The year 2008 will long be remembered as a year when corn based ethanol has seen tremendous change. The Energy Independence and Security Act, which passed in late 2007, gave a huge boost to the industry as it mandated an increase in biofuel production and use. In 2008, the industry witnessed record high prices on corn and crude oil. Ultimately, a big ethanol and distiller’s grain company--Vera Sun Energy-- filed for Chapter 11 bankruptcy leaving farmers with contracts wondering what will happen next. South Dakota is a major corn growing and ethanol producing state and this article assesses the relative magnitude of corn based ethanol on the local economy in terms of distribution of ethanol plants and corn disappearance ratios.ethanol, south dakota, farm policy

Jing Li

Yonas G. Hamda

English

Research Papers in Economics

            E C O N O M I C S 
   C O M M E N T A T O R 
South Dakota State University      No. 503                                     December 30, 2008
 
  Causality and Granger  
Causality 
 
 
by 
 
       Jing Li 
     Assistant Professor 
 
 
Causality is an important topic for empirical 
researchers. For instance, a researcher notices the 
quality of beef improves after beef price rises.   He 
wonders if the price change causes the quality change. 
If that is the case, then the government can use price 
as a tool to encourage improvement of beef quality. 
Otherwise the price tool would be useless. The 
researcher hopes certain statistical analysis is 
informative to address this issue. Unfortunately, the 
statistical and econometric methods commonly used 
can only provide an incomplete answer to his 
problem.  This short note is intended to show the 
degree to which causality can be established by 
statistics and econometrics.  
 
Strictly speaking, X causes Y if the next two 
conditions are both satisfied:  (1) a change in X is 
followed by a change in Y, and (2) all other factors 
are held constant.  It is relatively easy to show 
condition (1) is true.  The Granger causality test, for 
instance, can be used to prove (1).  On the other hand, 
condition (2) is nearly impossible to prove.  The 
difficulty is controlling all relevant factors.  Ideally, 
condition (2) could be confirmed or refuted by an 
experiment in which all relevant factors are under 
control.  In economics, however, we are most often 
working with non-experimental data.  To make 
matters worse, a typical economic phenomenon is so 
complicated that it involves some factors for which 
data are not even available.  As a well-known 
example, it is very difficult to obtain reliable data 
about people’s ability.             (Continued on page 2) 
 
        Dependency on the 
 Ethanol  Industry  
 
                      
            by 
 
Yonas G. Hamda 
Research Associate   
 
 
The year 2008 will long be remembered as a year 
when corn based ethanol has seen tremendous 
change.  The Energy Independence and Security Act, 
which passed in late 2007, gave a huge boost to the 
industry as it mandated an increase in biofuel 
production and use.  In 2008, the industry witnessed 
record high prices on corn and crude oil.  Ultimately, 
a big ethanol and distiller’s grain company--Vera Sun 
Energy-- filed for Chapter 11 bankruptcy leaving 
farmers with contracts wondering what will happen 
next.  South Dakota is a major corn growing and 
ethanol producing state and this article assesses the 
relative magnitude of corn based ethanol on the local 
economy in terms of distribution of ethanol plants and 
corn disappearance ratios.   
 
U.S. corn based ethanol has increased considerably in 
recent years.  In 1990, ethanol production was barely 
a billion gallons a year.  It took more than ten years 
before the industry doubled its production.  The 
industry has recorded more growth in the last five 
years since it started.  By 2002, production was just 
over 2 billion gallons a year and in 2007, close to 6.5 
billion gallons of ethanol was produced or 3 fold 
growth in 5 years (see figure 1). 
 
Distribution of Ethanol Plants 
As of September 2008, there were 165 ethanol plants 
in the U.S. with total capacity of 9.9 billion gallons a 
year.  With the expansion of five plants and the 
construction of 34 new plants, national ethanol 
        (Continued on page 2) 
  
 
(Causality …  continued from page 1) 
One may argue that we may ignore condition (2) due 
to its complexity. But we cannot, if we want to 
demonstrate the real causality. Go back to the beef 
example. Just because a quality change follows a 
price change, that does not mean the latter causes the 
former.  Let us imagine that the true cause for the 
improved quality is a new technology for raising 
cattle.  This new technology is introduced by the 
government for free.  Therefore, farmers’ adopting 
the new technology has nothing to do with the market 
price of beef. It just happens at the same time we see 
an increasing beef price, but clearly this price change 
is not the cause for improved quality.  Here, the new 
technology is a relevant factor that we must account 
for. 
 
Conceptually it is hard to show X causes Y because 
of uncontrolled factors.  Nevertheless, it is much 
easier to show X does not cause Y.  Condition (1) is a 
necessary condition for causality.  Hence if we can 
show condition (1) is false, then it must imply that X 
does not cause Y.  The Granger causality test uses this 
idea, and the null hypothesis of the Granger test is 
that X does not help when forecasting Y (so that a 
change in X leads to no change in Y).  
 
The Granger causality test is easy to use.  To show X 
does not Granger cause Y, the first step is to consider 
an autoregression for Y.  Next, we add lagged values 
of X as extra independent variables.  Finally we test if 
the coefficients of the lagged X are equal to zero.  We 
reject the null hypothesis if those coefficients are 
significantly different from zero.  
 
Suppose the coefficients of lagged X are close to 
zero, so that the null cannot be rejected. In this case, 
the lagged X is not informative about future values of 
Y.   Put differently, the time path of Y only depends 
on its own history, not on X.  In the time series sense, 
we can say that Y is exogenous because of its 
independence from other variables.  Some researchers 
then want to treat Y as the regressor to study the 
relationship between Y and X.  Typically a researcher 
has no prior knowledge about the direction of 
Granger causality. Therefore he may treat all 
variables equally, and use vector autoregression 
(VAR) to check whether X Granger causes Y, or vice 
versa.  
There are potential pitfalls for the Granger causality 
test.  First, some researchers have shown that the 
Granger test result sometimes is very sensitive to the 
number of lagged terms in the regression. In practice, 
we may show the test result is robust to the lag 
number by conducting the Granger test repeatedly for 
different lag numbers.  
 
The second issue is the stationarity of data.  In 
general, the Granger test follows nonstandard 
distributions when data are not stationary. However, 
when data are nonstationary but cointegrated, the 
distributions become standard again. This is a 
technical but important issue. The lesson is that we 
need to check stationarity and cointegration when we 
apply the Granger test to seemingly nonstationary 
data (such as Gross Domestic Product). 
 
Finally, it should be emphasized again that Granger 
causality is not the usual causality we have in mind. 
To be precise, we should label the Granger test as 
testing for predictive power.  If X Granger causes Y, 
the only implication is that X is useful for predicting 
Y, or X occurs prior to Y.  It remains unclear whether 
X really causes Y, since we do not know whether 
there is another factor.  On the other hand, if we can 
show X does not Granger cause Y, then it is safe to 
say that X must not be the cause for Y.  
 
 
 
(Dependency On …     continued from page 1) 
 
production capacity will increase to 13.8 billion 
gallons per year (table 1).  The top five states (Iowa, 
Nebraska, Illinois, South Dakota and Minnesota) 
account for two-thirds of the current ethanol 
production capacity.  In South Dakota 15 ethanol 
plants have the capacity to produce 874 million 
gallons a year or 9 percent of the nation’s ethanol 
producing capacity.  Three companies, Vera Sun, 
Poet, and Archer Daniel Midland, own 43 plants with 
total capacity of 3.6 billion gallons/per year, thus  
accounting for 36% of the nation’s ethanol production 
capacity (Qasmi, Hamda and Fausti, 2008).  
 
   
 
0
1000
2000
3000
4000
5000
6000
7000
m
i
l
l
i
o
n
 
g
a
l
l
o
n
s
Source: Renewable Fuel Association.
Figure 1. U.S. Ethanol production, 2000-2007
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Corn Disappearance Rates  
Large increases in ethanol production have in turn led 
to large increases in corn use by ethanol plants. One 
method of examining the amount of ethanol corn 
intake is to look at the change in the share of corn 
used in ethanol production in terms of domestic corn 
disappearance.  In 1990/91, less than 5% or 333 
million bushels of corn disappearance in the U.S. was 
accounted for by ethanol production.  By the 2007/08 
crop year, as much as 24%, or 3.1 billion bushels, of 
corn was accounted for by ethanol production (figure 
2).  During the same crop year period, the proportion 
of corn going into ethanol production was about 30% 
in Minnesota and Illinois, 40% in Nebraska, 50% in 
Iowa and 60% in South Dakota.  The increasing rate 
of corn use for ethanol has affected availability of the 
Table 1.   Ethanol Production Capacity, as of September 4, 2008. 
                    
Location/ 
Ownership 
Number     
of Plants 
Current Capacity       
(mil gal/yr) 
Number of 
Plants 
Expanding 
Number of 
New Plants 
Planned 
Under 
Construction/ 
Expansions 
Capacity        (mil 
gal/yr) 
Total  Capacity    
with New 
Construction/   
Expansion         
(mil gal/yr)  
State:             
Iowa  31  2,269  (19%)  2  9  1,265  3,534 (26%) 
Nebraska  19  1,347  (14%)  1  1  319  1,666 (12%) 
Illinois  9  1,035  (10%)  0  1  188  1,223   (9%) 
South Dakota  15  874    (9%)  1  0  18  892   (6%) 
Minnesota  19  827    (8%)  1  2  275  1102   (8%) 
Others  72  3,609  (36%)  0  21  1,725  5,334  (39%) 
USA  165  9,961 (100%)  5  34  3,790  13,751 (100%) 
Ownership:             
Vera Sun  13        1,290 (13%)  0  3  360  1,650 (12%) 
Poet  23        1,225 (12%)  0  3  195  1,420 (10%) 
ADM  7        1,103 (11%)  2  0  550   1,653 (12%) 
Others  122        6,343 (64%)  3  28  1,725  9,028 (66%) 
USA  165  9,961 (100%)  5  34  3,790  13,751 (100%) 
Source: Renewable Fuel Association       
        
 
 
commodity for feed and exports.  Moreover, it 
implies a heighted level of dependency and 
vulnerability of South Dakota’s  agriculture economy 
to the industry (Qasmi, Hamda and Fausti, 2008).    
 
Reference 
Qasmi, A. B., Yonas G. Hamda and Scott Fausti 
(2008) Ethanol Co-products: Trends in 
Production, Domestic Use, and Potentials for 
International Market Development. Paper 
prepared for presentation at South Dakota 
International Business Conference and Missouri 
Valley Economic Association annual meetings in 
2008.  
 
 
0%
10%
20%
30%
40%
50%
60%
70%
80%
0
1
/
0
2
0
2
/
0
3
0
3
/
0
4
0
4
/
0
5
0
5
/
0
6
0
6
/
0
7
0
7
/
0
8
p
e
r
c
e
n
t
a
g
e
 
f
r
o
m
 
t
o
t
a
l
 
d
i
s
s
a
p
e
r
a
n
c
e
Crop Year
Figure 2- Percentage of Corn used by Ethanol, 01/02-07/08
USA IA SD
 
 
*********************************************************** 
ECONOMICS COMMENTATOR 
*********************************************************************************** 
Department of Economics       
South Dakota State University     Phone: 605-688-4141 
Box 504   Scobey Hall        Fax:   605-688-6386 
Brookings, SD 57007-0895          E-Mail: Penny_Stover@ sdstate.edu 
120 copies of  this newsletter were produced at a cost of less than $100 
 
 
 
 
 
 
 
 
 
 
 
 
                                          
 
 
 
S
O
U
T
H
 
D
A
K
O
T
A
 
S
T
A
T
E
 
U
N
I
V
E
R
S
I
T
Y
 
 
 
 
D
e
p
a
r
t
m
e
n
t
 
o
f
 
E
c
o
n
o
m
i
c
s
 
 
 
 
 
 
 
B
o
x
 
5
0
4
 
 
 
 
 
B
r
o
o
k
i
n
g
s
 
S
D
 
 
5
7
0
0
7
-
0
8
9
5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
C
h
a
n
g
e
 
S
e
r
v
i
c
e
 
R
e
q
u
e
s
t
e
d
 
 

Ethanol Co-products: Trends in Production, Domestic Use, and Potentials for International Market Development. Paper prepared for presentation at South

Dependency on the Ethanol Industry

http://repec-sda.sdstate.edu/repec/sda/pdf/Commentator/No503.pdf

Dependency on the Ethanol Industry

Abstract

Similar works

Full text

Available Versions

Research Papers in Economics