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I would like to thank Benedict Clements, Emre Alper, David Grigorian, Hippolyte Balima, Daniel Hardy, Ravi Balakrishnan, Monique Newiak, Carlo Pizzinelli, Paolo Dudine, Cristian Alonso, Olivier Basdevant, John Hooley, Mahvash Qureshi, Nelson Sobrinho, and the AFR RAG as well as participants of the AFR External Sector Issues Network for useful comments and suggestions on the paper. I would also like to thank Tony Venables, Rick van der Ploeg, Christopher Adam, Sam Wills, Brock Smith, Tim Willems, and members of the OxCarre Group at the University of Oxford for their comments. Any remaining errors are my own.
An extensive literature on the ‘resource curse’ (Sachs and Warner, 1999) examines the plethora of mechanisms through which natural resources can be either a ‘curse’ or a ‘blessing’ (van der Ploeg. 2011).
The former develops a model of sovereign debt in which the possibility of permanent market exclusion following default generates an endogenous debt limit; below which it is always optimal to service outstanding debt, but above which default is optimal for the sovereign. The latter show that even non-permanent market exclusion can present a sufficient risk to the sovereign, yielding a similar result (see also Kletzer & Wright, 2000). Finally, Park (2017) shows that in a model with capital accumulation default can also occur in ‘good’ times. The model delivers a U-shape in the capital stock: at both low and high levels of capital, the economy has an incentive to default on its debt. Default in good times occurs when the economy has over-invested in capital during booms.
Others include the inclusion of long-term debt (Chatterjee & Eyigungor, 2012), CRRA lenders (Lizaro, 2017), misspecified default probabilities (Pouzo & Presno, 2016) and bailout risk (Fink & Scholl, 2016).
These six countries comprise the full set of oil-dependent emerging market economies for which we have quarterly GDP and interest-rate data and have outstanding USD denominated bonds.
The interest rate spread is the difference between the yield on 10-year USD denominated government bonds in each emerging economy and that of the US. The use of USD denominated bonds over local currency bonds strips out FX risk spreads that may eb country specific and focuses solely on credit risk, the object of interest.
The individual country correlations are: Angola = -0.6, Colombia = -0.63, Indonesia = -0.03, Mexico = -0.09, Nigeria = -0.28, and Russia = -0.56.
The output process is truncated if the sovereign chooses to default in any period. This truncation imposes a ceiling on output. A similar process applies to oil revenues under default.
Another option would be to use some form of hyperbolic preferences following work by Laibson (1997).
The individual country correlations are: Angola = 0.76, Colombia = 0.41, Indonesia = -0.06, Mexico = 0.14, Nigeria = 0.26, and Russia = 0.29.
Braeu (2010) finds that households in Canada tilt consumption toward the future, while Cashin & McDermott (2002) find that the dynamics of international capital flows to Australia during the 1990s, when the country was a net capital importer, were broadly consistent with utility maximisation under consumption smoothing.
Calculated based on data between 1995q1 and 2016q4. The risk-free rate set equal to the US government 10-year bond yield of 0.4% and an exogenous risk premium for emerging market 10-year bonds of 1.6%.
It is worth noting that while the mean GDP share has remained relatively stable over time, the export share has increased steadily.
Data on nominal GDP, national deflators and US CPI all come from the Global Financial Database. The data on oil prices is the quarterly average of the daily West Texas oil price in USD per barrel taken from the IMF’s IFS database.
This step avoids the double counting of oil revenues and keeps the calibration consistent with the model.
The use of a HP is fairly standard in the literature and comes with the usual caveats. See Hamilton (2018). Here we simply use the de-trended series to obtain average data moments for the size economies.
It is worth noting that the shock processes to these series are not very persistent. Infact the half-life of these shocks can be calculated using: h = Tlog(2)/log (xt/xT). This gives a half-life of 4.1 quarters for innovations to output and 2.2 for innovations to the oil price.
I use the 3rd, 6th and 9th positions for the high, medium and low states respectively.
The model predicts default (or a zero bond price) at relatively low debt-GDP ratios (sometimes below 10 percent) in adverse oil price and output states. This may not be in line with current experience in which actual debt ratios are as high as 75 percent, without default.
Sovereign spreads are typically a function of many factors, both supply and demand, and estimating them using a stylized model such as that presented in this paper is bound to be challenging. The model presented here is highly stylized and ignores several important factors that likely affect the pricing of sovereign debt in these economies, including: financial market imperfections and dynamics, risk sentiment, global push factors, government failures, the composition of external vs domestic debt, the choice of fiscal and exchange rate policy, etc.
I use 11 output states, 11 oil price states and 125 debt states.
Given the computational weight of solving and simulating the model for different parameter sets jointly, I choose 5 evenly spaced points covering each parameter range.
Given the discretised state-space and the numerical solution method, the data moments estimated from the simulations display significant estimation noise. I therefore display a smoothed version of these estimates in the figures.
The cross-correlation in the four countries in our sample ranges from -0.007 to +.077.