FX fair value estimates for 13 economies across LatAm at end-2023 underscore the idiosyncrasies of recent current account dynamics in the region…
…while also highlighting V- and L-shape nominal performance against USD since the pandemic in several countries,…
…even as the inflationary spike in ~2022 continues to abate across the main economies covered below.
As in other parts of the world, several Latin American economies saw their real exchange rates weaken during the pandemic only to rebound sharply amid the global inflationary shock. This v-shape trajectory of LatAm REERs is most evident in Peru and Costa Rica but is also visible to varying extents in Brazil, Colombia, the Dominican Republic, Mexico, and Panama.
REER trends in LatAm
In nominal terms against the dollar, the main currencies in the region weakened during 2020 before strengthening to varying degrees in the years since. Inflation was generally around the 2% mark in these economies in 2020 before peaking in 2022:
🇲🇽 Mexican peso: after dropping sharply during the early pandemic, the peso had mostly recovered by early 2021 and traded flat until October 2022. Since then, it has strengthened significantly, despite some wobbles circa October 2023. Inflation rose from 2% in 2020 to ~8.5% in 2022 before declining to the 4-5% range in 2023/Q1 2024.
🇧🇷 Brazilian real: weakened significantly in H1 2020 and has traded between flat and very moderate strengthening since. Inflation rose from 2% in 2020 to 12% by early 2022, and has remained mostly in the 4-6% range since late 2022.
🇨🇴 Colombian peso: a sharp drop in March 2020 before almost recovering by the end of the year. Then steady weakening until June 2022, when it dropped sharply, followed by a strong recovery throughout 2023. In early 2020, inflation stood at 4% but declined to sub-2% that year, before beginning to rise in H1 2021, culminating in a peak above 13% in late 2022/early 2023 and since declined to the 8-10% range.
🇨🇱 Chilean peso: came under pressure in March 2020 but only after having experienced a sharper drop in late 2019, so its decline during the pandemic coincided with a pre-existing weakening trend. By May 2021 it had more than recovered the early-pandemic weakness, then steadily weakened to October 2022. Subsequently, it bounced back in mid-2023 before declining again. Inflation hovered in the 2-4% range in 2020 before beginning a long steady rise from 2021 onwards, peaking at 14% in 2022 and declining to circa 4% by end-2023.
🇵🇪 Peruvian sol: a steady, significant decline from early 2019 to September 2021, followed by flat-to-moderate strengthening. Inflation stayed around 2% throughout much of 2020 before rising to around 8.5% in H1 2022, and then beginning to moderate in H1 2023, dropping to below 4% by the end of the year.
Regarding fair values, the broad REER trends described above don’t really shed that much light, as valuations depend on where underlying current account balances stand in relation to equilibrium.
FX fair value estimates for 13 economies across CEEMEA at end-2023 underscore the impact of the war in Ukraine.
Real effective exchange rates spiked in various countries following the successive pandemic-Ukraine shocks…
…although Morocco and Croatia appear to be bastions of REER stability in an otherwise volatile group.
One way to value a currency is to assess the link between current account balances and real effective exchange rates, which merge the nominal exchange rate with the ratio of domestic to trade-weighted foreign prices. The IMF uses a fair value model that compares “equilibrium” to “underlying” CABs, with any difference a result of REER misalignment. FX fair values are presented below.
REER trends in CEEMEA
Several economies in Central & Eastern Europe, the Middle East, and Africa have experienced real exchange rate appreciation in the past few years. The dual pandemic-Ukraine inflationary shock since 2021-2022 is in large part responsible for this: annualized inflation remained in double digits in the Czech Republic, Hungary, Poland, Estonia, and Croatia until early- to mid-2023.
Moreover, the Czech koruna, Hungarian forint, and Polish złoty all weakened significantly in nominal terms in 2022, but inflation was so strong that these REERs still rose that year. In 2023, REERs in these countries continued to climb while the koruna traded flat and the forint and złoty registered modest nominal gains.
Russia saw yearly inflation fall from ~11% at the beginning of 2023 to the 2-3% range in Q2 before rising to ~7% by year end, while the ruble weakened significantly, resulting in REER weakening.
South Africa experienced declining inflation and a minor depreciation of the rand in 2023, albeit on the back of significant currency weakening since mid-2021, causing the REER to slide.
Turkey remains an inflationary basket-case, having spent almost all of 2023 near or above 50% in annualized terms, resulting in the lira’s ongoing decline. The net effect has been for its REER to move sideways – but after many years of secular decline.
Turning now to fair values, a number of REERs in CEEMEA exhibit significant over- or under-valuation.
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FX fair value estimates for 13 economies across CEEMEA at end-2023 underscore the impact of the war in Ukraine.
Real effective exchange rates spiked in various countries following the successive pandemic-Ukraine shocks…
…although Morocco and Croatia appear to be bastions of REER stability in an otherwise volatile group.
One way to value a currency is to assess the link between current account balances and real effective exchange rates, which merge the nominal exchange rate with the ratio of domestic to trade-weighted foreign prices. The IMF uses a fair value model that compares “equilibrium” to “underlying” CABs, with any difference a result of REER misalignment. FX fair values are presented below.
REER trends in CEEMEA
Several economies in Central & Eastern Europe, the Middle East, and Africa have experienced real exchange rate appreciation in the past few years. The dual pandemic-Ukraine inflationary shock since 2021-2022 is in large part responsible for this: annualized inflation remained in double digits in the Czech Republic, Hungary, Poland, Estonia, and Croatia until early- to mid-2023.
Moreover, the Czech koruna, Hungarian forint, and Polish złoty all weakened significantly in nominal terms in 2022, but inflation was so strong that these REERs still rose that year. In 2023, REERs in these countries continued to climb while the koruna traded flat and the forint and złoty registered modest nominal gains.
Russia saw yearly inflation fall from ~11% at the beginning of 2023 to the 2-3% range in Q2 before rising to ~7% by year end, while the ruble weakened significantly, resulting in REER weakening.
South Africa experienced declining inflation and a minor depreciation of the rand in 2023, albeit on the back of significant currency weakening since mid-2021, causing the REER to slide.
Turkey remains an inflationary basket-case, having spent almost all of 2023 near or above 50% in annualized terms, resulting in the lira’s ongoing decline. The net effect has been for its REER to move sideways – but after many years of secular decline.
Turning now to fair values, a number of REERs in CEEMEA exhibit significant over- or under-valuation.
Following on from my estimates of current account equilibria in advanced economies, here I turn to emerging markets, which is after all the focus of this blog. I initially focused on AEs in an attempt to replicate as closely as possible an IMF empirical investigation of current account balances in this set of countries, as doing so is more methodologically prudent before expanding the analysis to EMs.
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The goal of this work is to understand what a country’s current account balance should be (see my previous post for a breakdown of what CABs are), based on relevant characteristics as identified by the IMF in its model. These include the cyclically-adjusted government budget balances, demographic dependency ratios, and income level, which are all variables that tend to change only gradually over time. As such, they can be thought of as “long-term” variables, especially the latter two, which can be useful in trying to conceptualize where a country’s CAB ought to be.
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In contrast, cyclical variables that are more volatile from year to year, such as real exchange rates, terms of trade, and domestic output gaps, as well as fiscal policy, theoretically should do a better job of predicting observed CAB readings. These can be thought of “short-term” variables, although I use the same fiscal variable in both the long- and short-run models.
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The “long-run” model fitted values in these charts are the closest approximation I have now for current account equilibria in these EMs. But my confidence in these results is low and will require additional work, for the reasons described below.
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Regretfully, I am far from satisfied with the results of this work so far, but have chosen to publish these interim conclusions in the interest of maintaining regular engagement with my audience. Worse still is that I had to exclude important EMs such as Indonesia from the analysis to maintain a balanced panel dataset, as data availability for some indicators didn’t go far back enough in time.
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The good news is that both models have overall statistical significance and that each of the regressors in the short-run model is statistically significant.
Short-run model results
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I’d still like to tweak the short-run model by adding a trade-weighted foreign output gaps as an independent variable and replacing the cyclically-adjusted budget indicator with a non-cyclically-adjusted budget variable. But, overall, I’m fairly content with the short-run model, as the fiscal, terms of trade, and real exchange rate regressors all behave as expected in relation to the CAB.
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For methodological reasons, I dropped the lagged dependent variable that featured in the short-run AE model, which has decreased the R2 readings in this short-run EM model, though I don’t see this as much of a problem.
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Surprisingly, the domestic output gap is not negatively-related to the CAB but exhibits a positive, strong, and significant association. A positive output gap, meaning that economic growth is above-trend, often results in increased imports, thus placing downward pressure on the CAB. And this is indeed what I found in the short-run model for AEs: a negative, strong, and significant relationship.
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Perhaps the reason behind the positive output gap-CAB relationship in EMs is to be found in exports as a common driver: strong exports could lead to a higher output gap and a higher CAB. Many EMs rely heavily on exports for economic growth, whether of manufactured goods, commodities, or services.
Short-run model variables
Deviation from the in-sample average of the general government cyclically-adjusted budget balance adjusted for nonstructural elements beyond the economic cycle, as a share of potential GDP. Countries with higher-than average government budget balances should be able to attract larger portions of global current account surpluses. This is confirmed by the positive coefficient at 1% significance.
Domestic output gap: actual output minus potential output in current USD (logarithmic difference). Economies in the boom phase of an economic cycle can experience strong import growth, appreciated exchange rates, and stronger remittance and primary income outflows, putting the current account under pressure. Unexpectedly, the coefficient is positive, and is also large and significant. Theoretically, exports could be a common driver of output gaps and CABs in EMs, as they are positively associated with both.
One-year change in the terms of trade, i.e. the ratio of the price of exports to the price of imports. The coefficient is positive and significant, as expected.
One-year change in the REER. The coefficient is negative and significant, as expected, because high REERs can lead to imports becoming relatively cheap, thus increasing import volumes, and lead to exports becoming relatively expensive, thus decreasing export volumes.
Long-run model results
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As for the long-run model, alas only two of its four independent variables are statistically significant after controlling for heteroskedasticity and autocorrelation: the budget surplus indicator and the old-age dependency ratio.
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The child-age dependency ratio and income level were both statistically insignificant, as was the case in the advanced economy model. As such, in further work on this I will be discarding these two regressors and replacing them with something related to private savings. Doing so would complement the public savings approach already captured by the budget variable.
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Moreover, the adjusted-R2 is laughably low in this model. While achieving a high R2 isn’t the most important consideration in constructing a good model with unbiased, efficient estimators, I’d still like to see something higher.
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One other area of improvement for this long-run CAB model would be to run the independent variables against underlying CABs – which have the cyclical impact of output gaps stripped out and real exchange rate effects worked in – rather than against observed, actual CABs.
Long-run model variables
Deviation from the in-sample average of the general government cyclically-adjusted budget balance adjusted for nonstructural elements beyond the economic cycle, as a share of potential GDP. Countries with higher-than average government budget balances should be able to attract larger portions of global current account surpluses. This is confirmed by the positive coefficient at 1% significance.
The deviation from the in-sample average of the child-age dependency ratio on the 20-64 year-old working-age population, i.e. people 19 and under. I tested this variable on the intuition that the child dependency ratio could well be negative, not only due to the income effect as noted by Faruqee and Isard, but also due to the large amounts of consumption (which pushes down savings, increases imports etc) associated with children’s parents at the height of their income generation, family activities, and the associated demographic profile that such countries might have. In this two-ways fixed effects model it is insignificant when standard errors are controlled for heteroskedasticity and autocorrelation. It is also unexpectedly positive.
The deviation from the in-sample average of the old-age dependency ratio on the 20-64 year-old working-age population, i.e. people 65 and over. My intuition with this variable is that it would be positive because of the high level of savings that elderly people have, despite doubts as to the degree to which the elderly can generate positive savings flows for themselves. The sign was positive, as expected, a statistically significant.
Deviation from the in-sample average of GNI per capita on a PPP basis, adjusted for the country’s output gap to equate the observation to what it would be if the economy were running at potential. Unexpectedly, the coefficient is negative: theoretically, greater availability of income and thus savings opportunities in wealthier countries should lead to a higher CAB. Yet this result is insignificant in the long-run model.
Following on from my estimates of current account equilibria in advanced economies, here I turn to emerging markets, which is after all the focus of this blog. I initially focused on AEs in an attempt to replicate as closely as possible an IMF empirical investigation of current account balances in this set of countries, as doing so is more methodologically prudent before expanding the analysis to EMs.
The goal of this work is to understand what a country’s current account balance should be (see my previous post for a breakdown of what CABs are), based on relevant characteristics as identified by the IMF in its model. These include the cyclically-adjusted government budget balances, demographic dependency ratios, and income level, which are all variables that tend to change only gradually over time. As such, they can be thought of as “long-term” variables, especially the latter two, which can be useful in trying to conceptualize where a country’s CAB ought to be.
In contrast, cyclical variables that are more volatile from year to year, such as real exchange rates, terms of trade, and domestic output gaps, as well as fiscal policy, theoretically should do a better job of predicting observed CAB readings. These can be thought of “short-term” variables, although I use the same fiscal variable in both the long- and short-run models.
The “long-run” model fitted values in these charts are the closest approximation I have now for current account equilibria in these EMs. But my confidence in these results is low and will require additional work, for the reasons described below.
Regretfully, I am far from satisfied with the results of this work so far, but have chosen to publish these interim conclusions in the interest of maintaining regular engagement with my audience. Worse still is that I had to exclude important EMs such as Indonesia from the analysis to maintain a balanced panel dataset, as data availability for some indicators didn’t go far back enough in time.
The good news is that both models have overall statistical significance and that each of the regressors in the short-run model is statistically significant.
Short-run model results
I’d still like to tweak the short-run model by adding a trade-weighted foreign output gaps as an independent variable and replacing the cyclically-adjusted budget indicator with a non-cyclically-adjusted budget variable. But, overall, I’m fairly content with the short-run model, as the fiscal, terms of trade, and real exchange rate regressors all behave as expected in relation to the CAB.
For methodological reasons, I dropped the lagged dependent variable that featured in the short-run AE model, which has decreased the R2 readings in this short-run EM model, though I don’t see this as much of a problem.
Surprisingly, the domestic output gap is not negatively-related to the CAB but exhibits a positive, strong, and significant association. A positive output gap, meaning that economic growth is above-trend, often results in increased imports, thus placing downward pressure on the CAB. And this is indeed what I found in the short-run model for AEs: a negative, strong, and significant relationship.
Perhaps the reason behind the positive output gap-CAB relationship in EMs is to be found in exports as a common driver: strong exports could lead to a higher output gap and a higher CAB. Many EMs rely heavily on exports for economic growth, whether of manufactured goods, commodities, or services.
Short-run model variables
Deviation from the in-sample average of the general government cyclically-adjusted budget balance adjusted for nonstructural elements beyond the economic cycle, as a share of potential GDP. Countries with higher-than average government budget balances should be able to attract larger portions of global current account surpluses. This is confirmed by the positive coefficient at 1% significance.
Domestic output gap: actual output minus potential output in current USD (logarithmic difference). Economies in the boom phase of an economic cycle can experience strong import growth, appreciated exchange rates, and stronger remittance and primary income outflows, putting the current account under pressure. Unexpectedly, the coefficient is positive, and is also large and significant. Theoretically, exports could be a common driver of output gaps and CABs in EMs, as they are positively associated with both.
One-year change in the terms of trade, i.e. the ratio of the price of exports to the price of imports. The coefficient is positive and significant, as expected.
One-year change in the REER. The coefficient is negative and significant, as expected, because high REERs can lead to imports becoming relatively cheap, thus increasing import volumes, and lead to exports becoming relatively expensive, thus decreasing export volumes.
Regression Results – 41 Emerging Economies
Dependent variable:
Current Account Balance, %GDP
panel
coefficient
linear
test
(1)
(2)
random.shortrun
random.pcse.shortrun
sur_dev
0.439***
0.439***
(0.056)
(0.065)
ogap_usd.logdiff
15.920***
15.920**
(3.543)
(8.019)
tot_1d
0.061***
0.061***
(0.022)
(0.022)
reer_1d
-0.072***
-0.072***
(0.021)
(0.018)
Constant
-1.054
-1.054
(0.735)
(1.022)
Observations
861
R2
0.097
Adjusted R2
0.093
F Statistic
92.337***
Note:
*p<0.1; **p<0.05; ***p<0.01
Long-run model results
As for the long-run model, alas only two of its four independent variables are statistically significant after controlling for heteroskedasticity and autocorrelation: the budget surplus indicator and the old-age dependency ratio.
The child-age dependency ratio and income level were both statistically insignificant, as was the case in the advanced economy model. As such, in further work on this I will be discarding these two regressors and replacing them with something related to private savings. Doing so would complement the public savings approach already captured by the budget variable.
Moreover, the adjusted-R2 is laughably low in this model. While achieving a high R2 isn’t the most important consideration in constructing a good model with unbiased, efficient estimators, I’d still like to see something higher.
One other area of improvement for this long-run CAB model would be to run the independent variables against underlying CABs – which have the cyclical impact of output gaps stripped out and real exchange rate effects worked in – rather than against observed, actual CABs.
Long-run model variables
Deviation from the in-sample average of the general government cyclically-adjusted budget balance adjusted for nonstructural elements beyond the economic cycle, as a share of potential GDP. Countries with higher-than average government budget balances should be able to attract larger portions of global current account surpluses. This is confirmed by the positive coefficient at 1% significance.
The deviation from the in-sample average of the child-age dependency ratio on the 20-64 year-old working-age population, i.e. people 19 and under. I tested this variable on the intuition that the child dependency ratio could well be negative, not only due to the income effect as noted by Faruqee and Isard, but also due to the large amounts of consumption (which pushes down savings, increases imports etc) associated with children’s parents at the height of their income generation, family activities, and the associated demographic profile that such countries might have. In this two-ways fixed effects model it is insignificant when standard errors are controlled for heteroskedasticity and autocorrelation. It is also unexpectedly positive.
The deviation from the in-sample average of the old-age dependency ratio on the 20-64 year-old working-age population, i.e. people 65 and over. My intuition with this variable is that it would be positive because of the high level of savings that elderly people have, despite doubts as to the degree to which the elderly can generate positive savings flows for themselves. The sign was positive, as expected, a statistically significant.
Deviation from the in-sample average of GNI per capita on a PPP basis, adjusted for the country’s output gap to equate the observation to what it would be if the economy were running at potential. Unexpectedly, the coefficient is negative: theoretically, greater availability of income and thus savings opportunities in wealthier countries should lead to a higher CAB. Yet this result is insignificant in the long-run model.
Building on recent work on how to measure deviations of actual GDP from potential GDP, known as an output gap, I’m pleased to reveal a world map of results for 2023. Remember that an output gap is positive when actual GDP is above potential – or trend – and negative when it is below. In the map below, countries in blue have positive output gaps in 2023, while those in orange and red are negative.
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While most countries are exhibiting above-trend GDP growth, there are some noteworthy pockets of below-trend output. Chief among these is a large negative output gap in Ukraine, clearly related to the ongoing war with Russia, and which also appears to have infected several of its neighbors in north-eastern and north-central Europe.
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Other countries with active conflicts or security-related concerns also seem to be well below potential: Sudan, Myanmar, Haiti, and Iraq.
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There are also some clusters of negative gaps in various regions: Latin America (Peru, Bolivia, Paraguay, and Chile), South/Southeast Asia (Pakistan, Nepal, Bhutan, Myanmar, Thailand, Laos), and West-Central Africa (Ghana, Burkina Faso, Gabon).
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As for the positive output gaps around the world, these are mostly in the range of 0-2.5% of potential GDP. Much of southern Europe is above this level: Portugal, Spain, Italy, Croatia, Montenegro, Albania, Greece. Farther east, Georgia, Armenia, Iran, and Tajikistan have also recorded above-trend output beyond 2.5%. Brazil and some parts of Africa (Libya, Republic of Congo, Democratic Republic of Congo, Botswana, Benin, and Liberia).
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The countries with the largest positive output gaps are in darkest blue: Guyana, Yemen, and Libya. The latter two have of course experienced significant conflicts over the past decade, suggesting that actual GDP is now well above trend as a result of those previous shocks. High positive output gaps can also be a symptom of economic overheating.
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Note that data for 2023 is absent for some countries in the map because the IMF did not provide actual GDP estimates for this year in its October 2023 World Economic Outlook. These include Sri Lanka, Afghanistan, Syria, Venezuela, and Cuba. Given high economic uncertainty and/or the absence of reliable data from these countries, fair enough.
Trend GDP: a visual primer
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So far in my writing about output gaps I haven’t made any visual presentations of what real and potential GDP look like. As explained previously, measuring potential GDP is complicated and data-intensive, so economists often use a shortcut: deriving a moving average of actual GDP readings as a proxy for potential GDP. The approach I have taken is known as Hodrick-Prescott filtering.
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As a result of the previously-noted pitfalls of using moving averages to measure potential GDP, I refer to the term of “trend” rather than “potential” GDP. As for “actual” GDP, this is data in national currency units using constant prices, meaning that it is real – and not nominal – GDP.
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The charts below provide examples actual and trend GDP. I’ve selected these countries because they are ongoing sovereign debt restructuring cases of interest, even if I only present them here for demonstrating how actual / real and trend / potential GDP relate to each other and output gaps:
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Output Gap = (Real GDP – Potential GDP) / Potential GDP * 100
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Sri Lanka is perhaps the most interesting case, even if the data is only through 2022: a sizable positive output gap – indicating potential overheating in the economy – preceded a sharp drop in GDP, leading to a negative output gap. Also currently in negative territory, Ghana’s GDP exhibits some of the same behavior, albeit with less volatility.
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Zambia sustained a positive output gap throughout most of the 2010s, until an economic contraction in 2020 led it into negative territory, though the gap turned positive again in 2023. Once one of the world’s fastest growing economies, Ethiopia’s economic growth has also been remarkably stable, despite the recent Tigray conflict. This makes for a more “boring” chart but is a credit to the country’s economy, with the output gap in marginally positive territory.
Building on recent work on how to measure deviations of actual GDP from potential GDP, known as an output gap, I’m pleased to reveal a world map of results for 2023. Remember that an output gap is positive when actual GDP is above potential – or trend – and negative when it is below. In the map below, countries in blue have positive output gaps in 2023, while those in orange and red are negative.
While most countries are exhibiting above-trend GDP growth, there are some noteworthy pockets of below-trend output. Chief among these is a large negative output gap in Ukraine, clearly related to the ongoing war with Russia, and which also appears to have infected several of its neighbors in north-eastern and north-central Europe.
Other countries with active conflicts or security-related concerns also seem to be well below potential: Sudan, Myanmar, Haiti, and Iraq.
There are also some clusters of negative gaps in various regions: Latin America (Peru, Bolivia, Paraguay, and Chile), South/Southeast Asia (Pakistan, Nepal, Bhutan, Myanmar, Thailand, Laos), and West-Central Africa (Ghana, Burkina Faso, Gabon).
As for the positive output gaps around the world, these are mostly in the range of 0-2.5% of potential GDP. Much of southern Europe is above this level: Portugal, Spain, Italy, Croatia, Montenegro, Albania, Greece. Farther east, Georgia, Armenia, Iran, and Tajikistan have also recorded above-trend output beyond 2.5%. Brazil and some parts of Africa (Libya, Republic of Congo, Democratic Republic of Congo, Botswana, Benin, and Liberia).
The countries with the largest positive output gaps are in darkest blue: Guyana, Yemen, and Libya. The latter two have of course experienced significant conflicts over the past decade, suggesting that actual GDP is now well above trend as a result of those previous shocks. High positive output gaps can also be a symptom of economic overheating.
Note that data for 2023 is absent for some countries in the map because the IMF did not provide actual GDP estimates for this year in its October 2023 World Economic Outlook. These include Sri Lanka, Afghanistan, Syria, Venezuela, and Cuba. Given high economic uncertainty and/or the absence of reliable data from these countries, fair enough.
Trend GDP: a visual primer
So far in my writing about output gaps I haven’t made any visual presentations of what real and potential GDP look like. As explained previously, measuring potential GDP is complicated and data-intensive, so economists often use a shortcut: deriving a moving average of actual GDP readings as a proxy for potential GDP. The approach I have taken is known as Hodrick-Prescott filtering.
As a result of the previously-noted pitfalls of using moving averages to measure potential GDP, I refer to the term of “trend” rather than “potential” GDP. As for “actual” GDP, this is data in national currency units using constant prices, meaning that it is real – and not nominal – GDP.
The charts below provide examples actual and trend GDP. I’ve selected these countries because they are ongoing sovereign debt restructuring cases of interest, even if I only present them here for demonstrating how actual / real and trend / potential GDP relate to each other and output gaps:
Output Gap = (Real GDP - Potential GDP) / Potential GDP * 100
Sri Lanka is perhaps the most interesting case, even if the data is only through 2022: a sizable positive output gap – indicating potential overheating in the economy – preceded a sharp drop in GDP, leading to a negative output gap. Also currently in negative territory, Ghana’s GDP exhibits some of the same behavior, albeit with less volatility.
Zambia sustained a positive output gap throughout most of the 2010s, until an economic contraction in 2020 led it into negative territory, though the gap turned positive again in 2023. Once one of the world’s fastest growing economies, Ethiopia’s economic growth has also been remarkably stable, despite the recent Tigray conflict. This makes for a more “boring” chart but is a credit to the country’s economy, with the output gap in marginally positive territory.
Angola, Pakistan, Egypt, Jordan, Argentina, El Salvador, Ecuador, and Belize are among the market-access countries most at risk of sovereign stress, according to the model presented below.Unsurprisingly, several advanced economies appear least at risk, including Norway, Ireland, Denmark, Singapore, the Netherlands, Luxembourg, Hong Kong, and Switzerland.
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Earlier this year I published the high-level initial results of a sovereign debt stress tracker, based on a model developed by the International Monetary Fund for countries that it classifies as having access to international markets. The IMF presented this model as part of its update to its Debt Sustainability Framework for Market-Access Countries in 2021, claiming at the time that it had performed significant robustness checks to ensure forecast salience. Time will tell how useful this tool is in predicting sovereign debt strains, and, in any case, it should only be used in conjunction with other analytical approaches.
Heatmaps
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Using the latest available data for 2023, the heatmaps below rank order countries by the probability of experiencing sovereign stress, as represented by the column farthest to the right. Neither the probabilities for the dependent variable nor any of the raw data readings for any of the independent variables is shown below. Instead, readers can see the percentile rank compared to the maximum value in each variable column, which is beneficial for visually detecting relative heat for each indicator.
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Lighter colors represent more risk, while darker colors represent less risk. Independent variables with negative coefficients, i.e. are negative predictors of sovereign stress, have been reversed in order to ensure color scheme coherence. These include institutional quality, the current account, and international reserves.
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The first heatmap below suggests that Angola, Pakistan, Egypt, Jordan, Argentina, El Salvador, Ecuador, and Belize are most at risk of experiencing sovereign debt strains. Looking across the independent variables for this group of countries:
They generally suffer from high external public debt burdens and from relatively poor institutional quality, though Argentina and Jordan fare better on those measures, respectively.
El Salvador is penalized relatively less on stress history, though this assumes spread widening in recent years remained under the IMF’s stress definition threshold (see “Model” section below).
One-year changes in general government debt in Angola, Egypt, and Argentina point to potential risks.
El Salvador, Jordan, and, to a lesser extent, Pakistan, appear to need some replenishing of their international reserve buffers.
Angola and – to a lesser extent – Argentina are marked down for surging REERs.
Pakistan and Egypt display relatively concerning public debt/revenue ratios.
Jordan stands out for poor current account performance.
Egypt, Jordan, and Ecuador exhibit high credit-to-GDP gaps, though several other countries fare worse on this measure.
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Each value is divided by the maximal value in that column, resulting in its own empirical percentile. Each value shown is the percent of observations with that value or below it. Sources: IMF, WGI, WB, Bruegel, BIS, author’s calculations.
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The second heatmap uses foreign currency general government debt to replace the external PPG debt indicator featured in the first heatmap (see explanation in “Data” section below). Neither of these indicators is ideal, as in both cases coverage for many countries is either lacking or data points are equal to zero. This is obvious in both heatmaps from the absence of dark-colored cells in the relevant column, meaning that many countries are zero. Overall country coverage on this variable is better in the first heatmap, but the second one provides value for countries where data is missing in the first one (e.g. Israel, Korea, Sweden).
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The eight countries most at risk of sovereign stress in this second heatmap are the same as in the first one, albeit in a slightly different order and except for Mexico replacing Belize. On this latter point, FX general government debt data – sourced from the BIS (see “Data” section below) – is missing for Belize, conferring on it an unfair advantage over Mexico and other countries where data are present for this indicator. In the first heatmap, external debt data is present for both Mexico and Belize, with the latter appearing more at risk than the former.
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Each value is divided by the maximal value in that column, resulting in its own empirical percentile. Each value shown is the percent of observations with that value or below it. Sources: IMF, WGI, WB, Bruegel, BIS, author’s calculations.
Interpretation
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Focusing on a country case helps illustrate ways to interpret the data in this model. Take Angola, as it appears most at-risk. Using heatmap (1), the brightest and thus most concerning data points are in the institutional quality, REER 3-year change, general government debt 1-year change, and external public and publicly-guaranteed debt columns. This suggests that the government and public sector more broadly are borrowing heavily, while prices and the exchange rate have also combined to rise quickly. Moreover, the institutions to set a good policy framework appear to be lacking. This is already a dangerous mix.
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On the other hand, Angola scores well on its current account balance and international reserves variables. This is easily explained by the fact that the country is an oil exporter, thereby keeping its current account balance high and accumulating foreign reserves from the proceeds of these oil sales to buyers abroad.
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While these oil exports provide Luanda with ample benefits, heavy reliance on a commodity-based export sector is also a double-edged sword. The result is often an appreciation of the exchange rate, making the economy less competitive for developing other industries: a classic case of Dutch Disease.
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More concerning still is the presence of high inflation. The country’s surging REER variable already suggests that prices are probably rising, as the overall increase is unlikely to be due to nominal exchange rate dynamics alone. Increases in government debt suggest potential fiscal profligacy, which can lead to undesirably-high inflation, the presence of which is confirmed by a glance at recent Angolan statistics. The credit-to-GDP gap, which measures the deviation from trend of credit to the non-financial private sector as a share of GDP, is not particularly alarming in Angola, but may be high enough to also be contributing to the rising price level.
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Angola also exhibits a high public debt-to-revenue ratio, which is worrying, given all the oil revenues that the country is seemingly raking in, suggesting that less borrowing and more fiscal discipline are likely needed. Recent sovereign stress is also a concern, indicating that, for all its natural resources, the government is unable or unwilling to pursue policies required to maintain macroeconomic stability.
Model
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To recap, the model’s dependent variable is the probability of sovereign stress, which the IMF has detailed criteria for defining – running the gamut from outright default to a mere spread widening beyond a certain threshold. Regarding the independent variables:
The first two represent how recently a country has experienced sovereign stress, and its government effectiveness and regulatory quality.
Other explanatory variables are macroeconomic in nature, including current account balances, real effective exchange rates – which also capture price changes, credit gaps to the private sector, and international reserves.
More specifically fiscal indicators include those on general government debt, foreign currency public debt, and public debt-to-revenue ratios.
With the exception of REERs and debt-to-revenue, these macro-fiscal indicators are all expressed as a share of GDP.
A global variable also features in the model, the VIX Index, which measures stock market volatility in the US, but is not presented in the heatmaps above, given its constance across countries.
Data
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In the first iteration of the tracker, 2023 data was captured for 43 market-access countries, including both emerging markets-developing economies and advanced economies. Thanks to more available data for this year and refinements in data capture, coverage has been expanded to 82 MACs in these heatmaps.
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Two similar heatmaps are presented in this article, with a difference in one of the independent variables and, as a result, slight changes to the overall results in the dependent variable. One of the IMF’s indicators is foreign currency public debt. In the first instance, the World Bank indicator for external public and publicly-guaranteed debt is used as the best available proxy for the IMF’s variable. While using this data from the World Bank remains the best possible option at this stage, there are some glaring omissions in coverage. For instance, the World Bank source suggests that Israel’s external PPG debt is equal to zero, which is clearly incorrect.
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As a remedy to the World Bank’s data deficiencies, a second heatmap applies data from the Bank of International Settlements on foreign currency general government debt, as a proxy for this indicator in the same overall model. The BIS data does fill in some of the World Bank gaps – e.g. Israel – but in fact covers fewer countries than the first source. As such, the first heatmap is still preferable.
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It is also worth noting that both the World Bank and BIS indicators differ from the IMF variable of foreign currency public debt. In the former case, external public debt differs somewhat from foreign currency public debt, even if virtually all external debt is in foreign currency. In the latter case, foreign currency general government debt excludes some types of debt that is covered under foreign currency public debt.
Angola, Pakistan, Egypt, Jordan, Argentina, El Salvador, Ecuador, and Belize are among the market-access countries most at risk of sovereign stress, according to the model presented below.Unsurprisingly, several advanced economies appear least at risk, including Norway, Ireland, Denmark, Singapore, the Netherlands, Luxembourg, Hong Kong, and Switzerland.
Earlier this year I published the high-level initial results of a sovereign debt stress tracker, based on a model developed by the International Monetary Fund for countries that it classifies as having access to international markets. The IMF presented this model as part of its update to its Debt Sustainability Framework for Market-Access Countries in 2021, claiming at the time that it had performed significant robustness checks to ensure forecast salience. Time will tell how useful this tool is in predicting sovereign debt strains, and, in any case, it should only be used in conjunction with other analytical approaches.
Heatmaps
Using the latest available data for 2023, the heatmaps below rank order countries by the probability of experiencing sovereign stress, as represented by the column farthest to the right. Neither the probabilities for the dependent variable nor any of the raw data readings for any of the independent variables is shown below. Instead, readers can see the percentile rank compared to the maximum value in each variable column, which is beneficial for visually detecting relative heat for each indicator.
Lighter colors represent more risk, while darker colors represent less risk. Independent variables with negative coefficients, i.e. are negative predictors of sovereign stress, have been reversed in order to ensure color scheme coherence. These include institutional quality, the current account, and international reserves.
The first heatmap below suggests that Angola, Pakistan, Egypt, Jordan, Argentina, El Salvador, Ecuador, and Belize are most at risk of experiencing sovereign debt strains. Looking across the independent variables for this group of countries:
They generally suffer from high external public debt burdens and from relatively poor institutional quality, though Argentina and Jordan fare better on those measures, respectively.
El Salvador is penalized relatively less on stress history, though this assumes spread widening in recent years remained under the IMF’s stress definition threshold (see “Model” section below).
One-year changes in general government debt in Angola, Egypt, and Argentina point to potential risks.
El Salvador, Jordan, and, to a lesser extent, Pakistan, appear to need some replenishing of their international reserve buffers.
Angola and – to a lesser extent – Argentina are marked down for surging REERs.
Pakistan and Egypt display relatively concerning public debt/revenue ratios.
Jordan stands out for poor current account performance.
Egypt, Jordan, and Ecuador exhibit high credit-to-GDP gaps, though several other countries fare worse on this measure.
Each value is divided by the maximal value in that column, resulting in its own empirical percentile. Each value shown is the percent of observations with that value or below it. Sources: IMF, WGI, WB, Bruegel, BIS, author’s calculations.
The second heatmap uses foreign currency general government debt to replace the external PPG debt indicator featured in the first heatmap (see explanation in “Data” section below). Neither of these indicators is ideal, as in both cases coverage for many countries is either lacking or data points are equal to zero. This is obvious in both heatmaps from the absence of dark-colored cells in the relevant column, meaning that many countries are zero. Overall country coverage on this variable is better in the first heatmap, but the second one provides value for countries where data is missing in the first one (e.g. Israel, Korea, Sweden).
The eight countries most at risk of sovereign stress in this second heatmap are the same as in the first one, albeit in a slightly different order and except for Mexico replacing Belize. On this latter point, FX general government debt data – sourced from the BIS (see “Data” section below) – is missing for Belize, conferring on it an unfair advantage over Mexico and other countries where data are present for this indicator. In the first heatmap, external debt data is present for both Mexico and Belize, with the latter appearing more at risk than the former.
Each value is divided by the maximal value in that column, resulting in its own empirical percentile. Each value shown is the percent of observations with that value or below it. Sources: IMF, WGI, WB, Bruegel, BIS, author’s calculations.
Interpretation
Focusing on a country case helps illustrate ways to interpret the data in this model. Take Angola, as it appears most at-risk. Using heatmap (1), the brightest and thus most concerning data points are in the institutional quality, REER 3-year change, general government debt 1-year change, and external public and publicly-guaranteed debt columns. This suggests that the government and public sector more broadly are borrowing heavily, while prices and the exchange rate have also combined to rise quickly. Moreover, the institutions to set a good policy framework appear to be lacking. This is already a dangerous mix.
On the other hand, Angola scores well on its current account balance and international reserves variables. This is easily explained by the fact that the country is an oil exporter, thereby keeping its current account balance high and accumulating foreign reserves from the proceeds of these oil sales to buyers abroad.
While these oil exports provide Luanda with ample benefits, heavy reliance on a commodity-based export sector is also a double-edged sword. The result is often an appreciation of the exchange rate, making the economy less competitive for developing other industries: a classic case of Dutch Disease.
More concerning still is the presence of high inflation. The country’s surging REER variable already suggests that prices are probably rising, as the overall increase is unlikely to be due to nominal exchange rate dynamics alone. Increases in government debt suggest potential fiscal profligacy, which can lead to undesirably-high inflation, the presence of which is confirmed by a glance at recent Angolan statistics. The credit-to-GDP gap, which measures the deviation from trend of credit to the non-financial private sector as a share of GDP, is not particularly alarming in Angola, but may be high enough to also be contributing to the rising price level.
Angola also exhibits a high public debt-to-revenue ratio, which is worrying, given all the oil revenues that the country is seemingly raking in, suggesting that less borrowing and more fiscal discipline are likely needed. Recent sovereign stress is also a concern, indicating that, for all its natural resources, the government is unable or unwilling to pursue policies required to maintain macroeconomic stability.
Model
To recap, the model’s dependent variable is the probability of sovereign stress, which the IMF has detailed criteria for defining – running the gamut from outright default to a mere spread widening beyond a certain threshold. Regarding the independent variables:
The first two represent how recently a country has experienced sovereign stress, and its government effectiveness and regulatory quality.
Other explanatory variables are macroeconomic in nature, including current account balances, real effective exchange rates – which also capture price changes, credit gaps to the private sector, and international reserves.
More specifically fiscal indicators include those on general government debt, foreign currency public debt, and public debt-to-revenue ratios.
With the exception of REERs and debt-to-revenue, these macro-fiscal indicators are all expressed as a share of GDP.
A global variable also features in the model, the VIX Index, which measures stock market volatility in the US, but is not presented in the heatmaps above, given its constance across countries.
Data
In the first iteration of the tracker, 2023 data was captured for 43 market-access countries, including both emerging markets-developing economies and advanced economies. Thanks to more available data for this year and refinements in data capture, coverage has been expanded to 82 MACs in these heatmaps.
Two similar heatmaps are presented in this article, with a difference in one of the independent variables and, as a result, slight changes to the overall results in the dependent variable. One of the IMF’s indicators is foreign currency public debt. In the first instance, the World Bank indicator for external public and publicly-guaranteed debt is used as the best available proxy for the IMF’s variable. While using this data from the World Bank remains the best possible option at this stage, there are some glaring omissions in coverage. For instance, the World Bank source suggests that Israel’s external PPG debt is equal to zero, which is clearly incorrect.
As a remedy to the World Bank’s data deficiencies, a second heatmap applies data from the Bank of International Settlements on foreign currency general government debt, as a proxy for this indicator in the same overall model. The BIS data does fill in some of the World Bank gaps – e.g. Israel – but in fact covers fewer countries than the first source. As such, the first heatmap is still preferable.
It is also worth noting that both the World Bank and BIS indicators differ from the IMF variable of foreign currency public debt. In the former case, external public debt differs somewhat from foreign currency public debt, even if virtually all external debt is in foreign currency. In the latter case, foreign currency general government debt excludes some types of debt that is covered under foreign currency public debt.
The World Bank Group-International Monetary Fund Annual Meetings drew to a close in Marrakech this past weekend, the first time these events have been held in Africa since the 1973 edition in Nairobi. While the Bank-Fund leadership expressed their usual endorsement of international cooperation and optimism for the future, this year’s agenda also explicitly aimed to address geopolitical fragmentation and fully acknowledged heightened threats to the goals of eradicating poverty; bolstering sustainable, inclusive growth; and preserving macroeconomic stability.
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The main problem at this year’s annuals wasn’t a new one and goes by many names: geopolitical competition, fragmentation, deglobalization, trade frictions, or decoupling. A whole host of challenges to multilateral financing efforts stem from the political obstacles to international cooperation that have emerged over the past decade, with the 2007-2009 Global Financial Crisis marking the end of America’s “unipolar moment” and ushering in a new, more competitive era. The prospects for a new capital increase for multilateral development banks, innovative hybrid financing solutions to boost World Bank lending, and sovereign debt restructuring processes are all suffering from the fractured backdrop.
IMF Global Policy Agenda
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The IMF’s policy priorities are a response to the main macroeconomic challenges in today’s global economy:
tame inflation
ensure financial stability
restore fiscal room
boost medium-term growth
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Indeed, inflation has not yet reverted to central bank targets in many countries, while the rapid rise in interest rates in the past few years have strained parts of the US banking system. At the same time, expansionary fiscal policies have pushed up yields on government debt in various countries, with the return of bond vigilantes evident in the US in 2023. The prospect of higher fiscal deficits can also sometimes undermine financial stability, as exemplified by the UK mini-budget straining pension schemes in September 2022. Tighter fiscal policy will be necessary in many countries to guard against future shocks, while appropriate reforms are also widely-needed to revive the dimmed outlook of medium-term growth.
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In parallel with the macroeconomic policy priorities, the Fund is pursuing complementary objectives. The IMF launched, with the government of Morocco, the Marrakech Principles for Global Cooperation, which include reinvigorating inclusive and sustainable growth; building resilience; supporting transformational reforms; and strengthening and modernizing global cooperation. These principles are a welcome attempt to stem the tide of global divergences, even if they are unlikely to meet with much success in the short term. In a similar vein, the IMF has attracted more funding for the interest-free Poverty Reduction and Growth Trust and for the climate change-focused Resilience and Sustainability Trust.
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Of note, the IMFC Chair committed to concluding the 16th General Review of quotas by December 2023, in light of agreement on a significant increase of quotas this year. Crucially, there seems to be support for quota realignment by June 2025 to reflect current economic realities, including through an updated quota formula. The IMFC has also called for the creation of a third chair on the IMF Executive Board for Sub-Saharan Africa, in order to improve the continent’s representation.
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Yet the IMF has not been able to deliver more in the way of impactful policy successes. One potentially high-impact policy area would be finding a solution for re-allocating SDR usage from the wealthy countries that don’t need them to the poorer countries that do. A further work-stream with outsized effects would be to do more to strengthen the Global Financial Safety Net, which includes the IMF’s toolkit, bilateral swap arrangements, regional financial arrangements, and international reserves – a tall order in the current environment.
Global Sovereign Debt Roundtable
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The official sector has achieved a modicum of progress on improving the sovereign debt restructuring architecture in recent months. Probably of most importance to private creditors is improved information-sharing during restructurings, with new possibilities for private lenders to access debt sustainability analyses and related elements at the same time as official creditors, under certain conditions. The Fund has highlighted the increasing speed from staff-level approval to Board approval, from 11 months in Chad in 2022, to 9 in Zambia, 6 in Sri Lanka, and 5 in Ghana most recently, while recognizing that this is still above the 2-3 month average in the past.
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The IMF maintains that external public debt strains are not currently as high as they were in the 1990s, even considering the existence of larger local debt markets, which has led to some observers wondering if there is a sense of complacency about pending risks in low-income countries. The IMFC welcomed progress in Zambia, Sri Lanka, and Suriname but called for more results in Ghana, Ethiopia, and Malawi, while also calling for stronger creditor coordination for sovereign debt restructurings occurring outside the Common Framework.
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One of the main pieces of news to come out of the meetings was that Zambia’s finance ministry and its official creditor committee signed a memorandum of understanding, thus formalizing the agreement reached in June, and paving the way for Zambia to seek comparable treatment from its commercial creditors. It was also revealed that Kenya may be seeking exceptional access to IMF support ahead of a $2 billion bond maturing in June 2024.
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There are some other minor new features in the sovereign restructuring framework, regarding cutoff dates (no later than staff-level agreement), state-contingent debt instruments (which shouldn’t be the norm), and the appropriate approaches to domestic debt (case-by-case) and SOE debt. Other areas remain contentious among the various creditor categories, such as appropriate discount rates to be used for NPV calculations for comparability of treatment. There is also no consensus on the treatment of arrears and on debt service suspensions during negotiations.
Show me the money: capital increases for MDBs?
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Despite the ongoing efforts of senior staff to convince donor countries to provide more resources for development, the World Bank Group’s ambitions will continue to lack requisite firepower. The cause is an absence of political will in most of the G7 countries to make sufficient financial commitments to development, as evidenced by a succession of broken Western promises. To be sure, some efforts are under way, such as Japan’s pledge to significantly raise its contribution to the IMF’s zero-interest loan tool, the Poverty Reduction and Growth Trust. For its part, the US may transfer $2 billion in additional funding to the World Bank Group this year, though this is a far cry from the scale that is needed.
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Additional annual financing required to meet the United Nations’ Sustainable Development Goals stands at around $3 trillion. The G20’s Capital Adequacy Review framework suggests that a general capital increase for the multilateral bank system, including the IBRD, could unlock $200 billion in annual lending, with a further $80 billion annually from balance sheet optimization (e.g. callable and hybrid capital). The Center of Global Development suggests that the international development finance system should boost its annual financing by $500 billion by 2030, with multilateral development banks providing $260 billion and national development finance institutions delivering the remainder. Private capital ought to match that half-trillion increase, for a combined public-private total of $1 trillion.
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Yet these figures still fall well short of the additional $3 trillion needed annually. By the CGD’s calculations, each dollar of new equity in MDBs can be leveraged for $15 of external sustainable investment financing, of which $7 in direct MDB lending and $8 in private capital. Assuming that private finance can be crowded in to such a degree is likely overly optimistic, as the CGD’s own figures indicate that MDBs currently mobilize only 60 cents for each dollar lent. Even so, public and private stakeholders will have to come up with financing solutions to achieve the SDGs, and this should be possible with enough political will: just look at the over $100 billion raised for Ukraine.
The World Bank’s Evolution Roadmap
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The World Bank Group’s recently-appointed president, Ajay Banga, has laid out a roadmap to enhance the organization’s effectiveness. More efficient balance sheet management should unleash $157 billion in additional lending over 10 years, while preserving the Bank’s AAA rating. These measures include increasing the loan to equity ratio, launching a hybrid capital instrument, and creating a portfolio guarantee mechanism. Similarly, management is also exploring solutions using callable capital and SDRs. An elegant approach to channeling some of 2021’s SDR 650 billion windfall could be to have the Bank issue SDR bonds, to be purchased by national central banks.
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A number of other changes are in the works under Banga. These include setting up a Global Public Goods Fund to grow concessional resources by attracting funding from governments and philanthropies, exploring maturities of up to 40 years for social and human capital investments, and exploring energy transition solutions. More importantly, efficiency gains are at the heart of the new strategy. There is an objective to slash project review and approval times by a third by simplifying procedures, while partnerships with other MDBs are already being pursued more actively so as to amplify impact. Similarly, Banga’s team plans on scaling knowledge-sharing in order to more easily share impactful solutions, and a private sector investment lab has already been set up to galvanize private financing.
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Banga’s plans to streamline processes seem like a requisite pre-condition for convincing donor countries to increase the Bank’s share capital, though even if his team can deliver, any new equity is far from guaranteed. Early signs of the new president’s first few months in the role have demonstrated his dynamism and communication skills, and future success in reforming the institution’s bureaucracy, while likely challenging to achieve, could yield significant development benefits. However, his team is reportedly difficult to approach internally, which could potentially delay progress.
