“We call for further analysis of the technicalities, opportunities, and challenges of state-contingent debt instruments, including GDP-linked bonds.” G20 (2016)
Gross government debt in advanced economies has surpassed 105% of GDP, up from less than 75% a decade ago. As a result, policymakers and economists have been looking for ways to make it easier to manage these heavier debt burdens.
One prominent suggestion is that countries should issue GDP-linked bonds that tie the size of debt payments to their economy’s wellbeing. Proponents point to two major advantages. First, these bonds reduce the likelihood of explosive paths for sovereign debt, lowering default risk. This would increase the maximum level of sustainable debt, and provide greater capacity for countercyclical fiscal policies (Blanchard et al. 2016, Benford et al. 2016). Second, GDP-linked bonds offer investors a low-cost way to diversify both domestically and internationally. Within a country, bonds with payoffs tied to GDP provide exposure to fluctuations in returns to labour as well as capital. The two are only weakly associated, with the correlation between growth in US labour income and capital income over the last half century less than 0.2. Internationally, a portfolio of GDP-linked bonds allows diversification of idiosyncratic, country-specific risks.1
We find this idea attractive, and see the expanding discussion of the viability of GDP-linked bonds as both warranted and useful (Bank of England 2016, Griffith-Jones and Sharma 2006). However, the practical issues associated with GDP data revision remain a formidable obstacle to the broad issuance and acceptance of these instruments.
How GDP-linked bonds work
Before getting to the key challenge of data revisions, let’s start with a brief description of the technical aspects of GDP-linked bonds. The recent interest in these instruments can be traced to the work of Robert Shiller (Shiller 1998, Kamstra and Shiller 2009). The idea is that governments should sell long-term bonds with a coupon equal to a constant fraction of nominal GDP. Shiller calls them “trills”, suggesting that their annual payment be one-trillionth of a years’ nominal GDP. (For the US, that would mean a current payment in the range of $18.86.) While Shiller observes that governments would find the stabilising properties of trills attractive, his primary focus is on their potential as a vehicle for retirement savings. What better way to insure your standard of living in retirement than to buy a share of your country’s (or, even better, multiple countries’) GDP?
Since Shiller’s original work, two types of GDP-linked bonds have been proposed. The first mimics the structure of inflation-indexed bonds. We call these GDP-principal-indexed bonds; they have a specific maturity and pay a coupon equal to a constant fraction of a principal that is indexed to GDP. To make this concrete, suppose that on 1 January 2015, the US Treasury issued a $100 face value bond with a 30-year maturity and a 2% coupon. At the time of issue, the bond has a reference level for nominal GDP. The proposal is that GDP levels be measured with a six-month lag, so the reference level is the end-2014 vintage reading for GDP in the second quarter of 2014, namely $17,328.2 billion. At the start of 2017, the value of the bond principal would be $106.747 for each $100 of face value, based on the end-2016 vintage reading for GDP in the second quarter of 2016 ($18,450.1 billion), while the coupon would be $2.13 (see Table 1).2
Table 1 GDP-linked bond examples: Principal-indexed vs. coupon-indexed
Note: GDP readings are December vintage for the second quarter of the year. So, for 1 Jan 2015, the GDP is the December 2014 release for the second quarter of 2014.
In the alternative structure for GDP-linked bonds, the coupon is set equal to the nominal GDP growth rate plus a fixed premium, while the principal does not vary. We will call these GDP-coupon-indexed bonds. To see how they work, again assume that at the beginning of 2015 the Treasury issued a $100 face value bond. But, now, the principal is fixed and the coupon is the sum of 2% plus the annual growth rate of nominal GDP (measured with a six-month lag). Because nominal GDP grew by 2.51% over the year to the second quarter of 2016 (based on end-2015 vintage data), the coupon payment at the start of 2017 would be $4.51.
One difference between these two structures is the timing of payments. In the first, the bulk of the compensation for nominal growth occurs at maturity, while in the second a larger proportion comes with the periodic coupon payments. This difference does not affect the government’s primary balance (that’s the government deficit or surplus excluding interest payments) for a given debt-to-GDP ratio.
However, a key purpose of issuing GDP-linked bonds is to provide the government with a cyclical cushion, allowing it to limit debt service when revenues are low. From this cash-flow perspective, the two structures are quite different. To see why, suppose that the government has debt equal to 100% of GDP during a recession when nominal GDP has fallen by 2%. With GDP-principal-index bonds that pay a 2% coupon, the government will owe bondholders 2% of GDP. With GDP-coupon-indexed bonds that pay a 2% premium over nominal growth, debt service will be zero.
This may seem like magic, but it is not. In the first case, the bondholders lose 2% of their principal, which equals 2% of GDP. That is, for the GDP-principal-indexed bonds, the coupon payment exactly offsets the loss in principal. The debt-to-GDP ratio is unchanged. In the second case, since the bond principal does not vary, with a 2% decline in GDP, the debt-to-GDP ratio goes up by 2%.
What this means is that a government that issues GDP-coupon-indexed bonds has an option. In a deep recession, the authorities can either use their primary surplus (assuming they have one) to keep their debt-to-GDP ratio constant by buying back bonds, or they can allow the debt-to-GDP ratio to rise automatically, and use the revenue that otherwise would have been used to service the debt for expansionary fiscal policy. Which of these is more attractive will depend on how financial markets price this option.
Why we don’t see GDP-linked bonds: The problem with data revisions
So much for the technical details. What about the obstacles? For example, could a government game this GDP-linked debt structure in the short run or the long run? It seems highly doubtful that a government would intentionally depress the real economy to reduce its debt service. Another roadblock could be the price: debt managers might find the risk premium that investors demand to be too high.3
In our view, the biggest obstacles are associated with the computation of the GDP index itself. First, a government could pressure national statistical agencies to understate nominal GDP. Absent strongly independent institutions, investors may shun GDP-linked issues. Second, there are the inevitable data revisions, on which we focus in the remainder of this column.
Data revisions are of two types: regular, periodic changes that result from the inclusion of more accurate information; and infrequent changes in methods. The first of these results from the late arrival of useful data (like information from tax authorities, census surveys, and the like). To see how important these are in the US, we examine the real-time dataset maintained by the Federal Reserve Bank of Philadelphia. Figure 2 is a histogram of the revisions from the third release of GDP – that’s the one that was published three months after the end of the quarter – to the most recently available estimate. Fully half of these revisions exceed one percentage point (at an annual rate), either up or down.
Figure 2 Frequency distribution: Size of revisions in quarterly seasonally adjusted annualized growth of nominal GDP, 1965-2014
Note: Revisions are measured as the difference between the one-quarter seasonally adjusted growth at an annual rate measured from the third release (normally three months after the end of the quarter) to the release using the current value. Each bin contains the fraction of 198 quarters with growth revisions that are in the specified range.
Source: Federal Reserve Bank of Philadelphia, and authors’ calculations.
From the government’s perspective, the key benefit of GDP-linked bonds comes from times when GDP falls significantly, such as the final quarter of 2008 in the US. However, it is at precisely such times that early-vintage readings of GDP are most likely to be revised. Indeed, the Figure 3 displays the evolution of successive vintage estimates of the nominal GDP growth rate from the third to the fourth quarter of 2008, starting with the first vintage in early 2009 and continuing to the most recent vintage in December 2016. In January 2009, the Bureau of Economic Analysis (BEA) reported that fourth-quarter nominal GDP had contracted at a 4.05% annual rate. By mid-2011, the estimate was -8.43%. Finally, in mid-2013, it was revised for the sixth time to its current level of -7.78%. These revisions would have fed directly into the value of GDP-principal-indexed bonds.
Figure 3 Vintage estimates of seasonally adjusted, annualised nominal GDP growth for the fourth quarter of 2008, 2009-2016
Source: Federal Reserve Bank of Philadelphia, and authors’ calculations.
In addition to these regular revisions, every so often there are large revisions based on fundamental changes in methodology. For example, in 2013, the BEA altered the classification of research and development – including intellectual property and software – in its comprehensive revision of the US national income and product accounts. Formerly treated as intermediate inputs and hence ignored, they were reclassified as investment, which is a final good. This adjustment raised the level of GDP on average by about 3.2%. At the time, Treasury debt held by private investors totalled $9.964.5 billion, or 62% of GDP. If this had all been in the form of GDP-principal-indexed bonds, the value of government debt would have jumped by $325 billion.
Is there a solution?
The solution proposed for data revisions of using a six-month lag seems inadequate even excluding methodological shifts. While it might be appealing to lengthen the lag, given that most recessions are relatively brief, lasting less than two years, this would reduce the cyclical benefits to the government debt manager. As for the comprehensive data revisions, one idea is to maintain two sets of estimates, one under the old methodology and one under the new. But this is likely to be confusing to many, and may invite scepticism about the quality of the data, especially over long periods of time.
That brings us back to where we started. We see clear benefits to a government from issuing GDP-linked bonds, especially the type where coupon payment rates vary with nominal growth. But establishing investor confidence in these instruments will require a better approach to the obstacles posed by data revisions and changes in methodology. This seems like an excellent challenge for economists and finance practitioners alike.
Bank of England (2016), “Making a reality of GDP-linked sovereign bonds,” G20, July.
Benford, J, M Joy and M Kruger (2016), “Sovereign GDP-linked bonds,” Financial Stability Paper No. 39, Bank of England.
Blanchard, O, P Mauro and J Acalin (2016), “The case for growth-indexed bonds in advanced economies today,” VoxEU.org, 16 February.
Bowman, J and P Naylor (2016), “GDP-linked bonds”, Reserve Bank of Australia Bulletin, September.
G20 (2016), “Communiqué,” G20 Finance Ministers and Central Bank Governors Meeting, Chengdu, China, 23-24 July.
Griffith-Jones, S and K Sharma (2016), “GDP-indexed bonds: making it happen,” DESA Working Paper No. 21, United Nations.
Kamstra, M and R J Shiller (2009), “The case for trills: giving the people and their pension funds a stake in the wealth of the nation,” Cowles Foundation Working Paper CFDP 1717, August.
Shiller, R J (1998), Macro markets: creating institutions for managing society’s largest economic risks. Oxford: Oxford University Press.
 For a description of the diversification benefits of holding foreign equities, see Cecchetti and Schoenholtz (2016).
 For advanced economies, estimates of the premium are in the range of 150 to 300 basis points, while for emerging market economies, they can exceed 500 basis points (Bowman and Naylor 2016).