Assessing Fiscal Stress

This paper develops a new index which provides early warning signals of fiscal sustainability problems for advanced and emerging economies. Unlike previous studies, the index assesses the determinants of fiscal stress periods, covering public debt default as well as near-default events. The fiscal stress index depends on a parsimonious set of fiscal indicators, aggregated using the approach proposed by Kaminsky, Lizondo and Reinhart (1998). The index is used to assess the build up of fiscal stress over time since the mid-1990s in advanced and emering economies. Fiscal stress has increased recently to record-high levels in advanced countries, reflecting raising solvency risks and financing needs. In emerging economies, risks are lower than in mature economies owing to sounder fiscal fundamentals, but fiscal stress remains higher than before the crisis.

Abstract

This paper develops a new index which provides early warning signals of fiscal sustainability problems for advanced and emerging economies. Unlike previous studies, the index assesses the determinants of fiscal stress periods, covering public debt default as well as near-default events. The fiscal stress index depends on a parsimonious set of fiscal indicators, aggregated using the approach proposed by Kaminsky, Lizondo and Reinhart (1998). The index is used to assess the build up of fiscal stress over time since the mid-1990s in advanced and emering economies. Fiscal stress has increased recently to record-high levels in advanced countries, reflecting raising solvency risks and financing needs. In emerging economies, risks are lower than in mature economies owing to sounder fiscal fundamentals, but fiscal stress remains higher than before the crisis.

I. Introduction

Recent fiscal difficulties around the world brought to the fore the importance of assessing fiscal sustainability risks both in advanced and emerging economies. Based on the conceptual framework presented in Cottarelli (2011), these risks can lead to a sovereign debt rollover crisis in the absence of fiscal adjustment. Various factors can impact these fiscal sustainability risks, including: (i) whether current and projected fiscal policies are consistent with solvency and liquidity requirements (Baldacci, McHugh and Petrova, 2011); (ii) whether uncertainty around this baseline—reflecting shocks to macroeconomic assumptions, fiscal policy, and contingent liabilities—has heightened; and (iii) whether non-fiscal factors (such as current account imbalances) and global financial market risk appetite have increased the likelihood of a fiscal crisis (IMF, 2011).

In this paper, we build a new index of fiscal stress that provides early warning signals of fiscal sustainability problems for advanced and emerging economies. Unlike previous studies, the analysis is not confined to sovereign debt default or near-default events. Fiscal crisis periods are defined as episodes of outright fiscal distress—public debt default/restructuring, need to access large-scale official/IMF support, hyperinflation—as well as extreme financing problems—spikes in sovereign bond spreads. In these cases, fiscal solvency is endangered and the government is forced to alter its policies to regain fiscal sustainability.

Another innovation of this paper is that the fiscal stress index is based on a set of indicators that measure the risk of fiscal sustainability based on current fiscal variables and their baseline projections using a consistent conceptual framework (Baldacci, McHugh and Petrova, 2011). For each indicator, thresholds are estimated on the basis of a univariate procedure that maximizes the likelihood of predicting a fiscal crisis. The fiscal stress index measures the number of indicators exceeding these thresholds, weighted by their relative signaling power.

The index can be used to assess the degree of fiscal stress in advanced and emerging market economies over time. Results show that fiscal stress risks remain elevated in advanced economies and well above the pre-crisis years. This owes to high solvency risks related to fiscal fundamentals and aging-related long-term budget pressures as well as record-high budget financing needs. Fiscal stress is lower for emerging economies, due to the rebuilding of fiscal buffers and more positive growth prospects than in mature economies. However, risks remain higher than in pre-crisis years also for these economies and point to continued vulnerabilities to shocks.

The rest of the paper is organized as follows. The next section surveys the literature on early warning systems, focusing on studies of fiscal crises. Section III elaborates the early warning methodology applied to developing the fiscal stress index. Section IV describes the data used and main results, and Section V concludes.

II. Literature Review

There is an abundant literature on Early Warning System (EWS) models, mostly focused on currency and banking crises. These empirical studies differ according to: (i) the definition of crisis events; (ii) the methodology adopted; and (iii) the set of indicators used. Also country coverage tends to be limited by data quality, with only a few studies focusing on both advanced and emerging economies (and even in these cases limiting the analysis to relatively small samples).

Previous studies typically focused on financial crises, with a few papers assessing the risk of public debt default. In the latter studies, the definition of crisis events typically covers only tail events: for example, Detragiache and Spilimbergo (2001) define public debt crises as events of outright default or rescheduling, while Manasse, Roubini and Schimmelpfennig (2003) further add the provision of a large-scale official financing support to the definition of fiscal crises. However, extreme rollover problems are more common than public debt default episodes across advanced and emerging economies in the last decades. A broader definition of fiscal crises could provide better information about changes in underlying fiscal sustainability risks, even in the absence of outright debt default (or near-default events triggering financial support of the official sector). In this paper, we define fiscal stress events to capture crisis episodes that encompass public debt default and near-default events, as well as severe deteriorations in the fiscal solvency risk outlook leading to fiscal sustainability risks (Cottarelli, 2011; IMF, 2011).

The empirical literature also differs with respect to the methodology used in the studies. Two approaches are common: the univariate “signaling” approach and the multivariate regression analysis of the crisis determinants. 2 The “signaling” approach was proposed in a seminal paper by Kaminsky, Lizondo and Reinhart (1998) on determinants of currency crises. It entails using each potential indicator of crisis events separately, identifying critical thresholds that signal such events with the lowest prediction error, and then averaging the number of indicators exceeding this threshold into a composite index. This is based on weights proportional to the signaling power of each indicator. The methodology has been used in subsequent empirical studies, including to assess fiscal vulnerability indicators that help predict financial crises in emerging economies (Hemming, Kell and Schimmelpfennig, 2003) and to assess the risk of sudden stops (IMF, 2007). The multivariate regression approach uses panel regressions (probit or logit) with a binary dependent variable equal to one if a crisis occurs and zero otherwise. The impact of a set of determinants on the crisis probability is then derived by estimating the model and testing the coefficients’ significance. Berg and Patillo (1999) use this approach to predict currency crises and find that the crisis probability increases with changes in the predictive indicators.3

Various studies have attempted to compare the performance of these two methods based on their success in correctly predicting crises (Appendix Table 2). Berg and Patillo (1999) and Berg, Borensztein and Patillo (2005) find that the multivariate probit model outperforms the “signaling” approach both in-sample and in cross-country predictions, while the “signaling” approach has a better out-of-sample performance. Overall, no approach emerges as the clear winner and results depend on the type of crisis risk assessed.

In this paper, the “signaling” approach is used.4 This framework is relatively simple and allows for a transparent mapping from a large set of fiscal indicators into a composite index of fiscal stress. Another advantage of the methodology is that it easily accommodates differences in data availability across variables, while using panel multivariate regression models would limit the number of predictive variables owing to data gaps.5 One limitation of this approach is that individual predictive variables cannot be tested for their conditional statistical significance. However, each variable contributes to the fiscal stress index with a weight proportional to its power in predicting a fiscal stress event.

The literature suggests several indicators that can help predict which countries are most vulnerable to banking crises. Frankel and Saravelos (2010) point to the importance of the level of international reserves, the real exchange rate and the current account in predicting financial crises. Similarly, IMF (2007) finds that external sector variables are important, in particular reserve coverage, the current account and external debt relative to exports.

Only a few studies focus on fiscal variables determinant of fiscal crises. While fiscal data are not as widely available as monetary or financial data, fiscal variables are also found to be relatively less powerful in predicting crises.6 Hemming and Petrie (2002) discuss fiscal vulnerability and potential fiscal indicators that might increase fiscal risks and Hemming, Hell and Schimmelpfennig (2003) use a large set of fiscal variables for 29 emerging economies over the period 1970-2000 to assess risks of currency, debt and banking crises

They find that the best fiscal indicators are short-term public debt, foreign-currency debt as well as other deficit measures.

In this paper, we rely on a parsimonious set of fiscal indicators that have been identified by Baldacci, McHugh and Petrova (2011) to measure fiscal sustainability risks under the medium-term scenario of the World Economic Outlook baseline projections. These indicators measure solvency risks based on current deficit and debt levels, and projected growth-adjusted interest rate on public debt. Indicators of long-term budget pressure associated with demographic aging, such as projected change in health care and pension expenditures, are also included. In addition to the solvency risk outlook, the framework also cover risks to fiscal sustainability stemming from sovereign asset and liability composition and financing requirements.

III. Methodology

A. Fiscal Crisis Episodes

A fiscal crisis episode is identified in this study as a period of extreme government funding difficulties (Cottarelli, 2011). Funding pressures could arise as a result of public debt build-up, contingent liabilities that become outright fiscal costs, negative revenue shocks, or unaddressed demographic-related spending pressures. Financing constraints may also tighten due to market perception that the composition of public debt impedes the repayment capacity of the government. The surveyed literature suggests four types of criteria to capture such events: (i) debt default or restructuring; (ii) implicit default; (iii) recourse to exceptional official financing; and (iv) a sharp deterioration in market access.

Previous studies used a combination of the first three criteria to identify fiscal crises: public debt default or restructuring, hyperinflation, and large-scale IMF-supported programs. A limitation of this approach is that it misses fiscal distress episodes that are severe enough to alter the attainment of macroeconomic stability and growth but do not result in defaults or near-defaults. Fiscal crises can manifest themselves differently since the mid-1990s, with the development of bond markets and a lower reliance of countries on bank loans (see Pescatori and Sy, 2007). Notably, some episodes of severe difficulties may not trigger a debt default or restructuring and would not be captured by the standard definition used in the literature.

This paper combines the criteria above with indicators of severe spikes in financing costs to obtain a more comprehensive set of fiscal crisis events. To identify periods of public debt default, debt restructuring, and high levels of IMF financing support, the same definition is used for advanced and emerging economies. The definition of default follows Standard and Poor’s, which classifies a sovereign in default if it is not current on its debt obligations (including exchange offers, debt equity swaps, and buybacks for cash). Restructuring and rescheduling are defined as any operation which alters the original terms of the debt-creditor contract. Public debt defaults include both commercial and official creditors. Large IMF-supported programs are those with access above 100 percent of quota.7 These are typically non-concessional loans and are provided as part of an adjustment program. Exceptional financing covers situations where near-default was avoided through large-scale IMF-supported programs.8

Implicit domestic public debt defaults are identified by criteria for high inflation, differentiated between advanced and emerging economies. High inflation episodes are those where the inflation rate was above 35 percent per year in the case of advanced economies, and 500 percent per year for emerging economies.9 The threshold for advanced economies was chosen on the basis of the average haircut on public debt in case of external debt restructuring. This follows Sturzenegger et al. (2006) and aims to capture implicit domestic defaults. The threshold for emerging economies is based on results by Reinhart and Rogoff (2010).10

Severe government bond yield pressures are also considered. This captures situations in which the government faces significant short-term market financing constraints.11 Periods when yield spreads exceeded two standard deviations above the country-specific mean were used to capture market financing pressure events for both advanced and emerging economies. In addition, for emerging economies periods were also included when the bond yield spreads exceeded 1,000 basis points (even if this level did not exceed two standard deviations from the mean) to capture countries that have exceptionally high credit risk spreads for long periods, reflecting high political risks and the consequences of past debt defaults (Pescatori and Sy, 2007).12

The resulting definition of fiscal distress13 events for advanced and emerging economies is presented in Table 1.

Table 1.

Definition of Fiscal Crisis Across Advanced and Emerging Market Economies

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Annual data for 29 advanced economies and 52 emerging economies covering 1970-2010 are used to identify fiscal stress events.14 Data on debt default and restructuring were obtained from Standard and Poor. Information about exceptional IMF-supported programs is based on the IMF’s Finance Department database. Long-term domestic bond spreads and, where available, 5-year credit default swap (CDS) spreads are used to capture sovereign yield spikes in advanced economies. Data on spreads of long-term domestic bond spreads relative to comparable U.S. bonds are used for emerging economies. Sourced of data on sovereign bond yields at annual and monthly frequencies include the IMF’s International Financial Statistics (IFS), Bloomberg, and Datastream.

On the basis of the definition used in the paper, there were 41 fiscal distress events in advanced economies and 135 events in emerging economies (Table 2).15 Advanced countries’ events were identified mainly by government bond yield spikes, with only a few countries experiencing episodes of access to exceptional financing. Five countries experienced high-inflation events in the period; only 7 out of 29 countries had no crises. In contrast, fiscal stress events for emerging economies frequently involved multiple types of crises. About 60 percent of the cases relate to IMF-supported programs (79 events) and a third to outright defaults and restructuring (each 52 events). However, in the last decade fiscal stress events were increasingly identified through severe bond yield spikes in these economies.

Table 2.

Summary of Events Across Advanced and Emerging Economies

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Source: Authors’ calculations.

The incidence of new fiscal stress events is clustered around specific periods (Figure 1). Prior to the recent financial crisis, several advanced economies experienced fiscal stress as a result of the oil boom of 1973 and the recession of the early 1990s. Many countries entered into fiscal distress after the onset of the recent crisis in 2008, with a few more new crises occurring in 2009-10. Among emerging economies, fiscal stress events were clustered around the public debt crises in the early 1980s, the Latin American and Asian crises of the 1990s, and the recent global financial crisis.

Figure 1.
Figure 1.

Incidence of Fiscal Crises

Citation: IMF Working Papers 2011, 100; 10.5089/9781455254316.001.A001

Sources: IMF International Financial Statistics; Bloomberg; Standard and Poor’s; and authors’ calculations.

The length of fiscal stress is on average 2½ years in advanced economies, and 3½ years in emerging economies. As a result the incidence of fiscal crises may not correspond to the number of countries which experience fiscal stress in any given year (Figure 2). Therefore, in discussing the results we present the number of countries in fiscal stress in parallel with the incidence of fiscal crisis events.

Figure 2.
Figure 2.

Countries in Fiscal Stress

Citation: IMF Working Papers 2011, 100; 10.5089/9781455254316.001.A001

Sources: IMF International Financial Statistics; Bloomberg; Standard and Poor’s; and authors’ calculations.

As expected, our approach identifies more crisis events than other studies (Table 3 and Appendix Tables 4a and 4b). This stems from a more comprehensive definition of crisis events and from the larger sample used. The differences in the events identified in the paper and those identified by Reinhart and Rogoff (2010) arise mainly from the use of access to large IMF-supported programs and of government yield spikes. Lastly, the timing of crises also differs occasionally from other datasets, either because of the differences in definitions or because of the window required between two separate events.

Table 3.

Summary Comparison of Events Across Studies1,2,3

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Events dated differently by only one year are considered common events. Events within our identified crisis durations are also considered common.

The number of total events and missed events in other studies do not add to 164 due to differences in the sample of countries and dates covered.

B. Fiscal Stress Thresholds

The estimation of fiscal stress thresholds for each indicator is based on the “signaling” approach (IMF, 2007; IMF, 2010). This consists of defining cut-off values for each fiscal indicator that discriminates between predicted crisis and non-crisis periods. If an indicator exceeds the cut-off level, the model issues a signal of an upcoming fiscal distress episode. The optimal cut-off point should balance the two types of statistical errors. The lower the threshold, the more signals the model will send (i.e., type II errors will decrease), but at the same time, the number of wrong signals rises (i.e., type I errors will increase). Using a higher threshold reduces the number of wrong signals, but at the expense of increasing the number of missed distress episodes.

Formally one can define an indicator variable at time t, dt, for the following j time periods as following:

dt={1 for  j,if xt1>C0,otherwise

where xt refers to a fiscal indicator and is a monotonically increasing function of crises probabilities and C represents a fixed cut-off for xt. 16 As mentioned, the signaling window j is set to one year in the analysis.

Two methods are commonly used to determine the optimal value of C: the minimization of the total misclassified errors and the maximization of the signal-to-noise ratio. To illustrate these methodologies, the true versus predicted occurrence of crises are reported in Table 4. This shows also the occurrence of type II errors (FN(C)) and type I errors (FP(C)).

Table 4.

True Versus Predicted Occurrence of Crises

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Under the total misclassified errors (TME) method, for each cut-off point C, the TME value can be expressed as the sum of type I and type II errors,

TME(C)=FN(C)NC+FP(C)NNC.

The optimal threshold C* is the value that minimizes TME(C). Due to the small number of fiscal crisis events relative to non-crisis periods, the TME methodology places greater weight on misclassifying fiscal crisis events, thereby yielding relatively conservative thresholds compared to other methods.

The signal-to-noise (SNR) ratio can be defined as the ratio of the percentage of correctly classified crises observations (1-type II errors) to the percentage of incorrectly classified non-crises observations (type I errors). For each cut-off point C, the SNR can be expressed as:

SNR(C)=TP(C)/NCFP(C)/NNC.

The optimal threshold C * under this approach is the value that maximizes SNR(C).

C. Fiscal Stress Index

A fiscal stress index is calculated based on the signaling power of each fiscal indicator. This entails two steps. In the first step, an index summarizing a cluster of fiscal indicators is calculated. If an indicator crosses its threshold, it is assigned a value of 1 in the cluster index and it is weighed proportionately to its predictive power. In the second step, the predictive power of the cluster indices is evaluated and the indicators are aggregated in the fiscal stress index based on their own predictive power and the predictive power of the cluster indices:

Overall index =gwgiwi,gdi

where wig is the weight of each individual indicator i in group g, wg is the weight of the group, and di is a dummy that takes the value of 1 if the indicator is above (below) the threshold, and zero otherwise.

IV. Results

A. Data

The analysis uses 12 fiscal indicators (Baldacci, McHugh and Petrova, 2011),17 classified into three clusters: basic fiscal variables, long-term fiscal trends, and asset and liability management (Appendix Table 5). The data were obtained from the IMF’s Fiscal Monitor, the IMF’s World Economic Outlook (WEO), the Bank of International Settlements (BIS), and United Nations databases. While some data are available for the period 1970-2010, most series start in the 1980s and are available for all countries only for the mid-1990s. Therefore, while the complete dataset is used to estimate the thresholds, the analysis focuses on the period after 1995.

The analysis of the fiscal indicators reveals that the global financial crisis started in 2008 has triggered a pronounced deterioration in the basic fiscal variables (e.g., public debt to GDP ratio and the cyclically adjusted primary balance as a ratio of potential GDP) in advanced countries, leading also to a sharp upturn in gross financing needs (Figure 3 and Appendix Table 6). With long-term pension and health expenditure costs on an upward trend, risks of fiscal stress are expected to have increased in recent years.

Figure 3.
Figure 3.

Trends in Selected Fiscal Indicators

Citation: IMF Working Papers 2011, 100; 10.5089/9781455254316.001.A001

Sources: World Economic Outlook; Bank of International Settlements, Dealogic; and authors’ calculations. See Appendix Table 5 for the definition of fiscal indicators.

In emerging economies, the basic fiscal indicators show that the deterioration in the cyclically adjusted primary balance had started before the outset of the crisis. Nonetheless, public debt to GDP has remained lower than historical levels. Asset and liability management variables have deteriorated since 2008, mostly on account of large deficit financing needs. However, financing conditions have also worsened, with short-term debt reaching levels seen during the Latin American and the Asian crises of the mid 1990s. Variables measuring long-term fiscal challenges are also trending up in emerging economies, but to a lesser extent than in advanced economies.

B. Indicator Thresholds and Weights

The estimation of the indicator thresholds is based on the performance of the TME and SNR approaches. The TME method performs better, in line with previous results in the literature (IMF, 2007).18 Nonetheless, adjustments to the TME methodology are necessary for several reasons. First, occasionally the TME solution is located close to the median of the distribution and in some case on the tail of the distribution where values of the indicators indicate low risk of fiscal distress.19 Second, trends and structural breaks in the data are likely over long time periods. Finally, data are reliably available only since the mid-1990s.

To maximize the predictive power of the indicator, the thresholds are estimated separately for advanced and emerging economies under the constraints that they are located on the risk-prone side of each indicator’s distribution relative to the 1995-2010 median (Figures 4a and 4b). This is obtained by removing a few outliers,20 which allows more robust threshold estimation.21

Figure 4a.
Figure 4a.

Advanced Economies: Fiscal Indicator Medians and Thresholds

Citation: IMF Working Papers 2011, 100; 10.5089/9781455254316.001.A001

Note: Median for the period 1995-2010. See Appendix Table 5 for the definition of fiscal indicators.Source: Authors’ calculations.
Figure 4b.
Figure 4b.

Emerging Market Economies: Fiscal Indicator Medians and Thresholds

Citation: IMF Working Papers 2011, 100; 10.5089/9781455254316.001.A001

Note: Median for the period 1995-2010. See Appendix Table 5 for the definition of fiscal indicators.Source: Authors’ calculations.

The estimated thresholds and the implied signaling power of the indicators determine the relative weight that a variable has in the fiscal stress index (Tables 5a and 5b). Signaling power is defined as one minus the total error and it is a measure of the statistical power of the variable. As discussed in Section II, predictive errors produced by EWS methodologies are typically non-negligible. The focus of the exercise, however, is on the relative performance of the fiscal variables and their role in detecting fiscal vulnerability. This is shown by the relative signal intensity for each variable (signaling power).

Table 5a.

Advanced Economies: Thresholds and Relative Weights of Fiscal Indicators

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Source: Authors’ calculations.
Table 5b.

Emerging Economies: Thresholds and Relative Weights of Fiscal Indicators

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Source: Authors’ calculations.

The top predictors of fiscal stress are different for advanced and emerging economies.22 In the advanced economies, government rollover pressures are associated with the size of financing needs and fiscal solvency concerns, while for emerging economies liquidity constraints are the main signal of fiscal stress. This finding underlies the different economic structure and weaknesses that characterize these countries. When advanced economies are vulnerable to market financing shocks, this is generally in response to evidence of an unsustainable debt path. With about one third of the fiscal stress index determined by international liquidity and the currency composition of government debt, emerging economies are more exposed to “original sin” problems and spillovers from financial markets.23

A logit regression is used to assess the ability of the fiscal stress index to provide early warning signals on fiscal sustainability risks. This is done by plotting the fiscal stress index and the probability of entering into fiscal stress (and remaining in stress after an episode has started). The fiscal stress index components are all significant determinants of fiscal stress episodes. The correlation is higher with basic fiscal variables, whereas the other components of the index have a lower correlation—although their coefficients are highly significant (Figure 5a). 24

Figure 5a.
Figure 5a.

Advanced Economies: Probability of Fiscal Crisis at Different Levels of the Fiscal Stress Index

Citation: IMF Working Papers 2011, 100; 10.5089/9781455254316.001.A001

Note: Cumulative marginal effect of the fiscal stress index and its components with 95-percent confidence bands.Source: Authors’ estimations.

In emerging economies, the relationship between fiscal crises and the fiscal stress index follows a similar pattern, with a narrower confidence interval than for advanced countries (Figure 5b). The correlation between the fiscal stress index and probability of experiencing a fiscal crisis is driven primarily by the asset and liability management variables for these countries. 25

Figure 5b.
Figure 5b.

Emerging Economies: Probability of Fiscal Crisis at Different Levels of the Fiscal Stress Index

Citation: IMF Working Papers 2011, 100; 10.5089/9781455254316.001.A001

Note: Cumulative marginal effect of the fiscal stress index and its components with 95-percent confidence bands.Source: Authors’ estimations.

The fiscal stress index has also a positive correlation with government bond yields and credit ratings.26 In advanced economies, the correlation of the fiscal stress index estimated for 2011 with sovereign bond yields as of March 2011 is significantly different from zero at 0.4, while it is lower (0.24) but still significant with credit ratings. However, the correlation of the basic fiscal variables component with bond yields and credit ratings is stronger at 0.5 and 0.4, respectively (Figure 6). In emerging economies, the fiscal stress index has a lower correlation with market risk prices and ratings. The correlation of the asset and liability management component of the index is, however, the strongest among the three subcomponents of the index.

Figure 6.
Figure 6.

Advanced Economies: Government Bond Yields, Credit Ratings and Fiscal Stress, 2011

Citation: IMF Working Papers 2011, 100; 10.5089/9781455254316.001.A001

Source: Authors’ estimations.

C. Fiscal Stress Index Trends

Fiscal stress has increased more rapidly in advanced than in emerging economies. In 2011, the fiscal stress index—weighted with countries’ PPP-GDP— is higher in advanced countries (Figure 7). 27 Overall, in advanced economies the fiscal stress index has doubled since 2006 and is at record-high levels. In contrast, in emerging economies the fiscal stress index is elevated, but still slightly below the peak experienced during the financial crises of the late 1990s.28

Figure 7.
Figure 7.

Fiscal Stress Index, 1995-2011

Citation: IMF Working Papers 2011, 100; 10.5089/9781455254316.001.A001

Note: PPP-GDP weights used to calculate the weighted average index.Source: Authors’ calculations.

Decomposing the fiscal stress index for advanced economies reveals that its increase since the mid-2000s is a result of a sharp deterioration in the basic fiscal variables—mainly debt GDP and the cyclically adjusted primary balance (Figure 8). The asset and liability management component has also peaked, contributing for about half of the increase in the index. Long-term fiscal indicators have also exerted continuous pressure on the fiscal stress index. In emerging economies, the main factors behind the increase in the fiscal stress index have been the basic fiscal variables, followed by the long-term fiscal trends. The asset and liability management component—mostly due to declining short-term debt to international reserves—has kept the index from increasing further.

Figure 8.
Figure 8.

Contribution of the Fiscal Stress Index Components, 1996-2011

Citation: IMF Working Papers 2011, 100; 10.5089/9781455254316.001.A001

Note: Unweighted fiscal stress index. It measures the change in the index compared to the base year in percent.Source: Authors’ calculations.

Focusing on the regional differences (Figure 9), in advanced economies, the fiscal stress index is highest in North America, although the peak levels of the index are observed in peripheral euro countries. In emerging economies, the fiscal stress index is markedly higher in Emerging Europe, followed by countries in the Middle East and North Africa.

Figure 9.
Figure 9.

Fiscal Stress Index Levels by Region, 2011

Citation: IMF Working Papers 2011, 100; 10.5089/9781455254316.001.A001

Note: Unweighted fiscal stress index.Source: Authors’ calculations.

In the last five years, the index has increased sharply in North America (Figure 10). This is mainly due to deterioration in the cyclically adjusted primary balance and a sharp increase in debt and gross financing needs. While in Asia and the Pacific the index has increased the least, it has been on an upward trend for the last 15 years. This is due to underlying demographic trends, putting pressure on the long-term fiscal component of the index, as well as rising debt and large gross financing needs.

In emerging economies, over the last five years the index has increased the most in Latin American countries, due to peaking cyclically adjusted primary deficits, in a few cases accompanied by declining debt maturity and international reserve coverage of short-term debt. In Emerging Europe, the index has remained elevated throughout 1996-2011. This is not only due to the solvency indicators, but also worsening asset and liability management risks—high ratio of foreign currency denominated debt and low reserve coverage of short-term debt—in addition to growing concerns about the long-term fiscal outlook.

Figure 10.
Figure 10.

Fiscal Stress Index Changes by Region, 1996-2011

Citation: IMF Working Papers 2011, 100; 10.5089/9781455254316.001.A001

Source: Authors’ calculations.

V. Conclusions

The fiscal stress index presented in this paper provides a signaling tool to assess exposure to fiscal sustainability risks and helps identify the factors underlying changes in fiscal stress risks. However, like similar early warning tools, the stress index does not attempt to predict crises, which are typically triggered by a combination of economic, financial, or political shocks. While signaling tools like the fiscal stress index presented here are important to assess vulnerabilities, they should be complemented by judgment-based approaches.

This paper calculates thresholds that identify the likelihood of fiscal stress for a large set of fiscal variables. These thresholds are based on an EWS methodology and are used to construct a summary index of fiscal sustainability risks for advanced economies and emerging markets. In contrast with previous studies, the fiscal stress index relies on a broader definition of crisis episodes, consistent with the conceptual framework developed by Cottarelli (2011). In calculating the fiscal stress index, this paper uses a parsimonious set of fiscal indicators proposed by Baldacci, McHugh and Petrova (2011).

The fiscal stress index is calculated for a large sample of advanced and emerging economies during 1995-2011. Results show that in advanced countries the top predictors of fiscal stress are indicators of gross financing needs and fiscal solvency risks. In emerging economies, the best predictors of fiscal stress are risks associated with public debt structure and exposure to spillovers from financial markets. Fiscal stress risk has increased dramatically across the world as a consequence of the global financial crisis. Risks are higher in advanced economies than in emerging economies, but remain higher than before the crisis in the latter group. North America and Europe are the regions were fiscal stress risks are highest.

There is scope for further extensions based on the analysis presented in this paper. In particular, bootstrapping methods could be used to gauge the uncertainty surrounding the point estimates. Another avenue of further research is to conduct the analysis using thresholds based on country-specific distributions (as in Hemming et al., 2003) instead of using an overall threshold, in order to control for country-specific characteristics. Using time-specific effects could also prove useful in view of the common factors that affect many countries during periods of global contagion.

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Appendix Table 1.

Pros and Cons of Early Warning Statistical Methodologies

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Appendix Table 2.

Comparison of Statistical Methodologies

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Appendix Table 3a.

Advanced Economies: Fiscal Stress Events

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Sources: IMF International Financial Statistics; Standard and Poor’s; and authors’ calculations.
Appendix Table 3b.

Emerging Market Economies: Fiscal Stress Events

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Sources: IMF International Financial Statistics; Standard and Poor’s; and authors’ calculations.
Appendix Table 4a.

Advanced Economies: Event Comparison Across Studies

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1 “n.a.” indicates that either the country or the year were not covered in the respective study. Empty cells indicate no crisis. The dating of the crises follows exactly the respective studies - i.e. we include events from other studies where two consecutive crises are separated by only one year.