CORN AREA RESPONSE TO LOCAL
ETHANOL MARKETS IN THE UNITED STATES:
A GRID CELL LEVEL ANALYSIS
MESBAH MOTAMED, LIHONG MCPHAIL, AND RYAN WILLIAMS
We measure corn and total agricultural area response to the biofuels boom in the United States
from 2006 to 2010. Specifically, we use newly available micro-scale grid cell data to test whether
a location’s corn and total agricultural cultivation rose in response to the capacity of ethanol
refineries in their vicinity. Based on these data, acreage in corn and overall agriculture not only
grew in already-cultivated areas but also expanded into previously uncultivated areas. Acreage
in corn and total agriculture also correlated with proximity to ethanol plants, though the relationship dampened over the time period. A formal estimation of the link between acreage and
ethanol refineries, however, must account for the endogenous location decisions of ethanol plants
and areas of corn supply. We present historical evidence to support the use of the US railroad
network as a valid instrument for ethanol plant locations. Our estimates show that a location’s
neighborhood refining capacity exerts strong and significant effects on acreage planted in corn
and total agricultural acreage. The largest impacts of ethanol plants were felt in locations where
cultivation area was relatively low. This high-resolution evidence of ethanol impacts on local
agricultural outcomes can inform researchers and policy-makers concerned with crop diversity,
environmental sustainability, and rural economic development.
Key words: corn acreage, ethanol refineries, biofuels, grid cell data, agricultural land use.
JEL codes: Q15, Q16.
This article examines the local crop selection decisions of US farmers in response to
the demand recently posed by markets in
ethanol. Specifically, we measure the effect
of an ethanol plant’s location and refining
capacity on area planted in corn and total
agriculture. We use high-resolution satellite
crop data and information on ethanol plant
locations covering much of the American
Mesbah Motamed ([email protected]) is a research
agricultural economist at the Economic Research Service of
the United States Department of Agriculture. Lihong McPhail
is an economist at the Commodity Futures Trading Commission. Ryan Williams is a geographical information systems
analyst at the Economic Research Service of the United States
Department of Agriculture. The authors gratefully acknowledge the helpful suggestions received from the editor and
three anonymous referees. For their useful feedback and comments, we also thank Carlos Arnade, Vince Breneman, Metin
Cakir, Benoit Delbecq, Charlie Hallahan, Sharad Tandon,
and Paul Westcott. All remaining errors are the authors’. The
views expressed here are those of the authors and may not
be attributed to the Economic Research Service, the United
States Department of Agriculture, or the Commodity Futures
Trading Commission. McPhail’s work was completed during
her service at the Economic Research Service.
Midwest over the time period 2006–2010.
These data offer an extraordinary opportunity to study producers’ planting decisions at
a microscale, a useful feature for understanding the localized impacts of nearby ethanol
plants.
Two concerns motivate our research. First,
the emergence of biofuels technology as a
substitute for petroleum-based energy inputs
has spurred economic growth in many rural
US economies. This growth stems from new
jobs created at ethanol plants (Low and
Isserman 2009), higher prices paid to feedstock producers, particularly in the neighborhood of ethanol plants (Gallagher, Wisner,
and Brubacker 2005; McNew and Griffith
2005), and possibly higher farm land values
(Henderson and Gloy 2009; Blomendahl,
Perrin, and Johnson 2011). While stronger
linkages to global energy markets have
driven much of this growth (Serra 2011), the
consequences of volatility in these same markets remain relatively unknown. Agricultural
producers, historically insulated from the
Amer. J. Agr. Econ. 98(3): 726–743; doi: 10.1093/ajae/aav095
Published online February 12, 2016
Published by Oxford University Press on behalf of the Agricultural and Applied Economics Association 2016.
This work is written by (a) US Government employee(s) and is in the public domain in the US.
Motamed, McPhail, and Williams
Corn Area Response to Local Ethanol Markets
727
Figure 1. Change in corn acreage from 2006 to 2010 and locations of ethanol plants
Note: Darker shades represent positive changes in corn acreage, and lighter shades are negative changes. Larger ethanol refining capacities are
depicted in larger circles.
demand-side fluctuations of global energy
prices, suddenly confront new exposures
particularly as their crop mix is increasingly
destined for ethanol feedstock, and less for
food and feed consumption. By establishing the link between physical ethanol plant
locations and the planting decisions in their
vicinity, we aim to highlight the possibly
increasing risk facing not only individual
farmers, but entire local economies.
The second concern pertains to land
use and the environment. Beginning with
Searchinger et al. (2008), most research on
the impact of biofuels has centered on just
one, albeit important, dimension of land use:
carbon emissions. Uncultivated land brought
into production through plowing and deforestation can release carbon-based greenhouse
gases that were previously sequestered in the
soil and vegetation. However, comparably
less attention has focused on other impacts,
namely the disruption of natural ecosystem
services and soil and water contamination
owing to increased fertilizer and pesticide
use, outcomes associated with greater input
usage and heavier rotations (Tilman et al.
2002). As described in Malcolm, Aillery, and
Weinberg (2009), ethanol plants incentivize
feedstock production among their nearest neighbors, driving land conversion and
greater input usage and leading to potentially serious environmental impacts at the
local level. Again, by documenting the local
land use and crop selection effects of ethanol
plants, this article highlights the connection between the growing biofuels sector of
the US agricultural economy and possible
environmental consequences.
To motivate this research visually, figure 1
shows the change in the US Midwest’s corn
acreage over the period 2006 to 2010 at the
level of individual grid cells of dimension
10 × 10 kilometers. Overall, about 8.2 million
acres in corn were added in this region, with
the average cell increasing its corn planting
area by 410 acres. But as the map shows,
the distribution of acreage changes is not
uniform; some locations, depicted in darker
shades, gained acreage in corn, such as northern Illinois and eastern Iowa. Meanwhile,
other regions, colored in lighter shades, saw
their area in corn fall, including southern
728
April 2016
Minnesota and eastern Nebraska. Figure 1
also depicts the location of ethanol plants
over the same time period as well as their
refining capacities. Figure 1 thus frames the
central question of this article: do ethanol
plants cause changes in the planting decisions
in their vicinity?
The intuition underlying our hypothesis
tests is straightforward. From the perspective
of a profit-maximizing crop farmer, proximity
to a market, in the form of an ethanol plant,
and the lower transport costs associated with
it may be an important criterion for choosing to plant corn. Moreover, the size of the
market for the feedstock, as represented by
the refining capacity of all the plants in a
farmer’s vicinity, may also help determine
a farmer’s crop selection. In this article, we
measure the response of three crop variables,
(1) corn area, (2) total agricultural area, and
(3) corn share of agriculture to the introduction and capacity expansion of an ethanol
plant, using railroad proximity as an instrument for ethanol plant location. Our results
show that neighborhood ethanol markets, as
represented by ethanol plants, as well as the
size of those markets, led to sizeable changes
in all three outcomes of interest.
Background
Beginning with the 2005 Energy Policy Act
and later with the 2007 Energy Independence
and Security Act, the United States Congress
introduced rules that mandated mixing biofuels with gasoline in increasing quantities, with
the ultimate goal of adding 15 billion gallons
of conventional (i.e., corn-based ethanol)
biofuel by 2015.1 In response to these policies, as well as market forces in the overall
energy sector, US production in ethanol
climbed from 3.9 billion gallons in 2005 to
13.3 billion gallons in 2010, and the number
of ethanol plants rose from 81 to 189 over
the same time period with refining capacity
nearly tripling (Renewable Fuels Association
2013; Yi, Lin, and Thome 2014).
Corn-based ethanol of course requires
corn feedstock, and feedstock must be cultivated on farmland. Beginning in 2005,
about 81.8 million acres were planted in corn,
yielding over 11 billion bushels. By 2010,
1
The precise volumes are adjusted yearly by the Environmental
Protection Agency in accordance with actual available supplies.
Amer. J. Agr. Econ.
corn planting area expanded by nearly 8%
to 88.2 million acres, and production rose
to 12.4 billion bushels (ERS 2013).2 Area in
soybeans similarly expanded. Over the same
time period, however, other major crops,
such as wheat and cotton, witnessed declines
in area.
Recent studies on the acreage response to
ethanol plants have reached similar conclusions, though the variables and data sets used
vary from study to study. Fatal and Thurman
(2014) rely on county-level data from 2002
to 2008 covering all corn producing counties
in the United States and found that an additional one million gallons in refining capacity
is associated with a five-acre increase in a
county’s corn acreage. Similarly, Miao (2013)
relies on county-level data in Iowa for the
period 1997–2009, finding significant associations between a county’s corn share and
its refining capacity. Brown et al. (2014),
starting with satellite data from the Cropland
Data Layer (CDL) and building a countylevel data set for the state of Kansas over
the period 2007–2009, also observe significant relationships between corn acreage and
distance to the nearest refinery.
Apart from its crop selection effects, the
emerging biofuels sector has raised questions
concerning its impact on other outcomes.
One stream of research focuses on simulating
the market-mediated effects on overall production or deriving estimates of greenhouse
gas impacts attributable to the production
changes. Given the global nature of the
question and the interaction between agriculture and other industry sectors, partial
and computable general equilibrium models
have been utilized to address these questions, including Searchinger et al. (2008) and
Keeney and Hertel (2009). The emphasis
here has been identifying and measuring
the indirect effects of planting decisions on
global environmental outcomes. Analytical
presentations have also illustrated producer
responses to market and policy shocks (Feng
and Babcock 2010). While this research
shines considerable light on the nature of
producer responses, further efforts to empirically capture producers’ response remains
constrained by data availability and quality
(Birur, Hertel, and Tyner 2008).
2
Since 2010, area in corn has grown to even higher levels—
97.4 million acres as of the 2013–2014 marketing year. Since our
analysis is based on data covering 2006–2010, we confine our
discussion to the same time period.
Motamed, McPhail, and Williams
In this article, we measure three types
of response to an ethanol plant’s introduction and capacity: (1) How much has corn
area changed? (2) How much has area in
total agriculture changed? (3) And how
much has the share of corn in total agriculture changed? Questions (2) and (3)
speak to the issues of extensification and
concentration. Extensification refers to the
expansion of agricultural activity onto land
that was heretofore uncultivated. We use
the term concentration to mean when a crop
is more heavily planted in a given area, for
example, it is more frequently rotated or
double-cropped.3
Data and Variable Selection
We collected five years of data, from 2006
to 2010, on crop areas, prices, geographic
factors, and ethanol plant locations and
capacities spanning twelve states across the
Corn Belt of the United States.4 A novel
feature of our data set is the spatial unit
of observation: a grid cell of dimension
10 km × 10 km. The average county size in
Iowa, by way of illustration, is about 570
square miles, implying that around 14 grid
cells fall in a typical county. With highresolution, regular spatial units such as grid
cells, we can observe movement and concentration of crop selection within counties,
a valuable feature, particularly as counties grow larger toward the western half of
the study area, and consequently obscure
more variation. Similarly, using uniform
grid cells, in contrast to spatial units defined
by county boundaries, helps ensure that an
observation’s refining capacity is not affected
by the unit’s size or shape. The data set
used in this analysis consists of 20,109 grid
cells.
Crop Selection
To represent producers’ crop selection, we
used the National Agricultural Statistics
Service’s annual Cropland Data Layer
Corn Area Response to Local Ethanol Markets
(CDL), which reports crop location and
type at a resolution of 30-square meters
across. These data are sensed remotely by
satellites, classified into crop types according
to multispectral rules, and ultimately groundchecked for validity. The highest quality data
cover the most agriculturally intensive areas
of the United States, namely, the Corn Belt
and the Mississippi River Delta. Overall, the
spatial coverage of the satellite data varies
across the years, but a consistent time series
of plantings from 2006 to 2010 exists for
twelve states that span much of the Corn
Belt, a feature that ultimately determined the
scope of our data set. This period coincides
with the boom of the ethanol industry and
thus allows us to capture the year-to-year
response of producers to the (1) introduction
of an ethanol plant and (2) an ethanol plant’s
capacity expansion.
The 30-square meter observations were
aggregated to 10 × 10 kilometer (=100
square kilometer, or about 39 square miles,
or 24,700 acres) grid cells. Three crop outcomes interest us: (1) the acreage of corn
planted in each 10 × 10 km cell, (2) total
agricultural acreage in each cell, and (3) the
corn share of agriculture. In 2010, about
3,400 corn acres were planted on an average 10 × 10 km cell. The highest valued
cell, reporting 18,248 acres planted in corn,
appeared in DeKalb County, Illinois. See
table 1 for a summary of the data. Of all the
states in the study region, Iowa had cells with
the highest average area planted in corn, just
over 9,000 acres.
Ethanol Plant Location and Capacity
To motivate our explanatory variable selection, it helps to first discuss prices. Prices
paid to producers are composed of two parts:
(1) the spatially uniform part that is determined by the national (or global) supply
and demand of the homogeneous corn commodity, and (2) the spatially variable part
reflecting the distance between the producer
and the terminal market, often referred to as
the basis. These parts are related as follows:
(1)
3
A possibly more intuitive term might be intensification, but
this word has already gained currency when referring to more
intensive use of inputs, e.g., fertilizers, to raise production. To
avoid any confusion, we simply use the term “concentration.”
4
These states are Indiana, Illinois, Iowa, Kansas, Minnesota,
Missouri, Nebraska, North Dakota, Ohio, Oklahoma, South
Dakota, and Wisconsin.
729
Pproducer = Pterminal − t(d).
Pterminal embodies part (1), the price paid
at the grain’s terminal market, e.g., an
ethanol plant. Part (2) is reflected in t(d),
the transport cost, which itself is a function
730
April 2016
Amer. J. Agr. Econ.
Table 1. Summary Statistics of Dependent Variables
Corn Area (acres)
2006
2007
2008
2009
Corn Share of
Agricultural Area
Total Agricultural Area (acres)
2010
2006
2007
2008
2009
2010 2006 2007 2008 2009 2010
cellsa
All grid
Mean
2,990 3,326
18,109 18,896
Maxb
Std. dev.
3,813 3,326
Grid-cell means by state
Illinois
6,919 7,969
Indiana
5,552 6,152
Iowa
8,349 8,722
Kansas
1,387 1,595
Minnesota
3,403 3,479
Missouri
1,311 1,477
Nebraska
4,322 4,175
North Dakota 728 1,308
Ohio
2,458 3,229
Oklahoma
66
139
South Dakota 2,131 2,556
Wisconsin
2,134 2,599
3,102 3,150 3,399 10,205 9,816 11,016 11,542 11,806 0.26 0.28 0.22 0.21 0.22
19,023 1,796 18,248 23,899 23,527 23,641 23,609 23,916 1
1
1
.99 1
3,874 3,834 4,014 7,721 7,099 7,337 7,171 7,297 0.23 0.25 0.21 0.21 0.22
7,473
5,528
8,484
1,748
3,223
1,152
4,243
1,192
2,858
122
2,231
2,187
7,549
5,542
8,612
1,757
3,095
1,537
4,284
1,112
2,556
159
2,236
2,349
8,346
6,109
9,086
2,117
3,383
1,578
4,416
1,011
3,120
181
2,296
2,587
14,867
12,337
16,649
13,094
8,274
11,803
9,456
15,736
7,343
3,535
6,228
4,366
15,946
14,126
18,350
9,079
7,626
11,793
7,220
9,871
11,465
6,624
6,420
5,215
16,102
14,192
18,517
10,665
10,021
12,467
8,245
12,358
11,476
6,167
8,493
7,895
16,143
14,267
18,585
13,458
10,000
12,614
8,355
13,216
11,519
6,635
9,217
7,956
16,811
15,095
18,712
13,721
10,288
12,662
8,600
12,333
12,107
7,035
10,083
8,049
0.43
0.42
0.48
0.10
0.35
0.08
0.37
0.04
0.37
0.02
0.23
0.46
0.46
0.39
0.46
0.17
0.39
0.09
0.48
0.12
0.20
0.02
0.25
0.47
0.42
0.35
0.44
0.17
0.19
0.07
0.44
0.08
0.16
0.02
0.16
0.23
0.43
0.35
0.45
0.12
0.18
0.09
0.41
0.07
0.16
0.02
0.15
0.22
0.47
0.36
0.47
0.14
0.20
0.09
0.45
0.07
0.19
0.02
0.15
0.26
a The
data set consists of 20,109 grid cells.
maximum value of the corn share, by definition is 1. For 66 observations, however, the corn share technically exceeded the overall agricultural
acreage by an average 0.32 acres. We attribute this discrepancy to slight imprecisions in the satellite data, particularly in locations where overall
agricultural area is very small. Nevertheless, in the data set, we capped all corn ratio values at 1.
b The
of distance d from the farm gate to the terminal market.5 As d falls, so does t, and the
overall price term Pproducer rises, implying
that closer distances result in effectively
higher prices received by producers. As
described in McNew and Griffith (2005), the
addition of an ethanol refinery potentially
reduces the distance over which grains must
be shipped, lowering the cost of transport,
and ultimately raising the price paid to the
producer. Pterminal accounts for most of the
final value of Pproducer , but any local variation
in Pproducer and ultimately, variation in local
acreage outcomes, is explained by t(d), for
which reason we focus our attention on the
distance-dependent part (2).
The lack of transport cost data compels
researchers to proxy the term t(d) in a variety
of ways. One straightforward approach is
to use the distance between each county’s
centroid and the nearest ethanol plant, as
in Brown et al. (2014), and test its effect on
that county’s acreage. While this appeals to
our intuition that distance matters to acreage
outcomes, it ignores the role of ethanol plant
5
This analysis abstracts away from whether transport costs are
absorbed by the corn producer (CIF) or by the purchaser (FOB).
Gallagher, Wisner, and Brubacker (2005) show how different
competitive scenarios can determine the assignment of these
costs.
capacities. A nearby refinery might be competitive in terms of transport costs, but if its
capacity is fully reached, any unsold corn in
the vicinity must be trucked to more distant
and costly terminal markets. A refinery with
greater capacity arguably signals that it can
absorb more feedstock, giving producers
additional incentives to plant more corn.
To account for both distance and capacity
simultaneously, Miao (2013) calculates a
county-specific index of refining capacities
based on the assumed circular supply areas
of nearby refineries. Similarly, Fatal and
Thurman (2014) construct a variable in which
counties within a certain radius of a refinery
are assigned an effective capacity that decays
in proportion to the county’s distance from
the refinery.
In this article, we capture both proximity
and capacity using data at the grid cell level
that frees us from county-level boundaries
and complex allocation methods. We begin
by using Breneman and Nulph’s (2010) georeferenced data set based on the Renewable
Fuel Association’s reports on ethanol plant
location, online date, and yearly refining
capacities. Table 2 presents a yearly summary of the plants and their capacities for the
whole sample as well as state-level numbers.
Using these data, we construct a “neighborhood capacity” variable defined by a
circle of radius 100 kilometers around each
Motamed, McPhail, and Williams
Corn Area Response to Local Ethanol Markets
731
Table 2. Summary of Ethanol Plants and Capacity in the Study Area
Annual Nameplate Capacity
(millions of gallons)
Number of Plants
Year
All states
Individual states
Illinois
Indiana
Iowa
Kansas
Minnesota
Missouri
Nebraska
North Dakota
Ohio
South Dakota
Wisconsin
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
84
97
128
165
158
4, 536
5, 649
7, 723
10, 954
11, 476
9
1
19
6
16
3
12
1
0
12
5
10
3
24
7
16
3
13
3
0
12
6
10
8
28
10
18
4
19
4
5
14
8
16
10
38
12
22
5
25
6
6
15
10
16
10
36
11
21
5
25
5
4
15
10
872
102
1, 204
167
555
141
571
26
0
630
233
979
210
1, 837
207
561
141
683
126
0
630
275
1, 058
491
2, 152
427
768
166
1, 008
167
330
699
457
1, 541
679
3, 261
487
1, 051
241
1, 476
271
383
1, 016
548
1, 730
807
3, 326
434
1, 122
241
1, 594
353
314
1, 016
538
grid cell and identify all the plants that fall
inside. We sum the plants’ capacities and
assign that value to each cell. Some cells’
neighborhoods will have no ethanol plants
and hence no capacity. By using such a neighborhood, we arbitrarily identify a geographic
market for a producer’s ethanol-destined
corn. Over the time period 2006–2010, many
cells observed changes in the total refining
capacity of their neighborhood as new plants
came online or existing plants expanded. As
a result, the average grid cell’s neighborhood
capacity rose from 69 to 177 million gallons.
Table 3 summarizes the neighborhood plant
capacity variable that will serve as the key
explanatory variable in the later regression
analysis and presents state-level figures as
well.
Endogeneity of Ethanol Plant Location
and Selection of Instrumental Variable
Not surprisingly, investors weigh several factors when choosing where to build or expand
an ethanol plant. A location’s transport
infrastructure, the presence of competitors,
water availability, policy environment, and
of course, the feedstock supply all influence
the plant’s final address (Breneman and
Nulph 2010; Lin and Thome 2014). Stewart
and Lambert (2011) specifically show that
counties with higher corn production are
more likely to attract plant investment. Since
our analysis measures the impact of ethanol
plant proximity on corn acreage response,
the concern arises that ordinary least squares
estimates will suffer from endogeneity bias.
Table 3. Summary Statistics of Key Explanatory Variable
100 km neighborhood refining
capacity (millions of gallons)
Year
2006 2007 2008
All grid cells
Mean
69
Max
720
Std. dev.
127
State-level means
Illinois
168
Indiana
27
Iowa
291
Kansas
29
Minnesota
97
Missouri
26
Nebraska
82
North Dakota
5
Ohio
0
Oklahoma
0
South Dakota 73
Wisconsin
44
87
991
154
193
51
404
34
107
29
101
22
9
0
73
52
2009 2010
119
170
1, 180 1, 450
182
250
222
144
468
59
139
33
149
25
97
10
90
84
316
196
682
69
191
58
229
44
124
10
135
116
177
145
263
363
200
694
62
205
61
248
49
106
9
138
109
To get around this issue, we propose an
instrumental variable (IV) that (1) correlates
with ethanol refinery locations and capacities
but (2) is uncorrelated with planting decisions at the grid cell-level. The US railroad
network satisfies both criteria. First, ethanol
plants and railroads are highly spatially correlated. See figure 2. Unlike petroleum and
gas, ethanol cannot be conveyed via pipes,
and consequently, ethanol producers must
rely on alternate modes of transporting their
fuel, namely, rails. Today, around 70% of
732
April 2016
Amer. J. Agr. Econ.
Figure 2. Corn acreage, railroads, and ethanol plant locations in 2010
Note: Darker shades represent greater area in corn. Railroads are represented with lines. Ethanol plants are represented with circles.
ethanol is shipped on rail (Association of
American Railroads 2014). And as detailed
in Stewart and Lambert (2011), access to
transportation networks, particularly railroads, significantly affects ethanol plant
location decisions. Critically, moreover,
railways in the United States predate the
construction of modern ethanol plants by
nearly 100 years, assuring us that the causal
relationship between ethanol plants and railroad locations proceeds in only one direction
(Rodrigue 2013).6
But are farmers’ planting decisions necessarily uncorrelated with railroads? With the
advent of rail transportation in America’s
Corn Belt in the middle of the nineteenth
century, railroads and farms interacted to
form tight linkages between corn-livestock
producers and the shippers that moved hogs
and cattle to packers in Chicago and ultimately to population centers along the East
6
Chapter 3 of Rodrigue (2013) presents a time series chart of
railroad miles in the United States that peaks around the year
1920.
Coast (Hudson 1994). Simultaneously, the
introduction of rail networks led to large land
clearings and conversion into agriculture,
while corn-livestock producers, originally
dependent on river transport, gradually reoriented their herds to rail networks (Atack and
Passell 2004; Atack and Margo 2009).
Today, however, trucks exclusively carry
grain from the farm to the first point of sale,
typically an elevator or processor (USDA
2007). In general, around 80% of corn sold
and moved inside the United States is transported by trucks, regardless of the final
destination (Sparger and Marathon 2013).
As detailed in Hamilton (2008), post-World
War I transportation technologies—these
included Henry Ford’s Model T, enclosed
cabins, hydraulic brakes, and pneumatic
tires—greatly improved truck transport
and loosened the grip of railroads on farmers’ transport decisions. At the same time,
the radio broadcasted daily information
about markets and prices, and almost suddenly, farmers enjoyed a new set of tools for
accessing faraway markets independently and
flexibly. With trucks, farmers could ignore
Motamed, McPhail, and Williams
strict railroad schedules for shipping their
product and adjust their deliveries over time
and space to receive higher prices. Farmers combined these advantages with the
increasingly vast network of paved roads that
stretched across the country (Hamilton 2008,
44–46, 115).
Indeed, trucks could not have succeeded
without roads. The US government literally
paved the way for truck transport. Over the
period 1923–1970, the length of the Federal
Aid Highway System grew from 169,000
miles to nearly 900,000 miles (Bureau of the
Census 1975). As a result of these changes,
truck transport increasingly displaced railroads, permitting farmers to specialize
in cash-grain production and away from
corn-livestock production, and ultimately
diminishing the importance of farms’ proximity to a railroad (Hudson 1994). Similarly,
Chicago’s historic meat packing industry,
once seated at the nexus of railroads that
carried livestock to its markets, faded as
truck transport permitted packing centers to
decentralize.
Ultimately, railroad proximity lost any
influence it might have had on farmers’
planting decisions (Hudson 1994). In fact,
concomitant with the rise of trucking, rail
networks inside the United States experienced significant rationalizations, as track
miles, which peaked at around 250,000 in
1916, fell to about 140,000 in 2010 (Rodrigue
2013). In grain-producing states, rail networks experienced notable declines over the
period 1974–2010, over 40% in Iowa, South
Dakota, Wisconsin, and Nebraska (Prater
et al. 2013).
Having established that modern agricultural planting decisions are effectively
independent of railroad proximity, a related
question remains: What if crop choices
historically determined the location of
nineteenth century rail networks? Such a
possibility might undermine the important
exclusion restriction that railroads operate
on modern crop decisions strictly through
the channel of ethanol plants. Evidence that
tracks were originally built in places to capture agricultural production is weak. While
early work by Albert Fishlow suggested that
railroads were built in already-established
farm regions in part to serve agricultural
markets, Robert Fogel’s account of the
Union Pacific Railroad indicated otherwise,
showing that the economic returns to the
early years of railroad activity were too low
Corn Area Response to Local Ethanol Markets
733
to have attracted private investment (Atack
and Passell 2004, 433–44). In fact, to narrow the gap between the private and social
benefits of railroad construction, the US
government in 1850 began to grant swathes
of land to states and railroad companies to
facilitate westward expansion, with the stipulation that they could be used to transport
troops for national defense purposes as well
as meet the needs of the US postal service
(Atack and Passell 2004). To our knowledge, no evidence appears to suggest that
federal lands were initially selected for their
agricultural properties or use.7
Based on these facts, we conclude that
railroad proximity likely exerts no effect
on modern crop selection decisions at the
grid cell level except through the channel of
ethanol refineries. Thus, the selection of the
present day US rail network as an instrument
for ethanol plant location appears appropriate. Insomuch as a cell’s total neighborhood
refining capacity is associated with its total
number of plants, we expect more plants to
rely on a denser network of rails. To this end,
we construct a railroad instrument analogous to the neighborhood refining capacity
variable by adding up the total length of
all railroads that fall inside each grid cell’s
100-km radius neighborhood. We calculated
this variable in ArcGIS software using ESRIprepared shape files of Class I and Class II
railroads based on data from the Bureau of
Transportation Statistics of the US Department of Transportation. Figure 2 presents
a map of the railroads used in the analysis.
Table 4 presents summary statistics of the
variable.
Additional Controls
We also introduce time-invariant geographical controls that plausibly determine long-run
crop selection patterns. Soil quality data are
taken from the National Commodity Crop
Productivity Index (NCCPI), which ranges
7
Atack et al. (2010) present estimates showing that countylevel agricultural revenues per acre (dubbed “yields”) partially
explained the probability of railroads being built. Setting aside
the fact that revenues from the nineteenth century are likely
to be uncorrelated with revenues today, the authors’ specification does not adequately control for the multicollinear presence
of population and urbanization, two variables which appeared
insignificantly in their regression. Moreover, their results arguably
suffer from the omission of other possibly correlated variables,
including access to agricultural technologies as well as prices.
734
April 2016
Amer. J. Agr. Econ.
Table 4. Summary Statistics of Instrumental
Variable
Total Neighborhood Railroad Length (km)
Mean
Min
Max
Std. Dev.
1, 170
0
3, 719
659
from 0 to 10,000, with higher values reflecting
higher productivity (USDA 2012). We use
annual growing degree days (GDD) averaged over the period 1961–1990 produced
by the University of East Anglia’s Climate
Research Unit to capture the cumulative
amount of heat exposure a crop receives, a
variable which is generally understood to
capture temperature effects on plant growth
better than simple average temperature
variables (New, Hulme, and Jones 1999).8
To represent moisture, we use the USDA’s
Natural Resources Conservation Service and
its PRISM spatial climate data set to obtain
monthly rainfall across March, April, and
May, the months during which rainfall matters most to spring plant growth, and average
them over the time period 2006–2010 (Miao,
Khanna, and Huang 2015).9
And finally, we control for a location’s topography by constructing a grid
cell’s “roughness.” Generally speaking,
the rougher a location’s terrain, the less
amenable it is to mechanized cultivation. To
capture roughness, we used data from the
U.S. Geological Survey and its Shuttle Radar
Topography Mission’s Digital Elevation Map
(SRTM-DEM) rendered in gridded format
at the 30-arcsecond level.10 For each observation (approximately equivalent to 1 square
kilometer), we observed its immediate nine
neighbors and calculated that observation’s
neighborhood standard deviation of elevation. Then, for each of the 30-arcsecond cells
that fall within our larger unit of analysis,
the 10 km × 10 km cell, we took the average of their unique neighborhood standard
8
Growing degree data were downloaded from Atlas of the Biosphere, a product of the Center for Sustainability and the Global
Environment, part of the Nelson Institute for Environmental
Studies at the University of Wisconsin-Madison. http://www.
sage.wisc.edu/atlas/maps.php?datasetid=31&includerelatedlinks=
1&dataset=31.
9
The PRISM precipitation data set was downloaded from
http://www.wcc.nrcs.usda.gov/climate/prism.html.
10
The SRTM-DEM elevation data set was downloaded from
https://lta.cr.usgs.gov/SRTM2.
deviations to arrive at a final roughness measurement. Table 5 presents the summary
statistics of the control variables used in the
later regression analyses.
Exploring the Data
Several questions interest us. First, how did
the geography of ethanol markets change
over time? Figure 3 reveals how neighborhood refining capacities expanded their
spatial extent over the time period of the
study. The coverage of these arbitrarily defined geographic markets in 2006 is
depicted in the lighter shade. The expansion
of these markets into new areas by 2010, due
to the installation of new ethanol plants, is
colored in the darker shade. Judging from
the map, new markets in ethanol refining
capacity appeared most notably in Indiana
and Ohio, with some infill visible in Illinois,
Iowa, and Wisconsin. Additions along the
periphery occurred throughout the study
area, and, interestingly, an “island” of new
capacity emerged in western North Dakota.
A follow-up question is: How did the
distribution of acreage change across the
cells over time? Specifically, did changes in
acreage occur uniformly across all cells? To
answer this, we plot kernel densities for the
years 2006 and 2010 for each of the three
outcomes of interest (See Figure 4). For
corn area, the data reveal that the number
of cells with zero acres planted in corn fell
over the time period, implying that corn
production expanded into whole new cells.
Meanwhile, for all other cells, area in corn
slightly rose. A similar story plays out with
total agricultural area.
For total agricultural area, the number
of zero-value cells dropped by nearly half
over the time period 2006–2010, and almost
everywhere, acreage in total agriculture
rose, with the largest gains appearing in
the right-most portion of the distribution,
where area was already high. Indeed, this
time period witnessed a sizeable entry of new
land, about 1,200 cells, into agriculture. Why
did the largest gains in area occur in cells
with already-large agricultural acreage? One
possible explanation appears in Figure 5,
which plots a local polynomial smoothing
of all cells’ total agricultural acreage in the
years 2006 and 2010 against one plausible
geographic variable, the time invariant soil
quality index. From the graph, it appears that
greater changes in area occurred in locations
Motamed, McPhail, and Williams
Corn Area Response to Local Ethanol Markets
735
Table 5. Summary Statistics of Control Variables
Variables
Mean
Min
Max
Std. Dev.
National commodity crop productivity index
Precipitation (mm)
Growing degree days
Roughness
4,137
74.7
2,508
7.06
0
26.8
1,268
0.2
9530
151.6
4,208
71.18
1,970
24.1
591.5
5.1
Figure 3. Expansion of ethanol refinery neighborhoods, 2006 and 2010
Note: Geographic coverage of ethanol markets in 2006 are represented by areas shaded in light gray. Market coverage that was added by 2010 is
represented by areas shaded in dark gray. Geographic markets are defined by any refining capacity located within a 100-km radius circle
surrounding a grid cell.
with higher NCCPI values, suggesting that
soil quality—this might be taken to proxy for
a location’s overall agricultural suitability—
helped determine which areas were expanded
over the time period.
Combining information from ethanol
markets and cell-level acreage changes,
we investigate the correlation between a
cell’s ethanol market capacity and the three
response variables, with a focus on changes
between 2006 and 2010. In a graph that anticipates our later regression results, Figure 6
plots fitted curves relating each of the three
outcomes of interest to each cell’s neighborhood ethanol refining capacity. Cells
that fall outside the arbitrarily defined geographic markets are represented by those
points where the capacity variable (on the
x-axis) equals zero. Not surprisingly, for
these outside cells, areas in corn and total
agriculture, as well as the corn share, are at
their lowest. Cells that fall inside an ethanol
market are represented by the positive values
of the refining capacity variable. Judging
from the steep rise in all three of the graphs
as the capacity variable switches from zero to
positive values, simply being located inside
an ethanol market correlates strongly with
higher area outcomes.11 Moreover, all three
variables continue to rise in value in the face
of rising neighborhood refining capacity.
The data in figure 6 are selected from two
time periods, 2006 and 2010. In each of the
three panels, the slope of the curve appears
steeper in the first period, 2006. While we
might infer that ethanol markets’ demand
11
The smallest nonzero value of neighborhood refining capacity
was 2 million gallons.
April 2016
736
Amer. J. Agr. Econ.
0.0004
25,000
2006
20,000
total agricultural acres
density
0.0003
0.0002
0.0001
2010
0.0000
0
5,000
10,000
15,000
2010
15,000
2006
10,000
5,000
20,000
corn acres
0
0.00008
0
2006
density
2,000
4,000
6,000
8,000
10,000
nccpi
0.00006
2010
Figure 5. Total agricultural acreage in 2006
and 2010 and National Commodity Crop
Productivity Index
0.00004
0.00002
0.00000
0
5,000
10,000
15,000
20,000
25,000
0.80
1.00
total agricultural acres
4
density
3
2
2010
1
2006
0
0.00
0.20
0.40
0.60
corn share of agriculture
Figure 4. Kernel density plots of distribution
of crop outcomes at the grid cell level, 2006
and 2010
Note: Plots are based on Epanechnikov kernel using Stata’s optimal
bandwith selection.
for corn slackened over time, the 8 million
new acres in corn that were planted during
the time period as well as the new cells that
experienced production for the first time lead
us to ask whether ethanol plants had varying
effects on crop outcomes depending on the
level of crop acreage at the time. In the estimation section below, we attempt to test for
precisely these heterogeneous effects across
the distribution of acreage outcomes.
each cell’s neighborhood refining capacity on
its crop planting outcomes. The panel allows
us to control for time-invariant unobservable
effects that operate at the grid cell level.
A simple Hausman test reveals that the fixed
effect (FE) model is preferred.
As discussed earlier, estimating the direct
impact of a cell’s neighborhood refining
capacity on its crop outcomes will result in
biased and inconsistent estimates. For this
reason, we use a grid cell’s neighborhood
railroad density as an IV for its neighborhood refining capacity. Based on the
predicted values from the first stage estimation, we then estimate the effect of each
cell’s neighborhood refining capacity on two
response variables: corn acreage and total
agricultural acreage. A positive coefficient
in the corn acreage equation suggests that
greater refining capacity in a given cell’s
neighborhood raises acreage in corn. A positive coefficient in the total agricultural
acreage equation could be interpreted as evidence of extensification, in which new acres
are being introduced into production, be they
in corn or other crops. The final form for the
model appears as follows:
(2)
it + β2 capacity
it
acreageit = β1 capacity
· γt + θ + γt + αi + εit
Estimating an Acreage Response Model
where acreage is one of the two acreage
response terms (in logs) for grid cell i in
it represents the
year t.12 The term capacity
With 20,109 grid cells observed annually over
the five-year period 2006 through 2010, we
construct a panel to estimate the effects of
12
In 2006, nearly one-fourth of the observations showed corn
acreage equal to zero. In 2010, this number fell to about one in
Motamed, McPhail, and Williams
Corn Area Response to Local Ethanol Markets
corn area (acres)
15,000
10,000
2006
2010
5,000
0
0
500000000
1.00e+09
1.50e+09
neighborhood refining capacity
(gallons)
total agricultural area (acres)
25,000
20,000
2006
737
effect, and the error term εit includes any
remaining time varying idiosyncratic effects
at the grid cell level.
Our third outcome of interest is the elasticity of corn share of total agriculture, which
we also refer to as corn “concentration.”
Directly estimating the response of this fractional variable raises concerns about the
model’s potential for predicting outcomes
outside the [0,1] bound. Fractional response
methods are potentially applicable, but a
more direct approach is to simply obtain
the difference between the coefficients on
refining capacity estimated in (1) and (2).
To illustrate this, consider simplified forms
of the equation described above for our two
response variables.
2010
15,000
10,000
5,000
0
500000000
1.00e+09
1.50e+09
neighborhood refining capacity
(gallons)
0.6
(3)
log acreagecorn = β log capacity + · · ·
(4)
log totalagtotalag = α log capacity + · · ·
Since both equations use the same data and
the same right-hand-side variables, including
the explanatory variable of interest capacity, we can obtain the elasticity of the corn
share directly by subtracting (4) from (3) and
rearranging terms to obtain:
2006
(5)
0.4
ratio
2010
log
corn
= (β − α) log capacity.
totalag
The term (β − α) thus captures the percent
change in the corn share of agriculture in response to a 1% change in the
neighborhood refining capacity.13
0.2
0.0
0
500000000
1.00e+09
1.50e+09
neighborhood refining capacity
(gallons)
Figure 6. Crop outcomes and neighborhood
ethanol markets for all cells (2006 and 2010),
fractional polynomial fit with 95% confidence
intervals shaded in gray
instrumented log neighborhood refining
capacity. We also include an interaction
between the capacity term and time dummy
γ to observe any variation in year-to-year
effects. State-level effects θ and time dummies γt absorb state- and year-specific
unobservables. The term αi represents the
time-invariant grid cell level unobservable
twenty. We assigned these zero-value observations the value of
0.1 to permit the calculation of its natural logarithm and facilitate
the estimation of the elasticity.
Testing for Heterogeneous Effects
Based on figure 4’s graphs that show acreage
changes varying with the level of acreage,
we estimate the impact of ethanol plants
at different points along the acreage distribution using the IV quantile regression
framework based on the method proposed
in Lee (2007).14 The idea here is to observe
at what levels of crop acreage ethanol plant
impacts are most greatly felt. Specifically, did
low-value cells exhibit the largest response?
13
For equations (1) and (2), we perform a block bootstrap
at the grid cell level with 100 repetitions to obtain a distribution of estimated β and α, from which we can generate a
distribution for the term (β − α), thus allowing us to infer standard errors and ultimately judge the latter term’s statistical
significance.
14
To implement this method, we used the cqiv command for
Stata, as written and described in Chernozhukov et al. (2012).
738
April 2016
Amer. J. Agr. Econ.
Table 6. Neighborhood Refining Capacity Effects on Acreage: Fixed Effects Specification
GLS Results
(1)
log corn
acres
Log neighborhood 0.0307∗∗∗
refining capacity (0.00106)
Capacity∗ 2007
−0.0461∗∗∗
(0.00105)
Capacity∗ 2008
−0.0428∗∗∗
(0.00107)
−0.0577∗∗∗
Capacity∗ 2009
(0.00109)
Capacity∗ 2010
−0.0661∗∗∗
(0.00107)
Constant
0.0307∗∗∗
(0.00106)
N
100,545
0.16
Within R2
0.45
Between R2
IV Results
(2) log total (3) constructed
agricultural
corn share
acres
elasticity
0.0261∗∗∗
(0.000993)
−0.0463∗∗∗
(0.000980)
−0.0618∗∗∗
(0.00100)
−0.0777∗∗∗
(0.00102)
−0.0779∗∗∗
(0.00100)
0.0261∗∗∗
(0.000993)
100,545
0.21
0.23
0.005∗∗
(0.002)
0.001
(0.002)
0.018∗∗∗
(.002)
0.021∗∗∗
(0.003)
0.012∗∗∗
(0.002)
(4)
log corn
acres
1.559∗∗∗
(0.142)
−0.269∗∗∗
(0.0303)
−0.447∗∗∗
(0.0439)
−0.492∗∗∗
(0.0469)
−0.442∗∗∗
(0.0470)
−6.686∗∗∗
(1.069)
100,545
−
0.48
(5) log total (6) constructed
agricultural
corn share
acres
elasticity
1.740∗∗∗
(0.159)
−0.259∗∗∗
(0.0340)
−0.504∗∗∗
(0.0492)
−0.580∗∗∗
(0.0527)
−0.514∗∗∗
(0.0528)
−5.131∗∗∗
(1.201)
100,545
−
0.22
−0.181∗∗∗
(0.012)
−0.010∗∗∗
(0.010)
−0.057∗∗∗
(0.010)
−0.088∗∗∗
(0.009)
−0.072∗∗∗
(0.010)
Note: Standard errors are reported in parentheses. Statistical significance levels are as follows: ∗∗∗ 1%, ∗∗ 5%, and ∗ 10%. All estimates include state
and year effects. The Within R2 for the IV results are not computable since the instrument does not vary with time. Note that the elasticity of the
log corn share of agriculture is computed by obtaining the difference in the coefficients from the log corn acre and log total agricultural acre models. Bootstrapped standard errors are reported for these constructed coefficients.
Results from the Main Specification
Table 6 present the results from the fixed
effects models. For purposes of comparison,
the naïve generalized least-squares (GLS)
estimates are reported alongside the more
appropriate IV (FE-IV) estimates. Across
both sets of estimations, the coefficients on
neighborhood refining capacity exhibit the
2
acreage response (elasƟcity)
Or were high-value cells more responsive?
Any differences in the estimated coefficients on the neighborhood capacity variable
would amount to evidence of heterogeneous
impacts of ethanol plants across the distribution of the outcome variables. For our
purposes, we estimate the coefficient on the
instrumented neighborhood ethanol capacity
variable at the 10%, 25%, 50%, 75%, and
90% quantiles.
In addition to varying effects over the distribution, we also wish to know whether the
ethanol plants’ effects changed over time.
Unfortunately, a full random effects panel
estimation with time dummies lies beyond
the IV-quantile estimation methodology we
employ. As a workaround, we confine the
regressions to two years of the data, 2006 and
2010, in order to observe whether any variations in the estimated parameters occurred
over time at the different quantiles.
1.5
1
0.5
0
2006
2007
-0.5
2008
2009
2010
year
corn acreage
total ag acreage
corn share
Figure 7. Yearly acreage response
neighborhood refining capacity, FE-IV
to
expected sign and are statistically significant.
Note that the IV results are dramatically
larger than the GLS-estimated coefficients.
From the FE-IV estimates, a one-percent rise
in a grid cell’s neighborhood refining capacity
in 2006 increases that cell’s acreage in corn
by over 1.5% and increases total agricultural
acres by over 1.7%. In that same year, the
calculated corn share elasticity is −0.18%,
implying that the response of total agricultural area was greater than corn’s response in
that year.
Figure 7 graphically summarizes the estimated coefficients over each year of the time
Motamed, McPhail, and Williams
Corn Area Response to Local Ethanol Markets
739
Table 7. Neighborhood Refining Capacity Effects on Acreage: Random Effects Specification
GLS Results
(1)
log corn
acres
Log neighborhood 0.0577∗∗∗
refining capacity (0.00105)
Capacity∗ 2007
−0.0440∗∗∗
(0.00108)
−0.0400∗∗∗
Capacity∗ 2008
(0.00111)
−0.0542∗∗∗
Capacity∗ 2009
(0.00113)
Capacity∗ 2010
−0.0631∗∗∗
(0.00111)
Log soil quality
0.719∗∗∗
index
(0.0137)
Log precipitation −0.493∗∗∗
(0.0923)
Log growing
7.559∗∗∗
degree days
(0.206)
Log roughness
−1.241∗∗∗
(0.0236)
Constant
0.0577∗∗∗
(0.00105)
N
100,545
0.15
Within R2
0.55
Between R2
IV Results
(2) log total (3) constructed
agricultural
corn share
acres
elasticity
0.0539∗∗∗
(0.000926)
−0.0440∗∗∗
(0.00100)
−0.0588∗∗∗
(0.00102)
−0.0742∗∗∗
(0.00104)
−0.0749∗∗∗
(0.00102)
0.590∗∗∗
(0.00846)
−1.307∗∗∗
(0.0569)
7.527∗∗∗
(0.127)
−0.551∗∗∗
(0.0146)
0.0539∗∗∗
(0.000926)
100,545
0.20
0.53
0.0039∗
(0.0023)
0.0014
(0.0023)
0.0179∗∗∗
(0.002)
0.0208∗∗∗
(0.0028)
0.0122∗∗∗
(0.0026)
(4)
log corn
acres
(5) log total (6) constructed
agricultural
corn share
acres
elasticity
0.501∗∗∗
0.283∗∗∗
(0.00802) (0.00513)
−0.129∗∗∗ −0.0670∗∗∗
(0.00863) (0.00522)
−0.184∗∗∗ −0.142∗∗∗
(0.00817) (0.00495)
−0.203∗∗∗ −0.181∗∗∗
(0.00807) (0.00490)
−0.196∗∗∗ −0.175∗∗∗
(0.00822) (0.00498)
0.252∗∗∗
0.362∗∗∗
(0.0113)
(0.00756)
−0.0329
−1.105∗∗∗
(0.0625)
(0.0418)
3.372∗∗∗
5.528∗∗∗
(0.151)
(0.101)
−0.542∗∗∗ −0.217∗∗∗
(0.0187)
(0.0125)
−25.58∗∗∗ −35.55∗∗∗
(1.052)
(0.704)
100,545
100,545
0.02
0.06
0.59
0.53
0.218∗∗
(0.087)
−0.062
(0.016864)
0.042∗∗
(0.027)
0.022∗∗∗
(0.030)
0.021∗∗∗
(0.028)
Note: Standard errors are reported in parentheses. Statistical significance levels are as follows: ∗∗∗ 1%, ∗∗ 5%, and ∗ 10%. All estimates include state
and year effects. Note that the elasticity of the log corn share of agriculture is computed by obtaining the difference in the coefficients from the log
corn acre and log total agricultural acre models. Bootstrapped standard errors are reported for these constructed coefficients.
Table 8. Results of Tests for Heterogeneous Effects
Quantile-specific Marginal Effect of Log Neighborhood
Refining Capacity
(1) FE-IV
(2) 10%
Log corn acreage
2006
1.559
2010
1.117
Log total agricultural acreage
2006
1.740
2010
1.226
(3) 25%
(4) 50%
(5) 75%
(6) 90%
0.399
0.288
0.433
0.264
0.130
0.200
0.048
0.094
0.024
0.051
0.308
0.093
0.119
0.066
0.020†
0.025
0.002
0.009
0.001
0.003
Note: For purposes of comparison, column (1) reprints the estimated effects from table 6. All estimates are statistically significant at the 1% level,
based on bootstrapping with 25 repetitions, with the exception of the coefficient marked †, which is significant at the 5% level.
period, showing the positive but diminishing
impact of ethanol markets over the years.
Corn area response declined by approximately one-third, while total area response
fell by a little under one-fifth. The corn
share response to neighborhood refining
capacity, constructed from the separate
estimates of corn and total area, remained
consistently negative over the time period,
reflecting the smaller response of corn area
relative to total area in each year of the time
period.
Echoing the graph presented in figure 6,
the estimated coefficients on the interaction
between neighborhood refining capacity and
the time dummies confirm the diminishing
impact of ethanol markets over the years in
our sample.
740
April 2016
Amer. J. Agr. Econ.
Table 9. Capacity Effects at Varying Neighborhood Radii, FE-IV
Log Corn Acres
Log neighborhood refining capacity
Capacity∗ 2007
Capacity∗ 2008
Capacity∗ 2009
Capacity∗ 2010
(1) 100 km
(2) 150 km
(3) 200 km
1.559∗∗∗
(0.142)
−0.269∗∗∗
(0.0303)
−0.447∗∗∗
(0.0439)
−0.492∗∗∗
(0.0469)
−0.442∗∗∗
(0.0470)
0.491∗∗∗
(0.0372)
−0.172∗∗∗
(0.0130)
−0.0151∗∗∗
(0.00274)
−0.205∗∗∗
(0.0170)
−0.192∗∗∗
(0.0173)
0.631∗∗∗
(0.0260)
−0.187∗∗∗
(0.0125)
0.123∗∗∗
(0.0219)
−0.0153∗∗∗
(0.00167)
−0.0523∗∗
(0.0221)
1.873∗∗∗
(0.152)
−0.0109∗∗∗
(0.00136)
−0.510∗∗∗
(0.0252)
−0.953∗∗∗
(0.0251)
−0.859∗∗∗
(0.0250)
0.670∗∗∗
(0.0464)
−0.0532∗∗∗
(0.0162)
−0.0273∗∗∗
(0.00342)
−0.316∗∗∗
(0.0212)
−0.252∗∗∗
(0.0216)
0.891∗∗∗
(0.0303)
−0.0462∗∗∗
(0.0146)
0.202∗∗∗
(0.0255)
−0.0297∗∗∗
(0.00195)
−0.015
(0.0258)
Log total agricultural acres
Log neighborhood refining capacity
Capacity∗ 2007
Capacity∗ 2008
Capacity∗ 2009
Capacity∗ 2010
Note: Each sets of results also reflects controls for year and US states.
Table 8 presents the results from the quantile regressions that tested for heterogeneous
effects across the range of the crop variables’
distributions. Across all three responses, the
largest effects were detected at the low end
of the distribution, that is, the 10% quantile, suggesting that neighborhood refining
capacity mattered much more among cells
with low values in corn area and total agricultural area. Based on this result, as well
as the graphs depicted in figure 4, ethanol
markets appeared to contribute to a sizeable
extensification of agricultural area during
the time period. However, these effects
diminished over time, as the 2010 parameters
estimates are consistently lower than in 2006.
These results appear to support the narrative
that ethanol production spurred cultivation in hitherto unfarmed and potentially
low-quality land.
Robustness Tests
The results from our main specification
clearly show how the introduction or
expansion of an ethanol plant in a given
neighborhood can drive crop selection
decisions. In this section, we test whether
our main results are robust to alternative
specifications.
Testing a Random Effects Specification
Our main specification used the fixed effects
estimator for which the effects of any timeinvariant determinants, such as long run
features of geography and climate, are
effectively wiped away. However, it may
be desirable to account for these features
when modeling crop selection outcomes. To
do this, we run a random effects (RE) model,
mindful of the condition that the unobservable grid cell effect must be uncorrelated
with the observable explanatory variable,
that is, a neighborhood’s ethanol refining
capacity. To our knowledge, the only unobservable features that could contribute to
an acreage outcome and also correlate with
ethanol plant capacities pertain to geography
and climate, for which reason we introduce
the aforementioned controls for soil quality,
growing degree days, rainfall, and roughness for each cell i to arguably minimize any
potential correlation.
The results from the RE-IV specification
appear in table 7. The RE-IV show qualitatively similar effects for corn and total
Motamed, McPhail, and Williams
agricultural acreage, except here the difference between the two effects is positive,
resulting in a constructed corn share elasticity that is positive, about 0.22%. The signs
on the time-invariant geographic controls
broadly conform to expectations.
Neighborhood Radius
In our original specification, the neighborhood capacity variable was based on
a 100-km radius around each grid cell.
The selection of this radius was somewhat
arbitrary, so to ensure that our results are
robust to different radius lengths, and corresponding market area sizes, we re-run the
FE-IV model using redefined neighborhood
capacity variables with radii of 150 and 200
kilometers. The expected magnitudes of
the effects of these larger neighborhoods is
arguably ambiguous. Larger neighborhoods
imply potentially greater refining capacities,
as more refineries get swept into a cell’s
potential market. But the transportation
costs to reach these potentially more distant
buyers can also be higher.
Bearing these factors in mind, table 9
shows how the effects of neighborhood
capacity vary over different sized market
areas. From the results, acreage response
remains positive and significant even up
to 200 km away, though the effect sizably
diminishes. Here we note that Fatal and
Thurman (2014) detected acreage responses
up to 286 miles (or about 460 km) away from
an ethanol plant, suggesting that our radius
could be considerably lengthened before the
effects fully disappear.
Conclusion
Understanding the effects of the biofuels
industry on the landscape of US agriculture
remains a priority both for policy makers
and researchers. The aggregate response
of US producers to the incentives posed
by higher ethanol prices is unmistakable,
but the local responses have yet to be fully
documented. In this article, we ask how
producers responded to the presence and
size of an ethanol market in their neighborhood. Controlling for the endogeneity of
an ethanol plant’s location and capacity,
we documented a significant and large
acreage response to ethanol markets, with
Corn Area Response to Local Ethanol Markets
741
larger effects detectable in locations with low
acreage in corn and overall agriculture.
Coupled with recent trends in co-locating
livestock feeding operations, the implications
for this adjustment in the spatial pattern of
crop planting include more intensive land
use in areas surrounding ethanol plants,
more concentrated environmental impacts,
and potentially stronger spatial linkages
between the food, feed, and energy sectors.
These outcomes may reflect the efficient
response of different producers to new
economic incentives, but any externalities
associated with these evolving arrangements
remain unknown. This article highlights these
changes, relying on annual microscale satellite data that facilitate our understanding
of planting decision variation over time and
space. Extensions to this analysis might incorporate data on local-scale environmental and
economic variables, for example, nitrogen
application or employment volatility, to link
ethanol markets to outcomes that directly
interest policy makers.
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