1
Appendix A: Dieselization, CO2 emissions and fuel taxes in Europe
Jesús Rodríguez López, Universidad Pablo de Olavide
Gustavo Marrero Díaz, Universidad de La Laguna
Rosa Marina González Marrero, Universidad de La Laguna
A. Calibration
In this appendix, using the DSGE model, we calibrate the economy of fourteen countries in
Western Europe as of 1999: Austria, Belgium, Denmark, Finland, France, Germany, Greece,
Ireland, Italy, the Netherlands, Portugal, Spain, Sweden, and the United Kingdom. This set of
countries will be labeled as EU14. We retrieve data from several sources and compute average
moments for these fourteen countries to target the steady state conditions of the model.
1999. Year 1999 is chosen as the pivotal moment in the calibration that follows. It is the year
when the Euro was launched. Figure A1 plots the log of GDP (left axis) together with its growth
rate (data come from Eursotat). The level has evolved following an upward trend from 1995 to
2007. After the Great Depression starting in 2008, still ongoing, the trend came to a halt
presenting a flat dynamic. The effects of the 2008 Great Depression, downsized growth rate
possibilities in Europe. The GDP growth rate was 1.5% for 1995‐2014, and 2.4% for 1995‐2007.
As of 1999, the growth rate was 2.3%, which encounters the average growth before the Great
Depression.
In summary, the choice of 1999 is grounded on these two facts: the date when the Euro was
launched, and a year when the EU14 economies as a whole were sited on their balanced
growth path.
‐3,1%‐2,6%‐2,1%‐1,6%‐1,1%‐0,6%‐0,1%0,4%0,9%1,4%1,9%
4,304,354,404,454,504,554,604,654,70
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
GDP EU15 (left axis) EU15 GDP Growth (right axis)
Mean growth (right axis)
Figure A1: GDP growth rate in EU15
Source: Eurostat
2
Investment rates and capital‐to‐output ratio. Table A1 reports the investment rates for the
EU14 countries (Eurostat). These rates are calculated using the nominal gross fixed capital
formation relative to nominal GNP. The last row presents the average rate, weighted by the
share of nominal GNP. The investment rates have been stable across the sample, falling after
the Great recession in 2008. Spain and Ireland present the most remarkable differences during
2005‐2009, due to the housing price boom, for which the investment rates are quite above the
average, 0.235 and 0.286, respectively. The average rates are also very stable and, excluding
the final period, for our calibration the investment rate can be set to 20% of GNP.
Table A1: Investment rates (over GNP) 1995‐2013
1995‐1999 2000‐2004 2005‐2009 2010‐2013
Austria 0,244 0,233 0,216 0,211
Belgium 0,201 0,197 0,211 0,201
Denmark 0,196 0,198 0,201 0,167
Finland 0,190 0,195 0,204 0,191
France 0,173 0,184 0,199 0,192
Germany 0,215 0,192 0,176 0,172
Greece ‐‐‐ 0,222 0,231 0,147
Ireland 0,228 0,269 0,273 0,136
Italy 0,195 0,208 0,209 0,185
Netherlands 0,217 0,200 0,196 0,171
Portugal 0,255 0,259 0,228 0,176
Spain 0,227 0,270 0,292 0,202
Sweden 0,167 0,175 0,185 0,181
United Kingdom 0,172 0,169 0,165 0,144
Average 0,198 0,198 0,200 0,177
Source: Eurostat
The EU KLEMS dataset provides series for the stock of capital aggregated over several assets:
Computing equipment, communications equipment, software licenses, transport equipment,
other Machinery and Equipment, non‐residential assets, and residential structures.
Unfortunately, series of capital are not available for the following six countries: Belgium,
France, Greece, Ireland, Portugal, and Sweden. Germany is included after 1991. We retrieve
series of capital and Value Added (VA). VA amounts to labor compensation plus capital
compensation. For each country n, we aggregate over the eight K/VA ratios using the share of
nominal VA ( ):
.
This ratio is represented in Figure A1, varying from 3.75 in 1991 to 3.65 in 2007. After 1999,
the ratio increased from 3.50 to 3.65, following the increase in the investment ratio. The
average ratio is 3.55 for 1993‐2007, which we will take to target the long run real interest rate.
3
Fuel, maintenance and repairs, cars insurances, and new vehicles expenditures. Table A2
reports consumption of both types of fuel (diesel and gasoline, millions of tons) for 1999 and
2011. Diesel consumption has increased from 1999 to 2011 and, except for Greece and the
Netherlands, gasoline consumption has decreased during the same period. In 2011 relative to
1999, on aggregate, diesel fuel consumption is 89% higher, and gasoline fuel consumption is
60% lower. The weighted ratio also reflects such an increase in the relative use of fuel (from
0.288 to 0.870).
Table A2: Fuel consumption (millions of tons)
1999 2011
Diesel Gasoline Ratio Diesel Gasoline Ratio
Austria 1,3 2,1 0,592 1,8 1,3 1,423
Belgium ‐‐‐ ‐‐‐ ‐‐‐ ‐‐‐ ‐‐‐ ‐‐‐
Denmark 0,3 2,0 0,135 0,9 1,4 0,622
Finland 0,3 1,9 0,148 0,9 1,4 0,651
France 9,8 13,1 0,747 14,4 6,2 2,339
Germany 6,2 31,1 0,198 11,8 21,0 0,559
Greece 0,1 2,7 0,045 0,1 2,9 0,048
Ireland 0,3 1,2 0,241 0,7 1,1 0,653
Italy 4,0 16,2 0,249 8,7 7,9 1,110
Netherlands 1,2 3,9 0,310 1,7 4,0 0,424
Portugal 1,1 2,0 0,579 2,4 1,2 2,016
Spain 2,5 7,2 0,352 8,1 4,1 1,966
Sweden 0,3 3,9 0,067 0,9 2,9 0,321
United Kingdom 4,1 21,7 0,186 7,2 13,1 0,544
Sum 31,4 109,1 59,7 68,6
Mean (weighted) 0,288 0,870
Source: Odyssee‐Mure
3,45
3,50
3,55
3,60
3,65
3,70
3,75
3,80
1991
1993
1995
1997
1999
2001
2003
2005
2007
Figure A2: Capital to Value Added, K/VA, 1991‐2007
Source: EU KLEMS and own calculations
4
In Tables A3 and A4 we present expenditures related to transport as a percentage of total
households’ consumption expenditures. Data have been retrieved from Eurostat data bases,
which provide a detailed structure for transport expenditures per household: fuel and
lubricant expenditures, maintenance and repairs, spare parts, accessories, other services, and
insurances related to transport. Data are only available for those years reported in Tables A3
and A4 (i.e. 1994, 1999, 2005, and 2010).1
As of 1999, fuel and lubricant expenditures (diesel, gasoline and lubricants) accounted for
3.78% of household’s consumption expenditure (Table A3). Except for Italy, there are not
remarkable differences among countries. Maintenance and repairs expenditures, also reported
in Table A3, account for a smaller fraction of consumption expenditures, 2.44%.
The share of cars insurance expenditures is reported in the last panel of Table A3. This item
includes car insurances and cannot be disaggregated from other insurance expenditure related
to transport needs. Hence, this percentage is an upward biased estimator for car insurances.
Unfortunately, this information is only available for 2005 and 2010. On average, these
insurances accounted for 1.5% of consumption expenditures. In our setting, however, car
insurances are part of fixed costs on cars’ owners operating costs, which include other items
such as parking fees, or taxes levied on the property (not the use) of a vehicle, information
unavailable in databases of Eurostat.
For comparative purposes, a survey on vehicles operating costs can be found in Victoria
Transport Policy Institute (2009, Chapter V). For instance, excluding depreciation and financial
costs, which are considered as fixed costs by these studies, the American Automobile
Association estimates that fixed costs range within an interval 40‐45% of total vehicle running
costs, based on an annual average mileage of 12500 miles. The Canadian Automobile
Association estimates that the role of fixed is sensibly higher, accounting for 74% of total costs.
Other studies reported in this survey, such as that of the U.K. Automobile Association, also
conclude that these fixed costs can account for a non negligible fraction of passenger vehicles
operating costs. In our case, fixed costs only account for 19.4% of total operating costs:
1.53.78 2.44 1.5
100 19.4%.
Taking account these estimates (and data limitations), we take 1.5% over consumption as an
estimate of fixed costs (car insurances, parking and tolls, and fees).
1 http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=hbs_str_t211&lang=en
5
Table A3: Transport costs (percentage
of households' expenditures)
Fuels and lubricants
Maintenan
ce and repairs, spare parts,
Insurance connected
accesories, and other services
with transport
1994
1999
2005
2010
1994
1999
2005
2010
2005
2010
Austria
2,99%
3,45%
3,82%
3,61%
4,62%
3,56%
4,29%
4,49%
1,70%
1,56%
Belgium
2,64%
3,02%
3,14%
2,99%
3,09%
3,95%
4,22%
4,15%
1,78%
1,75%
Den
mark
2,86%
2,90%
3,36%
3,09%
4,22%
3,32%
3,00%
3,25%
1,87%
1,72%
Finland
3,74%
3,65%
3,85%
4,06%
2,56%
1,95%
2,80%
4,27%
1,38%
1,48%
France
3,61%
3,90%
3,27%
4,20%
2,39%
2,22%
2,09%
1,79%
2,03%
1,93%
Germany
2,62%
3,05%
‐‐‐
4,03%
3,55%
2,84%
‐‐‐
4,44%
‐‐‐
1,43%
Greece
3,38%
2,83%
3,11%
4,20%
1,16%
2,59%
2,12%
1,55%
1,21%
1,38%
Ireland
3,86%
3,60%
3,35%
4,93%
1,20%
1,13%
1,33%
1,58%
1,68%
1,71%
Italy
5,54%
5,44%
5,63%
5,17%
3,77%
2,65%
2,33%
2,53%
2,82%
2,52%
Netherlands
2,68%
3,02%
3,48%
‐‐‐
1,95%
1,75%
2,10%
‐‐‐
‐‐‐
‐‐‐
Portugal
3,54%
3,70%
5,18%
6,86%
3,53%
2,11%
1,68%
2,40%
1,73%
1,26%
Spain
3,63%
3,99%
3,76%
4,41%
3,14%
1,68%
1,62%
2,98%
1,51%
1,68%
Swed
en
4,30%
3,67%
4,80%
4,01%
3,01%
1,79%
2,14%
1,82%
1,30%
1,24%
United
Kingdom
3,83%
3,71%
3,84%
5,17%
1,47%
2,09%
2,52%
3,27%
1,63%
1,90%
EU15 Average
3,61%
3,78%
3,17%
4,45%
2,88%
2,44%
1,83%
3,05%
1,51%
1,79%
Source: Eurostat, M
ean consumption by expenditure detailed COICOP level (in PPS), and own calculations
6
In Table A4 we report the expenditures in new motor vehicles as a percentage of households’
consumption. These expenditures accounted for 5.48% of consumption in 1999. This
percentage decreased to 3.72% and 4.17% for 2005 and 2010.
Table A4: New motor vehicles expenditures
(percentage of households' expenditures)
1994 1999 2005 2010
Austria 7,64% 6,39% 6,87% 5,35%
Belgium 4,17% 4,48% 4,29% 4,61%
Denmark 7,07% 5,48% 4,47% ‐‐‐
Finland 5,43% 8,74% 6,88% 6,34%
France 6,33% 6,20% 6,14% 6,16%
Germany 5,97% 5,64% ‐‐‐ 3,92%
Greece 3,62% 4,18% 3,86% 3,38%
Ireland 6,24% 6,72% 5,32% 4,35%
Italy 2,98% 4,17% 2,86% 2,69%
Netherlands 2,93% 3,81% 3,54% ‐‐‐
Portugal 7,10% 7,78% 4,59% 3,86%
Spain 3,45% 5,70% 4,22% 3,53%
Sweden 4,50% 5,77% 6,20% 3,31%
United Kingdom 4,11% 5,77% 5,14% 4,44%
EU15 Average 4,87% 5,48% 3,72% 4,17%
Source: Eurostat, Mean consumption by expenditure detailed COICOP level (in PPS).
Carbon cycle, emissions, vehicles energy efficiency, and damage function. We distinguish two
types of gases emitted by motor vehicles (Parry, Walls and Harrington, 2007): Greenhouse
gases and local air pollutants. Greenhouse gases (GHG henceforth) due to cars are carbon
dioxide (CO2), methane (NH4), and other gases (chlorofluorocarbons, CFCs). Greenhouse gases
and d air pollutants are not limited to the country where they are emitted but, instead, they
produce effects beyond the boundaries. The main cause of anthropogenic CO2 emissions is the
combustion of fossil fuel. NH4 has a larger global warming impact than CO2, but its emissions
are lower than those of CO2. Motor vehicles also emit other type of gases that produce a local
effect, such as carbon monoxide (CO), nitrous oxide (NOx), hydrocarbons (HC), or particulate
matters (PM2.5 and PM10). These gases have a negative impact on human health, the quality of
air, forests, goods and the environment.
In this paper, notwithstanding, we focus on the consequences of GHG emitted by passenger vehicles. CO2 is the main contributor to anthropogenic greenhouse effect because of changes in its concentration since the 19th century industrial revolution. We take 581 Gt of CO2 concentrated in the atmosphere as a reference (Golosov, Hassler, Krusell and Tsyvinski, 2014; Heutel, 2012). The effect of GHG is determined by their residence time in the atmosphere: the average time a molecule of a gas remains in the atmosphere before it is somehow abated. The bigger the residence time, the larger is the greenhouse effect of the gas. While the average residence
7
time of the GHG is greater than a century (see Essenhigh (2009) for CO2), those of local pollutants are estimated to be a few days (see Van Loon and Duffy (2005) for NOx and SO2; Weinstock (1969) for CO). Thereby, the GHG will impact global warming long afterward emissions cut‐backs are implemented. We assume that the stock of CO2, denoted by , evolves according to the carbon cycle proposed by Golosov, Hassler, Krusell and Tsyvinski (2014) (see references therein): A 20% of CO2 emitted, 0.2, remains in the atmosphere for thousands of years. Let denote this part of stock. A percentage of the remaining fraction has a half life of 300 years in the atmosphere (Archer, 2005), while the other fraction 1 exists the atmosphere into surface oceans and the biosphere in about one decade (IPCC, 2007). These two latter GHG stocks are denoted as and , respectively. Given that Golosov et al. (2014) focus on long run implications of CO2 concentration, they only consider the long lasting stocks and . We adapt this state equation to our business cycle frequency and take account of . Let be denoting the world flow of CO2 emissions. The total stock of CO2 is governed by the following motion:
, ,
1 1 , 1 1 1 .
The parameters are selected to match this carbon cycle: 0.2, 1 0.5,
4 10 . The percentage is calibrated to ensure that total emissions confront a half life of 200 years (Golosov et al., 2014):
1 0.5 1 1 1 1 . This produces 0.595 (Golosov et al. (2014) estimate 0.393). Golosov et al. (2014) consider a concentration of 802 Gt for 2000, for which 684 correspond to . We adopt the same values and distribute the other 118 Gt in coherence with previous
carbon cycle: 116.1755, and 1.8245. Section 2 reports evidence that diesel powered cars are on average 20% more energy efficient
than gasoline cars. On average, a diesel motor car burns 6.60 liters of diesel fuel per 100
kilometers, while a gasoline motor car burns a 20% more of fuel (about 7.94 liters per 100 km).
According to model’s notation, energy efficiency of both type of cars is represented in
parameters , . This implies the following ratio: .
.0.831.
The flow of CO2 emissions is represented according to the following structure:
,
where and denote emissions from the rest of the world and domestic emissions
from other sectors, respectively. Ñ denotes the amount of fuel j consumed, for
1,2. The quantity of CO2 emitted per liter of fuel j is accounted by . The share of EU14
over total world CO2 emissions is represented in Figure A2. This ratio has decreased over the
years, mainly due to the increasing contribution of countries such as China or India. CO2
8
emissions in EU14 countries accounted for 20% of world emissions in 1980, and decreasing to
10% in 2010.
CO2 emissions per liter of fuel combusted depend on the carbon content in the fuel
(Environmental Protection Agency (EPA, 2011)). After combustion, most of the carbon content
is emitted as CO2 and, in a minor proportion, as other pollutants (HC, CO). The EPA estimates
2.689 kg. of CO2 per liter of diesel, and 2.348 kg. of CO2 per liter of gasoline. Therefore, the
higher the carbon content per liter of diesel partially offsets the fuel efficiency in diesel
powered cars. This way the following relation can be set: .
.1.145.
The damage function affects the production of the final good :
exp 581 , 1
which requires calibrating , where 581Gt is the pre‐industrial stock of CO2. In this respect, we
borrow the calibration given by Golosov et al. (2014): 2.3792 10 . This implies that a
concentration of 802Gt, in excess of 581Gt, produces a 0.52% damage on output:
1 0.0052.
0,100
0,110
0,120
0,130
0,140
0,150
0,160
0,170
0,180
0,190
0,200
1975 1980 1985 1990 1995 2000 2005 2010
Figure A3: Share of EU14 over CO2 World Emissions
9
Notations and national accounts. Household’s budget is written as
1 , , ñ,
.
According to this notation, Gross National Income (GNP) can be expressed as
, or in terms of the numeraire final good as
1 , 1 .,
(prices are expressed in terms of ). Dividends account for non distributed incomes in the
automotive sector (due to homogeneity of degree 1 1 in the technology of the automotive sector):
, .,
2
Inserting this expression in GNP yields:
, 1 .,
3
In this model, the current account, , presents a deficit due to crude oil imports (or income
balance, ):
, ,
0. 4
The Gross Domestic Product (GDP) can be calculated as
,
. 5
As long as the government balances its budget every period, transfers from the public
sector to households can be written as
, , , Ñ .,
6
From this expression and household’s budget, we reach the following expression
, Ñ,
.
In aggregate models, consumption is actually the sum of non durable and services
consumption, which includes fuel ( ), maintenance and repairs of motor vehicles ( ), tolls
and car insurances ( ), and any other item related to consumption: food and beverages,
clothing and footwear, housing and utilities, health care, transportation services (net of fuel
and maintenance and repairs), recreation services, food services and accommodations,
financial services and insurances (net of car insurances), and other services. In Table A1 we
found that the investment rate can be set to 20% of GNP. This implies that 80% of GNP should
accommodate (private and public) consumption and net exports, plus new cars investment.
Using the information in Tables A1 through A5, in Table A6 we summarize the following GNP
shares for the different items of expenditure: investment I, fuel expenditures F, maintenance
and repairs MR, tolls and car insurances TI, new cars investment X. Consumption net of
previous expenditures, is taken residually, which represent 69.4% of GNP.
10
Table A5: Summary of EU Aggregates
Share over
Notation Reference Consumption GNP
Investment rate Table A1 ‐‐‐ 0,2000
Fuel expenditures Table A3 0,038 0,0302
Maintenance and repairs Table A3 0,024 0,0195
Car insurances Table A3 0,015 0,0120
New cars investment Table A4 0,055 0,0438
Consumption (net of F, MR, TI, X) ‐‐‐ 0,868 0,6944
Total ‐‐‐ 1,000 1,0000
The EU14 economy is calibrated to meet these aggregate moments when the model is
simulated. The stationary GNP is normalized to 1, and the rest of steady state accounts are set
according to the last column in Table A5 (* denotes stationary positions). According to model’s
notation: ∗ 0.2, 7
∗ ∗ ∗ ∗ ∗ ∗ Ñ∗ ∗
,
0.0302, 8
∗ ∗ ∗ ∗ Ñ∗ ∗
,
0.0195, 9
∗ ∗ ∗
,
0.0120, 10
∗,
∗ ∗
,
0.0438. 11
Vehicle fleet and operating costs. We assume that the stock of both types of vehicles follows a
geometric law of motion:
1 ,
for j = 1,2, with being the depreciation rate, including scrap and net exports of second hand
vehicles. The inverse ratio indicates the average lifespan of a representative vehicle. Table
A6 reports the percentage of the stock of diesel cars and an estimate of the lifespan according
to the law of motion, using the Odyssee‐Mure database. This database provides yearly series
for the stock of vehicles and the flow of new cars registered across countries in the European
Union and for diesel and gasoline vehicles. As of 1999, the stock of diesel motor cars
accounted for 18.2% of the stock of passenger cars. This percentage increased to 38.6% in
2011. For calibration, we propose:
∗ 0.182, ∗ 0.818.
The rates of in Table A6 have been estimated according to the law of motion:
,
, , ,
11
where , denotes the growth rate of , . Assuming , and taking a simple average for
each country, the weighted mean indicates an average cars lifespan of 12 years across
countries:2
112 4
140.0833. 12
This implies a yearly 8.33% depreciation rate of passenger vehicles per year.3
From these steady state conditions, the stationary new cars investment is given by (in
quarterly terms, and neglecting the growth components):
∗ ∗ 0.18212 4
140.0151,
∗ ∗ 0.81812 4
140.0681.
Table A6: Diesel passenger vehicles fleet
Percentage of diesel cars Average lifespan 1999‐2011
1999 2011 Δ ( + )/2 Lifespan (years)
Austria 35,3% 55,7% 20,5% 0,035 0,075 0,055 18,1
Belgium 38,7% 62,5% 23,8% 0,088 0,088 0,088 11,4
Denmark 5,7% 27,2% 21,5% ‐‐‐ ‐‐‐ ‐‐‐ ‐‐‐
Finland 9,5% 20,9% 11,4% 0,001 0,027 0,014 71,2
France 32,9% 58,9% 26,0% 0,040 0,070 0,055 18,3
Germany 13,3% 27,0% 13,7% 0,103 0,071 0,087 11,5
Greece 1,4% 1,2% ‐0,2% 0,030 0,009 0,019 51,4
Ireland 13,6% 29,8% 16,2% 0,034 0,064 0,049 20,4
Italy 13,5% 41,8% 28,3% 0,027 0,066 0,047 21,3
Netherlands 13,0% 17,4% 4,4% 0,070 0,049 0,059 16,8
Portugal 21,5% 46,5% 25,0% 0,020 0,052 0,036 27,7
Spain 24,0% 52,8% 28,8% 0,059 0,070 0,064 15,5
Sweden 4,6% 18,5% 13,9% 0,057 0,063 0,060 16,7
United Kingdom 12,3% 31,0% 18,6% 0,036 0,073 0,054 18,4
Average (weighted) 18,2% 38,6% 20,4% 0,055 0,067 0,061 16,4
Source: Odyssee‐Mure and own calculations
In Table A2, the ratio of diesel to gasoline fuel consumption was 0.288 in 1999, and in Table
A6, we estimate that the ratio of diesel motor vehicles accounted for 18.2% of the vehicle
stock in 1999. According to our definition, total fuel consumption is equal to kilometers
driven (Ñ ), times the stock of vehicles ( ), times fuel efficiency ( ). Fuel efficiency is
expressed as liters per kilometer driven (or gallons per mile), which for simplicity we assume
2 For Finland and Greece the lifespan is remarkably higher than in any other country. However, passenger vehicles in these two countries only account for 3.75% of total fleet in the set of countries under consideration. 3 The BEA recommends a hyperbolic law of motion for automobile accumulation and a shorter lifespan of 10 years (Fraumeni, 1997).
12
constant across the period. Yet, diesel powered cars are 20% more efficient than gasoline cars,
i.e. 0.831. Taking this information, we have:
∗
∗ 0.288Ñ∗
Ñ∗
∗
∗Ñ∗
Ñ∗0.1820.818
0.831, 13
which yields a stationary relative mileage
Ñ∗
Ñ∗1.56, 14
Hence, under previous assumptions, diesel motors cars are driven 56% more intensively than
gasoline powered vehicles on average.
Table A7 reports the fuel prices and the fuel taxes for the countries under consideration for
1999 (Weekly Oil Bulletin, European Commission4). Net of taxes, the price of diesel fuel is 8%
lower than the price of gasoline, on average. Diesel taxation is lower relative to gasoline
taxation, the United Kingdom being an exception. The weighted average indicates that the
diesel tax tends to be 37% higher than gasoline tax. We normalize to unity the price of
consumption, and consider the aggregate values reported in Table A7 for prices and taxes: ∗ 0.330, ∗ 0.357, ∗ 0.812, ∗ 1.111 .
Table A7: Fuel prices and tax rates, 1999
Country / / Relative
Austria 0,401 € 0,439 € 0,91 0,658 € 0,915 € 0,72 0,782
Belgium 0,386 € 0,396 € 0,97 0,665 € 1,104 € 0,60 0,701
Denmark 0,391 € 0,412 € 0,95 0,747 € 1,156 € 0,65 0,726
Finland 0,413 € 0,412 € 1,00 0,709 € 1,227 € 0,58 0,685
France 0,313 € 0,333 € 0,94 0,828 € 1,251 € 0,66 0,720
Germany 0,348 € 0,371 € 0,94 0,711 € 1,069 € 0,67 0,735
Greece 0,316 € 0,399 € 0,79 0,562 € 0,686 € 0,82 0,809
Ireland 0,415 € 0,404 € 1,03 0,752 € 0,848 € 0,89 0,932
Italy 0,385 € 0,428 € 0,90 0,877 € 1,162 € 0,75 0,794
Netherlands 0,405 € 0,449 € 0,90 0,747 € 1,225 € 0,61 0,688
Portugal 0,337 € 0,409 € 0,82 0,576 € 0,927 € 0,62 0,683
Spain 0,370 € 0,386 € 0,96 0,580 € 0,779 € 0,74 0,815
Sweden 0,502 € 0,430 € 1,17 0,750 € 1,153 € 0,65 0,791
United Kingdom 0,352 € 0,331 € 1,06 1,478 € 1,442 € 1,02 1,032
Average (weighted) 0,330 € 0,357 € 0,92 0,812 € 1,111 € 0,73 0,778
Source: Weekly Oil Bulletin, European Commission, and own calculations (PPP adjusted)
Given these figures, expressions (A8) and (A13) provides us a system of equations for the
quantities of fuel consumed. The solution is ∗ 0.0192, ∗ 0.0668 . If we normalize to
one the km. driven of a gasoline car, we finally reach Ñ∗ 1.56, Ñ∗ 1, and
0.0679, 15
0.0817. 16
4 https://ec.europa.eu/energy/en/statistics/weekly‐oil‐bulletin
13
Concerning the expenditures of maintenance and repairs, according to the National Accounts
in Table A5 (expression A9), and assuming , ∗ 1 (i.e. both cars have the same
needs of repair services), we calibrate
0.0195
Ñ∗ ∗ Ñ∗ ∗
0.01951.56 0.182 1 0.18
0.0177. 19
Using these values, the marginal costs of a kilometer driven in a car of type are given by:
∗ ∗ ∗ 0.0952, ∗ ∗ ∗ 0.1376.
We complete the information on costs per mile driven (i.e. fuel expenditures plus maintenance
and repairs expenditures, Table A3) with some estimates calculated at the individual vehicle
level. Variable costs are those that depend on miles driven: fuel costs and maintenance and
repairs costs. A number of studies have estimated these costs in nominal terms,5 varying from
one country to another due to differences in technology or taxation, among other things. For
example, the U.K. Automobile Association provides operating costs per mile for gasoline and
diesel cars from 1998 (www.theaa.co.uk): fuel costs, tyres, service labor costs, replacement
parts, and parking and tolls. The estimates are available on a monetary basis for cars of five
different price levels (categories), labeled from A (lowest price) to E (highest price). As of 2013,
these price intervals are respectively: Up to 16000 £, [£16000 £22000], [£22000, £26000],
[£26000, £36000], and over £36000. For the cost of maintenance and repairs per kilometer, in
Table A8 we report the relative cost, / , that is, diesel relative to gasoline. These costs
were higher for diesel cars from 1998 to 2001, and tend to be equal across the years. As a
reference, although this estimation solely refers to the UK automobile fleet, the assumption
seems accurate.
Finally, the (fixed) costs, which include cars insurances, parking fees and tolls, can be estimated
from the national accounts in Table A5 (expression A10):
∗ ∗ ∗ ∗ 0.0120.
As long as there is not available a reliable estimate of the participation of parking fees and tolls
on households expenditures, we use this ratio (1.2%) as a reasonable reference.
5 For a survey, see Victoria Transport Policy Institute (2009), Chapters II and V.
14
Table A8: Relative maintenance and repair costs: /
Vehicle category
A B C D E Mean
1998 1,048 1,041 1,103 1,044 ‐‐‐ 1,059
1999 1,047 1,040 1,103 1,044 ‐‐‐ 1,058
2000 1,046 1,039 1,102 1,045 ‐‐‐ 1,058
2001 1,047 1,040 1,104 1,045 ‐‐‐ 1,059
2002 1,000 1,000 1,000 1,000 ‐‐‐ 1,000
2003 1,000 1,000 1,000 1,000 1,000 1,000
2004 1,003 1,007 1,018 1,002 0,952 0,996
2005 1,004 1,008 1,019 1,003 0,952 0,997
2006 1,000 1,000 1,000 1,000 1,000 1,000
2007 1,001 0,994 1,016 1,022 1,058 1,018
2008 0,981 0,979 1,024 0,912 1,015 0,982
2009 0,948 0,946 0,976 0,962 1,039 0,974
2010 0,949 0,946 0,974 0,962 1,039 0,974
2011 0,981 0,987 0,952 1,001 0,840 0,952
2012 1,007 0,994 1,034 1,022 1,024 1,016
2013 0,984 0,957 1,001 1,064 1,023 1,006
Average 1,003 0,999 1,027 1,008 0,995 1,009
Source: U.K. Automobile Association
Crude oil price and fuel prices. Let , denote the real price of the North Sea Brent Barrel of
oil crude. In Table A9 we present an estimation of an 1 representation of the crude oil
price, ln , 1 ln ∗ ln , , , where , ~ 0, . Data on the
North Sea Brent price are retrieved from Reuters Ecowin Pro. These prices are expressed in
dollars and converted to Euros (the nominal exchange rate was downloaded from the
European Central Bank). The nominal price in Euros is deflated with the Eurostat harmonized
CPI aggregated for the EU14 (weighted by nominal private consumption). The 1 is
estimated through OLS. Due to an outlier observation for 2008:4, a dummy was included.
The intercept is not found statistically significant. For that reason, we assume a stationary
price for crude oil ln ∗ 0, or ∗ 1. Oil price shocks are found to be highly persistent 0.979, with volatility 0.121.
Table A9: Dependent variable is ln , , 1996:2‐2014:2
Variable Coefficient t‐Statistic p‐Value
Constant 0,006 0,2 0,837
ln , 0,979 35,8 0,000
Dummy2008:4 ‐0,616 ‐5,0 0,000
S.E. 0,121
Adj. 0,947
Sources: Reuters Ecowin Pro, European Central Bank,
Eurostat, and own calculations.
15
The first order conditions in the refinery sector imply the following fuel price equations:
,1
1
1.
Where , denotes the real price of fuel ( 1 is for diesel, and 2 for gasoline). After
log‐linearization, the following equation has been estimated through restricted OLS:
ln , / ln , / ,
Series of fuel prices are retrieved from the Weekly Oil Bulletin of the European Commission
(see footnote #4). The rental price of capital is approximated through a ten year bond
interest rate for the Eurozone (from European Central Bank), and the harmonized CPI series
previously described. The slope is taken as an estimate for .
The results are reported in Table A10. In both regressions, the intercepts are similar and
statistically significant, indicating that the relative price of fuel (net of taxes) is constant and
close to one. The slopes differ something (0.793 and 0.839, respectively). We use an
intermediate estimate, weighted by our calibration of kilometers driven (0.258 for diesel cars,
and 0.742 for gasoline cars): 0.827.
Table A10: Fuel price equations, 2005:1‐2014:2
Dependent variable Dependent variable
ln , / ln , /
Variable Coefficient t‐Statistic p‐Value Coefficient t‐Statistic p‐Value
Constant ‐5,042 ‐79,2 0,000 ‐5,043 ‐95,3 0,000
ln , / 0,793 23,9 0,000 0,839 30,5 0,000
Adj. 0,941 0,963
S.E. of regression 0,065 0,054
Sources: Weekly Oil Bulletin (European Commission), Reuters Ecowin Pro, European
Central Bank, Eurostat, and own calculations.
Parameters which require solving the model. Let stationary GNP be normalized to one, GNP =
1. Consider the steady state conditions given from expressions (A3), (A8) and A(11), i.e. GNP,
fuel expenditures and vehicle investment, respectively. Combining these three expressions and
0.827 at Table A10, we determine a stationary value for the final goods sector:
∗ 1 0.0438 1 0.827 0.0302 0.951. 20
Finally, from the steady state conditions, consider the following system of five equations:
,∗ ∗
,∗ ∗ 0.0438, 21
1 ,∗
ϛ1 ϛ Ñ∗ ϛ ∗
1 1, 22
16
1 ,∗
ϛ1 ϛ Ñ∗ ϛ ∗
1 1, 23
ϛ ∗ ϛ 1 ∗ Ñ∗ ∗,,
24
Ñ∗
Ñ∗
∗
∗
/ ϛ / ϛ
. 25
Equation (A21) reflects the accounting condition in Table A5 (see also (A11)), where new cars
investment accounts for 4.38% of GNP. Equations (A22) and (A23) are the stationary
representations of the Euler equations that determine the decision of cars purchases: the price
of a new car j is the present discounted sum of current and all future stream of services and
costs accruing to the owner. Equations (A24) and (A25) are the stationary representations of
the first order conditions that determine the decision of driving. This system of equations
provides values for ,∗ , ,
∗ , ϛ, , , conditional on , , , , , , . We use the
following conditional values: 0.2, 1.0405 . , , 0.8, 0.55,
0.1494.
The European Automobile Manufacturers Association (ACEA, 2013) reports the share of VAT in
net prices of new cars for countries in the EU. The rates go around 20% in the fourteen
countries considered. Although this tax rate may change across years, in this exercise we
conceive taxes as parameters, as though they were permanently fixed.
The time discount factor is chosen assuming a 4.05% secular interest rate (yearly terms). This
is a standard assumption in calibrating DSGE models and produces a yearly capital to GNP ratio
of 3.55 (see Figure A2).
The parameter in the Cobb‐Douglas production function of the final good sector is chosen
according to the labor income share in National Accounts. This share hovers around 2/3 of
total income for most of countries (Gollin, 2002), albeit in recent times it might have
experienced a downturn due to the inception of technological progress embodied in capital
assets, mainly computer equipment, software licenses and communication equipment. Figure
A4 illustrates the decay of the labor income share for Europe from 1970 to 2007, using the EU
KLEMS database. Two sectors have been removed to calculate this share: coke, refined
petroleum sector and nuclear fuel, and the sector producing transport equipment. The value
of this share peaks at the beginning of eighties (0.718), and decreases unevenly for the
following years. The average for 1991‐2007 is 0.658, reasonably close to the prior selected
( 2/3).
17
The choice of 0 is more contrived. This parameter determines the degree of substitution
of the services of both types of vehicles: / , with Ñϛ denoting the
services provided by vehicles burning fuel j. The restriction 1 is also imposed. For
1, total services are an arithmetic mean of both services and the substitution between
both type of cars is absolute. For 0, services are a geometric mean of both vehicles.
Complementary relation between both type of cars is for 0. Finally, for 1, services are a harmonic mean of both services. We require a certain degree of substitution between
both services and impose 0 1. Parameter ϛ needs to be set first.
Previous system of equations renders the following stationary prices for new cars: ,∗
2.2547, ,∗ 2.0685 , that is, a relative price 9% higher for diesel cars, ,
∗
,∗ 1.09,6 and the
parameters are ϛ 0.609, 0.0046, 0.224 .
Thereby, consider ϛ 0.609. From the static first order condition for the decision of driving, it
is straightforward to see that the short run individual elasticity of fuel demand with respect to
price ( ) can be written as
11 ϛ
, 26 .
The first component of this expression ( 1 ϛ ) can be interpreted as the elasticity of
kilometers driven with respect to the operating costs of vehicles, which in our case is forced to
be higher than unity (note that and ϛ are constrained to be within the (0,1) interval). Bento, Goulder and von Haefen (2009), using a similar definition of operating costs, report mean
values for this elasticity of ‐0.74, and a price elasticity for fuel consumption of ‐0.35.
6 Miravete, Moral and Thurk (2015) document this same relative price for new cars in Spain in 2000. They argue that the Spanish case is indeed a representative case in the automotive market.
0,600
0,620
0,640
0,660
0,680
0,700
0,720
0,740
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
Figure A4: EU15 Labor Income Share 1970‐2007
Source: EU KLEMS and own calculations
18
The second component of this elasticity represents the fraction of the operating costs
accounted for the fuel costs. In our approximation, these fractions are 75.6% for diesel cars
and 87.1% for gasoline cars. This provides us a range to restrict the price elasticity between ‐1
and 0. In practical terms, we consider the following elasticity of VMT: 1 ϛ 1.1. This is well above the 0.74 estimate reported by Bento et al. (2009). Given ϛ 0.609, this yields
0.149, which guarantees a certain degree of substitution between both services. Note also that this fraction is also sensible to the level of price: a decrease in the price and/or
taxation of fuel j, or an improve in the fuel economy of j‐powered cars makes the demand for
fuel j be more inelastic. Hence, given that prices are lower for diesel than for gasoline mainly
due to the lenient taxation, and that fuel economy is higher for diesel motor cars (in our terms
), the demand for diesel fuel is more inelastic compared with the demand for gasoline
fuel.
As an exercise, a 1% permanent price decrease of crude oil produces a 0.827% decrease in the
net price of both fuels (note the equivalence with 0.827). Given taxation (reported in Table A7), this implies short run price elasticities of ‐0.61 and ‐0.62 for diesel and gasoline,
respectively. In the long run, once the replacement effect are considered, these values
increase to ‐0.71 and ‐0.74, respectively.
The literature on transport Economics estimates that the price elasticity should range between
‐0.25 and ‐1. A comprehensive review of earlier literature can be found in Goodwin, Dargay
and Harly (2003): For the price effects, the aggregate fuel consumption is more elastic than
aggregate kilometers driven. Particularly, while the fuel‐price elasticity is ‐0.25 in the short run
and ‐0.60 in the long run, for the mileage these are ‐0.10 and ‐0.30, respectively. Espey (1998)
estimates a price elasticity of the demand for fuel ‐0.26 in the short run and ‐0.58 in the long
run. She documents a decline in the short run price elasticity of fuel consumption after 1974,
but an increase in the long run price elasticities. She also finds higher (price and income)
elasticities for European countries than for the US. Other estimates of these price elasticities of
fuel consumption are those of Goodwin (1992) and Graham and Glaister (2004): in both
estimates, values range around ‐0.25 in the short run to ‐0.75 in the long run.
More recently, however, Blundell, Horowitz and Parey (2012) have found a long run value of ‐
0.92 for the price elasticity and 0.29 for the income elasticity, using standard time series
methods. Yet, when they consider income distribution, they estimate that the price elasticity is
lower for low and high income households, an aspect not covered by our aggregate model.
These authors survey other estimates of the price elasticity and the income elasticity of fuel
consumption that are higher than those earlier documented by Goodwin, Dargay and Harly
(2003): Hausman and Newey (1995) estimate ‐0.81 and 0.37, respectively; Schmalensee and
Stoker (1999) report values within the range [‐1.13, ‐0.72] for the price elasticity, and [0.12,
0.33] for the income elasticity; Yatchew and No (2001) find values of ‐0.89 for the price
elasticity and 0.28 for the income elasticity; West (2004) finds price elasticity of −0.89.
Differences between these estimates can be due to the period and countries considered, the
type of data (time series, cross‐section, micro‐data), the frequency of data, or the
methodology they employed. All in all, the review of current and earlier literature suggests
19
that price elasticity has increased over time (Espey (1998), Goodwin, Dargay and Harly,
(2003)).
Further arguments on the choice of , , can be found at the end of this Appendix.
Capital allocations and depreciation rates. Consider the following system of eight equations:
∗ ∗ 0.2, 27
∗ ∗ ∗ ∗ ,,
28
∗ 1∗
∗ , 28
∗ 1∗ ∗
∗ , 1,2, 30
∗ 1∗ ∗
∗ , 1,2. 31
Equation (A27) is the stationary expression of capital law of motion. Expression (A28)
represents the feasibility condition for capital. Expressions (A29) through (A31) are borrowed
from the first order conditions of firms’ profit optimization. From Euler equation, we have that
the rental price of capital should be equivalent to the user cost of capital, where ∗denotes the
real interest rate, ∗ 1.0405 / 1 0.00997, and denotes the depreciation rate of
capital. Due to the logarithmic utility for consumption, the time discount rate must meet
1 ∗ .
Given ∗, ∗, ∗, ∗ , ∗, ∗ , ∗,
and , , , the previous system is solved for
0.0140 (5.6% yearly depreciation rate), ∗ 14.23 (3.55 in annual terms), and
allocations:
∗ 12.5432,
∗ 0.0434, ∗ 0.1632, ∗ 0.2872, ∗ 1.1860.
Similarly, given the stationary price of crude oil ∗ 1, we have
∗∗ ∗
∗ 0.0052, ∗∗ ∗
∗ 0.01972.
Finally, the scale parameters in the production functions of the automotive sector and the
refinery are given by ∗
∗ 0.0086, 32
∗
∗ 0.0205, 33
∗
⟨ ∗⟩ ∗ 2.520, 34
20
∗
⟨ ∗⟩ ∗ 2.329. 35
Hours worked and willingness to work. The Frisch elasticity of labor supply is set to 0.72 (see Heathcote, Storesletten, and Violante 2010; and Chetty et al. 2011). This value is calculated using a married couple as the notion of household. The preference parameter is chosen to ensure that in steady state the consumer devotes a fraction ∗ 0.31 of his time to labor activities and the steady state consumption rate ∗ 0.6945 in Table A5. This fraction of hours worked, ∗ 0.31, is often used for calibration of the US economy. Prescott (2004) estimates that this fraction should be lowered for European countries, particularly for the period that followed the productivity slowdown of the seventies. However, none of our results hinge on it. According to these assumptions, the static first order condition produces
∗
∗
1∗ / 11.3841. 36
Finally, it follows that the scale parameter in the Cobb‐Douglas production function ∗ 0.951 is determined as
∗
∗ ∗ ∗0.7880, 37
Where ∗ denotes the services from vehicles: ∗ / 0.6327. Neutral technological shocks. We assume that TFP can be decomposed into a linear trend and
an AR(1) fluctuation:
ln ln , ln ln , ,
, ~ 0, .
From EU KLEMS, the (log) EU15 TFP is regressed on a constant, a lagged value, and a linear
trend for period 1970‐2007. The results are reported in Table A11. The coefficient associated
with the lagged TFP is taken as an estimate of , a measure of business cycles persistency
(0.897). The series presents an upward trend (0.004). We take: 0.897, 0.005.
Table A11: Dependent variable , 1970‐2007
Variable Coefficient t‐Statistic
Constant 0,471 0,359
ln 0,897 0,080
Linear trend 0,004 0,002
Adj. R2
S.E. of regression 0,005
Source: Eurostat
21
Three parameters: , , . For choosing these parameters, we search that some simulated
second order moments meet certain sample counterparts. The search strategy consists in
finding some values within reasonable ranges. The value of must be set to guarantee two
properties: firstly, the elasticity of substitution between automobile services, 1 , that
we require to allow some degree of substitution, i.e. 0 1; secondly, the price elasticity of fuel, according to our early discussion (see expression (A26)), where ϛ is given (it can be determined independently of ). Recall 1 ϛ can be viewed as the elasticity of
kilometers driven with respect to the operating costs. Bento et al. (2009) estimate a value of
0.74 for such an elasticity, which in our case requires choosing a negative : diesel and
gasoline powered cars are complementarity. Actually, miles driven per car are found inelastic
(see Goodwin et al. 2005). After several checks, values for 1 ϛ ranging between 1.1
and 1.5 guarantee that the price elasticity of fuel can be set around ‐0.8, consistent with values
reported in Blundell et al (2012).
The second parameter determines the elasticity of new cars supply: 1 . The lower
is, the flatter the supply curve. Related literature recommends modeling this supply curve
with a sufficiently high degree of flexibility, i.e. low (Busse, Knittel and Zettelmeyer, 2009).
The reason behind this recommendation, coming from industrial organization arguments, is
that the fixed nature of many of the costs of new automobile production suggests that
marginal costs tend to be small relative to the average costs. In response to a displacement in
the demand for new cars, the automotive industry tends to shift productions of new cars more
than adjusting prices. Table A12 reports standard deviations of the growth rates of new
vehicles registration (units of vehicles) and real GDP growth (at 2005 prices). All series are
annual observations from 1991 to 1998. GDP presents a yearly volatility of 1.4% in the EU14.
New cars investment series are highly volatile relative to GDP. Standard deviations of diesel
cars and gasoline cars registrations are 11.4 and 8.2 times as much high as that of the GDP
growth, respectively. For that purpose, we have searched values of to guarantee a
sufficiently high degree of flexibility in the supply curve of new cars, , . In practical
terms, our search moves within 0.6 and 0.01.
The standard RBC model stands for 1, implying that effective hours worked equal hours
devoted to non‐leisure activities. However, this case makes consumption of durables decrease
in response to a positive shock to TFP, a prediction not supported by the data. Assuming,
instead, 1 helps the standard model to reconcile with the data (see Fischer, 2007). Note
that the choice of must ensure a positive value for the utility parameter , accounting for
the “willingness to drive”, determined in previous expression (A24).
Table A13 reports a summary of simulated correlations of and new cars investment , ,
for certain key values of , , . The DSGE model is simulated for a time horizon of 10000
periods, using the parameterization suggested in this Appendix and for alternative values of
the triplet , , . We use two shocks earlier described: a shock to TFP, and a crude oil
shock. Firstly note that correlations between , and are negative for 1, and the volatility of relative to , is rather small (not consistent with evidence in Table A12).
When is lowered, the simulated correlations turn out positive and the relative standard
deviations exceed one, although these values are quite below those reported in Table A12.
22
For 0.8 and 0.10, the simulated correlations and the simulated relative volatilities
apparently have the correct sign. However, for 0.149 (elasticity of miles driven with
respect to operating cost of 1.1), the correlation at lead values are higher than for lagged
values (especially when the model is simulated under only on shock to TFP), implying an
unrealistic property that new cars purchases are lagged indicators. For 0.548,0.1, 0.8 , all simulated moments have correct sign and value (relative standard deviations
are even above 3), but price elasticity of fuel consumption presents numerical values are
rather high. Yet, for simulations with only TFP shocks, the correlations turn out negative.
After several attempts, we found that the model reconcile with several sample moments (i.e.
fuel price elasticity, correlations and standard deviations) when is set to 0.55, indeed not
such a flat supply curve of new cars purchases. Fuel consumption is more inelastic when
0.149. Summarizing, our benchmark cases will take two values for (0.548 and 0.149),
0.55, and 0.8.
Table A12: Vehicles registration growth and GDP growth, 1991‐2008
Standard deviations Relative S.D.
Diesel Gasoline GDP Diesel Gasoline
Austria 0,121 0,133 0,010 11,7 12,8
Belgium 0,080 0,102 0,026 3,1 3,9
Denmark ‐‐‐ ‐‐‐ 0,015 ‐‐‐ ‐‐‐
Finland 0,326 0,193 0,032 10,2 6,0
France 0,092 0,147 0,011 8,1 13,0
Germany 0,125 0,088 0,012 10,1 7,1
Greece 0,334 0,209 0,020 16,3 10,2
Ireland 0,122 0,195 0,035 3,5 5,6
Italy 0,262 0,132 0,012 22,2 11,1
Netherlands 0,124 0,054 0,013 9,6 4,2
Portugal 0,184 0,136 0,054 3,4 2,5
Spain 0,180 0,126 0,015 12,2 8,6
Sweden 0,539 0,190 0,021 25,8 9,1
United Kingdom 0,186 0,094 0,014 13,7 6,9
Average 0,163 0,118 0,014 11,4 8,2
Maximum 0,539 0,209 0,054 25,8 13,0
Minimum 0,080 0,054 0,010 3,1 2,5
Source: Odyssee‐Mure, Eurostat and own calculations
23
Table A13: G
rid search for {,
, }
Correlogram
with respect to sim
ulated
/
‐4
‐3
‐2
‐1
0
1
2
3
4
0,548
0,55
0,80
0,006
1,000
0,807
0,851
0,897
0,947
1,000
‐‐‐
‐‐‐
‐‐‐
‐‐‐
0,008
1,460
0,611
0,620
0,629
0,640
0,652
0,624
0,596
0,570
0,544
0,008
1,313
0,702
0,721
0,739
0,761
0,783
0,751
0,719
0,688
0,657
0,548
0,55
1,00
0,0121
1,000
0,880
0,909
0,939
0,970
1,000
‐‐‐
‐‐‐
‐‐‐
‐‐‐
0,0106
0,875
‐0,679
‐0,696
‐0,713
‐0,728
‐0,742
‐0,722
‐0,700
‐0,678
‐0,657
0,0086
0,704
‐0,622
‐0,638
‐0,652
‐0,665
‐0,676
‐0,658
‐0,638
‐0,619
‐0,600
0,548
0,10
0,80
0,006
1,000
0,817
0,858
0,902
0,950
1,000
‐‐‐
‐‐‐
‐‐‐
‐‐‐
0,019
3,232
0,512
0,509
0,504
0,500
0,496
0,491
0,481
0,469
0,455
0,021
3,496
0,581
0,590
0,597
0,605
0,614
0,600
0,582
0,562
0,541
0,149
0,55
0,80
0,0063
1,000
0,792
0,841
0,891
0,945
1,000
‐‐‐
‐‐‐
‐‐‐
‐‐‐
0,0071
1,135
0,699
0,734
0,768
0,805
0,843
0,792
0,739
0,690
0,640
0,0074
1,173
0,711
0,747
0,782
0,821
0,859
0,808
0,756
0,707
0,658
0,149
0,55
1,00
0,0121
1,000
0,880
0,909
0,939
0,970
1,000
‐‐‐
‐‐‐
‐‐‐
‐‐‐
0,0093
0,762
‐0,645
‐0,662
‐0,677
‐0,691
‐0,703
‐0,684
‐0,664
‐0,643
‐0,623
0,0089
0,731
‐0,634
‐0,650
‐0,665
‐0,678
‐0,689
‐0,671
‐0,651
‐0,631
‐0,611
0,149
0,10
0,80
0,0061
1,000
0,790
0,839
0,889
0,944
1,000
‐‐‐
‐‐‐
‐‐‐
‐‐‐
0,0237
3,910
0,540
0,560
0,578
0,595
0,612
0,569
0,522
0,477
0,433
0,0252
4,169
0,547
0,568
0,587
0,605
0,623
0,579
0,531
0,485
0,440
24
Sectorial composition. From the EU KLEMS database we retrieve data to calculate the shares
of Gross Value Added (GVA) for the three sectors “Coke, refined petroleum and nuclear fuel”,
“Extraction of crude petroleum and natural gas and services”, and “Motor vehicles, trailers and
semi‐trailers”. These shares are reported in Table A14 for different periods and for our set of
countries. The first two sectors only account for 0.6% of GVA. These shares remain stable
throughout the period. The maximum share in “Coke, refined petroleum and nuclear fuel” is
observed for The Netherlands (2.0%). The maximum share in “Extraction of crude petroleum
and natural gas and services” is for the United Kingdom (2.2%). And the maximum share in
“Motor vehicles, trailers and semi‐trailers” is for Germany (6.0%). On average, these three
sectors account for a 3.3% of total GVA of the EU14, implying that the rest of the economy
accounts for 96.7% of GVA. In our model, the final good sector Y represents a 95.1% of GVA.
The difference 1.6% can be associated to market services. Hence, we believe that ∗ 0.951 provides an accurate representation of the EU14 economy.
25
Table A14: Sectorial composition (share of sector over Gross Value Added) 1995‐2007
Coke, refined petroleum and nuclear
fuel
Extraction of crude petroleum and
natural gas and services
Motor vehicles, trailers and semi‐
trailers
1995‐1999
2000‐2004
2005‐2007
1995‐1999
2000‐2004 2005‐2007
1995‐1999
2000‐2004
2005‐2007
Austria*
0,36%
0,38%
0,33%
0,31%
0,42%
0,18%
0,94%
1,15%
1,32%
Belgium*
0,53%
0,58%
0,72%
0,17%
0,13%
0,12%
1,59%
1,34%
1,18%
Den
mark
0,55%
0,71%
0,93%
0,71%
1,53%
2,02%
0,36%
0,32%
0,27%
Finland*
1,14%
1,54%
2,07%
0,40%
0,38%
0,42%
0,39%
0,38%
0,54%
France*
0,32%
0,28%
0,29%
0,16%
0,13%
0,01%
1,14%
1,17%
0,95%
Germany
0,71%
1,10%
1,43%
0,08%
0,09%
0,08%
4,84%
6,18%
6,72%
Greece
0,48%
0,96%
1,26%
0,06%
0,03%
0,02%
0,08%
0,08%
0,05%
Ireland*
0,04%
0,04%
0,05%
0,64%
0,47%
0,50%
0,21%
0,17%
0,18%
Italy
0,48%
0,30%
0,42%
0,32%
0,24%
0,19%
0,86%
0,70%
0,64%
Netherlands
1,47%
1,94%
2,73%
1,58%
1,52%
1,82%
1,04%
0,99%
0,90%
Portugal*
0,08%
0,07%
0,28%
0,48%
0,37%
0,34%
0,73%
0,72%
0,58%
Spain
0,42%
0,42%
0,26%
0,01%
0,02%
0,01%
1,81%
1,49%
1,23%
Swed
en*
0,17%
0,17%
0,18%
0,30%
0,26%
0,58%
2,20%
2,36%
1,98%
United
Kingdom
0,35%
0,25%
0,21%
2,03%
2,20%
2,42%
1,24%
0,87%
0,71%
Average
0,53%
0,60%
0,72%
0,54%
0,62%
0,66%
2,10%
2,18%
2,10%
Source: Eurostat and EU KLEMS
For all countries, the sector "Refined
petroleum" is m
erged within "Coke, refined
petroleum and nuclear fuel".
For all countries, the sector "Passenger motor vehicles" is m
erged within "Motor vehicles, trailers and sem
i‐trailers".
*: For these countries, the inform
ation rep
orted
for "Extraction of crude petroleum and natural gas and services" includes additionally two other sectors: "Mining
of coal and lignite; extraction of peat", and "Mining of uranium and thorium ores". EU KLEMS database provides the inform
ation of these three sectors aggregated
in the supra‐sector "'Mining and Quarrying".
26
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