THE RELATIONSHIP OF BIOETHANOL
PRODUCTION AND FOOD PRICES
RAJCANIOVA Miroslava, POKRIVCAK Jan
Faculty of Economics and Management, Slovak University of Agriculture, Nitra,
Slovak Republic
e-mail: miroslava.rajcaniova@fem.uniag.sk
World annual biofuel production has exceeded 100 billion litres in 2009. The development of
biofuel production is partly influenced by the government support programs and partly by the
development of oil prices. The main purpose of this paper is to analyze the statistical
relationship between biofuel and food prices. The article evaluates the relationship among the
following variables: fuel prices (oil, bioethanol and gasoline) and selected food prices (corn,
wheat and sugar). We conduct a series of statistical tests, starting with tests for unit roots,
estimation of cointegrating relationships between price pairs, evaluating the inter-relationship
among the variables using Vector Error Corrections (VECM) and Impulse Response Function
(IRF). The direction of causation in the variables is tested by means of Granger causality tests.
According to our results, there is a cointegrating relationship between oil and gasoline prices
in the period 2005 – 2007, while in the later period there is a log run relationship among all of
the variables.
Key Words: biofuels, crude oil, gasoline, bioethanol, wheat, corn, sugar, cointegration
Introduction
There has been a tremendous increase in production of biofuels1 in recent years (Figure 1).
Global production of biofuels reached 36 million tons which is 62 billion liters in 2007. Of
this amount around 85 percent of liquid biofuels is bioethanol, while remaining 15 percent is
biodiesel. In 2009 annual production of biofuels has already exceeded 100 billion liters.
Incentives motivating the rise of biofuel production come mainly from government support
programs.
In the EU, the biofuel directive (The Directive 2003/30/EC) sets that by 2010 the
European Union should reach the reference target of 5.75 percent share of biofuels in total
transport fuel use. By year 2020 the European Union has a mandatory plan to achieve 10
percent share of biofuels in transport fuels. Individual member states in order to achieve the
reference target can provide tax concession for the support of biofuel industry. European
Union also uses import tariff on denaturated and undenaturated bioethanol imports of 10.20
EUR per hl and 19.20 EUR per hl respectively which is an equivalent of 33.2 and 62.4
percent respectively in ad valorem terms. The import tariff on biodiesel is 6.5 percent.
European Union also provides 45 EUR per hectare to farmers that produce feedstock that are
used for production of biofuels (energy crops) or to generate heat or power. Set aside 2 land
can be used also for production of feedstock used for biofuels or for generation of heat of
power. Individual member states of the EU provide tax concessions. On average tax on
biofuels is 50 percent lower than tax on fossil fuels.
Due to government support programs the output of biofuel industry increased significantly
that led further to the decline of production costs as economy of scale and learning by doing
were realized.
The relationship between food and fuel prices
Biofuels involve the tradeoff between using scarce resources to produce fuel and to produce
food (Runge 2007, Msangi 2006, Rajagopal, Zilberman, 2007). An estimated 93 million tons
of wheat and coarse grains were used for bioethanol production in 2007, double the level of
2005 (OECD–FAO, 2008). Agricultural commodity prices have risen sharply over the past
several years. Food demand increases in growing Asian economies, supply suffered from the
adverse weather, but increasing biofuel demand also contributed to rising food prices (FAO,
2008).
Msangi et al. (2006) use the IMPACT model (International Model for Policy Analysis of
Agricultural Commodities and Trade, developed by the International Food Policy Research
Institute, IFPRI). The results show that when the demand for biofuels is growing very rapidly,
holding crop productivity unchanged, world prices for crops increase substantially. However,
when introducing second-generation cellulosic technologies and allowing for crop
productivity improvement, the impact on prices is small (Brännlund et al., 2008).
Along with emerging government policies, a major uncertainty in the future growth and
profitability of the corn-starch bioethanol industry is the stability and strength of the cornbioethanol-crude oil price relationship (Wisner, 2009). O’Brien and Woolverton (2009)
1 In this article we will consider only bioethanol and biodiesel but biofuels in a wider sense are all fuels derived
from biomass provided by agriculture, forestry, or fishery as well as from wastes of agro-industry or food
industr. Bioethanol and biodiesel are used as transportation fuels and are almost perfect substitutes for fossil
fuels produced from oil.
2
Set aside is an instrument used to reduce supply of agricultural commodities in order to keep prices high or to
lower export subsidies. Farmers put some land out of production for which they obtain compensation from the
EU budget.
quantify the recent relationships between bioethanol and motor fuel prices and confirm that
the corn market is closely related to the energy sector. A sizeable increase in corn processing
for bioethanol now tends to strengthen corn prices much more significantly than in the past.
However the report from Informa Economics definitively demonstrates that the role of
corn prices and bioethanol production in rising food prices is minimal at best. According to
the findings, only 4% of the change in the food CPI (Consumer Price Index) is explained by
fluctuations in nearby corn futures prices, even when the corn price is lagged to allow for the
effects to work. It is a complex and interrelated set of factors that contribute to food prices
(Bioethanol Industry Outlook, 2008).
Bioethanol in the EU is essentially produced from wheat and to a lesser extent sugar beet
(production from corn is marginal). Bioethanol is still a very minor outlet for EU cereals
(more specifically wheat) since it represents less than 1% of end use of the latter. According
to the EC (2007), about 1 million tons of white sugar equivalents were processed into
bioethanol in 2005, that is 5% of total domestic consumption. Sugar used for bioethanol is
today only slightly less than gross sugar exports (1.3 million tons in 2006) (Bamiere, et al.,
2007).
Methods and Data
As mentioned above, most of the literature review suggests the current increase in bioethanol
production was an important factor that led to the rise in food prices. The main goal of our
study is to check whether the relationship between fuel and food prices is statistically
significant. We expect to find a relationship between bioethanol prices and the prices of corn,
wheat and sugar3; in other words, we expect an increase in bioethanol price to lead to an
increase in the demand for corn, wheat and sugar beet and therefore, an increase in corn,
wheat and sugar prices.
The article evaluates the relationship among the following variables: fuel prices (oil,
bioethanol and gasoline) and selected food prices (corn, wheat and sugar). We analyze the
strength and direction of a possible linear relationship among the variables. We conduct a
series of statistical tests, starting with tests for unit roots and stationarity, estimation of
cointegrating relationships between price pairs, estimation of Vector Error Correction Model
(VECM) and Impulse Response Function (IRF). The direction of causation in the variables is
tested by means of Granger causality tests.
We use weekly data (April, 2005 to October, 2009) for gasoline, oil, bioethanol, corn,
wheat and sugar prices. The total number of data points is 221×6 weekly observations. Prices
are expressed in USD per gallon of fuel and USD per ton of food. German bioethanol prices
come from Bloomberg database (2005-2009). German gasoline prices and Europe Brent oil
prices are from Energy Information Administration (2005-2009) and German corn, wheat and
sugar prices come from Deutsche Boerse database (2005-2009). German prices are used
because Germany has been one of the most important bioethanol producers in Europe during
the observed period. To attain stationarity the series must be transformed. Logarithmic
transformation of the prices is used due to the assumed multiplicative effect (Johansen, 1995).
The use of the logarithm of the variables of the model implies that the corresponding
coefficients are now interpreted in percentage terms.
3
Main crops used for bioethanol production in EU include corn (27%), wheat (28%) and sugar beet (28%).
Results
Since early 2000 bioethanol prices in Europe have widely fluctuated. The highest price in the
period reached $3.94 per gallon in March 2008, while the lowest price at the amount of $1.33
per gallon was observed in September 2000. The bioethanol market in Europe was growing
slowly in 1990s. It took almost 10 years for production to grow from 60 million liters in 1993
to 525 million liters in 2004. High increase in production has been driven by the combination
of EU biofuel policy, reduction of production costs, and increase in oil prices.
Oil prices reached the peak in July 2008. Oil being the raw product from which gasoline is
produced has also a crucial impact on the development of gasoline prices. 2008 was also the
year when bioethanol production in Europe reached another peak, increasing significantly by
56%, from 1.8 billion in 2007 to 2.8 billion in 2008 (Figure 2, Table 1).
Correlation analysis confirms high and positive correlation between bioethanol and
gasoline prices (Table 2). As expected there is also a very high and positive correlation
between oil and gasoline prices (95.44%) because oil is the major input in the production of
gasoline. Correlation analysis revealed also high correlation between oil or gasoline prices
and corn or wheat prices (0.7054 – 0.7964 0.6926-0-7503 respectively). These results are in
line with Abbot et al., (2009), who found the crude/corn price correlation to be high and
positive at 0.80 for the period 2006-08. Positive correlation was observed also between
bioethanol prices and corn prices (0.6209) and between bioethanol prices and wheat prices
(0.6990). Sugar prices are negatively and insignificantly correlated to all of the other price
series.
Stationarity of the Time Series
Non-stationary time series can lead to statistically significant results due to purely spurious
regression. We therefore tested for the stationarity of the price series. We use two tests to
check for stationarity of time series: augmented Dickey Fuller (ADF) test and Phillips Perron
(PP) test. The lags of the dependent variable were determined by Akaike Information
Criterion (AIC). Both tests (table 3) show that all the time series (oil, gasoline, bioethanol,
corn, wheat and sugar prices) are integrated of order 1, i.e. non-stationary. To make them
stationary we therefore take the first differences. Zivot-Andrews (ZA) unit root test was used
to check for the presence of structural break in the data. According to the result of ZA test we
decided to devide the observed period into two time periods (summer 2008 was identified as a
breaking point in all of the time series).
Cointegration
The original time series are non-stationary and could be used for cointegration test. While
individual time series may be non-stationary a combination of two non-stationary time series
may be stationary (Engle and Granger, 1987). In such a case, these individual time series are
said to be cointegrated. If two time series are cointegrated, there exists a long-run equilibrium
relationship between them and ordinary least squares can be used to estimate parameters of
their relationship.
Johansen Cointegration Test allows for testing the cointegration of several time series.
This test furthermore does not require time series to be in the same order of integration. In the
Johansen Cointegration Test, the cointegration rank (number of cointegration relationships) is
obtained through the trace test.
As shown in the Table 4, the gasoline and crude oil time series are cointegrated as
expected, while other time series are not cointegrated in the first observed period. All of the
analyzed time series are cointegrated in the second period, except for the sugar-bioethanol
price relationship. These results are in line with Higgins et al. (2006). Higgins found
cointegration relationship for bioethanol and gasoline using US data for the period from 1989
to 2005 and also bioethanol price and corn price during the period of June of 1989 and August
of 2005. Zhang (2009) also confirmed cointegrating relationship among the US fuel prices
(gasoline, oil, and bioethanol). However this relationship was observed only in the bioethanol
boom period (2000-2007). Cointegration tests in Serra (2008) support the existence of a
(single) long-run relationship between bioethanol, corn and oil prices. Results from Zhang
(2009) yield cointegration relationship between bioethanol and corn prices for the 1989-1999
period. In contrast, results indicate no long-run relation between bioethanol and corn prices in
the 2000-2007 period. Campiche et al. (2007) tested the cointegration of corn, soybean oil,
palm oil, sugar and crude oil in two different time periods. No cointegrating relationship was
observed in the time period 2003-2005. Corn and soybeans, but not soybean oil were found to
be cointegrated with crude oil from 2006 through the first half of 2007. Ciaian and Kancs
(2009) tested the relationship between crude oil and nine major traded agricultural commodity
prices including corn, wheat, rice, sugar, soybeans, cotton, banana, sorghum and tea.
According to the Johansen cointegration test, there were no cointegration relationships in the
period 1994-1998, the prices of crude oil and corn and crude oil and soybean were
cointegrated in the period 1999-2003 and all nine agricultural commodity prices and crude oil
prices contained a cointegrating vector in the period 2004-2008.
Vector Error Correction Model
Table 5 presents the main results of the model (standard errors are presented in parentheses
below the estimates). We needed to run a causality test in order to explore if there is a
“Granger causality” among the analyzed variables. Granger causality tests highlight the
presence of at least unidirectional causality linkages as an indication of some degree of
integration. This implies that each market uses information from the other when forming its
own price expectations, while unidirectional causality inform about leader-follower
relationships in terms of price adjustments (Arshaad, Hameed 2009). Causality tests answer
the question which of the observed commodities is a price leader and which are the price
followers, or that none of the commodities is more important than the other (Ciaian, Kancs,
2009). X Granger causes Y if past values of X can help explain Y. Of course, if Granger
causality holds this does not guarantee that X causes Y. This is why we say “Granger
causality” rather than just “causality” (Koop, 2006).
We found a casual relationship runnig from sugar prices to bioethanol prices, from sugar
prices to gasoline prices and to oil prices. The long run behaviour of sugar prices was found to
be determined by oil prices and, rather surprisingly, not bioethanol prices. The same results
were obtained by Rapsomanikis and Hallam (2006). The results of Granger causality tests
show that there exist a long run unidirectional causality from oil price to the three agricultural
commodity prices, i.e, corn, wheat and sugar, but not vice versa. This result is also in line
with Arshad and Hameed (2009). There is also a bivariate causal relationship between
gasoline and oil prices. This feedback relationship between oil and gasoline prices is strong
with a significance level of one percent. Similar results were found by Zhang et al., (2010),
where in the long-run, oil prices are influencing gasoline prices which then impact bioethanol
prices.
Zhang (2009) explains that the increases in the price of gasoline are driving up bioethanol
and oil prices. Gasoline prices not only directly influence oil prices but also indirectly
influence them by impacting bioethanol prices which influence oil prices.
Impulse Response Function
Impulse Response Functions were performed in order to show how a shock in one variable
would persist in future periods. The forecast was made considering a two-month period. All
the agricultural commodities are affected by fuel prices. The impact of shock in oil price on
agricultural commodity is higher than vice versa. It seems that the responses disappear after
about two months. This is also true for the response of oil price to the shock in gasoline
prices. As Figure 4 shows, a sudden change in oil prices results in a small change in
bioethanol prices but the same shock in oil prices will lead to a strong response of the gasoline
prices. After a sudden increase, gasoline prices will then start decreasing after 7-10 days
following the shock. (Because of limited space we place only some of the graphs.) Similar
results were observed by Serra (2008). Bioethanol responses usually reach a peak after about
10 days of the initial shock and fade away after around 30-35 days (Serra, 2008).
Conclusion
The main purpose of this paper is to analyze the statistical relationship between between fuel
and food prices. In order to achieve our goal, we first collected weekly data for gasoline, oil,
bioethanol, corn, wheat and sugar prices from April, 2005 to October, 2009. In order to
account for structural break we devide the observed period into two periods: 2005-first half of
2008 and second half of 2008 – 2009. The results provide evidence of cointegrating
relationship between oil and gasoline prices in the first observed period, but no cointegration
between bioethanol, gasoline, corn, wheat and sugar prices. All the variables were found to be
cointegrated in the second time period, except for the sugar/bioethanol relationship. The
Granger causality tests suggest that there is long run Granger causality from oil to agricultural
commodity prices but not vice versa. There is also a bivariate causal relationship between
gasoline and oil prices.
After running an Impulse Response Function, we found out that all the agricultural
commodities are affected by fuel prices. The impact of shock in oil price on agricultural
commodity is higher than vice versa. It seems that the responses disappear after about two
months.
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Biofuels Production (Billiion Btu)
Appendix:
Figure 1: Development of biofuel production
1600
1400
1200
1000
800
600
400
200
2009
2007
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
0
Source: Energy Information Administration (EIA)
Figure 2: Development of fuel and food prices
600
4.5
4
500
USD / ton
400
3
2.5
300
2
200
1.5
USD / gallon
3.5
1
100
0.5
wheat
sugar
ethanol
Sep-09
Jul-09
May-09
Feb-09
Dec-08
Oct-08
Aug-08
Mar-08
May-08
Jan-08
Nov-07
Jul-07
Sep-07
May-07
Mar-07
Oct-06
corn
Dec-06
Aug-06
Jun-06
Feb-06
Dec-05
Oct-05
Jun-05
Aug-05
0
Apr-05
0
oil
Source: Bloomberg - bioethanol prices, EIA – gasoline prices, oil prices, Deutsche Börse – corn, wheat and
sugar prices
Table 1: Descriptive statistics
Variable
Obs.
Mean
Std. Dev.
Min
Max
Bioethanol
221
3.179071
0.493830
2.127750
3.937184
Gasoline
221
2.336150
0.546073
1.360992
3.906546
Oil
221
Corn
221
Wheat
221
223.5044
63.32195
159.7406
438.3451
Sugar
221
263.5448
66.00133
178.0000
478.0000
Source: Own calculation
1.710537 0.532111 0.881904 3.427380
157.4487 38.78819 112.9921 302.7559
Table 2: Correlation Matrix
Variable
Bioethanol
Gasoline
Oil
Corn
Wheat
Sugar
Bioethanol
1.0000
-
-
-
-
-
Gasoline
0.6095
1.0000
-
-
-
-
Oil
0.6192
0.9544
1.0000
-
-
-
Corn
0.6209
0.7054
0.7964
1.0000
-
-
Wheat
0.6990
0.6926
0.7503
0.7189
1.0000
Sugar
-0.1502
-0.0613
-0.0840
-0.2469
-0.3019
1.0000
Source: Own calculation
Table 3: Unit root tests
Level
Time series
ADF - Bioethanol
ADF - Gasoline
ADF - Oil
ADF - Corn
ADF - Wheat
ADF - Sugar
PP - Bioethanol
PP - Gasoline
PP - Oil
PP - Corn
PP - Wheat
PP - Sugar
None
1.159
-0.317
-0.771
-0.335
-0.323
-1.174
1.198
-0.193
-0.642
-0.318
-0.319
-1.165
Constant
-2.018
-2.493
-1.927
-1.723
-1.215
-0.525
-1.990
-2.165
-1.710
-1.690
-1.256
-0.600
First Differences
Constant
& Trend
-1.074
-2.501
-1.652
-1.645
-0.789
-0.671
-1.053
-2.151
-1.461
-1.577
-0.843
-0.754
None
-10.097***
-14.268***
-7.121***
-8.115***
-9.463***
-15.536***
-12.871***
-14.268***
-14.283***
-15.530***
-15.383***
-15.536***
Constant
-10.423***
-14.243***
-7.107***
-8.096***
-9.442***
-15.583***
-13.127***
-14.243***
-14.257***
-15.496***
-15.350***
-15.583***
Source: own calculation
Notes: *
significance at the 10% level
** significance at the 5% level
*** significance at the 1% level
Table 4: Johansen cointegration test
Bioethanol - Oil
Bioethanol-Gasoline
Gasoline - Oil
Corn - Bioethanol
Wheat - Bioethanol
Sugar - Bioethanol
Corn - Oil
Wheat - Oil
Sugar - Oil
Trace statistic
1st period
r=0
r=1
6.5268***
2.4871
***
7.7286
2.4374
27.8534
1.5808***
8.9679***
3.4706
6.7755***
3.1575
***
10.5491
3.7294
7.3342***
1.2181
8.8933***
1.4111
***
4.8814
1.1268
Trace statistic
2nd period
r=0
r=1
17.6422
5.2006**
18.5503
2.9888**
47.4243
2.0408***
14.4749
3.3527*
14.3638
1.8629*
9.9283***
0.0963
13.4742
1.1850*
13.8375
1.4355*
13.8882
0.0402*
Source: own calculation
Note: critical values at 10% significance level are: 13.33 (r = 0) and 2.69 (r = 1),
critical values at 5% significance level are: 15.41 (r = 0) and 3.76 (r = 1),
critical values at 1% significance level are: 20.04 (r = 0) and 6.65 (r = 1)
*** denotes failure to reject the null hypothesis at 1% level
** denotes failure to reject the null hypothesis at 5% level
* denotes failure to reject the null hypothesis at 10% level
Constant
and Trend
-10.453***
-14.208***
-7.113***
-8.102***
-9.510***
-15.622***
-13.186***
-14.208***
-14.254***
-15.491***
15.407***
-15.622***
Table 5: Vector Error Correction Model
D_Bioethanol
D_Gasoline
0.386**
-0.154
-0.0505
-0.0559
-0.0416
-0.046
0.169**
-0.0741
-0.222***
-0.0705
-0.159**
-0.0642
0.0006
-0.0027
0.32
-0.513
0.0492
-0.186
0.254*
-0.153
-0.0346
-0.246
0.276
-0.234
0.0476
-0.213
0.0027
-0.0088
LD.Bioethanol
LD.Gasoline
LD.Oil
LD.Corn
LD.Wheat
Ce
Constant
D_Oil
1.109**
-0.472
0.321*
-0.171
-0.187
-0.141
0.434*
-0.227
-0.443**
-0.216
-0.717***
-0.196
0.0018
-0.0081
D_Corn
D_Wheat
1.032**
-0.463
0.333**
-0.167
-0.175
-0.138
0.0606
-0.222
-0.510**
-0.211
-0.0467
-0.192
-0.0087
-0.0080
1.043**
-0.475
0.0818
-0.172
-0.0979
-0.141
0.342
-0.228
-0.718***
-0.217
-0.0901
-0.197
-0.0092
-0.0082
Source: own calculation
Note: Standard errors in parentheses below the estimate
*** p<0.01, ** p<0.05, * p<0.1
Figure 3: Impulse Response Function
IRF, oil, corn
IRF, oil, ethanol
IRF, oil, gasoline
IRF, oil, wheat
.4
.2
0
-.2
.4
.2
0
-.2
0
2
4
6
8
0
step
Note: order of variables: impulse variable, response variable
Source: own calculation
2
4
6
8