices of export prices of tires (both
interacted with the car ’s w eight), BLP-style instruments and differences of characteristics betw een gasoline
and diesel v ersions. The estimation of car rental purchases in the Paris suburban area appears to ha v e a
problem of w eak instruments (see S ection 4.2) and does not conv erge for all bootstrap dra ws, so that w e
giv e no bootstrap error ter m for it.
T able II.12: Demand elasticity for selected models with respect to fuel prices
model (segment) fuel CO 2 fuel η j f ∆ t D η j f ∆ t c η j f
(g/km) cons.
(L/km)
(%) (%)
A udi A6 (sedan) gasoline 236.9 10.2 − 0.22
( 0.03 )
∗∗∗ 1.17
( 0.22 )
∗∗∗ − 6.73
( 0.89 )
∗∗∗
A udi A6 (sedan) diesel 200.1 7.6 − 0.29
( 0.02 )
∗∗∗ − 18.20
( 1.55 )
∗∗∗ − 9.39
( 0.60 )
∗∗∗
Citroen C3 gasoline 147.8 6.4 − 0.34
( 0.02 )
∗∗∗ 2.46
( 0.23 )
∗∗∗ − 10.62
( 0.51 )
∗∗∗
Citroen C3 diesel 112.8 4.3 − 0.19
( 0.01 )
∗∗∗ − 13.48
( 0.69 )
∗∗∗ − 6.55
( 0.32 )
∗∗∗
Peugeot 307 (sport) gasoline 192.7 8.3 − 0.17
( 0.01 )
∗∗∗ 1.57
( 0.08 )
∗∗∗ − 4.29
( 0.21 )
∗∗∗
Peugeot 307 (sport) diesel 159.0 6.0 − 0.32
( 0.01 )
∗∗∗ − 18.62
( 0.87 )
∗∗∗ − 9.41
( 0.43 )
∗∗∗
Renault T wingo (compact) gasoline 137.0 5.9 − 0.32
( 0.01 )
∗∗∗ 0.86
( 0.03 )
∗∗∗ − 9.78
( 0.44 )
∗∗∗
Renault T wingo (compact) diesel 113.0 4.3 − 0.25
( 0.01 )
∗∗∗ − 15.62
( 0.93 )
∗∗∗ − 7.30
( 0.37 )
∗∗∗
Source: CCF A, authors’ calculations. Equation (4) is estimated by GMM separately for each type of con-
sumers. Standard errors in par entheses are estimated b y bootstrap (500 replications).

60 II Fuel T axes
D T esting for w eak instr uments
T able II.13: Conditional F-v alues of the w eak instrument test – instruments for the price
Private consumers
Not emplo y ed Y oung emplo y ed ( < 30) Emplo y ed ( > 30)
Income Lo w High Lo w High Lo w High
Urban 35.1*** 31.9*** 51.8*** 51.7*** 47.8*** 49.0***
Suburban/rural 31.7*** 37.6*** 64.8*** 70.9*** 51.3*** 51.5***
Paris urban 20.4*** 42.2*** 44.2***
Paris suburban 16.3** 36.6*** 39.4***
Firm purchases
Industry & Car T rade &
Agriculture rental services
Urban 42.2*** 11.5** 39.7***
Suburban/rural 45.9*** 34.9*** 37.2***
Paris urban 52.4*** 36.3*** 34.6***
Paris suburban 14.2** 14.1** 15.4**
Note: Stars denote conditional F-values be y ond the critical v alue (at 5% significance lev el) for dif ferent
lev els of maximal bias of the IV estimator relativ e to OLS; *** stands for a maximal bias of 5%, ** for 10%, *
for 20%.
T able II.14: Conditional F-v alues of the w eak instrument test – instruments for the market
shar e of the model within its segment s d j | s
Private consumers
Not emplo y ed Y oung emplo y ed ( < 30) Emplo y ed ( > 30)
Income Lo w High Lo w High Lo w High
Urban 68.1*** 71.3*** 61.4*** 62.4*** 60.7*** 53.6***
Suburban/rural 71.5*** 73.3*** 71.1*** 64.0*** 58.9*** 55.7***
Paris urban 53.4*** 58.3*** 49.3***
Paris suburban 36.5*** 53.5*** 54.8***
Firm purchases
Industry & Car T rade &
Agriculture rental services
Urban 45.9*** 34.9*** 37.2***
Suburban/rural 42.2*** 11.5** 39.7***
Paris urban 52.4*** 36.3*** 34.6***
Paris suburban 14.2** 14.1** 15.4**
Note: Stars denote conditional F-values be y ond the critical v alue (at 5% significance lev el) for dif ferent
lev els of maximal bias of the IV estimator relativ e to OLS; *** stands for a maximal bias of 5%, ** for 10%, *
for 20%.

8. Appendices 61
T able II.15: Conditional F-v alues of the w eak instrument test – instruments for the market
shar e of a fuel-type within its model nest s d f | j
Private consumers
Not emplo y ed Y oung emplo y ed ( < 30) Emplo y ed ( > 30)
Income Lo w High Lo w High Lo w High
Urban 26.5*** 22.6*** 32.6*** 32.3*** 41.9*** 44.7***
Suburban/rural 23.6*** 27.9*** 31.7*** 38.1*** 43.7*** 44.3***
Paris urban 15.0** 27.3*** 31.7***
Paris suburban 16.7** 22.2*** 26.5***
Firm purchases
Industry & Car T rade &
Agriculture rental services
Urban 24.2*** 21.6*** 25.8***
Suburban/rural 32.4*** 6.3* 28.6***
Paris urban 15.2** 21.0*** 20.8***
Paris suburban 11.7** 2.9 10.4*
Note: Stars denote conditional F-values be y ond the critical v alue (at 5% significance lev el) for dif ferent
lev els of maximal bias of the IV estimator relativ e to OLS; *** stands for a maximal bias of 5%, ** for 10%, *
for 20%.

62 II Fuel T axes
E Robustness checks: elasticities
T able II.16: Robustness checks: elasticities with respect to fuel prices of diesel share, a v-
erage fleet fuel consumption (L/km) and CO 2 intensity (g/km)
Diesel share Fuel cons. CO 2
η D η φ η CO 2
Main specification - including degenerate nests (gas- or diesel-only models)
Households 0.044
( 0.003 )
∗∗∗ − 0.015
( 0.001 )
∗∗∗ − 0.018
( 0.001 )
∗∗∗
Firms 0.017
( 0.003 )
∗∗∗ − 0.004
( 0.001 )
∗∗∗ − 0.006
( 0.001 )
∗∗∗
T otal 0.045
( 0.002 )
∗∗∗ − 0.011
( 0.001 )
∗∗∗ − 0.015
( 0.001 )
∗∗∗
Alternative specification - Nests (segment > model)
Households 0.042
( 0.003 )
∗∗∗ − 0.014
( 0.001 )
∗∗∗ − 0.017
( 0.001 )
∗∗∗
Firms 0.015
( 0.004 )
∗∗∗ − 0.004
( 0.001 )
∗∗∗ − 0.006
( 0.001 )
∗∗∗
T otal 0.044
( 0.003 )
∗∗∗ − 0.010
( 0.001 )
∗∗∗ − 0.014
( 0.001 )
∗∗∗
Main specification - BLP-instruments only
Households 0.033
( 0.003 )
∗∗∗ − 0.015
( 0.001 )
∗∗∗ − 0.017
( 0.001 )
∗∗∗
Firms 0.017
( 0.004 )
∗∗∗ − 0.003
( 0.001 )
∗∗∗ − 0.004
( 0.001 )
∗∗∗
T otal 0.039
( 0.003 )
∗∗∗ − 0.011
( 0.001 )
∗∗∗ − 0.014
( 0.001 )
∗∗∗
Main specification - without purchaser heter ogeneity
T otal 0.039
( 0.004 )
∗∗∗ − 0.028
( 0.003 )
∗∗∗ − 0.025
( 0.002 )
∗∗∗
Source: CCF A, authors calculations. Estimates rely on the parameters of Equation (4) estimated
b y GMM separately for each type of consumers. Standard err ors in parentheses are estimated b y
bootstrap (500 replications).

I I I
Comp etition b et w een fo r-p rofit and industry
lab els: the case of so cial lab els in the coffee
ma rk et
I pity the man who w ants a coat
so cheap that the man or w oman
who produces the cloth will
star v e in the process.
Benjamin Harrison
on the importance of fair trade.
63

64 III Label Competition
1 Introduction
Ov er the past decades, consumers ha v e become mor e and more interested in the social
and environmental impact of their consumption. Ho w e v er , most sustainability aspects
of a product are dif ficult for consumers to v erify , ev en after purchase, meaning that the
promise of a responsible pr oduction process is essentially a cr edence attribute that cannot
be v erified either before or after purchase. Fir ms increasingly use v oluntar y thir d-party
labels to solv e their credibility problem.
The cof fee market has a particularly large number of w ell-established sustainabil-
ity labels; the most important being Fairtrade, Rainforest Alliance, and UTZ Certified.
These target the w ell-being of farmers and the environmental impact of production. The
stringency of the labels v aries: Fairtrade, for example, guarantees a price premium for
far mers, while the price premia established b y UTZ Certified and Rainforest Alliance are
lo w er and not guaranteed. 1 When it comes to social sustainability labels, higher far mgate
prices are seen b y consumers as higher quality and justify higher prices.
When each fir m can of fer sev eral dif fer entiated products, v arious constellations of
product lines can arise. In the coffee example, an inter national comparison illustrates
this multitude of possible product line constellations: in Ger many , most roasters 2 of fer a
range of products including conv entional, i.e. not labeled, and labeled cof fee of sev eral
labels (head-to-head competition). In other countries, such as Finland, 3 cof fee roasters
ha v e specialized so that each label is only offered b y one roaster (market segmentation).
This paper establishes a model of label competition, betw een a for-pr ofit label and an
industry standard. T o start with, w e model the firms’ choice of a third-party label of fered
b y a for -pr ofit licenser in the first period. W e are interested in the interaction betw een
the licenser , which sets a license fee and a label quality , and fir ms, which decide on their
product line and their prices. Each firm can offer se v eral goods that are dif ferentiated
both horizontally betw een fir ms and v ertically through quality . In a second period, w e
allo w firms to establish its o wn labeling organization – an industry standard – that max-
imizes joint fir m profit. W e then analyze ho w the industr y standar d sets its quality and
what product lines are of fered in equilibrium.
In both periods, w e find that equilibrium product lines depend crucially on the de-
gree of (exogenous) horizontal dif ferentiation: the market is segmented if horizontal
1 Fairtrade Labeling Organizations International (FLO) guarantee a price premium at the farmgate of
$0.20/lb (since 2011) o v er the stock market price. UTZ Certified in 2012 reported sales prices that result in
an a v erage premium of $0.04/lb o v er the price index of the Inter national Coffee Or ganization; the prices for
Rainforest Alliance are not kno wn but they reported a premium of $0.11/lb in 2009 (Potts et al. 2014). In
2012, Fairtrade and Rainforest Alliance had similar market shares of 2-3% w orldwide while UTZ had
almost twice as much, with much larger market shares in countries like the United States, Germany , and
Great Britain.
2 The German coffee market is dominated b y JDE/Mondelez, Aldi, T chibo, Melitta and Dallma yr;
together they hold 90% of the market (V illas-Boas 2007, adjusted for the mer ger of JDE/Mondelez in 2015).
3 In Finland, per capita coffee consumption is the highest in the w orld. The a v erage Finn consumes 9-10
kg of roasted cof fee annually; approximately four cups per da y (V alkila et al. 2010). There are just tw o
major companies on the Finnish coffee market: Meira and Paulig.

1. Introduction 65
dif ferentiation is w eak, i.e. each label is offered b y one fir m only . In contrast, fir ms are
in head-to-head competition when horizontal dif ferentiation is strong, that is both firms
of fer all a v ailable labels. When there are tw o labels and horizontal dif ferentiation is in-
ter mediate, the industr y standard strategically distorts its quality do wnw ar ds in order to
induce a segmented product line.
Ov erall, w e illustrate why an industry facing a third-party label has an interest in
establishing its o wn industry standard: the presence of a second label reduces the fees
set b y the for-pr ofit licenser , and an additional v ertically dif fer entiated good increases
product lines, thereb y increasing o v erall demand. Moreo v er , for inter mediate lev els of
horizontal dif ferentiation, the industry standard strategically reduces competition b y re-
ducing product line o v erlap, thereb y increasing mark-ups.
W e further ask whether regulation in form of a minimum quality requirement for
labels, such as established in or ganic far ming, can increase w elfare. In the first period
with one label, a minimum quality requirement increases the label’s standar d, thereb y
increasing w elfare. W elfare increases if fir ms are in head-to-head competition, but the
minimum quality requirement cannot af fect the equilibrium pr oduct line. In the second
period with tw o labels, the social planner can set its minimum quality requirement such
that it pre v ents the industr y standard’s strategic do wnw ar d distortion, thereb y maximiz-
ing the number of labeled products. Whenev er the industry standard does not strategi-
cally distort its quality do wnw ards, the social planner aims at setting lower qualities than
the industry standard. In these cases, a minimum quality requirement does not bind and
does not impact w elfare: the duopoly firms in equilibrium differentiate too much from
conv entional market and too little from the higher label.
In the remainder of this paper , w e begin b y discussing the r elev ant literature and
explain the context of the cof fee market and fairtrade research. W e then explain the
model and each pla y er ’s objectiv es in S ection 2. W e first solv e the first period with only
the for -profit licenser in S ection 3. Then, w e solv e the model in the second period upon
entry of an industr y standard in S ection 4. For each period, w e explore whether there is
scope for a go v ernment-imposed minimum quality requirement. Finally , w e conclude in
S ection 5.
1.1 Related literature
Our model features both v ertical dif ferentiation betw een labels and horizontal dif fer -
entiation betw een fir ms. Methodologically , this study relies on a large literature using
the nested logit model established b y McFadden (1978). In particular , the v ersion of
Anderson and De Palma (1992) with multi-product firms allo ws us to explicitly model
the endogenous substitution elasticity betw een labels depending on label differ entiation.
Gallego and W ang (2014) use such a nested logit to account for horizontal and v ertical
dif ferentiation.
V on S chlippenbach and T eichmann (2012) and Y u and Bouamra-Mechemache (2016)
model ho w standar ds are used b y differ ent agents (retailers, resp. manufacturers) to
strengthen their bar gaining po w er within the v ertical supply chain. The choice of fir ms

66 III Label Competition
in duopoly adopting a labeled product line also relates to pr oduct line riv alr y (e.g. A v enel
and Caprice 2006). Cheng and Peng (2012) sho w the importance of strategic effects in
quality setting when a fir m can of fer more than one v ertically dif ferentiated pr oduct.
A gro wing literature is studying v oluntar y third-party certification, for a revie w see
Bonro y and Constantatos (2015). In particular , new er papers study the interaction be-
tw een sev eral labeling or ganizations and fir ms, focusing on endogenous quality le v els.
Fischer and L y on (2014) model the riv alry betw een an ecolabel set b y an NGO and an
industry-standard in the forestry sector and find that the industr y-standard lo w ers envi-
ronmental benefits e v en if consumers are perfectly informed. Poret (2016) models the
competition betw een tw o NGOs setting labels with different objectiv es. Similarly to
this study , Bottega et al. (2009) study the interaction betw een a regulator , an industr y
standard and a for -profit licenser . Ho w ev er , all these studies consider simple market
constellations (monopolist/single-good duopoly), following in particular the model b y
Hey es and Maxw ell (2004). Finally , a strand of literature explores the ef fect of consumer
confusion when sev eral labels coexist or monitoring is imperfect (Harbaugh et al. 2011,
Mahenc 2010, Mason 2011), whereas w e assume that consumers obser v e label quality
perfectly .
1.2 Cof fee market and fairtrade
In our model, the incumbent labeling organization maximizes its profit. Pre vious the-
oretical resear ch on fairtrade has modeled an NGO label maximizing farmer w elfare
(Podhorsky 2015, Richardson and St ¨
ahler 2014, Chambolle and Poret 2013). Ho w e v er , it
is dif ficult to argue that the FLO price policy is aimed at maximizing far mer w elfare. A
concise theoretical model b y Janvr y et al. (2015) sho ws ho w farmer rents are eroded b y
unlimited entry of farmers, such that in equilibrium all the price premium goes to the
licenser in for m of the farmer annual fee. Crucially , fairtrade guarantees prices, but not
sales, such that fairtrade-labeled far mers typically sell large pr oportions of their produc-
tion as conv entional coffee, i.e. without the label at w orld-market prices (e.g. V alkila and
Ny gren 2009, Panhuysen and Pierrot 2014). 4 Moreo v er , annual license fees are high for
both roasting companies and, in particular , for far mers, which contrasts with the idea
that an NGO maximizes label participation. 5
This paper concentrates on the impact of labels in the consumer country , excluding
the far mer from the picture: w e interpret fairtrade as a quality label. Fairtrade coffee is an
amply a v ailable commodity and farmers ha v e no market po w er . Johannessen and W ilhite
(2010) estimate that about 75% of v alue added in fairtrade coffee remain in the consumer
4 Panhuysen and Pierrot (2014) sho w that about a quarter of certified cof fee production is sold with a
label.
5 Under standard assumptions, an NGO label maximizes access to its label and sets its fee as lo w as
possible, that is equal to the cost of monitoring (cf. Bottega and De Freitas 2009), which is normalized to
zero in our model. If the cost of the label is zero, then our model predicts that it is alw a ys an equilibrium
for both firms to offer the label. Only in markets with v ery w eak horizontal differentiation, market
segmentation might be an additional equilibrium. Ho w ev er , this does not reflect the reality of cof fee
markets.

2. Model 67
country . Empirical e vidence suggests that far mers receiv e a higher price for fairtrade
cof fee than for conv entional coffee (Beuchelt and Zeller 2011, Dragusanu and Nunn 2014,
Ar nould et al. 2009), but the impact on income is small at best when controlling for
selection into the labeling scheme (Ruben and Fort 2012, Saenz S egura and Zuniga-Arias
2008, Beuchelt and Zeller 2011). Dragusanu et al. (2014) re view this literature in mor e
detail.
Nev ertheless, marketing and experimental resear ch has consistently sho wn that con-
sumers ha v e a positiv e willingness-to-pa y for fairtrade products (e.g. Basu and Hicks
2008, Pelsmacker et al. 2005, Loureiro and Lotade 2005). A rational consumer under -
stands that it is w elfare-enhancing for a far mer to sell more fairtrade cof fee, once he has
incurred the fixed entry costs of labeling. Moreo v er , Friedrichsen and Engelmann (2017)
and T eyssier et al. (2014) sho w that social image concer ns pla y a role, so that consumers
enjo y being seen buying fairtrade pr oducts. Another possible explanation of the wide-
spread support of the fairtrade system is that consumers are not a w are of the dynamic
ef fects of the fairtrade system leading to an excessiv ely large number of certified farm-
ers. W e assume that consumers deriv e a homogeneous positiv e utility fr om higher cof fee
prices at the far mer lev el, lea ving aside the debate whether these preferences are due to
social image, warm glow (Andreoni 1989), or pure altruism.
2 Model
W e analyze a game with tw o periods, each consisting of sev eral stages. The game inv olv es
tw o labeling organizations s = F , I , tw o horizontally dif ferentiated firms i = 1, 2, and ho-
mogeneous consumers which v alue quality positiv ely . Fir ms can offer sev eral v ertically
dif ferentiated products: they alw a ys supply a product of conv entional market quality q C
and can additionally opt for one or both labels. W e assume that fir ms cannot credibly
of fer qualities higher than conv entional market quality q C = 0 without getting labeled
b y a labeling organization. 6 The labeling organizations decide on qualities q F and q I ,
guaranteed b y their respectiv e label. The for-pr ofit licenser moreo v er sets a license fee L .
Subsection 2.4 pro vides a detailed o v er view of the game sequence.
2.1 Consumer demand
T o capture both horizontal and v ertical product dif ferentiation, w e specify consumer de-
mand using a nested logit model (cf. McFadden 1978, Anderson and De Palma 1992). In
our model, products become closer substitutes when their qualities become more similar .
This section deriv es the demand equations in the case where both fir ms of fer both labels.
The fir ms’ market shares and demand functions for other pr oduct line constellations can
be deriv ed analogously .
6 The certification and labeling process is assumed to be credible and to guarantee that labeled products
fulfill the quality requirements defined b y the licensers. W e further assume that consumers are perfectly
informed about the qualities chosen by the licensers.

68 III Label Competition
Figure III.1: Nested decision-making structure of consumers
Assume that each fir m of fers three products with qualities q F , q I , and q C , then Fig-
ure III.1 sho ws the decision structure of consumers. Each of the homogeneous consumers
buys one unit or opts for the outside good. Consumers decide if they w ant to buy any
product (decision betw een nest P , for product, and nest 0, for outside option). If con-
sumers choose nest P , they decide betw een pr oducts with and without labels (decision
betw een nests F I and C ). W ithin nest F I consumers choose betw een labels (betw een nests
F and I ). Finally , within each nest s with s = F , I , C consumers decide from which firm
they buy . Figure III.1 illustrates this decision structure, where the substitution parameters
µ F I , C , µ F , I and µ are explained belo w .
Proceeding backw ards, consider first consumers’ decision within nest s ( s = F , I , C )
betw een both fir ms’ goods. Each consumer chooses the fir m i that maximizes his indirect
utility
u s
i = ¯
u + v ( q s ) − p s
i + µ ϵ s
i , (1)
where ¯
u is the consumer ’s direct utility of the product, v ( q s ) denotes the additional
utility from consuming quality q s and p s
i the price of fir m i ’s product with quality q s . ϵ s
i
is an error term that is distributed with the extreme v alue distribution. In the example
of fairtrade cof fee, quality is defined b y the far mgate prices guaranteed b y the labeling
organization. The parameter µ > 0 measures the degr ee of horizontal dif ferentiation
betw een the tw o fir ms such that µ approaching zer o translates into perfect competition
within the final market. Consumers ha v e a homogeneous v aluation of quality ν ( q s )
which is strictly increasing and strictly conca v e in q s :
v s = v ( q s ) = √ q s
1 + q s . (2)
Integrating equation (1) o v er the distribution of the stochastic term ϵ s
i , as it is standard in

2. Model 69
nested logit models, 7 w e obtain fir m i ’s within-nest market shares P i | s for nest s
P i | s = exp ( ( ¯
u + v s − p s
i ) / µ )
exp ( ( ¯
u + v s − p s
i ) / µ ) + exp ( ( ¯
u + v s − p s
j ) / µ ) = exp ( ( ¯
u + v s − p s
i ) / µ )
exp ( A s / µ ) (3)
with A s = µ ln [ exp ( ( ¯
u + v s − p s
i ) / µ ) + exp ( ( ¯
u + v s − p s
j ) / µ ) ] (4)
A s measures the expected utility of nest s (giv en previous choices at higher nest le v els),
which is called the inclusiv e v alue in nested logit models.
Consider next the choice betw een F -labeled goods and I -labeled goods. The utility u s
of the nest s for s = F , I is then defined as
u s = A s + µ F , I ϵ s for s = F , I (5)
where ϵ s is a nest-specific error ter m that is distributed extreme v alue and substitution
betw een nests F and I is giv en b y
µ F , I = µ + v F − v I
1 + v F − v I . (6)
The specification of µ F , I implies that µ F , I approaches µ if the labels become more sim-
ilar , i.e. if q F approaches q I . As before, integrating ov er the stochastic term’s distribution,
w e obtain the market shares for nest F (and analogously for nest I ):
P F | F I = exp ( A F / µ F , I )
exp ( A I / µ F , I ) + exp ( A F / µ F , I ) = exp ( A F / µ F , I )
exp ( A F I / µ F , I ) (7)
with A F I = µ F , I ln [ exp ( A I / µ F , I ) + exp ( A F / µ F , I ) ] . (8)
Mo ving upw ards, consider no w the choice betw een choosing a labeled product or
choosing conv entional quality . The utility of the nest F I and of the nest C are defined as
u F I = A F I + µ F I , C ϵ F I and u C = A C + µ F I , C ϵ C (9)
where ϵ F I , ϵ C is a nest-specific error ter m distributed with the extreme v alue distribution
and µ F I , C characterizes the substitution betw een F I and C . In analogy to equation (6) w e
use the follo wing functional form
µ F I , C = µ + v F + v I ( v F − v I )
1 + v F + v I ( v F − v I ) (10)
Integrating giv es the market share of the conv entional products, giv en the consumer
7 S ee econometrics textbooks, e.g. T rain (2009), for more details on the deriv ation of market shares in the
standard nested logit.

70 III Label Competition
buys any product (nest P )
P C | P = exp ( A C / µ F I , C )
exp ( A C / µ F I , C ) + exp ( A F I / µ F I , C ) = exp ( A C / µ F I , C )
exp ( A P / µ F I , C ) (11)
with A P = µ F I , C ln [ exp ( A C / µ F I , C ) + exp ( A F I / µ F I , C ) ] . (12)
Finally , consider the choice betw een buying any of the considered goods or the out-
side good, i.e. a substitute from another product category or nothing. Again, the utility
of the nest P is defined as
u P = A P + γ ϵ P (13)
where ϵ P is a nest-specific error ter m distributed with the extreme v alue distribution and
the substitution betw een the fir ms’ products and an outside good is defined as
γ = 1 + µ . (14)
Nor malizing the outside good’s utility to zero, w e obtain the probability to buy any
product, i.e. the aggregated market shar e of both fir ms P P :
P P = exp ( A P / γ )
exp ( A P / γ ) + 1 (15)
with A = γ ln [ exp ( A P / γ ) + 1 ] . (16)
Note that the definitions of the substitution parameters ensure that w e alw a ys ha v e
0 ≤ µ ≤ µ F , I ≤ µ F I , C ≤ γ such that goods within a nest are equally or more similar than
goods from dif ferent nests. 8 Furthermore, consumers’ preferences exhibit lo v e of v ariety
as the inclusiv e v alues in all nests increase in the number of pr oducts of fered.
Summarizing and nor malizing the total mass of consumers to 1, demand for fir m i ’s
products can be written as
D F
i = P P P F I | P P F | F I P i | F , (17)
D I
i = P P P F I | P P I | F I P i | I , (18)
D C
i = P P P C | P P i | C (19)
2.2 Fir ms
Fir ms decide, first, which label to acquire and, second, ho w to set product prices. Con-
v entional quality q C can be of fered without any certification. Hence, w e assume without
loss of generality that fir ms alw a ys of fer q C and choose the profit maximizing price for
8 W e adopt the notation from Anderson and De Palma (1992), with substitution parameters at each nest
lev el, which is formally equivalent to the notation mor e common in econometrics (e.g. T rain 2009), where
the highest parameter γ is nor malized to 1 and substitution parameters σ k of lo w er nest lev els are defined
as µ F I , C / γ and µ F , I / µ F I , C and µ s / µ F , I . Therefore, our restriction on parameters (0 ≤ µ ≤ µ F , I ≤ µ F I , C ≤ γ )
is equiv alent to the restriction σ k ∈ ( 0, 1 ) in econometric w ork.

2. Model 71
this quality . 9
W e assume that marginal pr oduction costs c ( q s ) are equal for both fir ms, as w ell as
constant and linearly increasing in q s :
c ( q s ) = q s . (20)
W e define mark-up as dif ference of price p s
i and marginal cost q s . Fir m profits are
then sum of the demand D s
i multiplied b y the mark-up for each of its products, minus a
license fee L if the fir m offers label F . As an example, if both fir ms of fer F , I and C , the
fir m i ’s profits are giv en b y Π i : F I C | F I C :
Π i : F I C | F I C = D F
i ( p F
i − q F ) + D I
i ( p I
i − q I ) + D C
i p C
i − L (21)
= Π i : F I C | F I C − L , (22)
where for readability , Π i : F I C | F I C is the fir m’s gross pr ofit before pa yment of the fee to the
licenser .
2.3 Labeling organizations
Both labeling organizations do not face any costs. The first labeling organization is li-
censer F that maximizes its profit. The licenser ’s profit Γ is giv en b y the number of fir ms
of fering an F -labeled good multiplied b y its license fee L . The second labeling organiza-
tion is an industr y standard I that maximizes joint profit of both firms; it does not charge
any fees and has no o wn pr ofit.
Both labeling organizations strategically set the quality of their r espectiv e label q I and
q F . W e assume that qualities chosen b y the labeling or ganizations as w ell as the license
fee are public information, without any room for priv ate negotiation.
Licenser F is the established label and is challenged b y the industr y standard I . In
the first period, w e model the situation with only for-pr ofit licenser F . The second period
is modeled as a Stackelberg game: industry standard I enters and sets q I taking into
account the strategic adjustment of the licenser F ’s quality q F and license fee L .
2.4 Game sequence
W e analyze a game with tw o periods. In the first period t = 0, there is only one label,
of fered b y the for-pr ofit licenser . Licenser and fir ms pla y the following four stage game
with perfect infor mation:
Stage 0.1 : Licenser F sets its license fee L 0 and its quality q F
0 ;
Stage 0.2 : Fir ms i = 1, 2 choose which label to of fer , i.e. decide on their product line;
Stage 0.3 : Fir ms set the consumer prices p s
i for s = F , C ;
9 Stated differ ently , a firm’s decision not to offer quality q C is equiv alent to charging an infinitely high
price for this quality , which is nev er optimal for a fir m.

72 III Label Competition
Stage 0.4 : Consumers choose their fa v orite product and buy 1 unit or opt for the
outside good.
In the next period t = 1, the industr y standard I enters the market, so that there is an
additional stage 0, follo w ed again b y the previous four stages:
Stage 1.0 : Industry standard I sets quality q I ;
Stage 1.1 : Licenser F sets license fee L 1 and its quality q F
1 ; the licenser cannot under-
cut his pre vious quality: q F
1 > q F
0 ;
Stage 1.2 : Firms i = 1, 2 choose which label(s) to offer , i.e. decide on their product
line;
Stage 1.3 : Fir ms set the consumer prices p s
i for s = F , I , C ;
Stage 1.4 : Consumers choose their fa v orite product and buy 1 unit or opt for the
outside good.
Note that w e assume that the incumbent for-pr ofit licenser in t = 1 cannot decrease
its quality q F
1 belo w its equilibrium monopoly v alue q F ∗
0 from t = 0, without seriously
har ming its brand image. For simplicity , w e further assume that the licenser in the first
period does not anticipate the entry of the industr y standard in the second period. In the
follo wing, w e solv e the game b y backw ard induction.
3 Market equilibr ium with licenser F only
W e first look at the first period t = 0 before entr y of the industr y standard, i.e. with
only a for -profit licenser F . The game starts with licenser F setting its quality q F
0 and
fee L 0 . Both fir ms can decide to of fer an F -labeled product, there ar e thus three possible
market constellations: both fir ms offer F or one fir m offers F or no fir m offers F . Since
conv entional quality q C = 0 can be of fered without any certification, w e can restrict
the analysis to the cases where the product line of fered b y each fir m comprises at least
C . Additionally , there can be no equilibrium in which licenser F sells no license; as
consumers v alue quality and v ariety positiv ely and licensers ha v e no cost, choosing some
q F > q C and an arbitrarily small but positiv e license fee L , licenser F can alw a ys ear n a
positiv e profit.
3.1 Consumer pr ices
W e first compute product price equilibria in stage 0.3 for all pr oduct lines and label
qualities. Let y , z ∈ { F C , C } , w e use the notation Π i : y | z for a fir m i ’s profit when it pla ys y
and the other fir m pla ys z . Maximizing fir m profit 10 Π y | z with respect to prices, w e find:
10 W e omit the fir m index i if no confusion is possible.

3. Market equilibr ium with licenser F only 73
Lemma 1 For all possible pr oduct line constellations, there ar e unique equilibrium prices p s
i with
s = F , C and i = 1, 2 in stage 0.3; moreover
(i) when the pr oduct lines are symmetric (both firms offer the same qualities), prices ar e sym-
metric;
(ii) when firms compete head-to-head { FC , FC } (both firms offer all qualities), the symmetric
prices ar e given by the mar ginal pr oduction costs c ( q s ) plus a constant mark-up. 11
Proof. S ee Appendix on page 90.
W e let Π ∗
y | z denote a fir m’s reduced pr ofit when it pla ys y and the other fir m pla ys z ,
giv en optimal price setting b y both firms.
3.2 Product line decisions
T ur ning to stage 0.2 of the game and analyzing the fir ms’ product line decisions, w e com-
pute the fir ms’ best responses in choosing whether to of fer an F -labeled good. Assume
fir m 1 of fers F and C , then fir m 2’s best response is giv en b y
max { Π ∗
F C | F C − L 0 , Π ∗
C | F C } (23)
S olving the respectiv e maximization problem if firm 1 does not offer F and using sym-
metry allows us to numerically compute the equilibrium in stage 0.2 of the game.
Figure III.2 illustrates product line equilibria for differ ent v alues of license fee L 0 and
horizontal dif ferentiation µ . 12 The lo w er the horizontal product dif ferentiation µ , the
less profitable it is for both firms to offer the labeled good simultaneously ( { F C , F C } ), as
fiercer competition reduces their mark-ups. If the license fee L 0 is too high, neither of the
fir ms of fers the labeled good ( { C , C } ).
3.3 License fee
When deciding on its license fee L 0 , the licenser F has tw o options: it can aim at sell-
ing licenses for its label to both firms or it can decide to sell just one license. S elling
to both fir ms requires a lo w license fee, whereas selling to only one fir m allo ws for a
higher license fee. Maximizing its profits, the licenser sets the fee such that fir ms are just
indif ferent, i.e. at the edge of an area in Figure III.2, either fr om { F C , F C } to { F C , C } , or
from { F C , C } to { C , C } .
Assume that the licenser aims at selling its label license to both fir ms, inducing sym-
metric, head-to-head competition. The licenser then sets its fee such that both fir ms
prefer of fering F C rather than offering C ; the fee equals the deviation pr ofit giv en the
11 Considering a different nest structure Anderson and De Palma (1992) also obtain that equal mark-ups
are optimal.
12 Horizontal differentiation µ is b y definition betw een zero and infinity . Ho w ev er , our figures sho w only
the range until µ = 1 as the results do not change qualitativ ely for higher v alues of µ .

74 III Label Competition
0.2 0.4 0.6 0.8 1.

0
0.00
0.05
0.10
0.15
0.20

L 0

μ

Figure III.2: Product line equilibrium in stage 0.2 as a function of license fee L 0 and
horizontal dif ferentiation µ (for q F
0 = 0.23)
other fir m also of fers F C :
L s y m
0 = Π ∗
F C | F C − Π ∗
C | F C . (24)
W e v erify numerically , that this license fee L s y m
0 indeed ensures that both firms w ant
to of fer label F :
Π ∗
F C | F C − L s y m
0 = max { Π ∗
F C | F C − L s y m
0 , Π ∗
C | F C } for all µ and q F
0 (25)
Assume in contrast that the licenser intends to establish market segmentation { F C , C }
as an equilibrium in stage 0.2 of the game. The licenser then sets its fee such that the
fir m of fering F C does not w ant to de viate to of fering C only; the fee equals the deviation
profit of the firm offering F C giv en the other fir m offers only C :
L s e g
0 = Π ∗
F C | C − Π ∗
C | C . (26)
When the licenser sets this fee L s e g
0 , the other fir m could also start of fering F , leading
again to the symmetric head-to-head equilibrium, but w e v erify numerically that the
potential entrant w ould alw a ys be w orse off. 13 The license fee L s e g
0 thus ensures that
fir ms pla y the market segmentation equilibrium in stage 0.2 for all µ and q F
0 .
Summarizing, the licenser effectiv ely chooses the equilibrium pla y ed in stage 0.2 b y
setting its fee. The licenser ’s preference betw een both outcomes depends both on (exoge-
nous) horizontal dif ferentiation µ and on (endogenous) v ertical differentiation fr om label
quality q F
0 . The licenser induces the product line equilibrium that giv es him the highest
profit Γ 0 :
Γ 0 = max { 2 L s y m
0 , L s e g
0 } . (27)
Numerically , w e find that for strong market dif ferentiation with µ > 0.48, the licenser
13 W e alw a ys ha v e Π ∗
F C | F C − Π ∗
C | F C < Π ∗
F C | C − Π ∗
C | C : offering F is alw a ys more profitable when the other
firm does not offer F .

3. Market equilibr ium with licenser F only 75
prefers the head-to-head equilibrium for all q F
0 ; for w eak market dif ferentiation with
µ < 0.43, the licenser alw a ys prefers market segmentation. In the relativ ely small range
betw een these v alues, the comparison depends on label quality q F
0 .
3.4 Label quality
The licenser profit Γ 0 depends on label quality q F
0 , and each product line equilibrium has
dif ferent first-or der conditions. Using the env elope theorem, w e compute the licenser ’s
first-order conditions for optimal label quality q F ∗
0 :
∂ Γ 0
∂ q F
0
=
⎧
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎩
[ ∂ Π i : F C | F C
∂ q F
0
+ ∑ F , C
s
∂ Π i : F C | F C
∂ p s
j
∂ p s
j
∂ q F
0 ] −
[ ∂ Π i : C | F C
∂ q F
0
+ ∑ F , C
s
∂ Π i : C | F C
∂ p s
j
∂ p s
j
∂ q F
0 ] = 0 if Γ 0 = 2 L s y m
0
∂ Π i : F C | C
∂ q F
0
+ ∂ Π i : F C | C
∂ p C
j
∂ p C
j
∂ q F
0
= 0 if Γ 0 = L s e g
0 .
(28)
In the first line of equation (28), the licenser maximizes the de viation profit, that is
the dif ference betw een the equilibrium pla y ed and the most pr ofitable alter nativ e, taking
into account cross-price ef fects. The interests of licenser and industr y are not aligned: a
quality q F
0 that maximizes only the first element Π ∗
F C | F C w ould maximize joint licenser
and industry profits, while the licenser also w ants to make the firm’s best alter nativ e
(second bracket) less profitable b y reducing quality q F
0 in equilibrium. In the second line
of equation (28), the licenser maximizes its customer ’s profit.
The licenser has to trade of f selling tw o cheaper licenses for its label v ersus selling one
more expensiv e license. Let L s y m ∗
0 , resp. L s e g ∗
0 , denote the license fees with optimal quality
q F ∗
0 maximizing the license fee in the head-to-head, resp. segmented, case. The licenser
w ants to pla y the symmetric head-to-head equilibrium { F C , F C } if 2 L s y m ∗
0 > L s e g ∗
0 . 14 The
trade-of f crucially depends on horizontal dif ferentiation µ : the higher µ , i.e. the lo w er
the intensity of competition betw een the fir ms, the more profitable it is for a firm to of fer
a label that is also of fered b y the other fir m; and higher sur plus for the fir m directly
translates into higher license fees.
W e numerically solv e the first-order conditions of equation (28) for all v alues of hor-
izontal dif ferentiation µ , compare the resulting licenser profits for each equilibrium and
find that there is a single threshold:
Proposition 1 ( 2 L s y m ∗
0 − L s e g ∗
0 ) incr eases monotonically with horizontal differ entiation µ : above
µ = 0.46 , the licenser prefers symmetric head-to-head competition, selling two licenses; below this
thr eshold, it pr efers market segmentation, selling just one license.
Figure III.3 sho ws the optimal quality chosen b y licenser F : for µ < 0.46, it is more
profitable for the licenser to set a high quality and a high fee, attracting only one firm in
14 This is related to, but not equal to the comparison 2 [ Π ∗
F C | F C − Π ∗
C | F C ] v ersus [ Π ∗
F C | C − Π ∗
C | C ] , as the
licenser sets differ ent optimal qualities in each case.

76 III Label Competition
0.2 0.4 0.6 0.8 1.0

0.22
0.24
0.26
0.28
0.20
q
q F*
q 0
{FC,FC} {FC,C}

Figure III.3: Equilibrium quality q F ∗
0 in stage 0.1 as a function of horizontal dif ferentia-
tion µ
the market segmentation equilibrium. For µ > 0.46, licenser F chooses a lo w quality and
fee but sells its label to both fir ms, leading to the head-to-head equilibrium in stage 0.2.
W ithin a giv en product line equilibrium, the optimal quality q F ∗
0 generally increases with
horizontal dif ferentiation µ (for µ > 0.05).
3.5 Minimum quality requirement
In order to e v aluate the scope for regulatory inter v ention, w e define social w elfare. As
w e compute the label quality giv en the duopoly’s pricing game, these are second-best
v alues. Follo wing the example of or ganic certification, there is potentially scope for a
go v ernment-imposed minimum quality requirement for fairtrade labels. As in organic
certification, this standard w ould lea v e the conv entional market unchanged but raise the
label’s quality to a regulated minimum le v el q . W e assume that the social planner cannot
force labeling or ganizations to adjust do wnw ards.
In nested logit models, expected consumer sur plus S is the inclusiv e v alue of the
highest nest lev el; here, it is thus the inclusiv e v alue at the decision le v el to buy the
product or the outside good fr om equation (16). S ocial w elfare W is the sum of the
consumer surplus, the fir ms’ profits and the licenser fee:
S = γ ln ( exp ( A P / γ ) + 1 ) (29)
W = S + ∑
j = 1,2
Π j : y | z (30)
where Π j : y | z again denotes the profits of firm j when it pla ys y and the other fir m pla ys
z , with y , z ∈ { F C , C } .
W e find that the social planner w ants to maximize the number of a v ailable products.
Thus, the social planner alw a ys w ants both fir ms to offer F -labeled goods. Ho w ev er , for
horizontal dif ferentiation belo w µ < 0.46 the social planner w ould ha v e to decr ease the
label’s quality to induce the head-to-head equilibrium, which he cannot do b y assump-

4. Market entr y of industr y standard I 77
0.2

0.4

0.6

0.8

1.0

0.20

0.25

0.30

{FC,FC} {FC,C}
q
μ

Figure III.4: Minimum quality requirement q as a function of horizontal differentiation µ
(unregulated equilibrium quality q F ∗
0 from stage 0.1 in gra y)
tion. For w eak horizontal differentiation µ , the social planner sets the optimal quality for
the segmented market constellation { F C , C } . As sho wn in Figure III.4, the social planner
alw a ys sets a minimum quality requir ement abo v e the equilibrium quality of licenser F .
4 Market entr y of industr y standard I
In the beginning of the second period t = 1, an industr y standard I enters the market
and announces its quality . The for-pr ofit licenser F can then adjust its quality and license
fee. Fir ms can no w decide whether to of fer one or both labels. This increases the number
of possible market constellations, but w e can again restrict the analysis to cases where
each label is of fered at least b y one fir m.
W e assume that the for -profit licenser cannot under cut its quality q F ∗
0 from the pre-
vious period. This is motiv ated b y the obser v ed qualities of labels in the cof fee market,
where the incumbent licenser has nev er adjusted do wnw ar ds and new entrants ha v e
alw a ys established less stringent standar ds than the incumbent.
Our results sho w that the industr y benefits from introducing an industry standard,
because an additional good increases o v erall demand (less people opt for the outside
good) and reduces the license fee. Moreo v er , for inter mediate horizontal differ entiation,
one fir m stops of fering an F -labeled good, thereb y reducing competition in that nest and
pa yments to the licenser .
4.1 Consumer pr ices
W e find that Lemma 1 can be generalized to a situation with tw o labeling organizations:
Lemma 2 For all possible pr oduct line constellations, there ar e unique equilibrium prices p s
i with
s = F , I , C and i = 1, 2 in stage 1.3; moreover

78 III Label Competition
(i) when the pr oduct lines ar e symmetric (both firms offer the same qualities), prices are sym-
metric;
(ii) when firms compete head-to-head { F I C , F I C } (both firms offer all qualities), prices are
given by the mar ginal pr oduction costs c ( q s ) plus a constant and symmetric mark-up;
(iii) in partial market segmentation { F I C , I C } , the mark-up on the F -labeled product is higher
than the mark-up on the I -labeled pr oduct and the differ ence in mark-ups decr eases when
the label qualities become mor e similar .
Proof. S ee Appendix on page 91.
W ith y , z ∈ { F I C , F C , I C , C } , w e let Π ∗
i : y | z denote fir m i ’s reduced profit with unique
profit-maximizing prices when it pla ys y and the other fir m pla ys z .
4.2 Product line decisions
T ur ning to stage 1.2 of the game and analyzing the fir ms’ decision to offer one or both
labels, w e compute the fir ms’ best responses to each other ’s product line. Assume fir m 1
of fers F I C . Then, fir m 2’s best response is giv en b y
max { Π ∗
F I C | F I C − L 1 , Π ∗
F C | F I C − L 1 , Π ∗
I C | F I C , Π ∗
C | F I C } (31)
S olving the respectiv e maximization problem for all other strategies of firm 1 and
using symmetry allows us to fully characterize the equilibrium in stage 1.2 of the game.
The equilibrium pla y ed in stage 1.2 of the game depends on µ , as w ell as on qualities q I ,
q F
1 and fee L 1 .
4.3 License fee
As in the first period, when deciding on its license fee L 1 , the licenser has tw o options: it
can aim to sell its label to both fir ms or it can decide to sell it to just one fir m. In stage 1.1,
this trade-of f depends on µ as before and the quality q I previously set b y the Stackelberg
leader industry standard I . The relev ant cases are symmetric head-to-head competition
{ F I C , F I C } and full market segmentation { F C , I C } , as befor e, plus additionally partial
market segmentation { F I C , I C } . W e also compute equilibrium qualities and prices for
all other possible cases, but this section concentrates on the rele v ant cases, i.e. cases that
are equilibria under certain conditions.
Assume first that the licenser aims at inducing the symmetric head-to-head equilib-
rium { F I C , F I C } , i.e. fir ms compete on all labels. In this case, the licenser sets its fee L 1
such that neither of the tw o fir ms of fering F I C w ants to deviate to of fering I C only; the
fee equals their deviation pr ofit, giv en the other fir m also of fers F I C :
L s y m
1 = Π ∗
F I C | F I C − Π ∗
I C | F I C (32)

4. Market entr y of industr y standard I 79
Numerically , w e v erify that firms indeed pla y the head-to-head equilibrium in stage
1.2 of the game when the licenser sets its fee at L s y m
1 , as w e ha v e for all µ , q F
1 and q I :
Π ∗
F I C | F I C − L s y m
1 = max { Π ∗
F I C | F I C − L s y m
1 , Π ∗
F C | F I C − L s y m
1 , Π ∗
I C | F I C , Π ∗
C | F I C }
S econdly , assume that the licenser aims to establish partial segmentation – as w e call
the product line constellation { F I C , I C } – as an equilibrium in stage 1.2 of the game.
Then, the licenser sets its fee L 1 such that the firm offering F I C has no interest to deviate
to of fering I C ; the fee equals the deviation pr ofit of this fir m, giv en the other fir m of fers
I C : 15
L p s e g
1 = Π ∗
F I C | I C − Π ∗
I C | I C . (33)
Third, assume that the licenser aims to establish full market segmentation { F C , I C }
as an equilibrium in stage 1.2 of the game. The licenser sets its fee L 1 such that the fir m
of fering F C has no interest in deviating to of fer I C ; the fee equals the deviation profit of
this fir m, giv en the other fir m pla ys I C :
L s e g
1 = Π ∗
F C | I C − Π ∗
I C | I C . (34)
The second element of L p s e g
1 and L s e g
1 is identical, so that the licenser ’s preference
betw een full market segmentation and partial market segmentation is deter mined b y the
first element. If the licenser chooses the higher of these tw o fees with a license fee defined
as max { L p s e g
1 , L s e g
1 } , w e numerically v erify that both firms ha v e no interest in de viating
from the chosen constellation for all µ , q F
1 and q I . 16 In both cases, the licenser sells just
one license fee.
Summarizing this section, the licenser ’s profit Γ 1 can be written as:
Γ 1 = max { 2 L s y m
1 , L p s e g
1 , L s e g
1 } (35)
4.4 Label quality of incumbent licenser F
As in the case with only one label (period t = 0), the optimal label quality q F ∗
1 maximizes
the license fee. W e assume that the incumbent for-pr ofit licenser cannot decrease its
quality belo w its monopoly v alue, q F ∗
0 , without seriously har ming its brand image. For
simplicity , w e further assume that the licenser in the first period does not anticipate the
entry of the industr y standard in the second period. Using the env elope theorem, w e can
15 Theoretically , the possible alter nativ e profits are Π I C | I C and Π C | I C . How ev er , w e numerically ha v e
Π I C | I C > Π C | I C for all q I , q F
1 and µ .
16 W e numerically compute the equilibria for all L , µ , q F
1 and q I : for many parameter constellations, the
licenser cannot induce partial or full segmentation, but he can alw a ys induce the one that giv es him the
higher pa y-off.

80 III Label Competition
0.02

0.04

0.06

0.08

0.20

0.25

0.30

0.35

q F* (q I )
q 1

{FIC,FIC} {FIC,IC }
q F* (q I )
q I
q F*
q 0
q 1

Figure III.5: Reaction function of quality q F ∗
1 as a function of industry standard quality q I
for µ = 0.5 (equilibrium quality q F ∗
0 from the pre vious period in gra y)
again write do wn the corresponding first-or der conditions:
∂ Γ 1
∂ q F
1
=
⎧
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎩
[ ∂ Π i : F I C | F I C
∂ q F
1
+ ∑ F , I , C
s
∂ Π i : F I C | F I C
∂ p s
j
∂ p s
j
∂ q F
1 ] −
[ ∂ Π i : I C | F I C
∂ q F
1
+ ∑ F , I , C
s
∂ Π i : I C | F I C
∂ p s
j
∂ p s
j
∂ q F
1 ] = 0 if Γ 1 = 2 L s y m
1
[ ∂ Π i : F I C | I C
∂ q F
1
+ ∂ Π i : F I C | I C
∂ p I
j
∂ p I
j
∂ q F
1
+ ∂ Π i : F I C | I C
∂ p C
j
∂ p C
j
∂ q F
1 ] = 0 if Γ 1 = L p s e g
1
[ ∂ Π i : F C | I C
∂ q F
1
+ ∂ Π i : F C | I C
∂ p I
j
∂ p I
j
∂ q F
1
+ ∂ Π i : F C | I C
∂ p C
j
∂ p C
j
∂ q F
1 ] = 0 if Γ 1 = L s e g
1
(36)
In the first line of equation (36), the licenser sets its quality q F
1 combining the ef fect
on the fir m’s profits against the ef fect on the firm’s best alter nativ e. Both Π ∗
F I C | F I C and
Π ∗
I C | F I C increase in q F
1 as it increases the dif ferentiation betw een nests F and I , and de-
crease in q I as it decreases differ entiation betw een nests F and I . The tw o qualities are
strategic complements: the higher the quality q I of the industr y standard, the higher the
optimal quality q F ∗
1 of the licenser , allo wing him to set a higher fee L s y m
1 . In the tw o latter
cases of equation (36), there is no such strategic element and the licenser set its quality
q F
1 maximizing the profits of the firm offering F .
As an example, Figure III.5 plots the reaction function of the licenser quality q F ∗
1 to
industry standard quality q I for horizontal differentiation µ = 0.5. For small q I , the li-
censer induces partial market segmentation { F I C , I C } ; for large q I , the licenser induces
head-to-head competition { F I C , F I C } . In the head-to-head equilibrium, the licenser dis-
torts its quality do wnw ards to increase its license fee b y reducing the deviation pr ofit,
which explains the discontinuity in Figure III.5. W ithin a product line equilibrium, q F ∗
1 is
increasing in q I .

4. Market entr y of industr y standard I 81
0.1

0.2

0.3

0.4

0.5

0.6

0.1

0.2

0.3

0.4

0.0

μ
{FIC,FIC}
q I
{FC,
IC} {FIC,IC }

Figure III.6: Preferred product range equilibrium of licenser F as a function of quality q I
and horizontal dif ferentiation µ
Let L s y m ∗
1 , resp. L se g ∗
1 and L p s e g ∗
1 , denote the license fees with optimal quality , i.e.
quality q F ∗
1 maximizing the license fee in the head-to-head, resp. fully and partially seg-
mented, case. W e numerically compute the optimal qualities for all µ and q I and then
compare 2 L s y m ∗
1 , L p s e g ∗
1 , and L se g ∗
1 . Figure III.6 plots the resulting preferred pr oduct line
of licenser F .
Proposition 2 For str ong horizontal differentiation µ > 0.61 , the licenser F induces head-to-
head competition { F I C , F I C } independently of industry standard quality q I . For weak horizontal
differ entiation, µ < 0.05 , the licenser induces full market segmentation { F C , I C } . For intermedi-
ate values of µ , the pr oduct line equilibrium depends on industry standard quality q I (Figur e III.6).
Details on numer ical calculations: S ee Appendix on page 92.
The licenser prefers partial market segmentation o v er head-to-head competition if
L p s e g ∗
1 > 2 L s y m ∗
1 . Intuitiv ely , low quality q I decreases v ertical competition betw een nests
F and I , while lo w horizontal dif ferentiation µ increases competition within nests. Mor e
in detail, lo w er horizontal differentiation µ increases the benefit of being the only fir m
of fering an F -labeled good ( F I C | I C v ersus I C | I C ) and increases the potential fee L ps e g ∗
1 .
At the same time, a lo w er µ decreases the mark-ups on the F -labeled product when both
fir ms of fer F I C ( F I C | F I C v ersus I C | F I C ) and decreases the potential fee L s y m ∗
1 .
The licenser prefers full market segmentation o v er partial market segmentation when
Π ∗
F I C | I C < Π ∗
F C | I C . For lo w v alues of µ and q I , the competition within nests is so strong
that competing within a label market is not profitable: of fering F C is better than offering
F I C , giv en the other fir m offers I C .
4.5 Label quality of new entrant I
In stage 1.0 of the second period, industr y standard I sets its quality q I , anticipating the
equilibria in the follo wing stages of the game, in particular the reaction of licenser F . The
industry standard can influence the licenser b y strategically setting its quality q I . Propo-

82 III Label Competition
sition 2 and Figure III.6 sho w ed the le v els of horizontal dif ferentiation for which the
industry standard can set its quality such that the licenser pla ys a segmentation equilib-
rium. W e first compute the joint fir m profit in the three cases mentioned before: head-to-
head competition, partial segmentation, and full segmentation, subsequently comparing
these three cases. Generally , fir ms w ant to segment the market as much as possible: the
less product lines o v erlap, the higher joint fir m profit.
The industry standard I maximizes joint profit of both firms. When the licenser
induces head-to-head competition, w e can use the expr ession for licenser fee L sy m
1 from
equation (32) to get an expression for joint firm profit:
Π s y m = 2 ( Π ∗
F I C | F I C − L s y m
1 )
= 2 Π ∗
I C | F I C (37)
In a partially segmented setting, where both fir ms offer an I -labeled good, but only
one of them of fers an F -labeled product, w e can use the expression for licenser fee L p s e g
1
from equation (33) to get an expression for joint firm profit:
Π p s e g = ( Π ∗
F I C | I C − L ps e g
1 ) + Π ∗
I C | F I C
= Π ∗
I C | I C + Π ∗
I C | F I C (38)
For the full market segmentation equilibrium, w e can use the expression for licenser
fee L s e g
1 from equation (34) to get an expression for joint firm profit:
Π s e g = ( Π ∗
F C | I C − L s e g
1 ) + Π ∗
I C | F C
= Π ∗
I C | I C + Π ∗
I C | F C (39)
Comparing joint fir m profits Π s y m and Π p s e g , the industr y prefers partial market seg-
mentation { F I C , I C } o v er head-to-head competition if Π ∗
I C | I C > Π ∗
I C | F I C , i.e. if offering
I C is more profitable when the other firm offers I C than if the other fir m of fers F I C .
Numerically , this is almost alw a ys the case, because a firm offering F I C obtains a higher
o v erall market share than a firm offering I C . Only for w eak horizontal differentiation µ
and exceptionally large v ertical dif ferentiation (lo w q I and high q F ), the industry prefers
head-to-head competition and this extreme region is ne v er an equilibrium.
Comparing joint fir m profits Π p s e g and Π s e g , the industr y prefers full market segmen-
tation o v er partial market segmentation if Π ∗
I C | F C > Π ∗
I C | F I C . Numerically , w e v erify that
of fering I C is alw a ys more profitable if the other fir m offers F C than if the other fir m of-
fers F I C , because a fir m benefits from being the only firm offering I -labeled goods. Fir ms
thus alw a ys w ant to segment the market passing from { F I C , I C } to { F C , I C } . Ho w ev er ,
w e ha v e seen in the pr evious section that the licenser does not pla y this equilibrium
unless horizontal dif ferentiation µ is w eak.
Let us summarize the comparisons betw een the relev ant cases both for the licenser
and the industr y: the licenser w ants to pla y the head-to-head equilibrium when µ and q I
are high; the partially segmented equilibrium when µ is inter mediate and q I is lo w; and

4. Market entr y of industr y standard I 83
the fully segmented equilibrium when µ and q I are lo w (see Figure III.6). The industr y
alw a ys w ants market segmentation.
Combining this finding about the industry’s preferred market outcome with the li-
censer ’s reaction in Figure III.6 allo ws us to deter mine the equilibrium market constella-
tions that are determined b y the industry standard’s quality q I .
Proposition 3 Depending on the degr ee of horizontal differ entiation µ , the industry standard
sets its quality q I ∗ following
µ equilibrium q I ∗ q F ∗
1
> 0.61 head-to-head { F I C , F I C } arg max { Π sym } arg max { L sym
1 | q I ∗ }
[ 0.16, 0.61 ] partially segmented { F I C , I C } max { q I | L p s e g ∗
1 ≥ 2 L sym ∗
1 } arg max { L p s e g
1 | q I ∗ }
[ 0.13, 0.16 ] fully segmented { F C , I C } max { q I | L s e g ∗
1 ≥ L p s e g ∗
1 } arg max { L s e g
1 | q I ∗ }
( 0, 0.13 ] fully segmented { F C , I C } arg max { Π s e g } arg max { L s e g
1 | q I ∗ }
Details on numer ical calculations: S ee Appendix on page 93.
When horizontal dif ferentiation is strong ( µ > 0.61), the industr y standard maxi-
mizes its profit in the symmetric head-to-head constellation fr om equation (37) b y setting
quality q I under follo wing first-or der condition:
∂ Π s y m
∂ q I = ∂ Π ∗
I C | F I C
∂ q I + ∂ Π ∗
I C | F I C
∂ q F
1
∂ q F
1
∂ q I = 0 (40)
The industry standard I maximizes the firms’ sur plus from offering the label taking
into account that a higher q I also induces a higher q F
1 (see discussion in Subsection 4.4).
This strategic ef fect increases q I , relativ e to the solution maximizing only the direct effect
on Π ∗
I C | F I C .
If the industr y standar d can induce partial market segmentation { F I C , I C } with
a positiv e quality q I (i.e. when horizontal differentiation µ is inter mediate with µ ∈
[ 0.16, 0.61 ] ), then the industr y prefers this outcome o v er head-to-head competition. For
inter mediate horizontal dif ferentiation µ , the industry standard sets its quality lo w
enough to make the licenser just indif ferent betw een pla ying the head-to-head equilib-
rium { F I C , F I C } and partial market segmentation { F I C , I C } :
q I ∗ = max { q I | L p s e g ∗
1 ≥ 2 L s y m ∗
1 } (41)
Thus, the equilibrium quality q I ∗ is low er than the quality that solv es the first-or der
condition ∂ Π p s e g / ∂ q I = 0, but the gain of pla ying an equilibrium with fe w er products is
high enough to compensate for the distortion in quality q I . Graphically , the quality q I ∗
in Figure III.7 can be deduced from Figur e III.6, as it is on the border betw een the area
inducing { F I C , F I C } and the area inducing { F I C , I C } .
Similarly , if the industr y standard can induce full market segmentation { F C , I C } with
a positiv e quality q I (i.e. when horizontal differ entiation µ is sufficiently small with µ ∈

84 III Label Competition
q I*
q F* (q I* ) q 1
q F*
q 0

0.1
0.2
0.3
0.4

0.2 0.4 0.8 1.

0

q
{FIC,IC}
{FC,
IC}
{FIC,FIC}
0.6

Figure III.7: Equilibrium qualities q I ∗ and q F ∗
1 as a function of horizontal dif ferentiation µ
( q F ∗
0 from the pre vious period with one label in gra y)
[ 0.13, 0.16 ] ), then the industry prefers this outcome o v er partial market segmentation. The
optimal quality q I ∗ makes the licenser just indifferent betw een { F I C , I C } and { F C , I C } :
q I ∗ = max { q I | L s e g ∗
1 ≥ L p s e g ∗
1 } (42)
Again, the quality q I ∗ in Figure III.7 is graphically on the border betw een the area
inducing { F I C , I C } and the area inducing { F C , I C } in Figure III.6.
For w eak horizontal differ entiation µ ( µ ∈ ( 0, 0.13 ] ), the industr y standard can pla y
an interior solution to its first-order condition in the fully segmented constellation, max-
imizing profits fr om equation (39). Analogously to the head-to-head case, the first-or der
condition in case of full market segmentation is
∂ Π s e g
∂ q I = ∂ Π ∗
I C | I C
∂ q I + ∂ Π ∗
I C | F C
∂ q I + ∂ Π ∗
I C | F C
∂ q F
1
∂ q F
1
∂ q I = 0 (43)
Figure III.7 represents the equilibrium quality q I ∗ for dif ferent v alues of µ , as detailed
in Proposition 3. Comparing Proposition 3 with the results in the case with only one
labeling organization in Pr oposition 1, w e understand that the industr y standard ef fec-
tiv ely reduces the of fer of F -labeled products for horizontal dif ferentiation µ ∈ [ 0.46, 0.61 ]
(shaded area in Figure III.7).
Proposition 4 The industry benefits fr om introducing the industry standard I , because
(i) offering another vertically differ entiated product incr eases total demand;
(ii) for µ ∈ [ 0.46, 0.61 ] , the intr oduction of the industry standard induces one firm to stop
offering an F-labeled good, ther eby r educing competition and payments to the licenser;

5. Conclusion 85
(iii) at any given horizontal differ entiation µ , the intr oduction of the industry standard lowers
the license fee.
Details on numer ical calculations: S ee Appendix on page 93.
4.6 Minimum quality requirement
W e use the same definitions of consumer surplus and social w elfar e as in equations (29)
and (30). As before, w e find that w elfare increases in the number of products of fered.
The social planner w ants to counteract the industry standard’s ef fort to restrict product
lines and reduce o v erlap. Ho w ev er , a minimum quality requirement is only binding for
labels that are in equilibrium belo w this minimum standar d q . If the lo w er label is raised
to the minimum standard, then the licenser strategically adjusts the higher label.
T able III.1 sho ws ho w the social planner deter mines the optimal minimum standard
q . In the tw o polar cases – for v ery large and v ery small µ – where the industr y standard
pla ys an interior solution, the minimum quality q is not binding because the industr y
standard is alr eady too high, leading to ov er -dif ferentiation fr om the conv entional market
C relativ e to w elfare optimizing v alues. In these markets, a minimum quality requirement
cannot impact the status quo. A minimum quality requir ement can only ha v e a w elfare-
enhancing ef fect in the markets where the industry strategically distorts its quality to
induce market segmentation. In these cases, the social planner solv es the same equation
as the industry standard, albeit the industr y standard w ants to be marginally belo w the
solution inducing partial segmentation (resp. full segmentation) while the social planner
w ants to be marginally abo v e inducing head-to-head product competition (resp. partial
segmentation).
T able III.1: Minimum quality requirement q set b y the social planner as a function of
horizontal dif ferentiation µ
µ equilibrium pla y ed q
> 0.61 head-to-head { F I C , F I C } not binding
[ 0.50, 0.61 ] head-to-head { F I C , F I C } arg max { W sym }
[ 0.35, 0.50 ] head-to-head { F I C , F I C } min { q I | L p s e g ∗
1 ≤ 2 L sym ∗
1 }
[ 0.14, 0.35 ] partially segmented { F I C , I C } not binding
[ 0.09, 0.14 ] partially segmented { F I C , I C } min { q I | L s e g ∗
1 ≤ L p s e g ∗
1 }
[ 0.01, 0.09 ] fully segmented { F C , I C } not binding
5 Conclusion
Our model describes the interaction betw een tw o fir ms and tw o labeling organizations
of dif ferent quality; one of the labeling organizations is a for -profit licenser , the other one
is an industry standard. W e first model ho w a for-pr ofit licenser sets its fee and quality

86 III Label Competition
when it is the only labeling organization on a market with tw o firms. W e then allow
for the entry of an industr y standard and model the competition betw een tw o labels.
In order to model sensible substitution patterns, w e dev elop a discrete choice model
with both horizontal dif ferentiation (exogenously giv en) and v ertical dif ferentiation (from
endogenous product quality) using a nested logit.
Our results sho w that the equilibrium product line depends on horizontal dif feren-
tiation: the market is segmented if horizontal differentiation is w eak, while fir ms are in
head-to-head competition when horizontal dif ferentiation is strong. In summary , fir ms
seek v ertical differ entiation when horizontal dif ferentiation is lo w .
W e further find that the industry benefits from reducing o v erlap in the firms’ product
lines; against this background, the industr y standard can serv e as a coor dination tool
to induce market segmentation and increase profits. Interestingly , there are cases where
fir ms pla y the fully segmented equilibrium where not all firms offer I -labeled goods,
ev en though the industry standard char ges no license fee.
S ocial w elfare alw a ys benefits fr om head-to-head competition in our setting, reflecting
a fundamental lo v e of v ariety of consumers as w ell as a benefit from stronger competition.
This leads to a conflict betw een industr y and consumers, where the for mer w ant to
reduce product lines such that the y do not o v erlap and the latter w ant to maximize
product div ersity . A minimum standard set b y the regulator can impro v e the situation in
some cases. In other cases, ho w ev er , the industr y standard, set as an interior solution, is
too high relativ e to the w elfare-maximizing minimum standar d: fir ms in duopoly benefit
from dif ferentiating mor e than the w elfare-maximizing lev el.
Our results shed some light on product line decisions in complex markets like the one
for cof fee cof fee: as w e noted in the beginning, the product line equilibria in dif ferent
national cof fee markets are v ery different, with some featuring head-to-head competition
(Ger many) and others market segmentation (Finland), consistent with our theoretical
analysis. Moreo v er , our model explains why the cof fee industry collectiv ely has an in-
terest to introduce an industry standard. In practice, industr y-related labels like UTZ
and Rainforest Alliance ha v e gained popularity in recent y ears. As the marginal pro-
duction costs are lo w er , the global quantities of coffee sold under these industry-related
labels are three times higher than the quantity sold under the Fairtrade label (Panhuysen
and Pierrot 2014). It remains an open question ho w ev er , whether industr y standards
are strategically distorted do wnw ards in or der to decr ease competition. Ov erall, there
remains considerable scope for further resear ch: for example, our model is limited to the
strategic interactions within one countr y , whereas in practice labeling organizations set
their license fees on a global scale for many heterogeneous countries.

6. Bibliography 87
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90 III Label Competition
Appendices
A Proof of Lemma 1
In the symmetric head-to-head case { F C , F C } , profits are:
Π i : F C | F C = D F
i ( p F
i − q F ) + D C
i p C
i (44)
Analyzing the first-order conditions, w e find that there is a unique mark-up δ such
that
p F ∗
i = p F ∗
j = p C ∗
i + δ = p C ∗
j + δ (45)
with δ implicitly giv en b y
δ = 2 µ ⎛
⎝ 1 − µ
µ + γ [ 1 + ( exp ( A C / µ F , C ) + exp ( A F / µ F , C ) ) µ F , C / γ ] ⎞
⎠ (46)
Simple calculations sho w that with p s = δ + q s the right hand side of the last equation
is decreasing in δ , which establishes uniqueness. Further more, numerical calculations
sho w that the second or der conditions are satisfied at p s
i − q s . The same strategy applies
for the symmetric equilibrium { C , C } .
In the asymmetric segmented case { F C , C } , fir m i pla ying F C has the first-order con-
ditions: p F
i − q F
p C
i
= D C
i ∂ D C
i / ∂ p F
i − D F
i ∂ D C
i / ∂ p C
i
D F
i ∂ D F
i / ∂ p C
i − D C
i ∂ D F
i / ∂ p F
i
(47)
Substituting the demand functions and the respectiv e deriv ativ es leads to
p F
i − q F
p C
i
= 1 + ( µ F , C − µ ) exp ( p C
i / µ )
µ exp ( A C / µ ) (48)
The mark-up on the C -labeled good is identical to the mark-up on the F -labeled good
when their qualities are equal, i.e. µ F , C = µ . If the labels are v ertically differ entiated,
then the mark-up on the F -labeled product is higher , as this is the market where the fir m
of fering F C is in monopoly .
Further more, dif ferentiating both sides of the equation with r espect to p F
i sho ws that
the left-hand side is increasing in p F
i while the right-hand side is decreasing in p F
i . Addi-
tionally , using the solution of this equation numerical calculations sho w that
∂ Π i : F C | C
∂ p C
i
= ( p F
i − q F ) ∂ D F
i
∂ p C
i
+ D C
i + p C
i
∂ D C
i
∂ p C
i
= 0 (49)
has exactly one solution in p C
i .
Applying the same procedure for firm j w e obtain
∂ Π j : C | F C
∂ p C
j
= D C
j + p C
j
∂ D C
j
∂ p C
j
= 0 (50)

7. Appendices 91
has exactly one solution in p C
j .
B Proof of Lemma 2
In the symmetric head-to-head case { F I C , F I C } , profits are:
Π i : F I C | F I C = D F
i ( p F
i − q F ) + D I
i ( p I
i − q I ) + D C
i p C
i (51)
Analyzing the corresponding first-or der conditions, w e find again, as in Lemma 1 that
there is a unique mark-up δ :
p s ∗ − q s = δ with δ implicitly giv en b y
δ = 2 µ ⎛
⎝ 1 − µ
µ + γ [ 1 + ( exp ( A C / µ F I , C ) + exp ( A F I / µ F I , C ) ) µ F I , C / γ ] ⎞
⎠
Simple calculations sho w that with p s = δ + q s the right hand side of the last equation
is decreasing in δ , which establishes uniqueness. Further more, numerical calculations
sho w that the second or der conditions are satisfied at p s
i − q s . As mentioned in the proof
of Lemma 1, an analogous result holds for { F C , F C } and { C , C } .
In the { F C , I C } case, fir m i pla ying F C has the first-order conditions:
p F
i − q F
p C
i
= D C
i ∂ D C
i / ∂ p F
i − D F
i ∂ D C
i / ∂ p C
i
D F
i ∂ D F
i / ∂ p C
i − D C
i ∂ D F
i / ∂ p F
i
(52)
Substituting the demand functions and the respectiv e deriv ativ es leads to
p F
i − q F
p C
i
= Ψ µ exp ( p C
j / µ ) + µ F I , C exp ( p C
i / µ )
µ F , I exp [ ( v F − p F
i ) / µ F , I ] + µ F I , C exp [ ( v I − p I
j ) / µ F , I ]
with : Ψ = µ F , I exp ( A F I / µ F , I )
µ exp ( A C / µ )
Further more, dif ferentiating both sides of the equation with r espect to p F
i sho ws that
the left-hand side is increasing in p F
i while the right-hand side is decreasing in p F
i . Addi-
tionally , using the solution of this equation numerical calculations sho w that
∂ Π i : F C | I C
∂ p C
i
= ( p F
i − q F ) ∂ D F
i
∂ p C
i
+ D C
i + p C
i
∂ D C
i
∂ p C
i
= 0 (53)
has exactly one solution in p C
i . Applying the same procedure for firm j w e obtain
p I
j − q I
p C
j
= Ψ µ exp ( p C
i / µ ) + µ F I , C exp ( p C
j / µ )
µ F , I exp [ ( v I − p I
j ) / µ F I , C ] + µ F I , C exp [ ( v F − p F
i ) / µ F , I ] (54)
Again, while the left-hand side is increasing in p I
j , the right-hand side is decreasing in p C
j

92 III Label Competition
0.1

0.2

0.3

0.4

0.5

0.066

0.068

0.070

0.072

0.074

q F*
q 0

q F
q 1
L 1
L 1
L 1 L pseg
L sym

Figure III.8: License fee as a function of label quality q F
1 for µ = 0.4 and q I = 0.07 (with
q F ∗
0 = 0.28)
and
∂ Π j : I C | F C
∂ p C
j
= ( p I
j − q I ) ∂ D I
j
∂ p C
j
+ D C
j + p C
j
∂ D C
j
∂ p C
j
= 0 (55)
has exactly one solution in p C
j .
In the { F I C , I C } case, w e also compute the first-order conditions for the firm i pla ying
F I C . Substituting the demand functions and the respectiv e deriv ativ es leads to
p F
i − q F
p I
i − q I = 1 + ( µ F I , C − µ ) exp ( p I
i / µ )
µ exp ( A I / µ ) (56)
If the labels are v ertically dif ferentiated, then the mark-up on the F -labeled product is
higher , as this is the market where the firm offering F I C is in monopoly .
The proof for uniqueness of equilibrium prices w orks identically to the pre viously
sho wn full market segmentation { F C , I C } case.
C Calculations for Proposition 2
For deter mining the equilibrium in stage 1.1 where the licenser sets its fee and quality , w e
numerically compute for each v alue of industr y standard q I and horizontal differentiation
µ the optimal licenser quality q F ∗ for each of the three fees L s y m , L p s e g and L s e g . W e then
compare the reduced licenser pr ofit with optimal quality 2 L s y m ∗ , L p s e g ∗ and L s e g ∗ and
keep the case that maximizes licenser profits. This giv es us the reaction function of the
licenser q F ∗
1 ( q I ) sho wn in Figure III.5.
As an illustration, Figure III.8 plots the license fee as a function of label quality q F
1 for
horizontal dif ferentiation µ = 0.4. At this lev el of horizontal dif ferentiation, the licenser
chooses the highest fee betw een L s y m
1 and L p s e g
1 . Moreo v er , it cannot under cut the label
quality from the pre vious period with only one label q F ∗
0 dra wn as a gra y line. For
q I = 0.07, the maximum is such that the licenser chooses the partially segmented market
constellation. When q I increases, the symmetric equilibrium becomes more attractiv e and
the distance betw een the maxima of the tw o cur v es explains the jump on Figure III.5.

7. Appendices 93
0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.335

0.340

0.345

0.350

0.355

q I


sym (q I ,q F* (q I ))
{FIC,FIC} {FIC,IC }

pseg (q I ,q F* (q I ))

Figure III.9: Joint fir m profit Π as a function of industr y standard q I giv en licenser reac-
tion q F ∗
1 ( q I ) for µ = 0.4
D Calculations for Proposition 3
In order to determine the equilibrium in stage 1.0 where the industry decides on its
standard, w e first compute the licenser reaction in q F
1 and L 1 for each lev el of horizontal
dif ferentiation µ and each industry standard q I . W e then deter mine for each horizontal
dif ferentiation µ , the q I ∗ that maximizes joint fir m profit Π .
As an example, Figure III.9 sho ws the joint firm profit Π for dif ferent v alues of indus-
try standard q I , holding horizontal differentiation µ fixed at 0.4. There is a jump in the
cur v e, because the licenser F switches from partial segmentation to head-to-head com-
petition when q I increases abo v e 0.085. The joint fir m profit is maximized b y the cor ner
solution ensuring partial segmentation.
E Calculations for Proposition 4.(iii)
Figure III.10 sho ws that the license fees are systematically lo w er upon entr y of the indus-
try standard. In the first graphic, with µ = 0.6, w e compare head-to-head competition
license fees L s y m
0 and L s y m
1 for dif ferent v alues of licenser quality q F and industr y standard
q I ; L s y m
1 is alw a ys smaller . In the second graphic, with µ = 0.1, w e compare segmented
(resp. partially segmented) market license fees L s e g
0 and max { L p s e g
1 , L s e g
1 } for dif ferent v al-
ues of licenser quality q F and industr y standard q I ; max { L p s e g
1 , L s e g
1 } is alw a ys smaller .

94 III Label Competition
0.0

0.2

0.4

0.2

0.3

0.4

0.5
0.6

0 .06

0 .08

0.1

0.3

(a) License fee for head-to-head competition for possible v alues of q I and q F
t with µ = 0.6
0.0

0.2

0.4

0.2

0.4

0.02
0 .04
0 .06
0 .08

0.5

0.3

0.1

(b) License fee for segmented equilibria for possible v alues of q I and q F
t with µ = 0.1
Figure III.10: Comparing license fees from t = 0 and t = 1

IV
Offset credits in the EU ETS: a quantile
estimation of firm-level transaction costs
This is, of course, a v er y
unrealistic assumption.
Coase (1960)
on the Coase theorem.
95

96 IV Carbon Of fsets
1 Introduction
The EU Emissions T rading System (EU ETS) aims at achieving the EU’s carbon emission
goals at minimum cost. Instead of imposing a tax, the policy deter mines an emission cap
and lets the market deter mine the equilibrium emissions price. Ideally , all fir ms incur
the same price for emissions and abatement is realized where it is cheapest, such that the
aggregate abatement cost is minimized.
Ho w e v er , abatement and certificate costs are not the only costs arising from an emis-
sions trading scheme: just like any other regulation, this policy has to be implemented b y
fir ms, causing a wide range of administrativ e, managerial, and infor mation-related trans-
action costs. T ypically , such frictions are unobser v ed b y the econometrician. Presumably ,
many fir ms themselv es do not track the v alue of their emplo y ees’ time and resources
spent in the course of EU ETS compliance and optimization. This chapter considers such
unobser v ed trading cost, i.e. transaction costs that are conditional on trading.
This chapter focuses on the possibility for fir ms to use not only European certificates
but also inter national of fset credits. The EU ETS is linked to the inter national certificate
market of the Ky oto Protocol. On aggregate, these additional foreign certificates increase
the cap for European polluters and decrease their compliance cost. Offset cr edits w ere
cheaper than European credits (European Union Allo w ances, EAUs) throughout Phase II
of the EU ETS (2008-2012). Ho w e v er , the EU limited the quantity of of fset cr edits b y a
fir m-specific of fset quota ( entitlement ). For the fir ms, offset usage w as an unambiguous
w a y to reduce compliance cost. Nev ertheless, o v er tw enty per cent of regulated fir ms did
not use any of fsets.
This chapter uses fir m-lev el data on of fset usage to estimate the distribution of fixed
trading costs, both for general entry into certificate trade and for offset use in partic-
ular . It brings together elements, first, from theoretical literature on transaction costs
in emissions trading; second, from empirical literature on transaction costs in European
emissions trading; and, third, from the small literature on the use of of fset certificates in
the EU ETS. Methodologically , this research uses binary quantile methodology .
While the abatement incentiv es of cap-and-trade schemes are amply discussed, most
of the literature does not consider transaction costs. Ho w e v er , emissions trading – just
like any other market transaction – is unlikely to be completely free of frictions. In
his seminal article, Coase (1960) underlines that the irrele v ance of initial property al-
location for final resource allocation holds only if “costs to use the price mechanism”
are negligible. The theoretical model of Sta vins (1995) focuses on v ariable (quantity-
dependent) trading costs, i.e. transaction costs arising from each certificate traded. Singh
and W eninger (2016) build on this seminal w ork and sho w what distinguishes the im-
pacts of v ariable and fixed (quantity-independent) trading costs. Fixed trading costs, as
analyzed in this chapter , suppress some of the potential trades and lead to capacity- and
certificate-underutilization; the y also make initial allocation non-neutral, as fir ms only
trade if their emissions and initial allocation are far a w a y from each other .
Empirical evidence on transaction costs in envir onmental policy is scarce, as McCann
et al. (2005) note in their literature re vie w . Literature suggests that transaction costs and

1. Introduction 97
other market imperfections ha v e hampered the impact of US environmental trading pr o-
grams (T ietenber g 2006, Hahn and Hester 1989). For example, Atkinson and T ietenberg
(1991) argue that trading is too scar ce to reach a cost-ef fectiv e outcome; they claim that
this inef ficiency stems from the bilateral, sequential nature of trades leading to frictions
and thus transaction costs in a broad sense.
Concer ning the EU ETS, the literature generally finds that small firms beha v e more
“passiv ely” and that many fir ms lack the inherent institutional capacity for activ e trad-
ing, for instance Sandoff and Schaad (2009) on a sample of Sw edish fir ms. Many Ger man
small and medium enterprises trade only at the end of the y ear and only if the grandfa-
thered allocation does not suf fice (L ¨
oschel et al. 2011). S chleich and Betz (2004) state that
for small fir ms, transaction costs likely exceed certificate cost. Zaklan (2013) sho ws that
most transactions take place betw een plants belonging to the same fir m, which might be
a w a y to reduce trading cost. Surv e ys sho w that large emitters face smaller per -tonne
transaction costs (Heindl 2017, Jarait ˙
e et al. 2010, L ¨
oschel et al. 2010, 2011). For example,
Jarait ˙
e et al. (2010) estimate that in Ireland per tonne transaction costs of the largest firms
we r e e 0.05 per tonne of emissions, while the y w ere up to e 2 per tonne for small fir ms.
This suggests that transaction costs are mostly composed of fixed (quantity-independent)
costs, potentially combined with smaller v ariable (per unit) costs. Ho w ev er , different au-
thors use dif ferent definitions of transaction costs, making literature comparison difficult.
S ome studies include monitoring, reporting and v alidation (MR V) costs that occur for all
regulated fir ms, while others concentrate on transaction costs that occur conditionally on
trading.
V irtually all empirical w ork on trading costs in the EU ETS relies on surv ey-data,
except Jarait ˙
e-Ka ˇ
zukausk ˙
e and Ka ˇ
zukauskas (2015) who use transaction data from Phase
I (2005-2007). They find that trading costs w ere a substantial factor inhibiting fir ms from
activ ely trading European certificates, but the y do not directly estimate their magnitude.
While the previously cited literature examines trading schemes with only one type
of certificate, few articles deal with linked schemes with tw o certificate types. T rotignon
(2012) sho ws that firms initially used few of fsets until 2011, when there w as a shar p
increase in of fset usage. He estimates the cumulated sa vings of fir ms at e 1.5 billion.
Eller man et al. (2016) pro vide an aggregate description through the end of Phase II in
2012.
Binary choice methods are an established w a y to identify latent v ariables that shape
beha vior around some cut-of f. In particular , one can identify unobser v ed costs from
obser v ed participation beha vior to some cost-sa ving or profit-yielding activity . Ander -
son et al. (2011) use this approach on the mar ginal costs of regulating fuel-standards
b y obser ving to what extent car producers use a regulatory loophole of known costs to
a v oid the regulation on cor porate fuel efficiency standar ds. Attanasio and Paiella (2011)
similarly identify fixed household costs of financial market activity from household’s par -
ticipation choice in the market. Conceptually , this resembles the present chapter , which
identifies fixed costs b y measuring the retur ns that fir ms for w ent b y a v oiding trade.
Quantile models are dev eloped b y Koenker and Bassett Jr . (1978), and applied to binar y

98 IV Carbon Of fsets
choice b y Kordas (2006). Belluzzo Jr (2004) uses them to estimate the distribution of
willingness-to-pa y for a public good, analogous to the present chapter: I measure trans-
action costs here from the observ ed “unwillingness-to-benefit” of fir ms. Going be y ond
usual estimation of the mean, this quantile methodology allo ws me to estimate the me-
dian as w ell as (a discrete approximation to) the whole distribution of transaction costs
across 19 quantiles.
This chapter pro vides both an analytical and empirical contribution to the literature.
First, it describes the obser v ed offset usage beha vior . Among the firms not using offsets,
there are mostly small fir ms and, more particularly , fir ms with generous free allocations
of European certificates. Across all firms, forgone rev enue fr om unused of fsets adds up
to around e 1.37 billion.
In a second step, I argue that firms’ reluctance to trade can be inter preted as transac-
tion costs. W ithout such unobserv ed transaction costs, the of fset entitlement w ould be an
unequiv ocal “free lunch” opportunity . The share of fir ms incurring this opportunity cost
can only be explained b y the interference of some unobser v ed frictions: trading costs, as
defined in this chapter , can include employ ees’ time/salaries, training and consultancy
costs. T rading costs are assumed fixed (quantity-independent) and pa y able whenev er a
fir m first decides to purchase emissions certificates in general or of fset credits in particu-
lar; therefore, they might also be called entry costs.
The theoretical section la ys out ho w trading costs change the firms’ optimization
problem. Building on the standard model, I introduce a second type of certificate and
fixed transaction costs. Such costs make the fir ms’ free allocation of certificates non-
neutral, as firms with allocations larger than their emission do not need to engage in
emissions trading: they can a v oid transaction costs of activ e trading, such that they are
less likely to use their of fset entitlement. The model establishes a link betw een, on one
hand, the decision to trade on the offset market and, on the other hand, both the initial net
allocation status and of fset entitlement. This relies on the fundamental assumption that
a fir m enters of fset trading if and only if (obser v ed) trading benefits exceed (unobser v ed)
trading costs.
The empirical section uses this insight to estimate the latent transaction costs ratio-
nalizing a fir m’s decision to not to enter the of fset market. T o the best of my kno wledge,
this is the first study to estimate costs using binary quantile regression. I identify the
distribution of tw o transaction cost components: general trading cost and offset-specific
cost. 1 The empirical results sho w that trading cost to the of fset market is lo w for most
fir ms, with a median of e 905. The general trading cost is much higher with a median
cost of e 7,770. Ho w ev er , the estimated distribution of these costs is highly skew ed, such
that the means are much higher than the medians ( e 21,519 for mean general entry and
e 83,675 for of fset market entr y), resulting from some lar ge outliers. Thus, a probit re-
gression of the conditional mean is misleading about the costs faced b y the majority of
fir ms. Although these transaction costs are often small compared to other production
1 Note that I only consider fixed transaction costs that are conditional on trading any amount. The ter ms
transaction cost and trading cost thus apply interchangeably .

2. Background 99
factors, they make the use of of fsets unprofitable for 21% of the firms. For bigger fir ms,
inv estment in offset certificates mostly r emains profitable.
The remainder of this chapter is or ganized as follo ws. After introducing the institu-
tional and legal framew ork of international offset certificates (S ection 2.1), I briefly ex-
plain the aggregate impact of of fset trading in the EU ETS (S ection 2.2) and the definition
of transaction costs in this context (S ection 2.3). I then set up a model of fir m-beha vior in
the reference case, i.e. without any transaction/entr y costs (S ection 3.1), which I extend
b y adding entr y costs (S ection 3.2). Finally , I present the data and some stylized facts,
explain the econometric methodology (S ection 4) and present the estimated distribution
of transaction costs (S ection 5).
2 Background
The EU ETS and the international offset credits are based on a complex regulatory frame-
w ork. This section briefly explains the key elements of this regulation. It further sketches
out the aggregate mechanics of introducing a second type of certificate into an emis-
sions trading system. Finally , this section explains in detail the specific transaction costs
examined in this chapter .
2.1 Institutional framew ork
Each y ear , the European Union issues EU emissions allo w ances (EUAs) that, in total,
equal the o v erall EU ETS emission cap. In Phase II – the period under study here – virtu-
ally all these certificates w ere distributed free of char ge to the regulated firms, according
to their historical emission lev els ( grandfather ed allocation ). At the end of each y ear , fir ms
ha v e to report their emissions and surrender certificates equaling their emissions: one for
each tonne of CO 2 . Other gr eenhouse gases are included as w ell, for instance methane
(CH 4 ) and nitrous oxide (N 2 O). Emissions of these other gases are conv erted with spe-
cific factors to CO 2 equiv alent masses; hence the use of tonnes of CO 2 equivalent (tCO2e)
as a unit measuring quantities of certificates. Used certificates disappear , while unused
certificates are banked , as the y remain v alid in subsequent y ears.
In order to coor dinate international emission reduction ef forts and to lo w er abatement
cost for EU-based companies, the EU linked its ETS to the inter national framew ork es-
tablished b y the United Nations Framew ork Conv ention on Climate Change (UNFCCC,
1992) and the Ky oto Protocol. According to these international conv entions, suitable
projects that sa v e emissions in unregulated parts of the w orld 2 can be v alidated and
certified b y UNEP . This procedure then generates Certified Emission Reductions (CERs,
from Clean De v elopment Mechanism) or Emission Reduction Units (ERUs, from Joint
Implementation) that can be used to co v er emissions in regulated parts of the w orld.
CERs and ERUs are commonly called international offset certificates . 3 The EU does not dis-
2 Ky oto “non-Annex I” countries, in practice mostly China, Ukraine and India.
3 CERs and ERUs can be used interchangeably under this legislation. I only use the ter m “offsets” from
here, as ev erything applies equally to CERs and ERUs.

100 IV Carbon Of fsets
tribute of fset certificates, meaning that fir ms can only use them after activ ely acquiring
them, either b y conducting projects generating offsets or b y buying them on the market.
W ithin their obligations under the EU ETS, firms could substitute a limited number
of European certificates with of fset certificates. Such a substitution is attractiv e because
of fset certificates are cheaper than European certificates. Ho w ev er , to ensure that the
bulk of emission reduction w as achiev ed domestically , the EU restricted the quantity of
of fsets usable b y each fir m. The exact definition of this quota depends on the national
go v ernment, but most countries computed it as a per centage share of the grandfathered
allocation, cf. T able IV .4 on page 122. This yields a fir m-specific offset entitlement , as a
product of firm-specific allocation and countr y-/sector-specific per centage share. While
European certificate allocations w ere distributed each y ear , the total offset entitlement
w as deter mined only in 2008; once fixed entitlements could then be used at any point in
time o v er Phase II.
Of fset entitlements w ere set in adv ance for the entire Phase II. In the middle of
Phase II (April 2009), EU Directiv e 2009/29/EC announced that the usage limits of cer-
tain of fsets w ould be transferable ( bankable ) into Phase III (2013-2020); 4 ho w ev er it w as
unclear what amounts and which types of certificates w ere inv olv ed. It w as clear that
“industrial gas” certificates, which constituted the bulk of of fsets traded (Eller man et al.
2016), w ould no longer be v alid. Due to institutional obstacles, the final regulation en-
suring the bankability and its conditions only appeared in No v ember 2013, 5 i.e. after the
original claims for Phase II expired. From the perspectiv e of a fir m acting during Phase II,
the end of Phase II had therefore to be considered as the temporal limit when planning
the use of its of fset entitlement. 6
An alter nativ e explanation for limited of fset use w ould be that offset use w as limited
b y supply side constraints. Ho w ev er , the data sho ws that of fsets w ere alw a ys amply
a v ailable: the central registr y of the UNEP sho ws that the number of of fsets generated
at the end of 2012 w as much higher than aggregate offset usage rights within the EU. 7
Of fset prices collapsed to virtually zero after the end of Phase II, which sho ws that the
EU ETS demand w as the driving force behind offset v aluation.
2.2 Why are of fset certificates cheaper?
Before looking at the impact of transaction costs, it is useful to consider the impact of of f-
set certificates in general (without transaction costs) and, in particular , to sho w why the y
4 Phase III mainly extended the pro visions of Phase II, in particular emission certificates fr om Phase II
remained v alid in Phase III. Important changes included new allocation rules, a reduction of free allocation
combined with an increase in certificate auctioning, and the inclusion of the air transport sector .
5 Commission Regulation (EU) No 1123/2013
6 S ee Appendix B on page 123 for more detail.
7 Theoretically , in addition to EU firm-lev el demand (analyzed in this chapter) there w as scope for
additional demand coming from the state-le v el; how ev er , at the state-le v el of the Ky oto framew ork, offsets
w ere perfect substitutes for Assigned Amount Units (AAUs). Giv en the large AAU o v erallocation to
ex-S o viet Union states (so-called “hot air ”), the evidence suggests that AAUs are usually sold far belo w the
price of EUAs, CERs, and ERUs (Aldrich and Koerner 2012).

2. Background 101
marginal abatement cost/prices
emissions (tCO 2 )
e ∗
unreg’d
p e
¯
e
p e
hi
¯
e ′
= ¯
e + ¯
q o
p o
hi
of fset supply high ( q o
hi )
e ∗
hi
p e
l o w = p o
l o w
e ∗
l o w
of fset supply lo w ( q o
l o w )
∆ p
Figure IV .1: Stylized illustration of aggregate market equilibrium with tw o alter nativ e
of fset supply lev els
ha v e been cheaper than European certificates. T ransaction costs are added in S ection 3.2.
Inter national of fset credits co v er emissions fr om geographic regions not pre viously in-
cluded in the scope of EU ETS. As such, they are a spatial flexibility mechanism (Stev ens
and Rose 2002) allo wing firms to abate where it is cheapest and ha v e the abatement cred-
ited via the creation of of fset credits. The introduction of of fsets increases the o v erall cap
imposed b y the EU ETS. Potentially , the cap could increase b y an amount equal to the
sum of all fir ms’ of fset quotas ( entitlements ). 8
Figure IV .1 illustrates the resulting market equilibrium: in an unregulated situation,
emissions ha v e no cost and fir ms emit e ∗
unreg’d . In an ETS without offset cr edits, the
standard r esult for emissions trading holds: the market clears at the regulated maximum
emission lev el ¯
e at price p e , equal to the marginal abatement cost at ¯
e (T rotignon 2012).
When of fsets are introduced, the y are perfect substitutes for Eur opean certificates up to
the quota. When of fsets are costly to produce (supply q o
l o w ), their a v ailability increases the
o v erall cap, lo w ers the price and mo v es the equilibrium to e ∗
l o w , where prices are set at the
lev el for which of fset supply clears. This equalizes European certificate and offset prices
p e
l o w = p o
l o w . When offset creation is cheap (supply q o
hi ), fir ms w ould like to buy more
of fset certificates than allo w ed and emit up to e ∗
hi . The aggr egate of fset quota ¯
q o binds in
that case. The resulting constrained equilibrium at ¯
e ′ = ¯
e + ¯
q o , no longer ensures equal
prices: European certificates trade at marginal abatement cost p e
hi at ¯
e ′ . The ov er -supply
of of fset certificates driv es their price do wn to p o
hi . The price differential ∆ p = p e − p o is
alw a ys positiv e or zero; its magnitude depends on the difficulty to generate of fsets and
on the stringency of the of fset quota.
2.3 Definition and interpretation of transaction costs
The EU ETS causes direct costs through abatement and certificate prices. Besides this
direct (and intended) cost, the EU ETS causes a number of (unintended) infor mation-
, administration- and management-related frictions, which in this chapter are broadly
8 S ee Mansanet-Bataller et al. (2011), Nazifi (2013) for more details from a finance perspectiv e.

102 IV Carbon Of fsets
understood under the ter m transaction costs.
I separate these costs into tw o parts according to their contingency: the first are “ad-
ministrativ e costs” due to mandator y actions, such as costs for monitoring, reporting and
v alidating emissions (MR V) as w ell as the EU registr y ser vice charges. These administra-
tiv e costs are una v oidable and thus cannot explain firms’ (non-)entr y to the offset market.
The second, generally kno wn as trading or entry costs, are the consequence of v oluntary
trading choices, such as information gathering, forecasting of certificate prices, finding
trading partners, bargaining, contracting, managing price risk, or finally simply the costs
of out-sourcing the whole trading pr ocess. This chapter concentrates on the latter , i.e.
trading costs, which are defined as all frictions that are important for a fir m’s decision
to activ ely enter the certificate market. While some fir ms ha v e to pur chase certificates,
others ha v e allocations large enough to a v oid any activ e inv olv ement in the certificate
market.
This definition is narro w er than in other w orks which consider the o v erall cost of
establishing, managing, monitoring and enforcing a policy (Krutilla and Krause 2010,
Joas and Flachsland 2016). 9 Ho w ev er it is also br oader than the definition used in some
of the literature, as it includes all frictions prev enting fir ms from entering the certificate
market, in particular it includes outsourcing costs and purely psy chological factors that
discourage managers from de v oting resources to certificate trading.
Heindl (2012) finds that infor mation-procurement alone – the biggest upfr ont cost –
costs fir ms about 17 emplo y ee-w orkda ys. He also finds that infor mation and trading
costs do not depend on fir m size. While this indicates fixed costs, most sur v eys present
their results on a per -tonne basis, i.e. inter preting them as v ariable rather than fixed costs,
cf. T able IV .1. None of them asks about offset-related costs. The brokerage fees of an
individual transaction are lo w , 10 while there are upfront entr y costs. Just as an example,
setting up a trading account at the ICE (the biggest exchange, clearing about 90% of
emission certificate trade in Europe) costs e 2,500 in direct fees, 11 while an individual
transaction thereafter costs only cents. 12
A multitude of news and data pr o viders (Point Carbon), consulting firms
(ICIS/T schach), and financial transaction ser vices (brokerage like TFS Green, exchange
platfor ms like ICE) ha v e emerged. The fact that fir ms use such costly ser vices indicates
a lack of cost-free infor mation. Moreo v er , descriptiv e management literature highlights
9 In particular , this chapter concentrates on costs bor ne b y fir ms and does not take into account what
Joas and Flachsland (2016) call “public-sector costs” borne by the regulatory authority .
10 Conv ery and Redmond (2007) establish a list of direct transaction fees: brokers ha v e large minimum
trade sizes and take betw een 1 and 5 cent fee per certificate (tCO2e). Exchanges take smaller trades and
charge betw een 0.5 and 3 cent per certificate.
11 As indicated on https://www.theice.com/fees ; retriev ed on 01/03/2015.
12 Inter nationally operating fir ms could decide to create offset certificates in their o wn plants abroad,
rather than purchasing the certificates on a market place. This chapter assumes that the large majority of
firms bought their certificates, which matches anecdotal evidence about offsets. How ev er , this claim cannot
be pro v en due to data restrictions. If this claim is not true, the estimations in this chapter remain v alid, but
their interpretation changes from trading costs to transaction costs in the generation of offsets.

3. Model 103
T able IV .1: Ov er view of transaction cost estimates per firm in the EU ETS in the literature
A v erage transaction costs Cost structure Scope T ime
Heindl (2012) e 4,193 infor mation
e 4,659 trading
e 12,223 MR V
fixed +
v ariable
Ger many 2009 and 2010
(y early)
Jarait ˙
ee ta l .
(2010)
e 71,860 early implementa-
tion
e 74,180 MR V
v ariable Ireland Phase I
L ¨
oschel et al.
(2010)
e 1.79/tCO2e if emissions
< 25,000t
e 0.36/tCO2e if emissions
≥ 25,000t
v ariable Germany 2009
L ¨
oschel et al.
(2011)
e 11,136 MR V and infor -
mation
e 2,654 trading
fixed +
v ariable
Ger many 2010
Jarait ˙
e-
Ka ˇ
zukausk ˙
e and
Ka ˇ
zukauskas
(2015)
sho w significance, no
magnitude
EU Phase I
Source: Cited studies and author ’s computation from estimates stated therein.
the discrepancy betw een actual and intended market practice: fir ms use simple heuristics
instead of fully optimizing their beha vior (e.g. V eal and Mouzas 2012). These anecdotal
elements support the idea of transaction costs, ev en though firms ma y rarely account for
them as such explicitly .
3 Model
First, a static model describes firm’s optimization problem in presence of tw o types of
emission certificates without transaction costs. In a second step, I examine ho w beha vior
changes in the presence of fixed transaction costs. Simply put, fir ms alw a ys w ant to use
of fset credits, unless transaction costs are higher than potential retur ns from using the
cheaper of fset credits. Giv en the institutional background, the model is static with just
one period corresponding to Phase II of the EU ETS.
3.1 Emissions trading with of fset credits: reference scenar io without trading
costs
For the purpose of this chapter , it is useful to look at fir ms’ optimization problem aggre-
gated o v er Phase II. As a reference case, this Subsection extends the standar d emissions
trading model with a second type of certificate and without adding trading costs. Fir ms
can separate the decision of emission lev els and pr oduced quantities from the partition-
ing betw een European and of fset certificates.
In the absence of of fsets, marginal abatement cost is constant across firms and equal
to the European certificate price p e in equilibrium (e.g. Montgomer y 1972). Each fir m i

104 IV Carbon Of fsets
jointly produces some quantity y and emissions e , maximizing profits:
max
y i , e i , q e
i , q o
i
π = y i − C ( y i , e i ) − p e ( q e
i − q e 0
i ) − p o q o
i , (1)
subject to e i = q o
i + q e
i , (2)
q o
i ≤ ¯
q o
i , (3)
where π is profit and C ( y i , e i ) production cost, which depends on emissions e i and out-
put y i sold at a price nor malized to 1. C ( y i , e i ) is assumed continuous and twice dif-
ferentiable. I assume that reducing emissions at a giv en production lev el increases cost,
C e < 0. 13 q o
i is the amount of of fsets and q e
i the amount of European certificates used.
At the beginning of Phase II, fir ms are giv en a free allocation of European certificates q e 0
i
and a fir m-specific of fset entitlement ¯
q o
i . They can buy and sell European certificates at
market price p e and offsets at price p o .
The fir m must simultaneously solv e three problems: decide on the produced quantity
y ∗
i , determine the emission lev el e ∗
i , and split compliance (i.e. an amount of certificates
equal to e i ) betw een the international offset and Eur opean certificates. T o satisfy the first-
order condition, emissions e ∗
i ha v e to be such that marginal abatement cost is equal to
the marginal certificate price, and pr oduction such that y ∗
i that marginal pr oduction cost
(including compliance cost) is equal to 1 (price nor malization).
The compliance cost is composed of the cost of buying the certificate quantities q e
i and
q o
i necessary to co v er the emission le v el e ∗
i , abatement cost and the forgone re v enue of
adjusting production relativ e to a pr oduction lev el that w ould be optimal at zero emission
cost. The marginal cost is either p e or p o depending on which type of certificate is used to
co v er the last (mar ginal) emission. Of fsets are perfect substitutes for Eur opean certificates
up to the quota; their price difference is thus zer o or positiv e: p e − p o = : ∆ p ≥ 0. 14
The result is straightforw ard: as a perfect substitute at a low er price, offset credits ar e
unambiguously preferable to European certificates, up to the regulated entitlement ¯
q o
i .
Only if emissions are abo v e ¯
q o
i , the fir m co v ers the remaining emissions b y using the mor e
expensiv e European certificates. Compared to a system with only European certificates,
the fir m sa v es an amount equal to ¯
q o
i ∆ p . The optimization problem can be simplified as
13 C y and C e denote the partial deriv ativ es with respect to y and e , respectiv ely . The production cost
function includes abatement cost, as the marginal cost of reducing emissions b y a tonne at same output
equals − C e (see Singh and W eninger 2016, for further details).
14 For the pur pose of this chapter , I only consider situations in which of fset certificates are strictly
cheaper than European certificates, as the alternativ e where both prices ar e equal is qualitativ ely not
differ ent from a system without of fsets. Moreo v er , the data re v eals that in practice there has alw a ys been a
clear price discount for offset certificates.

3. Model 105
follo ws: 15
max
e i
π ( y ∗ ( e i ) , e i ) = ⎧
⎨
⎩
y ∗ ( e i ) − C ( y ∗ ( e i ) , e i ) − p o e i , if 0 < e i ≤ ¯
q o
i
y ∗ ( e i ) − C ( y ∗ ( e i ) , e i ) − p e e i + ¯
q o
i ∆ p , if ¯
q o
i < e i
(4)
In the EU ETS, the of fset entitlement ¯
q o
i is, in practice, small compared to emissions.
V irtually all firms need to use European certificates in addition to of fsets, meaning that
the constraint in equation (3) is binding. The usual result that mar ginal abatement cost
are equalized across firms at the price lev el p e remains v alid.
3.2 T rading costs for both certificate markets
I no w assume that firms face some general entr y trading cost to enter any certificate
market, i.e. the cost of setting up a trading department no matter the type of certificates.
Only once the y ha v e such a trading department, they activ ely enter certificate trading
and can incur an additional cost contingent on entering the of fset market. They can
a v oid both costs if they only use their freely allocated European certificates. Fir ms with
emissions greater than their allocation ha v e to buy certificates and cannot a v oid the gen-
eral component of trading cost. Profit equation (1) has no w tw o additional fixed cost
ter ms:
π = y ∗ ( e i ) − C ( y ∗ ( e i ) , e i ) − p e q e
i − 1 e κ e − p o q o
i − 1 o κ o ,
= y ∗ ( e i ) − C ( y ∗ ( e i ) , e i ) − p e e i − 1 e ( κ e + 1 o ( κ o − ∆ p q o
i ) ) , (5)
where 1 o = 1 if f q o
i > 0 (6)
1 e = 1 if f q o
i > 0 ∨ q e
i − q e 0
i > 0 (7)
where a firm incurs general entr y trading costs κ e if it buys any certificates, but also needs
to pa y additional infor mation costs, κ o , to enter the less w ell-kno wn offset market. Fir ms
that are “long” in equilibrium, i.e. which receiv ed more free allocations than needed for
their emissions ( q e 0
i > e ∗
i ), are not obliged to purchase certificates. “Short” fir ms cannot
beha v e “autarkic” (Jong and Zeitlberger 2014): they must enter the market to buy some
certificates and, thus, consider the general trading cost κ e sunk when deciding about
of fset usage. The impact of transaction costs on offset usage and incurr ed total cost
depends on the relativ e magnitudes of κ o , κ o + κ e and ¯
q o
i ∆ p .
As usual with fixed entr y costs, fir ms enter trading if, and only if, profits are higher
with entr y relativ e to non-entry . Giv en the specific cost structure assumed here, short
fir ms enter the of fset market if κ o < ¯
q o
i ∆ p , while long fir ms enter if κ o + κ e < ¯
q o
i ∆ p . Thus,
entry to the offset market is a binary choice, yielding “all-or-nothing” beha vior . 16 In this
situation, grandfathered allocations create a discontinuity that impacts firm beha vior .
15 The allocation ter m p e q e 0
i in equation (1) is a choice-independent lump-sum transfer and can be
dropped from the maximization pr oblem.
16 This part assumes that fir ms ha v e emissions greater than their offset entitlement, which is the case for
o v er 98% of the fir ms.

106 IV Carbon Of fsets
This assumes fir ms take their allocation status as giv en when deciding about their
entry to the offset market. The fixed cost at emission lev el e i = q e 0 , i.e. the switching point
betw een short and long, could cause fir ms to restrict their emissions to q e 0
i . Appendix C
on page 124 for malizes this condition and tests whether there is any empirical e vidence
for such beha vior , i.e. bunching of fir ms at the threshold. While theor etically possible,
there is no empirical evidence for such an adjustment. T rading costs do not impact the
marginal cost-benefit trade-of f: both abo v e and belo w q e 0
i fir ms face a certificate price of
p e , such that the main mechanism of the ETS is independent of fixed transaction costs. 17
Let fir m “net allocation status” 1 l o n g
i be a dummy v ariable indicating that allocation
q e 0
i is larger than emissions e ∗
i , 18 and 1 o
i is again the dummy indicating the use of offset
certificates.
1 o
i = ⎧
⎨
⎩
1 if ¯
q o
i ∆ p > κ o + 1 l o n g
i κ e ,
0 other wise.
(8)
4 Data and empir ical research design
I use administrativ e data from the EU ETS. Descriptiv e data analysis re v eals four stylized
facts that my empirical analysis relies on: (a) offset certificates ar e cheaper than European
certificates; (b) virtually all fir ms ha v e emissions greater than their of fset entitlement; (c)
a non-negligible number of fir ms (22%) does not use their of fset entitlements; and (d) the
distributions of fir ms’ emissions and entitlements are highly dispersed.
4.1 Emissions, allocation and of fset entitlement
This chapter mainly relies on compliance data of the European ETS Registry (European
Union T ransaction Log, EUTL), which combines all member states’ national registries of
Phase II (2008-2012). This comprehensiv e administrativ e data comprises the allocated
European certificates, v erified emissions, and surrendered certificates (EUAs, CERs and
ERUs) for all 13,590 plants subject to the ETS.
I aggregate the data o v er Phase II, because of fset quotas w ere defined o v er the whole
period and could be used at any point during the phase, without any y early constraint,
so that the decision whether to use of fsets w as ultimately only rev ealed once, on the last
da y of Phase II. The data does not contain transactions per se , but all fir ms using offsets
must ha v e acquired them previously . Fir ms had no interest to stockpile offsets be y ond
17 An underlying assumption is that fir ms take prices as giv en: ev er y individual fir m is too small to
consider its o wn impact on the price lev el, i.e. it has no market po w er on the certificate market. On the
aggregate, p e depends on the number of firms using offset certificates. T o the extent that transaction costs
reduce access to the of fset market, they ar e neither neutral for p e nor , consequently , for y ∗ and e ∗ :
second-order ef fects decrease the of fset price p o and increases the European certificate price p e . While these
price effects ar e essential for a general equilibrium and w elfare assessment, they are not informativ e on
transaction costs and are be y ond the scope of this chapter .
18 The dummy variable is defined at the firm lev el, thus allo wing for cost-free within-firm trade.
Moreo v er , it includes dynamic considerations: giv en firms could bank certificates, 1 l o n g
i = 1 if the
cumulative sum of emissions does not exceed the cumulative sum of allocation in any y ear of Phase II.

4. Data and empir ical research design 107
the end of Phase II if they could also use them for compliance: in this chapter , of fset
usage is thus equated with of fset acquisition. Moreo v er , all fir ms which w ere “short”
in allocation, i.e. had emissions larger than their free allocation, had to buy certificates,
either European or of fset. 19
A matching with Bureau v an Dijk’s Orbis company database rev eals o wnership struc-
tures that link many of these individual plants. 20 This matching matters as the rele v ant
decision likely happens at the fir m lev el, ev en though regulation, allocation, and of fset
entitlements are defined at plant lev el. After some data cleaning, 21 around 9,000 plants
belonging to 4,578 fir ms remain. Ov er half of the plants belong to firms that own just
one plant.
The plant-specific of fset quota ( entitlement ) ¯
q o
i is the product of a country-specific
of fset percentage multiplied b y the plant’s free allocations q e 0
i o v er Phase II. For the
purpose of this chapter , the entitlement has been computed using this rule and v erified
using the Inter national Credit Entitlement tables published b y the EUTL in 2014.
Allocations ha v e been generous, such that 80% of the fir ms could co v er all of their
emissions using only grandfathered allocations; these firms are called the “long” fir ms
in the remainder of this chapter . Of fset entitlement ¯
q o
i is so small that only 2.8% of firms
are able to comply b y using offsets only . T able IV .2 sho ws that free allocation has, on
a v erage, been just abo v e emissions. Firms ha v e a wide v ariety of sizes, with some fir ms
o wning up to 158 plants and being activ e in 11 sectors or 17 countries.
Empirical
distribution
Ideal
compliance
emissions e i
0 ¯
q o
i q e 0
i ¯
q o
i + q e 0
i
only of fsets of fsets and free allocation of fsets, all. & purchase
3% of fir ms 77% of fir ms 8% 12% of fir ms
“long” “short”
4.2 Pr ice spread and realized savings
Daily price data for of fsets (CERs) and European certificates (EUAs) is a v ailable from
Intercontinental Exchange. Offsets are expected to trade at a lo w er price compared to
19 There are certainly some fir ms which entered the market without being legally obliged b y being short.
If many firms fall into this case, the ratio betw een of fset cost and general cost is biased to w ar d general cost,
while the o v erall distribution still holds. In presence of transaction costs how ev er , only short fir ms ha v e an
interest to buy additional European certificates.
20 For more infor mation on this extensiv e matching to the “global ultimate owner ” lev el, see Jarait ˙
e et al.
(2013); or their w ebsite http://fsr.eui.eu/CPRU/EUTL T ransactionData.asp x ; retriev ed on 06/09/2016.
21 Plants from countries that do not participate in the standard w a y , as described in S ection 2.1 (Estonia,
Iceland, Lithuania, Liechtenstein, Malta and Norwa y; 220 plants), and fir ms that ha v e of fset-use bey ond the
legal limit (most likely because of merger and acquisition transactions that are unobserv ed in this data set;
94 plants) are excluded. Also excluded are about 4,000 plants that nev er registered any emissions, ceased
existing in 2011/12, or ha v e their first emissions after 2009.

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