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  • DEPARTMENT OF ECONOMICS OxCarre Oxford Centre for the Analysis of Resource Rich Economies Manor Road Building, Manor Road, Oxford OX1 3UQ Tel: +44(0)1865 281281 Fax: +44(0)1865 271094

    Direct tel: +44(0) 1865 281281 E-mail:


    OxCarre Research Paper 134

    Dirty Little Secrets: Inferring Fossil-Fuel Subsidies from Patterns in Emission


    Radoslaw (Radek) Stefanski* Laval University

    *OxCarre External Research Associate

  • Dirty Little Secrets: Inferring Fossil-Fuel Subsidies from

    Patterns in Emission Intensities1

    Radoslaw (Radek) Stefanski

    Laval University and University of Oxford (OxCarre)

    April 4, 2014


    I develop a unique database of international fossil-fuel subsidies by examining country-

    specific patterns in carbon emission-to-GDP ratios, known as emission-intensities. For most

    but not all countries, intensities tend to be hump-shaped with income. I construct a model

    of structural-transformation that generates this hump-shaped intensity and then show that

    deviations from this pattern must be driven by distortions to sectoral-productivity and/or

    fossil-fuel prices. Finally, I use the calibrated model to measure these distortions for 170

    countries for 1980-2010. This methodology reveals that fossil-fuel price-distortions are large,

    increasing and often hidden. Furthermore, they are major contributors to higher carbon-

    emissions and lower GDP.

    1 An earlier version of this paper was circulated under the title Structural Transformation and Pollution.I would like to thank Thierry Brechet, Mario Crucini, Marine Ganofsky, Torfinn Harding, Philipp Kircher, DavidLagakos, Peter Neary, Bill Nordhaus, Fabrizio Perri, Rick van der Ploeg, Tony Smith and Tony Venables as wellas seminar participants at the University of Minnesota, the University of Oxford, Yale University, Universityof Calgary, Edinburgh University, University of St Andrews, Pontificia Universidad Catolica de Chile, LavalUniversity, Universite de Sherbrooke and the University of Surrey for helpful comments and discussion. I havealso benefited from comments of seminar participants at the Overlapping Generations Days Meetings (Vielsalm),the World Congress of Environmental and Resource Economists (Montreal), the Royal Economic Society Meetings(Cambridge), NEUDC 2013 (Boston), Midwest Macro (Minneapolis) and the Canadian Economics AssociationMeetings (Montreal). All errors are my own. Contact:

  • 1 Introduction

    An astonishing feature of international energy and climate policy is that fossil fuels - often seen

    as the primary contributor to climate change - receive enormous government support.2 Elim-

    inating these distortionary policies could in principle improve efficiency, provide a reprieve to

    strained government budgets whilst also lowering carbon emissions.3 Surprisingly, no compre-

    hensive database of directly measured, comparable fossil-fuel subsidies exists at the international

    level. As argued by Koplow (2009), this is both because of political pressure from the direct

    beneficiaries of subsidies and because of the immense complexity of the task given the profusion

    and diversity of subsidy programs across countries.4 Indirect measures of subsidies - such as

    the ones constructed by the IMF (2013) or the IEA (2012) - are based on the price-gap ap-

    proach. This methodology allows researchers to infer national subsidies by comparing measured

    energy prices with an international benchmark price. The key limitation of this technique is

    that it does not account for government actions which support carbon energy without changing

    its final price (Koplow, 2009).5 Furthermore, the data necessary for this exercise is limited and

    since estimates are based on energy prices measured at the pump, they incorporate significant

    non-traded components which biases estimates. In this paper, I develop a completely novel, in-

    direct, model-based method for inferring these carbon fossil-fuel wedges. I do this by examining

    country-specific patterns in carbon emission-to-GDP ratios, known as emission intensities.

    The method is based on two observations about carbon emission intensity. First, emission

    intensities follow a robust hump-shaped pattern with income. Figure 1(a) plots total CO2

    emissions per dollar of GDP for 26 OECD countries versus each countrys GDP per capita, for

    1751-2010. The graph suggests that middle-income countries produce dirtier output than rich

    or poor countries. Second, the emission intensity of later developers tends to follow a so-called

    envelope-pattern over time: the intensities of later developers rise quickly until they roughly

    reach the intensity of the UK - the first country to start the modern development process -

    after which, their intensity tends to approximately follow the same path as that of the UK. An

    illustrative example of this envelope-pattern is shown in Figure 1(b). In the graph, the obvious

    exceptions are China and the former USSR, which greatly overshoot this pattern.6 In this paper

    I argue that the extent to which countries like China deviate from the hump-shaped pattern,

    2 Rough, lower-bound estimates by IMF (2013) show that global fossil fuel subsidies in 2009 were on theorder of magnitude of US$ 480 billion.

    3 See, for example, IEA (2012), OECD (2012), IMF (2013) or Koplow (2009).4 Work by OECD (2012) is the only attempt to directly calculate carbon subsidies. These estimates, however,

    are only for a select number of countries and years and they are not comparable across countries.5 For example in the US, oil and gas producers receive support if they have older technology or access to

    more expensive reserves. As argued by Koplow (2009), the subsidy is not likely to change the market price ofheating oil or gasoline, simply because the subsidized producer is a very small player in the global oil market.

    6 Notice that whilst the above are illustrative, the hump-shape and envelope patterns are statistically robustas is shown in the Appendix.

  • 3





    0 10000 20000 30000GDP per Capita (1990 GK US $)


    of C




    n PP

    P U


    CO2 Emissions per unit of GDP

    (a) OECD CO2 Emission Intensity, 1751-2010





    1820 1840 1860 1880 1900 1920 1940 1960 1980 2000Ton

    s of






    US$ CO2 Intensity


    US Canada KoreaChina


    (b) Timing and Emission Intensity

    Figure 1: Carbon Dioxide Emission Intensity Patterns

    is indicative of different types of distortions within those economies. I then demonstrate how a

    simple model can be used to measure these distortions.

    To do this, I construct a model of structural transformation calibrated to the experience

    of the UK. The model reproduces the hump-shaped emission intensity by generating an en-

    dogenously changing fuel mix and energy intensity. I then examine cross-country differences in

    emission intensity through the lens of the model. In my framework, any deviation in a countrys

    emission intensity from the hump shape pattern is indicative of one of three distortions or wedges

    within that economy: 1) a wedge to agricultural productivity, 2) a wedge to non-agricultural

    productivity and a 3) subsidy-like wedge to fossil fuel prices. Following the language of Chari et

    al. (2007) and Duarte and Restuccia (2007), these wedges are objects that appear like shocks

    to productivity or prices in a standard model but in fact reflect a wider set of distortions,

    imperfections or government policies found in the data.

    The contribution of the paper is to show that the envelope pattern in CO2 emission intensities

    is a consequence of different starting dates of industrialization, which in turn are driven by cross-

    country wedges in agricultural productivity. Any other deviations from the hump-shaped pattern

    are symptomatic of either non-agricultural productivity wedges or subsidy-like wedges on fossil

    fuels. Given the calibrated, structural model I can then use data on a countrys CO2 intensity,

    the size of its agricultural sector and its GDP levels to measure the size of these three wedges -

    and in particular I can infer the size of the energy subsidy wedge across countries and over time.

    Before I construct a model that generates a hump-shaped intensity and use it to extract

    energy wedges, I first need to isolate the key drivers of emission intensity. To do this, I perform

    an accounting exercise on a panel of international data. I show that the hump-shape emission

    intensity is driven by two factors. First, the increasing part of the hump shape stems from a

  • 4

    changing fuel mix. As countries grow richer, they tend to shift from using clean, carbon-neutral

    bio-fuels (like wood) towards dirty, carbon positive fuels (like coal). This generates a rising

    impurity of fuels and an increasing emission intensity. Second, I demonstrate that in the data

    the declining part of emission intensity stems from falling energy intensity - the energy to GDP

    ratio of an economy. This captures the idea that over time an economy needs less energy to

    produce the same amount of output and hence will release less carbon per unit of output.

    To reproduc

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