Annual Review Draft Jan 31 Final Edit

7/28/2019 Annual Review Draft Jan 31 Final Edit

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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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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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24

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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25

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

Documents

Annual Review Draft Jan 31 Final Edit