In light of the U.S. shale oil boom and given that commercial shale oil production has been mostly confined to the USA, this paper disentangles the oil supply determinant into its U.S. and non-U.S. production components, and studies their impact on U.S. real personal consumption expenditures (PCE) and on U.S. index of consumer sentiment (ICS). First, I estimate a structural VAR model to identify and study the effect of structural shocks in the crude oil market on PCE and ICS. The results show that while PCE and ICS respond negatively to oil demand shocks, they respond differently to oil supply shocks. While ICS experiences transitory negative response to both oil supply shocks, PCE shows that the response of inflation and output differs depending on whether the oil supply shock is domestic or foreign. Second, I compute the forecast-error-variance decompositions to quantify the contribution of oil supply and demand shocks to the historical fluctuations in PCE and ICS. My findings reveal that most of the variation in PCE is explained by oil supply shocks—unlike ICS where most of its variation is explained by aggregate demand shocks. Third, I conduct a historical decomposition exercise to examine the contributions of structural oil shocks in explaining historical changes in PCE and ICS. My results indicate a strong presence of U.S. oil supply shock in boosting ICS during the 2014 period of the oil price drop. Finally, the results that not all oil supply shocks are alike are robust to alternative measures of real economic activity.
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While the 1980s and 1990s academic models on the transmission of oil price shocks consider fuel costs as exogenous shocks to the supply side of the economy, however, recent evidence provided by many economists and policy makers shows that oil price shocks play a major role on the spending side of the economy.
Baumeister and Kilian (2016b) provide a detailed quantitative and qualitative analysis showing that the cost channel of the transmission of oil price shock is minimal and that the demand channel is more important.
Baumeister and Kilian (2016b) indicate that the shale oil revolution is a large contributor to aggregate investment since 2010.
Throughout the paper, PCE stands for real personal consumption expenditures.
Where U.S. oil supply shocks account for about 61% of the variation driven by oil supply shocks.
In line with Gundersen (2018) providing evidence of the importance of both U.S. and OPEC oil supply shocks that account for a 33% of the variation in oil price.
Economou (2016) addresses similar question by disentangling the oil supply shocks of crude oil into exogenous and endogenous.
Baffes et al. (2015) indicate that oil supply shocks accounted for about twofold more than demand shocks in explaining the 2014 plunge in oil prices.
Hamilton’s index is an extended version of the OECD’s index of monthly industrial production in the OECD and 6 major additional countries. For details see Hamilton (2019).
These studies use different sample periods, econometric methods and/or data sets.
Ratti and Vespignani (2015) also consider a structural VAR model including OPEC versus non-OPEC oil production in examining global oil market.
The block-recursive structure of the reduced-form error term is composed of two blocks, with the first one represents the first four equations of the global crude oil market and the second one represents the last equation of PCE or ICS, interchangeably.
This assumption is plausible given that the USA is still considered an oil importing country with net imports of about 772,000 barrels per day by December 2018 and average oil production of about 13% of global oil production over the sample period.
I assume that non-U.S. oil production does not respond, within the month, to U.S. oil supply shocks, and reorder the variables so that U.S. oil production is ordered first and non-U.S. oil production next. The results are robust to this change.
Work by Barsky and Kilian (2002) provides evidence that a one-time oil price increase drives up gross output price measures such as CPI but not necessarily the price of value-added GDP deflator or PCE deflator.
Though CPI (calculated by the BLS) is the most well-known measure of inflation, the Fed prefers PCE price index (calculated by the BEA) in tracking inflation (Hakkio 2008). PCE is computed using data acquired through business surveys. This method tends to be more reliable than collecting data through consumer surveys, as in CPI. In addition to data collection dissimilarity, the actual indexes are calculated differently. The formula used to compute PCE allows for short-term changes in consumer behavior to be observed, while the formula used to compute CPI does not allow for these adjustments to be included. Thus, PCE results in a more comprehensive metric for measuring inflation. The Fed values this, because it can reveal the small fluctuations in inflation that can still be indicators of changes to the economy.
The index is computed based on monthly data collected by Drewry Shipping Consultants Ltd. for various bulk dry cargoes including grain, oilseeds, coal, iron ore, fertilizer and scrap metal. The index is available at http://www-personal.umich.edu/~lkilian/reaupdate_new.txt.
PCE quantity index is used in this paper.
The index with detailed information on its calculation is available at (https://data.sca.isr.umich.edu/fetchdoc.php?docid=24770).
The notation for the ICS and its sub-indices was first introduced in Güntner and Linsbauer (2018). See Güntner and Linsbauer (2018) for detailed description of the ICS and its sub-indices. A detailed description of the survey is available at https://data.sca.isr.umich.edu/fetchdoc.php?docid=45121.
In this paper, “partially significant” and “marginally significant” refer to “one-standard-error bands statistical significance” (see among others, Kilian 2009 and GL).
In this paper, I use “oil-specific demand” and “precautionary demand” interchangeably.
While PCE might respond positively to the increase in oil prices if oil enters the PCE basket, in this paper, I do not control for changes in the energy share in total consumer expenditures.
Huang and Mollick (2020) indicate that disruptions in world oil supply increase U.S. tight oil production significantly on impact.
Note that, as shown above, non-U.S. oil supply shocks have statistically insignificant impact on oil prices.
Kang et al. (2016) argue that surges in domestic income are associated with the increase in U.S. oil production.
Note that this immediate impact of foreign oil supply shocks on durables and non-durables is only marginally statistically significant.
Note that the results indicate that a negative U.S. oil supply shock has a negative significant effect on PCE goods, but positive significant effect PCE services. Thus, the results of statistically insignificant effect on aggregate PCE could be the outcome of aggregation across the spending components (see Alsalman and Karaki 2018 on the insignificant response of aggregate PCE to oil supply disruptions).
The negative and statistically significant response of PCE and its major components to aggregate demand shocks (in the long run) and to oil-specific demand shock is consistent with the literature and can be explained by the following channels. The discretionary income channel, the uncertainty channel associated with oil price shocks, the operating-cost channel that lowers consumption expenditures on durable goods are energy intensive in consumption, and the sectoral reallocation channel associated with frictions in the economy (see, among others; Bernanke 1983; Hamilton 1988; Davis and Haltiwanger 2001; Edelstein and Kilian 2009 Baumeister and Kilian 2016b).
The results reinforce Kilian (2009) results in that the demand side in the crude oil market is more important than the supply side in explaining changes in oil prices.
Inflationary in the sense that an increase in consumer expenditures would boost the demand side of the economy, and thus the price level, and vice versa for deflationary.
It could be that the structure of ICS data is not consistent with that of aggregate PCE.
A higher cumulative contribution of the two oil supply shocks is expected if U.S. oil producers responds to a disruption of non-U.S. production by raising supply and non-U.S. oil producers respond to a disruption of U.S. production by raising supply. In world production, the opposing effects are offsetting each other partially, leading to a lower combined contribution to the FEVD than when considered separately.
This effect is suggestive since the model in this paper does not include the change in above-ground global crude oil inventories.
In line with GL that the fall in U.S. consumer sentiment during 2003–2008 was attributed by the cumulative effect of aggregate demand shocks.
The index is available at: https://sites.google.com/site/cjsbaumeister/OECD_plus6_industrial_production.xlsx?attredirects=0&d=1.
To preserve space, the figures are available upon request.
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I am thankful to Ana Maria Herrera, Lutz Kilian and two anonymous referees for helpful comments and suggestions.
Conflict of interest
Zeina Alsalman declares that she has no conflict of interest.
This article does not contain any studies with human participants or animals performed by the author.
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Alsalman, Z. Does the source of oil supply shock matter in explaining the behavior of U.S. consumer spending and sentiment?. Empir Econ (2020). https://doi.org/10.1007/s00181-020-01900-9
- Consumer spending
- Consumer sentiment
- Oil price shocks
- Structural VAR