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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 References Aastveit, K. A., H. C. Bjrnland, and L. A. Thorsrud (2015): “What Drives Oil Prices? Emerging Versus Developed Economies,” Journal of Applied Econometrics, 30(7), 1013–1028. 2 Arezki, R., and O. Blanchard (2014): “Seven questions about the recent oil price slump,” IMF Blog. 2 Bernanke, B., J. Boivin, and P. Eliasz (2005): “Factor augmented vector autoregressions (FVARs) 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 Canova, F., and M. Ciccarelli (2013): Panel Vector Autoregressive Models: A Surveychap. 12, pp. 205–246. 8 CBS/AP (29 Feb. 2016): “Buffett Says U.S. Economy Weaker than He Expected but Growing,” CBS Money 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 Edelstein, P., and L. Kilian (2009): “How sensitive are consumer expenditures to retail energy prices?,” Journal of Monetary Economics, 56(6), 766–779. 10 Hamilton, J. D. (1996): “This is what happened to the oil price-macroeconomy relationship,” Journal of 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,” unpublished manuscript, University of Michigan and Federal Reserve Board. 5 Kilian, L., and R. J. Vigfusson (2011): “Are the responses of the U.S. economy asymmetric in energy price increases and decreases?,” Quantitative Economics, 2(3), 419–453. 4 Pesaran, M. H. (2006): “Estimation and inference in large heterogeneous panels with a multifactor error structure,” Econometrica, 74(4), 967–1012. 8 Stock, J. H., and M. W. Watson (2005): “Implications of Dynamic Factor Models for VAR Analysis,” 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