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Commodity Booms and Busts
Colin A. Carter, Gordon C. Rausser, and Aaron Smith*
Key WordsCommodity markets, asset bubbles, booms and busts
Abstract
Periodically, the global economy experiences great commodity booms and busts,
characterized by a broad and sharp co-movement of commodity prices. There
have been two such episodes since the Korean War. The first event peaked in
1974 and the second in 2008, thirty-four years apart. Both created major
economic and political shocks, including fallen governments and human
suffering due to high food prices. Each occurrence raised serious concerns over
food and energy security and led to more government intervention in the
commodity markets. While there is no simple explanation for what causes such
complex events, they do share similar characteristics. We find at the core of
these cycles a set of contemporaneous supply and demand surprises that
coincided with low inventories and macroeconomic shocks, and were magnified
by policy responses. In the next few decades the world faces the prospect of
continued increases in the demand for commodities and greater uncertainty
about supply. However, because market participants are likely to respond byincreasing inventory holdings and investing in new technologies, we see no
reason to expect an increase in the frequency of dramatic commodity booms and
busts.
* Colin A. Carter is Professor in the Department of Agricultural and Resource Economics
at UC Davis and Director of the Giannini Foundation; Gordon C. Rausser is the Robert
Gordon Sproul Professor in the Department of Agricultural and Resource Economics at
UC Berkeley; Aaron Smith is Associate Professor Department of Agricultural and
Resource Economics at UC Davis.
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I: Introduction
Commodity markets occasionally exhibit broadly based massive booms and busts.
These events affect the poors ability to purchase the most basic necessities such as food and
energy, and they often cause political unrest. Prominent riots generated by commodity
price spikes include the Peterloo Massacre in Manchester, England in 1819, the SouthernU.S. bread riots in 1863, and unrest in Haiti, West Africa and South Asia in 2008.
Commodity booms and busts thus resonate with the populace and affect social welfare in a
way that other asset price spikes do not.
Commodity booms and busts have the greatest economic and social impact on
developing nations, where most of the worlds population resides. Agriculture accounts for
a sizeable portion of economic activity in these countries, and households there spend a
large share of disposable income on food commodities. In addition, booms and busts can
have dire macroeconomic effects in developing countries because many of these economies
are highly dependent on commodity trade. In rich countries, booms and busts in energy
and industrial metal prices are more often salient than food price spikes. Most food in rich
countries is heavily processed, so the price of the raw commodity makes up a small fraction
of the retail price. On the other hand, energy prices have large effects on the retail cost of
transportation, heating, and cooling. Moreover, energy price spikes portend
macroeconomic recessions (Hamilton 2009).
Asset price booms and busts are not unique to commodity markets, although one of
the most widely cited examples of a price boom followed by a large crash took place in a
market for an agricultural commoditythe Dutch tulip mania of the 1630s. Other famousasset market booms and busts include the South Sea Company stock market crisis of 1719
20, the great stock market crash in 1929-32, the dot-com mania 1999-2000, the crash
following Japans asset price boom of 1986-91, and the global real estate boom and bust of
2003-08. The term bubble is often used to describe price booms and busts, especially in
the popular press. Most economists agree that an asset bubble exists when prices are driven
by trader beliefs peripheral to underlying supply and demand factors. For example, Stiglitz
(1990, p. 13) defined an asset price bubble as follows: [I]f the reason that the price is high
today is only because investors believe that the selling price will be high tomorrowwhen
fundamental factors do not seem to justify such a pricethen a bubble exists.
Garber (1990) studied spot and futures prices for rare tulip bulbs during the Dutch
tulip mania and found that market fundamentals were the most important factor driving
prices at that time, and not irrational behavior. However, Garber also concluded that during
the last month of the tulip bulb speculation, the rise and fall of common bulb prices was
possibly a bubble. His conflicting findings for the two periods of tulip bulb prices are typical
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of the literatureeconomists cannot easily distinguish bubbles from a major change in
market fundamentals. Similarly, Kindelberger (1978) found common links across asset
market booms and bustsprice peaks often occur after an exogenous shock that creates new
incentives for participants to purchase assets. Debt accumulation accelerates the process,
but then prices overshoot and finally asset prices tumble.Although some investors may purchase commodities for speculative reasons,
consumers and firms continue to demand physical commodities during commodity booms.
These buyers are not speculating on the future value of the commodity, so they would not
pay a price in excess of the marginal consumption value of the commodity. Assuming
stable preferences, the only way to slide up the demand curve and raise the market price to
this group of buyers is to reduce supply. A commodity bubble therefore implies that
speculators hold some inventory off the market with the expectation that they can sell it at a
higher price in the future, thereby reducing available supply and raising the current price
(Hamilton 2009). It follows that stockholding behavior provides an important clue to
explaining commodity booms and busts, along with other fundamental factors such as
supply and demand shocks, macroeconomic shocks, and policy responses.
Do we expect to see more or fewer commodity booms and busts in the coming years
as the world economy evolves? The long history of commodity price booms and busts
suggests they are inevitable. They occur in agrarian economies and industrial economies.
The events of 2007-08 suggest that the transition towards a knowledge-based economy
(Romer 1986) has not reduced the worlds vulnerability to commodity booms and busts. In
this article, we assess the likely size and frequency of future booms and busts giveneconomic changes such as continued globalization, population growth, urbanization,
increased regional specialization of agricultural production and trade, biofuel demand, and
climate change.
We explore the economics of commodity booms and busts using as examples the two
largest and most dramatic events since World War II1: 1973-74 and 2007-08. Broadly
speaking, both occasions experienced similar and sharp upward movements in commodity
prices and subsequent declines. We find there are no simple explanations for either event,
but each had at its core a set of contemporaneous supply and demand shocks that reduced
inventories to low levels. Macroeconomic events, cross-commodity linkages, and policy
responses with unintended consequences exacerbated these fundamental shocks. Viewed in
this light these two events are much more similar than different, and they provide a context
to assess possible future booms and busts.
1There was a smaller commodity boom during the Korean War and in 1979-80, but we do not analyze these
events in this paper.
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The article proceeds as follows. In Section II, we describe the 1973-74 and 2007-08
commodity booms and subsequent busts, and we characterize the magnitude of the price
variability. Market structures across commodity groups are explored in section III, where
we isolate the contributions of supply and demand differences across the various
commodity systems. Section IV outlines the important role of commodity stockholding,which normally serves to smooth price fluctuations. Macroeconomic linkages to commodity
prices are assessed in Section V, including the important role of exchange rates and interest
rates. In Section VI we examine the importance of general equilibrium or cross-commodity
linkages, including links through factor substitution and input costs. Temporary policy
responses to commodity booms are described in section VIII, where we explain why policies
such as export quotas often aggravate the volatility of world commodity prices and thereby
send the wrong price signals to domestic markets. Section IX concludes the paper.
II:Two Major Commodity Booms and Busts: 1973-74 and 2007-08
Chinais a big force in the extraordinary boom in commodities. Its
voracious appetite for everything from corn and wheat to copper and oil has
helped push up U.S. commodities prices by some 50% over the past 12
months. But China is by no means the whole story. Speculatorsincluding
small investorsare also playing a huge role...Barron's, March 31, 2008
[T]he commodity price boom is rooted in the Fed's weak dollar policy,and not in a change in relative prices due to rising global demand. Wall St
Journal, March 24, 2008
The above quotes from the financial press in the spring of 2008 summarize opposing
views as to what caused the most recent commodity boom and bust. When commodity
prices exploded in 2008,Barrons financial magazine attributed the boom to global supply
and demand fundamentals (also see IOSCO 2009), but at the same time the Wall Street
Journalargued the boom was due to low interest rates and a weak U.S. dollar (also see
Frankel 2008). Others have argued it was a speculative bubble (Khan 2009) and that index
fund speculation played a key role (Baffes and Haniotis 2010, and Gilbert 2010), arguments
that were countered by Irwin and Sanders (2010). Similarly opposing views were promoted
during and after the 1973-74 boom and bust. In this section, we briefly describe these two
major events.
Commodities are typically placed into several categories depending on their physical
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characteristics and end use. These categories are: energy (e.g., crude oil and natural gas),
cereal grains (e.g., corn, wheat, and rice), vegetables oils (e.g., soybeans and palm oil), softs
(e.g., sugar, coffee, cocoa, and cotton), metals (e.g., gold, silver, aluminum, and copper)2,
and livestock (e.g., hogs and cattle). Figure 1 shows the real prices for each commodity
category during the two boom-bust cycles.The 2008 price boom was characterized by price increases comparable to those in
1974. Crude oil prices increased about four-fold between 1972 and 1974, and tripled
between 2007 and mid 2008. Prices of cereal grains (e.g., corn, rice, and wheat) more than
tripled in the early 1970s before declining and then did almost the same thing from 2006-
08. Both of these boom-bust cycles exhibited a general sharp upward co-movement in the
prices of many commodities (especially food and energy), which calls for a common
explanation. However, there are also some notable differences between the two episodes.
For instance, agricultural commodities led the 1973-74 commodity boom but they moved
concurrently with energy prices in 2007-08. Cereal grains, vegetable oils, and energy
accounted for most of the 1973-74 commodity price spike, whereas in 2007-08 metals
joined these three groups to create four price leaders. In fact, most metals peaked earlier
than the other commodities in 2007. The soft commodities (such as coffee, sugar, cocoa and
cotton) played a much smaller role in the 2007-08 commodity boom, compared to their
huge price spike in the early 1970s.3 The livestock index exhibited an 80 percent increase in
1973-74, but very little change in 2007-08. Finally, the bust was much faster and more
coordinated in 2008 than following the 1973-74 boom. The emergence of the global
financial crisis in September 2008 and the associated macroeconomic slowdown was thecatalyst for the bust; no such event occurred in 1974.
The high price of crude oil was the poster child of the market frenzy in 1973 and
again in 2008. In October 1973, OPEC imposed an oil embargo on the United States and
many parts of Europe in response to several countries support of Israel during the Yom
Kippur War. Coupled with price controls imposed by President Nixon, the embargo led to
gasoline shortages in the U.S., which prompted long lines at gas stations and consumer
violence. The 2007-08 boom again saw oil prices in the headlines. According to Google,
oil was the sixth most common economics-related search of 20084, trailing only items
associated with the financial crisis. Moreover, the U.S. Congress held several hearings in
2008 that focused on factors contributing to the high price of crude oil.
2Other categorizations exist. For example, metals are sometimes divided into precious metals (e.g., gold and
silver) and industrial or base metals (e.g., aluminum and copper).3The November 1974 spike in softs was driven mostly by sugar. The other soft commodities exhibited mild
booms in both episodes.4See http://www.google.com/intl/en/press/zeitgeist2008/mind.html.
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Figure 1: Two Booms and Busts
Panel A: 1973-74
Panel B: 2007-08
Source: International Monetary Fund and the Commodity Research Bureau. Commodity Prices wereobtained from the IMF and deflated by the U.S CPI excluding food and energy. The metals indexrepresents industrial metals like copper, lead, tin, nickel, and aluminum.
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In the early 1970s, the Club of Rome (Meadows et al. 1972), a high-profile global
think tank, predicted a worldwide catastrophe within a generation due to a food shortage.
They were mostly concerned with densely populated countries like China and India facing
food shortages and inducing panic in the rest of the world. Their ideas were motivated by
the food crisis at the time and their projections were loosely based on the writings of Britisheconomist Thomas Malthus, who 200 years ago said the world would eventually face a large
scale famine because population growth would outstrip the food supply. In a 1990 book
called The Population Explosion, Paul and Anne Ehrlich built on this Malthusian theme
and argued that humans are on a collision course with massive famine. More recently,
environmentalist Lester Brown predicted in a 1995 book called Who Will Feed China that
demand from China would soon push food prices so high as to cause mass starvation.
These doom and gloom predictions all proved wrong. Figure 2 shows that the real
price of most commodities declined through the 1980s and 1990s. During this period, great
advances were made in reducing malnutrition in poor countries, as food production
outpaced population growth in developing countries outside Sub-Saharan Africa. If
anything, there was concern in the 1980s and 1990s over agricultural commodity prices
being too low, discouraging farm production in developing countries. Generous government
subsidies in rich OECD countries were blamed for over-supply and depressed food
commodity prices in the 1980s and 1990s (Anderson and Martin 2006). As Figure 2
reveals, non-energy commodity prices did not reach in 2008 the levels experienced in 1973-
74. For instance, in late 2007 and early 2008, long-grain rice futures prices increased more
than any other grain on the Chicago Board of Trade futures market and at the peak in April2008 reached $24 per hundredweight (over $500 per mt), but adjusted for inflation this
was 50% less than the peak of U.S. long-grain rice prices in late 1973. Energy prices stand
out in Figure 2 because the 1979 oil crisis caused real prices to double at a time when other
commodities were not booming. The cumulative energy price increase in the period from
1998-2005 was also much greater than for the other commodities, so energy prices entered
the 2007-08 boom-bust cycle at a relatively high level.
Real food prices stopped declining in the early 2000s, several years before the boom
occurred in 2007-08. As Piesse and Thirtle (2009) correctly point out, when food
commodity prices started to rise in late 2006, it was not an abrupt reversal of declining real
prices, but instead more of a change from stable to rising prices. The real prices of energy
and metals actually started increasing around 2002. For example, crude oil prices increased
from $25 per barrel in 2002 to $70 in mid 2007, an increase that was attributed to supply
and demand fundamentals, such as strong economic growth in China and India (Hamilton
2009). Oil consumption in China increased by 50 percent from 5.2 to 7.6 million barrels per
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day from 2002-07.
Even though the decline in real commodity prices stopped well before the 2007-08
boom, the price surge caught many by surprise (especially for agricultural commodities).
For this reason, and reflecting the fact that no major single world event marked its
beginning, the Economistmagazine referred to the 2007-08 commodity boom as a SilentTsunami. The sharp rise in food prices created a food crisis, with sharp price increases
giving rise to concerns about inflation in rich countries and worries over increased hunger
and political instability in poor countries. The mass media was abuzz with non-stop
descriptions of food hoarding and attempts by governments to manipulate the market to
calm the panic-like situation in many countries. An unprecedented World Food Crisis
Summit of political leaders was held in Rome in June 2008, organized by the UN Food and
Agriculture Organization (FAO). One objective of the summit was to try and find some
common ground for ways to alleviate the problem. The Summit focused on what gave rise
to the surge in agricultural commodity prices and its relationship to the concurrent surge in
non-agricultural commodity prices. Many experts (IFPRI 2008, FAO, 2008) predicted that
this was the beginning of a new permanent long-term upward shift in commodity and food
prices.
After the dramatic price crash in late 2008, these dire predictions appear just as
misguided as their Malthusian counterparts from the 1970s. It would thus appear on the
surface that the 2007-08 boom was not fundamentally different from the 1973-74 event;
both entailed temporary price spikes. What underlies claims that these events were
fundamentally different? One argument is that commodities have been financialized inthe past decade through the development of investment vehicles like commodity index
funds and the increased participation of hedge funds in commodity markets. Although
commodity index funds are indeed new, the hypothesis that futures market speculative
trading may exacerbate booms and busts is not. For example, Labys and Thomas (1975)
analyzed the effect of futures market trading on the 1973-74 boom by quantifying the
degree to which this speculation rose and fell with the switch of speculative funds away
from traditional asset placements and towards commodity futures contracts.
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RealPrice
Index(Jan
72=
100)
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Cereals
VegOil
Softs
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Livestock
Figure 2: Real Commodity Prices: 1957-2010
Source: See Figure 1.
Another hypothesis underlying claims that the 2007-08 boom was likely to be
permanent is that commodity demand will soon outstrip supply. Proponents of the Peak
Oil hypothesis (e.g., Simmons 2005) claimed that global production of crude oil had or wasabout to peak.5 A recent slowdown in the growth of crop yields also raised concern that
agricultural supply would be unable to grow fast enough to keep real prices from rising
(Alston et al. 2009). Moreover, the rise of biofuels led the UNs Food and Agriculture
Organization (FAO) and others, to claim that commodities are now more closely tied with
the prices of fossil-based fuels than they have ever been. As the world economy evolves, the
race between technology and scarcity will determine whether commodity prices continue
their long run decline or increase according to the Hotelling(1931) rule as scarcity bites.
5In August 2005, Simmons bet John Tierney and Rita Simon, the widow of Julian Simon, $2500 each that
the price of oil averaged over the entire calendar year of 2010 would be at least $200 per barrel (in 2005dollars).
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III. Market Structure
In this section, we describe the importance of the underlying fundamentals of
commodity supply and demand. We present a broad framework that encompasses the
commodity groups included in Figure 1. With the exception of livestock, these commodities
are storable. After presenting the economics of consumption and production in this section,we address storage in the next section. These commodities are all supported by market
structures that include physical or spot markets, futures markets, as well as forward
markets. Much of the volatility and the potential for booms and busts in each of these
various commodity markets can be traced to the inherent market structure characteristics.
In terms of the microstructure, we describe the behavior of four separate groups:
consumers, speculators, producers, and processors. Consumers purchase and use processed
commodities according to their preferences; they do not typically trade in futures markets
or engage in forward contracting. Speculators trade in futures markets, but they do not
generally handle the physical commodity. Producers include farmers, miners, and drilling
and extraction firms. Processors are intermediaries who convert the raw commodity into a
good for final sale and may include oil refiners, food processors, grain mills, and exporters.
Both producers and processors engage in futures markets and forward contracting. The
behavior of the four groups gives rise to three markets for which equilibrium conditions
must be satisfied.
Sudden supply shocks often hit commodity markets. These shocks may emanate
from climatic conditions, adverse weather, such as droughts, floods, or hurricanes, labor
strikes, pests and plant disease, or from geopolitical events such as wars and trade disputes.Such nonmarket supply shocks dominate short-run volatility in many commodity markets.
However, demand shocks also arise, as demonstrated in 2007-08 by the jump in demand
for grains as a feedstock for use in producing biofuel. Moreover, from the prospective of a
particular producing country, supply shocks in other countries appear as shock to export
demand. With these stylized facts in mind, we follow numerous authors (e.g., Hirshleifer
1988) and consider a two-period decision process for market participants. In the first
period, producers choose inputs to production, and producers, processors and speculators
take hedging positions in futures and forward markets. At the time of these first-period
decisions, the final demand is uncertain, as is the final amount of production. In the second
period, output and demand are realized. Processors choose how much to process,
consumers choose how much to purchase, and the participants in futures markets realize
their profits or losses.
To provide a concrete framework for understanding supply and demand of a diverse
set of commodities, our characterization of supply and demand makes several
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simplifications. The conceptual framework is represented in Figure 3. Without material loss
of generality, we assume that individuals do not migrate among groups due to asset fixities.
We also assume symmetry of information across market participants and we ignore
transactions costs. Because we focus on boom and bust cycles, we do not consider long run
supply and demand. Thus, we abstract away from the fact that commodity demandschedules tend to shift slowly over time with evolving technology, wealth and tastes, as
exemplified by the changes in commodity flows generated in recent years by strong
economic growth in Asia. Supply also exhibits a slowly moving component as, for example,
new seed technology improves crop yields or advances in mining and drilling technology
lower energy and mineral extraction costs.
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FirstPeriod SecondPeriod
futures
priceforward
priceraw
commodity
spotprice
finalgood
spotprice
PRODUCERSchoose
InputstoproductionNumberoffuturescontractsNumberofforwardcontracts
PROCESSORSchoose
NumberoffuturescontractsNumberofforwardcontracts
SPECULATORSchoose
Numberoffuturescontracts
PRODUCERSrealize
FuturesprofitsorlossesPRODUCERSchoose
Quantitysupplied
SPECULATORSrealize
Futuresprofitsorlosses
CONSUMERSchoose
Quantitydemanded(finalgood)
PROCESSORSrealize
FuturesprofitsorlossesPROCESSORSchoose
Quantitydemanded(rawcommodity)Quantitysupplied(finalgood)
Sup
plyShocks
Dem
andShocks
Figure 3: Commodity Supply and Demand Framework
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Risk aversion and uncertainty play important roles in this framework. Together, they
create an incentive for firms to hedge and thereby make the futures and forward markets
relevant. As pointed out by Rausser (1980) and Moschini and Hennessy (2001), the classic
hedging literature neglects basis risk and production uncertainty (e.g., Telser 1958, Feder et
al. 1980, Anderson and Danthine 1980). By treating production as fixed, these studiesproduce separation between the hedging and production decisions, which implies that the
cost of hedging and risk aversion do not affect supply. Other authors model production and
price risk jointly (e.g., McKinnon 1967, Newbery and Stiglitz 1981, Britto 1984, Lapan and
Moschini 1994). Joint modeling captures the fact price tends to be negatively correlated
with production because supply shocks have a negative effect on price. Hirshleifer (1988)
generalizes this framework by modeling jointly the behavior of the four types of market
participants described above.
Futures markets and forward contracting are the two main hedging tools used by
market participants to insure their exposures against unfavorable price movements. In
general, a forward contract is a bilateral agreement between two market participants to
transact a stipulated quantity and grade of the commodity at a specific price on a stated
future date. In our framework, producers may enter forward contracts with processors.
Futures contracts also specify the quantity, grade, time, and price of a future transaction of
the commodity. However, futures contracts are traded anonymously on exchanges rather
than bilaterally. If both deliver the same location and grade at the same time and if daily
interest rates are nonstochastic, then futures and forward prices must be identical (Cox et
al. 1981). For this reason, much of the commodity pricing literature treats them asidentical. In practice, forwards are customizable (little or no basis risk), which explains why
farm contracts with merchants commonly use them. Also, futures require participants to
post margin, which may not be costless for firms that are credit constrained. For instance,
the 2008 cotton price spike brought down three large U.S. cotton merchants who could not
meet their futures contract margin calls (Carter and Janzen 2009).
The participation of speculators in futures markets enhances liquidity, making it
easier for hedgers to transact at current market prices without encountering large bid-ask
spreads. The precise tradeoff between liquidity, margin costs, and basis risk varies widely
across firms, which explains why both futures and forward markets are actively used. The
fact that many participants use forwards suggests that the cost of doing so is lower for them
than futures and/or they value the basis risk insurance that they achieve with a forward
contract relative to a futures contract. To the extent that forward contracting reduces the
risk faced by producers, production is encouraged, which in turn mitigates boom-bust
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cycles.6
Consumers only enter this framework in the second period, so they are represented
by a market demand schedule. In the very short run, most of the commodity markets
feature highly inelastic demands (an exception would be meats and less so, cotton, Rausser
1982). Shifts in supply that move along a short-run inelastic demand schedule can clearlybe a major source of booms and busts. In the medium run, demand can adjust as people
buy fuel-efficient cars, processors use new ingredients, for example, high fructose corn
syrup instead of sugar. There is a vast literature on estimating demand elasticities. For food
and agricultural commodities, two well-documented sources are presented by Iowa State
University7 and the US Department of Agriculture.8 Roberts and Shlenker (2009) estimate
world supply and demand calories derived from corn, soybeans, wheat, and rice. These four
crops make up about three quarters of the caloric content in global food production. They
estimate a short-run demand elasticity of -0.04. For the large number of studies that have
been completed for energy commodity systems, see the Energy Information Administration
of the US Department of Energy and Rausser et al. 2004. Hughes et al. (2009) estimate
that the price elasticity of demand for gasoline in the U.S. was between -0.21 and -0.34
from 1975-80, and between -0.03 and -0.08 from 2001-06. These estimates are consistent
with others in the literature (Hamilton 2009).
The total supply function can be naturally decomposed into an asset supply (e.g.,
exploration and investment in mines in the case of precious and base metals, or land
cultivated for various agricultural commodities) and a productivity response in the short
run (e.g., rate of extraction for existing mines) or the yield or productivity of variousagricultural commodities (Chu and Morrison 1986). With respect to supply elasticities,
many empirical studies have estimated the area, land, or investment in exploration
elasticities, but very few studies that have estimated the productivity or yield elasticities.
For the area or land response elasticities, comprehensive sources are again Iowa State
University and the USDA (see footnotes 7 and 8), and an earlier study by Askari and
Cummings (1976). The latter study estimates more than 600 supply elasticities for different
commodities and countries. In accordance with economic theory, this study reveals long-
run elasticities that tend to be greater than short-run elasticities, and most of the numerical
values the elasticities for the short run are in the range of 0.0 to 0.3, with the second largest
6 In the U.S., forward contracting is threatened by recent Dodd-Frank Wall Street Reform and Consumer
Protection Act, 2010. This law seeks to push more hedging activity onto exchanges and to clear trades throughcentral clearinghouses. This change will likely raise the cost of hedging to firms and have a dampening impacton the production of risk averse firms.7 http://www.fapri.iastate.edu/tools/elasticity.aspx
8http://www.ers.usda.gov/Data/Elasticities/data/DemandElasData092507.xls
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frequency falling in the range of 0.34 to 0.67.
In addition to the core market parameters and the nonmarket supply factors, the
potential for booms and busts depends critically on the role of expectation formation
patterns across the various commodity market participants. Generally, much of the
literature imposes the expectation formation pattern as part of their maintained hypothesesin their empirical models.9 The most internally consistent empirical representations of
commodity price formation is rational expectations, first introduced by Muth (1961). This
formulation has been applied to commodity futures markets by Bray (1981), Danthine
(1978), Rausser and Walraven (1990), among others. More generally, Tirole (1982) has
demonstrated that unless agents have different priors about the value of a particular
commodity asset or are able to secure insurance in the corresponding market, speculation
(gains from trade) is ruled by rational expectations.
In all applications of rational expectations in commodity markets, for example by
Miranda and Helmberger (1988) and in macroeconomics, for example by Lucas and
Sargent (1981), rationality is only driven by benefits and the cost of collecting information
and data to formulate rationally-expected prices is swept under the rug or neglected. It can
be demonstrated theoretically that, for some economic environments, nave expectations
are in fact rational. In empirical models, this apparent paradox results from the failure of
rational expectations to incorporate the cost of collecting information on critical variables.10
Even though much of the empirical literature imposes an expectation formation
pattern as part of the maintained hypothesis, periods of booms, busts, or bubbles are
unlikely to arise in a rational world with constant risk aversion. In other words, for eachcommodity system, the operative expectations pattern depends critically upon the
economic environment. For example, the weights appearing in any convex combination
depends not only on the expected benefits, but also the cost of information used in forming
expectations. To be sure, bubbles or booms are most likely to emerge when many of the
market participants form their expectations of the future naively. In volatile commodity
markets, risk must also be recognized and is typically represented in terms of the
probabilities of (squared) deviations from an expectation. If the wrong expectation is used
to assess deviations, then risk facing agents can be either under- or over-estimated,
9However, certainly for those commodities that have both active futures and spot markets, sufficiently rich
data sets exist to discriminate across various expectation formation patterns. These patterns range fromrational expectation (the core principle in the efficient market hypothesis of Fama) to nave expectations(Bauman and Dowen 1998; Lakonishok et al. 1994; La Porta 1994). For further details, see Just and Rausser(2001).10
In the theoretical landscape, Grossman and Stiglitz (1976) have explicitly addressed this question,demonstrating the impossibility of informationally-efficient markets. To our knowledge, there has been noempirical application of this theoretical model.
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respectively.
Along with expectation formation patterns role in explaining booms and busts for
our two major events, 1973-74 and 2007-08, the market structure was an important
component in explaining and facilitating the price spikes that took place.
Numerous shocks to either supply or demand caused large price responses because short-run supply and demand elasticities are small. In the case of the 1973-74 commodity events,
a major shift in supply resulted in a spike in crude oil prices and all refinery energy
products. This shift was inward, and can be directly traced to OPEC imposing an oil
embargo on the United States as punishment for that countrys support of Israel triggered
by the start of the Yom Kippur War. In the case of grains, export demand shifted outward
as a result of both Russia and Asian demand expansion. In the case of Asia, particularly
Japan, the El Nino weather patterns dramatically lowered the anchovy fish catch with the
result of an increased protein demand, which expanded the demand for grains, especially
for animal feeds. Policy failures elsewhere (e.g., cheap food policies in poor countries) also
reduced food supplies in the early 1970s, but the overall reduction in the grain supply was
rather modest leading up to the crisis (Cooper & Lawrence 1975).
In the case of 2007-08, once again shifts in demand and supply played a crucial role.
Due to the rapid increases in income in many countries, including China, India, and Russia,
global energy demand shifted outward. This phenomenon also expanded the demand for
many grains, including corn and soybeans. For corn, public sector orchestrated incentives
also expanded the demand for biofuels, which made a major contribution to the spike in
feed grain prices. Mitchell (2008) identifies the rise in biofuels production in the U.S. andthe EU as the leading cause of higher agricultural commodity prices in 2007-08. Cross-
price elasticities assisted in further expanding the demand for biofuels due to the price
spikes in crude oil. (See Section 5 on cross-commodity linkages.) Long-term supply
response was largely unable to temper these demand expansions due to the low funding of
production oriented R&D. For example, Stoeckel (2008) suggests that global crop yield
growth was gradually slowing down and he attributes this to a decline in investment in
public agricultural research. Inward supply shifts due to nonmarket supply factors,
particularly weather shocks in Eastern Europe and Australia, contributed to price spikes in
food grains. Combined with the unprecedented extension of the multiyear Australian
drought reducing wheat production, transport cost increases emerging from energy
commodity systems simply made matters worse. For metals, supply also shifted inward,
particularly for platinum because of the closing of South African underground mines. This
shift created the foundation for new price records being broken almost on a daily basis in
2008, as consumers of this metal panicked over the security of supplies.
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With respect to the role played by market structure in contribution to higher
commodity prices in 2007-2008, there is a strong difference of opinion on the relative
importance of market structure versus other forces. For example, the price of corn more
than doubled between 2006 and 2008, but the U.S. government suggested that biofuels
policies were a relatively minor influence in the higher corn price (Lazaer 2008).Alternatively, Roberts and Shlenker (2009) estimate that biofuels demand has caused a 20-
30% increase in the average price of staple food commodities. The International Food
Policy Research Institute (IFPRI) in Washington DC, and the Organization for Economic
Cooperation and Development (OECD) in Paris both found that biofuels explained about
30% of the corn price increase in 2007-08. Some argued that biofuels played an even larger
role in explaining agricultural commodity price increases (FAO 2008).
In the context of the linkages among spot, futures, and forward markets, many
explanations have been sourced with the role of speculators. After accounting for the
market structure fundamentals, some economists have attributed part of 2007-08 oil price
spike to speculation driven by concerns about global supply and the development of new
investment vehicles such as commodity index funds (Hamilton 2009). Wray (2008) claims
that the rise of speculative investments in the commodity futures market (e.g., through
index trading) was the largest contributor to the rise in commodity prices before the bust in
2008, but Irwin and Sanders (2010) provide convincing econometric evidence against this
claim.
For both events, 1973-74 and 2007-08, the literature has generally recognized the
critical role of commodity stocks in any dynamic extension of Figure 4. Piesse and Thirtle(2009) argue the single most important factor in agricultural prices was low inventories, as
the stocks/utilization ratio for grains and oilseeds dropped to 15 percent in 1972 and 1973,
and did not touch such a low level again until 2008. They also identify the importance of
rising prices of fuel and fertilizer in 1973-74 and again in 2007-08, driving up the cost of
production and transportation. Radetzki (2006) argues that aggregate demand growth
played a key role in both the 1973-74 and 2007-08 commodity price booms. Trostle (2008)
recognizes the importance of fundamental supply and demand factors, emphasizing the
decline in stocks-to-use ratio for wheat, rice, and corn grains leading up to the boom
witnessed in 2008. Accordingly, we turn in the next section to the evidence on the role of
stockholding behavior in explaining booms and busts.
IV. Stockholding
Plentiful inventories provide a buffer against supply and demand shocks. In
response to such shocks, inventories can be drawn down, mitigating the impact on prices.
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When inventories are low, the lack of a buffer leaves the markets vulnerable to price spikes.
Thus, to explain commodity booms and busts, we need an understanding of what
determines stock levels and, in particular, what may cause inventories to be depleted.
Stock holders compare the current price of a commodity to the expected price at
some future date and the cost of carrying inventories (including the opportunity cost offunds). If the expected profit from holding inventories exceeds the payoff from selling the
commodity immediately, then stockholding firms may choose to store the commodity.
Conversely, if the expected future price is too low to compensate firms for the cost of
holding inventory, then they will not store. It follows that current as well as future
stockholding is determined by both current supply and demand and by expected future
supply and demand and is thus inherently dynamic.
The staple of the stockholding literature is the competitive rational storage model,
which originated with Williams (1936). Gustafson (1958) was first to solve for the optimal
storage rule in this model, and Williams and Wright (1991), Deaton and Laroque (1992,
1996), and Routledge et al. (2000) have made further important advances with this model.
The competitive storage model specifies that risk-neutral stockholding firms make rational
expectations about the future and act to maximize expected profit in a competitive market.
Moreover, it provides basic insights necessary for understanding the role of stockholding in
commodity booms and busts. In what follows, we examine extensions to the basic model
that permit non-rational expectations (Nerlove 1958), risk aversion (Keynes 1930;
Newberry and Stiglitz 1981; Gorton et al. 2007), technology or transportation costs
(Williams and Wright 1989, Brennan et al. 1997, and Carlson et al. 2007), governmentattempts at price stabilization (Newberry and Stiglitz 1981, Miranda and Helmberger 1988),
and a convenience yield, which is a positive flow of services from stockholding (Kaldor
1939). Throughout, we focus on the features of the model relevant to price booms and
busts.
Following Williams and Wright (1991) and Deaton and Laroque (1992), suppose the
inverse demand function for current use is ( )t t
P f D , wheret
D denotes the quantity
demanded and Pt denotes the commodity price in period t. Quantity supplied (St) is
determined by an iid(independent and identically distributed) harvest shock. Compared to
the two period framework depicted in Figure 3 of Section III, this dynamic setting adds an
additional type of firm, the speculative storage firms. These firms interact with producers
and processors to determine the raw commodity price. They provide an additional source of
demand for the raw commodity in the current period and an additional source of supply in
subsequent periods. Without loss of generality and for clarity of exposition, we assume that
the demand function is constant over time. This formulation captures the real-world feature
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of markets dominated by temporary supply shocks, and it abstracts away from slow-moving
supply and demand shifts.
Speculative storage firms may chose to store the commodity at volumetric cost per
period and face an opportunity cost of capital equal to r. Such firms will store an extra unit
of the commodity if the expected price next period, net of interest and warehousing costs,exceeds the current spot price. They will store fewer units if the current price is high relative
to the expected price next period. Thus, we have the intertemporal equilibrium arbitrage
condition:
1
1
1( ) 0
1
1( ) 0
1
t t t t
t t t t
P E P if Ir
P E P if Ir
Together with the market clearing condition that quantity demanded plus incoming
inventories equals quantity supplied plus outgoing inventories, this model produces astationary rational expectations equilibrium (see Williams and Wright 1991, and Deaton
and Laroque 1992). This equilibrium is characterized by a downward-sloping inventory
demand curve.
We depict this equilibrium in Figure 4. In any given period, total demand for the
commodity equals the horizontal sum of the inventory demand curve and the demand for
current use. Inventory demand in this figure is the difference between total demand and
f(Dt), which is demand for current use. When speculative inventories are positive, total
demand is relatively elastic because the market can respond to adverse shocks by drawing
down inventories. When speculative inventories are zero, total demand is relatively inelastic
because there is little capacity to draw down inventories.
A key feature of the competitive storage model is the restriction that inventory stocks
cannot be negative. It is impossible to borrow stocks from the future. As a result, this fact
can be a source of booms and busts. When a negative supply shock drives inventory to zero,
i.e., causes a stockout, the price is determined by the inelastic current demand curve.
Because this part of the curve is steep, even a small negative supply shock can cause a large
price spike. However, such price spikes tend to be short-lived as supply is replenished and
inventory begins accumulating again.
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f(Dt)
QUANTITYIt1+St
Pt
PRICE
Dt
TotalDemand
Supply
Consumption Inventory
Figure 4: Equilibrium in Competitive Storage Model
If consumption demand becomes more elastic in this model, average inventory levels
decline and stockout-induced booms and busts become more frequent and smaller in
amplitude (Wright and Williams 1991). Serial correlation in prices also declines as demand
becomes more elastic. In a set of influential papers, Deaton and Laroque (1992, 1995, 1996)
argue that, when calibrated to real data, the competitive storage model with iid supply
produces too little serial correlation. To match the data, they propose a competitive storagemodel with serially correlated shocks, which is also a specification favored by Routledge, et
al. (2000). Serially correlated shocks generate serial correlation by allowing the inventory
demand curve to shift, which occurs because the current shock provides information about
likely future shocks and therefore affects willingness to hold inventory. Cafiero et al. (2010)
show that Deaton and Laroques criticisms were overstated. With more accurate solution
techniques, Cafiero et al estimate a much smaller demand elasticity parameter, which
enables the model to more closely match the data by exhibiting strong autocorrelation and
infrequent stockout-induced booms and busts even with iidshocks. Carter and Revoredo-
Giha (2009) show that strong autocorrelation can also be induced by includingprocessors as
holders of inventory.
Much of the literature on competitive storage models focuses on agricultural
commodities. However, a parallel literature with a similarly long history exists for
exhaustible commodities such as metals, oil and gas. In a world with zero extraction costs
and known reserves the producers problem is the same as the competitive stockholding
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firm. In the absence of uncertainty, Hotelling (1931) shows that under certain conditions
prices should grow at the rate of interest as reserves are depleted. This rule is exactly the
intertemporal equilibrium condition with positive inventory shown above. Weitzman and
Zeckhauser (1975) and Pindyck (1980) have shown that this rule continues to hold when
demand is stochastic.The price history of exhaustible commodities does not match the predictions of this
simple theory. In fact, real resource prices have tended to decline over time. For this reason,
the recent literature has moved from treating resource extraction as an inventory problem
to a production problem. The resulting models incorporate features such as technological
progress (e.g., Lin and Wagner 2007) and adjustment costs (e.g., Carlson et al. 2007), and
they generate modified versions of Hotellings rule. Thus, as noted by Slade and Thille
(2009, p. 256), the often-cited fact that the Hotelling model is frequently rejected by the
datamust be interpreted with caution.
A competitive storage model for any resource predicts price spikes when inventories
become low. In the presence of production constraints, extraction cannot respond quickly
to shocks and above-ground inventories become important. Above-ground storage of
petroleum is limited by its high cost, with the result that stockholding behavior only affects
prices at horizons of a few months. This short horizon limits the applicability of the rational
storage model to petroleum commodities. In contrast, storage costs are minimal for
minerals suggesting that the rational storage model is more relevant for these commodities.
The market price of storage as determined by the futures term spread is often
negative during low inventory periods. Thus, not only do firms not run inventories all theway to zero, they appear willing to hold inventory at a loss. Working (1949) first
documented this phenomenon in Chicago wheat during the 1920s. Three separate theories,
each with a long history, have been proposed to explain the apparent willingness of firms to
store inventory under backwardation (distant futures prices lower than current spot or
nearby futures prices): (i) convenience yield, (ii) spatial aggregation, and (iii) risk aversion.
We address each of these explanations below, and we also address the implications of
market power and non-rational expectations for the applicability of the competitive storage
model.
Eastham (1939) posed an early version of the model in Figure 4, with the added
feature of heterogeneity among stockholders (Carter and Revoredo-Giha 2009). Some firms
hold stocks for speculative purposes, whereas others hold stocks as working inventories.
This formulation foresaw the development of convenience yield models, in which firms may
hold inventory because it produces a flow of services (Kaldor 1939, Brennan 1958, Telser
1958, Brennan and Schwartz 1985).
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The concept of convenience yield is somewhat vague. It has been described variously
in the literature as representing the value of an option to change production at short notice
to take advantage of market conditions (Litzenberger and Rabonowitz 1995), an option to
avoid shutting down a processing plant if supplies were to become scarce (Brennan 1958),
as a reflection of high fixed costs of acquiring or disposing of a batch of inventory(Bobenreith et al. 2004), or as a loss-leading strategy to draw in customers who pay for
merchandizing services (Paul 1970). Recent work by Carter and Revoredo-Giha (2007) and
Franken et al. (2009) demonstrates at the firm level that stockholding firms do hold stocks
at an apparent expected loss. These two papers provide support for the existence of
convenience yield, i.e., stocks that are held for reasons other than simple speculation.
Williams and Wright (1989) and Brennan et al. (1997) challenge the convenience
yield theory by pointing out that commodities are stored differentially across space. If
transportation costs are significantly large, then inventory at inconvenient locations may be
unable to be shipped out in a timely manner. Comparing prices at locations with no
inventory to other locations with positive inventory levels necessarily makes it seem that
firms are storing at a loss when they may not be. However, firm-level evidence of
convenience yield suggests that the aggregation argument is insufficient to fully explain
storage at a speculative loss.
Risk aversion may produce storage at an expected loss. Risk-averse processing firms
may be willing to hold inventory to avoid uncertainty over the price that they may have to
pay to acquire the commodity later. This uncertainty may be greater when inventories are
low and volatility is high, which would cause these firms to hold more inventories off themarket thereby exacerbating any price boom. However, the risk aversion literature tends to
focus on storage firms and thus finds risk premia of the opposite sign. Gorton, Hayashi,
Rouwenhorst (2007) provide some evidence of increased risk premia when inventories are
low. They argue that firms need to be encouraged to hold inventories in such periods
because of the price risk associated with future sale of those inventories. Their story is
consistent with the normal backwardation hypothesis of Keynes (1930), which was
reinforced by Carter et al. (1983), Bessembinder (1992), De Roon et al. (2000). Thus,
although risk aversion could exacerbate booms and busts, the literature tends to find that
the risk aversion displayed in the markets acts to dampen them.
Although the rational storage model presumes perfectly competitive markets, many
commodity markets display characteristics that violate these assumptions. For example,
OPEC openly attempts to collude on crude oil production, many countries set up
government-backed entities to control exports and imports, and a small set of
merchandising firms handle a high proportion of the international grain trade. Quantifying
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the impact of non-competitive behavior on a commodity market is challenging because of
strategic behavior and because some government-controlled marketing entities are driven
by political rather than economic motivations. However, as first noted by Adam Smith,
market power in storage markets implies too little storage on average. A firm with a
monopoly on storage space would profit by withholding space and setting prices abovecompetitive levels. Thus, market power does not explain why firms may choose to store at
an apparent loss. Nonetheless, with lower average inventory levels, the market would tend
to hit the steep part of the total demand curve more frequently and thus stockout-induced
price spikes would occur more often and with larger amplitude (Williams and Wright 1991).
Imprecise information about current inventory levels or about future demand and
supply can also reduce storage efficiency. Market participants may possess imprecise
information because of fundamental uncertainty, such as agricultural yields that are
sensitive to weather events. The nature of stockholding also affects information precision.
In many less-developed countries, substantial amounts of grain are stored in homes and on
small farms, making it difficult for markets to assess the quantity of inventory in storage
(Park 2006). Similarly, government entities frequently engage in extensive commodity
storage. Although it may not be the purpose, government storage can enhance inventory
measurement as in the strategic petroleum reserve in the United States or the large publicly
owned grain reserves that accumulated in the United States in the 1980s. In contrast, some
governments may have a strategic incentive to keep inventory levels secret, as happens for
petroleum in OPEC nations and grain in China.
Associated with the incomplete availability of commodity storage data is aninformation externality. Such an externality means that the social gains to information
collection exceed the private gains (Grossman 1977). In many markets, notably grains,
government agencies alleviate this potential externality by collecting and publishing
inventory information. However, even in the face of accurate inventory information, market
participants face substantial uncertainty that may be difficult to quantify. In such
circumstances, it may be efficient for market participants to resort to simpler expectation
formation rules. Examples of such rules include making output decisions based on the price
at the time of planting, which often provides the basis for the booms and busts of a cobweb
model (Kaldor 1934). Such expectations may exacerbate booms and busts if they lead firms
to interpret a temporary negative supply shock as a permanent shock. Such firms would
respond by hoarding the commodity, driving prices up further than they would under
rational expectations. Prices then crash back to earth once it becomes clear that excessive
inventory has accumulated.
Systematic errors in expectation sometimes arise in asset markets as market
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participants display irrational exuberance (Shiller 2006). In commodities, viewed
through the lens of a dynamic storage model, such inflated expectations about future prices
raises the demand for storage. Stockholding firms see profit opportunities in holding
additional stocks to profit from the expected higher prices. As with any outward shift in the
storage plus current use demand curve, we would expect to see increases in both the priceof the commodity and quantity in storage, all else equal. Only if the demand for current use
is perfectly inelastic will a price increase not raise inventory levels. Hamilton (2009) argues
that, because the demand for current use of crude oil may be very inelastic, such increases
in inventory could be imperceptible. However, all else equal, the prediction that a
speculation-fueled price increase causes inventory accumulation holds.
In practice, all else is not equal. As Kindelberger (1978) observes, episodes of
irrational exuberance tend to follow fundamental shocks. Market participants observe
prices increasing in response to the fundamental shocks and over-react, further bidding up
prices, and often claiming that this time is different (Reinhart and Rogoff 2009). In
commodities, the fundamental shock may take the form of a supply disruption, which raises
prices and reduces inventory holdings. If market participants overreact and induce
increased speculative demand for inventory, then prices rise further and inventories
accumulate. Thus, the net change in inventory from the fundamental shock and the
irrational exuberance may be positive or negative.
What role did dynamic stockholding behavior play in the two major events, 1973-
1974 and 2007-2008, described in Section II? To investigate this question, Figure 5 plots
annual price paths against inventory levels for six commodities. We display the three majorgrains (corn, wheat and rice), the largest energy commodity (crude oil), and an important
industrial commodity (copper). We also include cotton because it provides an interesting
contrast to the grains, because it did not experience a dramatic boom and bust in either
1973-1974 or 2007-2008. All price series span 1960-2010, except crude oil, which begins in
1974. For the agricultural commodities, we measure price in the middle of the crop year at a
time when the size of the previous crop is known and the weather shocks that will affect the
size of the upcoming crop have not been realized (March for corn, wheat, and cotton; April
for rice). For consistency across commodities, we also measure crude oil and copper prices
in March. We use log prices deflated by the CPI, so one unit on the vertical axis corresponds
to a 69% real price difference. We measure inventories using global stocks-to-use where
possible.11 For crude oil, we use the longer sample of United States stocks in Figure 5,
11Most rice stocks in China are held on-farm and never enter commercial channels, which makes rice data
from China unreliable. Moreover, in contrast to corn and wheat, estimated Chinese rice stocks display strongtrends that seem unrelated to prices. We therefore omit Chinese rice stocks.
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4.0
4.5
5.0
5.5
6.0
6.5
0 0.1 0.2 0.3 0.4 0.5
April
Price(Thai)
GlobalStocks/Use(excl.China)
Rice
19711976
20042010
4.0
4.5
5.0
5.5
6.0
0 0.1 0.2 0.3 0.4 0.5
MarchPrice
GlobalStocks/Use
Corn
19711976
20042010
4.5
5.0
5.5
6.0
6.5
0 0.1 0.2 0.3 0.4 0.5
MarchPrice
GlobalStocks/Use
Wheat
19711977
20042010
4.0
4.5
5.0
5.5
6.0
6.5
0 0.1 0.2 0.3 0.4 0.5
Ma
rchPrice
GlobalStocks/Use
Cotton
19711976
20042010
2
2.5
3
3.5
4
4.5
0 0.1 0.2 0.3 0.4 0.5
MarchPrice
GlobalStocks/Use
Copper
19711976
20042009
0
0.5
1
1.5
2
2.5
3
3.5
0 0.1 0.2 0.3 0.4 0.5
MarchPrice
USStocks/Use
CrudeOil
19741976
20042010
although OECD stocks data produce very similar results.
Figure 5: Real Prices and Ending Stocks-to-Use (1960-2010)
Notes: All stocks-to-use ratios detrended by linear trend. We exclude China from rice. Prices measured in March for corn,cotton, and wheat, and April for rice. U.S. prices for corn, cotton, wheat, copper, and crude oil; Thai prices for rice. Allprices deflated by all items CPI. Data sources: CRB, USDA, IMF.
Rational storage theory suggests a downward sloping demand curve for inventory
that becomes steep when inventories run low. The three grain commodities experienced
booms in 1973 and 2008, and also exhibited their lowest inventory levels at these times. In
contrast, cotton inventories in 1973 and 2008 were about average. Copper displays somesimilarities to the grains, with the 2008 boom and bust occurring in the face of relatively
low inventories. However, copper did not experience a 1973 boom. As was the case for
cotton, copper inventories were healthy in 1973, so although the real price was above
average during this period, the market did not exhibit a large price spike. These cases
illustrate the point that plentiful inventories enable commodity markets to cushion the blow
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of unexpected supply disruptions or jumps in demand by drawing down inventory. In
contrast, when stocks are low current supply and demand shocks must be met by a
reduction in current use.
As previously noted, crude oil is expensive to store above ground, so most storage is
largely operational. It follows that inventories are less volatile, which manifests in Figure 5as a price path that traces out a circle, rather than one that oscillates wildly as do the other
commodities. The lack of intertemporal profit-based storage also implies that price spikes
for crude oil are only weakly related to above-ground inventory levels.
The price spikes displayed in Figure 5 for corn, wheat, rice, and copper all follow the
same template. The price path follows a clockwise pattern. In the lead up to the spike,
stocks get run down and prices increase slightly. Then, when stocks reach a critical point,
the price spikes. In the ensuing year or two, stocks gradually get replenished and the price
declines. The price decline typically occurs more slowly than the spike.
Unlike the other commodities, corn prices spiked in 2008 at higher inventory levels
than in 1973, which suggests an outward shift in the demand for inventories. The source of
this demand increase was the dramatic growth of the biofuel industry. In the 2008 crop
year, over 30% of U.S. corn supply was diverted into ethanol production, up from just 14%
in 2005. This diversion has a significant impact on world corn prices because the United
State typically produces about 40% of the worlds corn and accounts for 60% or more of
global exports. This dramatic increase in U.S. corn ethanol production stemmed from
mandates in the Energy Policy Act of 2005. Because of the long lead time in building
ethanol plants, 2007 and 2008 ethanol production was essentially known by late 2006 andtherefore would have been incorporated in corn prices by late 2006 as stockholding firms
sought to increase storage in advance of the upcoming ethanol production boom.
In summary, the basic rational storage model predicts rare zero-inventory periods
that generate booms and busts. Modifications to the simple storage model that permit
convenience yield and account for spatial heterogeneity can explain the absence of
stockouts. Allowing for expectation errors due to high information collection costs or
irrational exuberance, or incorporating market power in storage may exacerbate price
spikes. However, none of these factors change the results that short-lived booms and busts
are relatively rare, and they tend to happen when stocks reach low levels. Consistent with
the theory, the 1973-74 and 2007-08 commodity boom-and-bust episodes exhibited
extreme price spikes only for those commodities with low stocks.
V. Macroeconomics and Commodity Prices
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Macroeconomic linkages are often used as the foundation for popular press
assessments of the causal source of booms in commodity prices. To determine the
quantitative impact of such linkages, it must be recognized that there are both forward and
backward linkages (Rausser 1985). The backwards linkages relate any sector of the
economy to real macroeconomic performance, including GNP, national income, tradebalance, and public sector expenditures. The immediate impact of backward linkages on
food commodity price booms is at best minimal, but some empirical evidence suggests that
energy price booms tend to lead to macroeconomic recessions (Hamilton 2009).12 Forward
linkages as well as possible feedback effects are a potential critical source in explaining the
magnitude of commodity booms and busts. These forward linkages directly relate money
markets to commodity market prices through two principal channels: interest rates and
exchange rates. As noted by Rausser (1985), these linkages became evident in the 1970s
with the move to flexible exchange rates, the rapid expansion of international markets, and
the emergence of a well-integrated international capital market.
The forward linkages between money markets and commodity markets follow
directly from a number of causal phenomena. First, the production processes for many of
the commodities is extremely capital intensive with the result that movements in real
interest rates have significant effects on the cost structure of producing and supplying such
commodities to their respective markets. Second, stock carrying in storable commodity
systems is sensitive to interest rates, while for non-storable commodities (for example, live
cattle and hogs) breeding stocks are interest rate sensitive. Including the influence of
interest rates on the value of currencies, many countries fiscal and monetary policies canexert pressure on both the demand side (export demand, stockholding demand) as well as
the cost side and thus supply to the relevant markets.
For our 1973-74 events the boom in commodity prices was in part the result of the
performance of money markets and foreign exchange rate markets. In particular, during the
early 1970s the Federal Reserve Bank expanded the U.S. money supply, with the effective
objective of accommodating increases in the real price of energy; other countries also
attempted to inflate away energy price shocks. The resulting inflation continued to evolve
until the Federal Reserve in October of 1979 adopted a policy of attempting to control
money supply directly, rejecting its previous policy of targeting interest rates (Rausser et al.
1986). As a result, what was a real-commodity price boom in the 1973-74 period suddenly
became a bust in 1981-82.
12For resource-based economies, particularly mining and energy, a hypothesis has emerged in the literature
that the expansion of resource-based commodities actually does harm to the macroeconomy. This so-calledresource curse hypothesis has been evaluated by Wick and Bulte (2009). Based on their assessment, theyconclude that this curse hypothesis should be rejected.
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The spike in commodity prices, particularly the precious metals in 1980, generated
feedback effects or second-round effects from the expansionary monetary policy of the
1970s. The rapid inflation reflected by a subset of the commodities ultimately led to tight
U.S. monetary policies which partially explained the subsequent collapse of all commodity
prices through much of the 1980s (Rausser et al. 1986). This collapse of prices wasinfluenced by real interest rates reaching all-time highs (measured in ex postreal terms),
helping to reverse the decline of the U.S. dollar that occurred through much of the 1970s.
This second-round effect on money markets was triggered by the inflation that can be
traced to the 1973-74 commodity price boom (Blinder and Rudd 2008, see Figure 6).13
Given the increasing integration of international markets, it is no surprise that
exchange rates play a role in the movements of commodity prices since the early 1970s.
Chen et al. (2010) show that the dollar exchange rates of commodity-producing countries
strongly forecast commodity prices during this period. The dominant exchange rate relates
to the U.S. dollar, which is a reserve currency and is also the currency in which most
international commodity transactions are denominated.14 Leading up to our second major
event (2007-08), the U.S. dollar lost almost 40% of its value from 2002 through 2008. The
fall in the value of the dollar was in part caused by the steady decline in U.S. short-term
interest rates, declining from 5% to just over 2% over the short period of time from
September 2007 to May 2008. Here again, as in the period of 1973-74, the forward
linkages from money markets to commodity markets were supportive not only as a result of
exchange rate movements but as well the movement in real interest rates.
The seminal theoretical paper that provides the lens for explaining the forwardlinkages from money markets is sourced with Dornbusch (1976). The empirical
underpinnings for this work can be traced back to Hicks (1974) and Okun (1975). Hicks and
Okun were the first to identify the macroeconomy as being composed of two types of
markets: flex-price or what they characterize as auction markets, and fixed-price or what
they characterize as customer markets. Given that the latter markets have sticky prices, the
speed of adjustment in such markets is much slower than it is for flex-price markets
following changes in monetary policy. As a result, disequilibrium in the real rate of interest
orchestrated by the central bank (too low relative to the long-run equilibrium real interest
rates) mean that the flex-price markets composed largely of commodities will overadjust,
and fixed-price markets will underadjust to expansionary monetary policies. Tight
13Booms in commodity prices often result in cost-push inflation (Phelps 1978), which does on occasion have a
tendency to induce a tightening of monetary and fiscal policies.14
For instance, if Venezuela exports oil to China, payments will most likely be made in U.S. dollars, thecurrency of settlement.
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monetary policy will have the opposite effect, with commodity or flex-price markets
overadjusting on the downside, resulting in contributions to bust cycles with once again
fixed-price markets underadjusting. Dornbusch used this basic framework to introduce the
notion of overshooting in exchange rate markets.
Exchange rates overreact to a monetary shock in order to compensate for thedisequilibrium arising in a more slowly adjusting goods market. In the Dornbusch
formulation, the long-run steady state remains unchanged while the exchange rate equates
(temporarily) demand and supply in both the exchange and goods markets. The
overshooting in exchange rate markets was extended to storable commodities by Frankel
(1986), Rausser et al. (1986), Stamoulis and Rausser (1988), and Sephton (1988).
Following the theoretical introduction of commodity price overshooting, sourced with both
real interest rate disequilibrium and exchange rate overshooting, a number of empirical
studies were conducted to test the theory. Frankel and Hardouvelis (1985) found that
overshooting results in real increases in commodity prices even if expectations are formed
rationally. Rausser et al. (1986) and Stamoulis and Rausser (1988), Ardeni and Rausser
(1995) found that such overshooting phenomena were insufficient to explain the price
spikes in 1973-74 and the bust that occurred in the 1981-82 through 1985 period. Their
empirical analysis illustrates that stockholding, basic market structure, cross-commodity
linkages, and public policies were also required to explain the booms and busts that took
place in many commodity markets. Their empirical results reject the hypothesis advanced
by Frankel (1995, 2008) that money market linkages are the principal explanation for
booms or busts in commodity prices.15
In the context of commodity futures markets, Rausser and Walraven (1990)
investigated the implications of different degrees of flexibility across markets by quantifying
the linkages among three groups of markets: interest rates, exchange rates and commodity
prices (corn, wheat, and cotton). Their results show that although all these markets
overreact to a shock, commodity markets do so to a much greater degree. Owing to their
much greater size, however, the welfare loss arising from overshooting is much larger for
interest rate and exchange rate markets. In the context of spot markets, similar questions
have been investigated by Lai et al. (1996) and Saghaian et al. (2002).
Both the 1973-74 and 2007-08 commodity booms were preceded by unusually high
world economic growth, which no doubt led to strong aggregate demand for commodities
(see Figure 6). In particular, income growth was very strong in lower middle income
15For a survey of forward linkages and feedback effects between money markets and commodity prices,
particularly agricultural prices, see Ardeni and Freebairn (2002).
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countries.16 The vertical line on the left hand side of Figure 7 indicates the approximate
beginning of the 1973-74 boom and the vertical line on the right marks the beginning of the
2007-08 boom. For the five years leading up to the first boom (1969-73), real GDP grew by
6.6% per year in middle income countries. Similarly, for the five years leading up to the
second boom (2003-07), middle income real GDP grew by 7.2 percent annually. In no yearbetween 1973 and 2003 did middle income GDP growth exceed six percent, and the average
over this interim period was 3.8%. Following each commodity boom, economic growth
slowed dramatically.
One apparent macroeconomic difference between the periods following the two
booms has been the path of inflation. The 1973-74 boom was followed by a prolonged
period of inflation in OECD countries (see Figure 6). Blinder (1982) argues that commodity
price shocks, along with the removal of the Nixon price controls in the U.S., were the most
important factors in producing this inflation. In contrast, the 2007-08 boom was followed
by the deepest recession since the Great Depression. The resulting contraction in aggregate
demand eliminated any inflationary pressure and caused deflation to become the principal
concern. However, inflation had been a concern of policy makers during the boom period.
In the August 25, 2008 Federal Open Market Committee meeting less than a month
before the emergence of the financial crisis participants expressed significant concerns
about the upside risks to inflation. Thus, without the collapse in global aggregate demand
beginning in September 2008, there may well have been significant inflation in consumer
prices. Essentially, strong global demand, especially in lower-middle income countries,
helped set the stage for the 1973-74 and 2007-08 commodity booms. This strong demandwas reflected in low real interest rates and strong GDP growth, and it contributed to the
reduction in inventory levels that made commodity markets vulnerable to supply and
demand shocks.
16The category of lower middle income is as defined by the World Bank. In 2008, per capita income in
middle income countries was $6,227 measured in PPP 2010 dollars.
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0
2
4
6
8
10
12
14
1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
GrowthRate
OECDInflation WorldGDP LowerMiddleIncomeGDP
1973 2007
Figure 6. World GDP Growth and Inflation
Source: http://data.un.org. Original Data from World Bank Indicators. Inflation is for highincome OECD countries and GDP growth is for the world economy, in real terms.
VI. Cross-Commodity Linkages
The economics of substitution and complementarity in supply and demand generate
cross-commodity linkages that create cascading effects of a boom or bust in one commodity
market on another. Supply substitutability is most relevant for agricultural commodities
because producers can choose which crop to plant on a fixed acreage base. In contrast to
agriculture, production of one mineral or energy commodity typically does not generally
crowd out production of another. Commodity complements are also an important feature of
agriculture, giving rise to strong cross-price elasticity relationships. For instance, when theprice of corn rises, this affects the profitability, supply, and price of pork because feed
accounts for over 70% of the cost of raising a pig.
In agriculture, petroleum commodities are important inputs to production through
direct fuel use and indirectly through nitrogen fertilizer and pesticides produced with
natural gas. During the both the 1973-74 and 2007-08 commodity booms, prices of
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ammonia, nitrogen, potash, and phosphate fertilizers more than doubled, placing strong
pressure on the cost of agricultural commodity production. The price of energy is also
linked to all other commodities through transportation costs. Figure 7 shows the ocean
freight index for bulk commodities constructed by Kilian (2009). It is evident that both the
1973-74 and the 2007-08 commodity booms were associated with significant increases infreight rates, which Kilian interprets as indicating strong real economic activity (aggregate
demand). The higher freight rates contributed to surges in delivered commodity prices.
Figure 7: Real Dry Cargo Index
80
60
40
20
0
20
40
60
80
100
1973 1978 1983 1988 1993 1998 2003 2008 2013
Source: Kilian (2009)
The use of food crops for biofuels is a relatively recent phenomenon and it
underscores the importance of cross-commodity linkages. Important countries in this
regard are the U.S., Brazil and the EU. These three countries account for 89 percent of
global biofuels production, including ethanol and biodiesel. Policies in the U.S. and the EU
have been criticized in particular because they promote the inefficient production of
biofuels (primarily from corn in the U.S. and rapeseed in the EU) through subsidies, trade
barriers, and mandated blending requirements. These policies draw corn and vegetable oils
out of the food market channels and divert them into ethanol and biodiesel on a large scale.
Another scheme in the EU called energy cropping is growing in importance as a result of
large subsidies, where land is taken out of food production and planted to maize or sugar
beets and then harvested for biofuel use.
The U.S. currently mandates 15.2 billion gallons of (ethanol equivalent) biofuel use in
2012, rising to 36 billion gallons by 2022. At the same time, the EU has mandated a 10%
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blend of biofuels into its fuel supply by 2020, and that 20% of its total energy mix come
from renewable sources. Statistics for 200917 indicate that the U.S. produced approximately
10.6 billion gallons of ethanol and 544.2 million gallons of biodiesel; Brazil produced 6.6
billion gallons of ethanol and 405.5 million gallons of biodiesel; while the EU produced
978.2 million gallons of ethanol and 2.7 billion gallons of biodiesel. In the case of the U.S.and the EU, biofuels represent a relatively minor share of the overall domestic fuel demand,
about 4 percent in the U.S. and 2 percent in the EU.18
Almost all of U.S. ethanol is currently produced with corn and by 2022,
approximately 50% of the ethanol production is forecast to be based on corn. In 2010, over
one-third of the U.S. corn supply will be diverted into ethanol production. As previously
noted, this has a significant impact on world corn prices. According to the FAO (2008), the
increase in global corn demand in 2007 was about 40 million metric tons, and 75% of the
growth in demand was attributable to ethanol production. The significant shift of corn into
ethanol has not only drawn acreage out of wheat and soybeans, but it has also reduced
available corn inventories. In crop year 2007-2008, U.S. corn acreage jumped 19% from the
previous year (rising to 93.6 million acres), and at the same time, soybean acreage fell by
16% from 75.5 to 63.6 million acres. It is no wonder that soybean prices then surged, an
indirect effect of U.S. ethanol policy on corn demand. Related markets also experienced
huge increases in prices, particularly fertilizers, which resulted in expanded production
costs justifying still higher commodity prices.
With few exceptions, historical prices of grains have not been highly correlated with
petroleum. But the UNs Food and Agriculture Organization (FAO) and others haverecognized that commodities are now more closely tied than they have ever been
suggesting agricultural commodity prices now move up and down with the prices of fossil-
based fuels. This perspective is partly based on the fact that the