68
ifo Beiträge
zur Wirtschaftsforschung
Effectiveness of Climate Policies:
Empirical Methods and Evidence
Julian Dieler
Institut
LeibnizInstitut für Wirtschaftsforschung
an der Universität München e.V.
Herausgeber der Reihe: Clemens Fuest
Schriftleitung: Chang Woon Nam
68
ifo Beiträge
zur Wirtschaftsforschung
Effectiveness of Climate Policies:
Empirical Methods and Evidence
Julian Dieler
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Preface
This volume was prepared by Julian Dieler while he was working with the Ifo Institute for
Economic Research. It was completed in December 2015 and accepted as a doctoral thesis
in May 2016.
At the latest since the adoption of the Kyoto Protocol in 1997 climate policies are permanently on the international policy agenda. The urgency to find effective and feasible strategies to curb greenhouse gas emissions increases as the carbon budget to reach the 2°C
goal will be exhausted in 2045 according to current estimations by the Intergovernmental
Panel on Climate Change (IPCC). Therefore decisionmakers should be provided with
thorough knowledge of climate policies and their effects by the scientific community. This
thesis contributes to the scientific discourse by analyzing the effectiveness and the development of climate policies.
Chapter 1 analyzes the degree of effectiveness of gasoline and diesel taxes in Europe by
estimating price and tax elasticities of fuel demand. The price or the tax elasticity is a typical measure to assess the effectiveness of policies which are designed as price mechanisms. Besides the insights into the European motor fuel market the analysis led to the
more general finding that anticipation effects have to be taken into account while analyzing the impact of a tax introduction or increase. Chapter 2 makes a further methodological
contribution in the area of fuel demand estimation. Especially in case of analyzing microdata, an often encountered problem in demand estimation is the large number of zeroobservations, which poses problems for standard regression methods. The study which is
the basis for Chapter 2 provides alternative empirical methods which constitute a remedy
to the problem of zeroobservations. Chapter 3 introduces a new climate policy indicator
which provides information about the stringency of climate policies in the OECD countries and can serve itself as an input in empirical analyses because of its empirical foundation.
Keywords: Anticipation effect, inventory effect, fuel demand, fuel price, fuel tax price
elasticities, tax elasticities, log of zero, model comparison tests, twopart models, climate
policies, carbon price, renewable subsidies, energy taxation, climate policy index
JELCodes: C01, C18, C21, C23, D12, H23, Q31, Q41, R41
Acknowledgements
I am sincerely grateful to all the people who have supported me on the way to my dissertation.
First and foremost, I would like to thank Karen Pittel for giving me the opportunity to write my
dissertation under her supervision. Karen was always approachable for questions and concerns
and she helped me throughout my dissertation with her invaluable ideas and comments which
always base on an impressive analytical rigor. Furthermore I am very grateful for the positive
atmosphere in our group at Ifo, which to a large extent goes back to her. I also owe to my second
supervisor, Frank Goetzke, with whom I had many fruitful discussions, leading to a substantial
extension of my skills in econometrics and to a nice joint paper with our colleague Colin Vance
whom I also want to thank for valuable comments and advice. Also Gebhard Flaig deserves my
thankfulness for being my third supervisor and for the pleasant collaboration on the empirical
research seminar for students we organize together.
Special thanks to HansWerner Sinn and Meinhard Knoche, representative for the Ifo Institute,
which was over all years an inspiring and motivating environment for conducting my research.
In addition I wish to thank present and former colleagues at the Ifo Institute, who also became
friends during the years, Anna Ciesielski, Marc Gronwald, Niko Jaakkola, Jana Lippelt, Waldemar Marz, Ana Maria Montoya Gomez, Johannes Pfeiffer, Markus Reischmann, Marina Riem,
MarieTheres von Schickfus, Christoph Schinke, Alex Schmitt, Suphi Şen, Christoph Weissbart
and Markus Zimmer. I was also fortunate enough to make some good friends at the University of
Munich. Especially I want to thank Nadjeschda Arnold, Jakob Eberl, Wolfgang Habla, Martin
Watzinger, Christopher Weber and Susanne Wildgruber for valuable conversations and experiences.
Many thanks go to Christian Beerman and Darko Jus for the exciting economic and noneconomic
discussions we had and all the memorable experiences we made together. For many unforgettable
moments and conversations in the mountains and off the mountains I wish to thank Wolfgang
Brummeisl and Steve Heinke.
Furthermore I would like to express my deep gratitude to Marion Handtke, who always supported
and encouraged me and with whom I share unforgettable memories.
Lastly, I would like to thank my family for their love and support. I am highly indebted to my
brother, as it was him, who gave me the idea of studying economics in first place. In addition, I
am very grateful for all the joint adventures and great conversations. The biggest thank goes to my
parents as they always supported all my endeavors and therefore without them, this dissertation
would not have been possible. I dedicate this dissertation to my family. Thank you.
Julian Dieler
Munich, December 2015
EFFECTIVENESS OF CLIMATE POLICIES:
EMPIRICAL METHODS AND EVIDENCE
InauguralDissertation
zur Erlangung des Grades Doctor oeconomiae publicae (Dr. oec. publ.)
an der LudwigMaximiliansUniversität München
2015
vorgelegt von
Julian Dieler
Referentin: Prof. Dr. Karen Pittel
Korreferent: Prof. Frank Goetzke, Ph.D.
Promotionsabschlussberatung: 11. Mai 2016
Contents
LIST OF FIGURES
IX
LIST OF TABLES
X
INTRODUCTION
1
CHAPTER 1: ANTICIPATION AND INVENTORY EFFECTS ON FUEL DEMAND: AN ANALYSIS OF PRICES AND
T AXES IN EUROPE
4
CHAPTER 2: T HE PROBLEM OF TOO MANY ZEROS: METHODS TO ESTIMATE FUEL PRICE ELASTICITIES
4
CHAPTER 3: CLIMATE P OLICY MEASURE INDEX
5
1 ANTICIPATION AND INVENTORY EFFECTS ON FUEL DEMAND: AN ANALYSIS OF
PRICES AND TAXES IN EUROPE
1.1
1.2
1.3
1.3.1
1.3.2
1.3.3
1.3.4
1.4
1.5
1.6
INTRODUCTION
DATA
MODELS OF FUEL CONSUMPTION
Least Squares Estimates of Fuel Consumption
IV Estimates of Fuel Consumption
Anticipation Effect and Temporary Persistency of a Motor Fuel Tax
Policy Implications
CONCLUSIONS
REFERENCES
APPENDIX
2 THE PROBLEM OF TOO MANY ZEROS: METHODS TO ESTIMATE FUEL PRICE
ELASTICITIES
2.1
INTRODUCTION
2.2
THEORETICAL BACKGROUND OF FUEL PRICE ELASTICITY ESTIMATION
2.3
MODEL COMPARISON T ESTS
2.4
DATA
2.5
RESULTS
2.6
CONCLUSION
2.7
REFERENCES
2.8
APPENDIX
A1. Bias after adding a constant
A2. Confidence Intervals of the mean errors for LOOCV and the differences in count probabilities
A3. Regression results of all models in the loglog specification
A4. Firststep regression results of the Hurdle and the ZeroInflated models
3 CLIMATE POLICY MEASURE INDEX
3.1
3.2
3.3
3.4
3.4.1
INTRODUCTION
SHORT OVERVIEW OF EXISTING CLIMATE POLICY INDICES
DATA
THEORETICAL FRAMEWORK AND METHODOLOGY
Taxes on Energy Products
7
8
11
14
14
21
24
25
25
28
29
33
34
35
41
42
44
48
49
50
50
51
53
55
56
57
59
64
66
67
VIII
Contents
3.4.2
Subsidies for Renewable Energy
3.4.3
Carbon Pricing
3.4.4
Aggregation of the Subindicators
3.5
RESULTS
3.5.1
Comparison of Climate Policy Stringency between the OECD Countries
3.5.2
Climate Policy Stringency over Time
3.5.3
Sensitivity Analyses
3.6
CONCLUSION
3.7
REFERENCES
3.8
APPENDIX
A1. Carbon Pricing Mechanisms in OECD countries
A2. Rankings of different climate policy indices including the CPMI
A3. Development of the crosscountry rankings over time
68
68
69
73
74
76
78
80
82
85
85
86
87
IX
List of Figures
F IGURE 1: NUMBER OF POLICY CATEGORIES IN WHICH THE COUNTRIES APPLY POLICY MEASURES
F IGURE 2: T AXES AND PRICES PER LITER IN 2005 EUROS FOR GASOLINE
F IGURE 3: GASOLINE AND DIESEL CONSUMPTION
F IGURE 4: GASOLINE TAXES AND NET PRICE IN CURRENT VALUES (GERMANY)
F IGURE 5: DEVELOPMENT OF GASOLINE PURCHASES FOUR MONTHS BEFORE AND TWELVE MONTHS AFTER A
TAX CHANGE
2
12
13
16
24
F IGURE 6: DEVELOPMENT OF DIESEL PURCHASES FOUR MONTHS BEFORE AND TWELVE MONTHS AFTER A TAX
25
CHANGE
F IGURE 7: GASOLINE RETAIL PRICE COMPOSITION IN DECEMBER 2006 AND 2010
29
30
F IGURE 8: DIESEL RETAIL PRICE COMPOSITION IN DECEMBER 2006 AND 2010
F IGURE 9: 95% CONFIDENCE INTERVALS OF THE MEAN ERRORS FOR THE TEST STATISTICS OF THE LOOCV
53
METHOD AND THE DOCP METHOD
F IGURE 10: EMISSIONS AND GDP IN THE UNITED STATES
60
F IGURE 11: STRUCTURAL OVERVIEW OF THE CPMI
66
F IGURE 12: CROSSCOUNTRY COMPARISON OF THE CPMI FOR THE YEAR 2012
74
F IGURE 13: DEVELOPMENT OF THE CLIMATE POLICY STRINGENCY OVER TIME IN GERMANY AND THE UNITED
STATES
76
F IGURE 14: RESULTS OF THE SENSITIVITY ANALYSIS WITH RESPECT TO THE WEIGHTING FACTOR
79
F IGURE 15: CROSSCOUNTRY COMPARISON OF THE CPMI FOR THE YEAR 1995
87
F IGURE 16: CROSSCOUNTRY COMPARISON OF THE CPMI FOR THE YEAR 2000
87
X
List of Tables
T ABLE 1: SUMMARY STATISTICS
T ABLE 2: OLS ESTIMATES
T ABLE 3: OLS ESTIMATES FOR DECOMPOSED RETAIL PRICE
T ABLE 4: OLS ESTIMATES OF AGGREGATED DATA
T ABLE 5: FAVORED OLS ESTIMATES COMPARED TO HETEROSKEDASTIC AND AUTOCORRELATIONROBUST
FGLS ESTIMATES
T ABLE 6: FIRST STAGE ESTIMATES
T ABLE 7: IV ESTIMATES
T ABLE 8: DATA SOURCES
T ABLE 9: ALL TAX CHANGES IN COMPARISON TO TAX INCREASES AND DECREASES ONLY
T ABLE 10: DESCRIPTIVE STATISTICS
T ABLE 11: RANKINGS OF THE MODEL COMPARISON TESTS
T ABLE 12: MARGINAL EFFECTS
T ABLE 13: COMPLETE REGRESSION RESULTS
T ABLE 14: P ROBIT REGRESSION RESULTS OF THE FIRST STEP FOR THE HURDLE AND THE ZEROINFLATED
MODELS
T ABLE 15: METHODOLOGICAL OVERVIEW OF EXISTING CLIMATE POLICY INDICES
T ABLE 16: RANKINGS OF THE DIFFERENT INDICES APPLIED TO A SUBSAMPLE OF COUNTRIES WHICH ARE
COVERED BY ALL INDICES
T ABLE 17: REGRESSION RESULTS FOR THE WEIGHTING FACTORS OF THE THREE POLICY CATEGORIES
T ABLE 18: DECOMPOSITION OF THE CPMI FOR SELECTED COUNTRIES IN 2012
T ABLE 19: DECOMPOSITION OF THE TIME SERIES CPMI FOR GERMANY AND THE UNITED STATES FOR
SELECTED YEARS
T ABLE 20: OVERVIEW OF C ARBON PRICING MECHANISMS IN OECD COUNTRIES
T ABLE 21: OVERVIEW OF THE RANKINGS DISCUSSED INCLUDING THE CPMI
13
15
18
20
21
22
23
31
32
43
44
46
54
55
62
63
71
75
77
85
86
Introduction
In 1990 the Intergovernmental Panel on Climate Change (IPCC) stated in its First Assessment Report “Emissions resulting from human activities are substantially increasing
the atmospheric concentrations of the greenhouse gases […]. These increases will enhance the greenhouse effect, resulting on average in an additional warming of the Earth's
surface”.1 This statement by a body of the United Nations Framework Convention for
Climate Change (UNFCCC) cannot be praised highly enough: First, because it unambiguously stated this causality in such an early phase of the climate debate and second, because it implies a large degree on consensus within the IPCC. Nowadays the prevailing
consent among the scientific community is that manmade climate change exists. The literature on anthropogenic global warming (AGW) has been extensively analyzed by Cook
et al. (2013). Their study confirms that out of almost 12 000 scientific publications on
climate in peerreviewed journals, the ones which express a position on AGW, 97.1%
“endorsed the consensus position that humans are causing global warming”.2 While occasionally climate sceptics still struggle to reason against global warming, there is no doubt
that a global temperature rise will substantially harm all natural and human systems
worldwide. From an economist’s perspective the public good character of the global climate is the basic cause of inefficient high emissions of greenhouse gases (GHG), and thus
not only justifies but requires political intervention to cope with climate change.
The adoption of concrete policy measures gained momentum in the 1990s with the implementation of the UNFCCC in 1994 and the adoption of the Kyoto Protocol in 1997
which established binding emission reduction targets for 37 industrialized countries. How
to achieve the national reduction goals and which instruments to implement was left open
and was, to prevent to further curtail national sovereignty, perceived as a matter of the
participating countries. The national reduction goals led to increased effort in the adoption
of national and international climate policies, which is illustrated in Figure 1.
1
2
IPCC (1990), “Working Group I: Scientific Assessment of Climate Change (Policymakers Summary)”,
IPCC First Assessment Report 1990.
Cook et al. (2013), “Quantifying the consensus on anthropogenic global warming in the scientific literature”, Environmental Research Letter 8.
2
Introduction
12
10
8
Russia
Australia
6
EU
Canada
4
Japan
2
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
0
Figure 1: Number of policy categories in which the countries apply policy measures. The time series for the EU
is an emission weighted average (own illustration).
Figure 1 depicts the number of different policy categories in which the respective governments have adopted policy measures. This representation serves as a simple policy diversity indicator for the 5 largest CO2 emitters among the countries that committed themselves to binding emission targets for the first commitment period (20082012) of the
Kyoto Protocol. Of course, the pure number of adopted climate policies is not very meaningful as long as their actual impact on the reduction of GHG emissions is neglected. The
policy categories shown in Figure 1 represent only policy measures which firstly, are
adopted in all countries considered in a comparable way and secondly, have the main goal
to reduce GHG emission. Among other measures, efficiency standards and subsidies on
renewable energy are included. In addition to the policies which primarily aim at climate
protection, every national government implements dozens of policies which affect GHG
emissions but were introduced for different reasons. The finest example is the consumption tax on petroleum products. Though having an impact on emissions its primary purpose is commonly of purely fiscal nature. Such policies are also referred to as climate related policy measures. Given the multitude of climate policies an assessment of the
performance of each policy with respect to emission reduction is necessary.
An assessment of climate policy performance can be conducted in several dimensions like
the aggregation and the objective. Considering the aggregation level it can be distinguished between analyzing countries with their aggregated policy set to reduce GHG
emissions and analyzing specific policy measures. On the countrylevel an assessment of
Introduction
3
the policy performance equals in most of the cases an analysis of a policy mix as in most
countries multiple policy measures affecting the climate are in force. In the analysis of
policy mixes it is important to account for potential interactions of the policies. An extensively debated interaction in Germany for instance, is the impact of the Renewable Energy
Act (EEG) on the EU Emission Trading System (ETS). The EEG is blamed to be – to a
certain extent – accountable for the price erosion in the EU ETS. While these policy
measures arguably act in opposite directions, other measures exhibit mutually beneficial
tendencies. Given this interaction of policies, most empirically founded policy analysis
are done on the policy level, analyzing the effects of single policy measures on a specific
outcome variable. One of the finest examples is the analysis of fuel taxes with regard to
their effect on consumption reduction.3 Besides the entity of the analysis, a further dimension in which policy analysis differs is the objective used to evaluate climate policies. The
evaluation measure could for example, be the pure number of certain policies like in Figure 1, the effectiveness of policy measures or even the policy efficiency. The first method
is rather naïve with limited value for the assessment or comparison of policy measures.
Effectiveness as a measure accounts for the impact of the analyzed instrument in respect
of a specific policy target. A common objective for the evaluation of the effectiveness of
climate policies is the quantity of avoided GHGemissions. The commonly used method in
the analysis of the policy effectiveness is the empirical analysis of historical data. Efficiency takes one step forward by considering the actual costs of the achieved emission
reductions. An additional assessment of the costs of a policy measure requires the consideration of general equilibrium effects. This is typically done by using computable general
equilibrium (CGE) models.
This dissertation focuses on the analysis and comparison of the effectiveness of climate
related policies by applying empirical methods. Chapter 1 analyzes the degree of effectiveness of gasoline and diesel taxes in Europe by estimating price and tax elasticities of
fuel demand. The price or the tax elasticity is a typical measure to assess the effectiveness
of policies which are designed as price mechanisms. Besides the insights into the European motor fuel market the analysis led to the more general finding that anticipation effects
have to be taken into account while analyzing the impact of a tax introduction or increase.
Chapter 2 makes a further methodological contribution in the area of fuel demand estimation. Especially in case of analyzing microdata an often encountered problem in demand
estimation is the large number of zeroobservations which poses problems for standard
regression methods. The study which is the basis for Chapter 2 provides alternative empirical methods which constitute a remedy to the problem of zeroobservations. Chapter 3
introduces a new climate policy indicator which provides information about the stringency
3
Brons et al. (2008) provides an overview of the existing literature on the price elasticity of gasoline demand (see Chapter 1 for the reference).
4
Introduction
of climate policies in the OECD countries and can serve itself as an input in empirical
analyses because of its empirical foundation.
In the following sections I will give a brief overview of the dissertation. Each chapter is
based on an individual research paper and thus is selfcontained. To emphasize that Chapter 1 and 2 originate out of joint work, I have kept the pronoun ‘we’.
Chapter 1: Anticipation and Inventory Effects on Fuel Demand: An Analysis of
Prices and Taxes in Europe
In a panel study for 11 European countries covering the period between 1990 and 2012 we
analyze the effectiveness of fuel taxes using monthly data. Because of its importance in
Europe our data also includes data on diesel consumption, which typically is neglected in
this kind of studies, since most of them are conducted for the United States, where Diesel
is less relevant. Our main contribution to the existing literature however, is the disclosure
and quantification of a tax anticipation effect. This anticipation effect lets consumers fuel
up their cars shortly before the implementation of a tax introduction or increase. Thereby
in the month of the tax increase the motor fuel purchases decrease for two reasons. First,
the consumers have to fuel less because they filled up their tanks at the end of the previous month and possibly second because they face a higher price due to the tax increase.
Taking anticipation effects into account therefore leads to decisively smaller tax effects
than it has been postulated in the literature so far.
This overestimation of the tax effect can also occur using quarterly data as many changes
in the tax regime are coming into force at the beginning of a year. If that is the case, the
anticipation effect overlaps two different quarters and leads thereby to overestimated tax
elasticities, similar to the case of monthly data.
By accounting for the anticipation effect the result of recent studies by Davis and Kilian
(2011)4 and Li et al. (2012)5, stating that there is a difference between tax and tax exclusive price effect, cannot be confirmed, at least not in the shortrun.
Chapter 2: The Problem of too many Zeros: Methods to Estimate Fuel Price Elasticities
Whereas the analysis in Chapter 1 was conducted using macrodata, data on country level,
the focus of the second study in the area of fuel demand estimation is on the analysis of
microdata. Microdata in this case means data on household level. A special feature of
household driving, or to put it more general consumption data, is the large number of zero
4
5
Davis, Lucas W., and Lutz Kilian (2011). “Estimating the effect of a gasoline tax on carbon emissions”.
Journal of Applied Econometrics 26(7).
Li, Shanjun, Joshua Linn, and Erich Muehlegger (2012). “Gasoline taxes and consumer behaviour”.
NBER Working Paper 17891.
Introduction
5
observations for consumption. In our dataset, a German household survey on driving behavior, the zeros account for a share of 15% of all observations. That means 15% of the
households in the sample did not drive in the observation period. This share is representative for the German population and nonnegligible from an econometric point of view, as
the workhorse model in this field, the loglinear model estimated by ordinary least squares
(OLS), is not able to deal with zeroobservations. Thereby estimating the price effect on
the demand of car driving with the loglinear model, leads to the omission of a substantial
fraction from the sample and therewith to biased estimates. The loglinearized OLS model
also suffers from an additional drawback which is of statistical origin. Loglinearizing
leads in most cases to an endogeneity of the error term, which in turn leads to a violation
of the exogeneity assumption of OLS.1 and thereby again to biased estimates. However
our analysis shows that the zeroproblem is the prevailing issue.
In our study we analyze both problems theoretically as well as empirically by conducting
a case study using the above mentioned German household mobility survey. Besides illustrating the problems of the loglinear model, we also propose alternative econometric approaches to estimate the effect of prices or taxes on the demand for driving. The alternatives comprise countdata models like the Poisson or Negative Binomial model and TwoPart models like the Hurdle or ZeroInflated models. To be able to establish a statement
which model performs best with respect to the dataset we have at hand, we conduct model
selection tests. The tests we apply have to be able to deal with models which base upon
different distributional assumptions (linear vs. poisson). Therefore we cannot use model
comparison tests which rely on the likelihood, like the Akaike Information Criterion
(AIC) or the Vuong test, for example. Our preferred model selection test is the Model
Confidence Set (MCS) developed by Hansen et al. (2011)6. The MCS even allows for a
ranking of the compared models. We find that the TwoPart models which model the data
generating process (dgp) as a twostep model fit the data best. The TwoPart models divide the dgp of how many kilometers the households drive into two decisions: The first
decision households have to make is to buy a car or not and the second one is, once they
bought a car how much to drive. As this theoretical structure of the TwoPart models represents the real decision process rather well, it is convincing that these models also empirically perform better than onestep linear models.
Chapter 3: Climate Policy Measure Index
Chapter 3 introduces a climate policy index measuring the stringency of the OECD countries with respect to climate related policies, taking into account not only the level of different policy categories, but also the effectiveness of the respective policy instrument with
6
Hansen, Peter R. et al. (2011), “The Model Confidence Set”, Econometrica 79 (2).
6
Introduction
regard to the reduction of GHG emissions. Thereby countries which are very active in a
policy category which at the same time is also very effective in reducing GHG emissions
perform best in the index called Climate Policy Measure Index (CPMI). In contrast to
most of the existing climate policy indices, the CPMI focuses on quantitative policy
measures to enhance the comparability of the different policies. The policy categories
which represent the subindicators of the CPMI are consumption taxes on fossil fuels, policies to promote the expansion of renewable energies and carbon pricing mechanisms.
Another distinguishing feature of the CPMI is the focus on policy variables only, which
makes it a purely inputvariableoriented index. Many of the existing indices, on the contrary, mix input variables like concrete policy measures with output variables like the progress in emission reduction or the degree of preserved biodiversity. Currently there are
only two other indices, the Climate Laws and Institutions Measure Index (CLIMI)7 and
the Climate Performance Index (CPI)8, which also solely include input variables in their
indices. But in contrast to these two indices the weighting of the subindicators of the
CPMI, which is based on empirics, varies over the policy categories. The CLIMI and the
CPI assume equal weights for all subindicators. For the weighting of the single policy
categories inside the CPMI, the impact of each policy category on the emissions is estimated. The resulting elasticities are used to calculate the weighting factors of the three
policy categories.
The CPMI does not only allow a crosscountry comparison between the OECD countries,
but also a comparison of the climate policy performance over time for each country, as the
CPMI provides data for the period between 1991 and 2012, so far.
The general findings of the CPMI are, that the policy stringency increases in all of the
OECD countries over the observation period and that there are seven countries9 out of the
33 OECD countries10, which always perform above the average of all OECD countries.
7
Steves, Franklin and Alexander Teytelboym (2013), “Political Economy of Climate Change Policy”,
Smith School Working Paper Series, Working Paper 1306.
8
Künkel, Nana, Klaus Jacob and PerOlof Busch (2006), “Climate policies – (the feasibility of) a statistical
analysis of their determinants”, Manuscript.
9
Denmark, Italy, Greece, Germany, Norway, Portugal and Spain
10
Iceland is missing due to data availability.
1 Anticipation and Inventory Effects on Fuel Demand
Chapter 1
Anticipation and Inventory Effects on Fuel Demand: An Analysis of Prices
and Taxes in Europe
Julian Dieler*, Darko Jus+, Markus Zimmer#
In recent years several studies improved the assessment of the effects of fuel prices and
taxes on fuel demand. We further contribute to this literature by including anticipation
and inventory effects in the analysis of motor fuel tax elasticities. Recent studies have focused on US data and gasoline consumption. By constructing a new data set we provide a
complementary study for European countries and extend the analysis to diesel fuel, which
is essential for the European market. Neglecting the reaction on anticipated tax increases
leads to upward biased estimates for the tax elasticities. We also reveal a difference in the
ability of gasoline and diesel consumers to react to announced tax changes. By including
anticipation effects we are able to narrow down the band for the tax elasticity from 0 0.39. Lastly we show that using quarterly data in the presence of anticipation effects may
also lead to upward biased results.
Anticipation and Inventory Effects on Fuel Demand
*
+
#
Ifo Institute for Economic Research at the University of Munich, Germany, +49/89/92241346,
dieler@ifo.de
Center for Economic Studies, University of Munich
Ifo Institute for Economic Research at the University of Munich
Chapter 1
8
1.1 Introduction
Fuel taxes have received increased attention in today’s policy agendas as they are a passionately debated topic in public. They are seen as an important instrument to raise tax
revenues on the one hand but also to curb carbon emissions on the other hand. Consequently in recent years the economic literature has advanced from assessing price elasticities only to include the effects of taxes on fuel consumption.
We analyze the effect of fuel taxes in Europe in the shortrun by estimating the response
of gasoline consumption triggered by fuel price changes. More specifically, we derive the
reactions to price and excise tax changes using a unique data set covering 11 European
countries. The data includes diesel consumption and prices in addition to gasoline, extending previous literature which has mainly focused on gasoline11. While excluding diesel
from the analysis for the United States seems to be valid, it is not the case for the European countries where major shares of private cars use diesel fuel. Consequently our study
analyses and compares gasoline and diesel consumption in a panel that covers the period
between 1990 and 2012 on a monthly basis.
We make five contributions to the existing literature. Our main contribution is the disclosure and the quantification of an anticipation effect in the demand for motor fuel that results from an increase in fuel taxes12. A potential mechanism might originate from consumers fuel up their cars shortly before the tax increase is implemented. Consequently
they refuel less in the month when the tax increase comes into force. This interpretation
needs some more clarification, since the sales volumes communicated to the statistical
offices are rather wholesale figures than actual private fuel demand. Observed reactions in
the data thus reflect the gas station manager’s response to expected tax changes. Since
taxes are determined at the date when the product is sold to the consumer, the tax change
itself should not motivate increased wholesales, since the gasstation owner cannot save
taxes through an early purchase. An increase in the reported fuel sales rather indicates the
reaction of the gas station manager to an expected increase of fuel sales prior to the tax
increase. While it is not clear if the gas station manager correctly anticipates the consumer
reaction it is certainly valid to assume that she is well informed and aware of fuel tax
changes. Most likely especially the gas stations which would have had to restock soon will
predraw their order. Nevertheless the effect is potentially essential, since gas stations
hold notable storage capacities. Thus there are two reasons for less consumption in the tax
month. In addition to the increase in the retail price of the motor fuel we would expect a
11
12
See e.g. Hughes et al. (2008), Davis and Kilian (2011) and Li et al. (2012).
Ours is the first paper to present this idea and we apply it to the European context. Anticipation effects
should be included in any empirical analysis of the effects of announced tax changes. This point is considerably strengthened by the fact a corresponding analysis from the American context by Coglianese, J.
et al. (2015) is forthcoming in the Journal of Applied Econometrics.
Anticipation and Inventory Effects on Fuel Demand
9
behavior that leads to fuel tanks being fuller before the tax change than in months without
a tax increase (be it in the cars itself or in the reservoirs of the gasstations). In terms of
the effect of taxes on fuel consumption, only the reaction to the increased retail price represents an actual decline in fuel consumption. The fuller tank in the beginning of a tax
month represents an anticipation effect, or an intertemporal shifting of fuel purchases with
no change in overall fuel consumption. Beneficial for the analysis is also the fact, that the
vast majority of tax changes come into effect on the first of a month and therefore the reported monthly sales volumes nicely separate the pre and posttax effect. Disregarding
the anticipation effect leads to an overestimation of the tax impact. Controlling for the
anticipation effect we find tax elasticities of
–0.30 for gasoline and –0.36 for diesel.
This is decisively smaller than the elasticities Davis and Kilian (2011) and Li et al. (2012)
find in their studies for the USA without controlling for the anticipation effect. Their estimates for the tax elasticities lie between –0.46 and –0.765. When we don’t control for
the anticipation effect we find similar results for the European gasoline market (–0.64).
In the literature of modeling crude oil markets, anticipation effects are already acknowledged to have an impact on demand and thereby on the crude oil price. Studies like Kilian
and Lee (2014) and Kilian and Murphy (2014) argue for the necessity of including the
stock of aboveground oil inventories in modeling the oil price, as the stock of inventories
is an indicator for the anticipation of future oil price movements.
A second contribution of our study is the construction of a new European dataset on a
monthly basis. Previous literature has either focused on US data (e.g. Hughes et al. 2008)
or on yearly data for the European countries (e.g. Pock 2010 and Liu 2004) as monthly
data was not readily available for Europe before. We collected data from many different
national institutions and compiled them into a single data set of comparable fuel tax
measures in 11 European countries.13
As a third contribution we analyze – in addition to the commonly studied gasoline consumption – the effects of fuel taxes on diesel consumption. Including diesel use in the
analysis is crucial for Europe as the share of diesel cars in the EU fleet amounts to 36.8%
and is increasing further.14 In the United States, dieselfueled cars are also becoming more
and more important, but currently they make up only 3% of the entire market. In Europe
the mentioned 36.8% of diesel cars are particular popular with frequent drivers. For frequent drivers it pays off to incur the higher initial costs of acquiring a diesel car as the
fuel costs are lower due to higher mileage and lower fuel prices. Additionally, most light
and heavy duty trucks, typically used for commercial purposes, use diesel engines. Fuel
13
14
See table 11 in the appendix for detailed information on the data sources. The dataset is available on
request.
Newest figures from 2011 (ACEA 2014).
10
Chapter 1
costs account for a major share of total expenses of these commercial users. Assuming
they react differently from people who only use their car to drive to the supermarket, we
expect to find a difference between the consumption behavior of diesel and the consumption behavior of gasoline. In addition to being more sensitive to fuel costs, commercial
fuel consumers also have better opportunities for intertemporal shifting due to bigger fuel
tanks or even large central fuel reservoirs (e.g. in trucking companies or farms).
Fourthly, taking the anticipation effect into account we find no significant difference between the effect of a tax change on fuel consumption and the effect of a net of tax price
change on fuel consumption. This is in line with a finding by Anderson et al. (2013) who
analyze consumer beliefs about future gasoline prices in Michigan. They find that consumers’ price forecasts do not respond differently to net of tax price changes and to tax
changes. This means consumers do not view tax increases as a more important reason than
pretax price changes to invest in more fuel efficient vehicles. Consequently a tax increase
should lead to similar gasoline consumption effects as a net of tax price increase does.
Using a similar approach to the one used in this paper, but without controlling for the anticipation effect, Davis and Kilian (2011) and Li et al. (2012), however, find indication for
differential impacts of tax changes and pretax price changes. Analyzing the impact of the
introduction of a carbon tax in British Columbia, Rivers and Schaufele (2013) also find
evidence for differences between tax exclusive price effects and tax effects. The authors
of these studies advance two arguments as possible explanations for their results. First,
they argue that tax changes attract more media attention than pretax price changes, and
that thereby tax changes are more salient and lead to larger responses of consumers. The
second possible explanation is that of Anderson et al. (2013): namely, that tax changes are
perceived to have a more persistent impact on the retail price than net of tax price changes
and, thus, a stronger influence on the vehicle choice. In their survey of consumer beliefs,
however, Anderson et al. do not find statistical evidence in support of this hypothesis.
Lastly, we make a further methodological contribution and show that using quarterly data,
in a calendar quarterly sense and in the presence of anticipation effects, may also lead to
upward biased results. When the anticipated event takes place in the first month of the
calendar quarter, which is rather often the case for tax changes, it might impact the behavior in the quarter before through the anticipation effect, and that this effect should thus be
controlled for.
The structure of our paper is as follows. We proceed in the next section by presenting a
summary of the quantitative results and relating them to the findings of the previous literature. In section we 3 explain our data in detail and present some summary statistics
before we address the different empirical methods applied and their respective results in
section 4. In the remaining section we draw some conclusions.
Anticipation and Inventory Effects on Fuel Demand
11
1.2 Data
The empirical analysis of the effect of vehicle fuel taxes on gasoline and diesel consumption is based on an original panel data set which has been compiled from a wide range of
sources. These sources include national governments and ministries, national statistical
offices, research institutes, private companies, and supranational organizations (cf. Table
8 in the Appendix). In total, our data set with monthly observations covers 11 countries
(Austria, Belgium, Denmark, France, Germany, Ireland, the Netherlands, Portugal, Spain,
Sweden, and the United Kingdom). The observation period is from 1990 until 2012. Unfortunately, the data are not available for all countries over the entire period. The key variables in our econometric analysis are the consumption of the two main vehicle fuel types
(“Diesel” and “Eurosuper”, a 95 octane gasoline, which we simply refer to as gasoline),
the respective consumer prices, and the taxes levied on these two products. The taxes consist of excise taxes, the valueadded tax (VAT), and additional fees that act like excise
taxes. In the remainder of the article tax stands for the sum of excise taxes, the VAT levied on excise taxes and additional fees that act like excise taxes. Consequently taxes according to our definition comprise only price exogenous elements and no elements which
change automatically with pretax price like the VAT, for example. Figure 2 illustrates the
evolution of taxes and retail price in the analyzed countries.
Figure 7 and 8 in the Appendix show exemplarily the composition of the gasoline and diesel retail price. Figure 3 shows the development of consumption of gasoline and diesel. The
sales of diesel grew due to the increasing spread of turbocharged engines in passenger
vehicles since 1988. Comparing the evolution of the consumption of diesel to that of gasoline, we observe in all countries – but especially in France, Sweden and the United Kingdom – a trend away from gasoline fueled cars to more fuel efficient diesel fueled cars. In
the case of gasoline, the increase stems from the fact that we use 95 octane gasoline,
which was not used widely before the end of the 1980s. In the subsequent years, it steadily
replaced 91 octane gasoline, previously the main type of gasoline. Therefore, the increase
in gasoline consumption in Figure 3 does not imply an overall increase in consumption, but
is the result of the substitution of 95 octane for 91 octane gasoline.
Chapter 1
12
Belgium
Denmark
France
Germany
Ireland
Netherlands
Portugal
.5
1
1.5
.5
1
1.5
Austria
1992 1997 2002 2007 2012
Sweden
UK
1992 1997 2002 2007 2012
1992 1997 2002 2007 2012
1992 1997 2002 2007 2012
.5
1
1.5
Spain
Taxes (in 2005 Euro/l)
Retail Price (in 2005 Euro/l)
Figure 2: Taxes and prices per liter in 2005 Euros for gasoline
The consumption curves of those countries that were severely affected by the financial
crisis (Ireland, Portugal, and Spain) exhibit a discontinuity in 2008. German consumption
of gasoline has two discontinuities: one at the end of 2007 when 91 octane gasoline was
taken “off the shelf” by most gas stations and a second one at the beginning of 2011 when
a new type of gasoline with a higher degree of bioethanol was introduced. Another observation from Figure 3 is the high seasonality of the consumption data which implies the necessity to control for the month of the year.
Data on the other control variables, including the number of working days per month, the
monthly oil price, and the monthly national currency to dollar exchange rate are taken
from OECD and World Bank databases, respectively.
300
200
1000
France
0
100
0
0
Denmark
13
1992 1997 2002 2007 2012
Germany
Ireland
Netherlands
Portugal
400
200
150
0
200
100
0
500
1992 1997 2002 2007 2012
600
1992 1997 2002 2007 2012
1000 2000 3000
1992 1997 2002 2007 2012
1992 1997 2002 2007 2012
1992 1997 2002 2007 2012
Spain
Sweden
UK
300
1000
200
1000
0
1992 1997 2002 2007 2012
1992 1997 2002 2007 2012
1500
400
2000
1992 1997 2002 2007 2012
2000
Consumption in 1000 toe
Belgium
500
200 400 600
Austria
1000 2000 3000
Anticipation and Inventory Effects on Fuel Demand
1992 1997 2002 2007 2012
1992 1997 2002 2007 2012
Gasoline
Diesel
Figure 3: Gasoline and diesel consumption
Gasoline
Variable
Consumption (in 1000 toe)
Tax exclusive price (in current Euros)
Tax (in current Euros)
Working days (monthly differences)
Absolute change of unemployment rate
Oil price (in current Dollar/barrel)
Mean
609.8
0.409
0.663
0.00430
0.0195
54.58
Std.Dev.
546.1
0.152
0.123
1.908
0.374
32.63
Min
55
0.143
0.336
6
2.400
9.820
Max
1935
0.821
0.974
5
2.200
132.7
Std.Dev.
922.6
0.186
0.155
1.910
0.376
33.43
Min
111.8
0.136
0.266
6
2.400
9.820
Max
3086
0.854
1.034
5
2.200
132.7
N=1861
Diesel
Variable
Consumption (in 1000 toe)
Tax exclusive price (in current Euros)
Tax (in current Euros)
Working days (monthly differences)
Absolute change of unemployment rate
Oil price (in current Dollar/barrel)
N=1829
Table 1: Summary statistics
Mean
1187
0.410
0.467
0.00505
0.0182
54.24
Chapter 1
14
Table 1displays summary statistics for the variables used in the regression analysis. The
“working days” variable is defined as the absolute difference in the working days from
one month to the next. All variables show fairly high variation. Especially with regard to
taxes one could be concerned about sufficient variation. Surprisingly tax changes are rather common. In fact we observe about 1.8 nominal tax changes per country and fuel type
per year or a total of 262 changes for gasoline and of 230 changes for diesel over the entire panel. For gasoline as well as for diesel the bulk of the changes are increases: 75% for
gasoline and 79% for diesel.
1.3 Models of Fuel Consumption
We extend the analytic structure used by Davis and Kilian (2011) by controlling for forward looking consumption behavior, in order to study changes in purchasing behavior
before the tax is implemented. Estimating this tax anticipation effect leads us to the conclusion that taxes are less effective in the short run than previously stated.
1.3.1 Least Squares Estimates of Fuel Consumption
To establish a common benchmark with similar studies, we start with the analysis of the
impact of price changes on monthly fuel consumption (cf. e.g., Hughes et al. 2008; Davis
and Kilian 2011).
A frequently employed specification in the literature links , the logarithm of fuel consumption in month t, linearly to , the logarithm of the average price of the respective
fuel in the same month, along with a vector of control variables and an unobserved idiosyncratic timevarying factor .
=
0
+
1
+
1
+
In the basic specification,
contains monthoftheyear fixed effects that are intended to
control for the strong seasonality of the consumption data.
However, since yt and pt are likely to follow a longrun trend, we estimate the equation in
differences of logs, i.e., in growth rates (Δ
Δ
=
0
+
= ln
1Δ
= ln
+
1
+
ln
):
(1)
This equation uses the pooled data set, meaning that countryspecific effects are ignored.
In the second step, we make use of the panel structure for the 11 countries in our data set:
Δ
=
0
+
1Δ
+
2
+
(2)
In this specification, Δyit is the monthly growth rate of fuel consumption in month t in
country i. Accordingly, Δpit is the monthly growth rate of the price of the respective fuel
Anticipation and Inventory Effects on Fuel Demand
15
in the same month t15 and in the same country i. In addition to the monthoftheyear fixed
effects included in ,
contains country and year fixed effects and other controls, such
as the change in the unemployment rate and the monthovermonth change in working
days. Their relevance will be explained below. Thereby, the unobserved idiosyncratic error term varies across countries and over time on a monthly basis.16
Gasoline
% change in consumption
% change in retail price
(1)
0.008
(0.083)
(2)
0.224***
(0.076)
Diesel
(3)
0.211***
(0.071)
(1)
0.101
(0.084)
(2)
0.198**
(0.079)
(3)
0.181***
(0.065)
change in working days
0.014***
(0.001)
0.027***
(0.001)
change in unemployment
0.015***
(0.005)
0.000
(0.007)
Month of the year indicators
Country indicators
Year indicators
No
Yes
Yes
No
Yes
Yes
No
No
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
R2
N
0.00
1861
0.35
1861
0.42
1861
0.00
1829
0.23
1829
0.46
1829
* p<0.1; ** p<0.05; *** p<0.01
Table 2: OLS estimates
Table 2 shows the results for the above mentioned estimation equations for both fuel
types. Columns (1) give the results of the basic model, i.e., the relationship between the
change in fuel consumption and change in its price using a pooled OLS regression (cf.
Equation (1)). The explanatory power of this model is rather poor, mainly due to the
strong seasonality of consumption (cf. Figure 3) which explains much of the variation. We
address this issue by using the panel structure of the data in column (2). Columns (2) depict the results of Equation (2) without controlling for changes in working days and
changes in the unemployment rate. The model’s explanatory power increases and the price
coefficient becomes highly significant. In the Columns (3) we add changes in working
days and changes in the unemployment rate. The number of working days in a month has
a decisive impact on gasoline and diesel consumption. As described in Section 1.2, the
number of working days sometimes varies quite substantially (up to a 6day difference
from the previous month). The positive impact of working days on fuel consumption may
15
16
Whereas t counts the number of the month from the beginning of the observation period (January 1990)
till the end (December 2012).
When we discuss a variable in the remainder of this article we refer to its growth rate, monthtomonth
growth concerning working days, or absolute change concerning the unemployment rate, unless otherwise stated.
Chapter 1
16
.2
.4
.6
.8
1
be attributed to commercial activity that requires fuel or simply because many individuals
are commuting to work by car. Similar reasoning drives the observed effect of including
changes in the unemployment rate. When the unemployment rate increases, fewer individuals commute to work. Unemployment can also be seen as a more general indicator for the
economic performance of a country. In an economic downturn, which is mostly accompanied by increasing unemployment there is less economic activity in general which leads to
decreasing fuel demand. Additionally a shrinking economy leads to tighter budgets of the
individuals in the economy. In these phases it is plausible that the individual substitute
away from fuel purchases in their spending decisions. And, indeed, for gasoline consumption, we find empirical evidence in support of this argument. Unemployment can also be
seen as a more general indicator for the economic performance of a country. In an economic downturn, which is mostly accompanied by increasing unemployment there is less
economic activity in general which leads to decreasing fuel demand. Additionally a
shrinking economy leads to tighter budgets of the individuals in the economy. In these
phases it is plausible that the individual substitute away from fuel purchases in their
spending decisions. And, indeed, for gasoline consumption, we find empirical evidence in
support of this argument.
1992
1997
2002
Taxes (in current Euro/l)
Figure 4: Gasoline taxes and net price in current values (Germany)
2007
Net Price (in current Euro/l)
2012
Anticipation and Inventory Effects on Fuel Demand
17
Our shortrun price elasticity estimates of around –0.2 (for both gasoline and diesel) are
very similar to those found in the literature (cf. the review study by Dahl and Sterner
1991).17 While the analysis of price elasticities may serve as a proxy on how consumption
is affected by tax changes (which naturally translate into price changes), Davis and Kilian
(2011) and Li et al. (2012) propose a different approach. They point out that decomposing
the effects of taxes and tax exclusive prices, instead of looking at the overall effect of a
changing gross price, provides further insight into consumer behavior. Such disentanglement allows separating the reaction to difficulttopredict and highly volatile net price
changes and the reaction to more easily foreseeable and persistent tax changes (cf. Figure
4).
Li et al. (2012) find evidence for a stronger reaction to salient retail price changes due to
tax changes (elasticity of –0.77) than to net price changes (–0.37). Their explanation for
this difference is motivated firstly, by tax changes being announced before they become
effective and secondly, by consumers perceiving them to be more persistent than tax exclusive price changes, as these market driven price changes, are always difficult to predict
in advance and fluctuate strongly. Therefore, at least in the short run, changes of consumption behavior should be larger with respect to tax changes. Differentiating between
individuals’ reactions to tax changes and to changes in the net price also raises an additional question: do individuals form expectations about the retail price and, if so, what
does this imply for their purchasing decisions? The variable
is commonly interpreted
as consumption of gasoline and diesel, while it is actually stating the purchase of fuel and
not its consumption. As a result the demand for motor fuel possesses features of stock
demand. Such features are not as pronounced as for crude oil (Kilian and Murphy 2014,
Kilian and Lee 2014), but up to a certain degree, car drivers can use their fuel tank and
gas stations and large commercial consumers can use their reservoirs for storage. Therefore the observed reduction of purchases in the month the tax increases can be either the
consequence of a reduction in consumption or of a depletion of fuel stocks. Figure 5 and
Figure 6 – which will be discussed in depth later – shed light on this issue. They show a
significant increase in purchases in the month before and after the tax increase. That indicates the accumulation of fuel stocks, which are subsequently depleted in the month of the
tax increase. Thus it seems unlikely that individuals really consume less fuel by e.g. not
using their cars to go to work or to take their children to school. Especially towards the
end of the month, before an announced tax increase is implemented, individuals could
choose to increase their purchase volume in anticipation of the tax increase. We control
for this by including the first lead of tax in our estimation equation. To analyze consumption or, rather, purchase behavior after the month of the tax increase, we also include the
17
Dahl and Sterner (1991) reveal a shortrun price elasticity of
0.26.
Chapter 1
18
lag of tax in our regression. These considerations lead to the following estimation equation:
=
0
+
1
+
+
2
1
3
+
4
+1
+
2
(3)
+
As in Equations (1) and (2), Δ stands for the first differences of the logs. The tax exclusive
fuel price is denoted by
and the tax of the current, previous, and next month are denoted by
,
, and
, respectively.
again contains the growth of the
unemployment rate and the difference in working days of the current month compared to
the previous one.
Table 3 summarizes the results of our disentangled analysis of the tax exclusive price and
the tax. Columns (1) show that we observe even larger differences between consumption
reactions to net price changes and consumption reactions to tax changes in the European
data than Li et al. (2012) found in the U.S. data. The shortrun tax elasticity of –0.64 is
decisively larger than the price elasticity of –0.07.
Diesel
Gasoline
% change in consumption
% change in net price
(1)
0.068**
(0.028)
% change in unit tax (lead1)
% change in unit tax
(2)
0.063**
(0.028)
(1)
0.074**
(0.029)
0.373**
(0.145)
0.635***
(0.182)
% change in unit tax (lag1)
0.837***
(0.171)
(2)
0.071**
(0.029)
0.323**
(0.152)
1.029***
(0.211)
0.250**
(0.121)
1.044***
(0.211)
0.371***
(0.131)
change in working days
0.014***
(0.001)
0.014***
(0.001)
0.026***
(0.001)
0.026***
(0.001)
change in unemployment
0.014***
(0.005)
0.014***
(0.005)
0.002
(0.007)
0.002
(0.007)
Month of the year indicators
Country indicators
Year indicators
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
R2
N
0.42
1861
0.43
1849
0.47
1781
0.47
1769
* p<0.1; ** p<0.05; *** p<0.01
Table 3: OLS estimates for decomposed retail price
A tax elasticity of –0.64 translates into a 5% consumption reduction in the month of a 5
cent tax increase, which is a large effect. These large values confirm the results Davis and
Kilian (2011) and Li et al. (2012) found for the United States. Next we check for intertemporal shifting of fuel purchases around the month of a tax change. The tax change next
Anticipation and Inventory Effects on Fuel Demand
19
month and the tax change in the previous month are statistically significant and nonnegligible in magnitude (cf. Columns (2)). The coefficient of the tax lead depicts the anticipation effect (cf.
in Equation (3)) and the lag of tax stands for the catchup effect
in Equation (3)).
after a tax change (cf.
It seems plausible that at least some consumers refuel their cars during the last few days
before a tax increase comes into force resulting in an adequate purchasing behavior of the
intermediate suppliers. Consequently, these consumers need to buy less fuel in the month
of the tax increase, independent of any changes in their consumption behavior. Thus the
effect, which we measure in the taxchange month is a combination of less fuel purchased
due to having refueled at the end of the previous month and less fuel purchased due to a
change in consumption behavior. According to these results, a 1% gasoline tax increase in
the next month leads to a 0.38% consumption increase in the current month. In the month
of the tax change, consumption decreases by 0.84% before increasing again in the following month by 0.25%.
Table 3 shows similar results for diesel. The tax and net price elasticities tend to be slightly higher for diesel than for gasoline. A possible explanation for this finding is that diesel
is to a large extent used commercially and commercial fuel customers, such as freight carriers, might be better positioned to shift fuel purchases across time (e.g. due to having
more storage capacity). However, the differences in the elasticities are not statistically
significant. Based on the arguments above, we should observe smaller tax effects after
aggregating the monthly data to calendar quarterly data (JanMar, AprJun, etc.). That is,
some of the intertemporal shifts should vanish over the course of a quarter. We use the
same methodology as we did for the monthly data analysis above (see Equation (3)), but
omit the lag and the lead of the tax variable and replace month of the year indicators with
quarter of the year indicators. As expected, the tax elasticity of consumption decreases
from –0.84 (cf. Table 3) to –0.31 for gasoline and from –1.04 to –0.50 for diesel (see column 1 and 2 in Table 4). Although aggregating to calendar quarters diminishes the anticipation effect, the tax effect is still overestimated, as more than 60% of the tax changes
occur at the beginning of a quarter. Thereby the anticipation and the tax effect act in two
different quarters and using quarterly data still exhibits a large tax effect driven by anticipation. Moving the calendar quarter one month forward so that the first quarter starts in
December instead of January leads to a decisive reduction of the tax effect (see column 3
and 4 in Table 4).
Now the estimated tax effect is statistically not differentiable from zero any more. An
even more precise way of identifying the tax effect net of the intertemporal shifting is to
pretend that the prefueling in the month before the tax change, the reduced purchases in
the tax month, and the return to the pretax behavior in the month after the tax change all
Chapter 1
20
happen in one month. We implement this test aggregating over these three months18 by
averaging the consumption, tax, and net price data. We call this period the tax period. 19
By leaving the estimation methodology unchanged compared to Equation (3), except for
excluding the lead and the lag of tax, we again obtain a smaller coefficient for the tax
elasticity than without aggregating (cf. Table 3 and Table 4). Thus, we find that the tax
elasticity is overestimated without controlling for the anticipation effect.
% change in consumption
% change in net price
Quarterly
Gasoline
Diesel
Quarterly (shifted)
Gasoline
Diesel
Tax period
Gasoline
Diesel
0.015
(0.035)
0.009
(0.028)
0.090*
(0.049)
0.043
(0.035)
0.114***
(0.029)
0.044
(0.033)
% change in unit tax
0.308**
(0.121)
0.503***
(0.133)
0.195
(0.149)
0.200
(0.166)
0.296*
(0.162)
0.356**
(0.170)
R2
N
0.52
614
0.45
587
0.71
623
0.74
596
0.37
1537
0.23
1476
* p<0.1; ** p<0.05; *** p<0.01
Table 4: OLS estimates of aggregated data.
To check whether the anticipation effect is driven by consumer’s behavior towards tax
increases or by their reaction towards tax decreases we run the regression separately for
tax increases and decreases. Table 9 in the appendix shows that the fuel pattern of predrawing fuel purchases which we discussed above is dominantly driven by tax increases.
This is not very surprising since tax increases dominate the tax changes (in total for gasoline and diesel: 507 increases against 134 decreases). As a further robustness check we
compare the OLS results with those of a feasible generalized least squares (FGLS) approach (see Table 5).
Allowing for autocorrelation of the AR(1) type and heteroskedasticity across panels, we
obtain similar results. The net price elasticity decreases for gasoline and stays the same for
diesel. For both fuel types, the net effect of the tax over the three months decreases as the
purchasereducing effect of the tax in the tax month shrinks and the anticipation and
catchup effects increase. As the results of the two methods do not differ substantially, we
are confident that our results are robust.
Estimating demand functions using the price as explanatory variable is routinely met with
the concern of biased coefficients due to endogeneity. Endogeneity could be caused by
18
19
In reality, this is often a period of four months since in countries like Austria, for example, an increase
in the mineral oil tax occurs in two steps over two consecutive months.
The “tax period” includes the month before the tax change, the month of the tax change, and the month
after the tax change.
Anticipation and Inventory Effects on Fuel Demand
21
reverse causality of price and demand. If the amount of fuel consumed locally has an influence on its price, the price elasticity would be underestimated by a simple least squares
approach. To account for the possible endogeneity of the price we apply a twostage least
squares (2SLS) approach in the next section. Skeptics might argue that taxes are endogenous as well as there might be the tendency of politicians to decrease taxes during periods
of economic downturn and to increase taxes during periods of economic boom. While this
might be true in the long run it is more than unlikely for monthly data. The subsequent
section will reflect on these concerns and evolve the analysis with a focus on the possible
endogeneity of the tax exclusive price.
Gasoline
Diesel
% change in consumption
% change in net price
OLS
0.063**
(0.028)
FGLS
0.036**
(0.018)
OLS
0.071**
(0.029)
FGLS
0.071***
(0.020)
% change in unit tax (lead1)
0.373**
(0.145)
0.428***
(0.106)
0.323**
(0.152)
0.402***
(0.101)
% change in unit tax
0.837***
(0.171)
0.699***
(0.111)
1.044***
(0.211)
0.952***
(0.107)
0.371***
(0.131)
0.379***
(0.099)
% change in unit tax (lag1)
0.250**
0.277***
(0.121)
(0.091)
* p<0.1; ** p<0.05; *** p<0.01
Table 5: Favored OLS estimates compared to heteroskedastic and autocorrelationrobust FGLS estimates
1.3.2 IV Estimates of Fuel Consumption
In the spirit of the recent approaches to separate the effects of tax exclusive price changes
and tax changes on consumption we instrument the net of tax gasoline price with the crude
oil price and with the DollarEuro exchange rate. For the crude oil price we use the spot
price of Brent, but this choice is arbitrary and of no special importance as the spot prices
of all reference oil grades (WTI, Brent, and Dubai crude) are highly correlated with a correlation coefficient larger than 0.95 (World Bank 2013). For the oil price to be a valid instrument for the fuel price in the estimation of the consumption equation, consumption of
gasoline and diesel should have no impact on the crude oil price, which is an assumption
our data appear to support. On average, each of the observed countries account for 0.9%
of the world crude oil consumption. Out of our sample countries, Ireland consumes the
smallest fraction (0.07% of world consumption) and Germany the largest (2.3%) (IEA
2013).20 Moreover, the amount of crude oil used to produce car fuels is even less important. Considering only the use of crude oil for light and heavyduty vehicle fuels,
20
The figures are for 2012 but appear representative of the observation period. Germany consumed the
biggest fraction of world consumption in the observation period in 1994 with 3.4%.
Chapter 1
22
Germany’s fraction of 2.3% of world consumption shrinks to 1.7%.21 Thus, from the perspective of a single country, the oil price should be assumed to be exogenous.
As second instrument serves the dollar exchange rate as it is an important determinant of
gasoline and diesel prices in European countries since oil, as the main input for producing
gasoline and diesel, is traded in U.S. dollars, but the final products are sold in national
currencies. To account for potential timing issues related to the price setting of mineral oil
firms we include the first lags of the oil price and the exchange rate. Hence, the firststage
equation is:
=
+ 3Δ
_
2 + 4Δ
(4)
+ 5Δ
_
+
+
2
1
Table 6 shows the estimates of the first stage for both fuel types. For our instruments to be
valid two conditions must hold: Firstly, the oil price as well as the dollar exchange rate
need to contribute to the explanation of the tax exclusive price of gasoline and diesel. This
condition holds as the F statistic of the first stage amounts to 259 and 131 for gasoline and
diesel respectively. Secondly, the instruments need to be exogenous. Since the number of
instruments exceeds the number of endogenous variables in the presented model it is advisable to apply an Overidentifying Restrictions Test for exogeneity. The result of the test
indicates the instruments to be exogenous in case of our IV approach.
Δ
0
+
1Δ
+
2Δ
1
% change in net price
% change in oil price
Gasoline
0.372
(0.014)***
Diesel
0.240
(0.014)***
% change in oil price (lag1)
0.250
(0.014)***
0.181
(0.014)***
% change in oil price (lag2)
0.033
(0.014)**
0.095
(0.014)***
% change in exchange rate
0.291
(0.052)***
0.159
(0.052)***
% change
rate(lag1)
0.246
0.253
(0.052)***
(0.053)***
0.38
2084
0.24
2136
in
exchange
R2
N
* p<0.1; ** p<0.05; *** p<0.01
Table 6: First stage estimates
The second stage estimation is identical to Equation (2), except for
as explained above.
21
These figures are from December 2012.
being instrumented
Anticipation and Inventory Effects on Fuel Demand
=
0
+
1Δ
+
2
+
3
1
+
4
+1
+
23
2
+
(5)
stands for the instrumented pretax price based on Equation (4). Table 7 shows the
The
results of the IV regression for both fuel types.
Our findings for this sample of European countries differ only slightly from previous studies, e.g. those of Davis and Kilian (2011) and Li et al. (2012) for the United States. Our
tax elasticity for gasoline consumption, at –0.82, lies in the range of the results of Davis
and Kilian (–0.46) and those of Li et al. (–0.77). What distinguishes our results from previous works is that we also quantify the anticipation and the catchup effect. Li et al.
(2012) and Davis and Kilian (2011) only hint at intertemporal effects.22 By not considering the effects in the previous and the following month of a tax change, the consumption
reaction to a tax change is overestimated. Therefore quantification of the anticipation effect is necessary.
% change in consumption
% change in net price
Gasoline
Diesel
0.040
(0.041)
0.012
(0.048)
% change in unit tax
0.821***
(0.173)
1.004***
(0.223)
% change in unit tax (lag1)
0.371**
(0.144)
0.370***
(0.126)
% change in unit tax (lead1)
0.380***
(0.145)
0.332**
(0.147)
change in working days
0.014***
(0.001)
0.026***
(0.001)
change in unemployment
0.014***
(0.005)
0.002
(0.006)
month of the year indicators
country indicators
year indicators
Yes
Yes
Yes
Yes
Yes
Yes
R2
N
0.43
1847
0.47
1767
* p<0.1; ** p<0.05; *** p<0.01
Table 7: IV estimates
The results from the IV approach do not substantially differ from those of the OLS estimation (see columns 2 in Table 3). If the net price was endogenous due to a reverse causality
with the consumption we would expect our price coefficient be biased towards zero in the
OLS estimation. But as the price coefficient becomes even smaller in the IV estimation it
22
Li et al. (2012, p. 11).
Chapter 1
24
is questionable whether reverse causality is an issue in our consumption and price data. As
OLS is the most efficient estimator given no endogeneity problems we regard the OLS
approach as preferred to the IV approach.
1.3.3 Anticipation Effect and Temporary Persistency of a Motor Fuel Tax
Figure 5 and Figure 6 illustrate the change in consumption behavior prior to and after a
tax change for gasoline and diesel, respectively. This graph was generated by including
additional lags and leads in the OLS model (Equation (3)) and then plotting the point estimates (solid line) and the 95% confidence interval around it (dashed lines). The time of
the tax change is indicated by month 0. In the observed period around a tax change, we
find significant tax effects only in the month before a tax change, in the month after a tax
change, and in the month of a tax change. The grey shaded area marks the period for
which we do not find statistical significant coefficients for the percentage change in tax.
Making more longrunoriented statements about tax elasticities is difficult, especially
with monthly data.
1
0.5
0
4
3
2
1
0
1
2
3
4
5
6
7
8
9
10 11 12
0.5
1
1.5
Figure 5: Development of gasoline purchases four months before and twelve months after a tax change
Anticipation and Inventory Effects on Fuel Demand
25
1
0.5
0
4
3
2
1
0
1
2
3
4
5
6
7
8
9
10 11 12
0.5
1
1.5
Figure 6: Development of diesel purchases four months before and twelve months after a tax change
1.3.4 Policy Implications
Comparing our results e.g. with those of the studies for the USA by Davis and Kilian
(2011) and Li et al. (2012) we find similar elasticities. The Least Squares point estimate of
the gasoline demand’s tax elasticity lies with 0.84 in the ballpark of the results of Davis
and Kilian ( 0.46) and Li et al. ( 0.77). But considering a concrete absolute tax change of
10 UScents per gallon, however, discloses the fundamental differences between the EU
and the USA. A 10 UScents increase of the gasoline tax per gallon leads according to
Davis and Kilian’s results for the USA to a consumption reduction of 1.4%. Imposing a 10
UScents tax increase per gallon which equates a 1.9 Euro cents tax increase per liter in
the EU would lead to 2.5% decrease of gasoline consumption and to a 4.5% decrease of
diesel consumption. Although the tax elasticities look rather similar at first glance, the
impact of the same absolute tax change differs a lot: on the one hand between the USA
and the EU, and on the other hand between gasoline and diesel consumption in the EU.
These differences are due to discrepancies in the tax levels. Whereas the average gasoline
tax in the USA amounts to 4.5 Eurocents per liter the same tax in Europe averaged over
the countries in our sample is more than ten times larger. The gasoline tax in Europe totals
52.5 Eurocents and the diesel tax 37.3 Eurocents.23
1.4 Conclusions
From our analysis of 11 European countries we draw three main conclusions. Firstly, in
analyzing short run tax elasticities of fuel demand, it is important to control for anticipation and inventory effects since neglecting these two effects leads to overestimation of the
23
These figures refer to the excise taxes only without VAT.
26
Chapter 1
tax effect. Secondly, the intertemporal shifting of fuel consumption only occurs in connection with tax changes and not with net of tax price changes. Thirdly, there is an indication
for a difference between gasoline and diesel consumption regarding the ability to shift fuel
purchases over time.
The main contribution of our analysis to the existing literature in the area of fuel demand
estimation is the identification and quantification of an anticipation effect. We show that
consumers and intermediate suppliers who expect a fuel price increase induced by a tax
increase in the following month bring forward part of their fuel purchases of the next
month to the extent of their storage capacities. This behavior is likely motivated by consumers fueling up shortly before a tax increase becomes effective, even if their tank is not
completely empty yet. They simply try to exploit the lower retail price once more. Accordingly we identify an increase in the consumption or more precisely in the amount of
fuel purchased by 0.37% (0.32%)24 in the month before the implementation of a 1% tax
increase. In the month of the tax increase these consumers naturally have to fuel less as
they have brought forward part of their usual monthly fuel purchase, though this reaction
has only little to do with a change in the consumption behavior. As a consequence of the
tax mainly causing an intertemporal shift in the purchasing pattern, the amount of fuel
purchased in the month after the tax increase rises again. Or stated differently: Drivers
return to their pretaxincrease purchase behavior. The aftertaxincrease effect of a 1%
tax increase amounts to a 0.25% (0.37%) consumption increase. Aggregating over the entire “tax period” we do not find a statistically significant effect of the tax, leading us to
conclude that considering only the consumption effect of the tax in the month of the tax
increase results in an overestimation of the tax impact. Regarding net of tax price changes
we do not find an anticipation effect which is plausible since pretax price changes are
difficult to predict.
These results provide the following insight: In the month in which a tax increase becomes
effective we observe a large reduction in demand which is mainly triggered by consumers
shifting fuel purchases to the prior month. As we do not observe such shifting prior to net
of tax price changes, we find different demand reactions to retail price changes dependent
on whether they are caused by tax changes or by pretax price changes. This is in line with
the findings of Davis and Kilian (2011), Li et al. (2012) and Rivers and Schaufele (2013).
According to these studies, larger tax elasticities are rationale since tax changes are announced and predictable and they are more persistent, especially compared to net of tax
price changes. The price elasticities in our preferred specification are –0.06 for gasoline
and to –0.07 for diesel, whereas the estimated tax elasticities are –0.84 for gasoline and –
1.04 for diesel. Our interpretation of these results, however is somewhat different. We
24
Figures in brackets refer to diesel consumption.
Anticipation and Inventory Effects on Fuel Demand
27
find that the announcement plays the biggest role in the difference between price and tax
elasticities, and that controlling for the anticipation effect results in the estimates of tax
changes and tax exclusive price changes being statistically undistinguishable from each
other.
As a third major result we find diesel purchases to react more strongly to a tax change
than gasoline purchases. We explain this finding by the large fraction of commercial users
among the diesel consumers. Firms like freight carriers might have more possibilities to
shift fuel purchases over time, for example, due to onsite fuel reservoirs, whereas private
users usually only use their car’s gas tank as storage capacity.
As an additional result of this study we show price and tax elasticities of fuel demand to
be rather constant as the results for our European dataset are in the same ballpark as those
from Davis and Kilian (2011) for an American dataset. This should not be expected upfront as the price and tax levels differ tremendously between the European countries and
the United States. Still nonlinearities in the reaction to price and tax changes are not dominant as the elasticities in comparable estimations do not vary significantly.
This study confirms the importance of accounting for intertemporal purchase shifting in
estimating tax elasticities of fuel demand. Neglecting the forwardlooking purchase decisions of fuel consumers results in an overestimation of the tax impact on consumption
behavior.
28
Chapter 1
1.5 References
Anderson, Soren T., Ryan Kellogg and James M. Sallee (2013), “What do consumers believe about future gasoline prices?”, Journal of Environmental Economics and Management 66: 383–403.
European Automobile Manufacturers Association (ACEA) (2014), The Automobile Industry Pocket Guide, p. 68.
Brons, Martijn, Peter Nijkamp, Eric Pels, and Piet Rietveld (2008), “A metaanalysis of
the price elasticity of gasoline demand. A SUR approach”, Energy Economics 30: 2105–
2122.
Coglianese, John et al. (2015), “Anticipation, Tax Avoidance, and the Price Elasticity of
Gasoline Demand”, Journal of Applied Econometrics (forthcoming).
Dahl, Carol, and Thomas Sterner (1991), “Analysing gasoline demand elasticities: A survey”, Energy Economics 13: 203–210.
Davis, Lucas W., and Lutz Kilian (2011), “Estimating the effect of a gasoline tax on carbon emissions”, Journal of Applied Econometrics 26(7): 1187–1214.
Hughes, Jonathan E., Chritopher R. Knittel, and Daniel Sperling (2008), “Evidence of a
shift in the shortrun price elasticity of gasoline demand”, Energy Journal 29(1): 113–
134.
IEA (2013), “OECD crude supply”, IEA Oil Information Statistics (database), doi:
10.1787/data00475en (accessed November 21, 2013).
Kilian, Lutz and Thomas K. Lee (2014), “Quantifying the speculative component in the
real price of oil: The role of global oil inventories”, Journal of International Money and
Finance 42: 7187.
Kilian, Lutz and Daniel P. Murphy (2014), “The Role of Inventories and Speculative
Trading in the Global Market for Crude Oil”, Journal of Applied Econometrics 29 (3):
454478.
Li, Shanjun, Joshua Linn, and Erich Muehlegger (2012), “Gasoline taxes and consumer
behaviour”, NBER Working Paper 17891.
Liu, Gang (2004), “Estimating Energy Demand Elasticities for OECD Countries: A Dynamic Panel Data Approach”, Statistics Norway Discussion Papers 373.
Pock, Markus (2010), “Gasoline demand in Europe: New insights”, Energy Economics 32:
54–62.
Rivers, Nicholas and Brandon Schaufele (2013), “Salience of Carbon Taxes in the Gasoline Market”, Available at SSRN: http://ssrn.com/abstract=2131468
World Bank (2013), “Crude oil, Brent”, “Crude oil, Dubai”, and “Crude oil, WTI”, Global
Economic Monitor (GEM) Commodities (database) (accessed November 21, 2013).
Anticipation and Inventory Effects on Fuel Demand
1.6 Appendix
Figure 7: Gasoline retail price composition in December 2006 and 2010 (nominal values in € cents)
29
30
Chapter 1
Figure 8: Diesel retail price composition in December 200625 and 2010 (nominal values in € cents)
25
As the time series in Belgium and Ireland starts later, the first observation is used, which is February
2007 and February 2008, respectively.
Anticipation and Inventory Effects on Fuel Demand
Time
period
Consumption
Taxes
Prices
Austria
1999–
2012
Belgium
2002–
2012
Denmark
19902012
France
1990–
2012
Federal Ministry of
Ecology, Durable
Development and
Energy
French Association of
the Petrol Industry
Federal Ministry of
Ecology, Durable
Development and
Energy
Germany
1990–
2012
Federal Office of
Economics and Export Control
Association of the
German Petroleum
Industry
ARAL, ADAC
Ireland
2002–
2012
National Oil Reserves Agency
Irish Tax and Customs
Europe’s Energy Portal
Netherlands
2000–
2012
Portugal
1998–
2012
ThomsonReuters
Ministry of Economy
ThomsonReuters
Spain
19902010
National Oil Reserves Agency
Spanish Tax Agency
Spanish Ministry of
Industry, Agriculture
and Tourism
Sweden
2000–
2012
Swedish Petroleum
and Biofuel Institute
Federal Tax Agency
Europe’s Energy Portal
UK
2000–
2012
Government Department of Energy
& Climate Change
Government Department of Revenue
& Customs
Europe’s Energy Portal
Table 8: Data sources
Federal Ministry of Economics
Federal Public Service Economy
Belgian Federation of
Petrol
Europe’s Energy Portal
Danish Oil Industry Association
Statistics Netherlands
ThomsonReuters
31
0.837***
(0.171)
0.250**
(0.121)
0.373**
(0.145)
0.014***
(0.001)
0.014***
(0.005)
Yes
Yes
Yes
0.43
1849
% change in unit tax
% change in unit tax (lag1)
% change in unit tax (lead1)
change in working days
change in unemployment
Month of the year indicators
Country indicators
Year indicators
R2
N
0.46
1717
Yes
Yes
Yes
0.012***
(0.004)
0.014***
(0.001)
0.328**
(0.143)
0.281**
(0.130)
0.869***
(0.160)
0.072***
(0.028)
increase
0.49
1481
Yes
Yes
Yes
0.51
1401
Yes
Yes
Yes
0.007
(0.005)
0.015***
(0.001)
1.504***
(0.357)
0.136
(0.311)
0.234
(0.380)
0.068***
(0.026)
decrease
0.51
1323
Yes
Yes
Yes
0.009**
(0.004)
0.015***
(0.001)
1.335***
(0.327)
0.126
(0.289)
0.106
(0.399)
decrease
> 1 cent
0.068**
(0.026)
* p<0.1; ** p<0.05; *** p<0.01
0.012***
(0.004)
0.015***
(0.001)
0.247**
(0.124)
0.329**
(0.138)
0.840***
(0.158)
Gasoline
increase
> 1 cent
0.072***
(0.027)
Table 9: All tax changes in comparison to tax increases and decreases only
0.063**
(0.028)
all
% change in net price
% change in consumption
0.47
1769
Yes
Yes
Yes
0.002
(0.007)
0.026***
(0.001)
0.323**
(0.152)
0.371***
(0.131)
1.044***
(0.211)
0.071**
(0.029)
all
0.49
1638
Yes
Yes
Yes
0.005
(0.007)
0.027***
(0.001)
0.396**
(0.198)
0.480***
(0.153)
1.297***
(0.226)
0.079***
(0.030)
increase
0.49
1395
Yes
Yes
Yes
0.008
(0.007)
0.027***
(0.001)
0.375*
(0.202)
0.493***
(0.180)
1.286***
(0.287)
Diesel
increase
> 1 cent
0.085***
(0.030)
0.47
1348
Yes
Yes
Yes
0.012*
(0.007)
0.027***
(0.001)
0.045
(0.212)
0.002
(0.173)
0.060
(0.246)
0.073***
(0.028)
decrease
0.48
1270
Yes
Yes
Yes
0.016**
(0.007)
0.026***
(0.001)
0.244
(0.267)
0.027
(0.202)
0.109
(0.329)
decrease
> 1 cent
0.082***
(0.030)
2 The Problem of too many Zeros: Methods to Estimate Fuel Price Elasticities
Chapter 2
The Problem of too many Zeros: Methods to Estimate Fuel Price Elasticities
Julian Dieler*, Frank Goetzke+ and Colin Vance#
The workhorse model to estimate price and income elasticities in the fuel demand literature is the loglinear model estimated using least squares. This method however, has to
deal with two flaws. The first one is general in nature and applies to all loglinearized
regression equations. It is the fact that the loglinearization leads to violation of the
OLS.1 assumption and thereby to biased estimates. The second problem of the loglinear
model is not specific to the estimation of fuel demand but it applies to many types of demand estimation on microlevel. It is the problem of a substantial share of zero observations for the dependent variable, which also causes biased demand elasticity estimates.
This study presents a set of models, which constitute remedies to the drawbacks of the loglinear model estimated by OLS and model selection tests to compare the goodness of fit of
nonnested models, which base upon different distributional assumptions. Applying the
tests to a German household travel survey, we show that the Hurdle Poisson model performs best.
The Problem of too many Zeros
*
+
#
Center for Energy, Climate and Exhaustible Resources, Ifo Institute for Economic Research at the University of Munich, Germany, +49/89/92241346, dieler@ifo.de
Department of Urban and Public Affairs, University of Louisville, Louisville, KY, USA
Department of Environment and Resources, RWI Essen, Germany and Jacobs University Bremen, Germany
34
Chapter 2
2.1 Introduction
Accurate estimates of income and gasoline price elasticity of demand for vehicle miles
traveled (VMT) are important for designing efficient and successful transportation policies. Many, if not most, empirical gasoline price elasticity values are derived by constantelasticity regression models with the convenient loglog (sometimes also called loglinear)
functional form (Basso and Oum 2007), or some other kind of related equation type, such
as the translog functional form (Wadud et al. 2010). This study provides an analysis of the
methods used most frequently to estimate gasoline price elasticities. Additionally we present model selection procedures to compare the different econometric approaches and illustrate the results using the German Mobility Panel (MOP 2013).
Review and meta studies like Espey (1998) and Hanly et al. (2002) show that the estimation method chosen makes a difference – although not a huge one but with regard to the
use of price elasticities for forecasts and policy advice also minor differences matter. For
the decision between fuel taxes or carbon prices and fuel efficiency standards it is important to be able to rely on as accurate elasticity estimates as possible. For example,
Frondel et al. (2008) claim that the rebound effect is analytically equivalent to the gasoline price elasticity. Therefore they argue that price mechanisms like taxes are superior to
efficiency standards. Gillingham et al. (2013) suggest that gas mileage regulation policies
are more effective than they are said to be. They argue that the rebound effect, which
counteracts efficiency standards, is overestimated relative to the price elasticity. Nevertheless the discussion about the appropriateness of the applied econometric methodology is
mostly not accorded the weight it deserves. With this paper we would like to contribute to
the methodological debate in the literature of estimating fuel price elasticities. Therefore
we analyze and compare several estimation methods which have already been used for the
estimation of price elasticites in vehicle travel or fuel demand in the past. Next to those
established methods we also include methods such as Poisson, NegativeBinomial regression and TwoPart models, which have been also applied for elasticity estimations in other
fields of economics like in the migration or trade literature (e.g. gravity models).
One of the most widely applied methods is the estimation of loglinearized constant elasticity demand functions. However since Haworth and Vincent (1979), it has become clear
that loglinearizing estimation regression models using a log transformation may lead to
biased estimates, and thus faulty elasticity values. Flowerdew and Aitkin (1982) took up
on Haworth and Vincent’s findings and showed that they also hold true for gravity models. As an example Flowerdew and Aitkin used a gravity model explaining migration in
Great Britain. More recently Silva and Tenreyro (2006) argued that the concerns of biased
and also inconsistent estimates due to loglinearization also play a role in lognormal
models to estimate bilateral trade. The possible inconsistency of elasticity estimates of
The Problem of too many Zeros
35
loglinear estimations has two reasons: Firstly, the loglinearization of originally multiplicative relations leads most probably to heteroskedasticity of the error term and secondly,
the existence of a considerable number of zero observations for the dependent variable.
The second problem arises only analyzing micro data as the occurrence of zeros in the
dependent variable is rather unlikely in case of aggregated car driving data.
To select the best performing model or set of best performing models we use three different statistical tests. All three tests use the predictive ability of the models as selection criteria to evaluate their performance. Since we are comparing models which do not all rely
on the same distributional assumptions we cannot resort to criteria based on likelihood,
e.g. the Akaike Information Criterion (AIC) or the Vuong test (Vuong 1989). The first
model selection procedure we apply is the Model Confidence Set (MCS) developed by
Hansen et al. (2011). The MCS is a model selection procedure which chooses a superior
set of models out of all models which are to be compared by repeated testing of equal predictive ability (EPA) of the analyzed models. Crucial for the MCS is the possibility to
choose freely a loss function serving as model selection criterion. To check the robustness
of these results we also apply the LeaveOneOut CrossValidation and the comparison of
the count probability of the observations against the count probability of the predictions.
For the German Mobility Panel we find that TwoPart models fit best as it accounts well
for the two decisions which are behind the data generating process (dgp) of the German
driving data. More precisely the Hurdle and the ZeroInflated model using the Poisson
regression model on the second stage perform best, which is surprising since both models
are rarely applied in the literature. An exception provides a study by Frondel and Vance
(2011) which analyzes the determinants of ridership in public transport using a countdata
model. Another important finding of this study is, that it obviously matters which model is
used. Looking at all analyzed models, we find a quite substantial divergence between the
estimates of the price elasticity. It ranges from –0.115 until –0.255.
The structure of the paper is as follows. The following section discusses the different estimation methods and their assumptions from a theoretical point of view. Section 3 shortly
presents the data we use for our empirical comparison of the different estimation models
followed by a description of the model selection procedures in section 4. In section 5 we
will present the estimation results of the elasticities as well as the results of the model
comparison tests. Finally we discuss these results and conclude in section 6.
2.2 Theoretical background of fuel price elasticity estimation
The theoretical foundation for fuel price elasticity regression models is a constant elasticity demand function like
driven,
=
. With
standing for the fuel price and
as amount of fuel purchased or distance
for income. As this relation is not deterministic
36
Chapter 2
and the respective elasticities
and
cannot simply be measured a stochastic term
accrues. Most commonly one thinks about additive error terms which would lead to the
following stochastic model:
=
+
(1)
The alternative would be a multiplicative connection of the error term.
=
(2)
From Espey’s (1998) metaanalysis it becomes clear that most of the studies, she included
in her comprehensive fuel price elasticity analysis, use Ordinary Least Squares (OLS)
regression methods to estimate price elasticities. OLS regression implies loglinearization
of the CobbDouglas type estimation equation. Independent from the assumption made
about the connection of the error term loglinearization of the demand function leads to
violation of the OLS.1 assumption. OLS.1 requires strict exogeneity of the errors for the
OLS estimates being unbiased. That means ( ′ ) = 0 with as the matrix of explanatory variables. After loglinearization the OLS.1 assumption for unbiasedness turns into the
following relation: (ln
ln ) = 0 The latter relation is rather unlikely to hold as the
expected value of the logarithm of depends both on its mean and on the higher order
moments of distribution (Silva and Tenreyro 2006, p. 642), which includes the variance.
The variance of again depends most likely on since most datasets have the property of
increasing variance of the errors with increasing value of the explanatory variables.
The second problem of loglinearization is the problem of the occurrence of zeros in the
dependent variable. In the estimation of fuel price elasticities this problem mainly arises if
the analysis bases on micro data, since data of driving or fueling behavior from individuals or households is mostly derived from surveys which only cover a limited observation
period, like a certain day or a certain week. That makes it rather likely to observe individuals who did not drive or fuel their car in that specific period. For more aggregated data
on the contrary this is a rather unlikely observation. Therefore the zeroproblem primarily
arises analyzing microlevel data. The problem of zero observations in the dependent variable lies in the simple fact that the logarithm of zero is undefined and thereby the loglinearization leads to omission of all individuals who did not drive in the observation period no matter whether they do not drive at all or whether it was simply a coincidence that
they were not driving in the observation period. Leaving out this information leads to an
underestimation of the fuel price elasticity since the decision of not having a car or not
driving might also be a consequence of the fuel price. To circumvent this problem empiricists typically add an arbitrarily small constant to the dependent variable. But adding a
The Problem of too many Zeros
37
constant to a nonlinearly transformed variable in turn leads to biased coefficients again as
we demonstrate in the Appendix.
As a solution to the problems of the loglinearization of the multiplicative demand function Silva and Tenreyro (2006) suggest the application of a pseudomaximumlikelihood
(PML) estimator which estimates the fuel or travel demand directly in multiplicative
form. Another possibility would be to use the nonlinear least squares (NLS) estimator
but this method weights large observations more which is not very efficient since they
exhibit also a larger variance and thereby calculating the coefficient one would rely more
on noisier observations. This flaw could be remedied by estimating a weighted NLS estimator but therefore a nonparametrically estimated function of the conditional variance
would be needed. The PML estimator Silva and Tenreyro (2006) propose gives equal
weight to all observations and does not require a nonparametric estimation of the conditional variance of the dependent variable. It is numerically equal to the Poisson pseudomaximum likelihood (PPML) estimator and assumes the conditional variance of the dependent variable to be proportional to the conditional mean of .
(  )=
(  ) with
=1
(4)
Therefore the PPML estimator is completely identified by its conditional expectation
(  ) = exp( ) (with = and = ( , ) in our application).
For
> 1 Cameron and Trivedi (1986) propose a full maximum likelihood estimator –
> 1 means that the data exhibits overdispersion.
the Negative Binomial (NB) model.
Under this special assumption, which can be tested, the NB model constitutes a more efficient estimator than the PPML estimator. Since variance and conditional expectation are
not equal for overdispersed data the NB model additionally to the conditional expectation
(  ) = (1 + ) exp( )) to be identified unambiguously. If,
needs its variance (
however, the variance condition of the NB model ( > 0) is not met, its coefficient estimates become inconsistent (Wooldridge 2002, p. 657).
An alternative approach to model automobile travel demand is to think of the dgp as a
twostep process. The first decision consumers have to make is to own a car or not. Then,
given the consumers possess a car, the second decision is how much to use it. There are
two models from the family of TwoPart models which are predestined to describe this
dgp: ZeroInflated models and Hurdle models. By allowing two different dgp’s ZeroInflated and Hurdle models account for excess zero kilometers driven caused by people
who do not own a car.
The first step of the ZeroInflated model consists of a binary count model like the Probit
model
Chapter 2
38
Pr(
= 0 ) = ∫
(
exp
)
= Φ(
)=
.
Where
stands for the probability of the household having no car and simply depicts
the number . Step two is composed of a count model ( ). Typically Poisson or Negative
Binomial models are applied as second step. The specialty of ZeroInflated models compared to Hurdle models is that zeros can materialize on both steps. That means consumers
either have no car and therefore drive zero kilometers, or they have a car but they decide
not to drive in the observation period. Equation 5 shows the count distribution of the ZeroInflated model.
Pr(
+ (1
(1
= )=
) (0)
) ()
=0
= 1, 2, …
(5)
Following equation 5 we can calculate the expected value as
E(  ) = (1
)
=
with
( ) for = 1, 2, … .
(6)
The variance which characterizes ZeroInflated models is depicted in equation 7.
(  )=
+
(1
)
(7)
From equation 7 can be seen that the variance is larger than the expected value
and
thereby zero inflated models are similar to the NB model able to account for overdispersion. The second TwoPart model, the Hurdle model accounts for over as well as for underdispersion. In return the Hurdle model does not allow zeros to occur on the second
step. In our case that would mean all people who have a car also drive in the observation
period. This restriction also gets clear from the count distribution:
Pr(
With
= (1
)⁄(1
= )=
()
=0
= 1, 2, …
(8)
(0)). Which leads to
E(  ) =
with
( ) for = 1, 2, …
=
(9)
and
(  )=
+
(1
).
(10)
The Problem of too many Zeros
39
Since can be both, smaller or larger one, the Hurdle model can additionally to overdispersed data deal with underdispersed data. However, the latter case might be rather rare in
car use data (Frees 2010).
Similar to the ZeroInflated and the Hurdle model the so called Heckit model, which goes
back to the seminal work by Heckman (1979), as well assumes a twostage dgp and is
regularly applied in the analysis of datasets with a large share of zero observations. But
there is a decisive difference between the TwoPart models and the Heckit procedure:
Dow and Norton (2003) make this distinction clear by differentiating between actual and
potential outcomes. Actual outcomes describe a dependent variable which can be fully
observed. For actual outcomes zero observations are a consequence of the true dgp. For
car use data that means that people can drive a positive amount of kilometers or they cannot drive which results in a zero observation. The zero is therefore the true observation in
this dgp. The potential outcome in contrast, cannot be fully observed. It is a latent variable
which is only observed for a fraction of the sample. For the rest of the sample it is often
set to zero and thereby creates excess zeros. The prime example for a potential outcome is
the wage offer (Gronau 1974) which can only be observed from the working part of the
population. Estimates of certain characteristics’ effects would be biased if the working
part of the population would differ systematically from the not working part of the population after controlling for the covariates. The original purpose of the Heckit model is to
correct for this selection bias. Its first stage consists similarly to the TwoPart models of a
Probit model:
Pr
=1
,
= Φ(
,
(11)
)
And the second stage is an OLS regression among the subsample for which
The overall marginal effect of
E
,
,
= 1 holds.
can be deducted from the following expected value.
,
= 1,
=
+ ( 
=
+
(
> 0,
)
(12)
)
With ( ) ≡ ( )⁄Φ( ) being the inverse Mills ratio, which controls for potential correlation between the error terms from both stages. It is the estimation of (  > 0, ) =
+
(
) where ( , )~ (0, Σ) and
Σ=
1
.
(13)
From Σ it can easily be seen that the Heckit model reduces to an OLS regression as the
inverse Mills coefficient
gets zero when the errors of both stages are uncorrelated. Alt
Chapter 2
40
hough the Heckit model originally was developed for analyzing potential outcome variables it can also be used to estimate regression equations with actual outcome variables as
it is the case in the fuel or travel demand literature. The marginal effects calculation of the
explanatory variables on the actual outcome variable, however, must be based on a different expected value which combines both stages (Dow and Norton 2003):
E( 
) = Φ(
)[
+
(
)]
(14)
Of course, this selection of models does not cover the whole range of models used in the
fuel demand or automobile travel demand literature but it comprises the workhorse model
in this literature – the loglinear regression model – and a few models which were brought
up to alleviate the caveats of the loglinear model. Another model being applied in this
strand of literature is the Tobit model. Due to theoretical arguments the applicability of
the Tobit model must be doubted in the analysis of automobile travel demand. Maddala
(1992, p.341) argues that the Tobit model should only be applied to censored data. That
means data whose observations are stacked into one category in case they are under or
above a certain threshold. This is, for example, often the case for pollutant data when the
measuring instrument can only detect pollutant concentrations above a certain level. However this is not appropriate for the type of data used for travel or gasoline demand analysis. The zeros are not a consequence of censoring but of the decision of people not to
drive. Maddala argues that instead of mechanically falling back to the Tobit model the
appropriate procedure would be to model the decision that produces the zeros. That is exactly what those models like the already described Hurdle and ZeroInflated models or the
Heckit model do.26
From a theoretical perspective the loglinearized demand function estimated with OLS
will presumably not perform well applied to micro driving data due to the endogeneity of
the logged error term and the occurrence of zero observations for kilometers driven. If the
driving data can be described as one decision by households, meaning that the analyzed
explanatory variables have equal impact on the decision of owning a car and driving, the
Poisson and the Negative Binomial model will be the models of choice. To decide between Poisson and Negative Binomial the crux of the matter is the conditional variance of
the kilometers driven. If it is larger than the conditional expectation, meaning the data is
overdispersed, the Negative Binomial model outperforms the Poisson regression. For the
case the dgp is assumed to be a stepwise process however, a switch to the TwoPart models is indicated. They model the dgp of the driving data as a twostep process. On the first
step consumers decide upon buying a car or not and on the second step, given they have
26
Nevertheless, we included the Tobit model in our analysis but we will not go into details about this
model in the main part.
The Problem of too many Zeros
41
bought a car, how much to drive with that car. Again there are two options with TwoPart
models. First the Hurdle models which do not allow zeros to occur on the second step
which means that Hurdle models assume every household which has a car to drive. Second there are the Zeroinflated models which relax this assumption. Finally we include the
Heckit model in our analysis. The Heckit model would be a good choice if there is a substantial share of households in the data who do not own a car and who would react differently to price or income changes if they had a car compared to those households in the
dataset who own a car. Before coming to the characteristics of our dataset we present the
model comparison tests in the next section.
2.3 Model Comparison Tests
One of the main contributions of this study is the proposition of tests for comparing the
suitability of different models for vehicle travel or fuel demand. For being able to compare not only nonnested models but also models which base on different distributional
assumptions. This is not possible with the standard model selection criteria like the
Akaike Information Criterion (AIC) or the Vuong test (Vuong 1989) as they hinge on the
maximum likelihood. Comparing likelihoods from two models with different distributional assumptions however is not very meaningful without some standardization. Therefor we
resort to model selection tests which rely on the model’s predictive ability.
Hansen et al. (2011) propose a convincing approach to determine the best performing
models given a certain dataset. They developed the Model Confidence Set (MCS) which
sorts out “bad performing” models and thereby leads to a selection of “good performing”
models – the MCS – that contains the best model(s) on a chosen significance level. The
structure of the procedure is as follows:
1. Definition of a set of models to be compared.
2. Test of the null hypothesis of equal predictive ability (EPA) of the models.
3. If the null hypothesis is rejected, the worst performing models are eliminated from
the comparison set and the null hypothesis is tested again. This process is repeated
until the null hypothesis is accepted und thereby the MCS is determined.
The basis for the elimination rule is provided by a loss function, which in our case is defined as a measure of the prediction’s deviance from the observed values. Next to revealing the best models the MCS also provides information about the data’s informative quality. An informative dataset leads to a MCS containing few, or at best only one model
whereas a less informative dataset leads to a MCS that contains more models. Besides the
differentiation between models being inside and outside the MCS, it allows a continuous
ranking of all models, since the MCS is a multilateral comparison method and no bilateral
method like the Vuong test, for example.
42
Chapter 2
To check whether this ranking is robust, we firstly use different types of loss functions
and secondly we apply further measures to compare the model’s goodness of fit. As first
robustness check we use a resampling method – the LeaveOneOut CrossValidation
(LOOCV). The principal idea of LOOCV is to take a subsample of the analyzed dataset
which consists of all observations but one, fit the model to the subsample and use the fitted model to predict the response for the leftout observation. This procedure is repeated
for all observations what leaves you with predictions for the observations. From these
pairs a mean squared error (MSE) is calculated. In this way we derive a MSE for each
model and use it as indicator for the comparison of the goodness of fit of the different
models for the given dataset.
Another comparative indicator is the mean of the differences between the observed count
probabilities of kilometers driven and the predicted count probabilities of kilometers driven (Liu and Cela 2008). For this purpose we first calculate the categorical distribution for
the kilometers driven, once for the observed parameter values and once for the predictions
of each of the models being compared. Then we take the differences between the observed
count probabilities and the predicted count probabilities at each value of kilometers driven. Taking the mean of these differences provides us with another goodness of fit measure
for the different models focusing on the probability distribution. For the remainder of this
paper this test is referred to as the Difference of Count Probabilities (DOCP) test.
2.4 Data
As case study for our model comparison we use a surveybased micro dataset on the mobility behavior of German households – the German Mobility Panel (MOP 2013). The
mobility panel results from two surveys. The first one asks facts about everyday mobility
and the second one is focused on automobile mobility only. The respondents of the everyday mobility survey are asked to fill in a travel diary over the time span of a week containing questions on the purpose of the travel, starting and end time, distance and so on.
Here all travels are questioned independent of the means of transport. In the second survey however, only a subsample of car owning households is interviewed and asked to fill
in a refueling diary over a period of two months with information about kilometers driven
between each refueling stop, the amount of petrol purchased and the price paid for the
petrol. In our observation period from 2000 until 2013 there are 2752 households in the
sample. Since the survey is organized as a panel, some households emerge several times.
At most one household stays in the panel for three years, which is rather short for making
use of panel methods. Additionally only 703 households stayed in the panel for all three
The Problem of too many Zeros
43
waves.27 Therefore we make a pooled crosssection analysis with household clustered errors.
Variable
Variable definition
Mean
Std.Dev.
Min
Max
Daily kilometers
Daily kilometers driven in km on
average in the observation period
28.43
24.57
0
202.6
Fuel price
Real fuel price in € per liter
1.389
0.134
1.085
1.687
Income (real)
Real net monthly household income
in €
Number of people living in the
household
1 if person is employed in full or
halftime job, 0 otherwise
2052
887.2
236.5
4376
1.863
0.967
1
7
0.487
0.500
Household size
Employed
Binary variable
N = 4891
Table 10: Descriptive Statistics
From the 2752 households there are 678 households which do not own a car. And these
678 households without a car are responsible for all the zero observations of kilometers
driven in our dataset. That means there is no household possessing a car which does not
drive in the observation period. The carless households stem from a randomly selection
from the general MOP survey such that they comprise 15% of the observations in a given
year. This share roughly corresponds to the actual share of carless households in Germany.
The participants of the survey are also asked to give some background information on
their household and personal characteristics including age, gender, amount of household
members and employment status among others. But since we are not after measuring the
size of a certain variable’s effect on the demand for driving but more after comparing different models with respect to their predictive ability, we keep the demand equation rather
basic. We concentrate on four main drivers of the demand for driving: fuel price, income,
household size and the employment status (see Table 10Table 1 for summary statistics of
these variables). Because in the original dataset the fuel price is only available for households which took part in the automobile mobility survey we use spatially interpolated
price data covering all of Germany by Frondel and Vance (2011). By spatial interpolation
Frondel and Vance succeeded in assigning fuel prices also to households which were not
asked for the fuel price in the survey. Having this data is very important for such kind of
analysis, as the fuel price might also be an important driver in the decision of buying a car
or not.
27
733 households responded twice and the majority of 1316 households only took part once. That makes a
total number of observations of 4891.
Chapter 2
44
2.5 Results
Most of the literature in fuel or travel demand estimation in the past used the loglinear
regression without testing the suitability of the model to the data at hand. In this section
we compare the goodness of fit of the models presented in section 2.2 on the basis of our
case study. We also present the estimation results of the different models to illustrate the
size of the potential deviations between the estimates of the different models.
Table 11 shows the rankings which result from the different model comparison tests. For
each of the test we specified two different loss functions – the squared error and the absolute value of the error – to check the robustness of the results towards changes of the loss
function. And it turns out that changing the loss function inside the testing procedures
leads to minor changes in the rankings. Comparing the ranks of the three tests with each
other it becomes clear that especially the MCS and the LOOCV test result in rather similar
rankings for the twelve different models analyzed. The reason for this finding is that these
two tests focus on a similar selection criterion which is the predictive ability whereas the
DOCP test puts more emphasis on the correspondence of the distributions of the observed
and predicted outcome variable.
Model Confidence Set
LOOCV
DOCP
squared
error
absolute
error
squared
error
absolute
error
squared
error
absolute
error
OLS
12
10
12
10
10
4*
Zerotruncated OLS
8
8
7
7
8
10*
PPML
5
5
5
5
2*
2*
Zerotruncated PPML
10
12
10
12
11
12*
NB
6
6
6
6
1*
1*
Zerotruncated NB
9
11
9
11
9
11*
Hurdle OLS
11
9
11
9
12
8*
Hurdle PPML
2*
1*
1*
1*
5*
6*
Hurdle NB
4*
4*
4*
4*
3*
3*
ZIP
1*
2*
2*
2*
6*
7*
ZINB
3*
3*
3*
3*
4*
5*
Heckman
7
7
8
8
7
9*
Model
Table 11: Rankings of the model comparison tests (models whose ranks are marked with a * are similar on the
95% significance level)28
28
See Figure 9 in the Appendix for an explanation of the significance level of the LOOCV and the DOCP
test. The MCS test produces the significance levels automatically as it is a joined test (see Section 3).
The Problem of too many Zeros
45
The models can be divided roughly into two groups – the onestep and the twostep models. The onestep models consist of OLS, PPML and NB. For each of them we estimated
one version including all observations and one version excluding the zero observations,
since both approaches are used for estimating fuel price elasticities from a loglinearized
demand function by OLS. For being able to do the analysis for all observations using
OLS, we applied the Inverse Hyperbolic Sine (IHS) transformation which adds a constant
to the outcome variable relative to its size. Thereby the zero observations are not dropped
because of taking logs. The IHS transformation reduces the induced bias by the added
constant and was first introduced by Johnson (1949). The twostep models include the
Hurdle model with OLS, Poisson and NB as secondstep model, the Zeroinflated model
with Poisson and NB as second step model and the Heckit model. Since Zeroinflated
models allow for zeroobservations on the second step as well, it does not make sense to
combine it with OLS like the Hurdle model.
Taking the MCS and the LOOCV test into account, which means looking at the predictive
ability of the models, the result which can be derived from theory gets confirmed. All
models which base on OLS do not perform well due to the zeroproblem but also due to
the endogeneity of the error. The endogeneity issue of the errors is illustrated by the ranks
of the zerotruncated versions of the onestep models as they abstract from the zeroproblem by looking at nonzero observations only. Also here PPML and NB outperform
OLS. The same holds true for the twostep models. Again the OLS version of the Hurdle
model ends up behind the other Hurdle models and the ZeroInflated models. So does also
the Heckit model although, we included an additional identifying variable to estimate the
first step of the Heckit model. We used the costs of the household’s car insurance as identifying variable since it can be seen as a fixed cost which might influence the decision of
buying a car but not the decision of how much to drive. Both the MCS and the LOOCV
test give rise to the assumption that the Hurdle model performs best for our case study.
This is completely coherent with theory. As has been stressed before the Hurdle model
assumes the dgp to be a result of two decisions: First, the households decide to buy a car
or not and once they bought a car they decide how much to drive and in contrast to the
ZeroInflated model it does not allow for zero observations on the second step which is
true for our dataset since all the zeroobservations stem from households without a car.
Furthermore overdispersion does not seem to play a big role in the data with respect to the
predictive ability. Although the likelihood ratio test finds the overdispersion parameter
being larger than zero and in general the NB model seems to make better distributive assumptions as the Poisson model as can be deducted from the DOCP test. Here the NB
models always outperform the Poisson versions of the four models (PPML, zerotruncated
PPML, Hurdle Poisson and ZIP). But as mentioned above, overdispersion leads merely to
less efficiency of the Poisson estimator and not to biased estimates. Additionally the rank
Chapter 2
46
ings of both Hurdle models and both ZeroInflated models are not statistically significant
on the 95% level which is indicated by the asterisks in Table 11. For the DOCP test the
amount of models between which no statistical significant differences exist is even larger
with 6, respectively all 12 models. That means there is no distinction possible according to
the second DOCP test. But apart from that test, all tests show that models relying on OLS
estimation perform significantly worse than the models which estimate the demand function directly in its multiplicative form like the Poisson and Negative Binomial regression
models. The good performance of the OLS model in the DOCP test with absolute value of
the error as loss function is relativized by the large variance of the mean compared to the
other models.
To assess if it is worth the effort to compare different models one should look at the variance of the coefficients being estimated since they are the values of interest. When there
are no notable differences in the size of the coefficients one could argue that the choice of
the correct model is not too important. Table 12 depicts the marginal effects defined as
elasticities calculated from the estimated coefficients.29
The first thing to be noticed is that there is a quite substantial variance in the estimated
elasticities between the different models. The price elasticity ranges from –0.261 (zerotruncated PPML) until very high –0.741 (OLS).
Onestep models:
Variables
OLS
Zerotruncated
OLS
PPML
Zerotruncated
PPML
NB
Zerotruncated
NB
Fuel price
0.741**
0.345*
0.473**
0.261*
0.483**
0.324*
Income (real)
1.186**
0.171**
0.533**
0.193**
0.543**
0.181**
Twostep models:
Variables
Hurdle OLS
Hurdle PPML
Hurdle NB
Heckman
ZIP
ZINB
Fuel price
0.603*
0.351*
0.418**
0.348**
0.351*
0.414**
Income (real)
1.085**
0.467**
0.454**
0.178**
0.467**
0.455**
* p<0.05; ** p<0.01
Table 12: Marginal effects
For the income elasticity the range is even larger: 0.171 (zerotruncated OLS) until –
1.186 (OLS). So it is worth to compare different models to estimate fuel demand elasticities. Part of the variance however can be explained by the differences regarding the specification. Considering the onestep models, the zerotruncated versions always lead to
smaller elasticities in absolute terms. This is due to the fact, that in these models only
29
Table 13 in the Appendix shows the complete regression results of the analyzed models.
The Problem of too many Zeros
47
households are taken into account, which have a car. The onestep models which include
all households combine the elasticities of the decisions of buying a car and how much to
drive. Especially the income effect is by far bigger for the investment decision to buy a
car than for the marginal decision of how much to drive once the car is in the garage. This
finding already is a strong argument for the application of twostep models since most of
the time researchers and politicians are interested in the overall price effect on driving
demand and thereby on fuel demand which in turn causes GHG emissions.
The second noticeable finding is that the price elasticities estimated by OLS and Hurdle
OLS which include all observations are beyond the range of elasticities found in the existing literature. Hanley et al. (2002) specify the range in their literature review beginning
with 0.25 for shortrun and ending with –0.6 for longrun elasticities. Since we are estimating a static model we cannot say something about the time dimension, but nevertheless
the nonzerotruncated OLS model yields marginal effects beyond the scope typically
found in the literature.
The third remarkable result is that the price elasticity estimate of the zerotruncated OLS
differs only slightly from those of our most preferred model, the Hurdle PPML. This
shows that the zeroproblem seems to be the largest problem with respect to the extent of
the bias as this is excluded by the zerotruncated OLS regression model.
Another noteworthy result is that the onestep zerotruncated count models (PPML and
NB) have diverse results, while their nonzerotruncated versions lead to very similar results concerning the price elasticities. That hints at the fact that the zeros cover the overdispersion which exists in the nonzero part of the sample for which the NB model controls and the PPML model does not. This explanation is confirmed by testing the
statistical significance of the overdispersion parameter , which is positive and statistical
different from zero. This is also the case for the sample including the zeros but here the
impact of the overdispersion is most probably outweighed by the effect of the excess zeros. This explanation would also explain the fact that the elasticity estimates of the twostep models using the Poisson model as second step differ from those using the NB model
as second step.
Another peculiarity attracts attention while comparing the marginal effects of the Hurdle
PPML with the ZIP model and the marginal effects of the Hurdle NB with the ZINB model: The models using a Poisson regression as second step (Hurdle PPML and ZIP) lead to
exactly the same results while this is not the case for the Hurdle NB in comparison with
the ZINB model. For understanding this result one should recall some of the features of
zeroinflation and the NB model in comparison to the Poisson regression. The two count
models differ in the capability of incorporating excess zeros. The NB model is able to explain more zeros than the Poisson model as it controls for overdispersion. The first step in
Chapter 2
48
the ZeroInflated models, in contrast to the first step of the Hurdle model, only explains
the zeros which are not explained by the model of the second step. In case of the ZeroInflated PPML all zero observations are attributed to the first step. This makes it equal to
the Hurdle PPML model, since the Hurdle model explains all zero observations per definition. In case of the ZINB model not all zeros are attributed to the Probit model on the first
step, as the NB model on the second step explains the zero observations to a certain extent
itself. This can be seen by looking at the coefficients estimated by the Probit regression in
the first step in each of the four models30.
Last, not only the statistical tests prove that there is little to no difference between choosing Hurdle or ZeroInflated models with count data models as secondstep but also the
estimation results of the Hurdle PPML model are rather similar if not equal to the ZIP
model as well as the results of the Hurdle NB and the ZINB model.
2.6 Conclusion
This study presents empirical models which are used, or should be used for the estimation
of fuel demand elasticities. At the same time we introduce methods to compare the empirical performance of the different models. Applying the different models and tests to a
German household dataset we show the importance of assessing the goodness of fit of the
respective model to the data at hand as estimates can differ quite substantially between
different estimation models.
A common phenomenon in the applied econometrics literature is the existence of a workhorse model in each branch which is adopted again and again without scrutinizing. In the
fuel or vehicle travel demand literature this is, for example, the loglinear model. Silva
and Tenreyro (2006) showed however, that the loglinear model has some flaws in the
application to right skewed data with a large fraction of zero observations in the dependent variable. This data pattern also applies to driving or fuel demand micro data as there
are always people who do not drive in the observation period especially in Europe where a
decisive fraction of households does not own a car. Therefore this paper provides an overview of empirical models which are applied in the estimation of price elasticities of travel
demand and those models which possibly should be applied. We compare the different
models with regard to their suitability in the estimation of price elasticities on theoretical
grounds. The models discussed make different assumptions concerning the distribution of
the analyzed data as well as concerning the data generating process which is behind the
data. For our specific dataset a twostep approach seemed to be most promising as it models the decision to drive and the decision how much to drive separately which fits very
well the large fraction of zero observations for kilometers driven.
30
See Table 14 in the Appendix for the regression results.
The Problem of too many Zeros
49
Besides the theoretical assessment of the models we present three different statistical
methods to compare the model’s empirical goodness of fit. The Model Confidence Set
(MCS), the LeaveOneOut CrossValidation and an approach which compares the count
probability of the predictions with the count probability of the true observations. Since the
selected models partly base upon different distributional assumptions we cannot resort to
typical model comparison tests like the Vuong test or measures like the Akaike or Bayesian Information Criterion as they rely on the likelihood as comparison measure which does
not allow the comparison of models with different distributional assumptions.
Applied to our case study – the German Mobility Panel – the model comparison tests
identify the TwoPart models with a Probit as first and Poisson regression as second step
as the best performing model. Further well performing models are the TwoPart models
with a Negative Binomial regression model as second step. The workhorse model – the
loglinear model – however is not among the best models according to our comparison.
One has to emphasize at this point that this is no general result as it can differ from sample to sample. But this result shall encourage researchers to test the goodness of fit of various models to their analyzed data. This is stressed even more looking at the variability of
the results depending on the different estimation methods. In our case the estimations of
the price elasticity vary between –0.261 and –0.741. The ratio of these estimates is larger
than the factor 2 which is crucial when it comes to the derivation of policy advice.
An interesting expansion of this study would be the application to another case study and
the question if the patterns shown for the German data hold true for other countries or regions as well.
In any case, this work shows that it is advisable to scrutinize the empirical model with
respect to the data and the hypothesis to be tested.
2.7 References
Basso, Leonardo J. and Tae Hoon Oum (2007), “Automobile Fuel Demand: A Critical
Assessment of Empirical Methodologies.” Transport Reviews 27 (4).
Cameron, Colin A. and Pravin K. Trivedi (1986), “Econometric Models Based on Count
Data: Comparisons and Applications of Some Estimators and Tests”, Journal of Applied
Econometrics 1 (1).
Dow, William H. and Edward C. Norton (2003), “Choosing Between and Interpreting the
Heckit and TwoPart Models for Corner Solutions”, Health Services & Outcomes Research Methodology 4.
Espey, Molly (1998), “Gasoline Demand Revisited: An International MetaAnalysis of
Elasticities”, Energy Economics 20 (3).
Flowerdew, Robin and Murray Aitkin (1982), “A Method of Fitting the Gravity Model
Based on the Poisson Distribution”, Journal of Regional Science 22 (2).
50
Chapter 2
Frees, Edward W. (2010), “Regression Modeling with Actuarial and Financial Applications”, Cambridge University Press, New York, NY.
Frondel, Manuel, Jörg Peters and Colin Vance (2008), “Identifying the Rebound: Evidence from a German Household Panel”, The Energy Journal 29 (4).
Frondel, Manuel and Colin Vance (2011), “Rarely enjoyed? A count data analysis of ridership in Germany’s public transport”, Transport Policy 18.
Gillingham, Kenneth, Matthew J. Kotchen, David S. Rapson and Gernot Wagner (2013),
“Energy Policy: The Rebound Effect is Overplayed”, Nature 493.
Gronau, Reuben (1974), “Wage ComparisonsA Selectivity Bias”, Journal of Political
Economy 82 (6).
Hanly, Mark, Joyce Dargay and Phil Goodwin (2002), “Review of Income and Price Elasticities in the Demand for Road Traffic”, Economic & Social Research Council
Transport Studies Unit publication 2002/13
Hansen, Peter R. et al. (2011), “The Model Confidence Set”, Econometrica 79 (2).
Haworth, J. M. and P. J. Vincent (1979), “The stochastic disturbance specification and its
implications for loglinear regression”, Environment and Planning A 11 (7).
Heckman, James J. (1979), “Sample Selection Bias as a Specification Error”, Econometrica 47 (1).
Johnson, N. L. (1949), “Systems of Frequency Curves Generated by Methods of Translation”, Biometrika 36 (1/2).
Liu, WenSui and Jimmy Cela (2008), “Count Data Model in SAS”, SAS Global Forum
2008, Paper 3712008.
Maddala, G.S. (1992), “Introduction to Econometrics”, Macmillan Publishing Company,
New York, New York.
MOP (2013), “Deutsches Mobilitätspanel”, Federal Ministry of Transport.
Silva, J. M. C. Santos and Silvana Tenreyro (2006), “The Log of Gravity”, The Review of
Economics and Statistics 88(4).
Vuong, Quang H. (1989), “Likelihood Ratio Tests for Model Selection and NonNested
Hypotheses”, Econmetrica 57 (3).
Wadud, Zia, Daniel J. Graham and Robert B. Noland (2010), “Gasoline Demand with
Heterogeneity in Household Responses.” The Energy Journal 31 (1).
Wooldridge, Jeffrey M. (2002), “Econometric Analysis of Cross Section and Panel Data”,
MIT Press, Cambridge, Massachusetts.
2.8 Appendix
A1. Bias after adding a constant
In the following we show how adding a constant to the dependent variable leads to biased
coefficients:
The Problem of too many Zeros
( )=
=(
( )+
)
or
( )+
=
=
( , ) ∑(
=
∑(
( )
51
+
+
̅ )(
)
(A.1)
̅)
insert ln( ) for :
∑(
̅ )(ln( ) ln( ))
(A.2)
∑(
̅)
Adding an infinitesimal constant to the dependent variable to circumvent the problem
after taking the logs.
of zeros leads to a biased estimator of
=
(ln( )
ln( )) =
ln(
+ 1)
(A.3)
ln( + 1)
The equality in equation A.3 only holds in special cases. Generally adding a constant
leads to a biased estimator of .
A2. Confidence Intervals of the mean errors for LOOCV and the differences in
count probabilities
LOOCV
Squared error
22.75
22.7
22.65
22.6
22.55
22.5
PPML
Hurdle PPML
Hurdle NB
ZIP
ZINB
Chapter 2
52
LOOCV
Absolute error
17.15
17.1
17.05
17
16.95
16.9
PPML
Hurdle PPML
Hurdle NB
ZIP
ZINB
DOCP
Squared error
0.001
0.0009
0.0008
0.0007
0.0006
0.0005
0.0004
0.0003
0.0002
0.0001
0
PPML
NB
Hurdle PPML Hurdle NB
Heckman
ZIP
ZINB
The Problem of too many Zeros
53
DOCP
Absolute error
0.03
0.025
0.02
0.015
0.01
0.005
0
Figure 9: 95% confidence intervals of the mean errors for the test statistics of the LOOCV method and the
DOCP method. The rationale behind the assessment of the statistically significant difference between the models
is as follows: For each test models are included according to their rank until the confidence intervals overlap.
Therefore the diagrams include the best models with overlapping confidence intervals plus the next model in the
ranking whose confidence interval does not overlap with the confidence intervals of the preceding models. For
the DOCP test with the absolute value of the error loss function this method does not allow a distinction between
the models.
A3. Regression results of all models in the loglog specification
Onestep models:
Variables
OLS
Fuel price
0.741**
(0.267)
Zerotruncated
OLS
0.345*
(0.134)
Income
(real)
1.186**
(0.073)
Household
size
0.473**
(0.146)
Zerotruncated
PPML
0.261*
(0.126)
0.483**
(0.163)
Zerotruncated
NB
0.324*
(0.127)
0.171**
(0.035)
0.533**
(0.041)
0.193**
(0.035)
0.543**
(0.052)
0.181**
(0.034)
0.627**
(0.065)
0.158**
(0.032)
0.186**
(0.037)
0.064*
(0.032)
0.269**
(0.038)
0.087**
(0.031)
Employed
0.172**
(0.059)
0.327**
(0.029)
0.257**
(0.033)
0.287**
(0.028)
0.274**
(0.033)
0.289**
(0.027)
Constant
5.810**
(0.538)
1.893**
(0.257)
0.792**
(0.300)
1.971**
(0.258)
0.922*
(0.379)
2.069**
(0.244)
4891
3968
4891
3968
4891
3968
17950.4
17982.9
7990.2
8021.6
114807.3
114839.7
68383.3
68414.7
41887.8
41926.7
34327.4
34365.2
N
AIC
BIC
Twostep models:
PPML
NB
Chapter 2
54
Variables
Fuel price
Hurdle OLS
0.344**
(0.133)
Hurdle PPML
0.261*
(0.126)
Hurdle NB
0.327*
(0.128)
ZIP
0.261*
(0.126)
ZINB
0.325*
(0.128)
Heckit
0.348**
(0.134)
Income (real)
0.171**
(0.035)
0.193**
(0.035)
0.180**
(0.034)
0.193**
(0.035)
0.181**
(0.034)
0.178**
(0.037)
Household
size
0.157**
(0.032)
0.064*
(0.032)
0.089**
(0.031)
0.064*
(0.032)
0.087**
(0.031)
0.161**
(0.033)
Employed
0.326**
(0.029)
0.287**
(0.028)
0.289**
(0.027)
0.287**
(0.028)
0.289**
(0.027)
0.326**
(0.029)
Constant
2.588**
(0.256)
1.971**
(0.258)
2.077**
(0.243)
1.971**
(0.258)
2.067**
(0.245)
1.837**
(0.279)
N
4891
4891
4891
4891
4891
4891
AIC
BIC
11867.0
11932.0
72291.3
72356.3
40038.9
40103.8
72291.3
72356.3
38230.8
38302.3
11902.2
11980.1
* p<0.05; ** p<0.01
Table 13: Complete regression results.
The Problem of too many Zeros
55
A4. Firststep regression results of the Hurdle and the ZeroInflated models
Variables
Fuel price
PPML
0.339
(0.272)
ZIP
0.339
(0.272)
NB
0.339
(0.272)
ZINB
0.336
(0.273)
Income (real)
1.029**
(0.078)
1.029**
(0.078)
1.029**
(0.078)
1.031**
(0.078)
Household size
0.627**
(0.072)
0.627**
(0.072)
0.627**
(0.072)
0.629**
(0.072)
Employed
0.085
(0.058)
0.085
(0.058)
0.085
(0.058)
0.086
(0.058)
Constant
6.851**
(0.566)
6.851**
(0.566)
6.851**
(0.566)
6.865**
(0.569)
* p<0.05; ** p<0.01
Table 14: Probit regression results of the first step for the Hurdle and the ZeroInflated models. Note that the
Probit model of the ZeroInflated models estimates the probability for the observation being zero and not 1 as it
is the case for the Hurdle model. Therefore the signs of the coefficients shift between the ZeroInflated and the
Hurdle models.
3 Climate Policy Measure Index
Chapter 3
Climate Policy Measure Index
Julian Dieler*
Climate policy has been a prominent topic in the political arena as well as in several
fields of research for many years already. Although protecting the climate is originally an
international issue, the up to now limited success of international climate negotiations has
resulted in the fact that most action against climate change has been taken on country
level so far. To enable the analysis of countries’ policies to reduce GHG emissions it is
useful to have a composite measure which integrates the multitude of individual policy
measures into one figure. This is what the Climate Policy Measure Index (CPMI) is designed for. It facilitates the analysis of the stringency and effectiveness of climate policy
in the countries. The CPMI provides crosscountry comparisons of the stringency of climate policy as well as a comparison of the development over time (19912012). The major
contribution to the existing indices in this field is the entirely datadriven aggregation of
the several subindicators to one composite indicator, which also considers the effectiveness of the respective policy category with respect to the reduction of the emission intensity of production. Thereby the CPMI provides a high degree of objectivity and transparency, which is of high importance for scientific applicability as well as for the informative
value for the public.
*
ifo Institute for Economic Research at the University of Munich, Germany, +49/89/92241346,
dieler@ifo.de
Climate Policy Measure Index
57
3.1 Introduction
The main purpose of indices is to integrate large amounts of information into a format
which provides an easily accessible overview of the underlying information. Wellknown
examples for indices are stock market indices like the Dow Jones or DAX. They comprise
price information about all the different stocks which are part of the particular stock market index and in practice they are interpreted even broader, namely as indicator for the
entire stock market’s performance. In the field of climate related policies the need for information condensation is at least as high if not even higher than on the stock market. For
climate policies, it is hardly possible to get a quick overview of the current effort of countries to curb greenhouse gas (GHG) emissions, not to mention of their success in doing so.
This is even the case looking at one country only without crosscountry comparisons. This
has several reasons, first of all the quantity of different policies. The United States, for
example, currently have more than 130 climate change related policies in place on national level.31 Second, it is difficult to compare the different policy measures as they differ
with regard to their type (e.g. economic, command and control or information and education instruments) and with regard to their target (e.g. electricity, heat or transport). And
third it is difficult to assess the relative importance of a policy, meaning the potential
GHG emission reduction by a certain instrument. Thus, it is not surprising that there is a
long list of indices dealing with climate issues.32 Surminski and Williams (2012) describe
the history of climate related indices as an evolution which started with indices measuring
outcome variables like emissions or degrees of environmental pollution. Nowadays the
focus shifts more and more towards looking for indicators which give information about
the input variables. Input variables can either be actual input factors like resources, or policy measures which can also be seen as input factors in the various processes which lead
to GHG emissions. Among the 33 commonly referenced environmental indices (Surminski
and Williams 2012) there are only 6 indices which include climate or environmental policies in their assessment. The way of incorporating policy measures varies considerably
between the different indices. The Climate Change Performance Index (CCPI) by Germanwatch (2013), for example, compile the policy oriented subindicator based on expert
opinions. By contrast the Climate Laws, Institutions and Measures Index (CLIMI) introduced by Steves and Teytelboym (2013) and the Index of Sustainable Energy (ESI)
from the European Bank for Reconstruction and Development (EBRD, 2008) rely on
scores they award to each considered policy in comparison to other countries’ effort in
31
32
This number is taken from the IEA Policies and Measures Database (IEA, 2015a). It depicts the number
of policy measures against climate change, which are currently in place and were introduced after 1973.
The figure stated must even be seen as a lower bound, as the list of the IEA is not completely exhaustive.
See Annex 1 in Surmsinki and Williamson (2012) for a comprehensive list of environmentally related
indices.
58
Chapter 3
this measure. These three examples already clearly show the difficulties implied by the
construction of a policy index: The first difficulty is finding a measurement method for
the different policies which cannot be measured metrically. This includes all regulatory
policies as well as information and education instruments. The second difficulty, once a
measurement method is found, is to make the indicators comparable between countries
(see Holzinger (2006) for an extensive discussion of comparability problems of indices).
Another challenge in the construction of policy indices is, of course, the data availability
problem which increases in the targeted number of countries and time periods the policy
index shall cover. The less countries, or the shorter the time frame covered by the index,
the better is the data quality, obviously. But at the same time the information value is very
limited for an index consisting of two or three countries only, as it is prone to problems of
sample selection. Another difficulty is finding meaningful weighting factors for the aggregation of the subindicators. This problem is in most cases solved by simply using
equal weights. The first two problems can at least to some extent be circumvented by focusing on metrically measurable policy instruments like taxes, subsidies or tradable permits. This approach is already applied by Künkel and her coauthors (Künkel et al. 2006)
in constructing their Climate Performance Index (CPI). In contrast to the indices mentioned earlier, the CPI concentrates on climate policy and does not assess the development
of several other subindicators like the development of GHG emissions, or the ecosystem
vitality which are outcome indicators of climate policies.
The Climate Policy Measure Index (CPMI) introduced in this study follows the basic approach of the CPI by focusing on metrically measurable climate policy measures. At the
same time it makes two significant contributions: Firstly, the CPMI covers 33 countries33
instead of 24 OECD countries covered by the CPI. Furthermore, the CPMI is constructed
for the continuous period between 1991 and 2013 instead of three single years (1992,
1997 and 2005) as it is the case for the CPI. The second contribution of the CPMI is
methodology wise. Instead of using educated guesses for the weights or equal weights the
CPMI uses data based weights for the subindicators of the different policies considered.
To be more precise, the weighting inside the subindicators is done by using shares reflecting the scope of the respective policy instrument. For the weighting of the subindicators, estimated elasticities are used, which describe the impact of the subindicator
on the emissionintensity of production. Thereby the stringency measure of the CPMI
consists of two parts: First, it consists of the level of the policy and second it consists of a
measure for the effectiveness in reducing carbon intensity. This implies that a high tax on
e.g. fossil fuel consumption alone is not enough to rank high in the CPMI. The tax also
has to be effective with respect to reduction of the carbon intensity. Both aspects will be
33
These are all OECD countries except for Iceland. The absence of Iceland is due to data availability.
Climate Policy Measure Index
59
referred to as stringency in the remainder of this study. The goal of the resulting index is
not only to provide information about the development of the stringency of a country’s
climate policies over time and relative to other countries but also to serve as a sound basis
for empirical analysis.
The overarching results of the index are that the stringency of climate policies seems to
have increased in recent years and that despite the variation in the rankings over time
there are few countries which always perform well with regard to the stringency of climate policies. The CPMI provides the possibility to track the strictness in climate policy
for each country included over time as well as a crosscountry comparison at specific
points in time. A byproduct of the empirical analysis for the calculation of weighting factors is the finding that the impact of climate policies is rather small expressed in elasticities and that subsidies on renewable energy are more effective than taxes on fossil fuel
products and carbon prices in decreasing the emission intensity of GDP.
The paper is structured as follows: The next section briefly summarizes the existing climate policy indices with regard to the applied methodologies and results. In the subsequent section, the data which is used to construct the CPMI is presented. Section 3.4 looks
at the theoretical framework behind the CPMI and the methodology applied to compile the
index. The following section 3.5 summarizes the results. Furthermore section 3.5 will present some sensitivity analyses to check for the robustness of different standardization
methods. Finally, section 3.6 discusses the results and concludes.
3.2 Short overview of existing climate policy indices
As already mentioned in the introduction there are a lot of indicators related to environmental and/or climate issues. Surminski and Williamson (2012) singled out 33 environment or climate related indices from a very comprehensive policy index survey conducted
by Bandura (2008), who identified 178 different indices over all fields (economic, political, social or environmental topics). Most of the 33 environmentally related indices assess
the environmental status, for example by measuring emissions of pollutants or the degree
of water pollution. The motivation for the construction of these so called outcome or impact indicators was to measure the success or failure of governments in their efforts to
improve the environmental status. When it comes to the measurement of countries’ strictness in environmental policies, however, outcome indicators are only helpful to a certain
degree, because the emissions of GHG, for example, have many other drivers apart from
the government’s effort to reduce emissions. A good example for the multiple dimensions
of GHG emissions is the recent shale gas revolution in the United States. The development of shale gas and its partial substitution of coal in the electricity production was definitely one important factor in bringing down total CO2 emissions in the United States.
Chapter 3
7000
60000
6000
50000
5000
40000
4000
30000
3000
2010 US$
Million Metric Tons
60
20000
2000
10000
1000
0
0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Total CO2emissions
CO2emissions from Coal
CO2emissions from Natural Gas
GDP per capita
Figure 10: Emissions and GDP in the United States (source EIA (2015a) and OECD (2015a), own diagram)
Figure 10 depicts total CO2 emissions as well as CO2 emissions from Coal and Natural
Gas which also includes shale gas. It can be seen that natural gas consumption is picking
up from 2005 onwards, whereas coal consumption starts shrinking at still increasing economic activity measured by GDP per capita. At the same time total emissions start decreasing which could be due to a partial substitution of coal by gas which has lower carbon content. Another important driver of CO2emissions is definitely economic activity as
can be seen in Figure 10 as well. Therefore the focus of indices which are supposed to
measure the strictness of environmental policies shifted in recent years more and more to
the attempt of measuring the policies impact directly as an input in the emission generating process. That means the strictness of climate policy is no longer measured by means
of outcome, like the amount of emissions, but it is measured by the design of single policy
measures which intend to curb emissions. The latter indicators will be called direct policy
indicators for the remainder of this study. In the following six indices will be presented,
which either consist of direct policy indicators mixed with other indicators, or solely focus
on direct policy indicators.
Table 15 gives an overview of currently available indices including direct policy indicators. They are compared with respect to their geographic and temporal coverage, the actual policy measures underlying the direct policy indicator and the applied weighting and
aggregation method. It is also depicted which indicator types are applied. Pure indicators
solely consist of direct indicators measuring the policy variables and mixed indicators
apply direct as well as outcome variables as indicators. Apart from the ISE (EBRD 2008)
all indices cover a large share of the OECD countries if not all of them. The ISE has a
Climate Policy Measure Index
61
special focus on the development of environmental policies in the transition countries and
they include merely Germany, the Netherlands, Spain and the UK as benchmark countries.
Especially the Environmental Performance Index (EPI) (Hsu et al., 2013), the CLIMI
(Steves and Teytelboym 2013) and the CCPI (Germanwatch 2013) cover a huge number
of countries up to 178 including all OECD and the five BRICS countries. The indices can
further be differentiated regarding the time horizons they cover: There are three indices
(CCPI, CDI (Birdsall and Roodman 2003) and EPI) which are compiled on a regularly
basis, meaning yearly or even twice a year (EPI). The other three indices were provided
only once or three times in case of the CPI (Künkel et al. 2006). The policy types covered
in the different indices vary a lot, as does the amount of policies included in the indices.
All but one index contain metric as well as nonmetric policies. Metric policies are, for
instance, taxes or subsidies which put tangible numbers on the consumption or production
of a special good which can be made comparable by means of normalization. This is not
the case for nonmetric policies like regulation on energy efficiency or information and
education measures, for example. Therefore those indices which contain nonmetric
measures use scores, which are attributed to the particular policies. These scores either
stem from surveys among experts (CCPI) or they are determined relatively in comparison
to national goals or in comparison to the design of equivalent policies in other countries
which are part of the sample. The only index which focuses on metrical measurable policies is the CPI.
Direct policy indicators
Methodology
Climate Change
Performance
Index (CCPI)
58 countries
(incl. OECD &
BRICS), annually since 2004
Structure: Policy is one of three
subindicators.
Weighting: Fixed weighting factors
based on educated guesses.
Commitment to
Development
index (CDI)
27 OECD
countries,
annually since
2003
95 countries
(incl. OECD &
BRICS), 2011
Policies: promotion of renewable energies, increase of efficiency and measures to reduce
GHG emissions
Measurement: Expert ratings
Policies: fishing subsidies and
regulation of illegal timber
exports
Measurement: relative scores
Policies: Broad scope of policies divided into 4 policy areas
(International cooperation,
Domestic climate framework,
Sectoral fiscal or regulatory
measures or targets and Crosssectoral fiscal or regulatory
measures)
Measurement: relative scores
Climate Laws,
Institutions and
Measures Index
(CLIMI)
Climate Performance Index
(CPI)
24 OECD
countries,
1992, 1997
and 2005
Policies: 6 areas of policies
(e.g. Industry, Energy Efficiency). As indicator for the climate
policy in each of these areas
there is at least one policy (e.g.
taxes, subsidies).
Measurement: Only metric
indicators, can be measured
directly
Policies: agricultural subsidies
and pesticide regulation
Measurement: relative scores
Structure: Environment is one of
six subindicators
Weighting: Fixed weighting factors
based on educated guesses.
Structure: Structured according
the policy areas, no nonpolicy
subindicators included
Weighting: Fixed weighting factors
based on educated guesses for
Policy indicators and for most of
the single policies except for the
sectoral policies. They are
weighted according to emissions
affected.
Structure: Structured according
the policy areas, no nonpolicy
subindicators included
Weighting: The subindicators for
the areas as well as the policy
measures in the different areas are
fix and equally weighted.
mixed
Coverage
pure
Index
Type
Chapter 3
62
178 countries
(incl. OECD &
BRICS), biannually since
2002
mixed
Structure: Environmental policy
indicators are included in the subsubindicator agriculture which in
turn is part of the subindicator
ecosystem vitality.
Weighting: Weights are set according to the assessment of the data
quality of the single variables and
the importance of the indicators
and categories with respect to
policymaking.
33 countries
Policies: subsidies, taxes
Structure: Index consists of three
Index of SusMeasurement: relative scores
components: Energy Efficiency,
(mostly fortainable Energy
Renewable Energy and Climate
mer USSR and
(ISE)
4 European
Change which are evaluated on
comparative
basis of the three pillars institutions, market incentives and outcountries),
comes. Policy measures are includ2008
ed in the first two pillars.
Weighting: The components are
equally weighted as well as the
policy measures inside the three
pillars.
Table 15: Methodological overview of existing climate policy indices (source: methodology reports of the indices,
own illustration)
Environmental
Performance
Index (EPI)
Climate Policy Measure Index
Country
63
CCPI
CDI
CLIMI
CPI
EPI
Australia
21
21
22
22
2
Austria
13
11
13
11
6
Belgium
8
8
11
16
22
Canada
22
22
21
10
17
Czech Republic
16
5
12
21
3
Denmark
1
6
6
3
10
Finland
15
3
1
1
13
France
5
10
2
7
19
Germany
10
9
10
4
4
Greece
19
15
16
19
16
Hungary
6
2
18
17
20
Ireland
7
17
9
12
14
Italy
9
13
14
9
15
Japan
20
20
15
13
18
Netherlands
14
12
8
5
9
New Zealand
17
16
17
14
11
Norway
12
19
5
6
8
Portugal
2
4
19
20
12
Spain
11
7
4
18
5
Sweden
3
1
7
2
7
Switzerland
4
14
3
8
1
ISE
4
1
5
3
2
18
18
20
15
21
United States
Table 16: Rankings of the different indices applied to a subsample of countries which are covered by all indices
except for ISE. The rankings stem from the most recent versions of the indices (source: own illustration).
The most prominent weighting method to aggregate all the single indicators is the use of
fixed weights. The CPI and the ISE use fixed and equal weights for aggregating the single
components of each indicator. The CLIMI uses fixed and equal weights as well, except for
the sectoral policy indicators, which are calculated with the help of each sector’s share in
emissions. Another datadriven weighting method is to make use of the data quality of
each policy indicator. This means to evaluate the indicators according to completeness,
number of missing observations or representativeness, for example (Hsu et al. 2013). The
Yale Center for Environmental Law & Policy uses data quality and expert opinion for
their EPI. Germanwatch (2013) and Birdsall and Roodman (2003) rely on expert opinion
only for determining the weighting factors of the CCPI and CDI.
In anticipation of section 3.4, which sets out the methodology of the CPMI, the contribution of the CPMI becomes very clear at this point. Similar to the CPI, the CPMI covers
metric policy measures only, in order to be able to rely merely on figures. What distinguishes the CPMI, however, from all existing climate policy indices is the fully data
Chapter 3
64
driven determination of the weighting factors and the inclusion of an effectiveness measure which is estimated empirically.
Regarding the multitude of different approaches and methods in compiling indices it is not
surprising that the results of different indices vary quite substantially. Table 16 shows the
country rankings for the reviewed indices applied to a subsample of 22 countries. This
subsample was selected as it represents the countries which are included in all indices except in the ISE. The intersecting set of the ISE with all other indices is rather small due to
ISE’s focus on transition countries. Table 16 emphasizes the importance of sensitivity
analysis of the outcome of an index with respect to the selection, weighting, normalization
and aggregation of variables (Freudenberg 2003).
3.3 Data
The CPMI covers all OECDcountries except for Iceland, which is due to data availability.
Thereby it covers nearly 40%34 of current worldwide CO2 emissions. The time horizon
includes the years from 1991 until 2013. The year 1991 is chosen because hardly any climate policies were passed before that date, at least not with the intention to reduce GHG
emissions. The United Nations Framework Convention on Climate Change (UNFCCC)
was founded in 1992 and can be seen as the starting point of international as well as national efforts to curb anthropogenic climate change (UNFCCC 1992). The latest year for
which it is possible at the moment to construct such an index due to data availability is the
year 2012.
As pointed out several times already, the CPMI consists exclusively of metric measurable
policy measures. Specifically, it is composed of three policy categories: Taxes on energy
products (oil and coal products, natural gas and electricity), subsidies on renewable energy
production and carbon taxes or emission trading systems (ETS), respectively.
The taxes on energy products stem from the IEA Energy and Prices Statistics database
(IEA 2015b). The International Energy Agency (IEA) provides taxes35 as well as prices on
a very disaggregated basis regarding the different energy products. In total the IEA Energy
and Prices Statistics comprise 14 different products, including multiple types of fuel oils,
diesel, different grades of gasoline, LPG, natural gas, two types of coal products (steam
and coking coal) and electricity. If there is more than one price or tax available per product and country, the IEA creates a representative price or tax series for that product (IEA
2012). Next to the differentiation according to the different product types, the taxes are
also distinguished by the sectors in which they incur. These sectors are industry, households and energy generation. Considering the unit of the prices and taxes, the user can
34
35
Calculated on the basis of emission levels from 2012 (EIA, 2015a).
Subsidies on fossil fuel consumption are also included and embodied as negative tax rates.
Climate Policy Measure Index
65
choose between national currency (nat. cur.) per unit and nat. cur. per ton of oil equivalent
(toe) which is a measure of the energy content.36 For constructing an index, the latter unit
is of course preferable as it is comparable over all energy products. The conversion into
nat. cur. per toe is done by the use of country specific conversion factors (IEA 2012, pp.
11).
Unfortunately, there is not such a rich database for policies which foster the energy production from renewable sources as there is for the taxes on energy. Considering one of the
most common policy measures to subsidize renewable energy production – feedin tariffs
– one would need differentiated tariffs according to the different technologies and the energy production of the respective technologies to calculate a weighted average feedin
tariff per country. So far there is no collection of such data for the OECD countries over
time. Therefore policies which promote renewable energy production are measured by a
proxy in the CPMI. The chosen proxy is the installed capacity of renewable energy production, or more precisely the growth of the installed capacity, which is the key figure
when it comes to the measurement of investment or production subsidies. The growth of
the installed capacities is a more direct measure than for example production shares of
renewables, since the energy production depends on even more factors than the installed
capacity, such as weather conditions or the structure of energy demand. In contrast to the
energy tax indicator, which is an input measure which directly measurs the policy activity,
the indicator for subsidies on renewables is a more indirect measure as it measures a first
policy output. Surminski and Williamson (2012) call this type of indicator an intermediate
indicator because although it measures a policy output it is not the intended final output of
the policy, which would in this case be the reduction of GHG emissions. The indicator for
the fostering of renewable energy sources includes all forms of renewable energy
sources.37 The data on installed capacities is taken from the IEA statistic OECD – net Capacity of Renewables (IEA 2015d). The capacity is measured in MW electricity equivalents (MWe) as the data includes plants which produce electricity as well as heat.
The third policy category consists of carbon pricing. This includes both carbon taxes and
ETSs. In the OECD countries there are currently 7 different trading systems in force (see
Table 20 in the Appendix for an overview of all carbon pricing mechanisms in OECD
countries). An eighth ETS, the Carbon Pricing Mechanism (EPM) of Australia, is also
covered by the CPMI. But this one has already been abolished after being in force for only
two years (2012/2013). In 28 of the 33 countries considered, there are (or were) trading
schemes in place. The CO2 emissions covered by the 8 ETSs amount to 6.12 billion tons
36
37
1 toe corresponds to an energy content of 107 kcal.
The renewable energy sources included are: Hydro, Geothermal, Solar, Tide/Wave/Ocean, Wind, Waste
and Biofuels.
Chapter 3
66
of CO2. That makes up approximately 17% of global CO2 emissions.38 A carbon tax is
applied in 12 of the countries included in the CPMI. Apart from Mexico, all carbon tax
countries also make use of an ETS, with Canada’s ETS and carbon tax affecting two different regions (Québec and British Columbia). Further ETSs which apply to parts of the
country only are the Japanese (Tokyo and Saitama) and the US (California and Northeast)
systems.
3.4 Theoretical Framework and Methodology
Climate Policy Measure Index (CPMI)
Taxes on energy products
Oil products:

Heavy fuel oils
Light fuel oil
Automotive Diesel
Gasolines
Automotive LPG
Gas:
 Natural gas
Subsidies for renewable
energy
Installed capacity of renewable energy sources
 Geothermal
 Solar (Photovoltaics/Thermal)
 Tide/Wave/Ocean
 Wind
 Waste
 Biofuels (solid/liquid)
Carbon pricing
 Emission Trading Systems
(ETS)
 Carbon taxes
Coal products:
 Coking coal
 Steam coal
Electricity
Taxes are recorded on sector level:
Households
Industry
Energy
generation
Figure 11: Structural overview of the CPMI (source: own illustration)
Following the classification by Freudenberg, the CPMI is a Composite indicator consisting of three thematic indicators, the three policy categories: taxes on energy products,
subsidies on renewable energy sources and carbon pricing. They in turn consist of different individual indicator sets (Freudenberg 2003, p. 7). Figure 11 gives an overview of the
structure of the CPMI. This section explains the structure in a bottom up style, meaning
that the composition of the three policy categories with their individual policy indicators
are explained first, and in a second step the aggregation to one single indicator is addressed.
38
Calculation is made for the year 2013.
Climate Policy Measure Index
67
3.4.1 Taxes on Energy Products
The energy taxes category covers taxes on all fossil fuels (oil products, natural gas and
coal products) and on electricity. In 2010 the combustion of fossil fuels accounted for
93% of all CO2 emissions in the OECD countries.39 As already pointed out in the data section, the tax data (IEA 2015b) is available on a highly disaggregated product level. Some
of the products listed in Figure 11 consist themselves of several sub categories of products
like Gasolines, for example. Figure 11 also illustrates the differentiation of the taxes with
respect to the three sectors: households, industry and the energy generating sector. In total
there are 23 different tax combinations between products and sectors, as not every sector
uses all products. One drawback of the Energy Prices and Taxes statistic is the not negligible amount of missing values. Especially problematic for the construction of an index
over time are missing values within a time series since this would be equivalent to the
abolishment of a tax which is a rather rare event (cf. Bauer 2006). Furthermore the database indicates the reason for missing values. It states if a missing value is not observed
due to the fact that it simply was not collected or due to the fact that the data point cannot
exist. In the first case, which is the case of a missing value in its original sense, a data
imputation method is applied in this study which perpetuates the tax in a constant way.
This extrapolation is used until new information is available or until the end of the respective time series. This prevents the energy tax indicator from unrealistic jumps and by using constant extrapolation there is no artificial variation introduced to the data, which
could harm the empirical analysis of the tax indicator. On average there were 2.5 data imputations necessary per country and tax time series which comprise of 36 years each, since
the tax data is available for a longer time period40 than the renewable energy capacity data, for example. In the second case, the case in which there is no tax, the observation is set
to zero.
For transforming the taxes of all 23 different productsector combinations into one single
indicator it is necessary to weigh and aggregate the single taxes. The weighting inside this
subindicator is done by using the respective yearspecific consumption. For this purpose
again, data from the International Energy Agency (IEA) is used41. This guarantees consistency with regard to the data collection and classification method. One could suspect
that, using consumption shares as weighting factors leads to a certain cannibalization especially of effective energy taxes in the CPMI, as an increase of those would lead to a
decrease in its share. But this is only the case if there is a high degree of between product
39
40
41
The remaining 7% stem from fugitive emissions and industrial processes. Source: own calculation on the
basis of IEA (2015e and 2015f).
The Energy Prices and Taxes statistic (IEA 2015b) provides data for the period from 1978 until 2013.
Consumption data sources for the different product types: Oil products (IEA 2015g), coal products (IEA
2015h), natural gas (IEA 2015i) and electricity (IEA 2015j).
Chapter 3
68
leakage, meaning that the consumption of the highly taxed product is substituted to a large
extent by another product; only then the denominator (i.e. total consumption) would stay
constant. For the case in which the increased tax leads to a true reduction in the consumption of fossil fuels, the share more or less stays the same. For the leakage case, on the other hand, it is sensible under policy effectiveness considerations that the share decreases.
Since the consumption data is provided in product specific units like liters in case of gasolines or tons in case of heavy fuel oils, it has to be standardized before calculating the sector and product specific weights. This is done by using product and country specific conversion factors42 which allow converting the product consumption units into toe. The
weighting based on the consumption allows the energy tax indicator to be interpreted as
the average tax paid on energy products per toe in the three observed sectors.
3.4.2 Subsidies for Renewable Energy
As explained in the data section, subsidies on renewable energy sources are represented
by the sum of the installed capacity of renewable energy technologies producing either
electricity or heat. To make both types of energy comparable the capacity is measured in
MWe. Since the main interest of the CPMI lies in the measurement of the stringency of
countries’ climate policies, the annual growth of the installed capacity is used to represent
subsidies on renewable energy technologies. If alternatively percentage changes were
used, countries which start from very low levels would have huge percentage changes in
the first years. Therefore absolute changes are applied. Since a 10 MWe expansion in the
United States, however, is to evaluate differently from a 10 MWe expansion in Luxembourg the absolute changes are corrected by country size by using the absolute growth in
installed capacity per capita.
3.4.3 Carbon Pricing
The carbon pricing indicator is composed of carbon prices achieved by ETSs and of carbon prices set by a carbon tax. For the crosscountry comparison with regard to carbon
prices two things are important next to the carbon price, provided that there is a carbon
pricing mechanism. First, the coverage of the pricing mechanism, meaning how many CO2
emissions are governed by the respective mechanism, and second, the number of pricing
mechanisms as in some countries there is an ETS as well as a carbon tax in force (see Table 20 in Appendix). To generate an average carbon price for each country, the single carbon price indicators, similarly to the energy tax indicator, have to be weighted. First of all
this is done by the share of emissions covered by the respective mechanism relative to the
total GHG emissions of each country. Data on total emissions is taken from the IEA
42
The conversion factors are taken from the IEA report “Energy Prices and Taxes: Methodology Notes”
(IEA, 2012).
Climate Policy Measure Index
69
(2015e). Both are measured in CO2 equivalents. In those 11 countries in which both instruments are applied simultaneously the average of the weighted carbon prices is taken.
This excludes double charging of companies, meaning it is assumed that taxpayers either
pay carbon taxes or buy emission certificates but not both. This simplifying assumption
has to be made due to missing information about the potential overlap of the systems. Anecdotal evidence shows that in many countries considered, taxpayers are governed by one
or the other system and not by both. The emissions covered by the ETSs are provided by
the status reports of the International Carbon Action Partnership (ICAP 2015). For the
European ETS (EU ETS) country specific data is available at the European Environment
Agency (EEA 2015). The information about the carbon tax coverage stems from the
World Bank (2014).
3.4.4 Aggregation of the Subindicators
To aggregate the three subindicators to one single index, the subindicators have to be
standardized and weighted. Standardizing is necessary because of the different units and
scales shown by the subindicators. Aggregating the taxes on energy products and carbon
pricing mechanisms could easily be done since both indicators represent a cost per unit
consumed, which could be converted in a way that they have equal basis. Transforming
the subsidy indicator into a cost indicator, however, is not so trivial. Therefore the indicators are standardized before they are weighted. The standardizing method chosen is the
most commonly applied method, namely the standard deviation from the mean as it has
several advantages compared to other methods. It puts all subindicators on a common
scale which is normally distributed with zero mean and standard deviation equaling one.
The zero mean prevents the CPMI from being dependent on the different scales of the
subindicators (Freudenberg 2003) and makes variables with completely different units
comparable. The scale of the subsidy indicator, for example, is by far larger than those of
the other two subindicators and also its unit is MWe compared to dollars per unit consumed. As the CPMI allows comparison of the policy performance between countries at a
certain point in time and comparison over time for individual countries, the standardization has to be done in two dimensions. For the crosscountry comparison, each subindicator is standardized per year over all countries. This means that each country’s performance is assessed in each of the three policy categories in relation to those of the other
countries in a specific year. To also account for differences in the purchasing power in the
different countries, the originally national currencies are converted to U.S. dollars using
Power Purchasing Parities (PPP). Thereby a 10$ tax in Chile can be compared with a 10$
tax in Denmark. For the withincountry comparison over time, the three policy categories
are standardized using each subindicator’s time series of the respective country to depict
the performance of a certain year relative to the performance of other years.
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70
Not only the weighting inside the subindicators, but also the weighting between the subindicators of the CPMI is data driven, which is one of the major contributions to the climate policy literature. Many composite indicators use equal weights or educated guesses
about the importance of the single components. For the CPMI the impact assessment for
the three subindicators is done empirically by estimating elasticities. The theoretical
background for the estimation equation is the Kaya identity which is oftentimes the foundation for the emission scenarios in Integrated Assessment Models (IAMs) (IPCC 2000).
The Kaya identity decomposes the anthropogenic CO2 emissions into four factors (Kaya
1990):
=
(1)
.
These four factors constitute at the same time the leverages to reduce GHG emissions
whereby the first two factors, population size and GDP per capita, are usually not part of
the choice set of politicians while trying to reduce emissions. The factors left, which can
be addressed by climate policy, are the last two: Energy intensity per GDP and emission
intensity of energy production. Since climate policies impact both intensity components
simultaneously, energy intensity of GDP and emission intensity of energy production are
combined to emission intensity of GDP. This modified version of the Kaya identity constitutes the basis for the estimation equation which shall produce the weights of the three
policy indicators. The empirical strategy follows a twostep approach in which the impact
of the policies on the emission intensity of GDP is estimated in a first step, and in a second step the modified Kaya identity is estimated with an instrumented emission intensity
of GDP.
First stage:
=
+
+
+
(2)
Second stage:
=
+
+
+
The regression model described in equation 2 is estimated by a twostage least squares
paneldata model using countryfixed effects in a loglog specification. The panel structure of the data is clarified by the subindices and , standing for the country and the year
respectively. The loglog specification provides the nice property that the coefficients can
directly be interpreted as elasticities. In the first stage, the individual impact of the three
policy categories on emission intensity is estimated. The emission intensity is taken from
Climate Policy Measure Index
71
an EIA database (EIA 2015b). In the second stage this estimated emission intensity43 is
regressed on the CO2 emissions (IEA 2015e). It should be noted that this twostep regression is not used as an instrumental variable approach but the twostep structure should
account for the channel through which policy measures can impact emissions in the
framework of the Kaya identity, namely through the channel of emission intensity of
GDP. The main goal of this exercise, however, is not to explain the determinants and their
impact on CO2 emissions, but to obtain weights for the three policy categories of the
CPMI. Therefore the focus lies on the regression results of the first stage. For completeness Table 17, however shows the regression results of both stages.
Emission intensity of GDP
(1st stage)
Emission intensity of GDP
Emissions
(2nd stage)
0.829**
(0.096)
Emissions (t1)
Population
GDP per cap.
Taxes on Energy
0.659**
0.163*
(0.031)
(0.071)
0.519**
0.654**
(0.081)
(0.077)
0.795**
0.763**
(0.031)
(0.089)
0.015**
(0.006)
Subsidies on Renewables
0.022**
(0.008)
Carbon Pricing
0.004**
(0.001)
2
R
N
0.84
627
0.88
627
* p<0.05; ** p<0.01
Table 17: Regression results for the weighting factors of the three policy categories
In addition to the regression model depicted in equation 2, the final regression includes a
lagged term of emissions to account for the dynamics in the development of CO2 emissions. Although the results of the second stage are not of major significance in this study,
they can serve as a plausibility check of the regression model. All factors of the second
stage are statistically significant and show the expected signs. It is not surprising that the
estimated emission intensity of GDP has a clearly positive impact on the emission level.
The same is true for the emissions from the year before which makes clear the trending
behavior. It is also not surprising that larger and richer countries emit more. Similarly to
the emission level, also the emission intensity of GDP depends positively on the level on
43
The estimated variable is labeled with a hat in equation 2.
72
Chapter 3
the emissions from the past period. The negative effect of country size in terms of population size cannot be interpreted causally, as it is highly correlated with the lagged emissions (correlation coefficient: 0.93). Thereby the negative population coefficient represents a smaller overall effect of past emissions rather than an impact of population on
emission intensity. This can be shown by leaving population out of the regression, which
leads to a smaller impact of the past emissions on the emission intensity. The coefficient
for GDP per capita, however, does measure the impact of GDP per capita as its correlation
with the past emissions is relatively small (correlation coefficient: 0.018). The elasticity of
GDP per capita shows the negative relation between income and emission intensity. The
most important result of the regression analysis, however, is the measurement of the climate policies’ impact on the emission intensity of GDP.
The first thing to be noticed is that the magnitude of the elasticities is relatively small. A
tax increase on energy products by 10%, for example, leads on average only to a reduction
of the emission intensity by 0.15%. This result is quite sensible as the demand for energy
products is rather inelastic. Typically taxes are more effective in the long run, but since
the regression model is a static model only year over year elasticities are estimated. Subsidies on renewable energy, here represented by growth of installed capacities of renewable energy plants, have a slightly larger elasticity in magnitude than taxes on energy consumption have. Nevertheless it is still small. This is surprising at first sight, as at least the
emissions from the electricity and heat production are impacted rather immediately by
more power plants using renewable energy sources. But two facts have to be kept in mind.
First, the so called rebound effect, which states that e.g. higher fuel efficiency reduces
consumption, which in turn leads to lower fuel prices, and as a result demand is increased
again. This goes back to Jevons’ Paradox (Jevons 1865). A second explanation might be
that capacity is not automatically equal to power submitted to the network. The capacity
installed has to be multiplied by the region and technologyspecific capacity factor,
which for most renewable energy technologies is far from being equal to one. Even smaller than the effect of renewable energy capacity is the effect of carbon pricing mechanisms.
This again is not so surprising looking at the design and the current prices of the ETSs.
Applying the estimated elasticity of the carbon pricing mechanism to the EU ETS, for
example, illustrates why the elasticity is so small. The current EU ETS price is around 8
USDollars per ton of CO2. Assuming now an increase by 375%, which would bring the
EU ETS price to 30 USDollars per ton of CO2, the resulting emission intensity reduction
would be 1.5%.
The weighting factors for each of the policy categories are simply calculated by the share
of the respective elasticity of the sum of the three elasticities. As the elasticities are estimated using a panel approach, it is implicitly assumed that all policies are equally effective in all countries of the sample and also equal effective over time. This leads to
Climate Policy Measure Index
73
weighting factors of around 38% for the taxes on energy products, 53% for the subsidies
on renewable energies and 9% for the carbon pricing mechanisms.
In case of missing observations for one of the three subindicators, may it be due to missing data or due to the fact that a country does not apply policies of a certain policy category in a certain year, the respective subindicator is treated as it would have taken the value
zero, which is equal to the mean of the distribution of the respective indicator. Given the
fact that there are quite a few countries, especially in the first years of the observation
period, which do not always apply policies from all three policy categories this is the most
consistent way to treat missing values. Also with respect of the fairness of the index it
seems sensible, as countries should have the possibility to choose which policies to apply
and they can always make up the absence of one policy type by acting more stringent in
the others.
The theoretical framework of the CPMI can be summarized by categorizing its components. As policy measures, the CPMI uses input variables in case of the taxes on energy
products and in case of the carbon pricing mechanisms. The policies to promote renewable
energy production are measured by an intermediate indicator, the growth in renewable
energy capacity. For the weighting inside the subindicators, for energy taxes and carbon
prices the coverage of the respective policy is used, which is also an intermediate indicator. The weighting of the subindicators is done using an impact indicator.
3.5 Results
For the interpretation of the results it is worth recalling the three main ingredients of the
index: taxes on the consumption of fossil energy products, the annual growth of the installed capacities of renewable power plants as proxy for the subsidies on renewable energy and carbon price mechanisms. Considering these three factors, it becomes clear that the
CPMI exclusively measures the political performance of governments to curb the emissions of GHGs and explicitly not the country’s performance with regard to environmental
friendliness in general. The latter issue is covered already by many environmental related
indices. Prominent examples are the EPI (Hsu et al., 2013) and the CCPI (Germanwatch,
2013). In these more general indices, policy is only one of many subindicators. The other
subindicators usually describe the current status of the environment and the consequences
of anthropogenic intervention, like fossil fuel or land use. That means these indicators mix
input and output variables to measure environmental performance. The CPMI, in contrast,
focuses on the assessment of current policy’s stringency, which is the input for the future
development of country’s environmental performance. This focus is important for policy
evaluation since it abstracts from historical or physiographic reasons for countries being
in a certain state of environmental friendliness. That implies that countries which already
run their economies at very low levels of emission intensity and do not have to apply very
Chapter 3
74
strict policies to stay on this level will perform worse than countries which currently consume large amounts of fossil fuels per GDP but at the same time adopt strict policies. This
is especially the case for the indicator of subsidies on renewable energy production. A
country like Norway, for example, has very limited scope to further develop the production of electricity and heat from renewables as the Norwegians already cover 99%44 of
their heat and electricity generation by hydro energy. Norway is a good example to visualize the policy focus of the CPMI because the large share of energy production from renewables is clearly no consequence of a super strict environmental related policy. It is
rather a consequence of the physiographic preconditions, which should not be included
when it comes to policy assessment.
In the remainder of this section, the rankings produced by the CPMI are presented: First
the crosscountry rankings and their development over time and then the development of
climate policy stringency within the 33 OECD countries. Subsequently the changes due to
robustness checks are presented.
3.5.1 Comparison of Climate Policy Stringency between the OECD Countries
As explained in section 3.4.4, the composite index is calculated from the weighted sum of
the standardized subindicators. The applied standardization method leads to zero mean
and a standard deviation equal to one. With regard to this distributional property the composite indicator is to be interpreted as follows: The values of the composite indicator can,
thanks to the standardization, be interpreted cardinally. Negative values indicate a below
average stringency of climate policy and positive values an aboveaverage stringency.
2
1.5
1
0
MEX
ISR
NZL
JPN
FIN
SVK
KOR
HUN
NLD
USA
CHE
FRA
IRL
CHL
AUS
GBR
AUT
CZE
ESP
LUX
POL
TUR
CAN
SVN
PRT
NOR
BEL
EST
DEU
SWE
GRC
ITA
DNK
0.5
0.5
1
1.5
Figure 12: Crosscountry comparison of the CPMI for the year 2012. (source: own illustration)
44
IEA (2015c).
Climate Policy Measure Index
75
Figure 12 shows the result of the crosscountry comparison of the CPMI. To get a feeling
for the size of the index values it should be noted that using a standard normal distribution, the probability of observing larger values than the standard deviation in absolute
terms amounts to around 16%, meaning that 68% of the observations lie between 1 and 1.
The probability of values larger than 1.5, like it is the case for Denmark, is 6.5%. Denmark is leading the 2012 CPMI which is mainly due to its high index value for the subindicator on renewable energy subsidies as Table 18 shows.
Table 18 depicts the index values of all subindicators as well as the values of the composite indicator for the 5 leading and for the 5 lowest performing countries of the 2012 index. It illustrates nicely which subindicators are driving the composite indicator. In the
first line of the table the percentage numbers in the brackets represent the weights of the
respective policy category. Although renewable energy subsidies have the largest
weighting factor, they are not dominating the overall result in all countries. In the case of
Greece, for example, the taxes on energy consumption are outweighing a below average
carbon price and a moderate index value for the promotion policies of renewables and let
Greece end up on rank No. 3 in 2012. The last three ranks are occupied by countries
which neither had a carbon tax nor an ETS in 2012. But this alone is not the reason for
them being at the end of the list. A nonexistent policy category is, as already mentioned
in section 3.4.4, treated as if the performance in the missing policy category were equal to
the mean performance of all countries.
#
Country
Composite Index
1
DNK
2
1.516503
Taxes on energy
products (38%)
1.319203
Subsidies for renewable energy (53%)
1.947698
Carbon pricing
(9%)
0.179337
ITA
1.187681
1.922957
0.9507561
0.4820045
3
GRC
1.181562
1.903111
0.8566154
0.0821725
4
SWE
1.115045
0.1823655
2.099523
0.7556109
5
DEU
0.9351041
0.4165622
1.513126
0.2838535
…
…
…
…
…
…
29
FIN
0.7963237
0.5202445
1.012222
0.6829728
30
JPN
0.8630255
1.614383
0.7381766
1.526999
31
NZL
0.9086341
0.7575453
1.171958
32
ISR
0.98918
1.212378
0.9999679
33
MEX
1.064404
1.394248
1.012201
Table 18: Decomposition of the CPMI for selected countries in 2012
Another interesting aspect is the development of the crosscountry rankings over time.
Although the index values, which are standardized on the crosscountry dimension, are
not comparable over time, it is still informative to compare how the rankings change over
time. Figure 15 and Figure 16 in the appendix depict the results from the crosscountry
Chapter 3
76
comparison of the CPMI for the years 1995 and 2000. The order of the countries follows
the order according to the 2012 ranking to allow for an easier comparison. The country
which is depicted on the far right in both figures is the country which performs best in
2012. Comparing the three cross sections, one recognizes that there are seven countries
which in all three years shown applied above average stringent climate policies. These
countries are Denmark, Italy, Greece, Germany, Norway, Portugal and Spain. On the other
end of the list there are nine countries45 which in the three crosscountry comparisons for
1995, 2000 and 2012 always lie below the average.
3.5.2 Climate Policy Stringency over Time
This subsection focusses on the development of the stringency for each country over
time. It should be noticed that these index figures are calculated differently from those in
the crosscountry comparison in the previous subsection. They differ with respect to the
standardization. The standardization for the index describing the development in a specific
country over time is done using the countryspecific time series of each subindicator: The
mean and the standard deviation are calculated e.g. for the weighted energy tax of Australia over the years from 1991 until 2012. Using this approach, the index values are not
comparable between countries, only within countries over time. Here, values below zero
indicate that the country was less ambitious regarding climate policy in that year than it
has been on average over the entire observation period. As an example Figure 13 shows
the development of the climate policy stringency of a country which compared to other
countries always performed above average (Germany), and the policy performance development of a country which performed below average (USA).
3
3
2
2
1
1
2011
2009
2007
2005
2003
2001
1999
1997
1995
1993
2011
2009
2007
2005
2003
2001
1999
1997
1995
1
1993
0
1991
0
1991
1
2
Figure 13: Development of the climate policy stringency over time in Germany (left) and the United States
(right). (source: own illustration)
In both countries an upward sloping trend becomes apparent, indicating an increase in the
stringency of climate policies in the observation period. In Germany the upward sloping
45
Australia, Chile, Hungary, Ireland, Israel, Netherlands, Japan, Slovakia, and USA
Climate Policy Measure Index
77
trend started in 1997, which was the year of the adoption of the Kyoto Protocol. In the
United States the upturn started a few years later: Until 2004, the CPMI curve is flat before it picks up in 2005. To also illustrate the development of the subindicators and their
impact on changes in the composite index, Table 19 shows the development of the three
sub indicators and the composite indicator for Germany and the United States over time.
To facilitate readability Table 19 only includes selected years, which reflect turning points
in the longterm development of the climate policy stringency of the two countries.
USA
Germany
Country
Year
Composite Index
Taxes on energy
products (38%)
Subsidies for renewable energy
(53%)
Carbon pricing
(9%)
1991
0.8285123
0.9308372
0.8977651
1995
0.6168438
0.4803099
0.8197761
2000
0.4644463
0.7292134
0.355675
2004
0.0143099
0.1261885
0.1167442
2005
0.1381289
0.0406204
0.2240553
0.0416831
2006
0.1499256
0.2049598
0.1088615
0.1610118
2007
0.0232697
0.5127357
0.030784
1.692772
2012
1.845303
2.045779
2.143751
0.7271517
1991
0.8034658
1.106913
0.7253025
1995
0.6580722
0.7324566
0.7179818
2000
0.6859618
0.5138606
0.9260562
2004
0.4814844
0.1317606
0.8129148
2005
0.0361335
0.2220805
0.0899931
2007
0.7607087
1.076566
0.6663826
2008
1.147765
1.656333
1.098572
0.6758973
2009
1.124437
0.8537427
1.637027
0.7414953
2010
0.7213451
1.200025
0.6358469
0.7665585
2012
2.379929
1.870009
2.98118
0.9897151
Table 19: Decomposition of the time series CPMI for Germany and the United States for selected years. (source:
own illustration)
The first noticeable point in the German development of the CPMI is in 2004, when the
indicator for subsidies on renewable energy lets the CPMI turn above average for the first
time. The kink in the CPMI curve in 2007 has two reasons. First it was the last year of the
first period of the EU ETS and the certificate price nearly dropped to zero. The second
reason is a decrease in the expansion of renewable energy capacity. The latter might have
been caused by investment uncertainties about future subsidies induced by discussions
Chapter 3
78
about an EEG novel in 2008. In the United States, the three indicators develop more congruent to each other than in Germany. The drop in 2010 is mainly caused by the introduction of the Regional Greenhouse Gas Initiative (RGGI) in nine northeastern states46 in
2008, which started off with rather low prices of around 2$ per ton of CO2. Another reason is the cutback in the expansion of renewable energy capacity. Germany and the United
States are of course only a small fraction of the entire sample of OECD countries but they
reflect nicely two general patterns which all countries in the sample exhibit. These are the
upward sloping trend in the climate policy stringency and the fact that the most volatile
measure in the CPMI is the subindicator on renewable energy subsidies measured by the
annual growth of installed capacities of renewable energy. To check the dependence of the
CPMI on the weighting factors of the three policy categories, the next subsection presents
some robustness checks.
3.5.3 Sensitivity Analyses
It should be stressed once again that the weighting factors are calculated based on estimates of the impact of the respective policy on the CO2 emissions. Thereby the weighting
factors of the CPMI are not subject to e.g. expert opinions on the importance of the individual policy categories, which is the case for many climate policy indices (see Table 15).
However, not only weighting factors based on expert opinions, but also empirically
founded weighting factors have to deal with uncertainty. Therefore it is necessary to conduct some robustness checks with respect to the weighting factors. The sensitivity analyses are illustrated using the most recent cross country results of the CPMI47, since the
weighting factors play a bigger role for the cross country comparison. The policy development of single countries is not so heavily influenced by the weighting factors as the
overall trend of increasing stringency also applies to the development of the three subindicators. But for the comparison of countries the weighting of the three policy indicators
has a larger impact as countries differ with regard to their policy focus. For instance, those
countries which mainly focus on energy consumption taxes and not so much on subsidies
for renewables are disadvantaged by the baseline weighting factors. In the baseline version of the CPMI, taxes on energy products have a share of 38%, subsidies on renewable
energy of 53% and carbon pricing mechanisms of about 9%.
The first robustness check is to decrease the weighting factor of subsidies. This is done by
using the lower bound48 of the 95% confidence interval of the estimate for renewable subsidies. A decrease of the subsidy weighting factor of course simultaneously increases the
46
47
48
Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New York, Rhode Island,
and Vermont.
The most recent result of the CPMI applies to the year 2012.
Lower bound in absolute terms as the elasticities of the three policy indicators are negative.
Climate Policy Measure Index
79
shares of the other two policy categories. The modified shares look as follows: energy
taxes 61%, renewable subsidies 24% and carbon pricing mechanisms 15%. While this
weighting factor modification leads to a substantial shift from subsidies to energy taxes,
the share of the carbon pricing mechanisms is still relatively small. To change that, a second robustness check is conducted by using the largest elasticity of the 95% confidence
interval of the estimate for the carbon price impact on CO2 intensity of GDP. The resulting weights are 56% for energy taxes, 21% for subsidies on renewables and 23% for the
carbon pricing mechanisms.
2
Weights
Energy taxes: 38%
0
Renewable energy subsidies: 53%
Carbon pricing: 9%
MEX
ISR
NZL
JPN
FIN
SVK
KOR
HUN
NLD
USA
CHE
FRA
IRL
CHL
AUS
GBR
AUT
CZE
ESP
LUX
POL
TUR
CAN
SVN
PRT
NOR
BEL
EST
DEU
SWE
GRC
ITA
DNK
1
1
2
2
Weights
Energy taxes: 61%
0
Renewable energy subsidies: 24%
Carbon pricing: 15%
MEX
ISR
NZL
JPN
FIN
SVK
KOR
HUN
NLD
USA
CHE
FRA
IRL
CHL
AUS
GBR
AUT
CZE
ESP
LUX
POL
TUR
CAN
SVN
PRT
NOR
BEL
EST
DEU
SWE
GRC
ITA
DNK
1
1
2
1
0
Weights
Energy taxes: 56%
Renewable energy subsidies: 21%
Carbon pricing: 23%
MEX
ISR
NZL
JPN
FIN
SVK
KOR
HUN
NLD
USA
CHE
FRA
IRL
CHL
AUS
GBR
AUT
CZE
ESP
LUX
POL
TUR
CAN
SVN
PRT
NOR
BEL
EST
DEU
SWE
GRC
ITA
DNK
2
1
2
Figure 14: Results of the sensitivity analysis with respect to the weighting factor. The first chart depicts the
reference CPMI. (source: own illustration)
Chapter 3
80
Figure 14 depicts the results of these two robustness checks in relation to the baseline
CPMI for the year 2012, which is shown in the first of the three charts. Again the order of
the countries is chosen according to the 2012 baseline country ranking.
The best performer of the 2012 baseline CPMI is depicted on the far right. As expected
the country ranking does change with changing weighting factors, but the overall picture
of those countries whose climate policy stringency is above average and those whose
stringency is below average turns out to be rather robust to changes in the weighting factors. This is because these countries have stringent policies across all three categories so
that the change in the weights does not matter for them. The only exceptions are Austria,
Canada, Luxembourg and Norway. These countries either switch from below average performance to above average performance (Austria) or vice versa (Canada, Luxembourg and
Norway).
3.6 Conclusion
The CPMI measures the stringency of climate policy in OECD countries.49 Stringency
means the levels of the respective policies weighted by their effectiveness in reducing the
emission intensity of production. It allows cross country comparisons per year as well as
within country comparisons over time. The time period covered goes from 1991 until
2012. It is thereby the CPMI which provides index values for the longest time series compared to other climate policy indices. Also, it covers the most important period for climate
policy as there has been hardly any policy measure before 1991 which was intended to
reduce CO2 emissions. The reason for 2012 being the current end of the observation period is data availability.
The major contribution of the CPMI, however, is that it is entirely data driven. This approach starts with the selection of variables as only quantitative policy measures are included. Thereby the CPMI does not have to rely on expert opinions or other indices. Also
the construction of the composite index is purely data driven. The single policy variables
are weighted according to each policy’s scope. Taxes on fossil fuel consumption for example are weighted according to the consumption of the respective product. The aggregation of the subindicators is done using weighting factors from an empirical assessment of
the impact of the respective policy measures on the reduction of CO2 emissions. This data
based construction guarantees objectivity of the index and enhances the transparency of
the index’s composition. Both are important for the informative purpose of the index as
well as for the applicability in empirical analyses and in the calibration of Computable
General Equilibrium (CGE) models.
49
Except for Iceland which is missing due to data availability.
Climate Policy Measure Index
81
The CPMI consists of three subindicators which each contain several policy indicators:
First, the indicator on taxes on energy products, second the promotionindicator on renewable energy and third the indicator on carbon pricing mechanisms.
Next to the ranking of the countries according to their political effort to curb carbon emissions, the CPMI provides also two more general results. The within country comparison
over time shows that in all countries observed there is a trend of increasing stringency of
climate related policies. A second interesting finding stems from the crosscountry comparison. There are seven countries (Denmark, Italy, Greece, Germany, Norway, Portugal
and Spain) which perform consistently above average considering their effort in climate
policies.
This study and the index resulting from this study should be understood as the first step
into developing an index on the stringency of countries’ climate policy. There is still
scope for further modifications of the database as well as with regard to the methodology.
The database would benefit from being extended regarding the policy measures as well as
the countries. It would be interesting to include also the BRICS countries to relate the policy performances from the OECD countries to those from emerging economies.
82
Chapter 3
3.7 References
Badura, Romina (2008), “A Survey of Composite Indices Measuring Country Performance: 2008 Update”, UNDP/ODS Working Paper, February 2008.
Bauer, Michael W. (2006), “Politikbeendigung als policyanalytisches Konzept“, Politische Viertelejahresschrift 47.
Birdsall, Nancy and David Roodman (2003), “The Commitment to Development Index: A
Scorecard of RichCountry Policies”, Center for Global Development.
EBRD (2008), “Securing sustainable energy in transition economies”, April 2008.
EEA (2015), “European Union Emissions Trading System (EU ETS) data from EUTL”
(database), European Environment Agency. (Accessed on 21 November 2015)
EIA (2015a), “CO2 Emissions”, International Energy Statistics (database). (Accessed on
17 November 2015)
EIA (2015b), “Carbon Intensity”, International Energy Statistics (database). (Accessed on
19 November 2015)
Freudenberg, Michael (2003), “Composite Indicators of Country Performance: A Critical
Assessment”, OECD Science, Technology and Industry Working Papers, 2003/16,
OECD Publishing.
DOI: http://dx.doi.org/10.1787/405566708255
Germanwatch (2013), “The Climate Change Performance Index. Background and Methodology”, November 2013.
Holzinger, Franziska (2006), “Methodological Pitfalls of Convergence Analysis”, European Union Politics, Vol. 7(2).
Hsu, A., L.A. Johnson, and A. Lloyd (2013), “Measuring Progress: A Practical Guide
From the Developers of the Environmental Performance Index (EPI)”, New Haven: Yale
Center for Environmental Law & Policy.
ICAP (2015), “Emissions Trading Worldwide”, International Carbon Action Partnership
(ICAP) Status Report 2015.
IEA (2012), “Energy Prices and Taxes: Methodology Notes”, IEA 1st Quarter 2012.
IEA (2015a), “Addressing Climate Change”, Policies and Measures Database (database).
(Accessed on 11 November 2015)
IEA (2015b), “Enduse prices: Energy prices in national currency per toe”, IEA Energy
Prices and Taxes Statistics (database).
DOI: http://dx.doi.org/10.1787/data00440en (Accessed on 06 October 2015)
IEA (2015c), “OECD  Electricity and heat generation”, IEA Electricity Information Statistics (database).
DOI: http://dx.doi.org/10.1787/data00457en (Accessed on 21 November 2015)
Climate Policy Measure Index
83
IEA (2015d), “OECD – Net capacity of renewables”, IEA Renewables Information Statistics (database).
DOI: http://dx.doi.org/10.1787/data00467en (Accessed on 20 November 2015)
IEA (2015e), “Emissions of CO2, CH4, N2O, HFCs, PFCs and SF6”, IEA CO2 Emissions
from Fuel Combustion Statistics (database).
DOI: http://dx.doi.org/10.1787/data00431en (Accessed on 23 November 2015)
IEA (2015f), “CO2 emissions by product and flow”, IEA CO2 Emissions from Fuel Combustion Statistics (database).
DOI: http://dx.doi.org/10.1787/data00430en (Accessed on 23 November 2015)
IEA (2015g), “OECD product supply and consumption”, IEA Oil Information Statistics
(database).
DOI: http://dx.doi.org/10.1787/data00478en (Accessed on 06 October 2015)
IEA (2015h), “OECD – Coal statistics”, IEA Coal Information Statistics (database).
DOI: http://dx.doi.org/10.1787/data00554en (Accessed on 13 October 2015)
IEA (2015i), “OECD  Natural gas supply and consumption”, IEA Natural Gas Information Statistics (database).
DOI: http://dx.doi.org/10.1787/data00481en (Accessed on 13 October 2015)
IEA (2015j), “OECD  Electricity/heat supply and consumption”, IEA Electricity Information Statistics (database).
DOI: http://dx.doi.org/10.1787/data00462en (Accessed on 06 October 2015)
IPCC (200), “Emission Scenarios”, Special Report, Cambridge University Press, UK.
Jevons, William Stanley (1865/1965), “The Coal Question: An Inquiry Concerning the
Progress of the Nation, and the Probable Exhaustion of Our Coalmines”, In: Flux, A.W.
(Ed.), 3rd edition 1905, Augustus M. Kelley, New York.
Kaya, Y. (1990). “Impact of carbon dioxide emission control on GNP growth: Interpretation of proposed scenarios”, In: IPCC Energy and Industry Subgroup, Response Strategies Workshop, Paris, France.
Künkel, Nana, Klaus Jacob and PerOlof Busch (2006), “Climate policies – (the feasibility
of) a statistical analysis of their determinants”, Manuscript.
OECD (2015a), “Aggregate National Accounts, SNA 2008 (or SNA 1993): Gross domestic product”, OECD National Accounts Statistics (database).
DOI: http://dx.doi.org/10.1787/data00001en (Accessed on 14 October 2015)
Steves, Franklin and Alexander Teytelboym (2013), “Political Economy of Climate
Change Policy”, Smith School Working Paper Series, Working Paper 1306.
Surminski, Swenja and Andrew Williamson (2012), “Policy indexes – what do they tell us
and what are their applications? The case of climate policy and business planning in
emerging markets”, Grantham Research Institute on Climate Change and the Environment Working Paper, No. 88.
84
Chapter 3
UNFCCC (1992), “United Nations Framework Convention on Climate Change”, United
Nations 1992.
World Bank (2014), “State and Trends of Carbon Pricing”, Washington DC, May 2014.
Climate Policy Measure Index
85
3.8 Appendix
A1. Carbon Pricing Mechanisms in OECD countries
Country
Australia
Austria
Belgium
Canada
Chile
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Ireland
Israel
Italy
Japan
Luxembourg
Mexico
Netherlands
New Zealand
Norway
Poland
Portugal
Republic of Korea
Slovakia
Slovenia
Spain
Sweden
Switzerland
Turkey
United Kingdom
United States of America
Carbon tax
(BC)
ETS
Carbon Pricing Mechanism (EPM)
EU ETS
EU ETS
SPEDE (Québec)
EU ETS
EU ETS
EU ETS
EU ETS
EU ETS
EU ETS
EU ETS
EU ETS
EU ETS
EU ETS
Tokyo Cap and Trade Program/Target Setting
Emissions Trading System in Saitama
EU ETS
EU ETS
NZ ETS
EU ETS
EU ETS
EU ETS
EU ETS
EU ETS
EU ETS
EU ETS
Swiss ETS
EU ETS
Regional Greenhouse Gas Initiative (RGGI) (CT,
DE, ME, MD, MA, NH, NY, RI, VT), California
CapandTrade Program
Table 20: Overview of Carbon Pricing Mechanisms in OECD countries (source: World Bank (2014) and ICAP
(2015), own illustration)
Chapter 3
86
A2. Rankings of different climate policy indices including the CPMI
Country
CCPI
CDI
CLIMI
CPI
EPI
ISE
Australia
21
21
22
22
2
10
Austria
13
11
13
11
6
13
Belgium
8
8
11
16
22
3
Canada
22
22
21
10
17
15
Czech Republic
16
5
12
21
3
Denmark
1
6
6
3
10
4
Finland
15
3
1
1
13
19
France
5
10
2
7
19
12
Germany
10
9
10
4
4
Greece
19
15
16
19
16
Hungary
6
2
18
17
20
Ireland
7
17
9
12
14
11
Italy
9
13
14
9
15
2
Japan
20
20
15
13
18
20
Netherlands
14
12
8
5
9
New Zealand
17
16
17
14
11
21
Norway
12
19
5
6
8
18
Portugal
2
4
19
20
12
5
Spain
11
7
4
18
5
Sweden
3
1
7
2
7
9
Switzerland
4
14
3
8
1
16
United States
18
18
20
15
21
17
4
1
CPMI
7
6
1
5
3
2
Table 21: Overview of the rankings discussed including the CPMI. (source: own illustration)
22
14
8
Climate Policy Measure Index
87
A3. Development of the crosscountry rankings over time
2.5
2
1.5
1
0
MEX
ISR
NZL
JPN
FIN
SVK
KOR
HUN
NLD
USA
CHE
FRA
IRL
CHL
AUS
GBR
AUT
CZE
ESP
LUX
POL
TUR
CAN
SVN
PRT
NOR
BEL
EST
DEU
SWE
GRC
ITA
DNK
0.5
0.5
1
Figure 15: Crosscountry comparison of the CPMI for the year 1995, which is the first year of the index in
which index figures can be assigned to every country. (source: own illustration)
3
2.5
2
1.5
1
0
0.5
MEX
ISR
NZL
JPN
FIN
SVK
KOR
HUN
NLD
USA
CHE
FRA
IRL
CHL
AUS
GBR
AUT
CZE
ESP
LUX
POL
TUR
CAN
SVN
PRT
NOR
BEL
EST
DEU
SWE
GRC
ITA
DNK
0.5
1
1.5
Figure 16: Crosscountry comparison of the CPMI for the year 2000 (source: own illustration)
Curriculum Vitae
Julian Dieler
born on September 9, 1986 in Frankfurt am Main, Germany
05/2016 – present
Research Associate,
Ifo Institute – Leibniz Institute for Economic Research at the University of Munich, Germany
10/2011 – 05/2016
Ph.D. Candidate, Munich Graduate School of Economics, University of Munich, Germany
Junior Economist and Doctoral Student, Ifo Institute – Leibniz Institute for Economic Research at the University of Munich, Germany
04/2007 – 09/2011
Studies of economics (Diplom, equivalent M.A. in economics)
University of Munich, Germany
09/2010 – 02/2011
Studies of economics
Toulouse School of Economics, France
06/2006
Abitur (High School Diploma),
Mariengymnasium Arnsberg, Germany