Download Oil Prices and Consumption across Countries and U.S. States

Oil Prices and Consumption across Countries and U.S. States
PRELIMINARY
∗
Matteo Iacoviello†
November 25, 2016
Abstract
I study the effects of oil prices on consumption across countries and U.S. states. Using a newly
constructed quarterly dataset for 50 countries over the years 1975-2015, I show that oil price declines have
large and positive effects on the consumption of oil-importing countries, while depressing the consumption
of oil exporters. I then complement this analysis by estimating the heterogeneous effects of oil price declines
on car sales across U.S. states.
∗
Aaron Markievitz and Lucas Husted provided outstanding research assistance. The views expressed in this paper are solely
the responsibility of the authors and should not be interpreted as reflecting the views of the Board of Governors of the Federal
Reserve System or of anyone else associated with the Federal Reserve System.
†
Federal Reserve Board of Governors. Email: [email protected]
1
Introduction
Oil prices fell from 104 to 30 dollars a barrel between July 2014 and February 2016. This large, although
not unprecedented, decline has reignited the interest on the macroeconomic effects of fluctuations in oil
prices. The conventional wisdom of academics, policymakers and market practitioners is that declines in
oil prices boost global economic activity, as increases in consumption, especially for oil importers, outweigh
the negative effects on oil producers. This wisdom has recently been reiterated by Arezki and Blanchard
(2014), Bernanke (2016), and even Warren Buffett, amongst others.1 However, much of the research on the
global effects of oil price changes has only looked at a smaller set of countries (see e.g. Jiménez-Rodrı́guez
and Sánchez (2005) or Kilian (2008)), or has focused on a set of indicators, such as industrial production or
current account, whose responses may not easily be mapped into those of the GDP and consumption (see for
instance Aastveit, Bjrnland, and Thorsrud (2015) and Kilian, Rebucci, and Spatafora (2009), respectively).
This paper studies the effects of oil prices on consumption and GDP across countries and across U.S.
states. A hurdle in obtaining precise estimates of the global effects of oil price shocks is data availability.
To address this shortcoming, I put together a quarterly dataset containing data on GDP, consumption and
oil dependence for 50 countries accounting for about 80 percent of world GDP and consumption and 75
percent if world oil production. The coverage, which varies across countries, spans from 1975 to 2015. I
go great lengths to ensure that the data are consistent across countries and do not contain breaks. Using
this unique database, I estimate the effects of oil price shocks on different countries allowing for the effects
to vary based on each country’s oil dependence. I find that oil prices declines have large, positive effects
on the consumption and GDP of oil-importing countries, while depressing consumption and GDP of oil
exporters, and that over time the positive effects dominate. However, I also show that the boost among oil
importers occurs rather gradually, while the hit to oil exporters is realized fairly quickly, thus suggesting
that, in aggregate, the benefits from falling oil prices might occur only slowly.
I conclude by presenting complementary analysis showing that, for the United States, the effects of
oil price declines on consumption differ across states depending on their oil dependence. To this end, I
use state-level data on consumers’ new cars’ registrations – available quarterly from 1990 to 2015 – as a
proxy for state-level consumption. This dataset has important advantages over state-level data on personal
consumption expenditures, as the latter are still experimental, are available only annually, and cover a
shorter time frame. I then show that while U.S. states exhibit pronounced heterogeneity in oil dependence,
their responses to a common oil shock appear less marked than the analogous objects across countries. The
small differences may reflect the fact that, because cars run on gas, lower oil prices make car-buying more
attractive, even in oil-intensive states. However, the small differences may also reflect the close trade and
financial linkages within the United States dampening the effects of heterogeneity in oil production.
The paper proceeds as follows. Section 2 presents the results from an aggregate VAR which groups
countries accordingly to whether they are oil importers or oil importers. Section 3 uses a panel VAR to dig
1
See CBS/AP (29 Feb. 2016)
2
deeper into the heterogeneous country responses. Section 4 presents state-level panel VAR evidence for the
United States. Section 5 concludes.
2
Oil Prices, World Consumption and GDP: Aggregate VAR
As is well known, changes in oil prices may reflect changes in global demand as well as shocks to that are
specific to the oil market. Additionally, oil market shocks may reflect either shocks to actual oil supply,
or shocks to oil demand that reflect expectations of future changes in supply. Most of my analysis focuses
on the effects of oil price movements that are driven by oil market shocks, and that can be considered
somewhat exogenous to global economic activity. I isolate oil market movements by postulating that oil
market variables move on impact, and macro variables with a one–period delay, following an oil market
shock.
The channels by which a decline in oil prices affects global activity are well known. On the supply side,
lower oil prices raise output in the non–oil sector by reducing firms’ production costs. By contrast, lower oil
prices depress production and investment in the energy sector, which should restrain activity in oil-producing
countries. On the demand side, lower oil prices transfer wealth towards oil-importing countries and away
from oil exporters, and thus shift consumption towards the former. To the extent that oil importers spend
a substantial portion of the transfer, the net effects on global activity of an oil price decline are likely to be
positive.
Figure 1 plots oil prices against what I call the “consumption differential,” the difference in consumption
growth between importing and exporting countries. Over the past 40 years, whenever oil prices have fallen,
the consumption differential has risen. The correlation between oil prices and the consumption differential
is −0.31. It is important to highlight the consumption differential because it offers a simple way to control
for global demand–side effects that otherwise create a positive correlation between activity and oil prices.
Separately taken, in fact, the correlations of oil prices with importers’ and exporters’ consumption are both
positive, respectively at 0.1 and 0.38.
To further explore the differential responses of importers and exporters, I next consider a vector autoregressive model (VAR) aimed at quantifying the effects of lower oil prices on global economic activity. The
VAR uses two lags and quarterly data from 1975Q1 to 2015Q4 on oil prices in real terms (oil), oil production
(oprod), importers’ and exporters’ private consumption expenditure (ci and ce respectively), and importers’
and exporters’ GDP (gdpi and gdpe respectively).2 Consumption and GDP are constructed aggregating
data – using constant-dollar GDP weights – for 40 oil-importing and 10 oil-exporting countries. Importers
account for about 90 percent of world GDP, and the sample include countries which together represent about
four-fifths of world GDP. The countries are listed in Table A.1. The structural VAR representation thus
2
All variables are logged. The baseline specification include a linear and a quadratic trend in each equation of the VAR. See
Appendix A.1 for details on data construction.
3
takes the form:
Axt = B (L) xt−1 + εt
(1)
xt = (gdpit , gdpet , cit , cet , oilt , oprodt )0
(2)
Eεε0 = I
(3)
To isolate the effects of supply-driven changes in oil prices, I use a Cholesky decomposition of the
0
variance–covariance matrix of the reduced–form VAR residuals (Σ = A−1 A−1 ) whereby oil price shocks
affect oil prices and production on impact, and consumption and GDP with a one quarter delay. That is,
the reduced form errors can be decomposed as follows:






−1
e = A ε =





egdpi


 

egdpe 
 



eci 
=
 
ce
e
 
 
oil
 
e
 
eprod
a11
0
a21 a22
0
0
0
0
0
0
0
0
a31 a32 a33
a41 a42 a43 a44
0
a51 a52 a53 a54 a55
a61 a62 a63 a64 a65
0



0 



0 


0 


0 

a66
ε1


ε2 


3
ε 
.

ε4 

εoil 

ε6
(4)
In other words, oil shocks εoil are the joint innovations in oil prices and oil production in column 5 of A−1
matrix) that move consumption and GDP with a one period delay. Such assumption is in keeping with
a host of empirical VAR studies who order asset prices after the macroeconomic variables in a Cholesky
ordering of the residuals of a VAR, on grounds that asset prices are highly sensitive to contemporaneous
economic news or shocks (see for instance Kilian (2009), Stock and Watson (2005), and Bernanke, Boivin,
and Eliasz (2005)).
Figure 2 shows the response to an identified oil price shock that is rescaled so as to lead to a 10 percent
decline in real oil prices in the first quarter. The fall in the oil price boosts importing countries’ GDP. The
peak effect, which is close to 0.15 percent, is not immediate. Rather, it builds in slowly and peaks only
three years after the shock. Importers’ consumption increase slightly more than GDP, peaking at nearly 0.2
percent above baseline. By contrast, exporters’ consumption drops rapidly and exporters’ GDP declines even
more briskly, presumably due to lower activity in the energy sector. The response of production supports
the interpretation of the oil shock as being caused by news about higher future supply: in particular, oil
production move little on impact, but rises persistently above the baseline after the shock, peaking at 0.3
percent above baseline after four quarters.
A large body of empirical literature has looked at whether increases and decreases in oil prices have
different effects on economic activity. To consider this possibility, I augment the VAR specification with
the net oil price increase variable (noil) constructed by Hamilton (1996). In particular, I follow Kilian and
Vigfusson (2011) and construct the net increase in the real price of oil rather than the nominal price as in
4
Hamilton. Consequently, the model has a different linear structure depending on whether oil prices go down
or up. The augmented structural VAR representation thus takes the form:3
Axt = B (L) xt−1 + C (L) noilt−1 + εt
(5)
noilt ≡ max (0, oilt − max (oilt−1 , oilt−2 , oilt−3 , oilt−4 )) .
(6)
Figure 3 shows that the negative effects on world activity of an oil price increase are larger than the positive
effects from an oil price decrease (starting from an initial state in which oil prices are constant). Oil price
increases lead to a decline in consumption both across importers and exporters, with importers’ consumption
falling more, on impact, than exporters’ consumption. By contrast, oil price decreases lead to an increase
in consumption and GDP only across importers. The takeaway message from the figure is that the harm
done (to world GDP and consumption) from oil price increases is slightly larger than the good done (to
world GDP a consumption) from oil price decreases. Additionally, the timing of the effects of oil price
decreases and increases for importers is not the same: the negative effects of oil price increases materialize
fairly quickly, with a response that bottoms out after about 4 quarters, both for consumption and GDP. By
contrast, the positive effects of oil price declines materialize about half as fast, and are spread about equally
across the first and the second year after the shock.4
The top half of Table 1 summarizes the results from the VAR specification that allows for asymmetric
effects of oil shocks.
The benchmark VAR does not impose any restriction on the joint contemporaneous comovement of oil
production and oil prices in response to an oil shock. If one interprets the oil price decrease as the outcome
of a positive shock to oil supply shock, the ratio of the impulse responses of oil production to oil prices
can be interpreted as the elasticity of oil demand to oil prices. Given my estimates, such estimate turns
out to be very small, and close to zero in the short run. While this assumption appears palatable, it hides
the possibility that oil production must be driven by oil demand shocks that move prices only a little, thus
implying a very large oil supply elasticity, something that appears somewhat at odds with case studies and
historical episodes. Caldara, Iacoviello, and Cavallo (2016) discuss this issue in large detail. In Figure A.3
in the Appendix, I examine the robustness of the results to an alternative identification scheme that retains
the assumption that oil shocks affect GDP and consumption with a one–period delay, but decomposes the
variance–covariance matrix of the VAR residuals assuming joint oil demand and oil supply elasticities (the
elements of 2 × 2 lower right matrix A−1 ) in line with existing studies, as discussed in Caldara, Iacoviello,
and Cavallo (2016). In particular, I assume an impact oil supply elasticity of 0.1, which in turn implies an
oil demand elasticity of −0.24. When the identified oil ”supply” shock is scaled so that the impact on oil
prices is the same as that of my benchmark specifications, the response of oil production is now larger, but
the macro responses lie in the same ballpark.
3
Kilian and Vigfusson (2015) also consider an augmented VAR model with oil prices and net oil prices, but use it to study
the link between oil, consumer confidence and the excess bond premium.
4
I return to the issue when I consider a higher level of disaggregation across importers and exporters in the panel VAR.
5
3
Cross-Country Panel VAR
In the previous section I have assumed that the effects of an oil shock only depend on whether a country is
an oil importer or exporter, without any allowance for within-importer and within-exporter variation.
Figure 4 and Table A.1 show that there are important differences in oil dependence, both across countries
(top panel), and within country over time (bottom panel). Such differences are obviously lost when using
the simple distinction between oil importers and oil exporters. I measure oil dependence as the ratio of net
oil imports to total oil consumption, expressed in percent. That is,
dt ≡
oconst − oprodt
pot × (oconst − oprodt )
≡
pot × oconst
oconst
(7)
where pot is the dollar price of oil, which drops out from the calculations. Japan and the large majority
countries in the Euro area each have an oil dependence of nearly 100 percent, while the United States has
an oil dependence of about 50 percent. There is also substantial heterogeneity among oil exporters, with
Canada’s oil exports amounting to 70 percent of its domestic oil consumption, while Saudi Arabia’s oil
exports are nearly 300 percent of its oil consumption. Canada was a net importer for much of the 1970s
and the 1980s, but now exports a large share of its oil production. By contrast, the United Kingdom was a
large exporter throughout the 1980s and the 1990s, but is now a net importer.
Motivated by this heterogeneity, I estimate a hybrid panel VAR that enriches in two key ways the model
of the previous section. First, I use disaggregated quarterly data – on consumption expenditures and GDP
– for individual countries, rather than grouping together all oil importers and all oil exporters. Second, I
allow the response of consumption and GDP in each country to depend, in a simple and parsimonious way,
on that country’s time-varying oil dependence dt . The final dataset, again discussed in Table A.1, has a
coverage that varies across countries, with an average of 28 years worth of observations for each country, for
a total of more than 5, 000 observations.
Figure 5 illustrates why a country’s oil dependence goes a long way in characterizing the link from changes
in oil prices to changes in consumption expenditures. For each country, I run a simple OLS regression of log
consumption against its fourth lag and oil prices lagged four periods.5 That is:
ln ct = ρ ln ct−4 + β (1 − ρ) ln ot−4 + ui,t .
(8)
In the specification above, the country–specific coefficient β can be interpreted the long–run elasticity of
consumption to oil prices. If all countries were to respond the same to oil price shocks, one should not find
a strong sensitivity of β on a country’s average oil dependence. Yet as shown in Figure 5, dependence to oil
changes is strongly negatively correlated with each country’s oil dependence. In other words, a higher share
of oil imports implies a larger negative response of consumption to higher oil prices.
5
Both oil prices and consumption are detrended with a cubic trend. The exact detrending matters little for the results. The
results are robust to running a similar specification in growth rates.
6
Motivated by this finding, I set up a simple panel VAR where I allow the response of consumption and
GDP to oil prices to depend, across countries and over time, on each country’s oil dependence. Let ci,t and
yi,t denote country-specific log real consumption expenditures and log real GDP,6 and let Ct and Yt denote
PN
P
the corresponding world aggregates, so that Yt ≡ N
i=1 ω i,t ct , where ω i,t denote GDP
i=1 ω i,t yt and Ct ≡
weights. The VAR includes consumption, GDP, a measure of commodity prices zt , and oil prices ot . All
variables are measured in deviation from a quadratic trend.
The specification that I estimate is a recursive, hybrid panel VAR as follows:7
yt = γ yy yt−1 + γ yY Yt−1 + γ yc ct−1 + γ yC Ct−1 + γ yz zt−1
+ γ yo + γ yd g (dt ) oilt−1 + (δ yo + δ yd g (dt )) noilt−1 + εyt ,
(9)
ct = αcy yt + αcY Yt + γ cy yt−1 + γ cY Yt−1 + γ cc ct−1 + γ cC Ct−1 + γ cz zt−1
+ (γ co + γ cd g (dt )) oilt−1 + (δ co + δ cd g (dt )) noilt−1 + εct ,
(10)
zt = αzY Yt + αzC Ct + γ zY Yt−1 + γ zC Ct−1 + γ zz zt−1
+γ zo oilt−1 + δ zp noilt−1 + εzt ,
(11)
oilt = αoY Yt + αoC Ct + αoz zt + γ oY Yt−1 + γ oC Ct−1 + γ oz zt−1
+γ oo oilt−1 + δ oo noilt−1 + εoil
t .
(12)
The specification above is a standard panel VAR in (yt , ct , zt , oilt ) in which the ordering of the variables
allows shocks to oil prices to affect consumption, GDP and commodity prices with a one period delay.8
I depart from a standard panel VAR (of the kind proposed in Holtz-Eakin, Newey, and Rosen (1988))
in the following ways. First, I allow for cross-sectional heterogeneity, by allowing for the responses of
consumption and GDP to oil shocks to vary according to each country’s time–varying oil dependence di,t .
The specific functional form that I adopt allows for slope coefficients that depend on a logistic transformation:
g (dt ) =
1
.
1 + exp (dt )
(13)
Such specification allows for proportionally smaller different responses among exporters than would be
predicted by a simple linear specification. It is motivated by Figure 5, which shows how the heterogeneous
consumption response are approximately linear in g (dt ) , as assumed by the panel VAR above. Later, I
show that the results are little changed when I adopt an alternative measure of oil dependency constructed
normalizing oil imports by nominal GDP, instead of oil consumption. In the appendix, I also show – see
A.1 – that the results are little changed both when I adopt a slightly different transformation (in a best-fit
6
Each country’s consumption and GDP are quadratically detrended using a country–specific trend.
The actual VAR has two lags. I omit lags greater than 1 to save on notation. The results were similar moving from two to
four lags.
8
The specification above is written as a recursive VAR, by including contemporaneous values as regressors in a block-recursive
way. However, given the cross-equations restrictions embedded in the panel VAR, it does not produce errors that are perfectly
uncorrelated across equations, as is the case in a standard VAR. See [ ] for discussion.
7
7
sense) of the oil dependency measures.
Second, I allow individual countries’ consumption and GDP to depend not only on their own lags, but
also on lags of the world aggregates, thus allowing for dynamic interdependencies across countries. The world
aggregates are averages of the country–specific variables, as done for instance in the global VAR approach
proposed by Pesaran (2006) and discussed in Canova and Ciccarelli (2013) and Chudik and Pesaran (2014).
Third, I allow the real net oil price increase variable, noilt , to enter the specification, to account for the
potential asymmetric effects of oil shocks.
Figure 6 plots the impulse responses of consumption and GDP across a selected group of countries to a
10 percent oil price decline, when oil dependence is equal to its value in the 2015. The choice of countries
showcases the heterogeneity across responses that accounting for oil dependence generates. For the world
as a whole, the responses of consumption and GDP are positive, although not very large, and take time to
fully materialize. As shown in Table 1, after two years, the rise in world GDP is about 0.12 percent, mostly
driven by a rise in consumption, which peaks at 0.13 percent. The responses of consumption and GDP to
an oil price decline are negative for oil exporters, such as Canada and Norway.
The bottom half of Table 1 compares the responses across negative oil price shocks and positive oil price
shocks. Both for consumption and GDP, there is some asymmetry, although not particularly pronounced.
Again, as in the aggregate VAR of section 2, oil prices increases do more harm than the good afforded by
oil price decreases.
Figure 7 uses the country–specific VAR estimates to better quantify how the “exogenous” portion of the
2014-2015 oil price decline has affected consumption and GDP across countries. First and foremost, the
VAR attributes a large chunk of the oil price decline throughout that period to surprise innovations to the
oil price equation (the εot in the VAR above) which took place between 2014Q3 and 2015Q1. Other shocks
as well as the after–effects of shocks that took place before 2014Q3 play a much more limited role in driving
oil prices. Accordingly, the response of oil prices – shown in the top left panel – in deviation from baseline
since 2014Q1, closely mirrors, in percent terms, the actual path of oil prices. The top right panel shows
the response for the world. Consumption rises only gradually, peaking at about 0.8 percent above baseline
after two years. The response of GDP is initially even more tepid, likely reflecting some drag from declines
in investment in the oil sector, but builds to 0.7 percent by the end of 2017. The middle panel compares
the two largest world economies, USA and Euro Area. While the response in the U.S. mirrors that of the
world as a whole, the response of the Euro Area to the oil price decline is larger than in the United States,
reflecting the Euro Area’s larger oil dependence. In the bottom panel, Canada and Norway display the
typical responses of oil–exporting economies, with GDP and consumption falling markedly.
The responses shown in 6 rely on an oil dependency measure that potentially does not control for oil
usage as a share of overall economic activity. According to this measure, two countries that do not produce
any oil will have the same oil dependency of 1, regardless of how energy intensive the household and the
business sector are. I consider an alternative specification of oil dependence which scales oil imports (the
difference between oil consumption and oil produced) by GDP, so that oil dependence is assessed against
8
the size of the economy has a whole. That is,
daltt = 100 ×
pot × (oconst − oprodt )
nomgdpt
(14)
Such measure is more directly affected by oil price fluctuations than the baseline measure. As oil prices rise,
oil dependence rises among importers, and falls among exporters. This measure is larger, all else equal, for
countries that rely more on imported oil as a fraction of total activity, not as a fraction of oil usage only.
Another property of this measure is that it can potentially capture nonlinearities in the effects of oil price
shocks that depend on the level of oil prices. For instance, this measure of oil dependency tends to fall
(rise) among oil importers (exporters) as oil price fall. Figure 8 shows that the alternative oil dependence
measure, once transformed using the logistic transformation of equation 13, produces impulse responses to
a 10% decline in oil prices – starting from the oil dependency value of 2015 – that are broadly in line with
those of the baseline specification. I conclude from this specific finding that, to a large extent, the results
support that oil dependency matters, but that the results are little changed when adopting alternative
definitions of oil dependence. This result mostly reflects the very high correlation between the two measures
of oil dependence used here. The correlation between d and dalt is 0.79. The correlation between g (d) and
g (dalt) is 0.90.
4
State-Level Panel VAR
Just like countries in the world, U.S. states are very heterogeneous in their oil dependence. As shown in
Table A.2 and in Figure 9, states in the Northeast have an oil dependence of nearly 100 percent. By contrast,
Texas produces more oil than it consumes, while the oil dependence of states like Alaska and North Dakota
is almost off the charts, around minus 1,000 percent.9 Using the cross-sectional standard deviation of oil
dependency as a measure of heterogeneity, the U.S. states are more heterogeneous than the world’s countries
in terms of oil dependency.10
I analyze the consumption response to oil supply shocks using state-level data on consumers’ new car
registrations – available quarterly from 1989Q1 to 2015Q2 – as a proxy for state-level consumption. This
dataset has important advantages over state-level data on personal consumption expenditures, as the latter
are still experimental, are available only annually, and cover a shorter time frame.
I set up a panel VAR that mirrors the cross-country analysis discussed in the previous section. In each
state, car registrations depend on their own lags, lagged national car registrations, and lagged oil prices. Oil
prices, in turn, depend on their own lags and on national car registrations. As before, the VAR allows for
heterogeneous regional responses to an oil shock as a function of oil dependence.
9
In oil exporting states such as Alaska and North Dakota, petroleum industry accounts for about 25 percent of the state’s
GDP.
10
In 2013, the cross-sectional standard deviation of oil dependency across U.S. states was 2.1. The corresponding number for
the world’s countries was 1.4.
9
Let ct denote state-specific car registrations (in log terms), and let Ct denote the U.S. car registrations
P
(in logs), so that Ct ≡ N
i=1 ω i,t ct , where ω i,t denote state–specific weights. The VAR only includes ct , Ct
and oil prices ot (in logs). The maintained assumption here is that aggregate car registrations are a good
proxy for global oil demand that one wishes to control for, so that oil prices are not treated as exogenous
with respect to the world economy. Indeed, as also shown in Figure A.2, the plunge in U.S. auto registrations
during the global financial crisis seems to capture, at least in part, the concurrent business cycle contraction.
Accordingly, I estimate a panel VAR as follows:11
ct = γ cc ct−1 + γ cC Ct−1 + (γ co + γ cd g (dt )) oilt−1 + εct ,
oilt = αoC Ct + γ oC Ct−1 + γ oo oilt−1 + εoil
t ,
(15)
(16)
where the specific functional form I adopt for oil dependency is the same as before:
g (dt ) =
1
.
1 + exp (dt )
(17)
Figure 10 shows the predicted effect on car registrations of a 10 percent oil price decline triggered by
an εoil
t shock. For the United States in aggregate, such shock is associated with a gradual but substantial
increase in car registrations, which peak at 0.9 percent after about three years. Comparing the response here
with the aggregate consumption responses from the country VARs highlights how the response of vehicle
registrations is about 5 times larger than that of consumption. Such large response well lines up with the
findings in the literature: Edelstein and Kilian (2009) find that vehicle expenditures are highly sensitive to
movements in the price of gasoline. Coglianese, Davis, Kilian, and Stock (2016) find an estimate of −0.37
for the price elasticity of gasoline demand, although their numbers do not appear directly comparable to
mine, as they look at the cumulative effects of gasoline price changes.
Turning to the state-specific responses, the response in oil-importing states (not shown) is similar to
that of the United States in the aggregate. By contrast, major oil-exporting states such as Texas and North
Dakota suffer an initial decline in car registrations. Eventually, car registrations eventually rise in all states,
even in major oil producing states. This rise may reflect the fact that, because cars run on gas, lower oil
prices make car-buying more attractive, even in oil-intensive states. However, the across-the-board rise in
car registrations may also reflect the increase in overall U.S. aggregate demand, with close trade and financial
linkages within the United States dampening the effects of heterogeneity in oil production.
Similar results also emerge when I estimate a version of the panel VAR model above replacing car
registrations with state–level unemployment rates. Figure 11 shows that the heterogeneity in the response
of unemployment rates across states hews closely to the heterogeneity in the response of car registration.
11
The actual VAR has two lags. I omit lags greater than 1 to save on notation. The results were similar moving from two to
four lags.
10
On impact, lower oil prices lead to a transitory increase in unemployment in oil-rich states, before the
across-the-board decline in unemployment lowers the unemployment rate across all states.
5
Conclusions
In this paper I provide empirical evidence that lower oil prices provide a substantial boost to U.S. and
global growth. I do so using cross–sectional regressions, aggregate VARs, and panel VAR methods. Most
importantly, lower oil prices boost economies in direct proportion to their degree of oil dependency, with an
effect that is positive for oil importers, negative for oil exporters, and roughly neutral for oil independent
countries. A 10 percent decline in oil prices, for instance, boosts Euro Area consumption and GDP by about
0.2 percent, while it leads to a short–run decline in consumption and GDP in oil–exporting countries such
as Canada and Norway.
I complement my cross–country results by showing analogous findings in the cross–section of U.S. states.
The takeaway message from my analysis is that oil price declines work a lot like redistributive shocks
that transfer wealth from oil rich states and countries to oil poor states and countries.
11
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and the analysis of monetary policy,” Quarterly Journal of Economics, 120(1), 387–422. 4
Bernanke, B. S. (2016): “The relationship between stocks and oil prices,” Brookings Institution. 2
Caldara, D., M. Iacoviello, and M. Cavallo (2016): “Oil Price Elasticities and Oil Price Fluctuations,” Discussion paper, Federal Reserve Board. 5
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205–246. 8
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Watch, Web. 2
Chudik, A., and M. H. Pesaran (2014): “Theory and practice of GVAR modelling,” Journal of Economic
Surveys. 8
Coglianese, J., L. W. Davis, L. Kilian, and J. H. Stock (2016): “Anticipation, tax avoidance, and
the price elasticity of gasoline demand,” Journal of Applied Econometrics. 10
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Journal of Monetary Economics, 56(6), 766–779. 10
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Monetary Economics, 38(2), 215–220. 4
Holtz-Eakin, D., W. Newey, and H. S. Rosen (1988): “Estimating vector autoregressions with panel
data,” Econometrica, pp. 1371–1395. 7
Jiménez-Rodrı́guez, R., and M. Sánchez (2005): “Oil price shocks and real GDP growth: empirical
evidence for some OECD countries,” Applied economics, 37(2), 201–228. 2
Kilian, L. (2008): “Exogenous Oil Supply Shocks: How Big Are They and How Much Do They Matter for
the U.S. Economy?,” Review of Economics and Statistics, 90(2), 216–240. 2
(2009): “Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the
Crude Oil Market,” American Economic Review, 99(3), 1053–69. 4
12
Kilian, L., A. Rebucci, and N. Spatafora (2009): “Oil shocks and external balances,” Journal of
International Economics, 77(2), 181–194. 2
Kilian, L., and R. Vigfusson (2015): “The Role of Oil Price Shocks in Causing U.S. Recessions,”
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NBER Working Papers 11467, National Bureau of Economic Research, Inc. 4
13
Table 1: Response of Consumption and GDP to a 10 percent oil price shock.
Grouped VAR
Importers
0.13
0.10
10% Oil Price Decline
Consumption
GDP
10% Oil Price Increase
Consumption
GDP
Exporters
-0.23
-0.15
World
0.09
0.07
-0.10
-0.04
-0.16
-0.10
-0.17
-0.11
10% Oil Price Decline
Consumption
GDP
U.S.
0.13
0.11
10% Oil Price Increase
Consumption
GDP
-0.16
-0.12
Panel VAR
Euro Area
Canada
0.21
-0.10
0.14
0.03
-0.26
-0.18
0.11
0.01
Norway
-0.33
-0.06
World
0.12
0.11
0.39
0.15
-0.15
-0.12
Note: Consumption and GDP responses in the grouped VAR and in the panel VAR following a 10% oil
price shock of either positive or negative sign. The consumption and GDP responses are measured as the
average during the first 3 years (from quarter 2 through quarter 13) after a shock moving oil prices by 10
percent in the first quarter.
14
10
100
Figure 1: Oil Prices and Consumption Differential
-5
Consumption Differential
1975q1
1985q1
1995q1
2005q1
-100
-50
4-quarter % change
0
5
0
50
4-quarter % change
Real Oil Price
2015q1
Note: The figures plots real oil prices (right scale) against the difference in aggregate consumption growth
of oil exporters minus aggregate consumption growth of oil importers. The list of countries is in Table A.1.
Exporters and importers are aggregated using using weights based on each country’s GDP in constant US
dollars.
15
Figure 2: Aggregate VAR: Impulse Responses to Oil Price Shock
Oil Price
5
-10
0.2
Percent
Percent
0.15
0.1
-15
0
0
10
Oil Production
0.6
-0.4
0
20
10
20
0
GDP Importers
0.3
0.4
0
-0.2
0.05
10
20
GDP Exporters
0.2
Percent
0.2
0.2
0
Percent
Percent
-5
Cons. Exporters
0.4
0.2
0
Percent
Cons. Importers
0.25
0.1
0
-0.2
0
-0.2
-0.4
-0.1
0
10
20
-0.4
0
10
Quarters
Quarters
20
0
10
20
Quarters
Note: Aggregate VAR. Note: The shaded areas represent 16th and 84th percentiles constructed using 1,000
bootstrap replications.
16
Figure 3: Aggregate VAR: Comparing Negative and Positive Oil Price Shocks
Oil Price
20
-10
0
Percent
0
0
-0.2
-20
10
Oil Production
1
-0.6
0
20
10
20
0
GDP Importers
0.4
0
-0.2
-0.5
10
20
0
-0.2
-0.4
-0.4
0
20
0.2
Percent
Percent
Percent
0
10
GDP Exporters
0.4
0.2
0.5
-0.2
-0.4
-0.4
0
Cons. Exporters
0.2
0.2
Percent
10
Percent
Cons. Importers
0.4
-0.6
0
10
Quarters
Quarters
20
0
10
20
Quarters
Note: Aggregate VAR allowing for asymmetric effects of oil shocks. Note: The shaded areas represent 16th
and 84th percentiles constructed using 1,000 bootstrap replications.
17
Figure 4: Oil Dependency Across Countries
Norway
Saudi Arabia
Russia
Colombia
Venezuela
Ecuador
Iran
Canada
Mexico
Denmark
Brazil
United Kingdom
United States
Australia
India
Italy
Germany
Netherlands
Korea
France
Japan
Spain
Taiwan
Turkey
-6
-4
-2
Oil Dependency in 2013
0
1
1
-8
0
Brazil
United States
Canada
Mexico
Venezuela
-3
-2
Oil Dependency
-1
United Kingdom
1975q1 1980q1 1985q1 1990q1 1995q1 2000q1 2005q1 2010q1 2015q1
quarter
Note: Oil Dependency Across Countries and Over Time. The top panel shows oil dependency in 2013 for
a subset of the largest countries in the sample (Largest exporters and countries with share of world GDP
in 2013 larger than 1 percent). The bottom panel plots oil dependency over time for a select group of
economies.
18
Long-run response of consumption to a 1% oil price rise
-.1
-.05
0
.05
.1
Figure 5: Oil Dependency and Oil Price Effects
VE
GR
SF
IA
NO
RU
SA
EC
MY
BZ
CO
MX
CA
SK
IDAL
PE
DN
AR
US
UK
AU
PL
IS
CZ
LU
IR
CL
SP
IN FR
VS
PO
PH
SZ
BE
IT
TH HUFN
TK
SG
NE
JA
SD
NZ
IC
GE
KO
TW
0
.2
.4
.6
Oil dependence, normalized from 0 to 1 (0.5: oil independent)
.8
Note: Oil Dependency and Oil Price Effects. The normalized oil dependence nod measure is constructed
as a simple logistic transformation of my oil dependence od measure. That is, od = 1 − oprod/ocons, and
nod = 1/ (1 + exp (−od)).
19
Figure 6: Country-Level Panel VAR: Impulse Responses across Countries
World
Oil Prices
0.2
Percent
Percent
0
-5
0
-0.2
-10
0
5
10
15
20
0
5
USA
15
20
15
20
15
20
Euro Area
0.2
Percent
0.2
Percent
10
0
-0.2
0
-0.2
0
5
10
15
20
0
Canada
5
10
Norway
0.5
Percent
Percent
0.2
0
0
-0.2
-0.5
0
5
10
15
20
0
5
Quarters
10
Quarters
Consumption
GDP
Note: The shaded areas represent 16th and 84th percentiles constructed using 1,000 bootstrap replications.
20
World
Oil Prices
% from 2014Q1
Index: 2014Q1=100
Figure 7: Country Panel VAR: Contribution to GDP and Consumption of the
2014Q2-2015Q2 Oil Slump
100
80
60
1.5
1
0.5
2014 2015 2016 2017 2018 2019
0
2014 2015 2016 2017 2018 2019
Euro Area
% from 2014Q1
% from 2014Q1
USA
1.5
1
0.5
0
2014 2015 2016 2017 2018 2019
2
1
0
2014 2015 2016 2017 2018 2019
Norway
% from 2014Q1
% from 2014Q1
Canada
1
0
-1
2014 2015 2016 2017 2018 2019
1
0
-1
2014 2015 2016 2017 2018 2019
Consumption
GDP
Note: Historical Contribution of oil price shocks to Consumption and GDP since 2014Q2. Oil price shocks
are those estimated for the periods 2014Q2 through 2014Q4.
21
Figure 8: Country-Level Panel VAR: Impulse Responses across Countries using alternative
definition of oil dependence
World
Oil Prices
0.2
Percent
Percent
0
-5
0
-0.2
-10
0
5
10
15
0
20
5
15
20
15
20
15
20
Euro Area
USA
0.2
Percent
0.2
Percent
10
0
0
-0.2
-0.2
0
5
10
15
0
20
Canada
5
10
Norway
0.5
Percent
Percent
0.2
0
0
-0.2
-0.5
0
5
10
15
20
0
5
Quarters
10
Quarters
Consumption
GDP
Note: Oil dependence constructed normalizing net oil imports by nominal GDP. The shaded areas represent
16th and 84th percentiles constructed using 1,000 bootstrap replications.
22
Figure 9: Oil Dependency Across U.S.States
North Dakota = -12
Alaska
-8
Wyoming
New Mexico
-6
Oklahoma
Montana
Colorado
Texas
Utah
-4
-2
Oil Dependency in 2013
California
Louisiana
Alabama
Ohio
Michigan
Illinois
Pennsylvania
Indiana
Florida
Tennessee
Missouri
New York
Arizona
Virginia
Connecticut
Georgia
Maryland
Massachusetts
Minnesota
New Jersey
North Carolina
South Carolina
Washington
Wisconsin
0
1
0
California
Texas
Oil Dependency
-10
-5
Wyoming
Alaska
-15
North Dakota
Alaska oil dep.< -15 before 1998
1975q1 1980q1 1985q1 1990q1 1995q1 2000q1 2005q1 2010q1 2015q1
quarter
Note: Oil Dependency Across States and Over Time. The top panel shows average oil dependency in 2013
for a subset of the largest states in the U.S. (Oil producers and states with share of car purchases in 2013
larger than 1 percent). The bottom panel plots oil dependency over time for a select group of states.
23
Figure 10: State-Level Panel VAR: Impulse Responses of New Car Registrations to an Oil
Price Shock
Oil Price
5
1
Percent
Percent
0
-5
-10
0.5
0
-0.5
-15
-1
0
1.5
5
10
15
20
0
5
10
15
Quarters
Quarters
Cars: Oil Superrich
North Dakota
Cars: Oil Independent
Texas
1.5
20
1
Percent
1
Percent
Cars: Aggregate
1.5
0.5
0
-0.5
0.5
0
-0.5
-1
-1
0
5
10
15
20
0
Quarters
5
10
15
20
Quarters
Note: The shaded areas represent 16th and 84th percentiles constructed using 500 bootstrap replications.
24
Figure 11: State-Level Panel VAR: Impulse Responses of Unemployment Rate to an Oil
Price Shock
Oil Price
-5
-10
0
-0.1
-0.2
-15
0
5
10
15
0
20
5
10
15
20
Quarters
Quarters
Unemp: Oil Superrich
North Dakota
Unemp: Oil Independent
Texas
0.05
Percentage Points
0.1
Percentage Points
Unemp: Aggregate
0.1
Percentage Points
Percent
0
0.05
0
-0.05
-0.1
-0.15
0
-0.05
-0.1
-0.15
-0.2
0
5
10
15
20
0
Quarters
5
10
15
20
Quarters
Note: The shaded areas represent 16th and 84th percentiles constructed using 500 bootstrap replications.
25
Appendix A
Appendix A.1
The Data
Country-Level Data
My sample includes a total of 50 countries for which it was possible to assemble sufficiently long and reliable
data on quarterly real consumption and quarterly real GDP from the country’s official statistics.
Total world GDP accounted by the countries in the sample is about 80% of world GDP (of the richest
countries in the world, measured by total GDP in US dollars, the sample does not include China, because
of the lack of consumption data). Total world oil production accounted by the countries in the sample is
about 75% of world oil production. Because of the lack of data on consumption and GDP, the sample also
excludes, among the largest oil producers, United Arab Emirates, Iraq, Kuwait, Nigeria, Qatar, Angola,
Algeria and Kazakhstan.
In my aggregate VAR analysis, I construct GDP and consumption using data for real consumption and
real GDP using weights based on each country’s GDP in constant US dollars from the World Bank World
Development Indicators (if the data are not available, the weight is set at zero, and the weights of other
countries are changed accordingly). The list of countries, their oil dependence measure and sample weights
for the year 2013, and the data coverage are listed in A.1.
Oil dependency is constructed using annual oil production and consumption data from the BP Statistical
Review of World Energy (which has annual data dating back to 1965). An advantage of these data is that,
barring small changes in year to year inventories, they satisfy the identity that oil production must be equal
oil consumption over very long horizons. For countries that are small oil producers, the BP Statistical
Review does not report data (they show as missing in A.1). In that case, I set oil dependency to a constant
equal to 1 in all years, unless I find data from either the CIA World Factbook or from the International
Energy Agency that document oil production in that country. In that case, I reset oil dependency to a
constant value indicated in either source. In all these cases, the implied oil dependency is between 0.9 and
1, except for Estonia, for which the oil dependency is set at 0.65 (based on the IEA member country profile
for Estonia, 2014).
Appendix A.2
State-Level Data
The key variable included in the regression is “new car registrations, retail” broken down by state of registration. This measure is constructed using data from Polk’s National Vehicle Population Profile (NVPP),
which is a census of all currently registered passenger cars and light-duty trucks in the United States. Polk’s
New Registration Data provides detailed indicators for new vehicle registrations (including where the vehicle was registered). The “Retail” file represents new vehicles purchased by individuals, thus excluding
registrations of rental, fleet, government and other commercial use vehicles. As shown by Figure A.2, retail
registrations at the national level are very highly correlated with durable goods: Motor vehicles and parts
from the National Income and Product accounts.
A.1
Table A.1: Oil Dependency and Data Availability by Country
Country
United States
Japan
Germany
United Kingdom
France
Italy
India
Canada
Brazil
Korea
Spain
Mexico
Russia
Australia
Netherlands
Turkey
Saudi Arabia
Taiwan
Switzerland
Indonesia
Sweden
Belgium
Poland
Austria
Norway
Argentina
South Africa
Denmark
Thailand
Iran
Ireland
Colombia
Finland
Malaysia
Singapore
Greece
Israel
Venezuela
Portugal
Chile
Philippines
Czech Republic
New Zealand
Peru
Hungary
Slovakia
Ecuador
Luxembourg
Slovenia
Iceland
GDP Share
30.14
9.98
6.60
5.38
4.92
3.66
3.11
2.77
2.51
2.50
2.45
2.18
2.07
1.81
1.50
1.36
1.06
1.01
0.99
0.94
0.91
0.88
0.87
0.73
0.70
0.69
0.68
0.55
0.48
0.48
0.45
0.44
0.44
0.43
0.42
0.42
0.41
0.41
0.39
0.36
0.32
0.32
0.27
0.26
0.24
0.17
0.12
0.09
0.08
0.04
Oil Dependency
(Oil Imports
over Oil
Consumption)
0.47
0.97
0.93
0.42
0.97
0.91
0.76
-0.67
0.31
0.96
0.97
-0.42
-2.39
0.59
0.94
1.00
-2.80
1.00
1.00
0.45
0.96
0.98
0.93
0.87
-6.56
0.05
0.73
-0.14
0.63
-0.73
0.99
-2.37
0.95
0.19
0.98
0.98
0.98
-2.26
0.97
0.95
0.94
0.94
0.66
0.55
0.82
0.89
-1.13
1.00
0.99
1.00
Oil Consumption
Oil
Production
18961
4521
2408
1494
1664
1288
3727
2383
3048
2455
1194
2020
3179
1022
898
722
3000
980
249
1615
306
628
520
264
243
670
581
157
1255
2038
136
298
188
800
1235
295
226
825
242
357
297
183
151
228
127
75
247
70
54
17
10069
0
0
867
0
116
906
3977
2114
0
0
2875
10777
421
0
0
11393
0
0
882
0
0
0
0
1838
635
0
178
459
3525
0
1004
0
645
0
0
0
2687
0
0
0
0
0
104
0
0
527
0
0
0
Oil Dependency, Exporter Start
Alternative (Oil
Date
Imports over
GDP, Percent)
2.1
0
1975q1
2.6
0
1975q1
2.1
0
1975q1
0.5
0
1975q1
2.0
0
1975q1
1.9
0
1981q1
4.7
0
1996q2
-2.3
1
1975q1
0.9
0
1991q1
6.5
0
1975q1
2.9
0
1995q1
-2.5
1
1980q1
-13.4
1
1995q1
1.2
0
1975q1
3.5
0
1977q1
2.9
0
1998q1
-40.2
1
2005q1
6.5
0
1982q1
1.2
0
1980q1
2.2
0
1990q1
1.9
0
1975q1
4.0
0
1980q1
3.5
0
1995q1
2.0
0
1988q1
-12.0
1
1978q1
-0.4
0
2004q1
4.8
0
1975q1
-0.5
1
1991q1
7.0
0
1993q1
-13.5
1
1988q2
2.0
0
1997q1
-5.8
1
2000q1
2.4
0
1990q1
0.6
0
1996q1
14.1
0
1975q1
3.8
0
1975q1
3.1
0
1995q1
-17.8
1
1998q1
3.4
0
1995q1
4.7
0
1996q1
4.0
0
1981q1
2.8
0
1996q1
2.9
0
1987q2
1.8
0
1980q1
3.3
0
1995q1
2.6
0
1997q1
-11.0
1
1990q1
3.8
0
1995q1
3.8
0
1995q1
3.8
0
1997q1
End
Date
2015q4
2015q4
2015q4
2015q4
2015q4
2015q4
2015q4
2015q4
2015q4
2015q4
2015q4
2015q3
2015q3
2015q4
2015q4
2015q3
2015q2
2015q4
2015q4
2015q4
2015q4
2015q4
2015q4
2015q4
2015q4
2015q2
2015q4
2015q4
2015q4
2015q1
2015q3
2015q3
2015q4
2015q4
2015q4
2015q4
2015q4
2014q3
2015q4
2015q3
2015q4
2015q4
2015q3
2015q4
2015q4
2015q4
2015q3
2015q3
2015q4
2015q4
Note: Oil imports are defined as oil consumption minus oil production, in year 2013 (source: BP Statistical
Review of World Energy). Oil production is production of crude oil, tight oil, oil sands and NGLs (Thousand
barrels daily). Oil consumption is inland demand plus international aviation and marine bunkers and refinery
fuel and loss (Thousand barrels daily). In the aggregate VAR, ”exporters” are the countries with negative
oil dependency in 2013.
A.2
Table A.2: Oil Dependency by State
State
California
Texas
New York
Florida
Illinois
Pennsylvania
New Jersey
Ohio
Virginia
Michigan
Massachusetts
North Carolina
Georgia
Washington
Maryland
Tennessee
Minnesota
Indiana
Colorado
Wisconsin
Missouri
Arizona
Connecticut
Louisiana
Alabama
South Carolina
Oklahoma
Kentucky
Oregon
Iowa
Kansas
Nevada
Arkansas
Utah
Mississippi
Nebraska
New Mexico
New Hampshire
West Virginia
Hawaii
Idaho
Maine
Rhode Island
District Of Columbia
Delaware
Montana
South Dakota
North Dakota
Alaska
Wyoming
Vermont
Income
Share
13.22
8.22
7.57
5.75
4.26
4.16
3.48
3.35
2.84
2.72
2.71
2.68
2.67
2.35
2.24
1.82
1.81
1.77
1.76
1.75
1.73
1.73
1.53
1.34
1.24
1.21
1.14
1.12
1.11
0.97
0.91
0.77
0.76
0.75
0.71
0.62
0.53
0.48
0.46
0.45
0.41
0.38
0.35
0.34
0.29
0.28
0.27
0.27
0.26
0.22
0.20
Oil
Dependency
0.48
-0.12
1.00
0.99
0.93
0.96
1.00
0.91
1.00
0.92
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.97
-0.18
1.00
1.00
1.00
1.00
0.64
0.83
1.00
-0.89
0.96
1.00
1.00
0.07
0.99
0.82
-0.06
0.51
0.90
-2.32
1.00
0.65
1.00
1.00
1.00
1.00
1.00
1.00
-0.50
0.86
-11.60
-6.13
-2.54
1.00
Oil
Consumption
1,722
3,680
655
824
643
623
507
598
424
456
296
433
477
369
256
353
316
401
248
277
332
266
166
905
273
262
269
305
174
234
226
118
170
147
220
123
137
79
98
114
84
98
42
8
51
87
61
112
118
80
42
Oil Production
Exporter
891
4,138
1
10
43
24
0
52
0
35
0
0
0
0
0
1
0
11
293
0
1
0
0
322
47
0
509
13
0
0
210
1
30
156
109
13
454
0
34
0
0
0
0
0
0
131
8
1,406
842
284
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
1
0
1
1
1
0
Note: Oil dependency calculated as 1 minus the ratio of oil produced over oil consumed (in 2013) (source:
EIA: State Profiles and Energy Estimates). Oil production is production of crude oil by state (sumcrudestate). Production of crude oil by state is scaled up by the ratio of total U.S. oil production from the BP
Statistical Review (which including tight oil, oil sands and other NGLs and including offshore production) to
sumcrudestate (which is about 1.5), so as to facilitate comparison with oil dependency measures by country.
Oil consumption includes all petroleum products consumed, and lines up with oil consumption from BP
Statistical Review. Production and consumption are in Thousand barrels daily.
A.3
.1
.1
Figure A.1: Oil Dependency and Sensitivity to Oil Price Shocks across Countries:
Robustness
IA
ECRU
BZSK
COMY PE
AL
MX DN AU
IS
IR
PL
LU
CZ
INCL
VS
FR
SP
SZ
PO
PH
CA
BE
HU
IT
TK
FN
TH
AR US
NZ
JA
SG
SD
NE
IC
ID
UK GE
KO
TW
-8
-6
-4
-2
1: Oil Dependency
0
CO
-.05
0
NO
SA
SF GR
IA
EC RU
MY
MX
ID
-.1
-.1
-.05
0
SA
2
0
BZ SK
PEAL
AU
IS
IR
PL
LU
CZ
DN
IN CL
VS
FR
SP
SZ
PO
PH
CA
BE
HU
IT
TK
FN
AR
USNZTH
JA
SG
SD
NE
IC
GE
UK
KO
TW
.2
.4
.6
2: Transformed Dependency
.8
.1
R-squared=0.2057
-.05
0
NO
SA
ID
0
.2
BZ SK
PEAL
AU
IS
IR
LU
CZ
DN IN PL
CL
VS
FR
SP
SZ
PO
PH
CA
BE
HU
IT
TK
FN
AR
USNZTH
JA
SG
SD
NE
IC
GE
UK
KO
TW
CO MY
MX
.4
.6
.8
3: Transformed, Optimal
.05
IA
EC RU
VE
SA
0
.05
SF GR
-.05
VE
-.1
.1
R-squared=0.1910
-.1
Long run Elasticity of Consumption to Oil Price
NO
VE
GR
SF
.05
.05
VE
1
R-squared=0.2169
GR
SF
IA
RU
NO
EC
SK
BZ
MY
CO AL
AU
MX PE
IS
IR
PL
LU
CZ
DN
CL
IN
VS
FR
SP
PO
PH
CASZ
BE
HU
IT
TKTH
FN
ARUS
NZ
JA
SD
NE
IC
ID
UKGE
KO
TW
-40
-30
-20
-10
0
4: Alternative Dependency Measure
SG
10
R-squared=0.2138
Oil Dependence
Note: Panel 1 shows the cross-section of consumption–oil price elasticities across countries when the oil
dependency measure is the ratio of average oil imports to average oil consumption, where oil imports are
the difference between oil consumption and oil production: od = 1 − oprod/ocons. The red line is a from a
fitted OLS regression.
Panel 2 plots on the x-axis the logistic transformation of the oil dependency measure, nod =
1/ (1 + exp (−od)) .
Panel 3 plots on the x-axis the best-fitting logistic transformation. The best-fitting logistic transformation
is the oil dependency measure that results from the ML estimation of the logistic function: β = b0 + b1 ×
1/(1 + exp(−b2 (od − b3 ))), where the resulting oil dependency measure is nod∗ = 1/(1 + exp(−b2 (od − b3 ))),
and the estimated coefficients are b2 = 1.60 and b3 = −1.86.
Panel 4 shows the cross-section of consumption - oil price elasticities across countries when the oil dependency
measure is the ratio of net oil imports to nominal GDP (all expressed in current dollars), that is: odalt =
(oprod − ocons)/ngdp.
A.4
-40
% change, year-on-year
-20
0
20
Figure A.2: New Cars Registrations and Motor Vehicles Expenditures from NIPA
1990q1
1995q1
2000q1
2005q1
2010q1
2015q1
quarter
Car Registrations
PCE: Motor Vehicles and Parts
Note: Comparing NIPA expenditures on motor vehicle (in real terms) with new cars’ registrations
A.5
Figure A.3: Aggregate VAR: Sensitivity to Different Oil Supply Elasticity
Oil Price
5
Cons. Exporters
0.5
-5
-10
Alternative
Elasticity
Benchmark
-15
Percent
0.2
Percent
0.1
-20
-0.1
0
10
10
20
0
GDP Importers
0.3
0
0.1
10
Quarters
20
-0.2
-0.4
-0.1
0
20
0
0
-1
10
GDP Exporters
0.2
0.2
Percent
2
1
-0.5
-1
0
20
Oil Production
3
0
0
Percent
Percent
0
Percent
Cons. Importers
0.3
-0.6
0
10
Quarters
20
0
10
20
Quarters
Note: The Alternative Elasticity lines assume an oil supply shock under the assumption that world oil
demand elasticity is -0.24, and world supply elasticity is 0.1.
A.6