Chapter

Chapter 3. After the Boom: Commodity Prices and Economic Growth in Latin America and the Caribbean

Author(s):
Dora Iakova, Luis Cubeddu, Gustavo Adler, and Sebastian Sosa
Published Date:
December 2014
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Author(s)
Bertrand Gruss

Following a decade of rapid, broad-based gains, international commodity prices peaked in mid-2011 and have fallen somewhat since then (Figure 3.1). Many analysts now argue that the upward phase of the commodity “super cycle” that started in the early 2000s has run its course.1 Indeed, market futures show commodity prices softening further in the near term, reflecting an anticipated increase in commodity supply along with weaker demand from some of the major commodity-importing economies, notably China.2 While these projections are subject to large uncertainty, there is nonetheless wide consensus that the period of ever-increasing commodity prices has come to an end.

Figure 3.1Commodity Price Indices, 2000–19

(In U.S. dollars; 2005 = 100)

Sources: IMF, World Economic Outlook database; and author’s calculations.

Note: Based on international prices in current U.S. dollars. Values in shaded areas correspond to projections based on the prices of commodity futures prevailing at end-February 2014.

What would this imply for the commodity exporters of Latin America and the Caribbean (LAC)? The region is highly dependent on commodities and has certainly benefited from the recent commodity boom.3 Average annual output growth in the region increased from less than 2.5 percent between 1980 and 2002 to more than 4 percent in 2003–11 (Figure 3.2). More recently, however, growth has decelerated considerably. Average output growth fell from 4.6 percent in 2011 to 3.1 percent in 2012 and 2.7 percent in 2013. Some observers claim that the recent economic slowdown across the region is primarily linked to the end of the upswing in commodity prices, raising obvious concerns for the future (Roubini, 2013). Others have downplayed these concerns, pointing out that commodity prices are still higher than in the mid-2000s, let alone in the 1990s (Figure 3.1).

Figure 3.2Latin America and the Caribbean: Real GDP Growth

(Percent)

Sources: IMF, World Economic Outlook (WEO) database.

Note: Purchasing power parity GDP-weighted averages of all LAC countries. GDP growth in 2014 corresponds to April 2014 WEO projections.

This chapter explores the possible consequences of weaker commodity prices on economic growth in LAC over the medium term (defined here as the period 2014–19). The chapter starts by constructing a country-specific commodity price index aimed at capturing the impact of variations in commodity prices at the country level. Analysis of the recent commodity price boom finds that, for most LAC commodity exporters, the period does indeed stand out in a historical perspective. The chapter then uses prices of commodity futures to characterize the country-specific commodity price outlook for the medium term. For most countries in the region, the outlook is for a sharp decline in the growth rates of their country-specific commodity price indices. The level of these indices is nonetheless projected to remain well above the averages attained during the boom years.

The chapter then investigates whether it is the lower growth of commodity prices or their still-high levels that will matter most for output growth in the region in the coming years. To this end, the chapter uses a variant of the dynamic multi-country global vector autoregression (GVAR) model originally proposed by Pesaran, Schuermann, and Weiner (2004), an approach especially designed to model the interactions between many countries. In a first stage, individual vector error correction models are estimated for a large set of countries. These country-specific models are linked to each other by including foreign variables and, in the second stage, stacked into a global model, so that national and global variables are determined jointly. The model specification considers 13 LAC countries, including the 12 largest commodity exporters in the region, and includes a broad set of global variables related to commodity prices. The model is estimated using annual data starting in 1970 and ending in 2013, and is used to generate conditional out-of-sample GDP growth forecasts under different commodity price scenarios over 2014–19. The model is also used to assess the potential impact of slower-than-expected growth in China on commodity prices and output growth in LAC.

The quantitative exercise suggests that the end of the commodity price boom will imply a significant drag on growth for the commodity exporters of LAC. Even if prices were to remain stable at the relatively high levels attained in 2013, the annual average output growth rate over the medium term (2014–19) would be almost 1 percentage points lower than in 2012–13, and more than 1½ percentage points lower than during the boom years. Projected growth is even somewhat weaker when conditioning on the path for spot commodity prices suggested by futures prevailing at end-February 2014. The simulations also confirm that a slowdown in China’s growth represents a key downside risk for LAC commodity-exporting countries.

The work in this chapter is mainly related to two strands of the literature. The first strand includes several studies that analyze the macroeconomic effects of terms-of-trade disturbances. Some recent examples include Ahmed (2003), Broda (2004), Raddatz (2007) and Izquierdo, Romero, and Talvi (2008). A particularly close link exists with studies focused on the macroeconomic effects of shocks to commodity prices (De Gregorio and Labbé, 2011; Céspedes and Velasco, 2012) and on the link between commodity price fluctuations and economic growth (Deaton and Miller, 1996; Dehn, 2000; Collier and Goderis, 2012; Cavalcanti, Mohaddes, and Raissi, 2014).

The second strand to which this study is related is a growing literature using the GVAR framework to address a variety of issues, including forecasting economic variables for a large number of countries in the global economy (Pesaran, Schuermann and Smith, 2009) and analyzing the transmission of the international business cycle to Latin America (Cesa-Bianchi and others, 2011).4 To our knowledge, this is the first application of the GVAR methodology to analyze the link between commodity prices and growth. While most applications in the GVAR literature include one global variable (for example, oil prices), typically modeled as endogenous in the U.S. model, the application used here includes 14 global variables, including 13 country-specific commodity price indices, that are ultimately related to 33 underlying international commodity prices. These commodity price variables are not modeled as endogenous in any particular country model but in auxiliary non-country models within the GVAR. The model is extended to cover a large number of commodity exporters (especially in LAC, but also in other regions). Overall, the model covers 30 economies, accounting for more than 80 percent of world GDP.

Characterizing the Commodity Boom and Its Aftermath in Latin America and the Caribbean

While the increase in commodity prices during the recent boom was quite generalized, the magnitude of the increase differed considerably across categories (Figure 3.1): oil prices in current U.S. dollars almost quadrupled between 2003 and 2013 and metal prices tripled, while food prices doubled and prices of agricultural products rose “only” by about 50 percent. Before analyzing the linkages between commodity prices and economic growth in LAC countries, a metric is needed that captures the effects of commodity price variations at the country level. These, in turn, depend on the specific mix of commodities that the countries export and import. This section describes the country-specific net commodity price index (NCPI) used in this chapter, documents how it evolved during the recent boom for individual LAC countries, and uses commodity futures to infer how the NCPIs might evolve over the medium term.

A Country-Specific Net Commodity Price Index

With a few exceptions (for example, Deaton and Miller, 1996; Dehn, 2000; Céspedes and Velasco, 2012), most studies on the macroeconomic effects of commodity-price fluctuations have used either prices of individual commodities, aggregate (that is, not country-specific) indices of commodity prices, or standard terms-of-trade measures. None of these alternatives, however, is particularly suited for the purposes here. First, few commodity exporters are so specialized that focusing on just one commodity price is enough (except maybe for the case of some oil producers). Second, there tends to be substantial heterogeneity in price variations within aggregate commodity categories, so even if a country specializes in commodities that mostly belong to one given category (for example, metals), an aggregate price index is likely to poorly track the price variations of the specific commodities it trades. Third, broad terms-of-trade measures capture noncommodity price influences and are affected by the composition of exports (Deaton and Miller, 1996). Moreover, international commodity prices have been shown to be better at capturing the exogenous component of terms-of-trade shocks for commodity exporters than standard measures (Chen and Rogoff, 2003).

The country-specific commodity export price index proposed by Deaton and Miller (1996), which combines international prices and country-level data on export volumes for individual commodities, provides a better alternative.5 However, shocks to the price of imported commodities are also likely to matter for growth. For instance, an increase in the price of imported commodities (such as oil or primary intermediate inputs) is likely to reduce profit margins for firms and disposable income for households, weighing on domestic demand and output growth. In order to capture the net income effects from changes in commodity prices, the weights in the net index are based on net exports of each commodity.6 Accordingly, a commodity price increase that would imply a positive (negative) income shock if the country is a net exporter (net importer) of that commodity would be captured by an increase (decrease) of its NCPI.

To illustrate that looking at net commodity exports can make a difference in some cases, Figure 3.3 compares the NCPI with an export-based commodity price index for Colombia and Uruguay during 2000–13. For Colombia, both indices track each other very closely. In the case of Uruguay, however, the export-based index shows a gain of more than 20 percent over 2003–11, while the NCPI decreased by about 15 percent over the same period, mainly reflecting Uruguay’s high reliance on crude imports.7

Figure 3.3Comparison Between the Net Commodity Price Index and an Export-Based Index

(2012 = 100)

Sources: UN Comtrade database; IMF, World Economic Outlook database; World Bank, Global Economic Monitor; and author’s calculations.

Note: The export-based commodity price index (XCPI) is computed by replacing τi,j.t in equation (3.2) with τi,j,tx=Xi,j,t1/Σj=1jXi,j,t1. NCPI = net commodity price index. COL = Colombia; URY = Uruguay.

Deaton and Miller (1996) argue in favor of using fixed weights to construct the price index so as to ensure that endogenous supply responses to price changes do not affect the analysis. But the commodity mix traded by many countries has changed significantly over the last four decades (see Table A3.1 in Annex 3.1). For instance, coffee accounted for more than 40 percent of Brazil’s commodity exports in the early 1970s, but its share was less than 6 percent toward the end of the 2000s, falling from the first to the fifth position in the country’s commodity exports. Accordingly, the net income effect for Brazil from an increase in the international price of coffee is much lower now than what it was in the 1970s. Similarly, none of the top three commodities that represented 70 percent of Argentina’s exports in 1970–72 were among its top three commodity exports in 2010–12. In order to take this into account, the weights used in the index are allowed to vary over time. They are based on three-year rolling averages of trade values (to smooth fluctuations) and lagged one year (so that changes in the price index reflect variations in commodity prices rather than endogenous changes in volumes).

Taking these considerations into account, the annual change in country i’s NCPI is given by:

where Pj,t is the logarithm of the relative price of commodity j at time t (in U.S. dollars and divided by the IMF’s unit value index for manufactured exports);8 and Δ denotes first differences.9 Country i’s weights for each commodity price (τi,j,t) are given by:

where xi,j,t−1 (mi,j,t-1) denote the average exports (imports) value of commodity j by country i between t – 1 and t – 3 (in U.S. dollars).10,11

The Mid-2000S Commodity Boom

The average annual NCPI growth rate across the 12 largest commodity exporters in LAC turned positive in 2003, reached double digits in 2004, and remained positive and large until 2011, with the exception of 2009.12 The sustained increases in NCPIs along these years also stand out in a historical perspective (Figure 3.4). Given this, 2003–11 is referred to here as a “commodity boom” period for LAC.

Figure 3.4Net Commodity Price Indices of Commodity Exporters in Latin America and the Caribbean, 1970–2013.

(Average annual growth of NCPI across LAC countries; percent)

Sources: UN Comtrade database; IMF, World Economic Outlook database; World Bank, Global Economic Monitor; and author’s calculations.

Note: Simple average of net commodity price index (NCPI) annual growth rate of Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Honduras, Paraguay, Peru, Trinidad and Tobago, Uruguay, and Venezuela.

During the recent commodity boom, NCPIs in the region grew on average by 5½ percent per year (Figure 3.5). This increase is similar to that recorded by commodity exporters of other regions, such as Australia and Indonesia. But there are important differences across countries. For instance, Uruguay did not experience NCPI gains during this period. Other countries that also export mainly food commodities, such as Honduras and Paraguay, experienced a lower-than-average NCPI growth rate of about 3½–4 percent. On the other end, Bolivia, Ecuador, Colombia, and Chile experienced average annual increases in their NCPIs of more than 6 percent and, in the case of Venezuela, of close to 11 percent per year (similar to pure oil producers outside the region).

Figure 3.5Commodity Price Growth, 2003–11

(Average annual growth of net commodity price index; percent)

Sources: UN Comtrade database; IMF, World Economic Outlook database; World Bank, Global Economic Monitor; and author’s calculations.

The commodity price boom of the last decade is considered to have been unprecedented in its magnitude and duration (Erten and Ocampo, 2013b). But was it also unprecedented from the perspective of LAC commodity exporters? Comparing the average NCPI growth rate in 2003–11 (5½ percent per year), with its long-term average (−1.4 percent over 1970–2002) suggests that this is the case (Figure 3.4). Another way to see this is to compare the increase in NCPIs during 2003–11 with comparable periods since 1970.13 The left panel of Figure 3.6 shows the distribution of average NCPI growth rates over rolling nine-year windows for these commodity exporters. In all cases except Uruguay, the average annual NCPI growth rate during the recent boom was above the eighth decile of the distribution. Moreover, in many cases average NCPI growth during 2003–11 was at, or very close to, the sample maximum. By contrast, average NCPI levels observed during the last decade do not typically stand out in a historical perspective, except for Chile and Venezuela (Figure 3.6, right panel). In fact, in some countries (such as Honduras and Uruguay) the average NCPI level during 2003–11 is close to the sample minimum.14

Figure 3.6Commodity Price Growth and Level, 1970–2013

Sources: UN Comtrade database; IMF, World Economic Outlook database; World Bank, Global Economic Monitor; and author’s calculations.

Note: The black lines denote the range for the nine-year window averages of net commodity price index annual growth rates and level; the rectangle denotes the second through eighth deciles of its distribution; the marker denotes the average values in 2003–11.

Is the Commodity Boom Over for Latin America and the Caribbean?

Commodity prices seem to have passed their peaks within the current “super cycle” (Figure 3.2). Nominal fuel prices peaked in mid-2008, while the prices of metals, food, and agricultural raw materials peaked sometime in the first half of 2011. In the last quarter of 2013, in fact, 46 of 51 international commodity prices were more than 10 percent below the maximum values attained between 2000 and 2013—and eight of them were more than 50 percent below their peaks.

Going forward, most forecasts suggest that overall commodity prices will soften somewhat over the medium term. For instance, the prices of commodity futures prevailing at end-February 2014 suggested that the spot prices of fuel, food, and metals in 2019 will be 15, 12, and 6 percent lower, respectively, than in 2013, although they would still be 30, 23, and 17 percent higher than their average prices over 2003–11.

What does this general price outlook imply for the commodity exporters of LAC? Figure 3.7 shows the average projected NCPI growth rates over the medium term based on the current prices of commodity futures.15 The current market-based outlook for 2014–19 projects a sharp decline in NCPI growth rates across LAC, with an annual growth rate (averaged over time and across countries) about 6½ percentage points lower than during the commodity boom—and actually negative for most countries.

Figure 3.7Commodity Price Outlook, 2014–19

(Average annual growth of net commodity price index; percent)

Sources: UN Comtrade database; IMF, World Economic Outlook database; World Bank, Global Economic Monitor; and author’s calculations.

Note: Net commodity price indices (NCPIs) for 2014–19 are constructed from prices of commodity futures prevailing at end-February 2014.

1 Percentage difference between average NCPI levels in 2014-19 versus 2003-11.

Nevertheless, average NCPI levels during 2014–19 would remain about 13 percent higher than during the boom years according to commodity futures (Figure 3.7). Even by the end of the forecast horizon in 2019, the projected NCPIs in LAC countries are, on average, about 10 percent higher than the 2003–11 average, and more than 30 percent higher than in the 1990s.

There is considerable uncertainty surrounding commodity price projections, and the ability of futures to forecast commodity spot prices has often been questioned.16 Still, taking these market-based price projections as a benchmark, the question in terms of the impulse to economic growth seems to boil down to what will predominate. Will it be the potential positive effect from the still relatively high levels of commodity prices, or the negative effect from the sharp deceleration in price growth? The sections that follow address this question.

Growth in Latin America and the Caribbean After the Commodity Boom

Before examining the evidence, it is useful to briefly review the potential links between commodity prices and growth. Consider a commodity exporter that is growing at its steady-state rate and suddenly faces a positive commodity price shock that is expected to persist. The higher income resulting from the improved terms of trade would boost demand for consumption, supporting domestic output (along with an increase in imports). This positive cyclical impulse would be reinforced by the rise of investment in the commodity sector in response to improved profitability, as well as in sectors that face higher demand from the commodity sectors (for example, transportation, logistics, and so on). Higher investment, in turn, would expand the productive capacity of the economy. Thus, both potential and actual output would grow faster than in the absence of the commodity price shock.

This effect, however, will be temporary. Once investment and consumption have adjusted to the new commodity price outlook after the price shock, output growth would revert to its pre-shock level—unless the new investment leads to permanently higher productivity growth. Of course, the distinction between the shock period and the subsequent one can be diffuse in practice (due, for instance, to investment gestation lags). And other effects could play an important role in shaping the dynamics (for example, an intensification of capital inflows triggered by the commodity price shock, an appreciation of the real exchange rate, alternative policy responses to these phenomena, and so on). But the outlined mechanism would suggest that moving from a period of ever-increasing commodity prices to a period of still-high but nongrowing prices entails a deceleration of output growth.

What does the empirical evidence say about the link between commodity prices and growth in LAC? Before moving to a more formal framework, simple evidence is reported from the unconditional bivariate correlations between NCPI levels and NCPI growth rates on one side, and output growth on the other, for a sample of commodity exporters in LAC. The data in the left panel of Figure 3.8 do not point to any significant relationship between NCPI levels and output growth in LAC, at least since the 1970s. By contrast, the right panel suggests there may have been a positive relationship between the growth in NCPIs and output growth in these countries. We checked whether this relationship is weaker after the mid-1990s, when many countries in LAC started improving their policy frameworks (which could have lessened the link between commodity prices and economic activity). But this is not the case. In fact, the relationship is slightly stronger in the subsample starting in the mid-1990s.

Figure 3.8Latin America and the Caribbean: Commodity Prices and GDP Growth

(Deviation from sample average; percent)

Sources: UN Comtrade database; IMF, World Economic Outlook database; World Bank, Global Economic Monitor; and author’s calculations.

Note: Net commodity price index (NCPI) growth rates, NCPI levels, and GDP growth rates correspond to the average over three-year windows and are reported as deviations from their country-specific sample averages. NCPIs are adjusted by the share of commodity trade in GDP in order to identify the actual economic impact of commodity prices on output in a cross-country comparison (see footnote 11).

This simple pattern provides a prima facie indication that nongrowing commodity prices could be a drag on growth in LAC in the next few years, even if they were to remain steady at their current high levels. But this simple analysis does not control for other factors that might have affected output growth (for example, demand from trading partners, variations in external financing, and so on). Moreover, even if we take this evidence as suggesting that nongrowing commodity prices could be a drag on growth, a more relevant question is: how much of a drag? The next sections turn to a multivariate framework to investigate the underlying relationships and obtain quantitative predictions for concrete commodity price scenarios.

A Global Vector Autoregression Model for Commodity Prices and Output Growth

The multivariate analysis of the relationship between commodity prices and output growth is based on a variant of the GVAR model proposed by Pesaran, Schuermann, and Weiner (2004) and further developed by Dees and others (2007). The GVAR modeling strategy involves two main steps. In the first step, each country/region is modeled as a small open economy by estimating a country-specific augmented vector autoregressive model (VARX*) in which domestic variables are related to country-specific foreign variables (constructed as the cross-section averages of the domestic variables of the other economies) and global variables, both assumed to be weakly exogenous at this stage. In a second step, the estimated country-specific models are combined into a global model and linked consistently using a matrix of predetermined (that is, nonestimated) cross-country linkages.

Given that the main objective is to generate out-of-sample forecasts, conditioning on specific paths for commodity prices, this coherent framework for modeling international linkages has many advantages. First, the country-specific models allow for the possibility of cointegration and hence long-term relationships between domestic variables, and between domestic and foreign variables (including the commodity price variables). This is particularly suitable to capture the effects of both commodity price levels and commodity price changes on output growth—which is particularly relevant given the current commodity outlook. At the same time, by modeling these relationships at the country level, the model can cope with idiosyncratic differences across countries related, for instance, to the structure of the commodity sector (private versus public property, domestic versus foreign shareholders, and so on).

Second, combining the individual models into a global model ensures that key cross-country interdependencies (owing to observed and unobserved common factors, but also to trade and policy spillover effects) and general equilibrium dynamics are taken into account. It also implies that predictions for domestic and foreign variables are simultaneously determined. This ensures that the forecast exercise takes into account not only the direct effects from conditioning on a given commodity price scenario (through the implications it entails for the terms of trade), but also the indirect effects from the outcomes of such a scenario on output growth in other economies, exchange rates, capital flows to the region, and so on.

Model Specification

The version of the GVAR model developed here covers 30 economies, five of which (France, Germany, Italy, Spain, and the United Kingdom) are modeled as a group.17 The other 25 economies include 13 LAC countries, covering the 12 largest commodity exporters (Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Honduras, Paraguay, Peru, Trinidad and Tobago, Uruguay, and Venezuela) and Mexico; other commodity exporters outside the region (Australia, Indonesia, Iran, Nigeria, Norway, Qatar, and Saudi Arabia); and other large economies (Canada, China, India, Japan, and the United States). Altogether these economies account for more than 80 percent of world GDP.

In order to capture as many commodity cycles as possible, the model is estimated using annual data over the period from 1970 to 2013. This, however, conditions data availability and limits the number of observations, making it necessary to keep the country models as parsimonious as possible. All models include real GDP (yit), which is the main focus in the simulation exercises. The extent to which an income shock from rising commodity prices affects domestic output would depend, among other things, on how much of the bonanza is used to accumulate foreign assets or reduce external debt. It would also depend on how the commodity price shock affects relative prices. With this in mind, the country models also include the current-account-to-trend-GDP ratio (cait) to proxy for changes in net foreign assets excluding valuation effects, and the real exchange rate, defined as the nominal exchange rate in terms of U.S. dollars deflated by domestic consumer prices (eit - pit, as in Pesaran, Schuermann, and Weiner, 2004, and Dees and others, 2007).18 (See Table 3.1 for a summary of the model specifications and Annex 3.1 for data sources.)

Table 3.1Specification of the Country-Specific VARX* Models
Models for Commodity Exporters (excluding Pure Oil Producers)Models for Pure Oil Producers and Noncommodity ExportersU.S. Model
DomesticForeignDomesticForeignDomesticForeign
yityit*yityit*yityit*
eit - piteit - piteUSt*pUSt*
caitcaitcait
NCPIit
poiltpoilt
Source: Author’s estimates.
Source: Author’s estimates.

All country models include foreign real GDP as a country-specific foreign variable (yit*). As is common in the GVAR literature, the set of real exchange rates constitutes a closed system and therefore this variable is included as a country-specific foreign variable in the U.S. model and as an endogenous variable in all the other country models (Pesaran, Schuermann, and Weiner, 2004).

The weights used to construct the country-specific foreign variables (yit*) are derived from bilateral trade data. Given that trade linkages have varied considerably over the sample period, we use rolling three-year moving averages of annual trade shares to compute these weights.19 The weights used to link the country-specific models within the GVAR are based on the trade shares at the end of the sample (averages over 2010–12).

Most of the GVAR literature uses one global variable, typically the price of oil.20 Given the interest here in capturing the impulse to growth from commodity price cycles, we augmented the model significantly along this dimension, including 14 global variables related to commodity prices. More precisely, we include a country-specific commodity price index (NCPIit) as a global variable in the models for nonpure-oil commodity exporters (Argentina, Australia, Bolivia, Brazil, Chile, Colombia, Ecuador, Honduras, Indonesia, Peru, Paraguay, Trinidad and Tobago, and Uruguay).21 The models for pure oil exporters (Iran, Nigeria, Norway, Qatar, Saudi Arabia, and Venezuela) and for noncommodity exporters (all the remaining countries in the model), instead, include real oil prices (poilt) as a global variable.

At the end of the day, all variables are endogenous to the global model. The common approach in the literature has been to model the global variable (for example, oil prices) as endogenous in the model of the United States.22 With 14 global variables, however, including them in the U.S. model is not an option given the number of observations. Moreover, while the United States is a large consumer of oil, it is not the main consumer of many of the other 32 commodities used in the construction of the NCPIs—in fact, it is the largest world exporter of some of them, such as corn and wheat. Instead, we model the global commodity price variables in three auxiliary VARX* models (labeled Model A, B, and C in Table 3.2).23 The “domestic” variables in these models include the 13 NCPI series and the (real) oil price series (in Model A). The foreign variables in these models are the real output of the economies in the model weighted by their share in global trade (yAt*, which is the same for all three additional models), and the real price of oil (poilt) in two of these three auxiliary models (Models B and C).

Table 3.2Specification of the Commodity-Price VARX* Models
Model AModel BModel C
DomesticForeignDomesticForeignDomesticForeign
NCPIBOL,tNCPIAUS,tNCPIARG,t
NCPICOL,tNCPIBRA,tNCPIHND,t
NCPIECU,tNCPICHL,tNCPIPRY,t
NCPIIDN,tNCPIPER,tNCPIURY,t
NCPITTO,t
poiltpoiltpoilt
yAt*yAt*yAt*
Source: Author’s estimates.
Source: Author’s estimates.

The lag orders pi and qi of the VARX* models were selected on the base of the Schwartz Bayesian criterion. All models are either (2,1) or (1,1). We first used Johansen’s trace and maximum eigenvalue statistics to select the number of co-integrating relations, but then reduced the number of relations for a number of models to ensure the stability of the GVAR (similar to Dees and others 2007 and Cesa-Bianchi and others, 2011). The final specification has one cointegration relation in each model.

Overall, and despite data limitations given the broad nature of the sample, the validity of the assumptions made to specify the GVAR is supported by a number of specification tests.24 For instance, the weak exogeneity assumption can only be rejected for one out of the 58 country-specific foreign variables and global variables (and this could be by chance, as one would expect about three tests to fail using a 5 percent significance level, even if the hypothesis were valid in all cases). Importantly, the weak exogeneity assumption is not rejected for any of the NCPI variables, or for the foreign variables in the models of large economies such as the United States or China.

Assessing the Goodness of Fit of the Model

In order to get a sense of the adequacy of the model for purposes here (that is, to obtain out-of-sample growth projections under alternative commodity price scenarios), we compared the projections in the IMF’s World Economic Outlook (WEO) with the model’s unconditional out-of-sample forecasts for output growth.

Toward this end, we first computed the root mean squared error between the model unconditional forecast for each country and forecast horizon, from one to six years ahead, and the corresponding WEO projection for all six vintages between 2003 and 2008. The set of differences between the model and WEO projections was computed in the following way. The model was first estimated with data up to 2002 and its unconditional output growth forecasts for each country for 2003 through 2008 were compared with the April 2003 WEO growth projections for those years. Next, the model was estimated with data up to 2003 and its output growth forecasts were compared with WEO projections from the April 2004 vintage for 2004 through 2009. The process was repeated for all remaining vintages up to that of April 2008. The root mean squared error considering all countries, forecast horizons, and vintages was 2.9 percent, which is reasonably small, especially given that we are considering forecast horizons of up to six years ahead.

Second, we computed the root mean squared error with respect to actual data both for the model and for the WEO projections. As expected, the root mean squared error of the WEO projections was lower than the one of the model, but only by 0.8 percentage points. Again, this difference is quite small considering the simplicity of the underlying country models.

Results from the Global Vector Autoregression Model

This section reports the results from the two main exercises conducted with the GVAR model: producing conditional output growth forecasts for the commodity exporters of LAC over the medium term, and simulating the response of commodity prices and GDP growth in LAC to a potential shock to China’s GDP.25

Conditional Growth Forecasts—Scenarios

The model is used to generate forecasts for output growth over 2014–19 conditioning on certain assumed paths for the country-specific NCPIs and oil prices.26 These paths, in turn, correspond to three alternative scenarios for individual commodity prices. The first scenario simply assumes that commodity prices in current U.S. dollars remain constant over 2014–19 at their 2013 average levels. This simple scenario, labeled stable prices, is a key benchmark aimed at answering what could happen to economic growth in LAC if commodity prices were to remain high but stop increasing.

However, even if a scenario of stable prices is deemed to be likely, assuming constant prices for each individual commodity may be a stretch because it ignores important existing information regarding the different commodity markets. In particular, it may ignore plausible relative price variations over the medium term associated with developments that are already known (for example, the maturing of investment projects that would increase commodity supply). To take this into account, the second scenario (called futures) assumes that commodity prices evolve in line with the market price of commodity futures (prevailing at end-February 2014).27 As shown in Figure 3.1, commodity futures suggest that spot prices for broad commodity aggregates will remain stable or decrease moderately over the coming years.

The third and final scenario (called adverse) preserves the relative price variations implied by the futures scenario but assumes lower price growth, such that all commodity prices under the adverse scenario are 10 percent below those implied by the futures scenario by the end of the forecast horizon.

Figure 3.9 shows the average growth rate of the country-specific NCPIs and the (real) oil price assumed under the three alternative commodity price scenarios. It also reports their average growth rates over 2003–11 and, as a reference, the model’s unconditional NCPI growth forecasts for 2014–19. It is worth noting that in most cases the unconditional forecasts for the NCPIs imply relatively stable prices over the medium term and, in all cases except Uruguay, a sharp deceleration compared to the boom years.

Figure 3.9Projected Growth of Net Commodity Price Indices and Oil Prices under Alternative Price Scenarios, 2014–19

(Average annual growth rate; percent)

Sources: UN Comtrade database; IMF, World Economic Outlook database; World Bank, Global Economic Monitor; and author’s calculations.

Note: “OIL” corresponds to the real oil prices variable (poil) described in the text.

Conditional Growth Forecasts—Results

What would these commodity price scenarios imply for economic growth in LAC commodity exporters? The main result from the conditional forecast exercise is that even if commodity prices were to remain stable at the relatively high levels attained in 2013, as implied by the stable prices scenario, output growth in the LAC commodity exporters would be substantially lower than in recent years. On average, output growth would be about 0.8 percentage points lower than in 2012–13 and 1.8 percentage points lower than during the commodity boom (Figure 3.10).

Figure 3.10Projected Average GDP Growth under Alternative Price Scenarios

(Average annual growth rate; percent)

Source: Author’s calculations.

Note: Simple average for the 12 largest commodity exporters in Latin America and the Caribbean (Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Honduras, Paraguay, Peru, Trinidad and Tobago, Uruguay, and Venezuela).

The slowdown vis-à-vis the boom period would be quite generalized (Figure 3.11). In all countries except Paraguay, average projected growth over 2014–19 is lower than in 2003–11.28 The projected slowdown is particularly large in the case of Trinidad and Tobago and, to a somewhat lesser extent, in Argentina and Venezuela.29 Excluding these four cases, the slowdown under the stable prices scenario in the other eight countries ranges from 0.8 percentage points in Chile to about 2 percentage points in Peru. In all of these eight cases, the conditional projections under the stable prices scenario is within about ½ percentage point of the model’s unconditional growth forecast (except for Uruguay, were the unconditional growth forecast is 1.2 percent lower). Although the region slowed considerably in 2012–13 vis-à-vis the boom years, the model still predicts lower average GDP growth in 2014–19 for all counties except Brazil and Paraguay.

Figure 3.11Projected GDP Growth, 2014–19

(Average annual growth rate; percent)

Source: Author’s calculations.

Output growth for the average commodity exporter in LAC under the futures scenario would be about ¾ of a percentage point lower than under the stable prices scenario (Figure 3.10). If, instead, commodity prices evolve as in the adverse scenario, growth would be even lower by an additional ½ percentage point (with country-specific differences ranging from −0.1 to −1.2 percent), highlighting further downside risk.

The results in this section suggest that output growth in LAC commodity exporters over the next few years will be more affected by the lower projected growth of commodity prices than by the still-high levels of those prices. This means that even if commodity prices do not revert to their long-term trends anytime soon, the end of the period of ever-increasing prices is likely to imply a significant drag on growth for the region.30

Assessing the Effects of Lower Output Growth in China

Demand from China has been a key driver of global commodity prices in recent years (Erten and Ocampo, 2013b) and, consequently, of the favorable economic performance in many LAC countries during the 2000s. A key question, then, is what might be the impact on commodity prices and more generally on economic growth in commodity exporters of LAC if China’s economy slows down more than currently expected.

This section reports results from simulating the effects that shocks to China’s GDP may have on NCPI series’ and on output for the average commodity exporter in LAC. Given the difficulty of identifying the structural shocks in a GVAR framework, and as it is commonly done in the literature, we rely on the generalized impulse response function (GIRF) analysis developed in Pesaran and Shin (1998).31

Figure 3.12 shows a summary of the results from this exercise. A 1 percent decline in China’s GDP (relative to baseline) would lower the average NCPI of LAC commodity exporters by about 3 percent on impact—and in some cases the decline in the NCPI would be about 8 percent. Moreover, the average NCPI would still be more than 1½ percentage points below trend two years after the shock.32

Figure 3.12Response of Net Commodity Price Indices and GDP in Latin American and Caribbean Countries to a 1 Percent Decrease in China’s GDP (Relative to Trend)

(Percentage deviation from trend)

Source: Author’s calculations.

Note: The shaded area reports the range of deviations from trend of the net commodity price indices (NCPIs) for Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Honduras, Paraguay, Peru, Trinidad and Tobago, and Uruguay. The red solid line shows the simple average of the NCPI responses for these countries. The blue dashed line corresponds to the simple average of the deviations from trend of GDP for all LAC commodity exporters in the model (Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Honduras, Paraguay, Peru, Trinidad and Tobago, Uruguay, and Venezuela) and is reported in the right scale.

The relevance of China for commodity prices is likely to reflect its large (and increasing) weight in global demand, but also its relatively higher commodity intensity of demand.33 To try to assess whether the effects of a shock to China are different from those of a shock to global demand, Figure 3.13 compares the effect on the NCPI from a 1 percent decline in China’s GDP (relative to baseline) with that of an equivalent shock to the United States.34 While a U.S. output shock also has a large effect on the average NCPI in the region, the effect from the shock to China’s output is roughly 50 percent larger, even if China’s share in total trade is about 30 percent lower.

Figure 3.13Response of the Average Net Commodity Price Index of Latin American and Caribbean Commodity Exporters to a 1 Percent Decrease in GDP in China and the United States (Relative to Trend)

(Percentage deviation from trend)

Source: Author’s calculations.

Note: Average of the deviations from trend of the net commodity price indices for Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Honduras, Paraguay, Peru, Trinidad and Tobago, and Uruguay.

Turning to the response of output in LAC to a shock to China’s GDP, the model suggests that, for the average commodity exporter in the region, GDP would be about a ½ percentage point below trend three years after the shock. The output level would still be 0.3–0.4 percentage point below trend even six years after the shock. Figure 3.14 shows the cumulative drop in GDP with respect to the baseline, by country, three years after the shock. The model suggests a bigger-than-average drop in GDP for Honduras and Peru of about 0.9 percent. Brazil, a much less open economy, shows a smaller response, of about 0.1 percent.

Figure 3.14Response of GDP in Latin American and Caribbean Countries to a 1 Percent Decrease in China’s GDP (Relative to Trend)

(Cumulative response after three years; percent)

Source: Author’s calculations.

Note: The red line denotes the simple average across countries.

Overall, the results in this section confirm that slower growth in China represents a key downside risk for LAC commodity exporters, and that commodity prices are a key channel through which such a shock would affect the region.

Conclusion

International commodity prices skyrocketed during the first decade of the 2000s, boosting economic growth of commodity exporters around the world. But after peaking around mid-2011, commodity prices have since moved along a slightly decreasing path, and most projections suggest they are not likely to resume the upward trend observed in the past decade. This chapter analyzed what this turn in the commodity price cycle may imply for output growth in LAC using a GVAR model extended to include the 12 largest commodity exporters in the region and a rich set of country-specific commodity price indices. The model is also used to explore the potential effects of slower-than-expected economic growth in China.

The results suggest that the end of the commodity price boom will entail a significant drag on growth for the average LAC commodity exporter. Even in a context of still-high but nonincreasing commodity prices—which, ex ante, would appear as a rather benign scenario—these economies would grow significantly less than in the past decade. More precisely, if prices were to remain stable at the average levels attained in 2013, average annual GDP growth over the medium term (2014–19) would be almost 1 percent lower than in 2012–13 and more than 1½ percentage points lower than over 2003–11. If commodity prices were to evolve as was implied by commodity futures in early 2014, output growth would be on average even lower, by about ¾ of a percentage point.

The results also confirm that slower-than-expected economic growth in China represents a key downside risk for the region. A 1 percent decline in China’s GDP relative to trend would be associated with a decrease in the average commodity price index in LAC of about 3 percent—and even 8 percent in some cases. In the case of such an event, GDP in the average commodity exporter in LAC would fall by more than ½ a percentage point below trend about three to four years after the shock.

The results from this exercise are nonetheless subject to important caveats. First, the estimated model assumes stable relations (including policy responses to external shocks) over the period 1970–2013, but in fact most LAC economies underwent important structural transformations during that period. Moreover, many have significantly strengthened their policy frameworks more recently (for instance, by allowing greater exchange rate flexibility and reducing the procyclicality of fiscal policy). Second, the model does not take into account future developments that are already foreseen but not readily captured by key macroeconomic relationships (for example, planned structural reforms aimed at raising future potential output). To the extent that these changes have a direct bearing on future growth, the projections from the model used here are likely to have a downward bias.

Despite these caveats, the model results carry important policy implications for LAC commodity exporters. First, the recent slowdown in many LAC economies could be the result, to a large extent, of having passed the peak of the commodity “super cycle.” If that is indeed the case, using demand-side stimulus to keep growth at recent high rates would not be warranted and could give rise to problematic macroeconomic imbalances. Policies should focus instead on structural reforms to raise productivity and potential output growth. Second, policymakers in these economies should work to weaken the link between commodity prices and economic activity to avoid the boom-bust dynamics often associated with past commodity cycles. Fiscal policy needs to play a critical role in this regard by striking the right balance between building buffers and frontloading capital spending to raise potential growth. A formal fiscal framework that explicitly accounts for natural resource revenues, potentially including a stabilization fund, can support this effort. Exchange rate flexibility, underpinned by credible monetary and macroprudential frameworks, provides an additional buffer for shocks to the terms of trade.

Annex 3.1. Data Sources

The source for real GDP (yit) for all 30 economies is Penn World Table Version 8.0 (GDP at constant national 2005 prices, “q_gdp”). Real GDP growth rates from the IMF’s April 2014 World Economic Outlook (WEO) database were used to extend the real GDP series to 2013. The real exchange rate (eitpit) is constructed using data from the April WEO database as the nominal exchange rate in terms of U.S. dollars deflated by domestic consumer prices. The current account variable (cait) is the ratio of the current account balance in U.S. dollars from the April 2014 WEO database to the Hodrick-Prescott trend component of GDP in current U.S. dollars. The latter is constructed using nominal GDP from Penn World Table Version 8.0 (GDP at current national prices, “v_gdp”) and the nominal exchange rate from the April 2014 WEO database.

The source for commodity prices is the IMF’s International Financial Statistics (IFS) database. Due to data availability, 33 commodities prices with data since 1970 were used: aluminum, bananas, barley, beef, coal, cocoa, coconut oil, coffee, copper, corn, cotton, crude oil, fishmeal, hides, iron ore, lamb, lead, natural gas, natural rubber, nickel, palm oil, rice, shrimp, soybean meal, soybean oil, soybeans, sugar, sunflower, tea, tin, wheat, wool, and zinc. The price of crude oil is the simple average of three spot prices: Dated Brent, West Texas Intermediate, and the Dubai Fateh and poil is this average divided by the IMF’s unit value index for manufactured exports. The World Bank’s Global Economic Monitor database was used to extend the following IFS commodity price series back to 1970: barley, coal, iron ore, and natural gas.

Table A3.1Latin America and Caribbean: Main Commodity Exports in the 1970s and 2000s
1970–72FirstShareSecondShareThirdShareSum
ARGBeef35.1Corn25.1Wheat9.569.8
BOLTin58.7Crude oil12.8Zinc9.280.8
BRACoffee43.8Iron ore10.9Sugar9.864.5
CHLCopper88.4Iron ore7.8Fishmeal2.598.7
COLCoffee68.9Crude oil10.8Cotton6.185.7
ECUBananas47.5Coffee21.8Cocoa13.682.9
HNDBananas62.5Coffee19.1Beef10.091.6
PERFishmeal32.0Copper25.8Sugar8.566.3
PRYBeef39.4Cotton11.0Soybean meal10.260.5
TTOCrude oil92.1Sugar5.8Cocoa0.898.7
URYRice65.8Barley21.0Wheat12.499.2
VENCrude oil91.8Iron ore5.3Gas1.098.2
2010–12FirstShareSecondShareThirdShareSum
ARGSoybean meal26.5Soybeans12.6Soybean oil12.551.5
BOLGas59.2Zinc13.4Soybean meal6.378.9
BRAIron ore30.4Crude oil16.7Soybeans12.759.8
CHLCopper94.0Iron ore3.2Fishmeal1.198.3
COLCrude oil61.6Coal22.5Coffee7.591.6
ECUCrude oil71.3Bananas13.8Shrimp7.092.2
HNDCoffee44.6Bananas15.6Palm oil11.371.5
PERCopper51.6Zinc8.7Fishmeal8.568.9
PRYSoybeans43.4Beef19.2Corn9.171.7
TTOGas45.7Crude oil33.1Iron ore20.499.2
URYBeef36.9Soybeans24.5Rice12.273.6
VENCrude oil96.5Iron ore2.1Aluminum0.999.6
Sources: UN Comtrade database; and author’s calculations.Note: The table reports the average share of each country’s three main commodity exports in their total exports of the 33 commodities considered here.
Sources: UN Comtrade database; and author’s calculations.Note: The table reports the average share of each country’s three main commodity exports in their total exports of the 33 commodities considered here.

Figure A3.1Net Commodity Price Index Series for Latin American and Caribbean Commodity Exporters, 1970–2019

Sources: UN Comtrade database; IMF, World Economic Outlook database; World Bank, Global Economic Monitor; and author’s calculations.

Note: The net commodity price indices for 2014–19 (shaded area) are constructed from prices of commodity futures prevailing at end-February 2014.

Figure A3.2Net Commodity Price Index Series for Other Commodity Exporters, 1970–2019

Sources: UN Comtrade database; IMF, World Economic Outlook database; World Bank, Global Economic Monitor; and author’s calculations.

Note: The net commodity price indices and the real oil prices for 2014–19 (shaded area) are constructed from prices of commodity futures prevailing at end-February 2014.

The source for the export and import value data for individual commodities used to weight the commodity price series for each country is the UN Comtrade database (SITC Revision 1).

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See the “Commodity Market Review” in the October 2013 World Economic Outlook (IMF, 2013).

Adler and Sosa (2011) show that the degree of commodity dependence in LAC is not only high compared with other regions, such as emerging Asia, but also has increased over the last four decades.

See Di Mauro, Pesaran, and Hashem (2013) and Chudik and Pesaran (2014) for a review of recent applications using the GVAR framework.

The approach in Deaton and Miller (1996) has been used in many studies on the macroeconomic effects of commodity price fluctuations. Some examples are Dehn (2000) and Cashin, Céspedes, and Sahay (2004).

The price index in Collier and Goderis (2012) also takes into account net exports of each commodity, but excludes those goods for which the country is a net importer, while they are kept in the NCPI.

Using a different metric, Adler and Magud (2013) also consider that Uruguay did not experience a terms-of-trade boom in the 2000s.

Using an international manufacturing trade price index as a deflator is standard in the literature (Deaton and Miller, 1996; Erten and Ocampo, 2013b). It is preferred to the alternative of using consumer price indices from major economies, as these also include nontradables, which may distort price trends.

We use international prices from the IMF’s International Financial Statistics database for 33 commodities with data availability since 1970. See data sources in Annex 3.1 for more detail.

We use country-level trade values from UN Comtrade. Typically, more than one Standard International Trade Category is associated with each commodity price. For instance, the price of crude oil is linked with “crude petroleum” but also with “petroleum, partly refined (including topped crudes)” and other similar categories.

There are two variations of the NCPI used in the chapter, based on different specifications for the weights in equation (3.2). In the export-based index shown in Figure 3.3, τi,j,tx=xi,j,t1/Σj=1Jxi,j,t1 is used. The weights for the adjusted NCPI series used in the scatter plots in Figure 3.8 are given by τi,j,t=(xi,j,t1mi,j,t1)/GDPUSDi,t1, where GDP_USDi,t-1 denotes lagged GDP in U.S. dollars. This adjusted index is similar to the “commodity terms-of-trade” index used in some studies (for example, Spatafora and Tytell, 2009).

For the purposes of this chapter, we refer to commodity exporters as those countries whose share of commodity exports in total exports is higher than the average for a sample of 169 countries during 2000–12.

Identifying the timing, duration, and magnitude of succeeding super cycles for LAC economies is beyond the scope of this chapter. See Erten and Ocampo (2013b) and Jacks (2013) for recent analysis of commodity price cycles.

Figure A3.1 in Annex 3.1 shows the NCPI time series for LAC commodity exporters since 1970. Figure A3.2 shows the NCPIs for commodity exporters from other regions, as well as the time series for (real) oil prices.

We used prices of commodity futures prevailing as of February 28, 2014 to construct projected NCPIs. The only international price among the 33 commodities considered for which there were no data from futures was coconut oil, for which we assumed the price would remain constant at its average price in 2013. In any case, its share in commodity exports for the LAC countries in the sample is very low (and in all cases below 0.1 percent).

Many studies have nonetheless found that futures prices can be a reasonable guide for forecasting commodity prices. See, for instance, Chinn and Coibion (2010), Reichsfeld and Roache (2011), and Reeve and Vigfusson (2011).

See Gruss (2014) for more details and Pesaran, Schuermann, and Weiner (2004) and Dees and others (2007) for a thorough description of GVAR models. A textbook treatment of the GVAR model can be found in Garrat and others (2006).

The current account ratio is defined as a ratio of trend GDP (expressed in U.S. dollars), instead of actual GDP, in order to avoid the measure to be contaminated by contemporaneous movements in GDP.

For example, the share of China in total trade of goods among the 30 economies in the sample for the average LAC commodity exporter has increased from less than 1 percent in 1970 to 14.7 percent in 2012.

For instance, Pesaran, Schuermann, and Weiner (2004), Dees and others (2007) and Cesa-Bianchi and others (2011) include only one global variable, the price of oil, which is modeled as endogenous to the U.S. model. Cashin and others (2014) add the quantity of oil produced in the world, which is modeled as endogenous to the Gulf Cooperation Council group of countries.

An alternative strategy is to directly use the international prices of individual commodities. But this has at least two drawbacks: it is unfeasible (it would be impossible to include 33 global variables in each country-specific model); and it would implicitly assume that the basket of commodities traded by each country is constant over time.

A notable exception is the GVAR model in Gauvin and Rebillard (2014), in which metal and oil prices are not endogenous to the U.S. model or to any other country model, but are modeled in two auxiliary models for commodity prices.

The NCPIs were grouped in three models according to similarities in the commodity mix among countries (whether they mainly specialized in energy, metals, or food commodities).

See Gruss (2014) for detailed evidence from the model specification tests.

For the purpose of the applications in this chapter, the GVAR model is estimated using the toolbox by Smith and Galesi (2011).

To compute conditional output forecasts under alternative future paths for a set of endogenous variables in the model (all NCPIs and the oil price), we use the Kalman filter approach proposed by Camba-Mendez (2012).

Although this market-based scenario could be thought of as a neutral scenario, it has been argued that using futures to forecast spot prices may imply a downward bias. See “Special Feature: Commodity Price Forecasting” in the April 2014 World Economic Outlook (IMF, 2014).

This mostly reflects the difficulty of obtaining a good model fit for Paraguay, given the still-large weight of the agricultural sector in this economy and the strong swings in its GDP tied to weather-related supply shocks.

In the case of Argentina, the results could be contaminated by measurement issues in official GDP data. Alternative data sources have indeed reported significantly lower real GDP growth than the official data since 2008 (Coremberg, 2014).

Although our quantitative exercise is grounded in very specific price scenarios, the results overall are consistent with Dehn (2000) and Collier and Goderis (2012), who find that commodity price booms have positive short-term effects on output growth but either no long-term effects or even negative effects in some cases.

Having 91 variables in the model, exact identification would require imposing an incredibly high number of restrictions, so we do not attempt to identify the ultimate source of the disturbance. The GIRF approach reports how shocks to one variable (say, China’s GDP) affect the other variables of the system on impact and over time regardless of the source of the change, but taking into account the possibility that the error terms of the GVAR are contemporaneously correlated.

While differences in the construction of variables, samples, and so on make it difficult to do direct comparisons with results in other studies in the literature, the results reported here appear quantitatively plausible and in line with previous findings. For instance, the IMF Spillover Report on China (IMF, 2011) finds that a shock to real activity in China of 1 percent of GDP would lead to an increase in metals prices (in current U.S. dollars) of about 6 percent after six months. In our model, a similar shock would lead to an increase in the net commodity price index of metal exporters, such as Chile and Peru, of about 3.9 percent in the first year after the shock.

China’s consumption accounted for about 20 percent of global nonrenewable energy production, 23 percent of major agricultural crops, and 40 percent of base metals (Roache, 2012).

The share of China in total trade has increased dramatically in the last few decades. Considering only trade of goods among the 30 economies included in the model, China’s share in total trade increased from about 1½ percent in 1990 to 14½ percent in 2010–12. The U.S. share decreased from 23 percent to 21 percent in the same period.

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