CHAPTER 3

COMMODITY PRICE CYCLES
Drivers and Policies

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

CHAPTER 3

Commodity prices soared in 2021 following the broad-based decline in early 2020, with prices of several
commodities reaching all-time highs. In part, this reflected the strong rebound of demand from the 2020 global
recession. Energy and metal prices generally move in line with global economic activity, and this tendency has
strengthened in recent decades. Looking ahead, global macroeconomic developments and commodity supply
factors will likely continue to cause recurring commodity price swings. For many commodities, these may be
amplified by the transition away from fossil fuels. To dampen the associated macroeconomic fluctuations, thealmost two-thirds of emerging market and developing economies (EMDEs) that are commodity exporters need tostrengthen their policy frameworks and reduce their reliance on commodity-related revenues by diversifying
exports and, more importantly, national asset portfolios.

Introduction
Commodity prices soared in 2021, in large part
rebounding from the sharp declines that occurredin the global recession of 2020 (figure 3.1). The
broad-based surge, led by energy and metals, wasdriven by a strong recovery in aggregate global
demand, easy financial conditions, and fiscal
expansions in advanced economies. It was
amplified by weather-related supply disruptions
for both fossil and renewable fuels. In 2021, crude
oil prices rose by nearly 82 percent, with themonthly average price of Brent climbing to about$84 a barrel for the first time in seven years.
Natural gas and coal prices also surged. Metal
prices were up about 28 percent, supported by therecovery in global manufacturing, improved
prospects for a significant increase in infrastructure investment in advanced economies, andpandemic-related supply disruptions. Agricultural
prices increased about 14 percent, with food prices
rising the most in low-income countries (LICs).
Rising commodity prices, coupled with pandemicrelated
supply-demand
mismatches,
have
contributed to a jump in headline inflation rates
in many EMDEs and advanced economies.
Upward price pressures are expected to persist forsome time in EMDEs, especially LICs, on theback of elevated food prices, lagged effects ofhigher oil prices, and higher import prices
resulting from currency depreciations. Policy
trade-offs have become increasingly complex,
particularly in many EMDEs, as inflation has been

Note: This chapter was prepared by Alain Kabundi and Garima
Vasishtha, with contributions from John Baffes, Wee Chian Koh,and Peter Nagle.

rising even as employment has remained below
pre-pandemic levels.
The recent commodity price upswing has once
again brought to the fore the susceptibility of
EMDEs to large fluctuations in commodity prices.
Macroeconomic performance in commodity
exporters has historically varied closely in line with
commodity price cycles. This is especially so for
EMDEs that rely on a rather narrow set ofcommodities.1 Commodities are critical sources ofexport and fiscal revenues for almost two-thirds of
EMDEs, and three-quarters of LICs, and morethan half of the world’s poor reside in commodityexporting EMDEs (figure 3.2; World Bank
2018a). Dependence on commodities isparticularly high for oil exporters. On average, oil
exports accounted for 33 percent of total goods
exports by oil exporters over 2017-20—
considerably more than the 20 percent share formetals and the 15 percent share for agriculturalcommodities for metals and agricultural exporters,
respectively.2
Terms-of-trade shocks arising from commodity
price movements cause changes in relative prices
and have accounted for as much as half of thevariation in economic activity in EMDEs (DiPace, Juvenal, and Petrella 2020; Kose 2002). The
impact of terms-of-trade shocks can also be
asymmetric, with export price shocks being twiceas important as import price shocks for domestic

1 See IMF (2015a); Jacks, O’Rourke, and Williamson (2011);
Richaud et al. (2019); World Bank (2009); and World Bank
(2020d).
2 Commodity exporters are defined as countries where more than
20 percent of exports were concentrated in an individual commodity,
on average over 2017-2020.

113

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FIGURE 3.1 Commodity prices
Commodity prices soared in 2021, partly reflecting rebounds from the
sharp declines during the global recession of 2020. Among commodity
groups, energy prices registered the sharpest rebounds since their troughin April 2020.
A. Commodity prices

B. Commodity price growth sinceApril 2020

Index, 100 = January 2020
180
Energy Metals Agriculture

Percent
200

140

150

1,200

100

800

50

400

0

0

100
60

Nov-21

Jul-21

Sep-21

May-21

Jan-21

Mar-21

Nov-20Jul-20

Sep-20May-20Jan-20Mar-2020

economic activity (Mendes and Pennings
2020; Riera-Crichton, Végh, and Vuletin2015).
•

Percent
1,600

-50

-400
MetalsAgriculture

Energy(RHS)

Source: World Bank.
A. Monthly average commodity prices, in U.S. dollar terms. Last observation November 2021.
B. Bars show commodity price growth for each group from the trough in April 2020 to November
2021. Orange whiskers show the range of price increases across commodities for each group. Forenergy commodities, the minimum and maximum values indicate the increases in the price ofliquified natural gas in Japan and Europe, respectively. For metals, the minimum and maximum priceincreases were for iron ore and tin, respectively. For agriculture, Thai rice prices increased the least
while Palm kernel prices increased the most.

business cycles. There are three main macrofinancial channels through which commodity
price fluctuations can affect economic activity.
••

Currency channel and inflation. Commodity
price movements can be a source of currency
volatility for economies where a few
commodities represent a significant share ofexports. They can also trigger inflationarypressures, notably for commodity-importing
LICs for which food and fuel constitute alarge fraction of consumption. Exchange ratechanges as well as inflationary pressures canpose challenges for monetary policy (Drechsel,
McLeay, and Tenreyro 2019; Ha, Kose, andOhnsorge 2019; World Bank 2020a).
Fiscal channel. In commodity exporters,
declines in commodity prices can trigger
procyclical cuts in public expenditures becauseof reduced revenue from commodity
production and exports, while conversely,
increases in commodity prices can trigger
procyclical increases in public spending. Fiscal
policy thus often accentuates the impact of thecommodity price cycle on economic growthand increases the amplitude of cycles in

Financial channel. In commodity exporters,commodity booms are often associated withstrong growth in bank credit and increases in
lending to borrowers who may prove lesscreditworthy during normal times. Risingcommodity prices can also lead, incommodity exporters, to currency appreciation, lower country risk premiums, and larger
capital inflows. These surges in capital
inflows, if not invested wisely, can lead tofinancial crises when commodity prices
collapse, and financial conditions tighten. A
notable example is the Latin American debt
crisis of the 1980s (Eberhardt and Presbitero
2021; Kose et al. 2021; Reinhart, Reinhart,
and Trebesch 2016).

Recent events have highlighted how global tradeand supply disruptions as well as climate-related
events can amplify commodity price movements
and their impact on economic activity. Countries
dependent on fossil fuels are vulnerable, like othercountries, to climate change and to the global
efforts to mitigate it (Peszko, van der
Mensbrugghe, and Golub 2020). A better
understanding of commodity price movements
can, therefore, help policy makers design effective
stabilization policies, ensure financial stability, andundertake policies to improve development
outcomes.
Against this background, this chapter asks thefollowing questions:•

What are the main features of commodity
price movements?

•

How does the recent recovery in commodity
prices compare with such episodes after
previous global recessions and downturns?

•

What are the key drivers of commodity pricecycles?

•What are the policy implications?

Contribution to the literature. This chapter
contributes to the literature along four dimen-

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

Main findings. This chapter offers the following
main findings.

FIGURE 3.2 Importance of commodities
Many EMDEs are heavily reliant on commodity exports. The averageenergy exporter is more reliant on energy exports than the average
agriculture- and metal-reliant EMDE. Resource revenues are also an
important source of fiscal receipts, particularly for energy exporters.
A. Number of EMDE commodity
exporters

B. Resource rents in EMDE
commodity exporters

Number20

Percent of GDP
50

16

40

12
30

8

Iron

Aluminum

Coal

Wheat

Coffee10

Natural gas

20

0

Oil

4

Coppersions. First, it expands on the earlier literature oncommodity cycles by using a much larger set ofcommodities and a period that includes the
COVID-19 pandemic.3 Second, it is the first study
to compare the rebound in commodity prices after
the COVID-19-induced global recession in 2020
with the price recoveries after past recessions andslowdowns. Third, using a cutting-edge econometric approach, the chapter examines bothglobal and commodity-specific cycles for a large
number of commodities as well as their underlying
drivers. This contrasts with earlier literature thateither focuses only on a small set of commodities,
or examines commodity demand and supply
rather than aggregate demand and supply, or
simply documents the existence of comovement
without identifying the underlying drivers.4
Fourth, the chapter presents a rich menu of policy
options available to commodity-exporting EMDEs
for dealing with commodity price volatility.

Commodity price cycles have been highly
synchronized across commodities. On average, allcommodity prices were in the same cyclical phase
60 percent of the time. For commodities
intensively employed in industry, such as copperand aluminum, prices were in the same phaseabout 80 percent of the time. This synchronization was reflected statistically in a common

3 For the earlier literature, see, for example, Cashin, McDermott,and Scott (2002); Roberts (2009); and Rossen (2015).
4 For studies of commodity cycles, see Charnavoki and Dolado(2014) and Ha et al. (2019); for the roles played by demand andsupply shocks, see Jacks and Stuermer (2020), Kilian and Murphy(2014), and Stuermer (2017, 2018); and, for the comovement ofcycles, see Chiaie, Ferrara, and Giannone (2017).

0
Naturalgas

Oil

CopperCoffeeC. Share of EMDE exports for energy,
metals, and agriculture

D. Resource revenues as share of
fiscal revenues

Percent of exports
80

Percent of fiscal revenues
100

60

75

40

50

20

25
0

0

First, over the past five decades, commodity prices
have undergone repeated cycles. On average, frompeak to peak, cycles lasted almost six years. Pricesrose and fell by 1-4 percent per month over thecourse of the average cycle. Price slumps lasted
somewhat longer (39 months) than booms (30months). Booms, on average, were steeper (4percent per month) than price slumps (1 percent
per month).

115

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Oil

Natural gas Coffee

CopperNaturalgas

Oil

CopperCoffeeSources: Comtrade (database); International Monetary Fund; UNU-Wider (database); WITS
(database); World Bank.
Note: EMDEs = emerging market and developing economies.
A. Figure lists the number of EMDEs that primarily export a specific commodity. An EMDE is defined
as a commodity exporter if, on average in 2017-20, the value of exports of an individual commodity
accounted for 20 percent or more of total exports.
B. Unweighted average of resource rents as percent of GDP for EMDE commodity exporters ofnatural gas (13 countries), oil (44 countries), copper (14 countries), and coffee (13 countries). Total
natural resource rents of exporters of each commodity included in the figure are the sum of oil rents,
natural gas rents, coal rents (hard and soft), mineral rents, and forest rents. Countries relying on theexport of multiple commodities are included in the averages for each commodity. As an example, acountry identified as a natural gas exporter may be deriving resource rents from exports of bothnatural gas and oil; its share of resource rents is included in the averages for both oil and natural gas
since the breakdown of resource rents by individual commodity is not available.
C. Figure shows the median share of exports accounted for by oil, natural gas, copper, and coffee for
EMDE exporters of that commodity. Oil includes 20 EMDEs, copper 6, natural gas 5, and coffee 4.
Blue bars show medians and orange whiskers show interquartile ranges.
D. Unweighted average of resource revenues as a share of fiscal revenues for EMDE commodity
exporters of natural gas (5 countries), oil (25 countries), copper (4 countries), and coffee (5countries). Countries relying on the export of multiple commodities are included in the averages foreach commodity. Orange whiskers indicate the range between the minimum and maximum values.

factor that accounted, on average, for roughly 1525 percent of price variability for energy andmetals, but only 2-10 percent of price variability
for agricultural commodities and fertilizers. For
industrial commodities (which here include
energy, metals, and rubber), the synchronization
has become more pronounced over time. Since themid-1990s, on average, the common factor
accounted for about 30-40 percent of the

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variability in industrial commodity prices—twice
as much as during the full period since 1970.
Second, the rebound in prices from their trough in
April 2020 was faster and steeper than that after
previous recessions for almost all majorcommodity groups. The recovery in energy prices
was particularly sharp—about 50 percent in thethree months after their 2020 trough, with prices
surpassing their pre-crisis peak in about a year. In
contrast, after previous recessions, the medianrecovery in energy prices was less than 5 percent
over a 13-month period. This reflected theextraordinarily strong economic rebound from the
COVID-19-induced global recession, along withdifficulties in quickly restoring supply.
Third, for metals—the commodity group forwhich demand is most sensitive to economic
activity—the global business cycle has been themain driver of prices. Energy and metal prices
were in the same cyclical phase as the global
business cycle about 60 percent of the time,compared with about half the time for agriculturalcommodity prices. About one-third of pricetroughs coincided with global recessions.
Since 1996, global macroeconomic shocks havebeen the main source of commodity pricevolatility—which includes both cyclical andshorter-run movements. Global demand shocks
have accounted for 50 percent, and global supply
shocks for 20 percent, of the variance of global
commodity prices. In contrast, during 1970-96
supply shocks specific to particular commodity
markets—such as the 1970s and 1980s oil priceshocks—were the main source of variability inglobal commodity prices. These results suggestthat the role played by developments specific tocommodity markets in driving commodity pricevolatility may have diminished over time.
Fourth, the gyrations in commodity prices in
2020-21 are a reminder of the need for policies tomanage and contain the economic consequencesof such volatility, especially in the case of EMDEs.
Almost two-thirds of these countries rely heavily
on primary commodities for government andexport revenues. Real incomes in both commodity
exporters and importers have been severely

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

affected by changes in the terms of trade resulting
from commodity price movements. In the years
ahead, the challenges are likely to be compoundedby the effects on commodity prices of thetransition away from fossil fuels. Countries relying
heavily on commodities face two types of policy
challenges, which are related: first, smoothing
macroeconomic volatility induced by commodity
price swings and, second, reducing their relianceon commodities. The former requires thestrengthening of fiscal, monetary, and macroprudential frameworks. The latter, progress withwhich will help achieve the former, requires
structural measures, such as encouraging economic
diversification, particularly of exports, building
human
capital,promotingcompetition,
strengthening institutions, and reducing distorting
subsidies.

Main features ofcommodity cycles
Over the past five decades, global commodity prices
have been characterized by repeated cycles. Energy
and metal prices have tended to comove particularly
closely with global economic activity. The rebound inprices from their trough in April 2020 has been fasterand steeper than that after previous recessions formost major commodity groups.
Empirical approach
Methodology. Standard techniques used to study
business cycles are applied to 67 global
commodity prices (see table A3.4.1 for the list ofcommodities and their groupings). Specifically,
the procedure applied is a widely used algorithm
for dating business cycles, and largely follows
Harding and Pagan (2002) and Cashin,
McDermott, and Scott (2002) (annex 3.1).5 The
algorithm is applied to real commodity price series
at the monthly frequency. The sample includes

5 Other studies that have used this technique for commodity
price cycles include IMF (2012), Roberts (2009), and Rossen (2015),
with the latter two focusing only on metals. Note that this technique
is designed to analyze short-run cycles in commodity prices. For
identifying cycles at different frequencies, other techniques may be
more appropriate (for example, Baffes and Kabundi 2021).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

commodities spanning energy (10 prices), metals
and minerals (7 prices), precious metals (3 prices),
agriculture (food, beverages, and oil; 34 prices),
fertilizers (5 prices), and raw materials (8 prices).
To better capture the behavior of diﬀerent
agricultural commodities, separate indexes wereconstructed for the prices of annual and perennial
agricultural commodities.6
Definitions. A boom in commodity markets is
deﬁned as a trough-to-peak rise in commodity
prices; a slump as a peak-to-trough decline. A cycle
consists of both a boom and the subsequent
slump. The number of months between troughs
and peaks, and the magnitude of changes incommodity prices during this period, are theduration and amplitude, respectively. Slope isdeﬁned as the average monthly amplitude (that is,
amplitude
dividedbytheduration).
Synchronization of phases between commodity
prices (as well as between commodity prices andeconomic activity) is assessed by the concordance
statistic, which measures the proportion of timethat two series are concurrently in the same phase(see annex 3.2).
Data. Monthly average price data, in U.S. dollar
terms, are taken from the World Bank
Commodities Price Data (the Pink Sheet) for theperiod January 1970 to October 2021. Real priceseries are obtained by deﬂating the nominal series
by the U.S. consumer price index (CPI)The beginning of the sample period ischosen to ensure that the price formation process
reﬂects the post-Bretton Woods exchange rate
arrangements (with the transition to generalized
ﬂoating among the major currencies in 1971-73)
while the monthly data enable the measurement ofhigh-frequency ﬂuctuations. As an illustration ofcommodity price cycles, six major commodities
6 The annual agriculture index comprises cotton, maize, rice,soybean meal, soybean oil, and wheat. These commodities, often
termed crop commodities, are produced on an annual basis so thatland use (and other factor inputs) can change each crop year,
depending on demand and supply conditions. The perennial
agriculture index comprises cocoa, coffee Arabica, coffee Robusta,
natural rubber, and tea. These commodities are produced by trees,
often termed tree crops, and therefore cannot be substituted on anannual basis. Both indexes are constructed using the weights specified
in the World Bank’s Pink Sheet.

CHAPTER 3

are examined in detail—coal, crude oil,aluminum, copper, maize, and coﬀee. These
commodities are the most traded ones in theirrespective commodity groups. The exercise yields a
total of 538 peaks and 573 troughs since 1970,
about 17 on average per commodity—17 per basemetals and minerals commodity, and 15 perenergy and agricultural commodity. The numberof completed peak-to-peak cycles ranges from 2(rubber) to 12 (logs) for individual commodities(table A3.4.1). The average number of cycles
ranges from ﬁve for precious metals to eight for
metals and minerals.
Features of commodity price cycles
Duration. On average in the period January 1970October 2021, price booms lasted 30 months andslumps lasted 39 months (table A3.4.1 and ﬁgure3.3). The diﬀerence between the average durationof booms and slumps is particularly large for some
agricultural commodities.7 The relatively long
duration of slumps in agricultural commodities
could be driven by the relative persistence ofnegative shocks, such as those related to weather
and/or plant diseases, that do not generally aﬀect
the prices of energy and metals (IMF 2012).
Amplitude and slope. For all commodity groups,
the amplitude and slope of price booms werelarger than those for price slumps. The average
price increase during commodity price booms waslarger than the average price decline during slumps(ﬁgure 3.3). On average, the monthly speed ofcommodity price rises in booms (4 percent amonth) was much faster than that for commodity
price declines in slumps (1 percent a month). For
some commodities the diﬀerence in the speed ofprice increases and decreases was particularly large.
For instance, the average rise in real oil prices inbooms was about 8 percent a month while theaverage fall in oil prices in slumps was about 2
percent.

7 The somewhat longer duration of slumps than booms is
consistent with findings in the related literature (see Cashin,
McDermott, and Scott 2002; IMF 2012; Roberts 2009; and Rossen2015). It is also in line with earlier literature that found agriculturalcommodity prices to be characterized by long periods of doldrums
interrupted by shorter-lived spikes (Deaton and Laroque 1992).

117

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

FIGURE 3.3 Duration, amplitude, and slope
On average, price slumps have been longer in duration than price booms
for most commodities. The difference is especially pronounced amongsome agricultural commodities. Price booms have been more pronounced
than price slumps, on average. The speed of commodity price rises inbooms was also much faster than that for declines in slumps.
A. Duration of booms and slumps(indexes)
Booms

Months80

B. Duration of booms and slumps(commodities)
Months50

SlumpsBooms

Slumps

40

60

30

40

20

20

10

Coffee

Maize

CopperCrude oil

Aluminum0

CoalPrecious
metals

Metals andminerals

Fertilizers

Agriculture

Energy0

E. Slope of booms and slumps(indexes)
Booms

Coffee

Maize

CopperAluminum

Crude oilSlumpsF. Slope of booms and slumps(commodities)
Percent
8

SlumpsBooms

Slumps64

Coffee

Maize

CopperAluminum

Crude oil0
CoalPrecious
metals

Metals andminerals

Fertilizers

2Agriculture

EnergyPercent
14
12
10
8
6
4
2
0

Booms

Coal

Metals andminerals

SlumpsPrecious
metalsPercent
240
210
180
150
120
90
60
30
0

Booms

Fertilizers

Percent
350
300
250
200
150
100
50
0

Agriculture

D. Amplitude of booms and slumps(commodities)

EnergyC. Amplitude of booms and slumps(indexes)

Source: World Bank.
Note: Data from January 1970 to October 2021. Yellow whiskers indicate minimum and maximum
range.
A.B. Duration measures the average length (in months) of a phase (booms or slumps).
C.D. Amplitude measures the average real price change (in percentage terms) from trough to peak
for booms and from peak to trough for slumps.
E.F. Slope refers to the average monthly amplitude (i.e., amplitude divided by the duration).

Comovement in commodity cycles. Commodity
prices have been highly synchronized: More than
60 percent of the time, on average, all commodity
prices in the sample were in the same cyclicalphase. This price synchronization reﬂects anumber of factors. Since several sets of com-modities are close substitutes in demand, being
used for the same purposes, demand surges or
supply disruptions in one commodity market
aﬀect the prices of similar commodities. For
annual agriculture commodities, prices may be
synchronized because of the substitutability of
inputs to production, including land, labor, andmachinery. Energy is a key input in the
production of some metals (such as aluminum)
and an important cost component for most grains
and oilseed crops. Thus, increases in energy prices
will tend to put upward pressure on the costs ofproduction (and hence the prices) of thesecommodities (Baﬀes 2007).
The highest degrees of synchronization were
between copper and aluminum (81 percent of thetime), and between copper and crude oil (73percent of the time) (table A3.2.1). This likely
reﬂects the strong response of these commodities
to global economic activity as well as their joint
use in a wide range of applications. These results
are consistent with earlier ﬁndings that, among
energy commodities and metals, consumption ofaluminum, copper, and crude oil exhibited thestrongest responses to per capita income growth(Baﬀes, Kabundi, and Nagle 2021; World Bank
2018a).
Comovement with global economic activity.
Commodity prices are positively correlated withthe global business cycle. The share of troughs that
occurred during global recessions and slowdowns
was about 23 percent for both metals and minerals
as well as agriculture, followed by energy (10percent) (ﬁgure 3.4).8 The share of peaks
preceding global recessions was the highest forenergy (20 percent), followed by agriculture, andmetals and minerals. Energy and metal prices
comoved more strongly than agriculturalcommodities with global industrial production.
Energy and metal prices, on average, were in thesame cyclical phase as global industrial production
about 60 percent of the time. This contrasts withthe limited synchronization of food commodity
prices with global industrial production: Maize,

8 The episodes of past global recessions and slowdowns are takenfrom Kose, Sugawara, and Terrones (2020).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

A. Share of peaks before recessions
and slowdowns

B. Share of troughs in recessions andslowdowns

Percent
20

Percent
30

15

20

10

10

Agricultural commodity prices. Prices for annual
agricultural commodities declined only slightly
during the pandemic but increased sharply in late
2020 and early 2021. This surge in prices was
mostly driven by strong demand from China, inpart because of the recovery in demand for animal
9 Taken together, these ﬁndings are consistent with studies
showing that demand for energy and industrial metals is driven
primarily by economic growth, whereas demand for agriculturalcommodities is driven mainly by population growth (Baﬀes,
Kabundi, and Nagle 2021; World Bank 2018a).

Agriculture

Metals andminerals

C. Synchronization of industrial
production and key commodities

D. Synchronization of industrial
production and commodity groups

Share of time spent in the same phase
0.8

Share of time spent in the same phase
0.8
0.6

0.6

0.4

0.4

0.2

0.2

Agriculture

Fertilizers

Precious
metals

Metals andminerals

Maize

Coal

CoffeeAluminum

CopperEnergy0.0

0.0

Crude oil

Energy and metal prices. The collapse in theenergy price index in early 2020 was the steepestof any during global recessions in the past ﬁve
decades, and the subsequent recovery was likewise
the steepest (ﬁgure 3.5). Energy prices reboundedby about 50 percent within three months of theirearly 2020 trough, and surpassed their pre-crisis
peak in about a year. In comparison, the medianrecovery after previous recessions was less than ﬁve
percent in 13 months. Likewise, for metal andmineral prices, the pandemic-driven decline in2020 was steeper than that during most of theprevious global recessions. The subsequent pricerecovery was also faster than in previous episodes.
This was mostly a reﬂection of the relatively shortlived nature of the pandemic-related recession, arebound in demand from China due to strong
industrial activity, and supply disruptions in Latin
America.

0
EnergyAgriculture0Allcommodities

5

Metals andminerals

Event study. An event study is used to compare
the behavior of commodity prices during the 2020
global recession with price movements aroundglobal recessions and slowdowns over the past 50
years. For brevity, the results are presented for thesix major commodity indexes. During the 2020
global recession, the troughs in commodity prices
generally coincided with those in global economic
activity. This is in sharp contrast with previous,
more prolonged recessions, where commodity
prices continued to decline for several months
after the trough in economic activity.

Commodity price cycles tend to comove with the global business cycle.
Energy and metals comove strongly with global industrial production, while
agricultural commodities are less correlated with industrial production.

EnergyRecent commodity price movements
compared with historical experience

FIGURE 3.4 Synchronization of commodity price cyclesAllcommodities

for example, was in the same phase roughly 40
percent of the time (ﬁgure 3.4).9119

CHAPTER 3

Sources: Haver Analytics; World Bank.
A. Share of peaks in a 6-month window before global recessions and slowdowns.
B. Share of troughs in a 6-month centered window around global recessions and slowdowns.
C.D. Synchronization is measured by the concordance statistic which is defined as the proportion oftime that two price series are in the same phase. It is equal to one if both series are in the same
phase at any time. See Annex 3.2 for details. Dashed red line indicates 0.5. The global industrial
production series is derived by aggregating country-level industrial production series (from Haver
Analytics) with “industry value added” (from the World Bank’s World Development Indicators) used asweights. The series is incorporated in level terms.
D. Yellow whiskers denote minimum and maximum range.

feed after the Africa swine ﬂu outbreak in 2019,and higher energy costs (and, hence, fertilizer
costs). For the perennial agricultural price index—
which comprises coﬀee, rubber, and tea—theprice decline in 2020 was broadly in line withhistorical episodes, while the subsequent recovery
was faster.

Evolution of commodity
cyclesSince 1970, slumps in the prices of crude oil, coal,
aluminum, and copper have been associated mainlywith declines in global economic growth, geopoliticalevents aﬀecting supply, and the emergence of new
producers. In contrast, slumps in the prices of

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

FIGURE 3.5 Commodity prices around global recessions
and downturns
The recovery in commodity prices since the COVID-19 recession is in stark
contrast to those following previous recessions. The rebound in prices has
been exceptionally fast for most major commodity groups.

A. Energy

B. Metals and minerals
2020 global recession
Median (previous cycles)

Percent change
1802020 global recession
Median (previous cycles)

Percent change
60
40

120

20
0

60

-20
0

-40
-60

-60

-80
-100

-120
-8

-4

t=0

4

8

12

16

-8

20

-4

t=0

4

C. Perennial agriculture

D. Annual agriculture

Percent change

Percent change

2020 global recession
Median (previous cycles)

40

8

12

16

20

2020 global recession
Median (previous cycles)

130
100

20

70

0

40
-20

10

-40

-20

-60

-50
-8

-4

t=0

4

8

12

16

20

E. Fertilizers

-8

-4

t=0

4

8

12

16

20

F. Precious metals

Percent change
150

2020 global recession
Median (previous cycles)

Percent change
802020 global recession
Median (previous cycles)

60

100

40
50

20

0

0
-20

-50

-40
-100

-60

-150

-80
-8

-4

WTI—prices, in real terms) have experienced
eleven troughs since 1970 (table A3.1.1 and figure
A3.1.1). The troughs were associated primarily
with global recessions and Organization of thePetroleum Exporting Countries’ (OPEC)
decisions/agreements (figure 3.6).10

t=0

4

8

12

16

20

-8

-4

t=0

4

8

12

16

Global recessions. Four of the identified troughs(1975, 1998, 2001, 2020) in oil prices wereassociated with global recessions or slowdowns.
The global recession and oil price slump of 1975
followed the shock to world oil prices from the
OPEC price hike and the Arab oil embargo
initiated in October 1973. The sharp decline in oil
prices in 1998 was associated mostly withweakening global demand stemming partly fromthe 1997-98 Asian financial crisis, althoughcontinued expansion of OPEC production until
mid-1998 may have been another contributing
factor (Fattouh 2007). The trough in oil prices in2001 was triggered by weakening global growthfollowing the bursting of the dot.com bubble,
exacerbated by the disruptions and uncertainty set
off by the September 11 terrorist attacks in the
United States (Baffes et al. 2015; World Bank
2015a). The most recent trough, in April 2020,
followed the steepest price collapse on record.
Global oil demand dropped due to the deepest
global recession since the Second World War aswell as the widespread restrictions on transport
and travel, which account for about two-thirds ofglobal oil demand (Kabundi and Ohnsorge 2020;
Wheeler et al. 2020).

20

Source: World Bank.
Note: The horizontal axis represents the time period in months, where t=0 denotes the peak of global
industrial production before global recessions and downturns since 1970, as in Kose, Sugawara, andTerrones 2020. See table A3.4.2 for the specific episodes. The vertical axis measures the percent
change in the commodity price series from a year earlier. The blue line shows the trajectory of thecurrent commodity cycle around the COVID-19 recession, while the red line is the median of previous
cycles around a global recession or downturn. Gray shaded areas represent the range of observed
values in previous cycles. Data from January 1970 to October 2021.

agricultural commodities, such as coﬀee, have been
associated mostly with commodity-speciﬁc supply andpolicy shocks and less so with aggregate demand
shocks.
Crude oilOil prices (represented by the unweighted averageof Brent, Dubai, and West Texas Intermediate—

OPEC decisions/agreements. The oil price slump
in 1986 can mostly be attributed to changing
supply conditions as OPEC reverted to a
production target of 30 million barrels per day
after cutting production significantly in the early
1980s (Baffes et al. 2015; World Bank 2015a).
Real oil prices dropped by almost 60 percent from

10 Major spikes in oil prices have been associated with geopoliticalevents affecting supply. For instance, during the 1973 Yom-Kippur
War, OPEC’s member countries cut production, and Arab suppliers
imposed an oil embargo against Canada, Japan, the Netherlands, theUnited Kingdom, and the United States. Average real oil prices in1974 were more than four times their 1973 level. Subsequentdisruptions in oil supplies following the war between Iraq and theIslamic Republic of Iran, and the Iranian revolution caused oil prices
to more than double in 1979 compared to the previous year.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

FIGURE 3.6 Crude oil
Crude oil prices have experienced 11 troughs since 1970. These troughs
have been associated with global recessions and events related to theOrganization of the Petroleum Exporting Countries (OPEC). The declines inoil prices in 1985-86 and after 2014 were triggered by changes in OPEC’s
policy objective, in each case from price targeting to preserving market
share. Both episodes followed rapid expansions in non-OPEC oil
supplies—Alaska, North Sea, and Mexico (1985-86) and U.S. shale oil,
Canadian oil sands, and biofuels (2014-16).
A. Oil price

B. Oil production

Log price
5.5

Percent of global production
60
MexicoU.S.
OPECCanada
50

5.0
4.5

40

4.0

2020

2015

2010

2005

2000

1995

1990

1985

1980

1975

1970

1965

2020

2015

2010

2005

2000

1995

1990

1985

1980

0

1975

10

2.0
1970

20

2.5

Sources: BP Statistical Review; World Bank.
A. Real price of crude oil (unweighted average of Brent, Dubai, and WTI oil prices) in logs. Shaded
areas indicate global recessions and downturns as in Kose, Sugawara, and Terrones (2020), and theglobal recession of 2020. Data from January 1970 to October 2021. Red lines indicate OPEC-related
episodes in 1973, 1979, 1985, 1990, 1991, 1994, 1998, 1999, 2002, 2003, 2014, and 2016.

FIGURE 3.7 Coal
Coal prices have experienced eight troughs since 1970. The troughs havemainly been associated with global recessions, the emergence of new
producers, and policy-driven changes in China’s growth strategy involvinga shift toward services and less energy-intensive consumption.
B. Coal production

Log price
6.0
5.5
5.0
4.5
4.0
3.5
3.0
2.5
2.0

Percent of global
Percent of global
production United States Germany production
India
Indonesia
China (RHS)
25
60
50

20

40

15

30

10

20

2020

2015

2010

2005

2000

1995

1990

1985

5
1980

1975

A. Coal price

1970

10
0

0

C. Energy intensity of global GDP

D. Share of coal in electricity
generation

TOE per $1,000 GDP

Percent

TOE per $1,000 GDP
Coal

0.1

Energy (RHS)

0.3

China
Japan
United States

India
United Kingdom
World

100
0.2
0.1

75
50
25
2020

2015

2010

2005

2000

1995

0
1990

2020

2015

2010

2005

2000

1995

1990

1985

0
1980

0.0
1975

Policy-driven changes in China’s growth model.
The coal market changed significantly in the
2000s as rapid economic growth in China led to asurge in demand for coal, both for power
generation and for metallurgical uses. To meet thesurging demand, China rapidly increased its
domestic production as well as its imports of coal.
These developments slowed and went into reversein the 2010s, as China’s growth moderated andshifted from investment and manufacturing
toward less energy-intensive consumption andservices. This contributed to a steady decline inprices from 2011-15, with prices reaching a
trough by end-2015.

3.0

1970

Global recessions. The global financial crisis of
2007-09 and the subsequent global recession
resulted in a sharp fall in coal prices (figure 3.7).
Prices bounced back rapidly in 2010-11 as theglobal economy recovered, with China driving theincrease in demand. In 2020, the COVID-19
pandemic and the associated global recession
caused a drop in demand for coal, with its pricefalling by nearly 30 percent between January andAugust. While demand subsequently reboundedin 2021 alongside the economic recovery,
production was slower to recover, with weather
events, including flooding in China andIndonesia, causing disruptions (World Bank
2021a).

30

3.5

1965

Coal prices underwent eight troughs since 1970
(table A3.1.1). The troughs can mainly be
attributed to global recessions and slowdowns,
policy-driven changes in China’s growth model,
and the emergence of new producers.

North Sea

1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
2011
2014
2017
2020

Coal

1985

January to July 1986, followed by a prolonged
period of low oil prices during the next two
decades. Likewise, the slide in oil prices after 2014
was triggered by a change in OPEC’s policy
objective, from price targeting to preserving
market share. Muted demand and rising oil supply
from non-OPEC producers, including U.S. shaleoil, Canadian oil sands, and biofuels also
contributed to the decline in prices at this time
(figure 3.6).

121

CHAPTER 3

Sources: BP Statistical Review; Our World in Data (database); U.S. Energy Information
Administration; World Bank.
A. Real price of coal in logs. Shaded areas indicate global recessions and downturns as in Kose,
Sugawara, and Terrones (2020), and the global recession of 2020. Data from Jan. 1970 to Oct. 2021.
B. Share of coal production in global production.
C. Energy includes coal, natural gas, and oil. ToE stands for tonnes of oil equivalent. Aggregatescalculated using GDP weights at average 2010-19 prices and market exchange rates.
D. Share of electricity that comes from coal.

122

CHAPTER 3

Emergence of new producers. After increasing
steadily through the 1970s, real coal prices
declined through much of the 1980s and 1990s.
The fall in prices through the 1980s in partreflected the emergence of additional coalproducers, particularly China and Indonesia(figure 3.7). By the 1990s, China had overtaken
the United States as the world’s largest coalproducer, although technological improve-ments,
such as automated longwall mining systems anddraglines, sharply reduced the cost of producing
coal in the United States and ensured the country
remained a leading producer: the United Statesremained the world’s second largest producer ofcoal until about 2016, when it was overtaken by
India. In 2020, China accounted for about 50
percent of global coal production, while India
accounted for less than 10 percent.
Aluminum

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

Soviet Union, and Japan. Since then, major shifts
have occurred in the geographic location of
production with the arrival of new private
producers and conglomerates of state-owned
enterprises. The collapse in prices in the early
1990s was caused by the breakup of the Soviet
Union: countries that had been members of thebloc opened up and joined the global aluminum
market, which resulted in a large increase insupply, especially from the Russian Federation. In
the 2000s, China emerged as the world’s largest
aluminum producer, accounting for more thanhalf of global production compared to just 1
percent in 1970 (figure 3.8). Despite
environmental curbs in China (aluminumproduction is energy-intensive) and tariffs
imposed by the United States, Chinese aluminumproduction—55 percent of global supply—has
continued to rise, even during the COVID-19
pandemic.

Aluminum prices saw ten troughs since 1970.
Price cycles for major industrial metals, such asaluminum, copper, zinc, and lead generally follow
global economic cycles since demand for them isclosely related to global economic activity,
particularly industrial production. These metals
are used in a wide range of applications, withchanges in usage and related structural changes indemand occurring only slowly over time. The
troughs in real aluminum prices have generally
been associated with global recessions andslowdowns, and the emergence of new producers
and consumers.

Emergence of new sources of demand. Since
2000, the intensity of aluminum use in global
GDP has risen, reflecting strong demand from
EMDEs, rapidly growing aluminum-intensive
industries, and replacement for tin in canning, andfor copper in electrical wiring. Between 2000 and2010, China’s consumption of aluminum as ashare of global consumption increased threefold.
As China’s aluminum production expanded, thesupport to aluminum prices from these factors
faded.

Global recessions. Three of the identified troughs
in aluminum prices (1982, 2009, 2020) wereassociated with global recessions. In addition, theglobal slowdown associated with the 1997-98
Asian financial crisis was accompanied by a sharp
decline in aluminum prices. The most recent
trough, in April 2020, was associated with the
pandemic-related recession, with real aluminum
prices falling to their lowest level over the past
half-century. Prices have since rebounded with theglobal economic recovery.

Copper prices experienced eight troughs since1970 (table A3.1.1). The troughs have generally
been associated with global recessions or
slowdowns, technological innovations, shifts indemand away from copper to other materials forsome uses, and the emergence of new producers.
Additionally, U.S.-China trade tensions contributed to a steep decline in prices in the second halfof 2018.

Emergence of new producers. In the early 1970s,
aluminum production was highly concentrated ina few countries, notably the United States, theCopperGlobal recessions. The price troughs of 1999 and2001 stemmed, respectively, from the global
recession associated with the Asian financial crisisand the global slowdown of 2001. Similarly,
copper prices fell sharply during (and in someG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

Aluminum prices have experienced 10 troughs while copper prices haveundergone 8 since 1970. The troughs in aluminum and copper prices havegenerally been associated with global recessions or slowdowns and theemergence of new producers.

A. Aluminum price

B. Primary production andconsumption of aluminumLog price
9.0

Percent of global

8.5

80

40
7.5

20
0

The methodology identifies ten troughs for coffee(Arabica) prices since 1970 (table A3.1.1). The
troughs were associated mostly with weatherrelated supply shocks and the emergence of new
producers. The most recent, pre-pandemic troughin 2019 was driven largely by prior surplus
production in Brazil, the world’s largest coffee
producer.
Weather. Following historically low levels in theearly 1970s, real coffee (Arabica) prices tripledduring 1975-77 and reached a record high in April
1977, following a major frost in Brazil (Akiyama
and Varangis 1990). As supplies recovered, andproducing countries failed to extend the

2020

2000

2010

2020

Consumption

E. Copper production

2015

2010

2005

2000

2020

2015

2010

2005

0.0

2000

0.2

7.5
1995

0.4

8.0
1990

0.6

8.5

1985

9.0

1980

9.5

Kg per $1,000 of GDP
1.0
Aluminum Copper
0.8

1975

Log price
10.0

2020

2020

2015

2010

2005

2000

1995

1990

1985

1980

1975

2010

Production

D. Intensity of use of aluminum andcopper in global GDP

F. Copper consumption
Percent of global
Percent of global
United States Japan India China (RHS)
60

Percent of global
14
Peru
Congo, Dem. Rep.
12
Zambia10
Kazakhstan
8

25
20

40

15

6

10

4

20

5

2

0
2020

2018

2016

2014

2012

2010

2008

2006

2004

2002

2000

0

Coffee2000

C. Copper price1970

Emergence of new producers. After a decade oflargely stagnant mine production, the discovery of
new supply sources and new technologies, that
reduced processing costs, played an important rolein driving down copper prices from 2011 to 2015.
During this period mine supply grew strongly,
particularly in the Democratic Republic of Congo,
Kazakhstan, Peru, and Zambia (figure 3.8).
Between 2010-16, copper mine production
increased by 27 percent, almost three times as fast
as between 2004-10.

1970

7.0

Shifting demand. Over the past half century,
copper demand has been dampened bysubstitutions toward aluminum, plastics, and glass
fiber. Aluminum has been the predominant
substitute, gaining substantial market share andsuppressing copper’s relative price (Radetzki2009).

China
Russian Federation
India
United States
Canada

60

8.0

1995

Technological innovations. During the 1980s and1990s, technological innovations reduced costs ofcopper production. An important breakthroughwas the development of the solvent extraction andelectrowinning technology, which extracted
copper through dissolution and subsequent
electrolysis instead of mining. By 1995, thisprocess accounted for 27 percent of U.S. primary
copper output, up from 6 percent in 1980
(Radetzki 2009).

FIGURE 3.8 Aluminum and copper

0
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
2019
2021

cases after) the global recessions of 1982, 1992,
and 2020.

123

CHAPTER 3

Sources: World Bank; World Bureau of Metal Statistics.
A.C. Real price of aluminum and copper in logs, respectively. Shaded areas indicate global
recessions and downturns as in Kose, Sugawara, and Terrones (2020), and the global recession of2020. Data from January 1970 to October 2021.
B. Aluminum production and consumption as shares of global production and consumption,
respectively.
D. Aggregates calculated using GDP weights at average 2010-19 prices and market exchange rates.
E.F. Percent of global copper production and consumption, respectively. Figures show yearly
averages using monthly data.

International Coffee Agreement, real coffee prices
declined, reaching a trough in late 1992.11 Morerecently, in 2016, the end of a drought supported

11 The International Coffee Agreement operated an export-quota
system, which was first implemented in 1963, and continued
intermittently until 1989.

124

CHAPTER 3

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

FIGURE 3.9 Coffee

Global commodity prices

Coffee prices have seen 10 troughs since 1970. The troughs have been
associated mostly with commodity-specific supply and policy shocks andless so with variations in global aggregate demand.
A. Coffee priceB. Coffee production

Log price
3.5

Percent of global production
40
1980
30

3.0
2.5

2000

2020

VietnamMexico

2020

2015

2010

2005

2000

1995

1990

1985

1980

1975

1970

0.0

Indonesia

00.5

Côte d'Ivoire

10

1.0

Brazil1.5

Colombia20

2.0

Sources: U.S. Department of Agriculture; World Bank.
A. Real prices in logs. Shaded areas indicate global recessions and downturns as in Kose, Sugawara,
and Terrones (2020), and the global recession of 2020. Data from January 1970 to October 2021.
B. Major coffee producers and their share of global production since 1980.

the coffee harvest in Brazil and contributed to thetrough in coffee prices in that year.
New producers. Following a price recovery withpeaks in 1994 and 1997, both associated withweather events (frost in Brazil and El Niño in
Peru, respectively), prices began declining again in
response to supplies from a new entrant—
Vietnam. Vietnam’s emergence as a major
Robusta coffee producer altered the landscape ofthe global coffee market for the long term:
Vietnam now accounts for nearly 20 percent of
global coffee supplies, up from less than 0.1
percent in 1980 (figure 3.9). A few smaller coffee
producers have increased supplies as well,including Honduras, Nicaragua, and Peru.

Drivers of commodity
cyclesSince the mid-1990s, movements in commodity
prices, especially those of industrial commodities, havebecome more synchronized. A common global factor
explains about one-third to two-fifths of the variation
in industrial commodity prices, but only one-eighthof the variation in agricultural commodity prices.
Both aggregate demand and supply shocks have been
important drivers of this common global factor,
together accounting for almost two-fifths of its
variability.

Methodology. A dynamic factor model in thespirit of Kose, Otrok, and Whiteman (2003) is
adopted to analyze the comovement of commodity prices (box 3.1 and annex 3.3). The
common factor derived from 39 commodity priceseries (in real terms) represents global commodity
price growth (henceforth referred to as the global
factor).
Main components of global commodity prices.
The global factor plays an important role indriving fluctuations in industrial commodity
prices. During 1970-2021, it accounted for asmuch as 22-37 percent of the variation in theprices of base metals, rubber (used in tires andtubes), and platinum (used in catalytic converters),
and 18 percent of the variation in energy prices,on average. In contrast, only 2-14 percent of thevariation in the prices of agricultural commodities(excluding rubber), precious metals, and fertilizers,
on average, is accounted for by the global factor.
The relatively larger contribution of the global
factor in explaining the variation in the prices ofindustrial commodities reflects the strong responseof metal and energy consumption to industrial
activity (Baffes, Kabundi, and Nagle 2021). This
is in contrast to agricultural commodities (foodand beverages) where supply shocks, resulting
mainly from weather conditions and policies,
typically play a larger role than demand-side
factors.
Components of global commodity prices overtime. The contributions of the global factor toindustrial
commodity
pricesincreasedconsiderably over time. For energy, base metals,
and platinum prices, its estimated contribution
was roughly twice as large for the 1996-2021
period as for the whole sample period, 1970-2021.
This increased comovement in global commodity
prices from the mid-1990s accompanied a broader
trend
towardgreatercomovementinmacroeconomic variables, such as inflation andoutput (Eickmeier and Kühnlenz 2018; Ha, Kose,
and Ohnsorge 2019). The increased synchronization of commodity prices can partly be
attributed to trade liberalization and theG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

CHAPTER 3

BOX 3.1 Drivers of commodity cycles

Commodity prices have exhibited increasingly synchronized booms and busts in the past five decades. Macroeconomic shocks
have become the main source of the greater comovement in a broad set of industrial commodity prices (energy, base metals,
platinum, and natural rubber).

Introduction
The widespread collapse in commodity prices in early
2020, triggered by the pandemic-induced global
recession, was followed by a synchronized sharp
rebound, with several prices reaching all-time highs.
Synchronized booms and busts in commodity prices
have been common in recent decades: the most recent
preceding boom was the prolonged, broad-based rise in
prices in the early 2000s resulting from the increase in
the global demand for commodities that was triggeredby strong growth in EMDEs, especially China (Alquist,
Bhattarai, and Coibion 2020; Baffes and Haniotis
2010; World Bank 2015b). This upswing in prices was
disrupted by the global financial crisis of 2007-09, withprices declining in unison to early-2006 levels by theend of 2008.
Several studies have examined common cycles incommodity prices and their underlying causes. A
popular view in the literature that attempts to provide amacroeconomic explanation for this phenomenon isthat there is a common component of commodity pricefluctuations that may be captured by measures of theebbs and flows in global economic activity (Alquist,
Bhattarai, and Coibion 2020; Byrne, Sakemoto, and Xu
2020; Chiaie, Ferrara, and Giannone 2017; andMarañon and Kumral 2019). However, fluctuations in
global economic activity alone do not explain the evolution of commodity prices. Other factors, particularly
supply conditions within and across commodity
markets, are likely to be key determinants ofcommodity price cycles (Borensztein and Reinhart
1994; Cashin, McDermott, and Scott 2002). For
example, the 2006-08 spike and the 2014-16 collapse incommodity prices were caused by factors other thanglobal demand.
Fluctuations in factor input costs, for example the pricesof oil and other energy products, could affect a wide
range of commodity markets simultaneously. Energy isboth a key input in the production of metals and an

Note: This box was prepared by Alain Kabundi, and is based on
Kabundi and Zahid (forthcoming).

important cost component for most grain and oilseed
crops, through both direct (fuel prices) and indirectchannels (chemical and fertilizer prices). Thus, whenenergy prices increase, the costs of these commodities go
up concurrently (Baffes 2007). Similarly, for annual
crops, prices could be synchronized because of input
substitutability. Often weather patterns (for example,
the El Niño or La Niña phenomena) increase or reduce
production across a number of commodities (WorldBank 2015b). Yet another strand of the literature hasargued that the comovement in commodity prices ispartly a response to the financialization of commodities,
especially following the price boom of the late 2000s(Le Pen and Sévi 2018; Ohashia and Okimoto 2016).
Against this background, this box asks the following
questions:•

How has commodity price comovement evolved
over the past five decades?

•

What have been the main drivers of common cycles
in commodity prices?

•

How have the main drivers of common commodity
price fluctuations differed during historical episodesof commodity booms and slumps?

Global commodity prices
Data and methodology. Global commodity prices aredefined as the common factor among 39 monthly
commodity prices, derived from a dynamic factor
model as in Kose, Otrok, and Whiteman (2003).a The
commodity price data are obtained from the WorldBank Commodities Price Data (the Pink Sheet),which covers more than 70 commodity prices andindices. All price series are expressed in real terms
by deflating the nominal series, in U.S. dollar terms,
by the U.S. Consumer Price Index CPI). Series thata. A number of other studies use a similar framework to examine
determinants of commonalities in commodity prices; see, for instance,
Byrne, Fazio, and Fiess (2013); Lombardi, Osbat, and Schnatz (2010);
Poncela, Senra, and Sierra (2014); and Vansteenkiste (2009). However,they focus on a subset of commodities.

125

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

CHAPTER 3

BOX 3.1 Drivers of commodity cycles (continued)
FIGURE B3.1.1 Global commodity prices
Movements in global commodity prices have often been associated with global cyclical developments as well as
developments specific to commodity markets. Industrial commodity prices, such as those of energy and metals and minerals,
have been largely driven by global commodity price movements. Industrial commodity prices have become more
synchronized since the mid-1990s.
A. Global commodity price growth

B. Commodity price variation due to theglobal factor, 1970-2021 and 1996-2021(indexes)

C. Commodity price variation due to theglobal factor, 1970-2021 and 1996-2021(commodities)

Percent

Percent

30

60
50
40
30
20
10
0

Percent
60

10

RubberPlatinum

Tin

Zinc

Lead

NickelCopperIron ore

Coal

Aluminum0

Crude oil

2021

2016

2011

2006

2001

1996

1991

1986

1981

1976

-30

20

Fertilizers-20

1970-2021

30

Agriculture-10

1996-2021

40

Precious
metals01970-2021

50

Metals,minerals10

1996-2021

Energy

20

1971

126

Sources: Baumeister and Hamilton (2019); Ha, Kose, and Ohnsorge (2021); World Bank.
A. Year-on-year increase in global commodity prices (in U.S. dollars), defined as the common factor of 39 commodity prices in a one-factor dynamic factor model as in
Kose, Otrok, and Whiteman (2003). Gray bars indicate global recessions as identified in Kose, Sugawara, and Terrones (2020). Red lines indicate events specific tocommodity markets, including the first oil price shock of October 1973; the Iranian revolution in January 1979; the beginning of the Gulf War in August 1990; thememorandum of understanding between Australia, Canada, the European Union, Norway, the Russian Federation, and the United States to cut aluminum production;
OPEC meetings to ease production quotas (December 1985 and November 2014); and selected OPEC meetings to reestablish production quotas (December 2016,
1998-1999).
B.C. Share of variation in month-on-month growth of 39 commodity prices—3 energy commodities, 7 metal and mineral commodities, 3 precious metals, 5 fertilizers, and21 agricultural commodities—accounted for by the global factor and derived from a one-factor dynamic factor model as in Kose, Otrok, and Whiteman (2003). Bars show
consumption-weighted averages. Whiskers indicate range from minimum to maximum during 1996-2021 (B).

are either averages or close substitutes of other series
are excluded to avoid introducing price comovement byconstruction. This leaves 39 commodity prices. The
resulting common factor is a standardized (demeaned
with unit standard deviation) representation of global
commodity price growth.
Evolution of global commodity prices. Global
commodity prices often increased and declined with theglobal business cycle but also with specific events, inparticular those related to commodity markets (figure
B3.1.1). For example, prices peaked just before theglobal recessions of 1975, 1982, 1991, 2009, and 2020,
dropped during those recessions, and subsequently
rebounded. In addition, global commodity prices
underwent large swings around specific commodity
market events, such as the oil price jumps of 1973-74,
1978-79, and 1990-91.
Main components of global commodity prices. The
global factor has played an important role in driving
fluctuations in industrial commodity prices. During
1970-2021, it accounted for 18 percent of the variationin energy prices, on average, and 22-37 percent of thevariation in the prices of base metals, rubber (used intires and tubes), and platinum (used in catalytic
converters). It accounted for only 2-14 percent of thevariation in agricultural commodity prices (excluding
natural rubber), precious metal, and fertilizer prices.
The larger contribution of the global factor in
explaining the variation in industrial commodity prices
reflects the strong response of metal and energy
consumption to industrial activity, a relationship thathas been established by several studies. b Indeed, metal
prices, especially copper, are often considered barometers and leading indicators of global economic activity(Bernanke 2016; Hamilton 2015). This is in sharp
contrast to agriculture where supply shocks (primarily
driven by weather conditions and policies) dwarf
demand shocks. These results are consistent with otherstudies which find that the dynamics in industrial
commodity prices are mainly explained by transitoryb. See, for example, Davutyan and Roberts (1994); Labys, Achouch,and Terraza (1999); Marañon and Kumral (2019); Roberts (2009); andStuermer (2018).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

CHAPTER 3

BOX 3.1 Drivers of commodity cycles (continued)

shocks, whereas agricultural commodities respond more
to long-term shocks (for example, Baffes and Kabundi2021).
Main components of commodity prices over time. In
recent decades, the contribution of the global factor toindustrial commodity prices has increased considerably:
it doubled for energy prices and nearly doubled for basemetal and platinum prices during 1996-2021 comparedwith 1970-2021. This increased comovement in global
commodity prices from the mid-1990s was part of abroader trend toward greater comovement inmacroeconomic variables such as inflation and output(Ha, Kose, and Ohnsorge 2019; Eickmeier and
Kühnlenz 2018). Trade liberalization and expanding use
of financial instruments for commodity market trading
have also contributed to the increased synchronizationof commodity prices.

three categories: Global demand shocks, global supply
shocks, and commodity price shocks. The shocks are
identified using a set of sign restrictions on interactions
between the three variables in the FAVAR on impact(annex 3.3). The restrictions to identify the structural
shocks are consistent with theoretical predictions (Fryand Pagan 2011) and follow other empirical studies in
the literature (Charnavoki and Dolado 2014; Peersman
2005; Peersman and Straub 2006).
•

A positive global demand shock is assumed toincrease global industrial production, inflation, andcommodity prices.

•

A positive global supply shock is assumed to raise
global industrial production and reduce global
inflation; in commodity markets, it lifts global
consumption of commodities and, hence, raises
commodity prices.

•

A positive commodity price shock is defined asraising commodity prices and global inflation but
depressing global industrial production. Suchshocks could reflect a wide range of commodity
market developments that are unrelated to global
demand or supply, including geopolitical risks,financialization of commodity markets, andexpectations of future demand or supply pressures.

Drivers of commodity prices
Data and methodology. A factor-augmented vector
autoregression (FAVAR) model is estimated with threeglobal variables—global consumer price inflation, global
industrial production growth, and global commodity
price growth—all expressed in month-on-month log
changes over 1970-2021, in seasonally adjusted terms,
with twelve lags (annex 3.3). By construction, themethodology is designed to analyze the links between
short-term fluctuations in the global economy andglobal commodity markets, not long-term trends.
Global industrial
production is defined as the global economic activity
index of Baumeister and Hamilton (2019). c Global
consumer price inflation is defined as the medianheadline CPI inflation in up to 143 economies from
Ha, Kose, and Ohnsorge (2021). d
Identification of shocks. While the specific nature ofshocks changes over time, they can be grouped intoc. While the baseline regression relies on industrial activity as ameasure of economic activity, a robustness test is conducted using the
global composite purchasing managers’ index. The index is available atmonthly frequency and includes services activity. The results are
qualitatively robust to the use of this indicator.
d. The results remain qualitatively similar when CPI inflation for theOrganisation for Economic Co-operation and Development (OECD)
countries or the common factor of headline CPI inflation for 143
countries is used as a measure of global inflation.

Note that these global demand and supply shocks differ
materially from the commodity demand and supply
shocks modelled in Kilian and Murphy (2014) andothers (Baumeister and Hamilton 2019; Jacks andStuermer 2020). Here, an increase in both economic
activity and commodity prices can reflect either a global
demand or global supply shock—depending on
movements in global inflation. Either of these two
global shocks drives up commodity demand, consistent
with the definition of a commodity demand shock in
Kilian and Murphy (2014). But, in the latter anincrease in both economic activity and commodity
prices reflects a commodity demand shock, in contrast
to a commodity supply shock which is associated withan increase in commodity prices but a decline ineconomic activity.
Evolution of global demand, supply, and commodity
shocks. The model identifies a series of global demand,
global supply, and commodity price shocks from 1970

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CHAPTER 3

BOX 3.1 Drivers of commodity cycles (continued)
FIGURE B3.1.2 Contributions of global shocks to commodity prices
Global supply shocks have had larger and more persistent effects than global demand shocks on global commodity prices.
Since the mid-1990s, global demand and supply shocks have accounted for the lion’s share of global commodity pricevolatility. In the global recession of 2020, both global demand and commodity market shocks depressed commodity prices,while supply shocks supported commodity prices.
A. Global demand shocks

B. Global commodity market shocks

C. Response of global commodity prices
to 1 percent increase in global demand

Percent

Percent

Percentage point

15

15

10

10

5

5

0

0

-5

-5

-10

-10

1.2
0.8
0.4
0.0

D. Response of global commodity prices
to 1 percent increase in global supply

E. Contributions of global shocks tovariation in global commodity prices

Percentage point

Percent

Percentage points
20

2021

10

40

0.4

20

Global supply
Global demand
Global commodity markets

-20

0

4

8
Months121601970-2021

1996-2021

After

0.0

During

-30

1975

1982

1991

2009

After

60

After

0
-10

During

80

0.8

16

F. Contributions of global shocks tovariation in global commodity prices
around recessions and slowdowns

After

1.2

12

During1.6

8Months

After

2.0

4

During100

Commodity markets
Global demand
Global supply

0

During

2.4

2017

2012

2008

2004

2000

1996

1992

1988

1984

1980

1976

1972

2021

2017

2012

2008

2003

1999

1994

1990

1985

-0.8

1981

-20

1976

-15

-20

1972

-15

-0.4

2020

Sources: Baumeister and Hamilton (2019); Ha, Kose, and Ohnsorge (2021); World Bank.
Note: Global demand, supply, and commodity shocks identified using sign restrictions in a FAVAR of month-on-month, seasonally adjusted changes in global inflation,
global commodity prices, and global industrial production. All in year-on-year growth rates.
A. Gray shades indicate quarters with global recessions (1975, 1982, 1991, 2009, 2020) and global slowdowns (1998, 2001, 2012) as defined in Kose, Sugawara, andTerrones (2020).
B. Red lines indicate events specific to commodity markets, including the first oil price crisis in October 1973; the Iranian revolution in January 1979; the beginning of the
Gulf War in August 1990; the memorandum of understanding between Australia, Canada, the European Union, Norway, the Russian Federation, and the United States
to cut aluminum production; OPEC meetings to ease production quotas (December 1985 and November 2014); and selected OPEC meetings to reestablish production
quotas (December 2016, 1998-1999).
C. D. Solid line indicates cumulative median response of global commodity price growth to a 1 standard deviation (about 1 percent) increase in global demand (C), or
global supply (D). Dotted lines indicate 16-84th credible intervals.
E. Contribution of global demand, supply, and commodity market shocks to variance of month-on-month growth in commodity prices, based on sample covering 19702021 and sample covering 1996-2021.
F. Cumulative historical decomposition of global commodity price growth into global demand, supply, and commodity market shocks between the last month before
global recessions and the last month of global recessions (“During”) as well as between the last month of the global recession and 12 months later (“After”). Global
recessions are defined as in Kose, Sugawara, and Terrones (2020).

onward. These shocks have often been associated withturning points in the global business cycle and sharp
movements in oil prices (figure B3.1.2).

economy began to slide into a global recession or
slowdown.
••

Global demand shocks. Negative global demand
shocks were associated with global recessions (1975,
1982, 1991, and 2009) and slowdowns (1998,
2001, and 2012). Large positive global demand
shocks often occurred in the year before the global

Global supply shocks. The widespread rise ininflation amid slow growth during the 1970s andearly 1980s has been partly attributed to negative
global supply shocks—such as the 1973-74 and1978-79 oil price shocks and productivity growth
slowdowns that reflected expanding government

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

CHAPTER 3

BOX 3.1 Drivers of commodity cycles (continued)

sectors, macroeconomic volatility, and regulatory
uncertainty (Bjork 1999; Charnavoki and Dolado
2014; CBO 1981). The global economic recovery
starting in the late 1990s, however, has been
attributed to positive global supply shocks
associated with rising productivity growth linked toadvances in information technology, rapidly rising
investment, as well as widespread tradeliberalization and global value chain integration in
EMDEs, especially China. e•

Commodity price shocks. Positive commodity priceshocks were associated with the oil price crises in1974 and 1979, the beginning of the Gulf war in
1990, a memorandum of understanding amongmajor producers to cut aluminum production in
1994, and a general strike in República Bolivariana
de Venezuela in 2002-03 that disrupted oil
production. Organization of the Petroleum
Exporting Countries (OPEC) agreements to easeproduction cuts in December 1985 and November
2014, both in response to the emergence of new
producers, were associated with negative commodity price shocks. In addition, new producers
entered global markets for several commodities in
the 1980s (for example, coal and palm oil), the1990s (for example, aluminum, coffee, and grains),
and in the 2010s (for example, copper, soybeans,and shale oil), reflected declining or negative
commodity price shocks.

Drivers of global commodity prices
Responses of global commodity prices to global
shocks. Since 1970, on average, a 1 percent increase inglobal demand has raised global commodity pricegrowth by up to 0.4 percent over the subsequent six
months but the impact has dissipated thereafter. In
contrast, global supply and commodity market shocks
had longer-lasting impacts (figure B3.1.2). The
persistence of the response of commodity prices tocommodity market shocks can partly be attributed to a
low elasticity of supply because of considerable leadtimes between resource discovery and production
e. The important role played by supply shocks in the late 1990s is
consistent with other studies; see Charnavoki and Dolado (2014); Dieppe(2020); Kabundi and Zahid (forthcoming); Kotwal, Ramaswami, andWadhwa (2011); Topalova and Khandelwal (2011); World Bank
(2020c); and Zhu (2012).

(World Bank 2016). A 1 percent increase in global
supply raised global commodity price by 1.6 percentage
points over the following 7 months and the effectremained statistically significant for a year and a half.
Similarly, a 1 percent global commodity market shockthat raises commodity prices was followed by more than
1.1 percent higher commodity prices within 12 monthsof the shock that persisted for at least a year and a half. f
Contributions of global shocks to global commodity
prices. In the full data period since 1970, shocks
specific to commodity markets have been the main
source of variability in global commodity prices,
accounting for more than 60 percent of the variance of
global commodity prices. g These shocks have included
major disruptions to oil markets, the collapse of theSoviet Union in the case of agricultural and metals
markets, and the emergence of new producers of metals
and agricultural commodities in the 1970s, 1980s, and1990s. Since 1996, however, global macroeconomic
shocks have been the main source of commodity pricevolatility: global demand shocks account for 50 percent
and global supply shocks for 20 percent of the varianceof global commodity prices. h
Evolution of the contributions of global shocks tocommodity prices. Global recessions have typically
been associated with demand weakness and supply
disruptions (outside commodity markets) that depressedcommodity prices—although sometimes offset bycommodity-specific market developments; theseunwound in the rebounds of activity that followed
global recessions. Outside global recessions, in the
1970s and 1980s, positive global supply shocks haveoften depressed global commodity prices; this reversed
temporarily between 2000 and the global financial crisis
when rapid global value chain integration andproductivity growth—positive supply shocks—liftedcommodity prices. During global recessions, demandf. The magnitude of these estimates appears to be somewhat larger
than in Charnavoki and Dolado (2014), possibly because they restrict theelasticities of commodity demand to economic activity.
g. The predominant role of commodity-specific shocks in thevariability of commodity prices is in line with Charnavoki and Dolado(2014), Ha et al. (2019), and Jacks and Stuermer (2020).
h. These numbers refer to the variance decompositions for one-yearahead forecast errors of global commodity price growth. Over a mediumto long-term (5-10 years) forecasting horizon, the variance contributionof the global commodity shocks (57 percent) is greater than that of global
demand shocks (28 percent) since global commodity shocks are more
persistent than demand shocks.

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BOX 3.1 Drivers of commodity cycles (continued)

pressures on commodity prices were compounded bysupply pressures specific to commodity markets (1975,
1991, 2020); in 1975 and 2020, these were offset by
supply pressures resulting from large-scale tradeembargoes (1975; Jacks and Stuermer 2020) or widespread supply chain disruptions (2020; Mahajan andTomar 2020). Commodity price increases in recoveries
from global recessions were driven by an unwinding of
supply or commodity market shocks and, since 2000,
also by rebounds in demand. The most recent surge incommodity prices can be explained by the growth ofdemand, combined with supply bottlenecks.

Conclusion
The results provide evidence of a global cycle incommodity prices whose global determinants havebecome more important over time: the global
commodity factor has accounted for an increasing
fraction of commodity price volatility over the past two
decades. The role of the global factor in capturing pricemovements is largest, and has increased the most, forindustrial commodities, consistent with the close link
between demand for these commodities and global
economic activity. In contrast, it explains a smaller
fraction of ﬂuctuations in agricultural crops andfertilizers, since demand for these commodities is lessclosely linked to global economic activity. These results
suggest that a synchronized surge in industrial
commodity prices will likely have widespread eﬀects ona large set of EMDEs that are heavily dependent oncommodities for revenues.
Global macroeconomic shocks have become the main
source of ﬂuctuations in commodity prices, accounting
for more than two-thirds of the variance of global
commodity price growth. Collapses in commodity
prices during global recessions have been driven by
weaknesses in demand and supply disruptions outsidecommodity markets. The recent rebound in prices
following the COVID-19 recession is attributed to thegrowth of demand, combined with supply disruptions.

expanding use of financial instruments forcommodity market trading.12
Drivers of commodity prices
Methodology. To analyze the links between shortterm fluctuations in global economic activity andglobal commodity prices, a factor-augmented
vector autoregression (FAVAR) model is estimatedwith three global variables—global consumer priceinflation, global industrial production growth, andglobal commodity price growth—over 1970-2021(annex 3.3 and box 3.1). Global demand shocks,
global supply shocks, and commodity price shocks
are identified using a set of sign restrictions on theinteractions between these three variables on
impact.
Responses of global commodity prices to global
shocks. Since 1970, global supply and commodity

12 See, for example, Alquist, Bhattarai, and Coibion (2020);
Fernández, Schmitt-Grohé, and Uribe (2020); Stuermer (2017 and2018); and Tang and Xiong (2012).

market shocks have had longer-lasting impacts on
global commodity price growth than global
demand shocks. The persistent response ofcommodity prices to commodity market shocks
can partly be attributed to a low elasticity ofsupply resulting from the considerable lead times
between resource discovery and production(World Bank 2016). It can take anywhere from a
few years to several decades to develop resources,
depending on the type of resource, the size andgrade of the deposit, financing conditions, andcountry-specific factors (UNECA 2011).
Contributions of global shocks to global
commodity prices. Since 1996, global macroeconomic (demand and supply) shocks have been
the main source of commodity price volatility.
Global demand shocks have accounted for 50
percent and global supply shocks for 20 percent of
the variance of global commodity price growth.13

13 These numbers refer to the variance decompositions for oneyear-ahead forecast errors of global commodity price growth.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

This is in contrast to the 1970-96 period whenshocks specific to commodity markets—such as
the 1970s and 1980s oil price shocks—were themain source of variability in global commodity
markets, accounting for more than 60 percent of
the variance of global commodity price growth.
The responsiveness of commodity prices todemand shocks may have increased over time on
account of the rapid global trade and financial
integration that occurred during the late 1990s
and the 2000s.
Evolution of the contributions of global shocks tocommodity price movements. Global recessions
were associated with demand weakness and supply
disruptions that depressed commodity prices—
although sometimes offset by commodity-specificmarket developments. During global recessions,
global demand pressures on commodity prices
were compounded by pressures specific tocommodity markets (1975, 1991, 2020); in 1975
and 2020, these were partly offset by supply
pressures resulting from large-scale tradeembargoes (1975; Jacks and Stuermer 2020) or
widespread supply chain disruptions (2020;
Mahajan and Tomar 2020). Commodity priceincreases in recoveries from global recessions were
driven by an unwinding of supply or commodity
market shocks and, since early 2000, also byrebounds in demand. Consistent with this, thesurge in commodity prices in 2020-21 can be
explained by a strong resurgence of demand, andunusually widespread supply bottlenecks.

Policy options
EMDEs generally need to take policy steps to placethem on a firmer footing to manage futurecommodity price shocks, including those stemmingfrom the energy transition and the effects of climatechange on weather patterns. Countries can adjust
their fiscal, monetary, and macroprudential policy
frameworks to cushion the impacts of commodity
price movements. They can also take structural policy
measures to reduce reliance on commodities byencouraging diversification of exports and national
asset portfolios, and reducing distortions arising fromsubsidies.

CHAPTER 3

Need for policy action
The preceding analysis finds evidence of
substantial comovement between different
commodity prices and between commodity prices
and global economic activity, and thesecomovements appear to have intensified over time.
With commodity dependence—in relation to
fiscal and export revenues, and economic activity—being a persistent characteristic of many
EMDEs, these findings again bring to the fore thecomplex challenges faced by these countries inmaintaining economic resilience in the face ofcommodity price fluctuations. In LICs, thesechallenges are compounded by severely
constrained fiscal space and weak institutions. The
wide commodity price swings of the past two years
have further underscored the vulnerabilities of themany EMDEs highly dependent on commoditybased exports.
Countries whose exports are heavily concentrated
in one or a few commodities tend to experiencehigh volatility in their terms of trade and output
growth (Baxter and Kouparitsas 2005; Blattman,
Hwang, and Williamson 2007; Lederman and Xu
2007). Such macroeconomic volatility increases
uncertainty about prospects for growth, relative
prices, and the real exchange rate, which in turn isdetrimental to private investment. Governments
have had difficulties in establishing macroeconomic policy frameworks that are effective in
helping maintain steady growth in the face ofcommodity price swings (IMF 2015b; UNCTAD
2021). This chapter provides evidence that pricebooms, on average, have been more pronounced
than price slumps for all major commodity
groups. One of the policy lessons is that countries
can take steps before a crisis caused by a priceslump to reduce their exposure and create policy
space to prepare for future shocks, including bysaving windfall revenues and building fiscal buffers
during good times, such as the recent surge incommodity prices.
Further, the rise in inflationary pressures during
2020-21 has pushed up near-term inflation
expectations in many EMDEs, prompting several
central banks—in both commodity-exporting

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and -importing countries—to raise policy interest
rates. Remaining vigilant about incoming
economic data and being prepared to act quickly,
as needed, will be paramount if inflation
expectations become de-anchored.

associated with commodity price booms andslumps. Fiscal rules, appropriate exchange rate
regimes, prudential and regulatory policies, andcapital flow management measures are among thepolicy options.

Commodity cycles have often created financial
market booms and busts in EMDEs. These
involve international capital flows and the supplyof domestic credit. Commodity booms havefrequently encouraged a surge in capital inflows,
and a build-up of debt by domestic borrowers thatproved excessive when the bust arrived. Strong
growth in domestic credit has usually exacerbatedthe resulting financial stability issue. Such capital
inflows have also caused real appreciations of thedomestic currency—whether through nominal
currency appreciation or domestic inflation—that
damaged the competitiveness of the noncommodity sector, and hence held back economic
diversification. Surges in capital inflows andgreater risk tolerance by lenders, stemming from
commodity price booms, can thus lead to financial
crises when commodity prices collapse andfinancial conditions tighten.

Fiscal frameworks

The insights from this study, together with therecent impacts on commodity-dependent EMDEsof the commodity price fluctuations and global
trade and supply disruptions triggered by the
pandemic, present an opportunity to consider
policy options for coping with commodity pricecycles, including the one currently underway.
Recent events have also highlighted how climatechange is a growing risk, including to energy
markets, affecting both demand and supply(World Bank 2021a). Countries have multiple
policy options to smooth the near- and long-term
effects of commodity price swings and, more
broadly, to reduce their reliance on commodities.
These options naturally depend on countryspecific circumstances, but they can be grouped
into macroeconomic and regulatory policies, andstructural policies to encourage diversification ofnational assets (human and physical capital, andinstitutions) and exports.
Macroeconomic and regulatory policies
Fiscal, monetary, and regulatory frameworks canbe constructed to moderate business cycles

Challenge: Procyclical fiscal policy. Swings incommodity-based fiscal revenues in EMDEs tend
to result in procyclical fiscal policy: spending
typically rises when commodity prices are highand falls when commodity prices decrease.14 This
leads to instability in public investment—one of
the few sources of discretionary spending in many
countries. This spending procyclicality has been
attributed to governments’ inability to resist thetemptation to increase spending in response to a(possibly temporary) rise in government receipts
from taxes or royalties in booms (Frankel 2017).
In addition, this procyclicality tends to beasymmetric between booms and slumps: spending
typically rises more than proportionately during a
resource boom but falls less than proportionately
during a slump, reducing net public savings (Gillet al. 2014). For EMDEs, limited financial depth
during crises constrains government borrowing
and limits the use of fiscal policy as a countercyclical policy instrument (Caballero andKrishnamurthy 2004).
Fiscal rules and stabilization funds. A number ofcommodity-exporting EMDEs have enacted fiscal
rules that work in conjunction with stabilization
funds. These signal the intent of the government
to dampen, if not eliminate, the procyclicality of
government spending and safeguard long-term
fiscal plans. In practice, the effectiveness ofstabilization funds in moderating fluctuations ingovernment spending, and hence output, hasvaried across countries (Gill et al. 2014). Whilestabilization funds in many EMDEs have not been
deployed successfully, in part on account of poor
fiscal governance, the experience has been positive,

14 This historical procyclicality is well-documented. See, forexample, Arezki, Hamilton, and Kazimov (2011); Cuddington(1989); Frankel, Végh, and Vuletin (2013); Gavin and Perotti(1997); Ilzetzki and Végh (2008); Talvi and Végh (2005); andTornell and Lane (1999).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

on the whole, for smoothing the path ofgovernment spending (Sugawara 2014).15 Among
oil-exporting countries, stabilization funds havebeen associated with reduced macroeconomic
variability and lower inflation (Shabsigh and Ilahi2007). A firm long-term political commitment,
and an appropriate institutional framework,
provide the key to their effectiveness (Asik 2017;
Bagattini 2011; Ossowski et al. 2008).16 The latterincludes transparent governance of the stabilization fund itself, and prudent constraints on thediscretion of fund managers. Cross-country
evidence shows a strong causal link running frombetter institutions to less procyclical or morecounter-cyclical fiscal policy in EMDEs (Frankel,
Végh, and Vuletin 2013).
In sum, stabilization funds provide a tool that canbe used to help implement a fiscal policy that
promotes macroeconomic stability and that issustainable over time. But their mere existence
does not guarantee that governments will use
them in this way. Long-term political commitment to a steady and sustainable fiscal policy, asound debt management framework, debt
transparency, and good governance are essential tothe effective use of stabilization funds.
In addition to fiscal rules and stabilization funds,
the risk to fiscal revenues from commodity pricefluctuations can be mitigated through financial
hedging instruments. State-contingent debt
instruments can, in principle, also be used to help
better manage public debt in the face ofmacroeconomic uncertainty. In practice, however,these novel instruments face obstacles from highpremiums demanded by investors in the earlystages of market development, costly state
verification, and the possibility of moral hazard(Benford, Best, and Joy 2016).
Country examples. The experiences of Chile andNorway underscore the importance of the good
15 Political instability has been found to be detrimental to thesuccess of stabilization funds. Changes in government can lead tomodifications to the rules and operations of the stabilization fund, orits discontinuation (for example, post-2002 Républica Bolivariana deVenezuela or post-2003 Ecuador).
16 This is in line with the “voracity effect,” where countries withthe weakest institutions tend to spend more during revenue windfalls(Tornell and Lane 1999).

CHAPTER 3

governance of institutions and sound macroeconomic management for the functioning ofstabilization funds. Both countries have managed
relatively successfully their dependence on natural
resources by virtue of their institutional capitaland good fiscal frameworks, offering useful lessons
for other resource-rich countries (Gill et al. 2014).
Chile’s economic performance in recent decades is
an example of sound macroeconomic management, underpinned by a combination of
inflation targeting, a fiscal rule, a free-floatingexchange rate, an open capital account, andsovereign wealth funds (SWFs). Chile’s Fiscal
Responsibility Law is supported by the fiscal ruleand two formal SWFs (to manage budget
surpluses arising from copper price booms). While
there is broad consensus that these mechanisms
have helped limit the impact of external shocks onfiscal spending and the business cycle, trade-offs
have been apparent in the operation of thesefunds: rules leave substantial space for discretionand their implementation requires judgements(Addison and Roe 2018).
Norway is another example where fiscal rules havehelped discipline policies, while providing thenecessary flexibility to respond to shocks. An
example of financial hedging instruments isprovided by Mexico’s oil hedging program.
Mexico’s government has been hedging oil-relatedrisks to public finances for at least two decades
through this program.17
Monetary policy frameworks
Challenge: Constraints on monetary policy. A
challenge facing central banks of commodityexporting countries relates to the effects of booms
in the prices of their export commodities on
incomes and, hence, domestic demand.
Tightening monetary policy in the face of acommodity price boom can stem the demand for
credit and lead to an appreciation of the exchange
rate. These responses will, if well calibrated,
appropriately dampen the build-up of inflationarypressures. However, in the case of temporary

17 Ecuador, Ghana, and Uruguay have also relied on hedging
instruments to guard against oil price volatility.

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FIGURE 3.10 Structural policies and capital flow
volatility in EMDEs
Commodity-exporting EMDEs have undertaken significant reforms ofmonetary, fiscal, and financial sector policy frameworks over the last two
decades, particularly since the 2007-09 global financial crisis. Capital flow
volatility tends to be somewhat higher for commodity exporters thanimporters, highlighting the need to further develop macroprudential tools.

A. Commodity-exporting EMDEs withfiscal rules or inflation targeting

B. Commodity-exporting EMDEs
governed by fiscal rules, inflation
targeting, or flexible exchange rates

Index
Number of countries
Adopted inflation targeting
Adopted fiscal rules
25
7Central bank transparency (RHS)
6
20
5
15
4

Percent of GDP
60

3

10

2019

40

20

2

5

1

C. Macroprudential policy in EMDEs
2017

2000

0
Flexible exchange Inflation
rates
targeting

2018

2015

2012

2009

2006

2003

0
2000

0

Index
3.5

1999

2007

D. Volatility of capital inflows to
EMDEs, 1970 to 2020
Percent of GDP
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5

3.0
2.5
2.0
1.5
1.0
0.5

Fiscal rules

Percent of GDP
Commodity exporters
Commodity importers
0.08
0.04
0
-0.04
-0.08

0.0
EMDEs

Commodity
exporters

Commodity
importers

Exchange rate flexibility, inflation targeting.
Terms-of-trade volatility makes a country lesssuited to a fixed exchange rate and more suited toa flexible exchange rate regime, since it allows
accommodation of such shocks and, hence, acountercyclical monetary policy.18 For example,
during a commodity price collapse, an inflationtargeting central bank under a floating exchange
rate regime can continue to focus squarely on
keeping domestic inflation on target over the
medium term. In contrast, a central bank in a
fixed rate regime would be forced to tighten policy
in response to a negative terms of trade shock,exacerbating its negative domestic macroeconomic
impact. For such reasons, many EMDEs haveadopted an inflation-targeting, floating exchange
rate regime (figure 3.10). While inflation targets
are typically based on the CPI, some studies haveargued for other forms of inflation targeting—pegging the export price, pegging an export priceindex, and product price targeting—where prices
of export commodities are given greater weight(Frankel 2011).

Equity net
flows

Debt net
FDI netflows
flows (RHS)

Sources: Ceruti, Claessens, and Laeven (2017); Dincer, Eichengreen, and Geraats (2019); Ha, Kose,
and Ohnsorge (2019); International Monetary Fund; World Bank.
Note: EMDEs = emerging market and developing economies.
A. B. An economy is considered to be implementing a fiscal rule if it has one or more fiscal rules on
expenditure, revenue, budget balance, or debt. Inflation targeting as classified in the International
Monetary Fund’s Annual Report of Exchange Arrangements and Exchange Restrictions. Index ofcentral bank transparency is the sum of the scores for answers to the fifteen questions (min = 0,
max = 15) as specified in Dincer, Eichengreen, and Geraats (2019).
B. Flexible exchange rate regimes are defined as “floating” or “freely floating” exchange rate regimes.
GDP aggregation at 2010 prices and exchange rates. Gray line indicates 50 percent.
C. Sample includes 123 emerging market and developing economies (EMDEs). Unweighted averageof the Macroprudential Policy Index of Cerutti, Claessens, and Laeven (2017). The Macroprudential
Policy Index measures the number of tools used by authorities and is based on a simple sum of up to
12 including, but not limited to, the countercyclical capital buffer and loan-to-value ratios.
D. Bars indicate the standard deviation of net equity, debt, and foreign direct investment (FDI)
inflows as percent of GDP. Net inflows are defined as net acquisition of financial assets minus net
incurrence of liabilities. Yellow whiskers indicate the range of minimum and maximum net inflows
in percent of GDP.

shocks, there is a risk that a hike in interest rates,with the associated increase in short-term capital
inflows and appreciation of the domestic currency,
may negatively affect demand and activity in thenonresource sector and add to economic volatility.
If the shock is permanent, policy measures toboost investment and productivity in thenonresource sector may be called for (Masson2014).

In contrast, for very small, very open economies orfor LICs with less developed financial and foreignexchange markets, a fixed exchange rate can offersome advantages (Frankel 2017). In particular, if
the central bank is not able to commit credibly to
an inflation target, an exchange rate pegged to thecurrency of a country whose central bank has suchcredibility may provide an alternative, highly
visible, nominal anchor for the economy. However, persistently high inflation, which remains a
key challenge in many EMDEs, is incompatible
with both credible inflation-targeting and a fixedexchange rate. In such cases, the adoption of acoherent, consistent, and clearly communicatedset of policies, including sustainable fiscal policies,
will be required to establish credibility and reduce
inflation inertia (Drechsel, McLeay, and Tenreyro2019).
Country examples. Several commodity-exporting
EMDEs, including Indonesia, Thailand, South
Africa, and many Latin American economies, have

18 See, for example, Berg, Goncalves and Portillo (2016); Broda(2004); Céspedes and Velasco (2012); Edwards and Levy-Yeyati(2005); and Rafiq (2011).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

made progress toward enhancing their monetary
policy frameworks by adopting inflation targeting.
The Central Bank of Chile was an early adopter of
inflation targeting in 1990, when inflation stoodat about 25 percent. Under the regime, which forseveral years included capital controls to restrain
exchange rate appreciation, inflation fell gradually,reaching 5 percent by 1998 (Drechsel, McLeay,
and Tenreyro 2019). Judicious management of
the flexible exchange rate enabled Chile to avoid
the real exchange rate misalignments that plagued
Latin American countries with fixed rate regimes.
The policy framework acquired substantial
credibility such that from 1999 the government
was able to remove capital controls and allow thecurrency to float freely.
Prudential and financial regulatory policies
Challenge: Procyclical capital flows. Commodity
price fluctuations often lead to sharp movements
in asset and credit markets, and in international
capital flows, that amplify business cycles incommodity-exporting countries (IMF 2012).
Capital flows to developing countries tend to bepro- rather than countercyclical (for example,
Kaminsky, Reinhart, and Végh 2004). The
concentration of wealth in one sector in many
commodity exporters can exacerbate systemic risk,
highlighting the need for prudential tools to
promote financial stability (Arezki et al. 2018).
Micro- and macroprudential policies, capital flow
management measures. Since 2008, there has
been a significant push toward the enhancementof microprudential policies to strengthen theresilience of individual institutions as well as
macroprudential tools to limit systemic risk.
Macroprudential policies include capital andliquidity requirements for banks and otherfinancial institutions, countercyclical capital
buffers, restrictions on foreign currency borrowing, limits on loan-to-value ratios in housing
finance, and limits on the accumulation of shortterm debt. These policies might be more
important for resource-dependent countries thanothers, given the higher volatility in resourcerelated revenues and capital flows (Beck 2018;
figure 3.10). At the same time, microprudential
regulation and supervision can be strengthened to

CHAPTER 3

protect the safety and soundness of individual
financial institutions.
Another type of measure to manage volatility incapital flows in certain circumstances is capital
controls which, unlike macroprudential measures,are explicitly designed to limit capital flows andthe financial risks arising from these flows.
However, like other tools aimed at addressing risks
associated with capital flows, capital controls have
limitations and may have unintended side effects.
For instance, macroprudential policy measures
aimed at banks may encourage the provision of
credit by entities not covered by the regulations
and operating under less transparent or effective
regulatory frameworks, such as foreign and nonbank institutions.
Country examples. Chile has implemented anumber of macroprudential policies that havehelped foster financial stability since the 1980s.
Chile’s General Banking Act and its supervisory
approach to banking have strong macroprudential
components. Additionally, Chile’s monetary
policy framework, underpinned by a credible
inflation-targeting regime and a flexible exchange
rate, helps to prevent the accumulation offinancial vulnerabilities. Peru—another commodity exporter—raised reserve requirements onbank’s short-term foreign exchange liabilities in2007. This resulted in a lengthening of thematurity of foreign exchange liabilities, in turn
reducing the country’s vulnerability to swings incapital flows (Armas, Castillo, and Vega 2014).
With regard to the use of capital controls, Brazil isthe most cited example, because of its size among
EMDEs, its active experimentation with manydifferent forms of controls on capital inflows, andits integration with global financial markets.
Evidence suggests that the controls on capital
inflows implemented between late-2009 and 2014
had some success in segmenting the Brazilian
financial market from global financial markets(Chamon and Garcia 2016).
Structural policies
In addition to implementing policy frameworks tomitigate the macroeconomic consequences of

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commodity reliance, commodity exporters can
reduce their commodity reliance itself. Policies toencourage export diversification as well as reduce
distortions arising from subsidies can help achieve
this objective. Beyond diversifying exports andeconomic structures, which is difficult in practiceand many not be feasible in the short-run,governments could also focus more on diversifying
their “national asset portfolios”—the mix ofnatural resources, human and physical capital, andeconomic institutions (Gill et al. 2014; WorldBank 2021b).
For oil and gas exporters, the prospect of a longterm decline in demand for fossil fuels arisingfrom the energy transition presents an opportunity
to increase diversification. At the same time, itbrings new structural challenges not least in theform of risks to their narrow revenue andemployment base in carbon-intensive activities.
The need for diversification is particularly pressing
for higher-cost producers of fossil fuels as they willbe the first to be affected by stagnant or declining
demand. For agricultural exporters, the energy
transition and expected increase in biofuels may
provide an opportunity to increase production ofrelated crops. It may also lead to higher foodprices if rising biofuel production increases
competition for land and other inputs.
Encouraging export diversification
Challenge: Investment discouraged by export
volatility. Commodity price volatility leads toinstability in export earnings for countries that are
reliant on production of these commodities. This
creates macroeconomic risks that discourage
investment by risk-averse firms, and thus hinder
long-term growth. Export diversification could,
thus, help to stabilize export earnings and promote
growth in the long run (Bleaney and Greenaway
2001; Ghosh and Ostry 1994; Hesse 2008).
However, resource-rich countries face significant
challenges in achieving diversification (see forexample, Harding and Venables 2016). For
instance, in oil exporters that are members of the
Gulf Cooperation Council (GCC), several factors
have impeded diversification, including the effectof oil revenues on governance and institutions,and a tendency for oil revenues to lead to

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

overvalued real exchange rates, which damage thecompetitiveness of domestic non-oil activities(Callen et al. 2014). Oil exporters have faced
challenges in successfully diversifying exports
without facing either depletion, secular decline inprices or externally imposed sanctions (forexample, Ross 2019).
Domestic investment, vertical and horizontal
diversification, business climate reforms. A
holistic approach toward reducing commodity
reliance might involve the gradual scaling up of
domestic fixed investments in real assets while
diversifying the tradable sectors (Chang andLebdioui 2020). A prudent approach to resource
rent management can ensure the efficientinvestment of resource rents towards productive
capacity-building and long-term economic
development. With regard to the tradable sectors,
policies can support export diversification andsophistication by encouraging firms to take on
several stages of production (vertical diversification). Governments could also encouragefirms to diversify their output mix (horizontal
diversification) with an emphasis on innovationand technological upgrading (Cherif and Hasanov
2014).
Successful diversification experiences in threeoften-cited country cases, Malaysia, Mexico, andIndonesia, have also involved reforms to improvethe business environment, develop infrastructure,and support workers in acquiring skills andeducation to boost productivity (Callen et al.
2014). Cross-country studies find evidence thatdiversification of exports and government revenues
away from commodities strengthens long-term
growth prospects and resilience to external
shocks.19
Country examples. Indonesia, Mexico, andMalaysia have been offered as examples ofsuccessful diversification away from oil while Chile
has had some success in diversification away fromcopper (Callen et al. 2014; Cherif and Hasanov
2014; and Salinas 2021). While each country
followed its own trajectory, a few common lessons
can be drawn. First, diversification took a long
19 For example, Hesse (2008); Papageorgiou and Spatafora(2012); and World Bank (2018a).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

time and took off only when oil revenues began todiminish (for example, in Mexico).20 Second,
these countries created incentives for firms todevelop export markets and supported workers
seeking to acquire skills and education to help findemployment in new areas. Such incentives forfirms included the development of highproductivity industrial clusters, and horizontal andvertical linkages within these clusters; attracting
foreign capital, including by creating free tradezones, to promote technological transfer; using
export subsidies, tax incentives, and easier accessto finance to facilitate risk-taking byentrepreneurs; and investment in training (Callen
et al. 2014).
Encouraging asset diversification: Building
human capital
Challenge: Disincentives for investment in
human capital. In several oil exporters, nationals
are predominantly employed in the public sector,
which typically offers relatively high salaries andbenefits. The structure of the labor market does
not provide much incentive to invest in humancapital and work in the private sector (Callen et al.
2014). The resulting lack of skills hinders theprivate sector’s ability to create high paying jobs to
attract workers.
Investment in training and education. Diversification can be facilitated by government investment in training to increase the availability ofhighly skilled workers. Efforts could include
investment in higher education, especially inscience and technology and vocational education.
Human capital accumulation can promote export
diversification partly by promoting innovation,
including the development of new products (Giri,
Quayyum, and Yin 2019). More generally,education is a key driver of long-term economic
growth and poverty reduction, not only throughthe promotion of innovation, but also through thestrengthening of institutions, including throughthe development of well-informed electorates

20 For example, Malaysia embarked on its export-oriented strategy
in the early 1970s and achieved rapid increases in export
sophistication in the next two decades. Nevertheless, it took morethan 20 years to reach a level of sophistication comparable toadvanced economies.

CHAPTER 3(World Bank 2018b). Research shows that, in
EMDEs, social returns to education are potentiallyhigher than returns to physical capital investment(OECD 2012; Psacharopoulos and Patrinos
2004).
Country examples. The successful diversification
experiences of oil exporters are few. They point to
the importance of changing the incentive structure
for workers and firms, as well as social attitudes
towardinvestmentinhuman
capital,entrepreneurship, and private sector employment(Cherif and Hasanov 2014). Malaysia and
Mexico—two frequently cited examples ofsuccessful diversification-—invested in training
workers, upgrading their skills, and sponsoringcertain workers for specialized foreign training.
Malaysia used public agencies to incentivize thecontinual retraining and skills upgrading of
employees. In addition, several agencies were
tasked with helping firms, particularly small andmedium-sized enterprises, through consulting
services in technological upgrading and exporting
to international markets. Over time, these efforts
contributed to building a more highly skilledworkforce (Callen et al. 2014).
Encouraging asset diversification: Reducing
barriers to competition
Challenge: Dominance of state-owned enterprises, barriers to entry. In many natural resourcerich countries, state-owned enterprises play animportant role in exploiting the resources andmanaging the extractive sector. For example, in
several oil-exporting countries, national companies
dominate oil and gas exploration and generatesignificant revenue for the state while leaving
governments with the costs and risks of
exploration. Although these companies are among
the biggest oil companies in the world, andcontrol more than half of global oil and gas
production, some of them are significantly lessefficient and profitable than their private counterparts (IMF 2020). More broadly, state-owned
enterprises accounted for more than half of all
infrastructure project commitments in EMDEs in2017 (World Bank 2017). The dominance oflarge state-owned enterprises in major sectors insome EMDEs may prevent private firms fromcompeting on a level playing field. The heavy

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debts incurred by underperforming state-owned
enterprises have also led to some serious fiscal
risks.
Integrating the private sector. Diversification
could benefit from reducing barriers to entry ofprivate sector firms, including through the breakup or exit of some state enterprises. Establishing alevel playing field, facilitating entry of more
efficient firms, and encouraging orderly exit ofless-efficient firms, can contribute to increased
productivity and a more diversified export base.
Country examples. In some examples oftechnological progress in partnership with theprivate sector, in commodity-dependent EMDEs(such as, Thailand, Indonesia, and Botswana), thegovernment played a critical role by putting inplace mechanisms that enabled the private sector
to thrive (UNCTAD 2021). Chile offers examplesof public-private partnerships, such as the establishment of the Competitiveness and Innovation
Fund in 2005, financed through a levy on mining,which focuses on developing sector clusters withprivate sector participation and partial funding. In
Botswana, the close partnership between thegovernment and the private sector in diamondmining is another example of public-private
partnerships. In Malaysia, efforts to promote
exports and foreign direct investment were
facilitated by a transparent legal framework andbusiness-friendly regulations, which discour-aged
rent-seeking and provided a relatively level playing
field for domestic and foreign enterprises (Gill et
al. 2014).
Undertaking subsidy reforms
Challenge: Costly and poorly targeted subsidies.
Subsidies—explicit or implicit—can erode fiscal
space and detract from potentially moreproductive spending, including investment ininfrastructure, health, and education, as well as
poverty alleviation. Energy subsidies andoverconsumption may also lead to a deteriorationin the balance of payments due to lower exports(for energy-exporting countries) or higher energy
imports (for energy-importing countries). Suchsubsidies can also crowd out public investment
and encourage more intensive use of fossil fuels(Arze del Granado, Coady, and Gillingham 2012).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

Cut poorly targeted subsidies. Reforms to poorly
targeted subsidies in many EMDEs can help maketheir economies and public finances more resilient
to commodity price fluctuations. Following the
2014-16 oil price plunge, many energy-exporting
EMDEs undertook energy subsidy reforms todiscourage
wasteful
energy consumption,
reallocate spending to programs better targeted tothe poor, and restore fiscal space (IMF 2017;
World Bank 2020b; World Bank 2020c).
Likewise, energy-importing EMDEs took
advantage of lower oil prices during this period tobegin dismantling energy subsidies, which tend to
disproportionately benefit higher-income groups.
Other reforms undertaken by some countries
included raising taxes on energy or energydependent sectors, such as transportation,
alongside measures to prevent energy subsidies
from re-emerging if oil prices rebounded. Despite
such progress on subsidy reforms, there remains
significant further room in both energy exporters
and importers to address long-standing inefficiencies and reduce fiscal costs in the long run. To
protect the more vulnerable groups, subsidy cuts
can be accompanied by strengthened social
assistance programs.
Country examples. Morocco embarked on a fixedprice fuel subsidy program in late 2000 in
response to the then-large increases in oil prices.
The government absorbed the cost ofadministering lower retail prices while trying tolimit the regressivity of the scheme. As oil prices
continued to rise through the 2000s, the fiscal
costs of the subsidies also increased. In early 2012,
the government took several measures to reformthe price structure of fuels together with othersteps to increase funding for public transportationand subsidize medical expenses for the poor. Overthe next year and a half, subsidies continued to be
reduced, with most cuts occurring before the large
fall in oil prices starting in mid-2014. Fuel prices
were fully liberalized in 2015.

Conclusion
Commodity price cycles. The preceding analysis
finds evidence of substantial comovement between
commodity prices, and between commodity prices
and global economic activity. These comovements

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

appear to have intensified over time. The role ofthe common global factor in capturing pricemovements is largest, and has increased the most,for industrial commodities, consistent with theclose link between demand for these commodities
and global economic activity. Global demand
shocks, such as recessions, have accounted for halfof the variability of global commodity prices since
the mid-1990s. Commodity price shocks, such as
those arising from adjustment to long-term trends
in supply and demand have accounted for justunder a third of the variability in global
commodity prices since the mid-1990s.
Since many EMDEs, and especially LICs, remain
heavily reliant on commodities, these findings
underscore some complex, shared policy
challenges faced by these countries. In the years
ahead, the transition away from fossil fuels to lowcarbon technologies will cause profound, farreaching shifts in the pattern of demand andsupply for commodities, which can have major
macroeconomic consequences, including income
effects. For example, demand for metals andminerals required as inputs in renewable energy
generation will go up, while increased demand forbiofuels might put pressure on food prices. If a
synchronized surge in industrial commodity prices
were to happen, it will likely have widespread
effects on many EMDEs that are reliant oncommodities for revenues. In LICs, especially, thiswould test the strength of institutions and policy
frameworks, which tend to be weaker than inother EMDEs. In any event, the energy transition
will undoubtedly cause further swings incommodity prices, of a magnitude no less thanthat of the cycles observed in recent decades.
Macroeconomic policy options. To manage themacroeconomic volatility that often accompanies
heavy resource reliance, EMDEs are well advised
to adopt fiscal, monetary, and regulatory policy
frameworks that safeguard macroeconomic andfinancial stability. These include a forwardlooking fiscal policy framework that smooths
government spending during the ups and downsof resource-based revenues. For example, oil
exporters could use the current opportunity
afforded by higher oil revenues to rebuild policy
space and direct spending toward addressing

CHAPTER 3

139

TABLE A3.1.1 Peaks and troughs in price cycles forselected commodities since 1970
Commodity Peaks
Crude oil
1974M1
1977M1
1979M11
1987M7
1990M10
1996M10
2000M11
2008M7
2012M3
2018M10
Coal

Aluminum

Troughs
1970M12
1975M12
1978M10
1986M7
1988M10
1994M3
1998M12
2001M12
2010M7
2016M1
2020M4
1981M4
1970M12
1988M12 1987M6
1995M7
1994M4
2001M7
2000M3
2004M7
2002M8
2008M7
2005M11
2018M7
2016M1
2020M8
1975M3
1973M1
1980M2
1976M6
1983M9
1982M6
1988M6
1985M11
1995M1
1993M11
2000M9
1999M3

Commodity Peaks
Aluminum (cont.)
2006M5
2011M4
2018M5
CopperTroughs

2002M8
2009M2
2015M11
2020M4
1974M4
1972M10
1980M2
1977M8
1988M12 1986M11
1995M7
1993M11
2000M9
1999M3
2011M2
2001M10
2018M1
2016M1
2020M4

Coffee (Arabica)
1974M2
1977M4
1984M41991M3
1994M9
1997M5
2005M3
2011M4
2016M11

1971M10
1975M4
1981M6
1989M10
1992M8
1995M12
2002M8
2006M6
2013M11
2019M5

Source: World Bank.
Note: Data from January 1970 to October 2021. The analysis is based on real commodity prices.

longer-term challenges. With respect to monetary
policy, countries practicing credible, flexible,
inflation targeting, with a floating exchange rate,
have performed relatively well. In addition, a welldesigned regulatory framework for the financial
sector that fosters financial stability helps moderate
the procyclical tendency of capital inflows. Special
measures may sometimes be warranted to preventthe excessive build-up of foreign debt.
Structural policy options. To tackle themacroeconomic volatility induced by commodity
price cycles at its source, EMDEs can encourage
greater diversity of their economic base. Measures
include efforts to diversify exports, build humancapital, promote competition, and cut distorting
subsidies. The appropriate responses are, however,inherently country-specific, and need to be
tailored to country characteristics, such as thedegree of commodity reliance, fiscal space, theflexibility of markets, and the quality of domestic
institutions.

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

FIGURE A3.1.1 Commodity prices
A. Crude oil

B. CoalLog price
5.5

Log price
5.5

5.0

5.0

4.5
4.0

4.5

3.5

4.0

3.0
3.5

2.5
2.0

C. Copper

D. AluminumLog price
10.0

Log price
9.0

9.5

2020

2015

2010

2005

2000

1995

1990

1985

1980

1975

1970

2020

2015

2010

2005

2000

1995

1990

1985

1980

1975

1970

3.0

commodities; (2) a cycle (peak-to-peak or troughto-trough) must be at least 24 months, which is
the minimum time needed to encompass at least
two harvests for annual crops; 21 (3) smoothing is
not applied to the original price series; and (4) thealgorithm is applied to the actual level of prices(deflated by the CPI), rather than trend-adjusted
price series. After dating the turning points, theprice series can be separated into boom and slump
phases.

ANNEX 3.2 Methodology for
estimating concordance

8.5

9.0
8.0

2020

2015

2010

2005

2000

1995

1990

1985

1980

1975

2020

2015

2010

2005

2000

1995

1990

1985

1980

7.0

1975

7.5
1970

8.0

7.5

1970

8.5

Source: World Bank.
Note: Log real prices of commodities. Shaded areas denote slump phases (contractions). Turning
points are identified using the business cycle algorithm of Harding and Pagan (2002) with a phase ofat least 12 months long and a cycle of at least 24 months long. Data from January 1970 to October
2021.
A. The oil price shown is the unweighted average of Brent, Dubai, and WTI oil prices.

Comovement is measured by the proportion oftime that two time series Yi,t and Yj,t spend in thesame phase (Harding and Pagan 2002). Let Si,t be
a dichotomous variable that takes the value unity
when the series Yi,t is in a boom phase and zero
when it is in a slump phase. Sj,t is defined in thesame way for Yj,t. The degree of concordance inthe cycles of the two series, Yi,t and Yj,t , isC ij =

ANNEX 3.1 Methodology fordating turning points
Turning points are identified using the algorithm
proposed by Harding and Pagan (2002), which isitself based on Bry and Boschan (1971) that was
initially set out to identify business cycle
consistent with the NBER dating. Peaks (troughs)
in series yt are identified whenever {yt > (<) yt+k}
where k = 1, …, K, and K = 12 in line with
Cashin, McDermott, and Scott (2002). The
minimum cycle is 24 months. From these turning
points, a binary variable can be generated suchthat St = 1, when the commodity cycle is incontraction, while St = 0 in expansion.
The original Bry-Boschan algorithm is modified totake into account peculiarities of commodity
prices, following Harding and Pagan (2002) andCashin, McDermott, and Scott (2002).
Specifically, (1) a phase (from peak to trough orfrom trough to peak) must be at least 12 months
long owing to the dominance of the annual
production process in many agriculturalT
1T  ( S i ,t S j ,t ) +  (1 − S i.t )(1 − S j , t ) T  t =1t =1where T is the sample size. Concordance isbounded between 0 and 1, and two independent
random walks have a concordance of 0.5.
Harding and Pagan (2006) suggest a simple t-test
based on the correlation coefficient ρ between Si,tand Sj,t . Under the null hypothesis of no concordance, this coefficient is equal to zero. The
following regression can be used to estimate ρ andcalculate the heteroscedastic and autocorrelation
corrected t-statistic:
S j ,t
Si ,t
=α+ ρ
+ εt
σˆ Si
σˆ S j21 While different phase and cycle lengths might be more
appropriate for certain commodities, the approach adopted here has
the advantage of maintaining a consistent set of parameters enablinga systematic examination of commodity prices. Shorter minimum
phase lengths were chosen to examine the sensitivity of the identified
turning points to the choice of phase length. Although the exercise
yielded more turning points, for most commodity price series, at least
70 percent of the identified peaks and troughs overlapped with those
identified when the minimum phase of 12 months was used.

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CHAPTER 3

TABLE A3.2.1 Concordance matrix of real commodity prices
Coal
Coal

Crude oil

Aluminum

Copper

Maize

Coffee1

Crude oil

0.677***

Aluminum

0.671***

1
0.634***

1

Copper

0.713***

0.728***

0.809***

Maize

0.679***

0.635***

0.596*

0.720***

1

Coffee

0.493

0.546

0.520

0.543

0.506

1
1

Source: World Bank.
Note: Concordance measures the proportion of time that two price series are in the same phase. It is equal to one if both series are in the same phase at any time. ***, **, and * indicatesstatistical significance at the 1%, 5%, and 10% level, respectively, based on the hypothesis test proposed by Harding and Pagan (2006). Data from January 1970 to October 2021.

where i =1,…,N and t =1,…,T, and b1,i arefactor loadings that reflect the degree to which
fluctuations in yi,t can be accounted for by thecommon global factor. The idiosyncratic disturbance, εi,t , follows an AR(q) process.

where
S S 
Cov  i ,t , j , t  σ S σ S  Cov ( S , S )
j 
j ,t
i ,t
 i
ρ=
=
 S j ,t 
σ S j σ SiVar  σS  j 
Cov ( S j ,t , S i ,t )=Var ( S j ,t )Var ( Si ,t )ε i,t =ρ i (L)ε i,t-1 +ζ i,twhere ρi (L) is a lag polynomial operator,
ζ i ,t ∼ N ( 0 , σ 2ζ ) and E(ζ i,t ζ j,t ) = 0 for all I ≠ j.

σˆ Si and σˆ S are the estimated standard deviationjof Si,t and Sj,t respectively. The results of theconcordance in the cycles of commodity prices areshown in table A3.2.1.

ANNEX 3.3 Methodology:
Factor-augmented vector
autoregression
Econometric model
Consider a panel of N commodity price series,
each of length T. To ensure stationarity, the modelis expressed in growth rates. Suppose yi,t represents
the growth rate of the price of commodity i at time
period t. yi,t is assumed to be decomposed into
two components: a common global factor, ft , that
affects all commodity price series, and an
idiosyncratic disturbance, εi,t , that only affects an
individual commodity price series i.
The growth rate of commodity price series i attime period t can, thus, be written asyi,t= bo,i+b1,ift + εi,t(A3.3.2)(A3.3.1)

The global factor follows an AR(p) process suchthat
(A3.3.3)
f t =ϕ(L)f t-1 +v t
where ϕ(L) is a lag polynomial operator,
vt ∼ N (0, σ 2v ) with σ 2v normalized to 1 andE(ζ i , t v t )=0, innovations ζ i , t and v t are contemporaneously uncorrelated, all comovement isaccounted by the common factor. Thus, thegrowth rate of each commodity price is driven by
a global factor that affects all commodity prices
and an idiosyncratic disturbance.
To find the contribution of the global commodity
factor to the fluctuations in different commodity
prices, the variance of each observed variable can be
decomposed as follows:var (yi,t) = (b1,i)2 var ( ft)(A3.3.4)

The drivers underlying the dynamics of thecommon factor, ft , are identified using a factoraugmented vector autoregressive (FAVAR) model.
The model comprises, in addition to the common
factor estimated by the dynamic factor model
described by equations (A3.3.1)-(A3.3.3), amonthly index of world industrial production, qt ,developed by Baumeister and Hamilton (2019),

141

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

and a measure of global inflation, πt , proxied by amedian inflation rate constructed from theseasonally adjusted consumer price indexes of 143
countries developed by Ha et al. (2019). The data
covers the period from January 1970 to September
2021.
We employ the following general form of astructural FAVAR model:

B0 yt =  i =1 Bi yt −i + wt
p

(A3.3.5)

where yt is K × 1 vector that contains theendogenous model variables, wt is the K × 1 vectorof mutually uncorrelated structural shocks, B0 is
the structural impact multiplier matrix that
describes the contemporaneous relationships
among the model variables, Bi is the matrix ofcoeﬃcients, and p is the lag length. The reduced
form errors can be written as ut = B0−1 wt , where

ut = yt −  i =1 Ai yt −ip(A3.3.6)

A i = B0−1 Bi and E(ut ut')=Ʃu is a K × K reduced
form variance-covariance matrix. Thus, given the
structural impact multiplier matrix, B0, thereduced-form innovations can be represented asweighted averages of the mutually uncorrelated
structural shocks, wt. However, since the model
parameters are not uniquely identiﬁed, further
identifying restrictions are required to estimate B0.
The analysis identiﬁes three shocks: a global
demand shock, a global supply shock, and acommodity-speciﬁc shock as underlying drivers of
the common global factor of commodity prices.
The identiﬁcation scheme is based on sign
restrictions applied to the matrix B0. Speciﬁcally,
the following sign restrictions are used
Global Demand + + −   ut
 Global Supply−1
ut =  + − +   ut
 = B0 wt
 Commodity −specific  + + +   utwhere signs are imposed on the elements of theinverse of the structural impact multiplier matrix,
B0−1 , and all shocks are normalized to increase thecommodity price factor.
The impact sign restrictions on B0 are in linewith theoretical predictions and other empiricalstudies.22 For example, positive global demand
shocks are characterized by positive comovementin the global industrial production index, global
inﬂation, and the common commodity pricefactor. Examples of positive global demand shocks
include an unexpected ﬁscal stimulus focusing oncommodity-intensive investment that increases
overall demand for commodities. A positive global
supply shock, on the other hand, increases global
output, decreases global inﬂation, and increases
commodity prices. Examples of global supply
shocks include technological innovation that raises
total factor productivity. This would also include
productivity-boosting economic reforms such as
trade liberalization and privatization measures(Charnavoki and Dolado 2014). An increase inproductivity would raise the marginal product of
all commodities and increase their overall demandand prices.
A positive commodity-speciﬁc shock decreases
output, increases global inﬂation, and increases
commodity prices. Commodity-speciﬁc shocks aredesigned to account for innovations to commodity
prices that are orthogonal to global demand andglobal supply shocks, such as unexpected shifts in
speculative or precautionary demand forcommodities (see Charnavoki and Dolado 2014;
and Kilian and Murphy 2014). These can be due
to ﬁnancialization of commodity markets or
geopolitical tensions (see Kilian 2009; Tang andXiong 2012).
A closer look at the above examples of eachpositive structural shock indicates that all threeshocks (i.e., global demand, global supply, andcommodity market-speciﬁc) increase demand forcommodities. This is consistent with the view thata synchronized increase in commodity prices
should, typically, be a result of shifts in commodity demand rather than commodity supply(Bilgin and Ellwanger 2017). Commodity supply
shifts are generally more idiosyncratic and only
aﬀect individual commodities. For example, alabor disruption that curtails steel production may

22 Charnavoki and Dolado (2014) identify global demand, global
supply, and commodity market shocks to examine their effects on
macroeconomic aggregates for a small commodity-exporting
economy. Ha, Kose, and Ohnsorge (2019) employ a similar
identification scheme to investigate the drivers of global inflation.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

not impact mine output for aluminum. An
exception is disruptions in supply of crude oil.
Since crude oil is used for producing othercommodities, crude oil supply disruptions canincrease the cost of production for othercommodities. Thus, a commodity market-speciﬁc
supply disruption through this channel can resultin a joint surge in commodity prices. In ourframework, this eﬀect would be captured as acommodity market-speciﬁc shock.
Similar models employing sign restrictions havebeen widely used in related studies. Charnavoki
and Dolado (2014) use a similar model but with asmaller group of commodities. Ha et al. (2019)
replace commodity prices with oil prices.
Gambetti, Pappa, and Canova (2005) and
Melolinna (2015) replace commodity prices withdomestic interest rates. Baumeister and Peersman(2013) and Kilian and Murphy (2014) replace
inﬂation with oil production and commodity
prices with oil prices.
Estimation
The global commodity factor is estimated using
equations (A3.3.1)–(A3.3.3) based on a state-spacesystem where equation (A3.3.1) corresponds to an
observation equation and equations (A3.3.2) and(A3.3.3) correspond to the transition equation.
The estimation of this system follows the Bayesianstate-space approach of Kim and Nelson (1998).23
The estimation objective is to infer from theobserved data: (1) the path of common factor, ft ,and (2) all unknown parameters of the model. The
Bayesian approach views these as two vectors of
random variables. Inference in the Bayesianframework is based on obtaining the joint andmarginal distribution of these given the historical
data on commodity prices, that is, obtaining the23 The classical approach to state-space modeling, which is basedon maximizing the likelihood function with respect to all theparameters, can be computationally inefficient in large scale models.
The Bayesian approach based on Gibbs sampling works with smaller
components of the model by drawing from conditional distributionsof the parameters (Blake and Mumtaz 2012). Another approach isthe nonparametric Principal Component Analysis (PCA), which iscomputationally faster and commonly used. However, the parametricstate-space approach to estimating factor models gives more accurate
variance decomposition estimates compared to PCA-based methods.
Jackson et al. (2015) provide a comparison of the different estimation
methods.

CHAPTER 3

joint and marginal posterior distributions of thecommon factor and model parameters. However,since the joint posterior distribution of these
vectors is not analytically obtainable, Gibbs
sampling is used to sample from the posterior.
The observed data series are represented by thevector yt. The Gibbs sampling proceeds by taking adrawing from the conditional distribution of themodel parameters given the data yt and the factor
ft , and then drawing from the conditional
distribution of the factor ft given data yt and theprior drawing of the model parameters. The
estimation of the model parameters given thefactor, ft , is straightforward. Note that by treating
ft as a set of data, generating the unknown
parameters of the observation and state transition
equations is a standard application of Bayesian
linear regression. However, sampling from theposterior of the autoregressive (AR) coeﬃcients is
not simple since the conditional distributions of ρ(L) and ϕ(L) are unknown. Therefore, the AR
coefficients are sampled using a MetropolisHastings algorithm (see Chib and Greenberg
1994; Otrok and Whiteman 1998). The latterstep involving the generation of the vector, ft , isbased on the multimove Gibbs-sampling (or theforward-backward) algorithm as described byCarter and Kohn (1994). This procedure allows
generating ft from the joint distribution.24
Using the Markov property of the state equation,
the joint posterior of ft can be factorized intop(fT | yt) and p(fs | fs+1, yt) for all s =1 …, T–1.
Since these two components are normally
distributed given that error terms in theobservation and state transition equations arenormally distributed, we can draw from
distributions by computing their mean andvariance. The Kalman ﬁlter is used to compute themean and variance of p ( fT | yt) and a backward
recursion provides the mean and variance ofp( fs | fs+1, yt). Thus, the Carter and Kohn (1994)
forward-backward algorithm delivers a draw of ft.25

24 Note that single move Gibbs sampling generates elements of ft
one at a time from the conditional distribution. The multi-move
Gibbs sampling procedure is computationally faster and more
efficient (Kim and Nelson 1999).
25 For a detailed exposition, see Blake and Mumtaz (2012),
Jackson et al. (2015), and Kim and Nelson (1999).

143

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CHAPTER 3

The estimation procedure can be summarized in
the following four steps:
1.

Conditional on ft , sample b and Ω from theirposterior distributions (where E (ζ t ζ′t ) = Ω ).

2.

Conditional on ft , sample ϕ(L) from its
posterior distribution.

3.

Conditional on the parameters of the state
space, b, Ω, and ϕ(L), sample ft from its
posterior distribution as described above.

4.

Repeat steps 1 to 3 until convergence.

The prior distribution for each model parameter is
speciﬁed by the mean and the standard deviation.
However, the priors are weak except thatstationarity is imposed on the AR coeﬃcients inequations (A3.3.2) and (A3.3.3). The priors for
the AR coeﬃcients in equations (A3.3.2) and(A3.3.3) are N(0, 10) and N(0, 10). The prior is
speciﬁed over the roots of the polynomial for thevariance terms and then translated into priors forthe coeﬃcients. The prior on all factor loading
coeﬃcients in equation (A3.3.1) is N(0, 1), where
the zero restrictions are appropriately applied. For
the prior on the innovation variances in equation(A3.3.1), IG(8, 0.25 2) is used, where IG() denotes
the inverse-gamma distribution.26 The AR parameters for equations (A3.3.2) and (A3.3.3) areconstrained to be stationary. The model isestimated for each commodity over 10,000 draws
after a burn-in of 5000 draws.
To generate the structural impulse response
functions from the FAVAR model, the procedureof Rubio-Ramírez, Waggoner, and Zha (2010) is
followed. This is done by ﬁrst drawing a K × K
matrix, X, of independent N(0, 1) values. Then the
QR decomposition of X is generated such that X =QR and QQ' = 1. The candidate solution, B̃ , canbe obtained as PQ, where P is the Cholesky
decomposition of the reduced form residuals. The
candidate solution is used to construct impulse
responses that are checked against the maintained
sign restrictions. These steps are repeated many

26 Note that the priors are loose and experimenting with tighter
priors suggests that results are not sensitive to these changes.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

times (1.5 million times here) and the results arerecorded accordingly.27
Data
The commodity price data is obtained from theWorld Bank Commodities Price Data (The Pink
Sheet), which contains data on more than
70 commodity prices and indices. Series thateither aggregates or close substitutes of other series
are excluded to avoid introducing price comovement by construction.
For example, Malaysian logs are included but
Malaysian sawn wood is excluded since the latteris produced by cutting the logs longitudinally.
Since these two commodities are close substitutes,
any changes in the price of sawn wood will closely
follow the fluctuations in the price of logs.
Similarly, soybeans prices are assumed to also
represent soybean oil and meal prices; global sugar
prices are assumed to also represent U.S. andEuropean sugar prices. The consumptionweighted average of liquid natural gas prices in the
United States, Europe, and Japan represents global
LNG prices; the unweighted average of Dubai,West Texas Intermediate, and Brent oil prices
represents global oil prices; and the unweighted
average of tea at auctions in Colombo, Kolkata,and Mombasa represents global tea prices. Finally,for coffee, rice, rubber, only series with data
available from 1970 are used (arabica coffee, Thai
5 percent rice, Singapore/Malaysia rubber).
This leaves 39 commodity prices (all in U.S.
dollars): 3 energy (oil, liquid natural gas, coal), 7
metals and minerals (aluminum, copper, iron ore,lead, nickel, tin, zinc), 3 precious metals (gold,
silver, platinum), 5 fertilizers (diammonium
phosphate, phosphate rock, potassium chloride,
triple superphosphate, urea), and 21 agriculturalcommodities (maize, rice, soybeans, coconut oil,palm oil, bananas, beef, chicken, orange, shrimp,
sugar, cocoa, coffee, tea, cotton, logs from
Cameroon and Malaysia, rubber, sawn wood,
tobacco, wheat). The resulting common factor is a
27 Since sign identified structural vector autoregressions are set
identified, the median target method is followed for reporting theimpulse responses (see Fry and Pagan 2011). The standard deviationsof all three shocks are about 1 percent.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

CHAPTER 3

standardized (demeaned with unit standard deviation) representation of global commodity pricegrowth.
Robustness tests
The robustness check consists of using the global
composite purchasing managers’ index (PMI) asa measure of economic activity in place of the
Baumeister and Hamilton’s global industrial
production index. This is done to capture theincreasing importance of the service sector in the
global economy, accounting for more than twothirds of global economic activity. The global
composite PMI obtained from J.P.Morgan covers
the period July 1998 to September 2021, based ondata availability. Overall, the results are
qualitatively similar. Commodity market shocks
remain the underlying driver of dynamics in global
commodity prices, followed by supply shocks. The
forecast error variance decomposition (FEVD)
depicts a slight decline in the contribution of thecommodity price shock, from 63 percent to 55
percent. In contrast, the contribution of demand
shocks rises by 7 percentage points, to 28 percentof the variation in commodity prices. Finally, thecontribution of supply shocks remains unchanged
at 17 percent of the variation of commodity
prices.
The results remain unchanged when the
Baumeister and Hamilton (2019) global industrial
production index and median headline CPI
inflation are replaced, respectively, by the common factors of industrial production covering 72
countries, and headline CPI inflation for 143
countries. In the FEVD, the contributions ofdemand and commodity-market shocks to commodity price variations decrease only marginally,
from 21 and 63 percent to 19 and 60 percent ofcommodity price variations, respectively. The
contribution of supply shocks increases from 17 to
21 percent of commodity price variations.

ANNEX 3.4 Additional tables
TABLE A3.4.1 Characteristics of commodity price cycles
Commodity

Number ofcyclesAverage duration (months)

Average amplitude (percent)

Slope

Booms

Booms

Booms

SlumpsSlumpsSlumpsEnergyCrude oil, Brent*

7

29

34

169

-59

5.8

-1.8

Crude oil, Dubai

8

33

32

253

-58

7.8

-1.8

Crude oil, WTI*

6

30

26

154

-57

5.1

-2.2

Crude oil, Average

9

28

31

219

-52

7.8

-1.7

Coal, Australian

6

34

48

136

-51

4.1

-1.1

Coal, South African*

6

23

41

118

-46

5.2

-1.1

Natural gas, Europe

6

32

51

139

-51

4.5

-1.0

Liquefied natural gas,
Japan*

5

38

44

87

-46

2.4

-1.1

Natural gas, U.S.

6

41

41

243

-65

6.0

-1.6

Natural gas index

6

47

34

185

-59

4.0

-1.7

Metals and minerals
Aluminum

8

27

35

89

-46

3.4

-1.3

Iron ore, cfr spot

7

27

41

112

-35

4.1

-0.9

Lead

11

24

24

127

-45

5.5

-1.9

Nickel

6

37

44

248

-65

6.9

-1.5

Tin

8

30

34

104

-46

3.6

-1.4

Zinc

9

26

32

151

-50

5.9

-1.6

Copper

6

33

46

153

-54

4.7

-1.2

145

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CHAPTER 3

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

TABLE A3.4.1 Characteristics of commodity price cycles (continued)
Number ofcyclesAverage duration (months)

Average amplitude (percent)

Slope

Booms

SlumpsBooms

SlumpsBooms

Slumps

Gold

5

53

49

201

-49

3.8

-1.0

Platinum

4

39

72

179

-54

4.7

-0.8

Silver

7

36

39

186

-49

5.2

-1.2

Banana, Europe*

3

20

53

93

-55

4.5

-1.0

Banana, US

7

32

40

119

-51

4.1

-1.3

Barley*

8

31

36

102

-53

3.3

-1.5

Beef

8

28

36

54

-36

2.0

-1.0

Meat, chicken

9

31

27

27

-25

0.9

-0.9

Cocoa

8

31

35

96

-49

3.1

-1.4

Coconut oil

9

33

24

185

-61

5.7

-2.5

Coffee, Arabica

8

30

33

151

-54

5.1

-1.6

Coffee, Robusta

6

33

49

228

-63

7.1

-1.3

Groundnuts*

9

21

26

95

-49

4.5

-1.9

Groundnut oil*

9

24

33

97

-48

4.0

-1.5

Lamb*

7

29

46

56

-33

1.9

-0.7

Orange

9

21

38

109

-51

5.6

-1.3

Palm oil

8

28

35

130

-55

4.7

-1.6

Fish meal*

7

27

35

79

-40

3.0

-1.1

Rapeseed oil*

3

27

26

62

-32

2.4

-1.2

Rice, Thai 5%

7

31

45

140

-53

4.5

-1.2

Rice, Thai A.1*

6

37

24

115

-45

3.1

-1.9

Maize

7

26

45

90

-48

3.5

-1.1

Sorghum*

5

29

65

110

-54

3.9

-0.8

Soybeans

7

24

49

79

-52

3.4

-1.1

Soybean meal

9

30

35

123

-47

4.1

-1.3

Soybean oil

9

23

34

102

-48

4.6

-1.4

Sugar, EU

7

28

44

65

-36

2.4

-0.8

Sugar, US

5

36

60

147

-50

4.2

-0.8

Sugar, world

6

39

41

219

-68

5.7

-0.6

Tea, avg 3 auctions

8

28

36

71

-42

2.6

-0.9

Tea, Colombo

7

38

36

153

-51

4.1

-1.4

Tea, Kolkata

5

52

41

126

-62

2.5

-1.5

Tea, Mombasa

6

39

48

71

-46

1.8

-0.9

Shrimps, Mexican

8

25

40

52

-35

2.1

-0.9

Wheat, US HRW*

8

28

37

105

-50

3.8

-1.4

Wheat, US SRW*

8

29

31

87

-48

3.1

-1.5

Palm kernel oil*

4

29

23

139

-57

4.9

-2.5

Commodity
Precious metals

AgricultureG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2022

CHAPTER 3

147

TABLE A3.4.1 Characteristics of commodity price cycles (continued)
Number ofcyclesCommodity

Average duration (months)

Average amplitude (percent)

Slope

Booms

Booms

Booms

SlumpsSlumpsSlumpsAgriculture: Raw materials
Cotton, A Index

9

24

33

98

-50

3.0

-1.5

Logs, Cameroon

9

25

33

52

-28

2.1

-0.8

Logs, Malaysian

12

21

27

28

-37

3.5

-1.3

Rubber, SGP/MYS

5

37

55

263

-66

7.3

-1.2

Rubber, TSR20*

2

32

37

200

-50

6.3

-1.4

Tobacco, US import u.v.

7

30

43

30

-25

1.0

-0.6

Plywood*

9

23

30

42

-32

1.8

-1.1

Sawnwood, Malaysian

7

45

38

51

-35

1.3

-0.9

DAP

10

27

27

131

-45

3.9

-1.7

TSP

7

26

46

167

-46

6.4

-1.0

Urea

8

24

39

316

-60

13.2

-1.5

Potassium chloride

6

27

57

103

-42

3.8

-0.7

Phosphate rock

6

36

42

286

-55

8.0

-1.3

Fertilizers

Source: World Bank.
Note: Cycles denotes the number of completed peak-to-peak cycles. Turning points are identified using the business cycle algorithm of Harding and Pagan (2002) with a phase of at least 12
months and a cycle of at least 24 months. Duration measures the average length (in months) of a phase (booms or slumps). Amplitude measures the average price change (in percentage
terms) from trough to peak for booms and from peak to trough for slumps. Slope measures the average price increase per month (in percentage terms) during booms and the average pricedecline during slumps. Data from January 1970 to October 2021. No turning points are identified for four commodities in the sample (sunflower oil; two varieties of rice; and sawnwood –Africa) because of gaps in the data. * denotes missing observations at the beginning and/or at the end of the sample.

TABLE A3.4.2 Global recessions anddownturns since 1970

Global episodeTime period

Associated peakin industrial
production

Global recession

1974Q1-1975Q1

1973M11

Global recession

1981Q4-1982Q4

1981M9Global recession

1990Q4-1991Q1

1990M8Global downturn

1998

1997M12Global downturn

2001

2000M12Global recession

2008Q3-2009Q1

2008M1

Global downturn

2012

2012M12Sources: Kose, Sugawara, and Terrones (2020); World Bank.

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