Commodity Price Cycles: The Perils of Mismanaging the Boom

Sebastian Sosa; Gustavo Adler

doi:10.5089/9781463926649.001.A001

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Commodity Price Cycles

The Perils of Mismanaging the Boom

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Mr. Sebastian Sosa

Mr. Sebastian Sosa
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Gustavo Adler

Gustavo Adler
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Contributor Notes

Authors’ E-Mail Addresses: gadler@imf.org; ssosa@imf.org

Publication Date:: 01 Dec 2011

eISBN:: 9781463926649

Language:: English

Keywords:: WP; good; price; commodity price; export concentration; commodity price drop; price effect; commodity price behavior; price boom

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Commodity-exporting countries have significantly benefited from the commodity price boom of recent years. At the current juncture, however, uncertain global economic prospects have raised questions about their vulnerability to a sharp fall in commodity prices and the policies that can shield it from such a shock. To address these questions, this paper takes a long term (4 decade) view at emerging markets' commodity dependence, the history of commodity price busts and the role of policies in mitigating or amplifying their economic impact. The paper highlights the stark difference in trends between Latin America - one of the most vulnerable regions given its high, and rising, commodity dependence - and emerging Asia - which has evolved from being a net exporter to a net importer of commodities in the last 40 years. We find evidence, however, that while commodity dependence is an important ingredient, a country's ultimate degree of vulnerability to commodity price shocks is to a great extent determined by the flexibility and quality of its policy framework. Policies in the run-up of sharp terms-of-trade drops - especially when those are preceded by booms - play a particularly important role. Limited exchange rate flexibility, a weak external position, and loose fiscal policy tend to amplify the negative effects of these shocks on domestic output. Financial dollarization also appears to act as a shock "amplifier."

Abstract

I. Introduction

Commodity-exporting emerging markets have significantly benefited from the commodity price boom of recent years. At the current juncture, however, uncertain global economic prospects have raised questions about their vulnerability to a sharp fall in such prices—since these tend to be very sensitive to global activity—and the policies that can shield these countries from such a shock.

This paper answers these questions by examining the behavior of commodity prices and the trends in commodity dependence and export concentration in key emerging-market regions during the last 4 decades, as well as by drawing lessons from the history of economic performance of these countries during episodes of sharp movements in trade prices.² It pays particular attention to the role of macroeconomic policies and fundamentals (in the period preceding the shock) in shaping the economic impact, in terms of domestic output, of subsequent negative terms-of-trade shocks.³ Two complementary approaches are used: a cross-sectional study of episodes of sharp negative terms-of-trade shocks and a panel data approach. In both, the period of analysis is 1970–2010, for a sample of 64 emerging and large commodity-exporting advanced economies.

Unlike other studies—which have focused mostly on the traditional measure of terms of trade (export prices over import prices)—we rely on an adjusted measure that captures the income effect of changes in trade prices, taking into account the initial export and import ratios to GDP (that is, the direct impact of the changes in export and import deflators on the trade balance, given volumes). This measure delivers more robust results than the traditional measure of terms of trade, as it combines the latter with information on the country’s degree of trade openness.⁴

A number of interesting stylized facts arise from the historical perspective:

The recent price boom is remarkable in historical terms, but less so for food prices—which are still significantly below their 1960s–70s levels, after trending downward for several decades.
While commodity prices are sensitive to global output, some (e.g., food prices) are less so, forcing a distinction when assessing vulnerability across exporters of different commodities.
In contrast to previous price cycles, the current one has shown a high comovement of prices, mainly reflecting the dominant role of global demand as a key common driver of price changes.
Despite shifting trade structures in some countries, Latin America is—on average—as dependent on commodities today as 40 years ago. This is clearly the case in South America, while Mexico and Central America have significantly reduced such dependence. At the same time, the region has seen a trend towards more diversified export structures in most cases, with the exception of the large metal and energy exporters.
In contrast, emerging Asia has evolved from being a strong net commodity exporter in 1970 to being a net importer in 2010, while also recording a marked export diversification.

While a country’s degree of reliance on commodities is a key ingredient in determining the economic impact of commodity price shocks, we find little evidence that the induced terms-of-trade shock—even adjusting for the degree of trade openness—can explain, by itself, how countries fare during episodes of sharp trade price busts. This suggests that other factors play an important role in amplifying or mitigating such impact. Indeed, we find evidence that policies in the run-up of sharp terms-of-trade drops—especially when those are preceded by booms—play an important role in shaping the economic impact. Limited exchange rate flexibility, a weak external position, and loose fiscal policy tend to amplify the negative effects of these shocks on domestic output. Financial dollarization also appears to act as a shock “amplifier.” Interestingly, we find that a higher degree of financial integration with the rest of the world helps to buffer the shock when country fundamentals are relatively good, but not necessarily otherwise.

The paper is organized as follows: Section II presents key stylized facts on commodity prices that put the recent boom in historical perspective and provide insights about the idiosyncratic behavior of different commodities and the comovement among them. Section III documents the extent of emerging markets’ commodity dependence, highlighting key differences across regions and some marked shifts in individual countries’ trade structures. Section IV studies the role of policies and fundamentals—particularly during the boom phase of the commodity price cycle—in determining the impact on domestic output of the subsequent negative terms of trade shocks. Section V discusses key conclusions and policy implications.

II. A Historical Perspective on Commodity Prices

A historical view of commodity prices is not straight forward. With high world inflation in the 1970s and 1980s, nominal series can give a distorted picture of their behavior. At the same time, given that commodities are normally priced in U.S. dollars, marked movements in the value of U.S. dollar vis-à-vis other currencies can have a numeraire effect on commodity prices that does not reflect an intrinsic change in the relative price of these goods at a global level. To correct for these two factors, we construct series of prices in real terms that also strip the changes in the value of the U.S. dollar relative to other major currencies.⁵ References to commodity prices in the rest of the paper are always in real terms.

Except for a brief interruption during the 2008–09 global crisis, commodity prices have increased sharply over the last decade, with the IMF broad commodity price index reaching levels similar to those recorded during the price booms in the 1970s (Figure 1). This boom has been remarkable in historical perspective not only for its magnitude, but also because—unlike most previous booms—it has been broad based.

Figure 1.

Commodity Prices in Historical Perspective, 1958–2011¹

(Index in real terms, 2005 = 100)

Citation: IMF Working Papers 2011, 283; 10.5089/9781463926649.001.A001

Sources: IMF, International Financial Statistics; and authors’ calculations.¹ See the main text for an explanantion of how nominal prices are deflated to construct real price series.² Excludes gold and silver.³ Includes cereals, meat and fishery.

Energy and metal prices have tripled (in the latter case from record low levels) since 2003, and current prices are around the historic peaks of the 1970s. Food prices have also increased markedly, although their surge has been less spectacular (around 50 percent since 2003), and has only partly reversed the pronounced downward trend seen for several decades. Indeed, current prices are still about 50 percent below their average level of the 1970s.⁶ The evolution of agricultural raw material prices is similar to that of food prices, though the long-run decline has been less marked.

Interestingly, food prices have also been less volatile and their shocks more persistent than those of metals and energy (Table 1)—although there are also marked differences within the food category, as prices for cereals tend to display relatively higher volatility and lower persistence than those of meat and fishery (to a large extent reflecting weather-related idiosyncratic shocks).

Table 1.

Commodity Price Behavior, 1958–2011¹

Sources: IMF, International Financial Statistics and authors’ calculations.

^¹Based on monthly data.

^²Half-life of a unit shock (HLS) is the length of time (in years) until the impulse response of a shock to prices is half its initial magnitude.

^³Include corn, rice, soybeans, and wheat.

Table 1.

Commodity Price Behavior, 1958–2011¹

		Persistence		Volatility
		Autocorrelation (24 months)	HLS² (years)	Std. dev. (dlog) (percent)
Energy		0.70	9	8.2
Metals		0.70	9	3.9
Agricultural raw materials		0.26	2	3.0
Food		0.80	11	2.9
	Cereals ³	0.58	4	5.8

Sources: IMF, International Financial Statistics and authors’ calculations.

^¹Based on monthly data.

^²Half-life of a unit shock (HLS) is the length of time (in years) until the impulse response of a shock to prices is half its initial magnitude.

^³Include corn, rice, soybeans, and wheat.

Table 1.

Commodity Price Behavior, 1958–2011¹

		Persistence		Volatility
		Autocorrelation (24 months)	HLS² (years)	Std. dev. (dlog) (percent)
Energy		0.70	9	8.2
Metals		0.70	9	3.9
Agricultural raw materials		0.26	2	3.0
Food		0.80	11	2.9
	Cereals ³	0.58	4	5.8

Sources: IMF, International Financial Statistics and authors’ calculations.

^¹Based on monthly data.

^²Half-life of a unit shock (HLS) is the length of time (in years) until the impulse response of a shock to prices is half its initial magnitude.

^³Include corn, rice, soybeans, and wheat.

A look at the comovement across commodity prices suggests that the relationship between them has changed significantly over time, reflecting the varying nature of underlying shocks (Figure 2). Prices across all categories have tended to move in the same direction in the last decade, on account of the dominant role of global demand as a key common driver of price changes. They have also shown pronounced comovement in response to financial shocks, as seen during the 2008–09 crisis and, somewhat less markedly, in the second half of 2011. In previous decades, however—and particularly during past commodity boom-bust cycles—the correlation was lower, even negative in some cases. Clear examples are the first and second oil price shocks in the 1970s and the Gulf War shock in the early 1990s, in which the oil supply shocks triggered a slowdown in global economic activity, negatively affecting the demand for and price of other commodities (Figure A in Annex 1).

Figure 2.

Comovement across Commodity Prices¹

(Groupwise correlation across categories, percent)

Citation: IMF Working Papers 2011, 283; 10.5089/9781463926649.001.A001

Sources: IMF, International Financial Statistics and authors’ calculations.¹ Based on monthly percentage changes of IMF commodity price indexes for energy, metals, and food, and on a statistic for a likelihood ratio test in which, under the null hypothesis, all pairwise correlations are equal to zero (i.e., the correlation matrix is equal to the identity matrix). See Valdes (1997) and Pindyck and Rotemberg (1990) for details.

The importance of common (global) underlying factors in driving prices across different categories of commodities is confirmed by a statistical analysis of principal components (Table 2). In the last decade, the first principal component accounted for almost 85 percent of the variance of commodity prices, and prices of all categories were positively correlated with this underlying common force. This reflects to a great extent the increasing importance of China in global demand for commodities.⁷ In the 1970s and 1980s, in contrast, the first principal component accounted for about 65 percent of the variance of commodity prices, and whereas it was positively correlated with prices of metals and food, the correlation with energy prices was negative.

Table 2.

Global Factors and Commodity Prices: Principal Component Analysis

Source: authors’ calculations.

^¹Loadings of first principal component.

Table 2.

Global Factors and Commodity Prices: Principal Component Analysis

		1970s–80s	2000s
Share of variance explained by first principal component		0.63	0.84
Measure of comovement with common global factor¹
	Food	0.63	0.56
	Energy	-0.36	0.59
	Metals	0.68	0.58

Source: authors’ calculations.

^¹Loadings of first principal component.

Table 2.

Global Factors and Commodity Prices: Principal Component Analysis

		1970s–80s	2000s
Share of variance explained by first principal component		0.63	0.84
Measure of comovement with common global factor¹
	Food	0.63	0.56
	Energy	-0.36	0.59
	Metals	0.68	0.58

Source: authors’ calculations.

^¹Loadings of first principal component.

Finally, despite their high correlation—especially recently—a glance at the behavior of commodity prices during global recessions suggests that there are notable differences across commodities in their sensitivity to the global cycle, with food prices being significantly less sensitive—possibly reflecting higher supply elasticity and lower income elasticity of demand (relative to other commodities). This lower impact was visible during the 2008–09 Great Recession as well as in other slowdowns in the last four decades (Figure 3).⁸ These differences across categories suggest that the degree of vulnerability to a global slowdown may vary significantly even within the group of commodity-exporting countries, depending on the specific commodities countries specialize in.

Figure 3.

Commodity Prices During Global Recessions, 1960–2010¹

Citation: IMF Working Papers 2011, 283; 10.5089/9781463926649.001.A001

Source: authors’ calculations.¹Recessions defined on the basis of estimated output gap for advanced economies (using industrial production series). A slowdown is considered a recession if the output gap reaches at least –1. 5 percent of potential output for at least a quarter. Reported commodity prices are in real terms.²t corresponds to the month of the peak value of the cyclical component of output, before ouput falls below potential. Only the first 30 months are reported as length of recessions varies across cases. Oil shocks of 1969 and 1973 are excluded.

Consistent with global recessions being accompanied by lower commodity prices, net commodity exporters have been particularly affected during those episodes (Figure 4), displaying lower export (values and volumes) and domestic output growth than non-commodity exporters. Quantifying such impact, however, is challenging, as recent recessions have also been, in general, associated with tighter financing conditions for emerging markets (Figure 5), implying a “triple” shock—that is, weaker terms of trade, lower external demand, and tighter global financial conditions—for many commodity-exporting economies. This highlights the intrinsic difficulties of assessing the impact of price shocks, as many of these countries are hit by simultaneous and highly correlated shocks. In section IV we discuss the identification strategy to disentangle the effect of terms of trade shocks.

Figure 4.

Macro Performance of Commodity Exporters and Other Emerging Economies during Global Recessions, 1980–2010 ¹

(Median cumulative growth, peak year = 0)

Citation: IMF Working Papers 2011, 283; 10.5089/9781463926649.001.A001

Source: authors’ calculations.¹Median values are reported. Based on Hodrick-Prescott-filtered estimates of output gap for advanced economies, using industrial production. All recessions display the same pattern, except for the one following the 2008-09 financial crisis (the commodity price slump was short-lived in the latter case).²Year at which estimated output gap closes.

Figure 5.

Financial Conditions During Global Recessions, 1995–2010¹

Citation: IMF Working Papers 2011, 283; 10.5089/9781463926649.001.A001

Source: authors’ calculations.¹ Simple average. Recessions defined on the basis of output gap estimated from advanced markets’ industrial production series. A slowdown is considered a recession if the estimated output gap reaches at least –1. 5 percent of potential output for at least a quarter. (t) corresponds to month of the peak value of the cyclical component of output, before output falls below potential. Only the first 30 months are reported as length of recessions varies across episodes.

III. Commodity Dependence: Latin America VS. Emerging Asia

The degree of a country’s reliance on commodity exports can be gauged with two different measures, that provide distict information:

A measure of commodity dependence, defined as total net exports of these basic products relative to the country’s GDP. This indicator provides information on the potential effect that a commodity price shock would have on domestic output.⁹
A measure of commodity export concentration, defined as gross commodity exports in percent of total exports of good and services. This second measure, that factors in the degree of a country’s trade openness, can inform about the country’s ability to adjust to a given commodity price shock. This potential source of strength has been pointed out by some authors (see, for instance, Calvo and Talvi, 2005), who have stressed the role of the relative size of the tradable sector (vis-à-vis the non-tradable sector) in determining the economy’s ability to adjust to an external shock. In concrete, a larger tradable sector would entail a smaller real exchange rate depreciation in order to restore external sustainability in the event of a negative shock. In this vein, the higher vulnerability due to a growing share of commodity exports in GDP would be mitigated by an even more pronounced increase in exports of other goods.

Commodity Dependence

The degree of commodity dependence, as well as its evolution over time, differs significantly across emerging market regions (Figures 6 and 7, and Figures B, D, and F in Annex 1).

Figure 6.

Commodity Dependence: A Regional and Historical Comparison

Citation: IMF Working Papers 2011, 283; 10.5089/9781463926649.001.A001

Sources: World Integrated Trade Solutions database; and authors’ calculations.¹ Simple average for Argentina, Brazil, Chile, Colombia, Ecuador, Peru, Uruguay, and Venezuela.² Simple average for Mexico, Costa Rica, El Salvador, and Guatemala.³ Simple average for China, India, Indonesia, Korea, Malaysia, Philippines, and Thailand.

Figure 7.

Commodity Dependence and Export Concentration, 2010

Citation: IMF Working Papers 2011, 283; 10.5089/9781463926649.001.A001

Sources: World Integrated Trade Solutions database and authors’ calculations.

South America is the most commodity-dependent subregion, and this feature has become more pronounced over time (net commodity exports represented 10 percent of GDP in 2010, compared with 6 percent in 1970). Although the increase has been broad based, metals and energy still account for the largest shares of net commodity exports.
In contrast, Mexico and Central America have recorded sharp declines in net commodity exports, primarily as a result of falling agriculture exports and increasing energy imports. The subregion was a large commodity exporter in 1970 (8 percent of GDP) and currently shows balanced trade in commodities (still being a net exporter of agriculture goods but now also a net importer of energy).
The trends in emerging Asia greatly differ from those in Latin America, as the former has evolved from being a net commodity exporter (reaching around 6 percent of GDP in 1970) to being a net importer (almost 3 percent of GDP) in 2010. This shift has been mostly due to a sharp decline in exports of raw materials and an increase in imports of energy and metals. Most large emerging economies in Asia are now net importers of energy.
A comparison with some large commodity-exporting advanced economies (Australia, Canada, New Zealand, and Norway) also yields some interesting insights. As in the case of South America, dependence is high and has increased markedly in these advanced economies (from an average of 6 percent of GDP in 1970 to 13 percent in 2010).¹⁰

Commodity Export Concentration

While increasing dependence on commodities (as percent of GDP) has made some countries in South America more vulnerable to negative commodity price shocks, even larger increases in non-commodity exports (trade openess has grown markedly) have led in many cases to a more diversified export structure, arguably making these economies more flexible to withstand such shocks. Export diversification has been even more pronounced in Mexico and Central America, and—especially—in emerging Asia (Figures 6 and 7, and Figures C, E, and G in Annex 1):

Several countries in South America (Argentina, Brazil, Uruguay) have diversified away from commodities, although the latter still account, on average, for 60 percent of total exports of goods and services. Interestingly, this diversification has not taken place in the case of the heavy metal or energy exporters. Indeed, metal (Chile and Peru) and energy exporters (Colombia, Ecuador, and Venezuela) still have higher concentrations of their exports, with metals (energy) accounting, on average, for about 60 percent (80 percent) of total exports. Moreover, these countries exhibit a higher degree of commodity dependence, with commodity exports averaging 20 and 17 percent of GDP, respectively, compared to only 8 percent in other countries in the region.¹¹
In Mexico and Central America, the importance of commodity exports has also halved (from 50 percent of total exports to around 25 percent) between 1970–80 and 2010.¹²
Diversification in emerging Asia has been even starker, with commodity exports falling from about 60 percent of total exports in the 1970s to less than 20 percent in 2010.
Commodity-exporting advanced economies, in turn, have a less diversified export structure than 40 years ago. In fact, after declining slightly for three decades, the share of commodity exports in total exports increased sharply in the last decade for this group of countries, reaching—on average—60 percent in 2010. Interestingly, these countries appear to be more reliant on commodities—based on our dimensions of dependence and export concentration—than the emerging market regions in our sample.

Finally, the changing dependence on commodities in many countries has also been, at least partly, driven by growing commodity imports—reflecting primarily the increasing need for energy in rapidly expanding emerging market economies (Figure 6).

The share of commodities in Latin America’s total imports has increased markedly as a percentage of GDP (although not in terms of total imports) with energy remaining the main category of commodity imports across the region.

Emerging Asia has shown a similar pattern, with the exception of China—where commodity imports have increased both as a fraction of total imports and as a percentage of GDP.

In sum, Latin America remains—on average—as exposed to commodities-related risk as four decades ago, making it vulnerable to a sharp decline in commodity prices. At the same time, higher export diversification (as non-commodity exports have grown even faster) has arguably made many of the countries in this region more flexible to withstand such shocks. This is not the case, though, for energy and metal exporters in the region, which are today particularly vulnerable to a global slowdown, given their higher overall commodity dependence and their greater concentration in commodities—heightened by their exposure to commodities that are more sensitive to the global economic cycle. In contrast, emerging Asia has evolved from being a strong net commodity exporter in 1970 to being a net importer in 2010, while also recording a marked export diversification, turning the region less vulnerable to a negative commodity price shock (from the perspective of a pure price shock).

A country’s ultimate degree of vulnerability is, however, also determined by the flexibility and quality of their policy frameworks. The role of policies in shaping the impact of shocks is examined in the next section.

IV. What Explains Economic Performance in the Face of Terms of Trade Busts?

Although high commodity dependence can make emerging market countries vulnerable to a sharp turnaround in commodity prices, the potential economic impact of such a shock is not obvious. This is partly because the degree of commodity dependence can be mitigated by a diversified export structure (as discussed before) but also—and more importantly—because economic fundamentals and policies can play an important role in mitigating or amplifying the effects of price shocks.

Furthermore, the frequent concurrence of terms of trade and other external shocks (global growth or financial) makes it intrinsically difficult to observe the direct impact of price shocks, unless a multivariate setting is used (enabling the effect of different external factors to be disentangled from the price effect and its interactions with country economic fundamentals).

This section presents two complementary approaches for exploring the determinants of macroeconomic performance during episodes of sharp terms of trade drops—most of the time driven by commodity price shocks—in such a multivariate setting. The aim is to explain whether and to what extent country fundamentals and policies can shape the impact of these foreign shocks. The focus is on large terms of trade changes, as country fundamentals are likely to play a more important role when shocks are sizable (e.g., the flexibility of policy frameworks is more critical when the need for economic adjustment is large).

The first methodology entails a cross-sectional study of episodes of sharp negative terms of trade shocks that took place between 1970 and 2010 in a sample of 64 emerging and large commodity-exporting advanced economies. After documenting the behavior of key macroeconomic variables during these episodes, we explore the role of fundamentals in determining the overall impact of the external price shock on domestic output.

This approach is complemented by a similar exercise in a panel setting, which allows us to explore the importance of certain variables for which reliable data are available only for a shorter and more recent time span (e.g., degree of dollarization, fiscal stance).

Unlike other studies—which have focused mostly on the traditional measure of terms of trade (export prices over import prices)—we rely on an adjusted measure that captures the magnitude of the income effect of changes in trade prices, taking into account the initial export and import ratios to GDP (that is, the direct impact of the changes in export and import deflators on the trade balance, given volumes).¹³ Specifically:

{T O T}_{t}^{A} = \hat{P_{t}^{X}} (\frac{X_{t - 1}}{{G D P}_{t - 1}}) - \hat{P_{t}^{M}} (\frac{M_{t - 1}}{{G D P}_{t - 1}})

where $\hat{P_{t}^{X}}$ and $\overset{}{P_{t}^{M}}$ denote the percentage change in export and import deflators, respectively, and $\frac{X_{t - 1}}{{G D P}_{t - 1}}$ and $\frac{M_{t - 1}}{{G D P}_{t - 1}}$ are the previous-year ratios of exports and imports to GDP.

A. Cross-Sectional Approach

The cross-sectional approach entails assessing whether country fundamentals at the outset of the shock can explain cross-country differences in macroeconomic performance during the full length (i.e., from peak to trough) of episodes of sharp and negative terms of trade shocks. Episodes are identified on the basis of whether a country experienced a cumulative drop in its (adjusted) terms of trade of at least 3 percentage points of GDP, from peak to trough (with negative changes in at least two consecutive years). The threshold is somewhat arbitrary. It is set at a relatively high level to increase the likelihood of identifying an economic impact and at the same time not too high in order to preserve a reasonable sample size. By requiring at least two consecutive years of terms of trade declines, our sample does not include any episode around the 2008–09 global crisis, given that the commodity price drop proved to be short-lived.

This criterion identifies 98 episodes (Figure 8). The sample of countries includes both commodity exporters and importers, in order to disentangle the price effect from the effect of other external shocks that could be highly correlated with commodity prices (e.g., global growth). Terms-of-trade shocks induced by export prices do not appear to have a different impact than those induced by import prices.

Figure 8.

Episodes of Terms of Trade Busts¹

(Time span of each episode)

Citation: IMF Working Papers 2011, 283; 10.5089/9781463926649.001.A001

Source: authors’ calculations.¹ Episodes of negative (adjusted) terms of trade shocks of at least 3 percentage points of GDP, cumulative peak to trough. Lighter blue shading corresponds to episodes in which the terms of trade drop is not accompanied by a drop in real (U.S. CPI-deflated) export prices (i.e., the change in terms of trade comes from import prices).

Interestingly, although there is a prevalence of episodes during the commodity shocks of the 1970s and 1980s, there are still a fair number of episodes dated in the last two decades—although the latter reflect primarily shocks to commodity-importing countries arising from higher import prices, rather than from lower export prices. In most cases, terms of trade shocks have been quite persistent, with an average peak-to-trough time span of 4½ years.

The magnitude of these shocks has been wide ranging, and so has output performance during the episodes—suggesting that, despite being sizable, the price shocks cannot by themselves explain the differences in macroeconomic performance (Figure 9). In fact, only two-thirds of the episodes show negative cumulative growth (relative to the preshock average), and the fraction falls to one-half for the subsample of episodes taking place during the 2000s. This reflects the importance of other external factors in driving economic growth, as the latter period was characterized by favorable global growth and financial conditions.

Figure 9.

Terms of Trade Shocks and GDP Growth

(Cumulative, peak to trough)

Citation: IMF Working Papers 2011, 283; 10.5089/9781463926649.001.A001

Source: authors’ calculations.¹ Cumulative difference with respect to prebust (t–3 to t) growth. t is the last year before the drop in terms of trade.² Cumulative direct effect in adjusted terms of trade, in percent of GDP.

A breakdown by region indicates that, for shocks of similar magnitude, Latin America appears to have been more affected than other regions (Figure 10). Since our measure of terms of trade already factors in the countries’ degree of trade openness, this disparity in economic impact suggests that other economic fundamentals may have played a role in amplifying the impact of these shocks.

Figure 10.

Performance by Region

(Percent, simple averages)

Citation: IMF Working Papers 2011, 283; 10.5089/9781463926649.001.A001

Source: authors’ calculations.¹ Cumulative difference with respect to prebust (t–3 to t–1) growth, in percentage points.² Cumulative direct effect in adjusted terms of trade, in percent of GDP.

A glance at the dynamics of key macro variables around the episodes, comparing best and worst macro performers (in terms of GDP growth) offers some additional insights (Figure 11):

Figure 11.

Key Fundamentals of Best and Worst Performers in Terms-of-Trade Busts¹

(Centered at terms-of-trade peak year, t)

Citation: IMF Working Papers 2011, 283; 10.5089/9781463926649.001.A001

Sources: IMF International Financial Statistics, and authors’ calculations.¹ Statistics of upper and lower halves of the sample of episodes, ranked on the basis of the average GDP growth in [t+1,t+3] relative to the preepisode [t–3, t–1] average.

There is considerable difference between the best and the worst performers, with evidence suggesting that those growing faster before the shock (while external conditions were favorable) suffer the most with the reversal.
Interestingly, these two groups do not appear to have faced significantly different trade prices, either during the boom or the subsequent bust.
As most episodes of price busts were preceded by improving terms of trade, current account balances often strengthened before the negative shock.
However, underlying current accounts (stripped of terms of trade changes) weakened markedly.

Overall, macro performance during these episodes appears to reflect more than simply the effect of the terms of trade, to the extent that countries facing more favorable price dynamics do not appear to have outperformed the rest (Annex 2). These stylized facts reinforce the need for a multivariate setting in order to properly examine the determinants of output performance in the face of large terms of trade shocks.

With this aim, we first estimate a simple regression model to identify the effects of fundamentals on output performance during the episodes, controlling for the size of the shock and other external factors. The specification is as follows:

Y_{i} = α + β_{0} T O T_{i}^{A} + β_{k}^{'} X_{i} + β_{j}^{'} Z_{i} + ε_{i}

Where:

Yi is the output performance in episode i, (measured as the cumulative difference between annual growth during the episode and the average growth rate in the preshock period). The average growth rate is computed over the five-year period up to the shock. We also use the three-year period preceding the shock and the main results do not change significantly.
${T O T}_{i}^{A}$ is the (adjusted) terms of trade cumulative change during episode i;,
X_i, is a vector of variables reflecting fundamentals and policies in the run-up to the shock, and
Z_i, is a vector of controls (external factors).

We focus on the following explanatory variables that—to different degrees—reflect economic policies:¹⁴

The external position, as reflected by the current account, external debt or international reserves (either the level at the time of the shock or the change in the three years preceding the episode).
A measure of “de facto” exchange rate flexibility, using the classification of Ilzetzki, Reinhart, and Rogoff (2008).¹⁵
The occurrence of a credit boom during the three-year period preceding the shock, as identified by either Gourinchas, Landerretche, and Valdes (2001) or Mendoza and Terrones (2008).
We also explore the role of financial openness—which could determine the country’ s ability to obtain foreign funding to buffer the shock—using a measure of capital account openness based on the index constructed by Chinn and Ito (2008), as well as a measure of international financial integration, calculated as the sum of the countries’ total foreign assets and liabilities—in percent of GDP—from the updated and extended version of the Lane and Milesi-Ferretti (2007) data set. The use of two different measures of financial openness allows us to properly interpret the results of the regression, as discussed below.

While we include public debt in the set of explanatory variables, other fiscal and financial sector variables are not included in this approach.¹⁶ In particular, we do not include in the cross-section analysis any measure of the fiscal stance or the cyclical behavior of fiscal policy in the period preceding the episode. This reflects poor data quality and/or coverage for a number of episodes that date back to the 1970s and 1980s. In the case of financial variables (notably, financial dollarization), it is because insufficient variance across episodes in those decades also precludes proper econometric examination. These variables, however, are explored in the panel approach presented in the next section.

External factors used as controls are global demand (proxied by world real GDP growth) and global financial conditions (using the Chicago Board Options Exchange Market Volatility Index, VIX, and the 10-year U.S. Treasury Bond yield).

Following a “specific-to-general” approach, we first regress output performance on each of the fundamentals, controlling for the size of the shock and for external conditions. Then, we include all the relevant fundamentals (and control variables) in a single regression. A negative value in our dependent variable indicates a loss of output, therefore a positive (negative) coefficient on an explanatory variable implies that this variable mitigates (exacerbates) the negative impact of the terms of trade shock on output.

Regressions are estimated using ordinary least squares with robust standard errors. For the sake of brevity, in Table 3 we omit the coefficients of the external controls. Results may underestimate the effect of terms-of-trade shocks to the extent that some price movements arise from idiosyncratic shocks in countries with a large share in global supply of commodities (e.g., an unexpected increase in production of copper in Chile leading to a fall in international copper prices and, thus, on Chile’s terms of trade).

Table 3.

Cross-Sectional Approach: Results¹

Source: authors’ calculations.

^¹Regressions estimated using ordinary least squares with robust standard errors (in parentheses). Regressions include controls for global factors (coefficients not shown).

^²Cumulative change in the adjusted terms of trade measure, from peak to trough.

^³Current account balance, percent of GDP, in period t or change from t–3 to t, as indicated.

^⁴Exchange rate flexibility, based on Ilzetzki, Reinhart, and Rogoff (2008) de facto index, at t. Dummy = 1 if index is between 1 and 4 (fixed), 0 if between 4 and 13 (flexible).

^⁵Standard deviation of the monthly percentage changes of the nominal exchange rate (over a 12-month window).

^⁶Chinn and Ito (2008) capital account openness index, average t–2 to t.

^⁷Measure of financial integration, defined as the sum of total external assets and total external liabilities (in percent of GDP).

^⁸Cumulative change in the adjusted terms of trade measure, in the three-year period before the shock.

^*** p<0.01, ^** p<0.05, ^* p<0.1, + p<0.15.

Table 3.

Cross-Sectional Approach: Results¹

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)
Dependent variable: Cumulative output growth during the bust (Y_i), relative to trend growth
ToT ^{A 2}	0.264	0.284^*	0.205	-0.113	0.160	0.092	0.012	0.159	0.020	0.135	0.096
	(0.23)	(0.17)	(0.20)	(0.19)	(0.15)	(0.26)	(0.27)	(0.23)	(0.22)	(0.199)	(0.200)
CA (percent of GDP, level in t)³	0.568^*					0.615⁺	0.648^*			-0.105	-0.101
	(0.29)					(0.42)	(0.37)			(0.37)	(0.36)
CA_2 (percent of GDP, change t-3, t) ³		0.503^**						0.557^*	0.456^**
		(0.20)						(0.30)	(0.23)
ER Regime ⁴			-10.59^**			-9.084^*		-10.50^**		-9.589^*	-13.54^***
			(4.43)			(4.57)		(4.74)		(4.80)	(4.59)
FXVol ⁵				0.946^***			0.707^**		0.701^**
				(0.32)			(0.34)		(0.32)
KA openness ⁶					-2.030^*	-1.755	-2.036	-1.567	-1.991	-2.339⁺
					(1.21)	(1.50)	(1.50)	(1.58)	(1.45)	(1.45)
Fin. Integ. ⁷											0.071^***
											(0.02)
CA × ToT^A Prev ⁸										0.222^***	0.133^**
										(0.08)	(0.06)
ER Regime × ToT^A Prev ⁸										-0.277	-0.209
										(0.51)	(0.48)
ToT^A Prev ⁸										0.129	0.191
										(0.48)	(0.44)
Constant	-1.27	-3.43	0.62	-7.70^**	-3.86	-0.29	-5.68^**	-1.35	-7.57^***	-0.86	-6.60
	(2.75)	(2.34)	(2.70)	(2.97)	(2.40)	(3.19)	(2.83)	(2.67)	(2.78)	(3.25)	(4.36)
Number of observations	91	93	79	68	92	70	64	73	66	70	70
R ²	0.103	0.114	0.131	0.119	0.102	0.207	0.213	0.211	0.206	0.305	0.332