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1 We are grateful to Roberto Garcia-Saltos, Annette Kyobe, Akito Matsumoto, Robert Rennhack, Marzie Taheri, Bruno Versailles, Alejandro Werner, and various seminar participants for helpful comments. All errors are our own.
With exceptions, many of the recent studies do not distinguish between the underlying forces driving the price of oil (or the price of commodities more in general).
It is possible, in principle, to further decompose these structural shocks by country, so that e.g. the effect of an unexpected increase in oil supply by country x can be identified. See Aastveit, Bjørnland and Thorsrud (2015), and Mohaddes and Pesaran (2016a).
We have also performed all calculations in the paper changing the order of the first two variables, i.e. having oil supply responding to world aggregate demand contemporaneously. As indicated below, the only noteworthy qualitative difference we find in the results is that, with the alternative ordering, oil supply shocks take effect (on average) faster than world demand shocks (see Section 5). All results with the alternative ordering are available from the authors upon request.
The shift in OPEC’s role was reflected, for example, in Saudi Arabia’s decision not to cut its production as the oil price tumbled in the second half of 2014. See e.g. “Why Saudis Decided Not to Prop Up Oil,” The Wall Street Journal, December 21, 2014 (retrieved October 18, 2016).
The data are shown as deviation from the long-run real oil price. Let rpolr denote the long-run real oil price. The solid black line in Figure 4 depicts ln(rpot/rpolr). Thus, for example, the December 2015 value of -0.9 (last data point) means that the real oil price was estimated to be about 60 percent below its long-run level (rpot = exp(–0.9) × rpolr).
The first five episodes in Figure 5 are the same as those considered in Baffes et al. (2015) (Figure 1). The last episode, which is the period for which counterfactual simulations are produced below, is longer than the one considered by Baffes et al. (2015) (who focus on July 2014-January 2015).
18 lagged coefficients, 6 contemporaneous coefficients, and 3 intercepts.
For many countries the oil-specific shocks have significant effect in the first year, but no significant effect in the two years. These countries are not depicted in this Figure.
Given that shocks are orthogonal to country-level variables by construction, pooled OLS is a con-sistent estimator for the effects of the three shocks. This has been confirmed in separate regressions including fixed effects, not reported here.
Being a larger group, the oil importers regressions are estimated much more precisely.
The reason that the simulated nominal oil price in Figure 9 is not smooth (as one would expect from the predictions of a deterministic autoregressive process) is that it uses the actual US CPI index.
The effect of all three shocks (difference between the actual growth and the fourth column) is ap-proximately equal to the sum of individual effects of each shock. The relationship is not exact due to non-linearities that arise because of the base effect when calculating 2015 growth.
A country is defined as an oil exporters if, in 2013 (i.e. before the period under consideration), oil exports are greater than oil imports.
Being partial associations means that the association between net growth gains and openness controls for diversification, and vice versa. In other words the charts correspond to the graphical representation of partial regressions of output gain on exports diversification and openness respectively. Conditionality also explains negative values on the x-axis.
The export diversification index is a Theil index constructed from data for 12 sectors and 187 countries (see Papageorgiou and Spatafora (2012); the index is available at https://www.imf.org/external/np/res/dfidimf/diversification.htm). Openness is measure as the ratio of sum of exports and imports of goods and services to GDP.
Figure 10 excludes the estimates for Ukraine, for which we find very large net losses from the oil price shocks (see Table 3) that may be more reflective of idiosyncratic circumstances. If one includes Ukraine in the scatterplot, the level of linear statistical significance decreases. Despite this, Ukraine’s story is consistent with the above intuition: the overall estimated effect of the oil price decline in 2015 was negative and large (8.7 p.p) and exports diversification was low (11 percentile out of 173 countries).
One might think that the outcome is driven by large oil exporters (e.g., RUS) or countries with questionable quality data (e.g., VEN). In fact the results become even more pronounced if countries such as RUS or VEN are excluded from the sample: coefficient on the diversification index becomes more significant, while the coefficent on openness does not.