Policy Research Working Paper 5371

Placing the 2006/08 Commodity Price Boom 

into Perspective

John Baffes

Tassos Haniotis

The World Bank

Development Prospects Group

July 2010

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Abstract

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development 

issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the 

names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those 

of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and 

its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Policy Research Working Paper 5371

The 2006-08 commodity price boom was one of the 

longest and broadest of the post-World War II period. 

Apart from strong and sustained economic growth, the 

recent boom was fueled by numerous factors, including 

low past investment in extractive commodities, weak 

dollar, fiscal expansion, and lax monetary policy in 

many countries, and investment fund activity. At 

the same time, the combination of adverse weather 

conditions, the diversion of some food commodities 

to the production of biofuels, and government policies 

(including export bans and prohibitive taxes) brought 

global stocks of many food commodities down to levels 

not seen since the early 1970s. This in turn accelerated 

the price increases that eventually led to the 2008 rally. 

The weakening and/or reversal of these factors coupled 

with the financial crisis that erupted in September 2008 

and the subsequent global economic downturn, induced 

This paper—a product of the Development Prospects Group—is part of a larger effort in the department to gain a better 

understanding of the causes and consequences of the 2006–08 commodity price boom. Policy Research Working Papers 

are also posted on the Web at http://econ.worldbank.org. The author may be contacted at jbaffes@worldbank.org. 

sharp price declines across most commodity sectors. Yet, 

the main price indices are still twice as high compared to 

their 2000 real levels, begging once more the question 

about the real factors affecting them. This paper 

concludes that a stronger link between energy and nonenergy commodity prices is likely to be the dominant 

influence on developments in commodity, and especially 

food, markets. Demand by emerging economies is 

unlikely to put additional pressure on the prices of food 

commodities. The paper also argues that the effect of 

biofuels on food prices has not been as large as originally 

thought, but that the use of commodities by financial 

investors (the so-called ”financialization of commodities”) 

may have been partly responsible for the 2007/08 spike. 

Finally, econometric analysis of the long-term evolution 

of commodity prices supports the thesis that price 

variability overwhelms price trends.
Placing the 2006/08 Commodity Price Boom

into Perspective

John Baffes

THE WORLD BANK

jbaffes@worldbank.org

Tassos Haniotis

EUROPEAN COMISSION

anastassios.haniotis@ec.europa.eu

The views expressed in this paper are those of the authors and should not be attributed to their

affiliated institutions. We would like to thank Ataman Aksoy, Julie Dana, Betty Dow, Chris Gil‐

bert, Louis Goreux, Bernard Hoekman, and Gauresh Rajadhyaksha for comments and sugges‐

tions on preliminary drafts. Rachel Weaving provided excellent editing. The paper is a contribu‐

tion to the World Bank‐DFID Global Trade and Financial Architecture project.
Page 2

1. Introduction

The 2006‐08 commodity boom was one of the longest and broadest of the post‐

WWII period. The boom—and especially the 2008 rally, when crude oil prices

peaked at US$ 133/barrel (up 94 percent from a year earlier) and rice prices

doubled within just five months—has renewed interest in the long‐term behavior

and determinants of commodity prices, and raised questions about whether

commodity prices have reversed the downward course that most of them fol‐

lowed during most of the past century.1 It has also produced numerous calls for

coordinated policy actions at the national and international level to address food

availability and food security concerns.2

To put the recent commodity boom into perspective calls for a good un‐

derstanding of the key characteristics and determinants of long‐term commodity

price movements—and an appreciation of how limited this understanding is, es‐

pecially with respect to the conditions under which the recent boom unfolded.

Such a perspective is important in order to avoid policy pitfalls that in the name

of mitigating food security concerns or improving the functioning of the markets

may, in fact, exacerbate existing problems.

This paper has two objectives. The first is to analyze the nature of the re‐

cent boom, especially in food commodities, by examining which key factors fu‐

eled it and whether such factors are likely to remain in place in the long term.

The second objective is to place the boom into perspective by examining long‐

term trends and characteristics of commodity prices. The next section begins

with a discussion of recent price trends, including the causes of the boom as well

as a comparison with earlier episodes of high prices. Particular attention is paid

to three key (real or perceived) causes of the boom: excess liquidity and specula‐

tion, food demand growth by emerging economies, and use of some food com‐

modities to produce biofuels. Section 3 analyzes the long‐term behavior of com‐

modity prices, including stationarity, co‐movement among prices of food com‐

modities, and the price link between energy and non‐energy commodities. The

final section summarizes and discusses some policy issues, including the ratio‐

nality and viability of proposals for dealing with price spikes.

We conclude that a stronger link between energy and non‐energy com‐

modity prices is likely to have been the dominant influence on developments in

commodity, and especially food, markets. Demand by developing countries is

unlikely to have put additional pressure on the prices of food commodities, al‐

though it may have created such pressure indirectly through energy prices. We

also conclude that the effect of biofuels on food prices has not been as large as

originally thought, but that the use of commodities by investment funds may

have been partly responsible for the 2007/08 spike. Finally, econometric analysis
Page 3

of the long‐term evolution of commodity prices supports the thesis that price va‐

riability overwhelms price trends.

2. The Nature and Causes of the Recent Commodity Boom

The recent commodity boom emerged in the mid‐2000s after nearly three dec‐

ades of low and declining commodity prices (Figure 1). The long‐term decline in

real prices had been especially marked in food and agriculture. Between 1975‐76

and 2000‐01, world food prices declined by 53 percent in real US‐dollar terms.

Such price declines raised concerns, especially with regard to the welfare of poor

agricultural producers. In fact, one of the Doha Round’s chief motives (and also

one of its perceived main obstacles) was the reduction of agricultural support

and trade barriers in high‐income countries—a set of reforms that was expected

to induce increases in commodity prices and hence improve the welfare of low‐

income commodity producers (Aksoy and Beghin 2005). Starting in the mid‐

2000s, however, most commodity prices reversed their downward course, even‐

tually leading to an unprecedented commodity price boom.

Between 2003 and 2008, nominal prices of energy and metals increased by

230 percent, those of food and precious metals doubled, and those of fertilizers

increased fourfold. The boom reached its zenith in July 2008, when crude oil

prices averaged US$ 133/barrel, up 94 percent from a year earlier. Rice prices

doubled within just five months of 2008, from US$ 375/ton in January to $757/ton

in June.

The recent boom shares two similarities with the two earlier major com‐

modity booms of the post‐WWII period, during the Korean War and the early

1970s energy crisis (see Radetzki (2006) for a discussion of the three booms). Each

of the three booms took place against a backdrop of high and sustained economic

growth as well as an expansionary macroeconomic environment, and each was

followed by a severe slowdown of economic activity. And all three triggered dis‐

cussions on coordinated policy actions to address food and energy security con‐

cerns.

Yet the recent boom also shows some important differences from the pre‐

vious ones. By most accounts, it was the longest‐lasting and the broadest in the

numbers of commodities involved. It was the only one that simultaneously in‐

volved all three main commodity groups—energy, metals, and agriculture—with

its peak showing food and agriculture prices increasing less than all other com‐

modity prices (World Bank 2009). It was not associated with high inflation, un‐

like the boom of the 1970s (although the increase in food prices had some nota‐

ble, albeit short‐lived, impact on inflation). Finally, it unfolded simultaneously

with the development of two other booms—in real estate and in equity mar‐
Page 4

kets—whose end led most developed countries to their most severe post‐WWII

recession.

The recent boom took place in a period when most countries, especially

developing ones, sustained strong economic growth. During 2003‐07, growth in

developing countries averaged 6.9 percent, the highest five‐year average in re‐

cent history (Figure 2). Yet apart from broad and prolonged economic growth,

the causes of the recent boom were numerous, including macro and long‐term as

well as sector‐specific and short‐term factors.

Fiscal expansion in many countries and lax monetary policy created an

environment that favored high commodity prices.3 The depreciation of the US

dollar—the currency of choice for most international commodity transactions—

strengthened demand (and limited supply) from non‐US$ commodity consumers

(and producers). Other important contributing factors include low past invest‐

ment, especially in extractive commodities;4 investment fund activity by financial

institutions that chose to include commodities in their portfolios; and geopolitical

concerns, especially in energy markets.

In the case of agricultural commodities, prices were affected by the com‐

bination of adverse weather conditions and the diversion of some food commod‐

ities to the production of biofuels (notably maize in the US and edible oils in Eu‐

rope). That led to global stock‐to‐use ratios of several agricultural commodities

down to levels not seen since the early 1970s, further accelerating the price in‐

creases (Figure 3). Policy responses including export bans and prohibitive taxes

that were introduced in 2008 to offset the impact of increasing world food prices

contributed to creating the conditions for the “perfect storm.”

The weakening and/or reversal of these factors, coupled with the financial

crisis that erupted in September 2008 and the subsequent global economic down‐

turn, induced sharp price declines across most commodity sectors. But though

commodity prices have declined sharply since their mid‐2008 peak, they picked

up again recently and the key commodity price indices are still twice as high as

their early 2000s levels (Figure 4).

Thus the key question is whether at least some of the factors behind the

recent boom are more permanent in nature, and likely to remain in place. Past

experience reveals that food commodity price spikes were mainly driven by neg‐

ative supply shocks, with high prices often acting as the best incentive for miti‐

gating the shocks that generated them. Yet the pertinence of such experience for

future developments has been questioned, and in attempts to explain the current

boom, some factors have received considerably more attention (or even subjected

to a considerable amount of misinformation) than others. With this in mind, the

rest of this section examines the contributions made by three such factors, name‐

ly, excess liquidity and speculation, income growth and dietary changes in
Page 5

emerging economies, and the diversion of some food commodities to biofuel

production.

Excess liquidity and speculation

During the course of the recent boom, it has been argued often that fundamentals

do not tell the whole story and commodity prices have been driven, in part, by

factors that go beyond demand and supply considerations. Excess liquidity,

which is often placed within the broader context of speculation, is often argued

to have fueled a commodity bubble.

To gauge the importance of the issue, consider the following. Between the

second half of 2007 and the first half of 2008 production of petroleum increased

from 85.8 million barrels per day (mb/d) to 86.8 mb/d. Consumption fell from

86.5 mb/d to 86.3 mb/d. Prices should have fallen. In December 2007, crude oil

averaged US$ 90/barrel while in June 2008 it averaged US$ 132/barrel, almost

50% up. Recent figures on spare capacity give an equally perplexing picture.

During 2009, OPEC spare capacity stood at 6.3 mb/d while petroleum prices av‐

eraged $62/barrel. However, similar capacity levels during the early 2000s were

associated with $20/barrel. Stocks of key food commodities are 20% higher in

2009/10 compared to 2007/08; yet the nominal food price index averaged 23%

higher in December 2009 compared to a year ago, rather surprising given that an

often cited reason for the food price spike of 2008 was low inventories. Admit‐

tedly, the apparent “divergence” between commodity prices and fundamentals

deserves (and has received) attention.

While fundamentals have played a key role in commodity price move‐

ments, observations such as the above have led many researchers and analysts to

argue that the commodity price boom reflected, in part, excess liquidity and

speculation. In other words, a lot of “new” money (a result of excess liquidity)

chased too few assets and eventually found its way into commodity markets, in

turn causing a speculative bubble.

Various concepts related to speculation have been discussed often inter‐

changeably and have been analyzed in isolation from each other, or they are pre‐

sented in an oversimplified manner, especially in various editorials and blogs.

These concepts include: excess liquidity (one of the three sources of “new” mon‐

ey); index fund activity (the chief investment vehicle for the “new” money);

speculation (an activity not well‐defined but necessary for the functioning of fu‐

tures exchanges and sometimes the source of market manipulation); the role of

commodity futures exchanges (the nerve centers of commodity price discovery

mechanisms); inventories (which matter most for non‐perishable agricultural

commodities, mostly grains); and speculative bubble (a very broad and difficult
Page 6

to quantify issue). Not surprisingly, such complexity has led to different views

and mixed empirical evidence. The remaining of this section elaborates on these

concepts (see Appendix A for a discussion on speculation in commodity mar‐

kets).

Broadly speaking, the “new” money—which is not associated with physi‐

cal commodity transactions—can be linked to three sources:

 Diversification of investment vehicles. During the past decade or so, invest‐

ment fund managers noticed that existing asset classes were becoming increa‐

singly correlated. In their search for new (uncorrelated) assets they broadened

their portfolios by including assets from emerging economies. When these as‐

sets became themselves correlated (because of the systemic effect of all funds

invested in the same assets) fund managers began investing in commodities,

thus setting the stage for the so‐called ‘financialization of commodities’—a

role typically reserved for gold, after 1973. To the extent that commodities are

viewed as another asset class, diversification may have a permanent charac‐

ter.

 Rebalancing of investment portfolios. The rebalancing of investment portfo‐

lios by shifting funds from US$‐denominated (and other) holdings to com‐

modities added further inflows into commodity markets. The strong inverse

relationship between the US$ and commodity prices—especially crude oil—

has been invoked often as evidence for such rebalancing (see Medlock and

Jaffe 2009).5 The effect of rebalancing is less permanent than diversification

and depends largely on how long investors’ risk attitudes will favor com‐

modities or disfavor other assets.

 Excess liquidity. The low interest rate environment supported by many cen‐

tral banks resulted in excess liquidity, part of which found its way to com‐

modity markets (on top of the price increase due to expansion of physical

demand for commodities). It is believed that excess liquidity has been the key

reason behind the boom in real estate markets. The effect of excess liquidity is

likely to last for as long as interest rates remain low.

The key channel through which the “new” money found its way into

commodity markets is index funds. The most widely used and closely watched

indices are the Dow Jones‐AIG and S&P Goldman Sachs Commodity Index (also

known as DJ‐AIG and S&P‐GSCI). About 95 percent of funds indexed to com‐

modities are replicated by these two indices. The funds take long positions in

commodity futures exchanges by buying contracts and, prior to expiration, roll‐

ing them over. While there are no precise estimates on their size, a broadly ac‐

cepted range as of mid‐2008 was $250 to $300 billion (Masters 2008). A report by

a major commercial bank estimated that an additional $60 billon went into com‐
Page 7

modities during 2009, placing the 2009 total estimate to $230‐240 billion, margi‐

nally lower from 2008 due to weaker commodity prices. Although these amounts

of money represent about one percent of the global value of pension and sove‐

reign wealth fund holdings (both key contributors to index funds, with the glob‐

al value of these two groups estimated at $20 and $4 trillion, respectively) they

are large compared to the size of commodity markets.6

The effect of the “new” money on commodity prices has been associated

with speculation that might have led to a price bubble. The views on the subject,

however, have been, for the most part, extreme. For example, Krugman in a se‐

ries of New York Times blogs and editorials, not only rejected the view that specu‐

lation fueled the boom but also dismissed the idea that commodity trading activ‐

ity in futures exchanges may have affected commodity prices at all, arguing that

“a futures contract is a bet about the future price. It has no, zero, nada direct ef‐

fect on the spot price” (New York Times, June 23, 2008). Others too have argued

that speculation played no role. Wolf is of the opinion that “if speculation were

raising prices above the warranted level, one would expect to see inventories pil‐

ing up rapidly, as supply exceeds the rate at which oil is burned. Yet there is no

evidence of such a spike in inventories” (Financial Times, May 13, 2008). Frankel

cited the Congressional testimony by the chief economist of the Commodities Fu‐

tures Trading Commission (CFTC) on April 3, 2008 to support in his weblog (Ju‐

ly 25, 2008) that “The evidence does not support the claim that speculation has

been the source of, or has exacerbated the price increases.” Verleger (2009), based

on IEA (2009) analysis, concluded at his CFTC testimony that the increase in

crude oil price between 2007 and 2008 was caused by the incompatibility of envi‐

ronmental regulations and speculation had nothing to do with the price rise.7 The

IOSCO Task Force (2009), formed at the request of G‐8 to examine the issue of

speculation in commodity markets, found that economic fundamentals, not spe‐

culative activity, are the possible explanation for recent price changes in com‐

modities.8 Wright (2009) echoed similar views by noting that if long futures posi‐

tions were behind the grain price spike of 2008, stocks would have increased.

At the other end of the spectrum, Soros (2008) called commodity index

trading at his US congressional testimony intellectually unsound, potentially des‐

tabilizing, and distinctly harmful in its economic consequences. Eckaus (2008)

and Khan (2009) authored papers entitled “The Oil Price Is Really a Speculative

Bubble” and “The 2008 Oil Price Bubble”, respectively. Calvo (2008) noted that

speculation and low inventories are not necessarily inconsistent with each other

and concluded that “... [increases in] commodity prices are the result of portfolio

shift against liquid assets by sovereign investors, sovereign wealth funds, partly

triggered by lax monetary policy, especially in the US.” Roubini (2009) said this

on the early 2009 crude oil price increase: “... improving fundamentals ... justify
Page 8

oil going from $30 to maybe $50. I think the other $30 is all speculative demand

feeding on it—speculators and herding behavior.” Similar views were echoed by

others as well, including Medlock and Jaffe (2009) and Wray (2008).

The empirical evidence on the subject has been mixed. Two IMF studies

(2006, 2008) found no evidence that speculation had systematically influenced

commodity prices. A similar conclusion was reached by a series of studies under‐

taken by the US Commodities Futures Trading Commission, the agency that re‐

gulates US futures exchanges.9 Sanders, Irwin, and Merrin (2008) expressed skep‐

ticism about the assertion that speculation has led to bubbles in agricultural fu‐

tures prices.

Other authors share somewhat different views. Robles and others (2009)

identified speculative activity in the futures market as a source of the 2007/08

agricultural commodity price increases. Plastina (2008) concluded that between

January 2006 and February 2008, investment fund activity might have pushed

cotton prices 14 percent higher than they would otherwise have been. In the non‐

ferrous metals market, Gilbert (2007) found no direct evidence of the impact of

investor activity on the prices of metals, but found strong evidence that the fu‐

tures positions of index providers had affected the prices of soybeans (though

not of maize) in the US futures exchanges. Perhaps, the strongest evidence is a

subsequent study by Gilbert (2010: 420) who concluded that “By investing across

the entire range of commodity futures, index‐based investors appear to have in‐

flated food commodity prices.”

Why is the empirical evidence mixed? Often it is argued that despite the

fact that investment fund activity remained steady or even increased between

mid‐ and end‐2008, prices declined sharply during that period. Hence, the argu‐

ment goes, investment funds did not affect commodity prices. However, that is

based on the logic that if investment fund activity continued to increase, then

prices would increase forever (or, at least they would not decline), an unlikely

outcome. Supply and demand fundamentals will prevail, eventually. Thus, from

an empirical perspective, the key question should be whether investment fund

activity contributed to the recent boom (or any boom, for that matter), not

whether it affects commodity prices in general. Consequently, examining the role

of investment funds in commodity markets requires first, the identification of the

boom period and second, either analyze that period in isolation–perhaps within

a univariate modeling framework–or use of earlier periods (or some other prior)

as counterfactuals.

Identifying the “suspect” period will contain a large element of subjectivi‐

ty and hence will inevitably lead to differing opinions. However, there are at

least three reasons why investment fund activity may have influenced commodi‐

ty prices. First, investment in commodities is a relatively new phenomenon, and
Page 9

funds have to date flowed mostly in, not out, implying that some markets may

have been subject to extrapolative price behavior; that is, high prices leading to

more buying by investment funds, in turn leading to even higher prices, and so

on. In fact, that may have been the case with the sovereign wealth funds, whose

revenue comes from commodities, which in turn it is invested in commodities.

Second, index funds invest on the basis of fixed weights or past performance cri‐

teria, and hence investment often behaves differently from what market funda‐

mentals would dictate. Third, the large size of these funds compared to commod‐

ity markets may exacerbate price movements. Or, as Soros (2008: 3) characteristi‐

cally put it “... the institutions are piling in on one side of the market and they

have sufficient weight to unbalance it.”

What conclusion can be derived from of all this in the context of excess li‐

quidity and speculation? Was investment fund activity at least partly responsible

for the recent commodity boom? Any commodity‐related activity on the financial

side is unlikely to alter long‐term price trends, which will ultimately be deter‐

mined by market fundamentals. But, such activities can induce higher price va‐

riability in the sense of exacerbating the length and the amplitude of price cycles,

as they most likely did during the ‘perfect storm’ of 2007/08.

Dietary changes and income growth in middle­income countries

Typically agricultural price booms are linked to supply shocks such as weather

events or animal diseases that disturb normal production patterns. Supply

shocks were no exception in the recent boom. Droughts played a major role in

the reduction of dairy exports from New Zealand. Australia’s grain production

was severely affected by the three droughts experienced during 2002/08 (a highly

unusual weather pattern, often linked to global warming). The 2009 drought in

South America affected the oilseeds market for more than a year. But the magni‐

tude of these events, although cumulative and coming after a long period of

normal weather patterns, falls short of explaining the extent of the food price

spike.

Could a demand shock offer a more plausible explanation, as has often

been suggested? For this to have been the case, such a shock should have taken

place either unexpectedly and suddenly or through a rapid shift in long‐term ex‐

pectations about food demand patterns. It has often been argued that a structural

shift has taken place in the demand for grain by emerging countries, including

China and India, and especially during the past decade when these two countries

experienced high income growth. The June 2009 issue of National Geographic, for

example, noted that “... as countries like China and India prosper and their

people move up the food ladder, demand for grains has increased.” Similar ar‐
Page 10

guments have been advanced by noted scholars as well. Krugman argued that

“... there’s the march of the meat‐eating Chinese—that is, the growing number of

people in emerging economies who are, for the first time, rich enough to start

eating like Westerners” (New York Times editorial, April 7, 2008). Likewise, Wolf

asked “So why have prices of food risen so strongly?” and then answered “...

strong rises in incomes per head in China, India, and other emerging countries

have raised demand for food, notably meat and the related animal feeds” (Finan‐

cial Times, April 29, 2008). Indeed, the size of China and India, which together ac‐

count for 27 percent of the world’s population, implies that even a minor change

in their pattern of demand growth has a major effect on world market prices.

But a closer look at the growth trends of population and income over the

past decades, coupled with those of demand for food commodities, shows no

evidence that food demand growth accelerated either in China and India or in

the world as a whole. Table 1 summarizes demand growth patterns for a number

of key food commodities since 1961 for four 12‐year periods roughly correspond‐

ing to four price cycles: the period of the “green revolution” (1961‐72); the after‐

math of the two energy shocks (1973‐84), the recovery of agricultural prices until

their mid‐1990s price spike (1985‐96), and the last period until the recent price

peak (1997‐08). The data clearly show that demand growth has slowed for most

grains—including those used for feed, reflecting a slowdown in the growth of

demand for meat.

During the most recent decade, despite a clear acceleration of GDP growth

since 2003, stronger demand for agricultural products both at world level and in

China and India has been the exception—it occurred in maize and in soybeans

(driven by demand for edible oils), and was rather mixed in grains, but certainly

did not occur in meats or dairy products (see Table 2 with a breakdown of the

1997‐2008 period for the world, China, and India). Similar findings on the role

(or, the non‐role) of China and India have been discussed in Alexandratos (2008:

673) who emphatically stated that “... their [China’s and India’s] combined aver‐

age annual increment in consumption (both growth rates and absolute incre‐

ments) was lower in the years of the price surges, 2002‐08, than in the preceding

period 1995‐2001.” FAO (2009) arrived at nearly identical conclusions.

These developments reflect the huge gap that existed during the price

boom between the fundamentals of agricultural markets and the corresponding

price levels. No other example demonstrates this better than the fact that the

highest price increases took place in two commodities—wheat and rice—where

food demand was stagnating and yet were widely explained as being driven by

strong food demand. Thus, while supply shocks may explain some of the price

pressures in certain food commodities, by contrast demand growth accelerated

in recent years in commodities used for biofuels, such as maize and edible oils.  
Page 11

Biofuels

The increasing interaction between the price movements of energy and non‐

energy commodities during the boom focused attention on the impact of grow‐

ing demand for biofuels, including for maize‐based ethanol (mainly in the US)

and oilseed‐based biodiesel production (mainly in Europe). During the boom,

maize and crude oil prices moved in tandem, pointing to an emerging new and

fixed relationship between them. Obviously, maize and its use for ethanol moved

into the picture as significant factors affecting price developments. But how

much impact was there, and was there a similar one in oilseeds, resulting from

their use for biodiesel?

The contribution of biofuels to the recent price boom, and especially the

price spike of 2007/08, has been hotly debated. Mitchell (2009) argued that bio‐

fuel production from grains and oilseeds in the US and the EU was the most im‐

portant factor behind the food price increase between 2002 and 2008, accounting,

perhaps, for as much as two thirds of the price increase. Gilbert (2010), on the

other hand, found little direct evidence that demand for grains and oilseeds as

biofuel feedstocks was a cause of the price spike.

FAO (2008) compared a baseline scenario, which assumes that biofuel

production will double by 2018, to an assumption that biofuel production will

remain at its 2007 levels; it concluded that in the latter case grain prices would be

12 percent lower, wheat prices 7 percent lower, and vegetable oil prices 15 per‐

cent lower than in the baseline scenario. OECD (2008) arrived at similar conclu‐

sions for vegetable oils, finding that their prices would be 16 percent lower than

the baseline if biofuel support policies were abolished; eliminating biofuel subsi‐

dies would have smaller impacts on the prices of coarse grains (‐7 percent) and

wheat (‐5 percent). Rosegrant (2008), who simulated market developments be‐

tween 2000 and 2007 (excluding the surge in biofuel production), concluded that

biofuel growth accounted for 30 percent of the food price increases seen in that

period, with the contribution varying from 39 percent for maize to 21 percent for

rice. Looking ahead, Rosegrant found that if biofuel production were to remain

at its 2007 levels, rather than reaching its mandated level, maize prices would be

lower by 14 percent in 2015 and by 6 percent in 2020.10

Banse and others (2008) compared the impact of the EU’s current mandate

to (i) a no‐mandate scenario and (ii) a mandate whereby the US, Japan, Brazil al‐

so adopt targets for biofuel consumption. They estimate that by 2020, in the base‐

line scenario (no mandate), cereal and oilseed prices will have decreased by 12

and 7 percent, respectively. In the EU‐only scenario, the comparable changes are

‐7 percent for cereal and +2 percent for oilseeds. By contrast, under the “global”

scenario (adding biofuel targets in US, Japan, and Brazil) oilseed prices will have
Page 12

risen by 19 percent and cereal prices by about 5 percent. The European Commis‐

sion’s own assessment of the long‐term (2020) impacts of the 10 percent target for

biofuels (i.e. that renewable energy for transport, including biofuels, will supply

10 percent of all EU fuel consumption by 2020) predicts fairly minor impacts

from ethanol production, which would raise cereals prices 3‐6 percent by 2020,

but larger impacts from biodiesel production on oilseed prices; the greatest pro‐

jected impact is on sunflower (+15 percent), whose global production potential is

quite limited. Taheripour and others (2008) simulate the biofuel economy during

2001‐06. By isolating the economic impact of biofuel drivers (such as the crude oil

price and the US and EU biofuel subsidies) from other factors at a global scale,

they estimate the impact of these factors on coarse grain prices in the US, EU, and

Brazil at 14 percent, 16 percent, and 9.6 percent, respectively.

A joint US Department of Agriculture and Department of Energy assess‐

ment (USDA/USDE 2008) concluded that the recent increase in maize and soy‐

bean prices appears to have little to do with the run‐up in prices of wheat and

rice. It found that if the amounts of corn used for ethanol and edible oil used for

biodiesel in the US had remained unchanged at their 2005/06 levels, prices in

2007/2008 would have been 15 percent lower for maize, 18 percent for soybean,

and 13 percent for soybean oil. The assessment also concluded that the impact of

biofuels production in 2007 was a 3‐4 percent increase in retail food prices and a

0.1‐0.15 percent increase in the all‐food CPI.

Clearly US maize‐based ethanol production, and (to a lesser extent) EU

biodiesel production) affected the corresponding market balances and land use

in both US maize and EU oilseeds. Yet, worldwide, biofuels account for only

about 1.5 percent of the area under grains/oilseeds (Table 3). This raises serious

doubts about claims that biofuels account for a big shift in global demand. Even

though widespread perceptions about such a shift played a big role during the

recent commodity price boom, it is striking that maize prices hardly moved dur‐

ing the first period of increase in US ethanol production, and oilseed prices

dropped when the EU increased impressively its use of biodiesel. On the other

hand, prices spiked while ethanol use was slowing down in the US and biodiesel

use was stabilizing in the EU.

Yet while the debate has focused mostly on the amount of food crops that

have been diverted to the production of biofuels, and the resulting effect on pric‐

es, less attention has been paid to a more important issue linked to this develop‐

ment—the level at which energy prices provide a floor to agricultural prices.

Analytically, this is a very complex issue; in addition to the prices of the respec‐

tive commodities (energy and feedstock for biofuels), it involves numerous other

elements, including subsidies, mandates, trade restrictions, and sunk costs of the

biofuel industry. Therefore, analysts often use various rules of thumb to express
Page 13

a perceived new relationship between agricultural and crude oil prices. One such

rule is that the price of maize expressed in US$/ton is roughly double the price of

crude oil in US$/barrel (thus a US$ 75/barrel price for crude oil would corres‐

pond to US$ 150/ton for maize). Other commentators (in the US) have argued

that a price of US$ 3/gallon of gasoline at the pump is the level at which the ma‐

ize price is determined by the crude oil price. The World Bank (2009) reported

that crude oil prices above US$ 50/barrel effectively dictate maize prices; this

conclusion was based on the strong correlation between the maize price and

crude oil prices above US$ 50/barrel and the absence of correlation below that

level. The US Government Accountability Office (2009: 101) while acknowledg‐

ing that economists have disagreed about the circumstances that would make the

2009 US biofuel mandates non‐binding (i.e. biofuels become profitable at current

energy prices), it gave a range between $80 and $120 per barrel (the range was

based on anecdotal evidence based on interviews). The empirical basis of such

rules is linked to the issue discussed in the next section.

3. Commodity Prices: Longer‐term Trends

This section focuses on three key characteristics of commodity price behavior:

lack of trends, co‐movement among prices, and a special case of the latter, i.e.,

the link between energy and non‐energy commodity prices.

Trends, cycles, and everything in between

The long‐term behavior of commodity prices was first examined systematically

by Prebisch (1950) and Singer (1950), who noted that since the late 19th century

the prices of primary commodities had been declining relative to the prices of

manufactured goods (often referred to as the barter terms of trade). They warned

of potential problems for producers of primary commodities, and in fact the no‐

tion of declining terms of trade formed the cornerstone of the industrialization

policies that many developing countries pursued during the 1960s and 1970s.

The so‐called Prebisch‐Singer (PSH) hypothesis has been, perhaps, one of

the most researched topics in commodity price behavior. Early research (e.g.,

Spraos 1980; Sapsford 1985; Grilli and Yang 1988), which focused mainly on

identifying trends, supplied broad support for PSH. However, later authors

found that prices did not simply move along a linear trend but instead contained

strong stochastic elements, i.e., long and irregular cycles, thus producing more

mixed results (e.g., Cuddington and Urzua 1989; Cuddington 1992). Studies us‐

ing better econometric techniques and longer time series allowed for structural

breaks (e.g., Leon and Soto 1997; Zanias 2005; Kellard and Wohar 2006). And
Page 14

very recent literature, focusing on non‐linear or time‐varying alternatives (e.g.,

Balagtas and Holt 2009), finds even less support for PSH.

All this research is perhaps best summarized by Cashin and McDermott

(2002) who concluded that the downward trend in real commodity prices is of

little policy relevance because it is small when compared to the variability of

prices. Or as Deaton (1999: 27) succinctly put it, “what commodity prices lack in

trend, they make up for in variance.”

Commodity price variability is at the core of the current policy debate.

The difficulty associated with describing past price behavior, and hence with

making inferences regarding future trends, can be inferred from Figure 1; the

conclusions reached depend on what time period is chosen for analysis. Statisti‐

cally, this difficulty reflects the problem of nonstationarity, i.e. the fact that the

average price does not exist in the statistical sense. Table 4 shows the results of

an analysis of stationarity for prices of six food commodities (wheat, maize, rice,

soybeans, soybean oil, and palm oil). For sensitivity purposes, we report results

from two tests, with and without trend, both in nominal and real terms (we also

used US CPI in addition to MUV and the results were remarkably similar). All

lend strong support to non‐stationarity, thus reaffirming the conclusions reached

by Cashin and McDermott (2002) and Deaton (1999).

The fact that commodity price variability overwhelms trends has a num‐

ber of key implications. On the methodological side, analysis involving prices

needs to recognize that correlations may not be meaningful unless certain condi‐

tions are met (see next section), and also that because a mean or a trend of the

price series cannot be properly defined, the variability in prices is difficult to cal‐

culate.

On the policy side, attempts to introduce mechanisms with price triggers

(as has often been proposed recently) are likely to fail. In fact, the absence of

trends (or simply put, the non‐existence of an “average price”) may be the key

reason why earlier price stabilization (or other) mechanisms failed.11 When prices

stay low for long periods, stabilization funds run out of resources, and when

prices stay high for long periods, stabilization funds tend to be misused. Consid‐

er, for example, that the agricultural commodity price index (shown in Figure 1)

exceeded its period average (equal to 173) in all years during 1948‐71 and fell be‐

low it in all years during 1981‐2007.

Co­movement

Because some agricultural commodities can be substituted for one another (e.g.

various edible oils), while resources on the input side (e.g., land, labor, and ma‐

chinery) can be shifted from one crop to another, the changes in fundamentals or
Page 15

policy actions in one market will eventually be transmitted to other markets as

well. Thus, assessing how the prices of various food commodities move with re‐

spect to each other is paramount in understanding the way and the degree to

which market conditions and policies affect prices. Examining such relationships

ultimately comes down to estimating the degree of price co‐movement among

various commodities.

While the general subject of price co‐movement has been extensively stu‐

died in the literature, analysis of the co‐movement among prices of different

commodities is scarce. (For a brief literature review of price co‐movement and

the reasons why the issue has not been adequately researched see the Appendix

B.)

Here we analyze the co‐movement of prices using a simple econometric

model. The degree of co‐movement was analyzed among six food commodity

prices, using ordinary least squares with annual data from 1960 to 2008: Pt

i = μ +

β1Pt

j + β2MUVt + β3t + εt, where Pti and Ptj denote the logarithm of commodity price

i and j in year t (expressed in nominal dollar terms), MUVt denotes the deflator, t

is the time trend, and εt denotes the error term; μ, β1, β2, and β3 are parameters to

be estimated.

The results are reported in Table 5. Because prices are non‐stationary (see

previous section) examining the stationarity properties of the error term is a cru‐

cial step in establishing the validity of the model. All the regressions show strong

performance, with an average R2 of 0.84 and with unit root statistics that strongly

confirm the stationarity of the error term. Moreover, in all cases the slope esti‐

mate of the price variable is significant at the 1 percent level.

The results imply that it is important not to analyze commodity markets

in isolation from one another, because the impact of events that seemingly affect

one market will eventually be equalized among most commodity sectors. Con‐

sider, for example, the palm oil/soybean oil parameter estimate of 0.97 and an R2

of 0.93 (Table 5, bottom row). This suggests an almost synchronous movement of

palm and soybean oil prices, despite the fact that soybean oil is an annual crop

produced chiefly in North and South America and palm oil is a tree crop pro‐

duced almost exclusively in East Asia. The implication is that, whether biofuel

mandates are applied to one or the other edible oil market, the effect will be

eventually diffused among all edible oil markets. Not surprisingly, policies fa‐

voring biofuel production in the name of environmental benefits may in fact lead

to less desirable outcomes. That is, the environmental benefits from switching

from fossil fuel use to, say, rapeseed‐based biodiesel in Europe or soybean oil‐

based biodiesel in the US may be less than the environmental costs of expanding

palm oil production in East Asia.12 Similarly, prices of wheat, maize, and soy‐

beans—key food crops, produced primarily in the US, EU, and South America—
Page 16

show an equally large co‐movement, as their R2 averaged 0.93, much like that of

palm and soybean oil.

For inflation, by contrast, the estimated coefficient is either not significant‐

ly different from zero or, in the few cases where it is significant, it is small. And

the time trend parameter estimate is almost always zero—implying that there is

either no trend or the same trend for all prices.

The energy/non­energy price link

It has become increasingly clear that the energy price increases of the last few

years have a permanent character. In the 20 years between 1984 and 2004, the

price of crude oil averaged a little more than US$ 20/barrel in real 2000 terms.13

Now most analysts and researchers believe that the “new” equilibrium price of

oil will be three to four times higher than this, with proportional changes taking

place in all other types of energy, at least in the long term. If such assessment is

correct, then high energy prices coupled with the high energy intensity of agri‐

cultural commodities imply that developments in non‐energy (especially food)

markets will depend strongly on the nature and degree of the price links be‐

tween energy and non‐energy commodities.

The channels through which energy prices affect other commodities are

numerous (see for example FAO 2002; Baffes 2007; World Bank 2009). On the cost

side, energy enters the aggregate production function of most primary commodi‐

ties through the use of various energy‐intensive inputs and, often, transport of

outputs over long distances. Some commodities have to go through an energy‐

intensive primary processing stage. In other cases, the main input may be a close

substitute to crude oil, as when nitrogen fertilizer is made directly from natural

gas. And, to the extent that some commodities are used to produce biofuels (to

some degree a response to high energy prices), another important dimension is

added to the energy/non‐energy price link (see earlier discussion on biofuels).

We examined the energy/non‐energy price link by estimating a regression

similar to the one used for the co‐movement estimates above.14 The results for

eleven commodity price indices are presented in Table 6. They show that energy

prices explain a considerable part of commodity price variability; the adjusted R2

of all regressions averaged 0.85. Specifically, the parameter estimate of the non‐

energy index (top row of Table 6) is 0.28, implying that a 10 percent increase in

energy prices is associated with a 2.8 percent increase in non‐energy commodity

prices, in the long run.

Three earlier studies—Gilbert (1989), Borensztein and Reinhart (1994), and

Baffes (2007)—that estimated the elasticities of non‐energy commodity prices

with respect to energy prices reported these as 0.12, 0.11, and 0.16, respectively
Page 17

(Table 7, top row).15 When the sample underlying the current analysis is adjusted

to match the samples used in these studies, the pass‐through coefficient becomes

remarkably similar, at 0.13, 0.12, and 0.18, respectively.

Underlying these aggregate pass‐through coefficients for non‐energy

commodity prices are variations within sub‐indices. Among the sub‐indices, the

highest pass‐through elasticity is in fertilizer, at 0.55—not surprisingly, since ni‐

trogen‐based fertilizers are made directly from natural gas. Interestingly, the fer‐

tilizer and energy price increases during the recent boom were in line with those

experienced during the first oil shock: from 1973 to 1974 phosphate rock and

urea prices increased four‐fold and three‐fold, while the crude oil price increased

from US$ 2.81/barrel to US$ 10.97/barrel.

The pass‐through elasticity for agriculture, estimated at 0.27, reflects a

wide range among the components of the agriculture index: beverages (0.38),

food (0.27), and raw materials (0.11). For the components of the food price index,

by contrast, the elasticity estimates fall within a very narrow range: cereals (0.28),

edible oils (0.29), and other food (0.22). Based on the same regression, Table 8 re‐

ports parameter estimates for the six food commodities under consideration. The

estimates for all six fall within a narrow range, from a low of 0.27 in maize to a

high of 0.36 in soybean oil. This result contrasts sharply with estimates for met‐

als, which show a high degree of diversity (see Chaudhri 2001; Baffes 2007).

A number of key conclusions emerge from these results. First, the prices of

most commodities respond strongly to energy prices, with the response further

strengthening in periods of high prices (the values of the estimated elasticities

increase considerably when the recent boom is included in the analysis). More

importantly, the difference between the last two estimates (last two columns of

Table 7) indicates that the effect of energy prices on the prices of all commodities

has increased considerably when the recent boom is taken into consideration—

suggesting that the energy/non‐energy price link has strengthened (see earlier

discussion). Many observers have attributed such strengthening of the relation‐

ship to the use of biofuels, which also coincides (roughly) with the boom. Yet, it

is important to note that the strengthening of the effect of energy on non‐energy

prices is more pronounced in non‐food commodities (e.g. raw materials and

metals) than in food. Thus, common factors appear to have played a more prom‐

inent role in the recent boom (see Vansteenkiste 2009; Gilbert 2010).

Second, food commodity prices respond to energy prices by moving in a

very synchronous manner since the elasticities fall within a very narrow range

(from 0.25 to 0.36). Such result not only emphasizes the interdependence of agri‐

cultural markets (as discussed earlier) but also indicates that since a key deter‐

minant of food commodity prices is energy prices, analyzing food markets re‐

quires an understanding of energy markets as well.
Page 18

Third, though the transmission elasticities of energy prices to non‐energy

prices are broadly similar to one another, this is not the case with the inflation

coefficient, estimates of which vary considerably in sign, magnitude, and level of

significance. The inflation coefficient is positive and significantly different from

zero only for agriculture and some of its sub‐indices, and effectively zero for

metals and fertilizers. This implies that the relationship between inflation and

nominal commodity prices is much more complex and, perhaps, changing over

time. This may not be surprising if one considers that during 1972‐80 (a period

that included both oil shocks) the MUV increased by 45 percent, and that during

2000‐08 it increased by only half as much. The increases in the index of nominal

non‐energy prices during these two eight‐year periods were identical, at 170 per‐

cent.

Lastly, the estimates of trend parameters are spread over a wider range

than the estimates of energy price pass‐through and inflation. For example, the

aggregate index of non‐energy prices shows no trend at all, while the index of

metal prices shows an almost 2 percent positive annual trend and the index of

agriculture prices shows a 1 percent negative annual trend. Further, the trend pa‐

rameter estimates of the agriculture sub‐indices vary considerably, from 0.08 for

raw materials to  ‐3.12 for beverages—confirming the point made earlier that

commodity prices do not exhibit well‐defined trends, even when one accounts

for the effect of energy prices.

4. Concluding Remarks

Numerous factors have contributed to the recent commodity boom, and have

been analyzed extensively in the literature. Yet their relative weight continues to

be an area of contention. In this paper we examined three key factors whose role

has been somewhat controversial: speculation, the growth of demand for food

commodities by emerging economies and the role of biofuels. We conjecture that

index fund activity (one type of “speculative” activity among the many that the

literature refers to) played a key role during the 2008 price spike. Biofuels played

some role too, but much less than initially thought. And we find no evidence that

alleged stronger demand by emerging economies had any effect on world prices.

Although tentative, these conclusions provide insights into the determinants of

the future path of commodity prices, which is still uncertain.

Central among the uncertainties is the relationship between the prices of

energy and of food commodities. Our examination of the key characteristics of

longer‐term commodity price behavior revealed a strong link between energy

and non‐energy prices, which increased considerably during the recent boom; it

also revealed that co‐movement among the prices of food commodities is very
Page 19

strong. The latter implies that events taking place in one sector (e.g., increased

demand for maize for the production of ethanol) will affect other markets (e.g.,

for wheat) through reallocation of resources, especially land. It also implies that

policy changes in one market may affect other markets. For example, expecta‐

tions about the use of corn for biofuels could result in high wheat prices even in

the presence of record levels of wheat stocks. Our results also show that agricul‐

tural commodity market fundamentals appear, in the short term, to be playing

somewhat less of a role than in the past, tending to be overshadowed by the

much stronger pull of energy prices.

Our conclusion about the long‐term evolution of commodity prices is con‐

sistent with earlier literature, and supports the thesis that price variability over‐

whelms price trends. Variability is such that the average price does not exist in

the statistical sense (i.e., prices exhibit non‐stationary behavior), and the conclu‐

sions reached about trends depend on what time period is chosen for the analy‐

sis.

Despite its simplicity, this conclusion has important implications. Follow‐

ing the recent food price spike, there have been calls for policy actions, essential‐

ly aiming to alleviate the impacts of price spikes on developing countries,

through reliance on some level of buffer stocks (whether physical or virtual). His‐

tory has not been kind to collective measures designed to prevent the decline or

reduce the variability of prices. What type of measures would be more pertinent

to mitigate any undesired effects of price variability would depend on the better

understanding of the factors that not only affect, but also potentially alter, long‐

term price trends.
Page 20

Endnotes

1 Numerous authors have analyzed the recent commodity boom. See, for example, Abbott, Hurt,

and Tyner (2008), Timmer (2008), Gilbert (2009), Mitchell (2009), Piesse and Thirtle (2009), Sarris

(2009), Trostle (2008), and Coady, Dorosh, and Minten (2009). Agricultural Economics devoted an

entire issue to the subject (Masters and Shiverly 2008).

2 See, for example, Von Braun and Torero (2009) on virtual reserves and Mendoza (2009) on rice

insurance mechanisms. Today’s discussions call to mind those in earlier booms (for example,

Meadows and others 1972).

3 Calvo (2008) and Frankel (2007) have argued that interest rates played a key role during the

boom.

4 Although underinvestment has been cited very often as the key factor in the boom, this assess‐

ment is essentially derived ex post. Certainly, any level of past investment will be considered low

at high prices and high at low prices. Yet, research reported in World Bank (2009) shows that the

level of investment was “right” at the time it was made. For example, during 1980‐2007, R&D and

investment expenditures by major multinational oil and gas companies track very closely output

prices (as evidenced by their strong correlation with energy prices, R2 = 0.95). Similarly, public

R&D agricultural expenditures follow agricultural GDP.

5 This inverse relationship is in addition to the effect of the US$ exchange rate against currencies

of commodity producing and consuming countries mentioned earlier.

6 Information on pension and, especially, sovereign wealth funds is not widely available. The re‐

cently published first annual report on sovereign wealth funds is an attempt to fill this gap (SWF

2009).

7 Yet, Verleger (2009: 2) argued that “The collapse in oil prices from July 2008 to December 31,

2008 can be tied to ... the possible liquidation of futures positions.” Which begs the question: If

the liquidation of futures positions were partly responsible for the collapse in oil prices, should

not the taking of such positions be responsible for the corresponding increase in oil prices?

8 The IOSCO (2009: 3) report’s recommendations, which were based on reviewing other studies,

were: (i) understand with greater clarity the role of speculative and commercial activity in com‐

modity futures markets; (ii) gain a more comprehensive view of trading activities in, and the

structure of, the underlying markets that may affect price formation on commodity futures mar‐

kets; and (iii) detect, deter, and prosecute manipulation and other trading abuses involving

commodity futures, and related commodity markets.

9 See, for example, Büyükşahin, Haigh, and Robe (2008) and Commodity Futures Trading Com‐

mission (2008).

10 The models used in the studies discussed in this section are the following: FAO (2008) and

OECD (2008) used AGLINK; Rosegrant (2008) used IMPACT; Banse and others (2008) used

GTAP‐E; EU (2008) used ESIM‐PE; Mitchell (2009) used simple statistical analysis; and Gilbert

(2009) used a CAPM‐type econometric model.

11 Such failed arrangements include the 1962 International Coffee Agreement (and a subsequent

series of agreements) to restrict exports and boost coffee prices, the 1972 International Cocoa

Agreement, and similar efforts by producers of cotton and grains; the International Tin Agree‐

ment; and the International Natural Rubber Organization. For a discussion of such agreement see

Gilbert (1996) and Radetzki (2009).
Page 21

 12 A large body of literature discusses this issue; see for example, Searchinger and others (2008)

and Fargione and others (2008).

13 The low energy prices between mid‐1980s and early 2000s prompted most analysts to argue

that the high prices of the 1970s were an aberration and that the pre‐1973 levels were the norm.

For example in its March 6, 1999 edition, the Economist’s leader article entitled “Drowning in Oil”

concluded that (p. 19): “$10 might actually be too optimistic. We may be heading for US$ 5.

Thanks to new technology and productivity gains, you might expect the price of oil, like that of

most other commodities, to fall slowly over the years. Judging by the oil market in the pre‐OPEC

era, a ‘normal’ market price might now be in the US$ 5‐10 range. Factor in the current slow

growth of the world economy and the normal price drops to the bottom of that range.” Indeed,

most energy analysts were forecasting real prices to average between US$ 15/barrel and US$

20/barrel in the long run. For example, the World Bank’s nominal crude oil price forecast in 1999

was US$ 18/barrel for 2005 and US$ 19/barrel for 2010. The December 2008 WTI futures contract

opened at US$ 18.88 in January 15, 2002, when it was first introduced. During 2008, crude oil

prices averaged US$ 97/barrel, almost five times higher than the highest forecasts.

14 Here the model is the same as the one used above for co‐movement, except that Pti denotes the

prices of food commodities and Pt

j denotes the energy price index. The rest of the coefficients and

variables have the same interpretation. However, in contrast to the price co‐movement regres‐

sions, regressing food prices on energy prices has a well‐defined endogeneity pattern; energy

affects food prices but not vice versa. Thus, we estimated only the regressions with the energy

price index (along with deflator and time trend) as the explanatory variables. The estimates can

be viewed as energy price transmission elasticities rather than just co‐integration parameters. See

Baffes (2009) for the structure of the indices.

15 Table 7 indicates that the elasticities for food commodities are higher than those for raw mate‐

rials and metals. This is consistent with the input‐output table of the GTAP database, which

shows that the direct energy component in the US agriculture and manufacturing sectors is 12

percent and 3 percent respectively.
Page 22

TABLE 1

ANNUAL GROWTH (PERCENT) IN GLOBAL GDP, POPULATION,

AND CONSUMPTION FOR SELECTED COMMODITIES

1961‐72 1973‐84 1985‐96 1997‐2008

MACRO VARIABLES     

Population 2.0 1.8 1.6 1.3

GDP, real 5.5 3.4 2.8 3.0

CONSUMPTION     

Rice 3.3 2.7 1.9 1.2

Wheat 3.9 2.9 1.4 0.9

Maize 3.7 2.5 2.7 2.8

Soybeans 4.8 2.6 5.5 4.0

Palm Oil 8.4 10.2 7.7 8.8

Beef 3.2 1.8 1.1 1.0

Pork 3.7 4.9 2.7 2.2

Poultry 12.1 6.9 6.8 4.0

Source: Authors calculations based on FAO, FAPRI, World Bank, and UN data.

TABLE 2

ANNUAL GROWTH (PERCENT) IN CONSUMPTION OF SELECTED COMMODITIES

  ‐‐‐‐‐‐ WORLD ‐‐‐‐‐‐‐  ‐‐‐‐‐‐‐‐ CHINA ‐‐‐‐‐‐‐‐  ‐‐‐‐‐‐‐‐‐ INDIA ‐‐‐‐‐‐‐‐‐ 

1997‐2002 2003‐08 1997‐2002 2003‐08 1997‐2002 2003‐08

GDP 2.9 3.3 8.4 10.4 5.1 8.5

Wheat 1.3 2.7  ‐0.7 0.2 2.7 1.8

Rice 1.6 1.5 0.5  ‐0.6 1.0 1.9

Maize 1.8 3.6 2.8 3.4 1.9 5.5

Soybeans 5.8 3.3 16.1 8.7  ‐1.4 7.1

Palm Oil 10.5 7.5 22.1 8.2 21.4 7.2

Beef 0.8 1.6 3.8 2.8  ‐0.5  ‐2.2

Pork 2.7 1.3 2.8 1.2 1.0 0.4

Poultry 4.7 3.8 4.9 4.6 17.6 7.7

Source: Authors calculations based on FAO, FAPRI, World Bank, and UN data.
Page 23

TABLE 3

KEY BIOFUEL STATISTICS

2000‐01 2002‐03 2004‐05 2006‐07 2008‐09

Biofuels as a share of global grain and oilseed area (percent)

EU oilseeds 0.00 0.06 0.15 0.24 0.34

US maize 0.13 0.27 0.37 0.76 1.11

Land used for US ethanol from maize as a share of (percent)

US Maize area 3.63 7.32 9.45 18.03 27.54

US Grain area 0.99 2.00 2.79 5.68 8.44

World grain area 0.16 0.32 0.43 0.85 1.26

Notes: The shares have been calculated based on average world yields.

TABLE 4

STATIONARITY STATISTICS FOR KEY FOOD COMMODITY PRICES, 1960‐2008

Without Trend    With Trend

ADF PP    ADF PP

REAL (MUV‐Deflated) SERIES        

Wheat  ‐2.32  ‐1.70   ‐1.83  ‐1.47

Maize  ‐1.59  ‐1.49   ‐1.84  ‐1.83

Rice  ‐1.59  ‐1.71   ‐3.08  ‐1.92

Soybeans  ‐1.72  ‐1.59   ‐2.09  ‐1.96

Soybean oil  ‐1.34  ‐2.06   ‐0.96  ‐2.28

Palm oil  ‐1.41  ‐2.14   ‐1.31  ‐2.47

NOMINAL SERIES        

Wheat  ‐1.63  ‐0.84   ‐3.10  ‐2.02

Maize  ‐1.69  ‐1.22   ‐2.76  ‐2.21

Rice  ‐2.53  ‐1.62   ‐3.40*

  ‐2.27

Soybeans  ‐1.32  ‐1.23   ‐2.11  ‐2.14

Soybean oil  ‐1.08  ‐1.17   ‐1.99  ‐2.56

Palm oil  ‐1.41  ‐2.14   ‐1.31  ‐2.47

Notes: ADF and PP denote the Augmented Dickey‐Fuller (Dickey and Fuller 1979) and Phillips‐

Perron (Phillips and Perron, 1988) statistics for unit roots. Asterisks in this and the following

tables denote significance at 10% (*), 5% (**), and 1% (***) levels. The corresponding t‐statistics are

‐2.60, ‐2.93, and ‐3.58 for the tests without trend and ‐3.18, ‐3.50, and ‐4.16 for the tests with trend.

The ADF statistic corresponds to the MacKinnon one‐sided p‐value. The lag length of the ADF

equations was determined by minimizing the Schwarz‐loss function while the bandwidth of the

PP statistic was based on the Newey‐West method.
Page 24

TABLE 5

PARAMETER ESTIMATES: CO‐MOVEMENT REGRESSIONS

μ 1 2 100*3 Adj‐R2 ADF

Maize‐Wheat 0.29

(1.37)

0.85@

(14.40)

0.06

(0.77)

‐0.33

(1.35)

0.94  ‐4.86***

Soybeans‐Wheat 0.90@

(3.20)

0.78@

(10.21)

0.19@

(1.99)

‐0.45

(1.40)

0.92  ‐5.20***

Wheat‐Rice 0.34

(1.01)

0.60@

(8.31)

0.26@

(2.36)

0.41

(1.02)

0.90  ‐4.54***

Soy oil‐Wheat 1.75@

(3.90)

0.97@

(7.83)

‐0.08

(0.51)

0.03

(0.05)

0.81  ‐6.54***

Wheat‐Palm oil 0.02

(0.02)

0.63@

(6.64)

0.39@

(3.26)

‐0.04

(0.10)

0.87  ‐5.30***

Maize‐Soybeans ‐0.16

(0.54)

0.85@

(10.82)

0.04

(0.39)

‐0.01

(0.92)

0.91  ‐6.18***

Rice‐Maize 0.48

(0.99)

1.02@

(7.24)

0.13

(0.81)

‐0.86

(1.54)

0.77  ‐5.60***

Palm oil‐Maize 1.27@

(2.69)

1.09@

(8.00)

‐0.04

(0.27)

‐0.17

(0.31)

0.79  ‐5.64***

Soy oil‐Maize 1.45@

(3.64)

1.12@

(9.72)

‐0.13

(0.98)

0.39

(0.86)

0.86  ‐6.90***

Rice‐Soybeans 0.00

(0.01)

1.01@

(7.13)

0.06

(0.32)

‐0.79

(1.41)

0.76  ‐5.19***

Soybeans‐Palm oil 0.54

(1.48)

0.53@

(7.75)

0.41

(3.96)

‐0.35

(0.91)

0.89  ‐4.54***

Soy oil‐Soybeans 0.89@

(2.09)

1.12@

(9.82)

‐0.23

(1.61)

0.47

(1.04)

0.86  ‐7.73***

Palm oil‐Rice 1.70@

(1.48)

0.64@

(5.40)

0.18

(1.01)

0.01

(0.02)

0.69  ‐5.15***

Soy oil‐Rice 1.94@

(3.71)

0.64@

(5.80)

0.12

(0.53)

‐0.71

(0.86)

0.74  ‐5.71***

Palm oil‐Soy oil ‐0.13

(0.44)

0.97@

(16.83)

0.08

(1.03)

‐0.54@

(1.79)

0.93  ‐4.32***

Notes: All regressions were run in both directions. (@) denotes parameter estimate significant at

the 5 percent level. We report the direction with the largest ADF statistic. The variances have

been estimated using White’s method for heteroskedasticity‐consistent standard errors. For other

notes see table 4.
Page 25

TABLE 6

PARAMETER ESTIMATES: PRICE INDICES REGRESSED ON ENERGY PRICE INDEX

μ 1 2 100*3 Adj‐R2 ADF

Non‐Energy 3.03@

(6.54)

0.28@

(5.24)

0.12

(0.68)

‐0.01

(0.02)

0.90  ‐3.35**

Metals 3.77@

(4.80)

0.25@

(3.14)

‐0.17

(0.60)

1.93@

(2.31)

0.82  ‐3.30**

Fertilizers 3.58@

(4.12)

0.55@

(4.79)

‐0.30

(0.95)

0.39

(0.48)

0.81  ‐3.97***

Agriculture 2.51@

(6.90)

0.26@

(5.54)

0.33@

(2.43)

‐0.99@

(2.73)

0.90  ‐3.81***

Beverages 1.83@

(3.10)

0.38@

(4.87)

0.55@

(2.63)

‐3.12@

(5.22)

0.76  ‐4.95***

Raw materials 1.85@

(4.16)

0.11@

(2.15)

0.51@

(3.15)

0.08

(0.19)

0.91  ‐3.15**

Food 2.91@

(7.11)

0.27@

(4.93)

0.21

(1.39)

‐0.71

(1.80)

0.85  ‐3.85***

Cereals 3.13@

(5.94)

0.28@

(4.23)

0.17

(0.89)

‐0.87

(1.76)

0.78  ‐3.83***

Edible oils 3.33@

(6.16)

0.29@

(4.51)

0.12

(0.58)

‐0.80

(1.50)

0.80  ‐2.82*

Other food 1.86@

(6.28)

0.22@

(3.81)

0.45@

(4.44)

‐0.42

(1.18)

0.89  ‐3.60***

Precious metals ‐1.40@

(3.58)

0.46@

(9.40)

1.05@

(7.61)

‐1.75@

(3.68)

0.98  ‐3.91***

Notes: See Tables 4 and 5.

Source: Baffes (2009).
Page 26

TABLE 7

COMPARING LONG‐RUN TRANSMISSION ELASTICITIES

Holtham

(1988)

1967:S1‐

1984:S2

Gilbert (1989)

1965:Q1‐

1986:Q2

Borensztein &

Reinhart (1994)

1970:Q1‐

1992:Q3

Baffes (2007)

1960‐2005

Baffes (2009)

1960‐2008

Non‐energy — 0.12 0.11 0.16 0.28

Food — 0.25 — 0.18 0.27

Raw materials 0.08 — — 0.04 0.11

Metals 0.17 0.11 — 0.11 0.25

Notes: Holtham uses semiannual data, Gilbert and Borensztein & Reinhart quarterly, and Baffes

along with the present study annual. Gilbert’s elasticities denote averages based of four specifica‐

tions. Holtham’s raw materials elasticity is an average of two elasticities based on two sets of

weights. ‘—‘ indicates that the estimate is not available.

Source: Holtham (1988), Gilbert (1989), Borensztein and Reinhart (1994), Baffes (2007, 2009).

TABLE 8

PARAMETER ESTIMATES: INDIVIDUAL COMMODITIES ON ENERGY PRICE INDEX

μ 1 2 100*3 Adj‐R2 ADF

Wheat 3.27@

(6.50)

0.30@

(5.02)

0.12

(1.49)

‐0.49

(1.07)

0.84  ‐4.35**

Maize 3.15@

(6.23)

0.27@

(4.66)

0.13

(0.70)

‐0.74

(1.58)

0.80  ‐3.49**

Soybeans 3.58@

(8.11)

0.26@

(4.92)

0.25

(1.51)

‐0.82

(1.83)

0.82  ‐3.85***

Rice 3.57@

(5.14)

0.25@

(2.67)

0.32

(0.26)

‐1.62@

(2.78)

0.58  ‐4.05***

Palm oil 4.94@

(6.44)

0.35@

(3.72)

‐0.01

(0.02)

‐0.95

(1.38)

0.63  ‐3.16**

Soybean oil 5.25@

(7.83)

0.36@

(4.13)

‐0.09

(0.39)

‐0.42

(0.53)

0.70  ‐2.56

Notes: See tables 4 and 5.
Page 27

Figure 1

Commodity Price Indices (Real, MUV‐deflated, 2000=100)

Figure 2

Real GDP Growth (Annual Percent Change)

1

0

50

100

150

200

250

300

350

1948 1953 1958 1963 1968 1973 1978 1983 1988 1993 1998 2003 2008

Agriculture

Source: World Bank

Korean

war

Oil

shocks

Recent

boom

‐4

‐2

0

2

4

6

8

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Developing countries

Source: World Bank
Page 28

Figure 3

Global Grain Stocks‐to‐Use Ration (Percent)

Figure 4

Commodity Price Indices (Nominal, 2000=100)

3

10%

15%

20%

25%

30%

35%

40%

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Including China

Source: US Department of Agriculture

4

100

200

300

400

500

Jan‐04 Jan‐05 Jan‐06 Jan‐07 Jan‐08 Jan‐09 Jan‐10

Agriculture

Source: World Bank
Page 29

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APPENDIX A. WHAT IS SPECULATION?

Blaming speculative activity in periods of sharp price increases is not uncom‐

mon. Pushed by such a belief, numerous attempts have been made to restrict or

even close the US futures markets. Markham (1987) reports that during 1907‐09

(60th US Congress), 25 bills were introduced, designed to prohibit futures trad‐

ing—for a comprehensive treatment of all attempts to regulate and/or prohibit

futures trading see Cowing (1965). Baffes and Kaltsas (2004) describe how nu‐

merous cotton futures exchanges were shut down during the first half of the 20th

century as the result of government intervention, again because of perceived

speculative activity. Schaede (1989) documents similar attempts for the Dōjima

rice market in Japan, often cited as the world’s oldest futures exchange—it began

operation in 1730.

What makes the recent debate on speculation interesting is that it has di‐

vided even noted scholars and analysts. Apart from the insufficient empirical

evidence on the subject, such division partly reflects the different types of “spe‐

culative activity” that analysts and economists refer to. Indeed, the lines between

hedgers and speculators, between physical and financial transactions, as well as

between legal and illegal trading activities are complex, blurry, and go beyond

text‐book definitions. To gauge such complexities, consider the traditional sepa‐

ration of the place in which transactions take place (physical versus financial) and

the actors involved (hedgers versus speculators), as depicted in table A1. The first

column shows hedging transactions by producers, consumers, and traders (with

the banks as intermediaries) that take place either in physical or financial mar‐

kets (the latter in commodity futures exchanges). This is the typical text‐book

case. The picture becomes complicated, however, when speculators engage in

physical transactions by holding inventories, keeping resources in the ground, or

engaging in various types of market manipulation. A more complex picture

emerges when speculators engage in financial transactions (often combined with

physical transactions).

Hence, understanding the complexities and the controversial nature of

speculation ultimately comes down to understanding the right‐bottom cell of the

Table A1. One way to analyze speculation is to map its sources and its effect on

commodity markets to the place where transactions take place, the actors in‐

volved, and their motivation.

Place of transaction

Commodity transactions take place either in futures exchanges or physical mar‐

kets. Speculation taking place in commodity exchanges forms the backbone of

the functioning of the futures markets by injecting the necessary liquidity to
Page 37

complete the transactions. This is the type of speculative activity that was typi‐

cally given as a reason for the closure of commodity futures exchanges men‐

tioned above.

In the physical market, on the other hand, traders may buy and hold large

quantities of commodities with the expectation that an upward movement in

prices will generate profits (often called hoarding). This is the type of speculative

activity that Paul Krugman and Martin Wolf (among others) have referred to.

Unless such activity entails market manipulation (in which case it would be an

illegal activity), it is the intertemporal equivalent of Adam Smith’s “invisible

hand:” traders buy at current prices to sell later when (in their opinion) the mar‐

ket will be tight, thereby balancing the market and hence reducing price variabil‐

ity. There is no evidence that hoarding took place during the recent boom, as

known inventories in almost all commodities reached historical lows. However,

in the case of extractive commodities—especially crude oil—one may well argue

that “targeting” output to levels below what the market fundamentals dictate (as

is the case with OPEC quotas) is a form of hoarding. The difference is that the

commodity is kept in the ground, rather than in above‐ground and therefore off

the market storage facilities.

Actors involved

Apart from the hedgers (e.g., producers and consumers) with interest in the

physical transaction of commodities, two other actors have been operating in the

market during the last two or so decades with purely financial incentives and no

transactions in the physical markets. They are hedge funds and commodity trad‐

ing advisors (CTAs). During the past few years, investment funds (mostly

pension funds and sovereign wealth funds) also entered the financial markets. It

has been argued that these groups (mostly the latter) may have affected com‐

modity prices.

 Hedge funds. These undertake investment and trading activities in a broad range of

assets, including commodity markets. A hedge fund may trade and invest in com‐

modity asset classes in order to “hedge” the diverse risks inherent in their portfolios.

In such a case, taking a position in the futures market for a particular commodity or

commodity class can represent an investment in a non‐correlated asset that provides

diversification benefits to the overall portfolio. Hedge funds have existed for decades

and their effect on commodity markets is typically of short term nature (i.e., they af‐

fect short term price movements).

 Commodity trading advisors (CTAs) are asset managers that operate almost exclusively

in commodity markets. They invest for portfolios under management and for clients

with the objective of earning profits from market volatility. Perhaps, CTA activity

reduces price volatility since they trade on the basis of market fundamentals and
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technical analysis.

 Investment funds. They include sovereign wealth and pension funds which during the

past few years began including commodities in their portfolio mix as another asset

class. Their chief motivation has been asset diversification. In addition to the way

these funds invest in futures markets (i.e., fixed weights and past performance crite‐

ria), it is their sheer size that matters most (see discussion in main text).

Motivation

Very often speculation takes place in the form of market manipulation. This re‐

fers to illegal activity typically isolated in one or a few commodity markets. It can

take place in the physical or financial markets (often it involves both). Well pub‐

licized cases are the US onion market in the 1950s, where onion producers ar‐

gued that traders in the Chicago Mercantile Exchange cornered the market (this

resulted in the passage of the Onions Futures Act which prohibited futures con‐

tracts on onions); the Hunt brothers who attempted to corner the silver market in

the late 1970s and early 1980s; Sumitomo’s chief copper trader, Yasuo Hamana‐

ka, who cornered the copper market in the 1990s; and the BP cornering of the

propane market in 2006, which resulted in a US$ 300 million fine. Such activity is

not known to have prevailed during the recent boom.

TABLE A1: “SPECULATION” IN COMMODITY MARKETS

ACTOR

PLACE

HEDGERS SPECULATORS

PHYSICAL

 Producers/consumers

 Traders

 Banks

 Holding inventories (e.g., hoarding)

 Keeping resources in the ground (e.g., OPEC)

 Market manipulation (e.g., cornering the

market)

FINANCIAL

 Producers/consumers

 Traders

 Banks

 Investment funds (e.g., pension funds, sove‐

reign wealth funds)

 Investment and diversification instruments

(e.g., CTAs, hedge funds)

 Market manipulation (e.g., cornering the

market)
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APPENDIX B. CO‐MOVEMENT OF COMMODITY PRICES

The general subject of commodity price co‐movement has been examined exten‐

sively and in various contexts. Yet some topics have received much more atten‐

tion than others. The topics that have received less attention matter most in the

current policy debate.

Overall, the research on commodity price co‐movement falls largely with‐

in two strands. The first examines co‐movement among prices of the same com‐

modity in different locations within the market efficiency context, a phenomenon

also known as spatial market integration or the law of one price. While most of

these studies examine co‐movement in a bivariate context, some use models ca‐

pable of examining it within a multivariate setting. This topic has been studied

extensively (see Fackler and Goodwin 2001 for a literature review). A less re‐

searched subject (though one more useful for policy analysis purposes) within

that strand has been the co‐movement between world and domestic commodity

prices, a relationship that includes a policy dimension. Specifically, these studies

examine whether world price signals have been fully transmitted to domestic

markets or instead have been subjected to policy distortions (see, for example,

Baffes and Gardner 2003; Mundlak and Larson 1992). Now, for agricultural

products, a research project led by Kym Anderson has produced a consistent

global database that includes prices received by farmers and paid by consumers

in 75 countries (Anderson and others 2008). That database is expected to generate

more research on the subject.

The second strand of literature examines price co‐movement (or lack the‐

reof) among different commodities. The genesis of this literature goes back to

Granger (1986: 218), who wrote: “If xt and yt are a pair of prices from a jointly ef‐

ficient, speculative market, they cannot be co‐integrated ... if the two prices were

co‐integrated, one can be used to help forecast the other and this would contra‐

dict the efficient market assumption. Thus, for example, gold and silver prices, if

generated by an efficient market, cannot move closely together in the long run.”

Granger’s assertion led to research in commodity markets (e.g., MacDonald and

Taylor 1988) and other markets as well, notably exchange rates (see, among oth‐

ers, Baillie and Bollerslev 1989; Hakkio and Rush 1989). This research was later

questioned on several grounds including the fact that co‐movement reflects res‐

ponses to common fundamentals rather than market inefficiencies. (See, for ex‐

ample, Agbeyebbe 1992; Baffes 1993; Dwyer and Wallace 1992; and Sephton and

Larsen 1991.)

A similarly controversial subject has been the “excess co‐movement” hy‐

pothesis first discussed by Pindyck and Rotemberg (1990) who, after analyzing

price movements of seven seemingly unrelated commodities (cocoa, copper, cot‐
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ton, crude oil, gold, lumber, and wheat), concluded that these prices co‐moved in

excess of what the macroeconomic variables could explain. A number of likely

explanations were given, including an incomplete model, endogeneity of the ma‐

croeconomic variables, rejection of normality assumption, and bubbles or market

psychology. Subsequent research, however, challenged the excess co‐movement

hypothesis on data and methodological grounds (see Ai, Chatrath, and Song

2006; Cashin, McDermott, and Scott 1999; Deb, Trivedi, and Varangis 1996; and

LeyBourne, Lloyd, and Reed 1994). In a more recent paper, Vansteenkiste (2009)

concluded that commodity price co‐movement is driven mainly by exchange

rates, interest rates, and the price of crude oil.

One may argue that the rejection of the efficient market hypothesis in the

presence of co‐movement argued by Granger (1986) corresponds to Pindyck and

Rotemberg’s (1990) bubbles or market psychology explanation for excess co‐

movement—provided that prices used in Granger’s sense have been adjusted ac‐

cordingly by the fundamentals. Perhaps, the fact that two leading articles on

price co‐movement among different commodities have been somewhat contro‐

versial may have led to a slowing down of research in that area as well.