Embed Size (px)
Citation preview
SHUTTLE TRAIN ADOPTION STRATEGY
A Thesis Submitted to the Graduate Faculty
of the North Dakota State University
of Agriculture and Applied Science
By
Weijun Huang
In Partial Fulfillment of the Requirements for the Degree of
MASTER OF SCIENCE
Major Department: Agribusiness and Applied Economics
June 2003
Fargo, North Dakota
ii
iii
ABSTRACT
Huang, Weijun; M.S.; Department of Agribusiness and Applied Economics; College of Agriculture, Food Systems, and Natural Resources; North Dakota State University; June 2003. Shuttle Train Adoption Strategy. Major Professor: Dr. William W. Wilson. The elevator industry is experiencing a new trend of adopting shuttle train
technology during the last decade. Adoption has changed elevators’ service area
and market shares that has given elevators new options for strategic movement.
The primary objective of this thesis is to determine factors affecting shuttle adoption
in the elevator industry. An understanding of the factors that affect these adoption
decisions can lead to a better understanding of the future adoption of other elevators.
Three categories of data, own-elevator characteristics, agronomic characteristics,
and competitive factors, are analyzed to determine which elevators are more likely
to adopt shuttle trains.
Data on the elevator industry include the elevators in the nine states in the
central United States that are on the BNSF, CP, and UP rail lines. Data on the
agriculture are available for five years: 1996, 1997, 1998, 1999, and 2000. The
adoption model is estimated using a Logit model.
The results show that the nearest elevator’s shuttle train adoption decision, the
elevator’s own storage capacity, the Herfindahl Index of crop diversity, and the
standard deviation of yield of crops in a county are the important determinants of
shuttle train adoption decision for an elevator as a whole. The nearest elevator’s
shuttle adoption decision is especially important in determining the probability of
adoption.
iv
ACKNOWLEDGMENTS
First, I would like to thank my adviser, Dr. William W. Wilson, for his untiring
guidance, input, and help in completing this thesis and my study. From him, I know
what an outstanding professor is.
Thank you also to committee member Dr. John Bitzan, who taught me two
courses at NDSU in the past. From the very beginning of my thesis writing, he has
been spending lots of time to discuss with me.
I also would like to thank other committee member, Dr. Steven Shultz for his
suggestions and help, and Dr. Rodney Traub for his responses to many questions
and kind support throughout this process.
A special thankfulness is also expressed to Bruce Dahl for his research expertise
and much assistance.
I would like to thank all professors who taught and helped me during my
master’s program at NDSU: Dr. David Lambert, Dr. Won Koo, Dr. Demcey
Johnson, Dr. Eric Devuyst, Dr. Cheryl Devuyst, Dr. William Nganje, Dr. Cheryl
Wachenheim, and Dr. Eric Schuck.
Finally, I am very thankful to the secretaries of the Department of Agribusiness
and Applied Economics.
v
TABLE OF CONTENTS
ABSTRACT.…………………….………….………….………...…………………iii
ACKNOWLEDGMENTS……………..…………….…………...…………………iv
LIST OF TABLES………………..……..….……….……..……..……………..…vii
LIST OF FIGURES……………….……...………………………………………viii
CHAPTER 1. INTRODUCTION……………….………….………………….……1
Background………………....………...…...………………………………….1
Problem…………………………………………………………..…………...4
Objectives….……………………………………………..………………..…6
Methods and Procedure………………………………………………………7
Results……………………………………………………...………………...7
Organization………………………………………….……………………....8
CHAPTER 2. REVIEW OF LITERATURE …………..……………………....….9
Introduction……………………………………….…..….…………............9
Empirical Industry Studies……………….…………………………............9
Elevator Studies…………………………………………………………….18
Rail Studies on Unit/Shuttle Trains……………………………………..….21
Summary……………………………………………………………………22
CHAPTER 3. COMPETITION THEORY IN AN OLIGOPOLISTIC MARKET..26
Introduction………………….…………….………………………………..26
Market Structure…….…….………..……………………………………..26
vi
Strategic Interactions………………………………………………………31
Capacity and Price Competition (Cournot and Bertrand Models)………….36
Summary…………………………………………………………………….49
CHAPTER 4. EMPIRICAL PROCEDURES………………………..…………...51
Introduction…………………………………………………………............51
Model Specification…………………………………………………………51
Data Sources and Behavior…………………………………………………66
Estimation: Procedure and Methods ………………………………………..70
Summary…………………………………………………………………….73
CHAPTER 5. RESULTS………………………………………………………….75
Introduction……………………………………………………....................75
Data Behavior: Significance, Insignificance, and Multicollinearity……....75
Empirical Results…………………………………………………………...78
Impacts of Alternative Spatial Representation of Variables…………….....98
Summary and Results………………………………………………….......103
CHAPTER 6. CONCLUSIONS…………………………………………………106
Summary of Results………………………………………………………106
Limitations of the Study…….…………………………………………..109
Need for Further Study……………………………………………………111
BIBLIOGRAPHY………………………………………………………………...112
vii
LIST OF TABLES
Table Page 5.1. Summary of Independent and Dependent Variables………..……………..76
5.2. Correlation of Variables Tested in the Estimation…….………………...…79
5.3. Shuttle Adoption Decision Estimation and Analysis of Maximum
Likelihood Estimates…………………………………………..…………..80
5.4. Comparison of Adoption Model Estimations and Analysis of Maximum Likelihood Estimates.………………...……………………………………87
5.5. Model Fit Statistics…………………………………………………………90
5.6. Goodness of Fit Test Results…………………………………………….....90
5.7. Regression Models Selected by Score Criterion…………………………..92
5.8. Comparison of Analysis of Maximum Likelihood Estimates with 10 Variables in the Models.…………………………………………...……92
5.9. Marginal Effects of Each Variable………………………………………...93
5.10. Summary of Four Alternative Variables….……….……………………….100
5.11. Impacts of Alternative Spatial Representation of Variables …………….101
viii
LIST OF FIGURES
Figure Page 3.1. The Draw Area Changes Before and After Adoption…………………..…..42
3.2. Sequential Game Between the Two Closest Elevators………..……….……44
3.3. Simultaneous Game Between the Two Closest Elevators………..…………46
4.1. Binary Logit Response Curve……………………………………………....54
4.2. Elevators Studied in 9 States.………………………………………………67
4.3. Elevators that Adopted Shuttles………………………………………..…..68
5.1. Goodness of Fit-1….………………….…………………………….………91
5.2. Goodness of Fit-2…...……..……………………………………….……..…91
5.3. Marginal Effect of Own Storage Capacity of Elevator i….………..…….…94
5.4. Marginal Effect of Storage Capacity of the Nearest Elevator, j…..………...95
5.5. Marginal Effect of Distance to the Nearest Elevator, j……….……………95
5.6. Marginal Effect of Number of Elevators in a County….………….…….....96
5.7. Marginal Effect of Yield…………….…………………………………......96
5.8. Marginal Effect of Standard Deviation of Yield….......………...….…..97
5.9. Marginal Effect of Herfindahl Index of Crop Diversity…...………….…....97
1
CHAPTER 1
INTRODUCTION Many external factors influence the ability of the agricultural sector to compete in new,
emerging, and traditional markets. Efficient transportation is vital to the continued and
improved competitiveness of the agricultural sector. One of these factors includes
changing rail technologies and operational practices. Railroads and elevators must have
the necessary information and analysis to participate in the process of change, which will
continue. As an attempt to provide some of the information that will enable railroads and
elevators to make better decisions, this study addresses the issue which is important to
future adoptions of shuttle train technology: factors that affect shuttle train technology
adoptions.
The cost advantage available to shuttle-equipped facilities has implications for farmers,
elevators, local processors, and rural communities. Just as unit train rates were
instrumental in redefining local grain flow patterns in 1980, shuttle train rates also have the
potential to influence local grain distribution patterns dramatically. This pattern will
determine infrastructure employment for the local grain market, and provide signals for
decision makers in establishing policies and distributing limited resources to maximize
returns to the user group. Elevators’ investment decisions are based on profit-maximizing
goals. It is important to identify and understand these factors.
Background
Grains are bulky, low-valued commodities. Because of the long distances from
2
producing areas to terminal markets, rail is often the most efficient mode for transporting
grain in northern America and central Canada. The grain handling and transportation
system in these areas has changed dramatically over the past two decades. There have been
technological changes and productivity gains in rail operations, handling, and shipping; and
substantial investment throughout the system. These changes were triggered, in part, by
changes in the regulatory mechanisms that were adopted in 1980. However, other
important factors have also contributed. They include underling economies of scale in rail
operations, handling and logistics management, as well as service competition. In addition,
the competitive environment in the handling and railroad sectors is sufficiently intense that
these cost savings have ultimately been passed on to farmers and decision makers
throughout the system (Wilson et al., 1998).
Several U.S. and Canadian railroads have been in the process of developing and
offering more sophisticated mechanisms for grain shipping. One of them is commonly
referred to as shuttle train operations. In 1997, Union Pacific Railroad (UP) introduced
published rates for 100+ car trains; Burlington Northern Santa Fe Railway (BNSF) also
introduced a $100 to $150 discount to the 52-car train rates for the 104-car train contract
rates.
Movements using shuttle train operations moved 7 percent of the corn, 5 percent of the
soybeans, and 11 percent of the wheat in 1998. Among these 100+ car shipments, 84
percent went to export, ranging from 68 percent and 72 percent of bean and corn shipments
to almost all (99 percent) wheat shipments (Klindworth, 2000).
Shuttle train technology typically has three important features. First, it offers a contract
to shippers to use a train over a specific period. Therefore, shuttle trains are able to cycle
3
continuously between origin and destination. The contract between the shipper and the
railroad, in which the shipper commits to loading and shipping multiple 100+ car trainloads
of the heavy axle, 286,000 pound cars each of corn, wheat, soybeans, milo, etc., covers a 6
to 9 month period on the railroad in exchange for a lower rate and a guarantee of a certain
level of rail service. Second, shuttle train operation has a tightly scheduled spot window by
the railroad and penalties paid by the carrier if that window is not met, typically 15-hour
loading requirements for the shipper and penalties upon the shipper if the train is not
released within the 15-hour window. Third, shuttle train rates provide incentive
allowances, discount rates (or rate spreads) off 54-car rates, for shuttle train shippers if the
shipper meets all of the requirements of the shuttle contract between the carrier and the
shipper.
A 100+ car shuttle train can load 440,000 bushels of grain. Covered hoppers in a
shuttle train are two to three times more productive than those in conventional service. A
shuttle train can move a third more grain with a third fewer cars, which improves
equipment utilization and availability, and enhances the railroad’s ability to meet customer
needs. It increases capacity and efficiency, improves logistics, and reduces costs. The
market impact that the shuttle train operations have is obviously easy to describe
(Klindworth, 2000): (1) Real savings are accrued by those shuttle shippers when they can
set up shuttle train operations and can qualify the multi-trip discounts, especially the 24 trip
discounts; (2) Some, although not all, of this transport cost savings gets passed back to
farmers surrounding the shuttle train facility, in that the shuttle train shipper would be
expected to increase bid prices to meet his shuttle obligations; (3) The gathering territory
of shuttle facilities increases significantly; (4) Not only are there significant rate reductions
4
to the shuttle train loaders, but surrounding 54-car shippers can no longer compete
effectively through co-loading because those privileges are typically canceled when the
shuttle train contract goes into effect; (5) The promotion of 100+ car shipping programs
along with the cancellation of co-loading privileges seemingly has a coercive effect of
forcing grain traffic into larger units; and (6) The gathering area of firms that becomes
shuttle train loaders gets bigger, and farmers truck longer distances to get the higher bid
prices.
The aim of the shuttle train technology is to improve the efficiency of grain shipping.
This technology has become a highly efficient form of transporting grain in American and
Canadian prairies. Some railroads that have not adopted shuttle train technology are
considering adopting this new potential mechanism. Those railroads that have adopted the
programs so far are planning to expand this service. The grain shuttle network is growing.
More elevators are making investments to update their facilities to adopt shuttle trains. For
example, Burlington Northern Santa Fe Railway (BNSF) is the largest grain-hauling
railroad in the United States. Of the elevators on the BNSF’s rail lines, 131 elevators have
adopted shuttle trains by 2001 (“2001 Grain Elevator Directory,” 2001).
Problem
Although shuttle train technology has been introduced for several years and most
elevators and rail companies see its cost efficiency, there are many situations where,
particularly, a large number of elevators in shuttle service areas are still not included in this
operation. For example, on the BNSF rail system, only 6.67% of elevators have adopted
shuttle trains by 2001 (“2001 Grain Elevator Directory,” 2001).
5
Shuttle adoption strategy remains somewhat unclear in some aspects in order to
expand this mechanism from the railroad point of view, such as the relationship between
the adoption decisions and cost related factors: crop density, crop diversity, distance
between competing elevators, capacity of elevators, and other variables.
Shuttle adoption is an easier location decision for a group of elevators than an
individual elevator. For individual elevators, adopting shuttle trains definitely is a strategic
decision in terms of a huge investment and new operation mechanisms. However, shuttle
train technology adoption is largely affected by the geographic location, its competitors’
situation, and agronomic situations. Whether an elevator adopts shuttle train technology is
affected by several key factors. First, it is affected by the difference between the payoffs
created by adopting shuttle trains and not adopting shuttle trains. If the revenue received
after the adoption is greater than the investment made to update facility to fit shuttle train
operation and the costs incurred, all elevators would adopt shuttle trains. Second, economy
has a great impact on adoption. In a good harvest or a bad harvest year, or in a booming
year or in a slumping year, the number of elevators that adopt shuttle trains would be very
different. Third, for elevators in an area where agriculture is a main industry, this situation
is obviously different from the non-agriculture area; crop production density and diversity
would directly change the adoption decision; therefore, the adoption strategy would also be
different. Fourth, competitions from other transportation modes can decrease the
incentives of shuttle adoptions; if a barge can be reached in less than 100 miles, it would no
doubt have an impact on shuttle adoption.
Since shuttle train technology is efficient for railroads and elevators in terms of
transportation cost savings, there is a tendency for overexpansion of shuttle train adoptions.
6
However, the higher bidding prices offered by elevators are attractive to farmers and will
definitely increase elevators’ draw areas. If all elevators expand their facilities to adopt
shuttle trains, it will mean that some elevators cannot attract enough farmers to buy grain
and cover the investments for adoption. It means only some elevators should adopt shuttle
trains, not all of them.
The above discussion shows that the shuttle adoption-related issues are geographically
related. So far, studies on shuttle train adoption are scarce and tend to focus on one aspect
of adoption or on the individual elevator basis. Those analyses are more micro-
emphasized, focusing mainly on cost analysis from an individual elevator’s standpoint, or
on effects of policy changes or technology development which need revenue and cost, or
profit comparison, analysis. No study takes all elevators in a large area as a whole picture
to analyze the influential factors of shuttle adoption to accept a 7000-foot train and
load/unload the train.
Objectives
The overall objective of this study was to analyze the key factors of shuttle train
technology adoption from the elevator perspective. Besides the agronomic factors, this
study emphasized more on geographical factors that affect an elevator’s shuttle adoption
decision, such as the effect of distance between the competitive elevators, influences from
shuttle adoption by a competitive elevator, the number of elevators in an area, etc.
Specific objectives were to (1) develop a database of the present situation of shuttle
adoption. The data will be converted to a Geographic Information System (GIS) file,
which contains an elevator’s characteristics and its geographic characteristics, such as zip
code, Fip-code, location, storage capacity, shuttle adoption situation, distance to the
7
competitor elevator, etc. and (2) develop a shuttle adoption decision model capturing
critical components of shuttle adoption on the BNSF, UP, and CP rail lines in the nine
central states in the United States and try to explain their relationships among these factors.
Particular attention was paid to the key factors associated with this technology that affect
the shuttle train technology adoption decision for shipping grain.
Methods and Procedure
Econometric models based on entry and exit theory were used to evaluate shuttle
adoption decisions. The estimation model contains distances among elevators, storage
capacity, crop diversity, yield, and density to evaluate the effects of key factors on shuttle
train adoption from the collected data. GIS software, Arcview 3.2, was used in this study to
manipulate and calculate the data of geographic relationships among elevators and
agricultural information from available data resources, such as distance to the nearest
elevator and port, area of a county, crop production, etc. Estimation of the shuttle adoption
model was conducted by using econometrics to test the reliability of the results given a
certain set of circumstances facing elevators. The shuttle adoption model was selected
based on the Chi-square values and with a maximum likelihood procedure that performs a
correction for collinearity. The limitations of this study and need for further research in
this area were also discussed.
Results
The study provided conclusions and recommendations on the factors that affect the
adoptions of shuttle train technology mainly for grain elevators in the wheat production
belt in the USA based on the empirical model. The study provides a description of these
8
variables considering the geographic situation, agronomic factors, characteristics of
elevators, etc. The research benefits railroads and elevators, then consequently the farmers,
if the shuttle train technology can potentially increase their profits or decrease their costs.
Organization The thesis is organized into six chapters. Chapter 2 provides additional background on
shuttle trains and reviews prior research that is related to this study. Chapter 3 discusses
the theory of competition, market entry and exit, and locations. Chapter 4 presents model
specification, variable definitions, data estimation, and data source analysis. Chapter 5
shows data behavior, empirical results, and the strategies for the adoption of shuttle train
technology for grain shipping and further research in this area. The summary and
conclusions are contained in Chapter 6.
9
CHAPTER 2
REVIEW OF LITERATURE
Introduction
Extensive research has been done on entry and exit in many industries, as well as on the
rail industry. There are a few studies on the elevator industry. However, very few studies
have been done on the elevators’ shuttle train technology adoptions. To better explain the
characteristics of elevators, previous studies pertaining to the empirical industry studies on
entry, exit, and innovation adoption; elevator studies, such as competition, entry, exit, and
agronomic influences; and rail studies on shuttle trains were surveyed. The problems
addressed, findings, and general conclusions in these studies are presented. This review
follows a procedure that begins with a general and broad aspect, and moves to a narrower
aspect that relates to this study. At the end of this chapter, the key points addressed in the
studies are discussed.
Empirical Industry Studies
Entry
Firm level and industrial characteristics are used as explanatory variables by studies on
entry. Some studies have analyzed a specific industry and others a number of industries.
Along with a few others, market share of plant, capital requirements, industry profitability,
industry concentration, industry rate of growth of output, advertising intensity,
diversification, demand growth, capacity utilization, firm innovation, extent of multi-plant
10
operations, and firm success have been found to be the important factors in explaining
entry. The following section is some earlier studies reviewed.
In a study on the determinants of entry in manufacturing industries, Orr (1974)
estimated the relationship between entry and the determinants: industry profit rate, capital
requirements, advertising intensity, research and development intensity, risk, concentration,
and industry rate of growth of output by using a linear regression model. His estimate
result showed that capital requirements, advertising intensity, and concentration are
significant barriers to entry. Industry size consistently has a positive impact on entry.
Research and development intensity, and risk are modest barriers to entry while profit rates
and industry growth rates have a positive, but weak, impact on entry.
After examining the impacts of demand growth, diversification, product promotion, and
concentration on price-cost margins and subsequent rates of entry in manufacturing
industries, Duetsch (1975) concluded four results. First, firms in a perfectly competitive
industry will earn high profits in the short run and thereby attract the entry of new firms to
the industry in the long run. Both entry by new firms and expansion of existing firms may
be encouraged by growth in industry demand. With rapid growth in demand, entry by
outsiders does not require capturing sales from established firms, so this barrier to their
entry will be lower. Second, industry diversification does deter the entry of new firms.
Potential entrants behave as though they expect diversified firms to practice predatory
pricing, and potential entrants are unable to obtain sufficiently detailed information about
industry profitability to warrant entry. Third, industries characterized by intense product
promotion appear to present significantly higher product differentiation barriers to the entry
of new firms. Fourth, concentrated industries are more likely to be entered than
11
unconcentrated industries. New entrants apparently prefer to enter industries in which
there exists some potential for collusive activity, even though their entry will reduce the
potential for subsequent collusive activity.
Spence (1977) argued that entry in an industry selling a relatively homogeneous
product is likely to be affected by the relationship between demand and industrial capacity.
In general, entry can be deterred by investment decisions (one of which is capacity) when
existing firms have enough capacity to make a new entrant unprofitable. This capacity
need not be fully utilized in the absence of entry, which can also result in higher prices and
lower levels of output. Capacity and other forms of investment are effective entry-
deterring variables, partially because they are irreversible in plant and equipment, and they
represent preemptive commitments to the industry.
It is often argued that incumbent firms may deter entry by making the entry investment
before the rival does. However, Judd (1985) showed a different conclusion. He used an
extensive form diagram of the entry-exit game to illustrate that multi-product incumbent
firms may exit in response to entry. Once entrants are in an industry, an incumbent will
often want to withdraw some goods to prevent competition with the entrant from reducing
profits on other goods. Such a reaction makes entry more attractive to a potential entrant.
The equilibrium industry structure is less likely to be monopolistic as the goods are better
substitutes, as exit costs are low, and as the competition between producers of the same
good is more intense. Credible preemption by a multi-product incumbent may be
impossible unless its costs of exit are high. Low exit costs substantially weaken the ability
of an incumbent to keep entrants out of a differentiated industry and result in multiform
12
equilibriums without assuming decreasing returns to scale or diseconomies of scope for
firms.
Karakaya and Stahl (1989) tested six market entry barriers in consumer and industrial
markets after summarizing nineteen different market entry barriers identified in a literature
review: cost advantages of incumbents, product differentiation of incumbents, capital
requirements, customer switching costs, access to distribution channels, and government
policy. They modeled market entry decisions of 137 executives in 49 major U.S.
corporations with a decision-making instrument consisting of 32 market entry
opportunities. Each respondent’s decisions were modeled by regression analysis. From the
findings by Karakaya and Stahl, all six barriers were perceived as important factors to
consider in making market entry decisions in industrial goods markets. These six barriers
influence executive decision makers in the market entry decision. The cost advantages of
the incumbent barrier, the capital requirement barrier, and the product differentiation of the
incumbent barrier are perceived as the most, second, and third critical for all market entry
decisions in the study. The other three barriers, customer switching costs, access to
distribution channels, and government policy barriers, differ in importance depending on
the market entry decision model, such as early or late entry, and consumer or industrial
goods. The limitation of this study is that most of the firms studied are considered to be
large and successful; the results are applicable only to large and successful firms.
Bresnahan and Reiss (1991) proposed an empirical framework for measuring the
effects of entry in concentrated markets. Building on models of entry in atomistically
competitive markets, they showed that the number of producers in an oligopolistic market
varies with changes in demand and market competition. These analytical results structured
13
their empirical analysis of competition in five retail and professional industries. Using data
on geographically isolated monopolies, duopolies, and oligopolies, they studied the
relationship between the number of firms in a market, market size, and competition. Their
empirical results suggested that competitive conduct changes quickly as the number of
incumbents increases. In markets with five or fewer incumbents, almost all variations in
competitive conduct occur with the entry of the second or third firm. Once the market has
between three and five firms, the next entrant has little effect on competitive conduct.
Yip (1982) surveyed managers in markets that experienced entry over the period of
1972-1979. He selected 36 instances of entry from 69, which he judged to be the most
successful: 21 by direct investment and 15 by acquisition. Managers in the industries that
were challenged by these entrants reported that only 29 percent of the entries were viewed
as “serious” threats when they occurred. Only 30 reported that they responded to entry
with price competition, which happened only in the case of direct entry, rather than when
new management takes over an existing firm. In a perfectly contestable market, if demand
and technological conditions do not change, incumbents should not be expected to take
entrants seriously because entry would not be viable.
Lieberman (1987) examined how incumbent firms in chemical product industries
responded to entry by estimating equations specifying investment rates for established
firms and new entrants. He found that entry into industries with relatively high
concentration levels is typically followed by an expansion of capacity by the incumbent
firms. Incumbent firms in concentrated industries do not respond positively to expansion
by other incumbents. On the other hand, incumbents in industries that are relatively not
concentrated do not increase their investment activity in response to new entry. In
14
relatively concentrated industries, incumbent firms invest to retard the rate of growth of
new entrants, but they do not necessarily invest to prevent entry. In an economy with
stationary technology and demand, these observations are inconsistent with the contestable
market.
However, the conclusions from Lieberman’s results depend on the assumption of a
stationary environment. In the case of entry to coincide with advances in technology or
with new information that leads to optimistic expectations of demand growth, both entrants
and incumbent firms would react with greater output so that entrant and incumbent
capacity expansion would be positively correlated (Gilbert, 1989).
Bresnahan and Reiss (1991) measured responses to entry by observations from markets
(primarily services in rural areas) that can support no more than a few firms, which allows
them to isolate the competitive effects of a discrete entry decision. They estimated a
critical market size at which monopoly profits are sufficient to support a single firm by
examining a cross-section of markets. In the same way, they estimated a critical market
size that can just support two firms in the same market. They argued that, if firms are
equally efficient and entry does not result in price cutting, then the size of the market that
supports two firms should be about twice as large as the size of the market that just
supports one firm. However, if entry results in aggressive price competition, then for two
firms to survive, the market would have to be more than twice as large as the market that
can support a single firm. These results suggest that, at least in some markets, established
firms respond aggressively to entry, which lowers the profit an entrant can expect and
should act as a deterrent to potential competitors.
15
Exit
In a study that identifies factors affecting entry, exit, and acquisitions in the U.S. baking
industry, Mattson (2000) stated that the perishable nature of the product also has an effect
on plant location. The distribution of bakery plants in the United States is closely related to
the distribution of population. The baking industry is a local industry rather than a
geographically concentrated industry because the products cannot be shipped great
distances. Large baking firms will often have a number of plants in different geographic
regions.
In declining industry because of shrinking demand, pressures will build up for a firm’s
capacity to be eliminated. Ghemawat and Nalebuff (1985) analyzed who exits first, large
firms or small firms. Survivability is inversely related to size in firms with asymmetric
market shares and identical unit costs: the largest firm is the first to leave and the smallest
firm the last. A small firm can profitably “hang on” longer than a large firm. Sufficiently
strong scale economies can, by conferring cost advantages on larger firms, overturn this
outcome. The required cost advantages for large firms to outlast smaller ones are
substantial. In a duopoly market, a firm that is still active in the market chooses between
continuing to operate at initial capacity level and exiting costlessly and completely from
the market. Entry by other firms is precluded by setup costs. Reentry is not allowed. Even
if reentry is possible, it would not occur in an equilibrium. In an oligopoly market, the last
firm to exit is the one with the longest profitable tenure as a monopolist; the final two firms
are the duopoly pair with the longest profitability, etc.
With samples tracking changes in producer concentration and the coefficient of
variation in firm sizes, Lieberman (1990) used the “shakeout” and “stakeout” models to
16
find out if large or small firms exit first in a declining industry. His conclusion was that
small-share firms exhibited high rates of exit and that small-scale plants are most likely to
close even though the exit sequence is presumably affected by numerous factors in addition
to the size-related determinants.
Market Size
In an attempt to introduce a new measure of geographic market size based on the
Census of Transportation, Weiss (1972) concluded that distance-shipped measures of
market size are less likely to be misleading and more easily interpreted than output-
dispersion measures of market size, especially for industries in which a significant amount
of cross-hauling occurs. The advantages that distance-shipped measures of market size
have in properly accounting for cross-hauling make them clearly preferable on conceptual
grounds for use in empirical work concerning the effects of geographic market size.
Distance shipped and dispersion measure different things. Distance shipped seems to
measure market size more directly and is more closely related to transport cost proxy. The
significant and expected relationship of distance shipped with transport cost, dispersion,
and margin in spite of the crude indexes used provides some basis for believing that
distance shipped is reasonably measured. This measure can be used in judging the viability
of new plant locations and in determining the geographic area within which concentration
should be measured.
In a case study of the newsprint industry in North America, Booth et al. (1991)
estimated a dynamic empirical model of regional capacity expansion and pricing in an
oligopoly. The model shows that capacity expansion is negatively related to the
concentration ratio, suggesting that higher concentration levels reduce the levels of
17
capacity expansion, which indicates some weak coordination. Firms appeared to adjust
capacity to meet changing demand conditions while tacitly coordinating their expansion to
share the market.
Innovation
Case (1992) formulated a model that a farmer’s expected profit from adopting a new
technology is a function of the own characteristics and the neighbor’s expected profits.
Since the neighbor’s expected profits are a function of their characteristics, any farmer’s
expected profit becomes a function of his/her own and his/her neighbors’ characteristics.
The farmer will adopt the new technology if the expected profit is positive. Case’s model
is strictly cross section; that is, a farmer examines his/her own characteristics and those of
his/her immediate neighbors and, based on this information, decides whether to adopt the
technology. However, the diffusion of a technological innovation is a dynamic process: the
innovation is first adopted by a few individuals and gradually spreads to the remainder of
the population.
Dubin (1995) presented a dynamic logit model of diffusion to capture whether a firm
will adopt innovation in a time period. He discussed the model and presented the
derivatives necessary for maximizing the likelihood function and for construction of the
information matrix. His model shows that a firm’s unobserved utility (expected profit)
from adopting an innovation is a function of its own characteristics plus its distance from
previous adopters. In his model, each element represents the influence firm j has on firm i.
Dubin simulated spatially autocorrelated data and used them to estimate the model. He
simulated the data in a two-stage process. In the first stage, he simulated standard Logit
observations by using two independent variables and a constant term. In the second stage,
18
he allowed non-adopters to be influenced by their proximity to the stage-one adopters
through the influence function.
Elevator Studies
There are a number of studies on elevators. Most of these studies use agronomic and
elevator characteristics as explanatory variables.
Entry and Exit
Since the widespread introduction of multi-car, shuttle train, and trainload fares on
grain, the U.S. grain elevator system has consolidated into a smaller number of high
capacity train-loading facilities. Some elevators are abandoned, and some are merged. For
example, from August 1, 2000, to July 31, 2001, nine grain elevators in North Dakota
closed, from 443 to 434, and the average capacity per elevator increased from 569,500
bushels to 578,500 bushels. The number of elevators is the lowest since record-keeping
began; North Dakota had 2,031 elevators in 1915. A new shipping fee structure will result
in the closure of even more local elevators (Johnson, 2001).
Vachal et al. (1997) used a log-odds model to estimate the survival of elevators as a
function of explanatory variables. Variables tested in the model included storage capacity
of elevator, number of extra services provided, bushels of grain handled, average bushels of
grain handled by the elevators in the county, concentration index for the diversity of grains
handled by elevator, proportion of the elevator’s grain shipped by truck, and dummy
variable for elevators owned by large grain companies. Their results showed that the first
four variables have positive signs and the other three have negative signs, which means that
the first four increase survival time and the other three decrease survival time of elevators.
19
They found, in North Dakota alone, the number of licensed grain elevators has declined
by nearly 18 percent, and the average storage capacity has more than doubled since 1979.
In their analysis, the time from start to closing for an elevator depends, among other
factors, (1) positively on elevator storage capacity because of its expected negative impact
on grain handling costs. Elevators realize economies of size in grain handling and volume
discounts through large shipments; larger capacity elevators are expected to realize lower
per bushel costs and, thus, have a longer survival time; (2) positively on bushels of grain
handled by the elevator due to economies off utilization realized by grain elevators. The
greater the turnover experienced by a given grain elevator, the lower the per unit costs; and
(3) negatively on the concentration index for the diversity of grains handled because the
wider the range of products and services provided by a particular grain elevator, the less
susceptible the elevator will be to the reductions in the demand for a particular type of
grain or service.
Other Studies
Most studies in the agricultural field take yield as an important variable if there are
agronomic variables included in the studies. Most of the time, agronomic factors are
indivisible from elevator studies. Yield, crop production, crop concentration, production
density, area, etc. are the important ones among these factors.
The Herfindahl Index is a measure of concentration that has been utilized in the
industrial organization literature to assess concentration within an industry. This measure
takes into account both the size and distribution of market shares by firms. The Herfindahl
Index score is calculated as follows:
Herfindahl Index = [ ∑(msi)2 ],
20
where msi is the market share for firm i.
The reciprocal of the Herfindahl Index is referred to as the numbers-equivalent of
firms. If one believes that the relative size of the largest firms is an important determinant
of conduct and performance, as economic theory suggests, then the Herfindahl Index is
likely to be more informative (Besanko et al., 2000).
Dahl and Wilson (1997) introduced this index into the measure of concentration to the
shares of planted acres for hard red spring wheat and durum varieties when they studied the
factors affecting the supply of spring wheat. Large values for the index score indicate
greater concentration in a few dominant varieties. They found that, with the Herfindahl
Index, all states show a decline in the concentration variety shares of hard red spring wheat
planted acres. Durum exhibits a similar pattern as hard red spring wheat. Their result
shows that the Herfindahl Index does have implications for the supply of quality hard
wheat and that the Herfindahl Index is capturing a different aspect of crop variety
concentration.
In the study of the North American grain marketing system for prairie transportation
and elevators, Vachal et al. (1997) stated that the average straight-line distance from a rail
line to the nearest barge-loading facility has a negative and significant effect on the rail
lines abandonment. The shorter the distance, the greater the effect: the competition from
barge increases rail line abandonment.
21
Rail Studies on Unit/Shuttle Trains
Studies on shuttle trains are very few compared with other studies, such as entry, exit,
or general rail transportation studies. Studies on shuttle trains mainly focus on policy or
macro economical impacts of shuttle train technology.
Vachal (2001) did an analysis on the potential impact of shuttle train shipments on
North Dakota’s local grain industry. In this case study, sensitivity analysis was employed.
A 10 to 15 million bushels handle volume is required for a feasible shuttle operation. Two
percent of the elevators in that study may have up to 32 percent of the average annual
production of wheat, barley, and corn. This market share of production translates to an
average 16.5 million bushels per facility. This potential concentration of bushels has
implications for roads, short-line railroads, bridge infrastructure, processors, communities,
and elevator industry. This bushel requirement also shows that redistribution of bushels in
the elevator industry seems imminent.
This case study shows that rail rates have a substantial effect on the reach of an
elevator’s draw area. When the shuttle rate is replaced with an applicable unit train rate,
draw areas contract. The largest and smallest percentage decreases in acreage are 73 and
23 percent, respectively, in some areas. Shuttle rates provide elevators with an opportunity
to penetrate a new grain draw territory. The additional territory attributed to shuttle train
rates is estimated to be 18 to 76 percent farther than the most distant market available to the
elevator when it utilizes unit train rates, depending on commodity and location. When unit
train rates are applied for estimating draw area volumes, it appears that shuttle train rates
increase an elevator’s draw area by approximately 50 percent.
22
In a study on North Dakota agricultural sector freight analysis, Berwick et al. (2001)
found that the draw area for the shuttle facilities is estimated to be a 60-mile radius. Where
shuttle facilities are built, truck traffic over local and state roads in the draw area would
significantly increase. Shuttle rail rates are, in today’s grain industry, the railroad’s most
competitive rate. Shuttle rates are available to shippers equipped to meet specific volume,
transaction, and operational commitments.
Summary
Both firm level characteristics and industrial characteristics are used as explanatory
variables in studies on entry. The determinants of entry include industry profit rate, capital
requirements, advertising intensity, research and development intensity, risk, concentration,
and industry rate of growth of output (Orr, 1974). In an industry which is selling a
relatively homogeneous product, entry is likely to be affected by the relation between
demand and industry capacity (Spence, 1977).
Entry can be effectively deterred by investment (capacity) decisions when existing
firms have enough capacity to make a new entrant unprofitable. Industry diversification
and industries characterized by intense product promotion deter the entry of new firms. In
addition, concentrated industries are more likely to be entered than are unconcentrated
industries (Duetsch, 1975). Important entry barriers also include cost advantages of
incumbents, product differentiation of incumbents, capital requirements, customer
switching costs, access to distribution channels, and government policy (Karakaya and
Stahl, 1989).
23
An incumbent will often want to withdraw some goods to prevent competition with the
entrant from reducing profits on other goods. The equilibrium industry structure is less
likely to be monopolistic as the goods are better substitutes, as exit costs are low, and as the
competition between producers of the same product is more intense (Judd, 1985).
In declining industries, survivability is inversely related to size for firms with
asymmetric market shares and identical unit costs: the largest firm is the first to leave and
the smallest firm the last. By conferring cost advantages on larger firms, sufficiently strong
scale economies can overturn this outcome. In a duopoly market, a firm that is still active
in the market chooses between continuing to operate at initial capacity level, and exiting
costlessly and completely from the market (Ghemawat and Nalebuff, 1985).
Expected profit from adopting a new technology is a function of the own characteristics
of a firm and the neighbor’s characteristics. The diffusion of a technological innovation is
a dynamic process: the innovation is first adopted by a few individuals and gradually
spreads to the remainder of the population (Case, 1992). The expected profit from
adopting an innovation is also a function of its own characteristics plus its distance from
previous adopters (Dubin, 1995).
The U.S. grain elevator system has consolidated into a smaller number of high capacity
train-loading facilities; some elevators are abandoned, and some are merged. The shipping
fee structure will result in the closure of even more local elevators (Johnson, 2001). The
survival time for an elevator is positively affected by storage capacity, number of extra
services provided, bushels of grain handled, and average bushels of grain handled by the
elevators in the country; and negatively affected by concentration index for the diversity of
24
grains handled, proportion of the elevator’s grain shipped by truck, and a dummy variable
for elevators owned by large grain companies (Vachal et al., 1997).
Size or storage capacity is always an important factor in any elevator study.
Capacity expansion was negatively related to the concentration ratio, and higher
concentration levels lead to reduce levels of capacity expansion, indicating some weak
coordination (Booth et al., 1991).
A volume of 10 to 15 million bushels handled is required for a feasible shuttle
operation. A small percentage of the elevators take up to a large percentage of the average
annual production of crops right now. Rail rates have a substantial effect on the reach of an
elevator’s draw area. When shuttle rates are replaced with applicable unit train rates, draw
areas contract. The additional territory attributed to shuttle train rates is 18 to 76 percent
farther than the most distant market available to the elevator when it utilizes unit train rates.
When unit train rates are applied for estimating draw area volumes, shuttle train rates
increase an elevator’s draw area by approximately 50 percent (Vachal, 2001).
Distance shipped seems to measure market size more directly, and is more closely
related to the transport cost proxy in judging the viability of new plant locations and in
determining the geographic area within which concentration should be measured (Weiss,
1972).
Yield, crop productions, crop concentration, production density, area, etc. are the
important factors among those agronomic ones. The Herfindahl Index is a measure of
concentration that has been utilized in the industrial organization literature to assess
concentration within an industry. Dahl and Wilson (1997) introduced this index to measure
25
the concentration to the shares of planted acres for hard red spring wheat and durum
varieties, which is capturing a different aspect of crop variety concentration.
26
CHAPTER 3
COMPETITION THEORY IN AN OLIGOPOLISTIC MARKET
Introduction This chapter focuses on the relevant theoretical concepts that pertain to explaining and
analyzing competition in an oligopolistic market in this study. Theories of market structure
and competition strategies used by firms in an oligopolistic market were explored. The
theoretical foundations and their applications and characteristics were discussed
respectively in the different sections.
Market Structure
Because of the geographical conditions and their own characteristics, elevators do not
have monopoly powers, the ability to act in an unconstrained way. The elevator market is a
market that has only a few service providers in an area, or a market, which are 8.7, on
average, in a county in this study. In general, an elevator competes mainly with the
elevators surrounding it, especially the nearest neighboring elevator. It is more reasonable
to expect that the competition strategies of any one elevator will affect market price and
every other elevator’s production levels: some elevators attract farmers from competitors.
It is a market in which the actions of individual elevators materially affect the market price
level, oligopoly. To identify the structure of the elevator market and the strategic
interactions, one needs to identify the products and characteristics of this market.
Products or Services
Product or service is one of the most important factors in the analysis of market and
strategic interaction. To identify competitors, one also needs to name products (services)
27
substituted for its own. In general, two products, X and Y, are substitutes if, when the price
of X increases and the price of Y stays the same, purchases of X fall and purchases of Y
rise. When firms are direct competitors, the strategic choices of one directly affect the
performance of the other. This situation would be the case for the elevator industry.
Services (products) among elevators are homogeneous and close substitutes because
(1) they have the close service performance characteristics: purchase grains from farmers,
store them in warehouses, unload from and load to vehicles, and provide information to
farmers. The services are almost the same among elevators; (2) they have the same or
similar occasions for use. All elevators provide their services in the same seasons as other
elevators; their customers are similar, farmers; (3) the services are offered in the same or
close geographical market. From the data in this study, there are eight elevators in a
county, on average. The main differences among them are the locations or the distances to
the farmers’ barns. Elzinga and Hogarty (1978) claimed that a geographical market and the
competitors within it are properly identified if two conditions are satisfied: the firms in that
market draw most of their customers from that area and the customers residing in that area
make most of their purchases from sellers in that market.
In general, two products are in different geographic markets if (Besanko et al., 2000)
(1) they are sold in different locations, (2) it is costly to transport the goods, and (3) it is
costly for consumers to travel to buy the goods. For elevator services, before an elevator
adopts shuttle trains in a county, every elevator has its own geographic market. It costs
more to ship crops to an elevator that is farther away than a closer elevator. Therefore,
elevators only compete in the marginal area where farmers are indifferent to which elevator
they would sell their crops. However, once one elevator has adopted shuttle trains, this
28
balance is lost. The discount rates received by the elevator from the railroad gives the
elevator the ability to mark up its board price offered to farmers, which leads to a re-
dividing of the geographical market. Those areas that are not in the marginal area before
will become the marginal market after one elevator adopts shuttle trains. In other words,
the services of two elevators are substitutes to each other now in those areas.
Bulow et al. (1985) first introduced the terms of strategic complements and strategic
substitutes. If one firm cuts its price of one product and causes its direct competitor to cut
the price, the two firms’ prices are strategic complements. If one firm increases its
production quantity or capacity, and causes its direct competitor to decreases its production
quantity or capacity, then the two firms’ production volumes are strategic substitutes.
Prices are usually strategic complements, whereas quantity and capacity decisions are
usually strategic substitutes. The reason why the concept here is important is that it tells us
how a firm expects its rival to react to its own decisions. When two firms’ prices are
strategic complements, one firm’s aggressive behavior leads its competitors to behave more
aggressively as well. When actions are strategic substitutes, aggressive behavior by a firm
leads its rival to behave less aggressively.
In the elevator market, the two situations discussed above happen at the same time if
one elevator adopts the shuttle train. The increase in board prices by elevator i will force
competitor j to increase board prices. The increase in capacity by elevator i will also put
pressure on its competitor, j. Since elevators’ prices are strategic complements, one
elevator’s aggressive behavior, adopting shuttle trains, will lead its competitors to behave
more aggressively.
29
Location and Distance
Numerous researchers have focused on how geographical characteristics and market
situations affect competition results or decision choices. The characteristics of materials
and products, and the distances over which they must travel are two important factors.
Several location theories are presented as follows.
Von Thunen’s Theory. Von Thunen’s theory was advanced in the early 1800s. Its
primary interest was the allocation of agricultural land. Under his theory, a location
decision is determined by differences in land rents that can be offered by competing
producers of various products. His model assumes that the products are transported to a
single city in the center of the plain for consumption and used the example of a plain
consisting of wheat and potato producers. Wheat is assumed to be more costly to produce,
but cheaper to transport. At distances close to the city, potatoes are cheaper to produce. As
the distance from the city increases, potatoes become more costly to produce than wheat
because of the higher transport costs. Potato producers are, therefore, willing to outbid
wheat producers for land close to the city. Likewise, wheat producers are willing to pay
more for land farther from the city. Differences in production and transportation costs
between the two products determine where each will be produced. Von Thunen’s theory
predicts a pattern of land use characterized by a series of concentric rings, each devoted to
the type of production that can offer the highest land rent. His theories were intended for
agricultural land use but can be applied to urban economics in a modern industrial society
(Greehut et al., 1987).
Weber’s Theory. Alfred Weber, mainly interested in industrial location choice,
advanced his theories in the early 1900s. His purpose was to find the location that
30
minimized total costs. Weber was interested in why industries locate in broad geographic
regions and why they select one city or zone over another. Weber defined primary factors
as those that determine a choice of broad geographic region. Two primary factors were
identified: transportation costs and interregional variations in labor costs. Weber defined
secondary factors as those that determine the distribution of industries within regions.
These factors included the potential for economies of scale and agglomeration. Weber
hypothesized that a firm locates to minimize the sum of transportation costs for inputs and
the transportation costs for the finished product (Greenhut et al., 1987).
Locational-interdependence Theory. A limitation of Weber’s theory is its treatment
of competition (Greenhut et al., 1987). It assumed that each firm takes the locations of its
competitors as given or acts as a monopolist. Locational-interdependence theories relax
this assumption and consider the spatial implications of noncollusive oligopoly. Hotelling
made the first major contribution to this theory. He analyzed the pattern of location for two
sellers of homogenous product when buyers of that product are evenly distributed over a
bounded linear market: the two sellers would locate next to each other at the center of the
market (Grennhut et al., 1987).
Some researchers have estimated models of spatial dependence with continuous
random variables. However, relatively little work has been done on incorporating spatial
dependencies into models with qualitative dependent variables (Dubin, 1995). There are a
number of applications for these types of models. For instance, since neighborhood quality
affects rents and since neighborhood quality is, in part, affected by maintenance decisions,
a landlord’s decision to invest in maintenance might be affected by prior maintenance
expenditures of nearby landlords. Whether a county grows in population might depend, in
31
part, on its proximity to fast growing counties. Whether a firm adopts a technological
innovation might depend on its proximity to adopters. These applications will help to
illustrate the model used in this study.
Strategic Interactions
Entry and Exit: Conditions and Barriers
Entry is pervasive in many industries and may take many forms. An entrant may be a
new firm that did not exist before it entered a market, may be a firm diversifying its
product line, or may be a firm diversifying geographically: the firm sells the same product
in other geographic markets (Besanko et al., 2000).
Different studies make different assumptions about what new competitors expect to
happen if they enter a market. A profit-maximizing, risk-neutral firm should enter a market
if the sunk costs of entry are less than the net present value of expected post-entry profits.
Post-entry profits will vary according to demand and cost conditions, as well as the nature
of post-entry competition. If the potential entrant expects post-entry competition to be
fierce, then it is more likely to stay out. Even when the potential entrant believes that post-
entry competition will be relatively mild, it may not enter if there are significant barriers to
entry.
There are three entry conditions (Bain, 1956):
(1). Blockaded entry. Incumbent needs to do nothing to deter entry. For example,
production in an industry requires large fixed investments; the entrant may expect post-
entry profits to be low; or it sells an undifferentiated product and expects price competition
to be fierce.
32
(2). Accommodated entry. Structural entry barriers are low, and either
(a) entry-deterring strategies will be ineffective or (b) the cost to the incumbent of trying to
deter entry exceeds the benefits it could gain from keeping the entrant out. It is typical in
markets with growing demand or rapid technological improvements. Entry is then so
attractive that the incumbents should not waste resources trying to prevent it.
(3). Deterred entry: If the entry is not blockaded, (a) the incumbent can keep the entrant
out by employing an entry-deterring strategy and (b) the cost of the entry-deterring strategy
is more than offset by the additional profits that the incumbent will enjoy in the less
competitive environment.
The elevator industry is under an accommodated entry condition because its structural
entry barriers are low: entry-deterring strategies are ineffective, and the cost to the adopter
of trying to deter entry, adopting, exceeds the benefits it could gain from keeping others
out. Any effort to prevent others is useless.
An incumbent firm should analyze the entry conditions in its market and choose
an entry-deterring strategy based on these conditions. If entry is blockaded or
accommodated, the firm need do nothing more to deter entry. If entry is deterred, the firm
should engage in a predatory act.
Besanko et al. (2000) described three main types of structural entry barriers: (1) control
of essential resources: an incumbent is protected from entry if it controls a resource
necessary for production; (2) economies of scale and scope: when economies of scale are
significant, established firms operating at or beyond the minimum efficient scale will have
a substantial cost advantage over smaller entrants; and (3) marketing advantages of
33
incumbency: a firm sells different products under the same brand name, also known as
umbrella branding.
At the most general level, entry-deterring strategies will succeed only if two conditions
are met: (1) the incumbent earns higher profits as a monopolist than it does as a duopolist
and (2) the strategy changes entrants’ expectations about the nature of post-entry
competition. Assuming that the incumbent monopolist’s market is not perfectly
contestable, it may expect to reap additional profits if it can keep out entrants. Besanko et
al. (2000) discussed three ways in which it might do so.
The first way is limit pricing, the practice whereby an incumbent firm can discourage
entry by charging a low price before entry occurs. The entrant, observing the low price set
by the incumbent, infers that the post-entry price would be just as low or even lower, and
entry into the market would, therefore, be unprofitable. Once entry occurs, it would make
no sense for the incumbent to continue to suppress price. The lost profit opportunities from
having previously set the limit price are sunk. Now that the entrant is already in the
market, the incumbent should acquiesce and maximize future profits.
The second way is predatory pricing, the practice of setting a low price in order to drive
other firms out of business. The difference between predatory pricing and limit pricing is
that limit pricing is directed at firms that have not yet entered the market, whereas
predatory pricing is aimed at firms that have already entered. The predatory firm sets its
price below cost with the expectation that it will recover whatever losses it incurs after
entrants or competitors have been driven from the market, and it can exercise market
power.
34
The third way is capacity expansion, the practice of carrying excess capacity. By
holding excess capacity, an incumbent may affect how potential entrants view post-entry
competition and, thereby, blockade entry. An incumbent that holds excess capacity may
signal its intention to cut prices if entry occurs. Such a signal can be effective if the entrant
is uncertain about the incumbent’s intentions and it believes that aggressive incumbents are
more likely to hold excess capacity than are accommodating incumbents.
In the elevator industry, the first strategy does not seem to be used. The ability for an
elevator to make up its board prices is because of its shuttle operation, which leads it to get
discounted rates from the railroad. It does not need to raise its price by lowering its profits.
In addition, its higher board prices do not discourage its competitor to adopt shuttle trains.
Instead, it encourages its competitor because these prices are from the railroad, not from
itself. The second deterring strategy, predatory pricing, automatically takes effect once an
elevator adopts shuttle train technology. The difference here is that a shuttle adopter sets
its board price higher above cost because of the discount from the railroad. The third
deterring strategy is not effective in the elevator market, which will be discussed in the next
section.
The number of producers in an oligopolistic market varies with change in demand and
market competition using data on geographically isolated monopolies, duopolies, and
oligopolies; competitive conduct changes quickly as the number of incumbents increase.
In markets with five or fewer incumbents, almost all variation in competitive conduct
occurs with the entry of the second or third firm. Once the market has between three and
five firms, the next entrant has little effect on competitive conduct (Bresnahan and Reiss,
1991).
35
In a study that examines the strengths and limitations of the alternative theories on the
role of potential competition in industrial organization, Gilbert (1989) stated that strategic
behavior to deter entry is an activity that intentionally compromises productive efficiency
to protect an established market. Established firms strategically choose products, locations,
outputs, advertising, R & D, or other competitive actions that are motivated primarily by
their consequences for entry, rather than by efficiency considerations. An enormous
economic literature has examined the theoretical scope for strategic entry deterrence.
However, the evidence is not a reliable index of how often strategic entry deterrence is
attempted or how often it works.
The empirical literature gives mixed signals on the importance of strategic entry
deterrence. Gilbert and Lieberman (1987) found that firms in concentrated chemical
product industries could preempt that expansion of rival established firms by investing in
new capacity, but it was not possible, from the available data, to discern whether such
behavior is intentional or profitable. Moreover, Lieberman (1987) did not find evidence to
support preemptive capacity expansion designed to deter new entry. A possible
explanation for Lieberman’s results is the difficulty of committing to entry-deterring
investment. Gilbert (1986) found that the technological characteristics of most industries
are such that a single established firm could not commit to a production level that
prevented entry, even if it had the desire to do so. For most industries, the fact that some
costs are sunk is not sufficient for a single firm to maintain observed levels of output,
which is a necessary condition to deter entry.
Exit is the reverse of entry, the withdrawal of a product or service from a market, either
by a firm that shuts down completely or by a firm that continues to operate in other
36
markets. A risk-neutral, profit-maximizing firm will exit if the value of its asset in its best
alternative use exceeds the present value from staying in the market. However, exit
barriers can limit the incentives for the firm to stop producing even when the prevailing
conditions are such that the firm; had it known with certainty that these conditions would
prevail, would not have entered in the first place (Besanko et al., 2000).
Exit barriers commonly arise when firms have obligations that they must meet whether
they cease operations, such as labor agreements, and commitments to purchase raw
materials or to input suppliers. Relationship-specific productive assets will have low resale
value and are, thus, a second exit barrier. Government restrictions are often a third exit
barrier.
In the elevator market, the second barrier is the main barrier for an elevator to exit since
most elevators focus on very limited service area besides their storage and buying/selling
functions; their assets have very limited values for other usages. The huge sunk costs are
not refundable.
Capacity and Price Competition (Cournot and Bertrand Models)
Models of oligopolistic competition that used game theoretic principles date back to
Cournot (1838) and Bertrand (1883). Cournot and Bertrand emphasized the dependence of
the equilibrium on the firms’ expectations. In the Cournot model, competitors conjecture
the quantities supplied by the other firms; that is, they assume that other firms will do
whatever is necessary to sell the conjectured quantities. The resulting Nash equilibrium in
quantity strategies is called “Cournot equilibrium.” In the Bertrand model, firms
conjecture the prices of the other firms; that is, they assume that other firms will sell at
37
certain prices regardless of how much they sell. The resulting Nash equilibrium in price
strategies is called the “Bertrand equilibrium.” The two models make different
predictions.
The economics literature has produced many models of how firms should and do
behave in oligopolistic markets. A central element of many models is also the careful
consideration of how firms respond to each other and to opportunities in the market. Two
of the most important oligopoly competitions are quantity competition and price
competition.
For the quantity competition in an elevator market, any increase in board prices by one
elevator will attract a larger draw area and take customers away from the neighboring
elevators. The intuition behind this assumption is that crop production is a factor that is not
affected by the elevators’ competition. If one elevator increases its board prices, it can
only attract farmers from its competitors, but it cannot increase total market demand -- the
total crop production volume. Even if all elevators increase their board prices at the same
time, in the short run, the total amount of crops they could buy from farmers is still the
same, and they are not able to create new market demand. Therefore, quantity competition,
increasing total purchasing amount results in decreasing board prices, is not the nature of
competition in the elevator market as a whole. It will not be discussed in detail here.
In the elevator market, there are only a few players. In this type of “small number”
situations, a key part of making strategic decisions, investment in new facilities, pricing,
and so forth is anticipating how rivals may react.
For the price competition in the elevator market, Bertrand’s model (Bertrand, 1883)
applies. Each elevator selects a price and stands ready to meet all the market demands
38
(purchases from farmers) for its service at that price. Each elevator selects a price to
maximize its own profits, given the price that it believes the other firm will select. Each
elevator also believes that its pricing practices will not greatly affect the pricing of its rival.
In Bertrand’s model, rivalry between two elevators is enough to achieve the perfect
competitive outcome, and price competition is particularly intense in this setting because
the elevators’ services are substitutes. Since elevators must make upfront investments in
the facility to adopt shuttle trains, competition can be unstable. After an elevator adopts
shuttles, it can increase its market share dramatically. The additional territory that
producers would travel for the higher elevator board prices are 3, 20, and 24 miles,
respectively, if the board prices are increased by 2, 5, and 10 cents per bushel, assuming
that the only difference is in board piece and no consumer loyalty (Novak and Schlecht,
2000).
For many firms that reduce prices, customer switching represents the biggest source of
sales gain (Besanko et al., 2000). Customers are more willing to switch from one seller to
another when the product is homogeneous, that is, the characteristics of the product do not
vary across sellers. When products are homogeneous, customers will switch from one
seller to another to obtain a better price. Elevators’ services are clearly homogeneous in
general. This situation intensifies price competition because elevators that increase board
prices can expect large increases in purchases. When an elevator increases its board prices,
it expects to increase its business (buying more from farmers). In this market with
substitutive services, the sales increase for an elevator may come from these sources:
purchases from farmers who were not planning to sell to the elevator or its competitors, or
39
purchases from farmers who were planning to sell to a competitor elevator but switched to
take advantage of the high price.
This result forces its competitor to take action: exit the market because a large part of
its customers moves away to its competitor, or get the lost market share back from its
competitor and from other neighboring elevators by increasing its board price. The second
solution can only be reached by adopting shuttle trains in order to get discount rates from
railroads. If not, it is impossible for this elevator to compete for long because it cannot get
any additional revenue from this market to cover the increase in board prices.
Game Theory and Strategies Interaction
A predominant industry structure in the real world is oligopoly, competition among a
few firms. Such “close” competition engenders in each firm a concern for the actions of its
competitors. This situation is in sharp contrast to perfect competition in the economies
where the number of firms is so large (relative to the market) that each firm can ignore the
others; any firm’s actions are “too small” to affect the others. Non-cooperative game
theory is a natural vehicle for modeling oligopolistic competition. The firms become the
players, and the competitive situation being modeled provides the rules of the game
(Friedman, 1977).
Games that have a finite number of strategies always have an equilibrium (Nash, 1950).
Sometimes, a pure strategy equilibrium will not exist in a finite game, but there will be a
mixed strategy equilibrium. In the language of economics, the equilibrium is inefficient for
the two firms; there is an alternative assignment of strategies that will make both firms
better. However, this outcome is not a general feature of non-cooperative games.
40
Nash equilibrium is a central concept of the non-cooperative game theory. It is a
prediction about how rational and intelligent firms will compete. An equilibrium is a list of
strategies (pure or mixed), one for each firm, with the property that no firm would like
unilaterally to change its strategy. In other words, for each firm, its strategy in the
equilibrium is the best response to the others’ strategies in the equilibrium, where the
“goodness” of a strategy is determined by the firm’s utility function. Equilibrium is
defined in terms of strategies, not moves, and strategies are played simultaneously in any
game. Dynamics are not part of the definition of an equilibrium (Moorthy, 1985).
From the above discussion, an oligoplistic market’s characteristics and game theory can
explain the competition behaviors of elevators. The game of competition in shuttle
adoption is more of a game of complete information in which the rules of the game are
common knowledge among the elevators other than a game of incomplete information.
Every elevator knows the rules, and every elevator knows that every other elevator knows
the rules. The reason is that it includes a complete description of the game (Moorthy,
1985): (1) the number of elevators, (2) their feasible sets of actions at every juncture in the
game, (3) their utilities (profit) from the shuttle adoption, (4) the sequence of moves, and
(5) the structure of information about adoption (the structure of information about shuttle
trains is common knowledge among elevators).
When only one elevator adopts shuttle trains in an area, it can reap a fruitful benefit by
taking its competitors’ market shares. If its competitors fight back, in the end, no one can
gain any positive results from this game. The following example can illustrate this
procedure.
41
Assume that there are several elevators, a, b, c, d, e, f, g, h, i, j, k, and l, in a county and
every elevator occupies a square area as shown in Figure 3.1. If elevator i adopts shuttle
trains as a first mover, its draw area will increase 50 percent (18 to 73 percent increases;
Vachal, 2001) from the original area, i, to the single-dashed line square. This increase
comes from its neighbors, b, e, g, j, a, c, f, and k, respectively, given all other situations are
equal.
Shuttle adoption by elevator i is a crucial strategic movement to its rival elevators
because the adopter will increase its board prices, which will invite a price war. This board
price increase can be compensated by the discount from the railroad. Elevator i views price
competition as a competitive process to get a larger market share. However, this price
increase attacks its competitors. The adopter will inevitably increase its sales volume, or
market share, from its expanded draw areas. This action makes it a more aggressive
competitor, which asserts dominance and forces its rivals to back off. With obvious price
differences, non-adopters do not have the ability to compete with the adopter, given other
conditions unchanged. A shuttle adopter’s movement makes being aggressive an
undesirable strategy for its neighboring elevators. Its adoption action will not generate the
desired response from its competitors because this adoption is not credible for other
elevators. The first mover expands its handling capacity in the hope that other elevators
will then abandon their own decisions to adopt shuttles.
When several elevators are competing in the same area, the elevator that adopts
shuttle trains can gain significant early mover advantage. It may benefit from the effect
that its service has on farmers’ perceptions by being the first to adopt the shuttle trains.
Another advantage is that it creates a structural entry barrier to its competitor: economies of
a b c d
e i j h
f g k i
Figure 3.1. The Draw Area Changes Before and After Adoption.The solid line is the areas before any elevator adopts shuttle trains;the single-dashed line is the area where elevator i adopts shuttle trains;and the double-dashed line is the area where elevator j adopts shuttle trains.
43
scale and scope. As discussed above, the market potential for an elevator is relatively
certain and does not change dramatically over the years. When economies of scale are
significant, this first adopter could gain a larger market share and have a cost advantage.
All these factors put the direct competitors of the first shuttle adopter in a difficult
situation. The dedicated productive assets have a low resale value and are a big exit barrier
for non-adopters, which is the most important reason that makes a non-adopter elevator
unwilling to accept the fact that let its competitor elevator moves first (adopts shuttle
trains) and does not follow the competitor (or even exits this market). The sunk costs, the
costs associated with these investments, arise because an elevator that has already
committed to a particular business has invested in resources and organizational capabilities
that are likely to be specific to that business and are less valuable if that elevator switches
to another business, which means even bigger losses.
One of the neighbors of elevator i, elevator j, takes a tit for tat reaction, adopting shuttle
trains to get the lost market back from i. After j adopts shuttle trains, its draw area will
reach the double-dashed line square area. However, for the area neighboring with i, j can
only return to its original boarder line. The result is that elevator j’s adoption pulls the first
adopter’s, (elevator i) draw area down as itself now. If all elevators surrounding i and j
follow the same movement, in the end, every elevator will return to the same draw area as
before it adopted shuttle trains. The consequence of this game procedure can be simplified
as a two-firm competition game as shown in Figure 3.2. Assume that before shuttle
adoption, each elevator’s payoff is 100 dollars. If one elevator adopts shuttle trains and
increases its draw increase by 50 percent, as discussed earlier, which all comes from its
competitor, its competitor’s payoff decreases by 50 dollars. Its payoff increases by 30
πi πj j doesn’t adopt 100 100
50 130
i doesn't adopt j adopts
80 80 j adopts i adopts 130 50
j doesn’t adopt Figure 3.2. Sequential Game Between the Two Closest Elevators.
j
j
i
45
dollars after considering the costs for adoption, assuming this cost is 20 dollars.
As illustrated by the game tree in Figure 3.2, if both elevators adopt shuttle trains, they
will both get into a worse situation than before. The only good outcome for one elevator is
that it adopts shuttle trains and the competitor does not follow. If all elevators in the same
area follow the same action and adopt shuttle trains, every elevator will return to its
original situation, or the same draw area as before. This result cannot happen since some
small elevators do not have enough capacity or turnover to meet the railroad’s requirement.
The above strategic interaction is a game of “one moves after another,” which is mostly
seen in today’s elevator market. However, there is also another type of game, “two
elevators with each confronting an expansion decision at the same time, or one moves
simultaneously with another.” From the data set used in this study, there are such
situations that two elevators at the same area made adoption movements at the same time.
When two elevators adopt shuttles at the same time, the final outcome is the same as
sequential adoptions. The difference is that, in a sequential game, the first mover can reap
all the benefits, payoff of 130 dollars, until a follower adopts shuttles and an equilibrium is
reached, payoff of 80 dollars for both elevators (Figure 3.2). In a simultaneous adoption
game, once adoption is finished, the two elevators’ payoffs are 80 dollars (Figure 3.3).
The competition game illustrates this basic dilemma of competition, which is why this
game is called a “prisoners’ dilemma game.” In the absence of any means to agree
credibly to set prices at the collusive level, competition leads inexorably to a less desirable
outcome for both firms (Moorthy, 1985).
In the elevator industry, each player recognizes that its actions affect the degree to
which the outcome is either cooperative or competitive. Since it is assumed in this
πi πj j doesn’t adopt 100 100
50 130
i doesn't adopt j adopts
j
80 80 j adopts i adopts 130 50
j doesn’t adopt Figure 3.3. Simultaneous Game Between the Two Closest Elevators.
2
1
i
47
study that elevators mainly compete directly with their nearest neighboring elevators, game
theory can explain the behavior of competition for each elevator.
The traditional presentation of the prisoners’ dilemma was by Luce and Raiffa (1957),
which is a key feature of decisions in an oligopolistic market. The prisoners’ dilemma
arises because, in pursuing its self-interest, each party imposes a cost on the other that it
does not take into account. Elevators provide services that are similar but not perfect
substitutes because of the different locations. In the shuttle adoption game, elevator i’s
adoption will hurt its neighbor elevator, j, because elevator i will attract more farmers than
before it adopts shuttle trains. Unlike a perfectly competitive market, which sets quantity
and lets the market determine price, each elevator here sets price; the market then
determines volume as a function of both elevators’ prices. Each elevator’s quantity is
increasing in its own price and decreasing in its competitor’s price.
For the reasons discussed above, no cooperative equilibrium can be reached after this
elevator’s first move. If the non-adopter does not adopt shuttle after its neighbor has
adopted shuttle trains, the only result it can get is its business situation getting worse, in the
end exiting this market. Alternatively, it fights back to its competitor, the first adopter, by
adopting shuttle trains also. The first result is unfavorable to the non-adopter. The second
one is unfavorable to both elevators, and it will drive them to the non-cooperative
equilibrium, both of them offering higher board prices to farmers. While this outcome is
unfortunate from the viewpoint of the elevators’ mangers, it is a favorable outcome from
the viewpoint of farmers.
Elevator i’s shuttle adoption movement makes being aggressive an undesirable strategy
for its neighboring elevators. Its adoption action will not generate the desired response
48
from its competitors because i’s adoption is not credible for elevator j. Elevator i expands
its handling capacity in the hope that other elevators will then abandon their own decisions
to adopt shuttles.
The difference in board prices is the key factor that leads a balanced equilibrium to
being unbalanced. Once an elevator adopts shuttles, it has first mover advantages and will
force its rivals to the situation in which they have to fight back by taking the same
movement as the adopter, or exit the elevator market and get out of this business.
Both situations are not good to the follower: fighting back will lead to the prisoner
dilemma because of the fixed market potential; doing nothing to the competitor’s challenge
or even exiting is unendurable.
Note that the outcome of the sequential-move game differs significantly from the
outcome of the simultaneous-move game. In the sequential game, the elevator’s decision
problems are linked through time: elevator j can see what i has done, and i can thus count
on a rational response by j to whatever action it chooses. In the sequential-move game,
elevator i’s shuttle adoption decision has commitment value; it forces elevator j into a
position where j’s best response yields the outcome that is most favorable to i. By contrast,
in the simultaneous-move game, j cannot observe i’s decision, so the adoption decision no
longer has commitment value for i. Because of this reason, the choice of adoption by i is
not nearly as compelling as it is in the sequential game. In the elevator market so far, most
shuttle adoptions are sequential. There are situations where more than one elevator adopts
shuttle trains at the same time.
49
Summary
The grain elevator market is a market that only has a few service providers in an area,
an oligopoly. An elevator competes with the elevators surrounding it, especially the
nearest neighboring elevator. Services among elevators are homogeneous and close
substitutes because they have the close service performance characteristics and the same or
similar occasions for use, and they are offered in the same or close geographical market.
The differences among them are the locations or the distances to farmers’ barns.
Elevators only compete in the marginal areas where farmers are indifferent to which
elevator they should sell their crops. This balance changes immediately once one elevator
adopts shuttle trains. The services of two elevators are substitutes to each other now in
these areas.
Under Von Thunen’s theory, location decision is determined by differences in land
rents that can be offered by competing producers of various products. Weber’s theory is
mainly interested in industrial location choices that minimized total costs. Locational-
interdependence theories relax the assumption that each firm will take the locations of its
competitors as given, or act as a monopolist and consider the spatial implications of
noncollusive oligopoly.
The elevator industry is under a blockaded entry condition for adopting shuttle trains
because it requires large, fixed investments. The relationship-specific productive asset is
the main barrier for an elevator to exit. The sunk costs, the costs associated with these
investments, arise because an elevator that has already committed to a particular business
has invested in resources and organizational capabilities that are likely to be specific to that
50
business and are less valuable, or low resale value, if that elevator switches to another
business, which means even bigger losses.
Two of the most important oligopoly models are quantity competition and price
competition, the Cournot (1838) and the Bertrand (1883) models, respectively. Quantity
competition is not the nature of competition in elevators as a whole. For the price
competition in the elevator market, Bertrand’s model can apply.
When only one elevator adopts shuttle trains, it can reap a fruitful benefit by taking its
competitors’ market shares, for a 50 percent increase in volume. If all elevators
surrounding the first adopter follow the same movement, eventually, every elevator will
return to the same draw area as before it adopted shuttle trains.
It is assumed in this study that elevators compete directly with their nearest neighbor
elevator. Game theory can explain the behavior of competition for each player. In the
shuttle adoption game, one elevator’s adoption hurts its neighbor elevator. No cooperative
equilibrium can be reached after this elevator’s first move. If a non-adopter does not adopt
shuttles after its neighbor has, the only result it can get is its business situation getting
worse, eventually exiting this market or fighting back to its competitor, the first adopter, by
also adopting shuttles. The first result is unfavorable to the non-adopter. The second one
is unfavorable to both elevators, and eventually, it will drive them to the non-cooperative
equilibrium, both of them offering higher board prices to farmers. While this outcome is
unfortunate from the viewpoint of the elevators’ owners, it is a favorable outcome from the
viewpoint of farmers.
51
CHAPTER 4
EMPIRICAL PROCEDURES
Introduction
The objective of this study was to analyze factors affecting shuttle adoption. The
previous chapter explained the theory of competition, entry, and exit in the elevator
industry. This chapter explains the empirical shuttle adoption model used for estimation.
Since this study is an exploratory study, a number of variables were explored to determine
which ones have significant effects and the directions of these effects based on theories
used in previous studies discussed in Chapter 2. Regression analysis and tests were used to
determine the factors that have an influence on shuttle adoption.
Explanatory variables for the shuttle adoption model consist of three categories: own-
elevator characteristics, agronomic characteristics, and competitive factors. This chapter
describes model specifications for shuttle adoption. This phenomenon is defined as a
function of different explanatory variables. The independent variables for the model are
defined, and its measurements are discussed. Following the discussion of model
specification is an explanation of the estimation techniques used. The next section explains
the data used and their sources, followed by an analysis of the data. The chapter concludes
with the Summary.
Model Specification
The Logit model was used in this study. Its theory and goodness of fit were explained.
52
Theory of Logit Model
Logit Model. Binary results (for example, yes and no) and ordinal results (for
example, good, normal, and bad) arise in many fields of studies. The popular models that
correct for the problems of the linear probability model are Logit and Probit models. Both
of these models estimate probabilities of an event occurring between 0 and 1. The
difference between the two models is that the Probit model uses the cumulative normal
function and the Logit model uses the logistic function. Estimation with these models is
done by maximum likelihood. However, the most widely used binary choice specification
is the Logit model. Its popularity is due to the fact that the formula for Logit choice
probabilities is readily interpretable, particularly compared with other qualitative choice
models, and that the parameters of the Logit model are relatively inexpensive to estimate
(Train, 1986).
Logistic regression analysis is often used to investigate the relationship between these
discrete responses and a set of explanatory variables. Several texts that discussed logistic
regression are Greene (2000), Collett (1991), Agresti (1990), Train (1986), Cox and Snell
(1989), and Hosmer and Lemeshow (1989).
Greene (2000) provides the following description of the Logit model. The model needs
to produce predictions such that
lim Pro (Y = 1) = 1 ,with f (X) +infinity
and
lim Prob (Y = 1) = 0 with f (X) -infinity
Any proper, continuous probability distrubution, in principle, could be used.
53
If the dependent variable is a 0-1 binary variable, the predicted values of it are expected
to fall with the 0-1 interval. For the binary results models, the result, Y, of an individual or
an experimental unit can take on one of two possible values, denoted by 1 and 0 (for
example, Y = 1 if shuttle train is adopted; otherwise, Y = 0).
It should be noted that, when the dependent variable is qualitative and is represented by
a dummy or binary variable, special estimating problems arise. If a linear regression line is
used to determine the best fit, there will be some values of explanatory values that will
predict a negative value for the dependent variable and some that will predict a value
greater than 1. Estimated probabilities cannot be greater than 1 or less than 0. The
estimated probabilities less than 0 could be converted to 0 and those greater than 1 could
simply be converted to 1, which is the linear probability model.
The logistic distribution is commonly used, which yields the Logit model:
Prob( Y=1) = e f (X) / (1+e f (X) ) = 1 / (1+e – f (X) ).
The linear logistic model has the form:
logit (p) = log (p /(1-p)) = β0 + β’ X,
where X is a vector of explanatory variables and p ≡ Pr (Y=1⏐ X) is the result probability
to be modeled, β0 is the intercept parameter, and β’ is the vector of slope parameters.
Generally, the Logit model has an S-shaped distribution curve as shown in Figure 4.1.
The logistic model shares a common feature with a more general class of linear models,
that a function g = g(µ) of the mean of the result variable is assumed to be linear related to
the explanatory variables. Since the mean, µ, implicitly depends on the stochastic behavior
of the result and the explanatory variables are assumed to be fixed, function g provides the
link between the random (stochastic) component and the systematic (deterministic)
54
0
0.2
0.4
0.6
0.8
1
1.2
x
prob
abili
ty
Figure 4.1. Binary Logit Response Curve.
component of the result variable, Y. For this reason, Nelder and Wedderburn (1972)
referred to g(µ) as the link function. One advantage of the Logit function over other link
functions is that differences on the logistic scale are interpretable regardless of whether the
data are sampled prospectively or retrospectively (McCullagh and Nelder, 1989).
The logistic procedure enables researchers to choose one of these link functions,
resulting in fitting a broader class of binary response models of the form:
g (p) = β0 + β’ x
The logistic procedure fits linear logistic regression models for binary or ordinal result
data by the method of maximum likelihood. The maximum likelihood estimation is usually
carried out with either the Fisheher-scoring algorithm or the Newton-Raphson algorithm.
Researchers specify starting values for the parameter estimates.
55
Goodness of Fit. There is no universally accepted goodness of fit measure for the
Logit model or any qualitative dependent variable models. The three criteria are often used
with the Logit model to measure how well the model fits the data. In the “LOGISTIC”
procedure of SAS software, goodness of fit is calculated as follows: suppose the model
contains s explanatory effects, then k is the total number of response levels minus one. For
the jth observation, let $pj be the likelihood ratio test of the observed response.
The first criterion is -2 Log Likelihood:
− = − ∑2 2LogL wf pj jj
j
log( $ ) ,
where wj and fj are the weight and frequency values of the jth observation. For binary
response models using events/trials syntax, this model is equivalent to
− = − + − −∑2 2 1LogL w f r p n r pj j j j j j j
j
{ log( $ ) ( ) log( $ )} ,
where rj is the number of events, nj is the number of trials, and $pj is the estimated event
probability.
The second criterion is Akaike Information Criterion:
AIC = -2 Log L + 2 (k +s).
The third criterion is Schwarz Criterion:
SC LogL k s fj
j
= − + + ∑2 ( ) log( ) ,
where k and s are as defined previously.
The -2 Log Likelihood statistic has a chi-square distribution under the null hypothesis
(that all the explanatory effects in the model are zero). The procedure produces a p-value
for this statistic. The AIC and SC statistics give two different ways of adjusting the -2 Log
likelihood statistic for the number of terms in the model and the number of observations
56
used. These statistics should be used when comparing different models for the same data;
lower values of the statistic indicate a more desirable model (SAS/STAT User’s Guide,
1999).
The likelihood ratio index is also often used to measure the model fitting. The statistic
measures how well the model, with its estimated parameters, performs compared with a
model in which all the parameters are zero (which is usually equivalent to having no model
at all). This comparison is made on the basis of the Log likelihood function, evaluated at
both the estimated parameters and at zero for all parameters.
The likelihood ratio index is defined as
ρ = 1 – (LL(β∗) / LL(0)),
where LL(β∗) is the value of the log likelihood function at the estimated parameters and
LL(0) is its value when all the parameters are set equal to zero. If the estimated parameters
do no better, in terms of the likelihood function, than zero parameters, then LL(β∗) =
LL(0), and ρ = 0. Zero is the lowest value that ρ can take. At the other extreme, suppose
the estimated model was so good that each sampled decision maker’s choice could be
predicted perfectly. In this case, the likelihood function at the estimated parameters would
be 1 since the probability of observing the choices that were actually made is 1, and since
the log of one is zero, the log likelihood function would be zero at the estimated
parameters. With LL(β∗) = 0, ρ = 1. One is the highest value that ρ can take.
It is important that the likelihood ratio index is not at all similar in its interpretation to
the R-squared used in regression. R-squared indicates the percentage of the variation in the
dependent variable that is “explained” by the estimated model. The likelihood ratio here
has no intuitively interpretable meaning for values between the extremes of 0 and 1. It is
57
the percentage increase in the Log likelihood function above the value taken at zero
parameters. The meaning of such a percent increase is not clear. In comparing two models
estimated on the same data and with the same set of alternatives, it is usually valid to say
that the model with the higher ρ fits the data better. Therefore, increasing the value of the
Log likelihood function is preferable (Train, 1986).
Model Specification
The economic theory states that when a firm makes the decision to adopt innovation, it
makes benefit-cost calculation based on the utilities achieved by adopting and by not
adopting. In this study, an elevator is assumed to adopt shuttle trains if the expected payoff
(π1) is greater than non-adopted payoff (π2). The shuttle adoption decision follows
generally from the condition that, to adopt shuttle trains, expected payoffs, E(π1), should
exceed returns from the non-adopt alternative, E(π2); i.e., E( π1 ) > E(π2). Expected
payoffs from adoption depend on the rate spreads, high handling efficiency of a large
volume and more volume of grain purchased from larger draw areas, and the costs of
adoption and investment while payoffs from non-adoption depend on the outside option
(i.e., opportunity costs of not adopting shuttle trains).
Most of these variables are not directly observable. It is not possible to observe the
expected payoff of an elevator, E(πi), or revenue and costs. What is observed is whether
the elevator adopts the shuttle train. Adoption is observed if E( π1) > E( π2). To avoid the
complications of such a framework, proxies are used to measure variable effect in the
adoption strategy. The index function approach to modeling adoption is employed. The
58
difference between expected payoffs from adoption and from non-adoption is modeled as a
reduced form and estimated using a Logit model.
The difference between adoption and non-adoption is modeled as an unobserved
variable, Y*, such that
Y* = E ( π1 ) - E (π2 ).
The shuttle adoption model is specified to explain the adopting shuttle decision. The
basic premise is that elevators maximize expected payoffs associated with shuttle adoption.
Since decision makers are rational, adoption choice is conditioned by expectations of
which payoff is likely to be higher if shuttle trains are adopted than if shuttle trains are not
adopted. The expected payoff of an elevator is not observed; instead, it is observed
whether the elevator adopts the shuttle trains. Therefore, the empirical model is defined as
follows:
Shuttle1i = 1, shuttle adopted if Y* = E( π1) - E(π2 ) > 0 vs.
Shuttle1i = 0, shuttle not adopted if Y* = E( π1) - E(π2 ) ≤ 0.
Shuttle1i is the decision of shuttle adoption at elevator location i, with i =1, 2, ..., N. It
is a binary variable. N is the total number of elevators studied.
P(Shuttle1i = 1) = P (Y* > 0) = P (βi X i ≥ ε i )
= 1- F (-(Xi βi )),
where P(.) is a probability function and Xi is a row vector of explanatory variables. In this
study, these explanatory variables are own-elevator and agronomic characteristics, and
competitive factors surrounding elevators. βi is a vector of coefficients (to be estimated)
which represent the influence of the explanatory variables on the profitability of shuttle
adoption.
59
εi is the error term, assumed here to have mean error, and either a standardized logistic
distribution or a standard normal distribution. F is the cumulative distribution function for
εi. The second equality is based on the assumption that the error term, εi, comes from a
logistic distribution or a normal distribution.
The base model of shuttle adoption follows directly from the previous section and
incorporates much of it in the development. The shuttle adoption model is a cross section;
that is, an elevator examines its own-elevator characteristics, agronomic characteristics,
and competitive factors and, based on this information, decides whether to adopt shuttle
trains. The model estimated is given by
Log [Pshuttle1i / (1 - Pshuttle1i )]
= f (RrUPi, RrCPi, Capacity1i, Capacity2j, Shuttle2j, Nearelei,
Noinfipi, Yieldi, Herfindahli, SDi….),
where the variables are defined in the next section. The model can be rewritten as
Pshuttle1i (Shuttle1i =1) = Exp(β0 + Xi βi) / (1+ Exp(β0 +Xi βi ) )
= 1 / (1 + Exp-(β0 + Xi βi ))
or
logit ( p) = Shuttle1i = β0 + Xi βi
Variable Definition
Variables consist of two categories, dependent and independent variables. The
dependent variable, Shuttle1i, is the decision of shuttle adoption at location elevator i.
Shuttle1i = 1, shuttle adopted vs.
Shuttle1i = 0, shuttle not adopted.
60
Fifteen independent variables were chosen at first. They are defined as follows.
RrUPi, RrCPi, and RrBNSFi (1 or 0). Railroads UP, Canadian Pacific Railroad
(CP), and BNSF are studied in this study. UP, CP, and BNSF all have large numbers of
elevators that have adopted shuttle trains on their rail lines. These three railroads are
encouraging elevators to adopt shuttles. The binary variables measure how an elevator on
a specific railroad line is willing to adopt shuttles or how a railroad company affects the
shuttle adoption. If three of these variables are included in the model, it will cause perfect
collinearity. Only two binary variables among the three can be included in the model. UP
and CP are tested in this study. The two included dummy variables will show the effects of
being on their rail lines relative to the excluded rail line, BNSF. From the data collected, it
can be found that different railroads have different impacts on an elevator’s adoption
decision. If these three variables are put together to find which would have positive effects
on shuttle adoption, some of them must have positive signs and also negative signs at the
same time.
Capacity1i (1,000,000 bushels). It is an elevator’s own storage capacity. To the extent
that grain elevators realize economies of scale in grain handling and volume discounts
through large shipments, larger capacity elevators are expected to realize lower per bushel
costs and thus, have a longer survival time, holding other factors constant (Vachal et al.,
1997). From this point of view, an elevator with higher storage capacity could adopt
shuttle trains easier than an elevator with smaller storage capacity since a large storage
capacity usually also means large handling capacity. Furthermore, if an elevator has a
large storage capacity, it would need less investment in its shuttle adoption because it
possesses a larger fixed facility already. This cost-saving advantage lets it compete with its
61
smaller competitor much easier. Therefore, it is easier to adopt shuttle trains. In addition,
on average, an elevator with a large storage capacity would have a larger draw area than a
smaller storage capacity one. After the adoption of shuttle trains, a larger elevator does not
need to increase draw area to cover its investment on shuttle adoption more than an
elevator with smaller storage capacity. The sign of the parameter for this variable is
expected as positive.
Capacity2j (1,000,000 bushels). This is the size or storage capacity of the nearest
competitor elevator, j. An elevator feels less pressure from its neighbor elevator of smaller
capacity because a small rival is usually weak. If an elevator of its own large capacity has
a tendency to adopt shuttles, consequently, the nearest elevator with larger storage capacity
will discourage elevators surrounding it from adopting shuttle trains. To emphasize the
effects, this variable is included in the model. The sign of parameter for this variable is
expected different from Capacity1i, or should be negative.
Shuttle2j (1 or 0). It is a binary variable that shows how the nearest competitor
elevator’s shuttle adoption situation affects elevator i’s decision. If adopted, it is 1;
otherwise, it is 0. In this study, it is assumed that only the nearest elevator’s shuttle
adoption decisions would have a main effect on elevator i’s shuttle adoption decision. Any
other elevators’ adoption situations have a much weaker effect on elevator i even if there
are two competitor elevators in the same closest location. This assumption neglects the
impacts of any other elevators’ shuttle adoption on elevator i if they are not the nearest
competitor elevator to elevator i. The reason for this simplification is due to, first, the
survey result to elevator managers and, second, the incapability of software to calculate all
62
the distances among all the huge number of elevators. The sign of parameter for this
variable is expected as negative.
Nearelei (miles). This variable is the distance between elevator i to its nearest
competitor elevator, j. Once an elevator has adopted shuttles, it has to expand its drawing
area because it has to offer higher bids to farmers in order to increase its handling volume
and its revenue so that it can get back the investment in adoption. If the value of this
variable is large, then competition is weak from competitor j. Elevator i will feel less
competition pressure. This variable measures this impact.
Theoretically, an elevator can have competitive pressure from all elevators surrounding
within a certain radius. It is important to consider these other elevators in the study. If
only the nearest competitive elevator is being taken into consideration, it neglects the
effects of all other elevators. This treatment does not totally fit the real-world situation and
has a certain degree of distortion. The reason why this study only studies the nearest
competitive elevator, again, is the incapability of software. It is impossible for more than
2000 elevators to get all distances to their surrounding elevators. However, this
simplification still does, to a very large degree, reflect the distance impact on the shuttle
adoption decision of any elevator. The sign for this variable is expected as negative.
Noinfipi (number of elevators in county). This variable is the number of elevators in
a county. It assesses the effects of the number of elevators on shuttle adoption decisions.
According to the strategy theory, the more competitors in a market, the less competition is
in that market since each firm has a smaller market share. In any county, crop production
is a relatively stable number. The more elevators a county has in it, the closer each
elevator is to each other, and the milder the competition is. On the other hand, given a crop
63
production level, more elevators means less handling volume for every elevator. If every
elevator wants to expand its handling volume to adopt shuttles, the results will be that some
elevators would not get the extra customers to cover their increasing fixed costs incurred
after adopting shuttle trains.
The sizes of counties are different. Therefore, a large county in size tends to have more
elevators in it, and a smaller county in size tends to have fewer elevators in it.
Nevertheless, on average, this variable gives a good illustration of the dependent variable.
The sign of parameter for this variable is expected as negative.
Eledenfipi (1,000 acre/per elevator). This variable is the density of elevators in a
county. In any county, given the areas are the same, the more elevators, the less intense the
competition, and it would have an impact on the shuttle adoption decision.
Yieldi (bushels/acre). It is average crop production per acre for the main crops. Yield
measures the total main crop production densities in a county. It relates directly to the
average draw diameter for every elevator with the same capacity. If yield is high, given
other conditions are the same, the elevator would have more crops to handle. In other
words, with the same draw distance, its volume handled is larger. An elevator located in a
higher-volume county is more likely to adopt shuttle trains because it has a larger volume
to cover the costs needed in order to compensate the investment for adopting the shuttle
program. Crop productions that are on the top in the county are included in the model. In
this analysis, these crops are wheat, corn, soybean, sorghum, barley, and sunflower seed.
The figures of these crops used in the analysis are averaged from main crops for each year
of 1996 to 2000 and then are averaged to a single-year figure. The sign of the parameter
for this variable is expected to be positive.
64
SDi (bushels/acre). It is standard deviation of the yield of crops in a county. A large
fluctuation in crop production means uncertainty. If crop production in a county changes
year after year, the revenue for an elevator is also highly uncertain. Therefore, if an
elevator locates in this county or in the period with high fluctuation in crop production, this
factor would discourage the elevator from adopting shuttle trains. From this point, the sign
for this variable is expected as negative.
Herfindahli (between 0 and 1). It is the Herfindahl Index of crop diversities, which
equals the sum of each crop production share of all crops in the county. If letting Si
represent each crop production share in the county, Herfindahli = Σ (Si)2. This variable is
expected to have a positive signed parameter. The reason is based on that if this index is
high, the production of the main crops is high; then, the volume of main crops handled by
each elevator is high. In this situation, it is easier to load or unload one grain than if the
elevator has to switch from one crop to another. Therefore, it is easier for the elevators to
adopt shuttle trains in a county with a high Herfindahl Index of crops. However, as
discussed in Chapter 2, if an elevator can only handle one crop all the time, it can also have
associated risks.
Cropproi (1,000,000 bushels). This variable represents total crop production of the
county in which an elevator locates. In this study, main crops are included in the analysis:
wheat, soybeans, barley, corn, sorghum, and sunflower seeds. Crop productions vary
greatly among all the studied counties because the area changes dramatically from one
county to another. A low crop yield county with a large area might have the value of this
variable as a high crop yield county with a small area. Therefore, from the very beginning
65
stage of this study, it was assumed that this variable does not have a significant effect on
shuttle adoption.
Proelei (1,000,000 bushels/per elevator). Proelei measures average main crop
production per elevator in the county. It is directly related to the average handling volume
for all elevators and decides the market potential of an elevator, or how much volume of
crops an elevator can probably get in a county. If this volume is high, given any other
conditions the same, an elevator would have a larger amount of grain to handle. For an
elevator, the handling volume is one of the key factors to affect its shuttle adoption
decision. This high volume means a short drawing distance for the elevators with the same
capacity, or with the same draw distance, its volume handled is larger. An elevator located
in a higher-volume county would have a better chance to adopt shuttle trains.
Areai (1,000 acres). It is the area of a county where elevator i locates. When the
amount of elevators in a county is given, this variable could directly measure the average
distance for farmers to deliver their grains to elevators and how elevators are dispersed.
Again, if only this single variable is taken as the indicator to measure the farmer’s delivery
distance, it is obviously not sufficient. However, since numerous studies found that area
has important effects on location decision or market range, this study estimates this
important variable for the purpose to confirm that this variable itself has no direct impact
on shuttle adoption.
Portdisi (miles). It measures the effect of distance from an elevator to the nearest port,
which reflects the impact of barge competition. If this distance is short, the competition
from barges is intense; otherwise, it is weak. In some areas, especially the areas near lakes
or rivers, this competition can affect transportation patterns.
66
εi. This last term is an error term. If the payoff is not known, the elevator’s choice
cannot be predicted perfectly. Since some of the elevator’s decisive elements are known,
an educated guess can be made as to the elevator’s choice. In particular, the probability
that the elevator will choose each alternative can be stated. This variable contains all other
unobserved factors that are correlated and affect shuttle adoption.
Data Sources and Behavior
For the availability of information, only elevators on the BNSF, UP, and CP rail lines
are studied. Elevator level data used in this study are collected from several sources. One
is the database of elevators obtained from the three railroads. These databases contain
information about elevators’ names, locations (state and county), zip codes, mailing
addresses, storage capacities, and track capacities. Another source is the membership
directories distributed by the Grain & Feed Associations of the states: South Dakota,
Montana, Minnesota, Colorado, Kansas, Nebraska, and Texas (Figures 4.2 and 4.3).
Originally, over 2400 elevators came from these data sets. Due to the reason that some
elevators lack the information needed for analysis, such as storage capacity, exact
geographic location, etc., those elevators are deleted from the data set, which is around 5
percent of total number of elevators. After the deletion, 2309 useable elevator observations
are left and used for estimation. Because all of those deleted elevators are generally much
smaller in storage capacity than the average sizes of the total elevators, this deletion does
not bring a large deviation to the final analysis result.
Another data source is the website published by the United States Department of
Agriculture (USDA) and National Agricultural Statistics Service (NASS) which gives all
# ###########
### ##
###
#
##
##
##
# ###
### # ##
#####
###
##
#
##
####
#
#
## ##
#
#
######
####
#
# ### # ##
##
#
##
######
#
### #
#####
# ###
##
#
## ## #### # ###
#
## #
# ###
###
#####
##
### #
#
##
#
# ##
##
# ###
## #
###
#
#
#######
###### #
## ###
#
#
##
#
###
#
##
#
## #
#
##
#
####
#
####
# ##### ### ###
###
#
##
##
#
##
##
##
##
# ###
## ###
# #
##
#
#
# #
#
#
##
#
## ####
#
#
#
##
#####
####
## #
##
##
#
###
##
### #
# #
###
#
##
# ##
##
##
#
#
#
#
#
####
##
#
###
# #
## ###
#
##
####
## #
############
###
##### ## ##
#####
###
###
## ###
###
##
##
# ## #
##
#
#
##
##
#
#
#
# #####
##
#####
######
# #
#
##
#
#
#
#
#
#
#
#
#
#
# #
#
###
#
#
#
##
##
###
#
##
#
#
# #
#
#
#
######
##
#
###
### #######
##
#
###
#
###
#
# ## ### ##
# ##
#
### ##
###
##
# #
#
##
# ##
#
#
#
##
###
##
## #
#
#
###
#
#
#
##
# ##
# ## ###
#
#
#####
##
# ##
#
# #
#
#
###
#
# ##
###
#
#
#
# ##
# #
###
##
# ##
##
#
#
#
# #
##
##
#
####
#
#
###
# ##
###
##
#
#
#
# ##
##
#
#
####
###
#
##
#
# ##
##
##
#
# #
# #
#
# ###
#
###
##
##
###
##
## #
## #
###
#
# #
##
###
##
#
#
#
#
# #
## ### #
# #
#
## #
#
# #####
##
#
##
#
###
##
#
##
##
# #
### ###
#
# ##
### #
####
###
#
#
##
#
###
# #
# #
#
#
####
##
# #
#
##
##
##
##
#
###
####
#
####
#
#
# ####
# ####
#
#
#
# ###
###
# ##
#
#####
#### ##
###
# ##
#
#
### ##
## #
###
## ### #
## ####
##
# # ##
# ###### ###
##
## ###
#
##
#### ###
#
# # ## # #
##
#
#####
#
# #
#
#### #
# ##
#######
## # # ###
##### ##
# #
### ## #
### #
### ###
# #
#### ## #
### ##
######
##
## ### ##
## ##
## ##
# ### #
# #
##
#### #
##
####
###
# #
#
#
##
###### # #
###
# # #
####
## #### #
#
####
# ##
## #### ##
#
# ##
#
#
## ###
# # # ###
#
######
##
#####
#
#
###
##
##
#
##
##
#
##
#
#
##
##
# ##
#
##
#
##
#
##
#
## ##
#
###
##
###
####
##
####
#
##
#
##
### #
#
######
####
####
## #
##
###
###
####
###
#
##
## ##
#
#
####
###
##
######
# #
#
# ##
#
##
###
##
#
#
##
#
##
## ###
#
#
#
#
# ##
#
#
#
#
##
#
#
#
# #
#
#
##
#
#
##
#
##
#
##
###
#
######
# #
##
##
#
#
#
#
#
#
## ## ####
###
##
##
#
Figure 4.2. Elevators Studied in 9 States.
67
# ##
# ###
##
##
##
##
#
######
##
#
#### #
#
#
#
#
#
#
#
##
#
###
#
#
#
#
## #
## ###
######
#
###
#
## ## ##
###
#
#
#
###
#
###
##
#
#
###
Figure 4.3. Elevators that Adopted Shuttles.
68
69
geographic data and the aggregate agricultural data for five years: 1996, 1997, 1998, 1999,
and 2000. All geographic data are used on the county basis to match other data. The five-
year agricultural data are cited from the USDA-NASS website, which includes crop
production, yield and harvest area, etc. These numbers are calculated. An average figure
for each county is used in the analysis. For the crop category, only main crops were
calculated: wheat, soybeans, corn, barley, sorghum, and sunflower seeds. Other crops were
not included in the study because they are either not suitable for use or are too small in
volume. All crop productions used in this study are in the unit of bushels. Some data from
USDA-NASS that are in tons (such as sorghum for silage or corn for silage) or pounds
(such as sunflower seeds) are converted into bushels: 1 ton is converted into 39.4 bushels
(Murphy, 1993).
Every elevator’s location is defined by zip code because there is no way to obtain
accurate latitude and longitude data. Such a treatment is good enough to use in this study
for the reason that a few miles’ difference or so is not a big issue for an elevator to change
its shuttle adoption decision and zip code areas are usually not very large. Besides, the
large number of observations offset any minor distribution error. When more than one
elevator is in the same zip code area, it is treated as being in the exact same geographical
location. The distance between them is zero.
The nearest neighboring elevator analyzed in the study is defined as the elevator with
the shortest geographic distance between the two zip codes. It might be more appropriate
to use road distance or railroad distance, but it is not practical to get these data. The
shortest distances used in this analysis between any competing elevators are accurate
70
enough to the actual distance; the distortions from these distances are very tiny. The mean
values of the shortest distances are correct.
Estimation: Procedure and Methods
Estimation Procedure
First, mean values of all variables were calculated. Then, the Logit model was
estimated by putting the original data into it with different combinations. The above 15
variables were tested. Some of them were significant, and some are not. Those variables
that were not always significantly important were dropped from the model, and those that
were significant all the time were kept. Those variables that were in between were tested
again by checking their Pearson Correlation Coefficients. If a variable is correlated with
several other variables, it is dropped from the model. The goodness of fit was also tested
during the procedure of selecting variables to choose a fit model. After the model
specification was determined, marginal effects of each variable were estimated to show the
changes in the dependent variable, given a change in each independent variable.
ArcView 3.2 is a software that helps solve spatial problems with geographical
information. In this study, the function of “Nearest Feature” of ArcView 3.2 was used to
calculate the distance to the nearest elevator for every elevator. At first, major efforts were
made to calculate distances from an elevator to all its neighboring elevators. However, this
function could not be found which was the only reason why this study took the nearest
neighboring elevator as a main rival. As explained before, zip code was taken as the
position for each elevator, and ArcView 3.2 only calculated the geographical distance, or
71
straight-line distance between elevators. The model estimation was made by using the
software SAS System 8.
All variables that could be related to the dependent variable, Shuttle1i, were tested,
which were 15 of them as discussed above, plus the cross effects, the distance to the nearest
elevator times the shuttle adoption situation at elevator j. In the estimation, the
“SELECTION=SCORE” method of SAS System 8 was employed to select the model. It
uses the branch and bound algorithm of Furnival and Wilson (1974) to find a specified
number of models with the highest likelihood score, Chi-squared statistic, for all possible
model sizes, ranging from 1, 2, 3 effect models, and so on, up to the single model
containing all 15 explanatory effects to determine which effects are active. The method is
applied automatically. For instance, if there is one effect that is included in the models, the
SCORE selection method displays the 15 best models; if 2 effects are included in the
models, the result displays the 15 best models of combinations (that is, the 15 models are
arranged in an order from the highest Chi-square statistic to the lowest), and so on, until all
explanatory effects are included in the model. Therefore, a total of 211 different
combinations of the 15 variables are obtained.
The basic selection criteria were based on the Chi-square distribution of 10 degrees of
freedom at the 5 percent level of significance. If a variable was insignificant at the 0.05
significance level in any estimations of different independent variable combinations, then
the variable would be treated as insignificant and would be removed from the model; those
variables that were always significant were kept in the model. These significant variables
were RrUPi, RrCPi, Capacity1i, Capacity2j, Shuttle2j, Nearelei, Noinfipi, Yieldi,
Herfindahli, and SDi. The independent variables that were tested in the estimations and
72
were removed after estimation are as follows: Cropproi, Areai, Eledenfipi, Proelei, and
Portdisi. The cross effect variable, the distance to the nearest elevator times the shuttle
adoption situation at elevator j, was dropped also since it was highly correlated with
variable Nearelei in the estimation.
The goodness of fit of model was tested by using the Akaike Information Criterion
(AIC), the Schwarz Criterion (SC), and the negative of twice the log likelihood
(-2LogL) for the intercept-only model and the fitted model. AIC and SC are used to
compare different models, and the ones with smaller values are preferred. In “testing
Global Null Hypothesis: Beta =0,” the likelihood ratio test and the efficient score test for
testing the joint significance of the explanatory variables are done by using SAS System 8.
The likelihood ratio index was also used to measure the model fitting. The likelihood
ratio index is defined as ρ = 1 – (LL(β∗) / LL(0)). With the estimated parameters, it was
compared with a model in which all the parameters were zero. The comparisons were
made on the basis of the log likelihood function, evaluated at both the estimated parameters
and at zero for all parameters.
Marginal effects of each variable were estimated by taking a partial derivative of
PShuttle1i with respect to each variable and plugging in the mean values of that parameter
from the basic model estimation to show the changes in probabilities of shuttle adoption,
given a change in each variable. The formulas used for marginal calculations are as
follows:
∂P( Shuttle1i = 1) / ∂ (Xi) = Exp-(β0 + Xi βi) / ( 1+ Exp-(β0 + Xi βi))2
73
Summary
The shuttle adoption decision follows generally from the condition that expected
payoffs, E (π1), from adoption exceed returns from the non-adopt alternative, E (π2); i.e., E
( π1 ) > E (π2 ). An elevator payoff, E(πi), from adopting shuttle trains is a function of the
rate spreads from the railroad, high handling efficiency of a large volume and more volume
of grain purchased from larger draw areas, and the costs of adoption and investment. Most
of these variables are not directly observable and are, questionably, endogenous. What is
observed is whether the elevator adopts the shuttle trains. Adoption is observed if E(π1)>
E(π2). To avoid the complications of such a framework, proxies are used to measure the
adoption strategy; the index function approach to modeling adoption is used. Explanatory
variables are own-elevator characteristics, agronomic characteristics, and competitive
factors, and other data are included.
The difference between expected payoffs from adoption and non-adoption is modeled
as a reduced form and estimated as an empirical shuttle adoption model using the Logit
procedure. The formula for Logit choice probabilities is readily interpretable, particularly
compared with other qualitative choice models, and the parameters of Logit models are
relatively inexpensive to estimate.
There is no universally accepted goodness of fit measure for the Logit model. A
statistic, the likelihood ratio index, is often used with the Logit model to measure how well
the model fits the data. The statistic measures how well the model, with its estimated
parameters, performs compared with a model in which all parameters are zero. This
comparison is made on the basis of the log likelihood function and evaluated at both the
estimated parameters and at zero for all parameters. In comparing two models estimated
74
on the same data and with the same set of alternatives, the model with the higher log
likelihood ratio usually fits the data better.
All the elevators’ data used in this study are on the rail lines of BNSF, UP, and CP.
After the deletion of unusable data, 2309 useable elevator observations are left and used for
estimation. All geographical data and the aggregate agricultural data are for the years 1996
to 2000.
The estimation is made by incorporating the 15 variables into the adoption model with
211 combinations. Marginal effects of each variable are calculated by taking a partial
derivative of PShuttle1i with respect to each variable. Then, each marginal effect trend is
calculated by plugging in different values of that variable, keeping other variable values
constant.
75
CHAPTER 5
RESULTS
Introduction
This chapter presents results from the empirical model and marginal analysis conducted
on key variables. It is organized into four sections. The next section presents data
behavior. Empirical Results and analysis are presented in the third section. The fourth
section presents a summary of the results.
Data Behavior: Significance, Insignificance, and Multicollinearity
In the analysis, 15 independent variables were included in the model at first:
Capacity1i, RrUPi, RrCPi (and RrBNSFi), Capacity2j, Shuttle2j, Nearelei, Noinfipi,
Eledenfipi, Portdisi, Areai, Cropproi, Herfindahli, Yieldi, SDi, and Proelei. Two-thousand-
three-hundred-nine useful observations were used in the analysis. The statistical results of
these 15 variables are reported in Table 5.1.
Of the 2309 observations, 68.2 percent of elevators, 1574, are on the BNSF rail line;
26 percent, 601, are on the UP rail line; and 5.8 percent, 134, are on the CP rail line. This
result highlights the fact that BNSF is the largest grain carrier in the United States.
As for the capacity of elevators, the average storage capacity, Capacity1i, is 1.3448
million bushels with a standard deviation of 2.5531 million bushels. The average storage
capacity of the nearest competitor elevator, Capacity2j, is 2.21 million bushels with a
standard deviation of 4.17 million bushels. The major difference in capacity between
76
Table 5.1. Summary of Independent and Dependent Variables
Variable Number Mean Unit Std. Dev. Min. Max.
Shuttle1i 2309 0.078 1 or 0 0.2682 0 1
RrBNSFi 2309 0.682 1 or 0 0.4659 0 1
RrUPi 2309 0.260 1 or 0 0.4389 0 1
RrCPi 2309 0.058 1 or 0 0.2338 0 1
Capacity1i 2309 1.3448 Million-Bushel 2.5531 0.001 32
Capacity2j 2309 2.2101 Million-Bushel 4.1652 0.002 32
Shuttle2j 2309 0.159 1 or 0 0.3657 0 1
Noinfip 2309 8.71 # 6.205 1 29
Nearelei 2309 4.20 Miles 7.5061 0 129
Eledenefii 2309 139.15 1,000 Acres /elevator 191.56 8 2295
Proelei 2309 1.9105 Million-Bushel/elevator 1.971 0 25
Areai 2309 712.1 1,000 Acres 468.9 82 3985
Cropproi 2309 11.9658 Million-Bushel 8.2236 0 66
Herfindhali 2309 0.456 0 to 1 0.498 0 1
Yieldi 2309 20.11 Bushels/Acre 14.465 0 65
SDi 2309 3.27 Bushels/Acre 1.9496 0 10
Portdisi 2309 222.50 Miles 131.768 1 666
Capacity1i and Capacity2j reflects the data treatment used in the analysis that, if there is
more than one elevator in the same zip code area or in the same place, only the elevator
that has adopted shuttle trains and/or that has the highest storage capacity is concerned.
The huge standard deviation values are the result of large spread of storage capacity of
each elevator: ranging from 0.001 million bushels to 32 million bushels.
About 7.8 percent of elevators, 180 of them, have adopted shuttle trains; 92.2 percent of
them have not adopted yet, although these percentages are increasing year by year. Among
these 180 elevators, 100 elevators that adopted shuttles are on the BNSF rail line; 70 are on
the UP rail line; and 10 are on the CP rail line. From the result, it shows that 6.4 percent of
77
elevators on the BNSF rail line adopted shuttles, 11.6 percent on the UP rail line, and 7.5
percent on the CP rail line; the total number of elevators that adopted shuttles on the BNSF
rail line is the highest. On the other hand, 15.9 percent of elevators that are the nearest
elevators have adopted shuttles among the nearest-neighboring elevators. The reason for
this result is that, if there are more than two elevators in the same zip code area, the
elevator that adopted shuttles is taken as the main competitor.
The average distance to the nearest competitor elevator, Nearelei, is 4.2 miles, ranging
from the shortest, 0 miles, to the longest, 129 miles. The main reason for this wide spread
is due to the different situations where agricultural production, land, and populations have
big variances. Therefore, the number of elevators changing dramatically results in this
number change in a similar way.
The average area of counties, Areai, is 712,105 acres, ranging from the smallest, 82,432
acres, to the largest, 3,984,448 acres. The average crop production is 11.966 million
bushels, ranging from 0 to 65.47 million bushels. The average Yieldi of crop per acre is
20.11 bushels, and the highest reaches 65 bushels per acre.
Herfindahli has an average value of 0.456 with a standard deviation of 0.498. The
minimum value of this variable is 0; the maximum value is 1, representing no crop
production and only 1 crop in the county, respectively.
SDi shows a mean value of 3.266, ranging from 0 to 10.37, with a standard deviation of
1.95. This result reflects big fluctuations in crop productions year after year in different
counties.
Noinfipi, the average number of elevators in a county, is 8.713, ranging from 1 to 29,
reflecting the difference between agricultural areas and industrial areas.
78
Portdisi, the average distance to the nearest port, is 222.5 miles, ranging from the
shortest, 1 mile, to the longest, 666 miles, depending on the locations of the elevators.
Eledenfii is 139,150 acres per elevator. The standard deviation is 191,560, much higher
than the mean value. The maximum value of this variable can even reach 2,295,420 acres
per elevator. This result is also because of the difference in agricultural situations in
different counties or states. Another interesting result is that the overall average area in a
county, Areai, divided by the overall average number of elevators in a county, Noinfipi,
results in 81,729 acres per elevator. However, the mean value of Eledenfii, the average
area per elevator in a county (from the individual average, then to average them in total), is
139,147 acres per elevator, which is 50 percent higher. This difference between these
calculations results from the large variance. This result shows, again, that geographical
conditions in different counties differ significantly.
The values for the other variables, as shown in Table 5.1, have a very similar
distribution as the variables discussed in the previous section due to the reason of the
differences in agricultural situations in different counties and states.
Empirical Results
Base Model Result
The specification tests involved estimating different combinations of variables in the
model. From these results, a base model, which contains 10 variables, was chosen. The
variables that are significant or have an important effect on the shuttle adoption decision in
the base model are RrUPi, RrCPi, Capacity1i, Capacity2j, Shuttle2j, Nearelei, Noinfipi,
Yieldi, SDi, and Herfindahli. The correlation test results and empirical results of the model
are shown in Tables 5.2 and 5.3.
Table 5.2. Correlation of Variables Tested in the Estimation
Pearson Correlation Coefficients, N = 2309, Pro > |r|, under H0: Rho=0
RrUP RrCP Capa1 Capa2 Shuttle2 Yield Herfinda SD Nearele Noinfip Proele Area Portdis Eledenfi Produ
RrUP 1
RrCP -0.15 1
Capa1 0.12 -0.08 1
Capa2 0.09 -0.09 0.38 1 Shuttle2
0.06 -0.04 0.14 0.28 1
Yield 0.05 -0.07 0.09 0.01 0.14 1
Herfinda 0.06 -0.09 0.05 0.05 0.06 0.10 1
SD -0.00 -0.03 0.06 0.04 0.08 0.69 -0.12 1
Nearele 0.06 0.00 -0.12 -0.13 -0.09 -0.13 0.09 -0.19 1
Noinfip -0.10 0.04 0.19 0.37 0.11 -0.02 -0.11 0.02 -0.29 1
Proele 0.03 0.04 -0.07 -0.13 -0.01 0.42 0.17 0.28 0.22 -0.38 1
Area -0.12 0.09 -0.10 -0.10 -0.06 -0.37 0.07 -0.39 0.09 0.02 0.02 1
Portdis -0.25 0.17 -0.15 -0.12 -0.09 -0.39 -0.00 -0.27 0.03 -0.02 -0.09 0.45 1
Eledenfi 0.01 -0.03 -0.12 -0.15 -0.10 -0.30 0.15 -0.37 0.49 -0.42 0.26 0.56 0.20 1 Produ -0.09 0.11 -0.04 -0.08 0.06 0.57 -0.05 0.43 -0.10 0.22 0.47 0.19 0.01 -0.18 1
79
80
Table 5.3. Shuttle Adoption Decision Estimation and Analysis of Maximum Likelihood Estimates
Variables Parameter Estimate Wal Chi-square Pr >Chi-square Intercept -3.2938 147.3847 <.0001
Capacity1i 0.1894 41.2109 <.0001 RrUPi 0.4007 4.8619 0.028 RrCPi 0.6627 3.4036 0.065
Shuttle2j 1.5191 72.2716 <.0001 Capacity2j -0.0502 4.8131 0.028
Nearelei -0.0321 4.1658 0.041 Noinfipi -0.0312 3.2411 0.072
Herfindai 0.3732 4.1383 0.042 Yieldi 0.0136 10.4402 0.001
SDi -0.067 1.5429 0.214
It can be seen, that except SDi, almost every Chi-square value of the variables is or
almost is significant at the significance level 0.05 (Table 5.3). This result shows that
almost all variables have significant effects on the dependent variable: Shuttle1i, the shuttle
adoption decision of elevator i.
Several variables do not have significant effects on the shuttle adoption decision
of elevator i, or they are the important factors which do not affect the shuttle
adoption decision. Several independent variables are highly correlated; some follow the
common trends and cause high multicollinearity. Therefore, these variables are dropped
from the model. Although they are not included in the model, they do give a good
illustration of the other variables and the model itself, and make the base model better to
illustrate the relationship between the shuttle adoption decision and its dependent factors.
Each variable is discussed as follows.
Capacity1i shows an always-significant impact in the estimations on shuttle adoption
among all variables in the model with a positive value of parameter, 0.1894. A positive
81
sign shows that the large storage capacity elevator is more likely to adopt shuttle trains. If
the storage capacity is higher, the average fixed handling costs are lower. On average, high
storage capacity also means high handling volume (Vachal, 2001). It is easier for the
elevator to update its facility to fit the requirements to adopt shuttles. This result matches
the results by some earlier studies that large firms have a higher possibility to make
innovation than small firms.
It was expected that all railroads, RrUPi, RrCPi, and RrBNSFi, could have positive
effects on shuttle adoption because they all have been promoting adoption in recent years.
The estimation results show that this expectation is correct. From the estimation results
(Table 5.3), it can be seen that UP and CP have parameter values 0.4007 and 0.6627,
respectively. Since this study is comparing the three railroads, the above result means that,
if the model is run without intercept term and involves the variable RrBNSFi, the relative
results for UP, CP, and BNSF are -2.8931, -2.6311, and -3.2938, respectively. They
indicate that CP is more effective to promote shuttle adoption than UP and that UP is more
effective than BNSF. In any estimations with different variables included in the adoption
model, these three variables always have similar results and do not change much.
Although the percentage of elevators that adopted shuttle trains on the CP rail line is lower
than that on the UP line, it does have a greater parameter value than UP. This result can
probably be explained by two reasons. One is that CP does have a strong impact and an
effective policy on elevators’ shuttle adoptions. Another reason is that the sample size of
the elevators that are on the CP rail line in this study is small, which could lead to a big
deviation. Only 10 elevators from 134 elevators on the CP rail line in this study adopted
shuttles; it seems that 10 elevators is still not big enough.
82
The result of this study contradicts many people’s thoughts. BNSF has the largest
number of elevators on its rail line and is the largest grain carrier in the United States, and
its promotions on shuttle adoption in recent years were impressive. From the estimation
results of this study, it can at least be said that CP and UP have the same effectiveness on
the promotion of shuttle adoptions as BNSF.
The nearest elevator’s storage capacity, Capacity2j, shows a much weaker impact on
the shuttle adoption decision. Although it also has a negative sign of parameter as
expected, its value, -0.0502, is much smaller than Capacity1i, less than one-third. It shows
the fact that the storage capacity of an elevator itself is more sensitive than the storage
capacity of its nearest competitor elevator. If the nearest competitor elevator’s storage
capacity is large, then this situation will put pressure on its neighboring elevators because it
is easy for that large elevator to adopt shuttle trains and leave it in a fair condition in terms
of draw area and average handling costs. This result explains, again, that the competition
in the elevator industry is intense because elevators with large capacity are more likely to
adopt shuttle trains, and an elevator of large capacity does not stimulate its neighbors to
adopt shuttle trains.
Shuttle2j has a strong effect on the dependent variable. If the nearest competitor
elevator has adopted shuttle trains, it stimulates its nearest elevator to adopt shuttle also.
Almost all the values of other parameters are less than 0.1, and only this variable is 1.54.
The positive sign of the parameter means that, if an elevator adopts shuttle trains, its
nearest competitive elevator would take a tit-for-tat strategy to avoid being in a worse
competitive situation.
83
As discussed in Chapter 3, if one firm adopts a new technology and if this action will
lead it to expand its market share, its direct competitor only has two choices: withdraw
from this market (having been defeated by its competitor), or compete with this new
movement directly and try to fight back against its opponent. From the estimation results,
it can be seen very clearly that, in the elevator industry, elevators use this tit-for-tat strategy
to react to their nearest direct competitors’ first movement: adopting shuttles also as their
competitors did. The exit theory can explain this reaction. The relationship-specific
productive asset is the main barrier for an elevator to exit the elevator industry. The sunk
costs, the costs associated with these investments, arise because an elevator that has already
committed to a particular business has invested in resources and organizational capabilities
that are likely to be specific to that business and are less valuable, or have lower resale
value, if that elevator switches to another business, which will mean even bigger losses.
Therefore, adopting shuttle trains is a better choice, as its competitor did. Although it is
possible for a non-adopter to operate at the same board prices as before, the non-adopter
will eventually face a decision between upgrading facilities and backing off from this
market. Both of these decisions are critical to any firm.
Results for Nearelei show that the distance between two elevators has a negative effect
on shuttle adoption. The farther away two elevators are, the less likely an elevator will
adopt shuttle trains. This result can be explained by the competition theory. If two
elevators are close, any change in draw area is much more sensitive than the situation if
two elevators are far apart because the marginal effects are different for any given change
in distance. Therefore, the competition is more intense for two closer competitors, and it
induces an elevator to make a movement first even if it does not want to do so. If this
84
distance is great, the pressure from competition is weak; the competition would not be
strong enough to stimulate an elevator to adopt shuttle trains.
Variable Noinfipi has a negative value as expected. From the competition theory, the
more firms compete in a market, the less intense the direct competitions are. In a
competitive market, such as only a few elevators in a county, one firm making any
marketing efforts will lead to immediate reactions from its competitors. On the other hand,
if every firm is satisfied with its present situation and does not want to stimulate a new
round of competition that might lead to the result of the prisoner dilemma, an innovation
effort could possibly be hard to take. The situation is the same here: if there are more
elevators in a county, the chance of an elevator adopting shuttle trains is lower than if there
are fewer elevators in the same county. In this study, the average number of elevators in a
county is 8.7. Any movement by one elevator has just a small effect on other elevators if
this number is big; it decreases the intention to adopt shuttles. This result matches the
study result by Bresnahan and Reiss (1991) that competitive conduct changes quickly as
the number of incumbents increase; in markets with three to five firms, the next entrant has
little effect on competitive conduct, and competition becomes much milder.
If there are more elevators in a county, given an area, higher Noinfipi, and smaller
Nearelei, the distance to the nearest competitor elevator is shorter; Vis-à-vis, high Nearelei
means low Noinfipi. The negative signs of these two variables from the estimation results
imply that the effects on the shuttle adoption decision from these two factors are offset by
each other to some degree, although both of them are significant factors on the shuttle
adoption decision.
85
Variable Yieldi, with a positive sign of parameter, shows a stable and strong effect on
the shuttle adoption decision in the estimation, and its parameter value never changes much
with different variables in the model. If the yield of crops in a county is high, an elevator is
more likely to adopt shuttle trains; if yield is low, elevators are less likely to adopt shuttle
trains.
High yield means, given any other situation the same, an elevator with a specific
handling volume would have a smaller draw area, or with the same draw area, its handling
volume is bigger. As a result, if an elevator can increase its draw area by a certain
distance, a high yield area would mean a greater increase in volume, which can create more
profits and, therefore, is more attractive to elevators. From estimation results, the
parameter value of this variable, 0.0136, is the smallest among all the variables in the
model.
The Herfindahli Index shows a significant and positive effect. In any model with
different variables, this variable never changes its significance. All nine states in this study
have big differences for this index among elevators, or more exactly, this index shows a
wide deviation. Even though some counties are close to each other, their Herfindahli
Indexes differ a lot.
If the Herfindahli Index is high, then there are fewer crops in this county. Therefore,
elevators handle fewer crops. With the same handling capacity, a high Herfindahl Index
means high volume for every single crop handled that helps adoption because the two main
requirements for shuttle adoption are large volume, and high loading and unloading
capacity. If an elevator deals with too many kinds of crops, it increases the difficulties for
86
it to reach the economy of scale. Having this variable in the model is reasonable from any
point of view.
SDi shows a negative effect on shuttle adoption in the model, which implies that a big
fluctuation in crop production discourages elevators to make investments on shuttle
adoption since it means an uncertain market or demand. Therefore, one has the reason to
believe that there are more elevators adopting shuttle trains in a good harvest year than a
bad year.
Specification Alternatives
Besides the variables discussed in the previous section, four other variables are also
estimated in the process to deduce to the base model. Even though they are not included in
the base model or the best-fit model, they do help to explain the affective influences
although they do not show significant effects in the base model (Table 5.4). They are
discussed as follows.
Portdisi has the least relationship with the dependent variable in the estimation.
Therefore, water transportation is not an important factor to affect shuttle adoption since
the competitive pressure is not strong enough here. This variable shows a negative sign of
parameter, which indicates that, if a port is far away from an elevator, the elevator is less
likely to adopt shuttles because the competition from other modes of transportation is
weaker and the pressure to make changes is smaller. This result fits the economic theory.
This result reflects a fact that, in the long period of development of different
transportation modes, the locations of ports and rail lines are chosen by market demand and
economic evolution. Only one new technology change, such the shuttle train operation, is
not big enough to change the transportation structure or pattern. Besides, after an elevator
Table 5.4. Comparison of Adoption Model Estimations and Analysis of Maximum Likelihood EstimatesVariables Estim. Pr > χ2 Estim. Pr > χ2 Estim. Pr > χ2 Estim. Pr > χ2 Estim. Pr > χ2 Estim. Pr > χ2 Estim. Pr > χ2 Estim. Pr > χ2
Intercept -3.893 0.0001 -2.7982 0.0001 -3.1705 0.0001 -3.5987 0.0001 -3.5077 0.0001 -3.201 0.0001 -3.3621 0.0001 -3.2449 0.0001Capacity1 0.1894 0.0001 0.1798 0.0001 0.1552 0.0001 0.1545 0.0001 0.1953 0.0001 0.1885 0.0001 0.1874 0.0001 0.1829 0.0001
Shuttle2 1.5191 0.0001 1.5167 0.0001 1.4247 0.0001 1.5654 0.0001 1.5523 0.0001 1.5267 0.0001 1.5024 0.0001Yield 0.0136 0.0012 0.0139 0.0001 0.0127 0.0001 0.0127 0.0001 0.0107 0.0006 0.0103 0.001
Capacity2 -0.05 0.0282 -0.0639 0.0053 -0.0626 0.0055 -0.0616 0.0057 -0.0643 0.0041BNSF -0.401 0.0275 -0.4672 0.0058 -0.4446 0.0089 -0.4668 0.0062
Herfindal 0.3732 0.0419 0.4279 0.0145 0.4553 0.0095Nearele -0.032 0.0412 -0.0242 0.113Noinfip -0.031 0.0718
SD -0.067 0.2142Eledenfi
AreaCroppro
CP 0.2603 0.486Proele
Portdis
Variables Estim. Pr > χ2 Estim. Pr > χ2 Estim. Pr > χ2 Estim. Pr > χ2 Estim. Pr > χ2 Estim. Pr > χ2 Estim. Pr > χ2 Estim. Pr > χ2
Intercept -2.991 0.0001 -2.8482 0.0001 -2.6311 0.0001 -2.6871 0.0001 -2.6968 0.0001 -2.7344 0.0001 -2.7446 0.0001 -2.744 0.0001Capa1 0.1888 0.0001 0.1873 0.0001 0.1875 0.0001 0.1895 0.0001 0.1869 0.0001 0.1885 0.0001 0.1875 0.0001 0.1875 0.0001
Shuttle2 1.5245 0.0001 1.5136 0.0001 1.5049 0.0001 1.5023 0.0001 1.4989 0.0001 1.5037 0.0001 1.5039 0.0001 1.5038 0.0001Yield 0.0099 0.0016 0.0133 0.0015 0.0124 0.0037 0.013 0.0026 0.0151 0.0045 0.0156 0.0038 0.0157 0.0036 0.0157 0.0063
Capa2 -0.053 0.019 -0.0515 0.0243 -0.0508 0.0256 -0.0488 0.0323 -0.0501 0.0292 -0.0492 0.0324 -0.05 0.0303 -0.05 0.0307BNSF -0.456 0.0076 -0.4396 0.0104 -0.4289 0.0126 -0.4376 0.0111 -0.4389 0.0109 -0.401 0.0285 -0.3967 0.0306 -0.3966 0.0337
Herfindal 0.4198 0.0181 0.3665 0.0452 0.3814 0.0375 0.3806 0.0381 0.3728 0.0425 0.3776 0.0402 0.364 0.05 0.3641 0.0512Nearele -0.031 0.0504 -0.032 0.0417 -0.0259 0.1253 0.0227 0.1897 -0.0214 0.2167 -0.0218 0.2085 -0.0228 0.1953 -0.0228 0.1954Noinfip -0.029 0.0893 -0.0298 0.0822 -0.0381 0.0437 -0.0477 0.0309 -0.0451 0.0393 -0.0454 0.0384 -0.0422 0.0621 -0.0422 0.0625
SD -0.0665 0.2228 -0.0765 0.1617 -0.0743 0.1753 -0.0687 0.2154 -0.0697 0.2099 -0.0719 0.1978 -0.0719 0.1978Eledenfi -0.00093 0.2974 0.00172 0.1886 -0.0019 0.1557 -0.00182 0.1694 -0.00221 0.1594 -0.00221 0.1598
Area 0.00028 0.3464 0.00038 0.2429 0.000367 0.2639 0.000446 0.22 0.000447 0.2226Croppro -0.0112 0.4983 -0.0124 0.4574 -0.0193 0.3597 -0.0193 0.3684
CP 0.2484 0.518 0.2394 0.5338 0.2398 0.5417Proele 0.0366 0.5875 0.0366 0.5884
Portdis -3.2E-06 0.9968
88
adopts shuttles, it can only increase the draw distance a little. Compared with the average
distance to the nearest elevator, 7.5 miles, the average distance from elevator to port, 222.5
miles, is too small. Because of this reason, in the estimation, this variable is the variable
that always shows insignificance with the least significant Chi-square values than any other
variables and is always last to be selected into models by the SAS program. It is excluded
from the model at the very early stages of estimation.
Cropproi, total crop production, shows a correlation with three variables, Yieldi, SDi,
and Proelei, in the multicollinearity test. If Cropproi is in the model, it always shows an
insignificant effect on the dependent variable. From these results, it has to be dropped
from the model. When Yieldi is in the model, the variables Proelei and Cropproi become
insignificant, and these results are coincidently identical with the correlation test.
Proelei, the total crop production per elevator, gives an undesired result in the
estimation. Actually, this variable is related to the average volume handled by each
elevator. It is the combination of two variables: total crop production and the number of
elevators in that county. It makes more sense if this variable is in the model. However,
from Table 5.4, this variable is the next least significant to the variable Portdisi. It also has
a very weak impact on shuttle adoption in the model. In addition, this variable shows a
correlation with Yieldi. Since Yieldi shows a good result and is always selected in the
estimation by the SAS program, it has to be dropped from the model.
Areai shows the insignificant effect in the models with different variable combinations.
If this variable is in the model, it also changes Nearelei from significant to insignificant.
The reason is that Nearelei is the function of Areai and Noinfipi . Including both of them in
the model can lead to correlation. Areai cannot be considered in the model. This result fits
89
the real-world situation because the areas of different counties are widely spread, and they
are divided by human beings according to other standards. This variable shows an
insignificant effect, which is rational.
Eledenfipi is very similar to Noinfipi in influence on Shuttle1i. It has a relatively close
correlation to Noinfipi and Nearelei. Even though it was desired very much to include this
variable in the model, the variable had to be deleted from the model because Noinfipi
showed a better result.
In the estimation, the cross effect variable, the distance to the nearest elevator times the
shuttle adoption situation at elevator j, was also tested. However, it was dropped from the
model since it was correlated with variable Nearelei, the distance to the nearest elevator.
After excluding these variables from the model, the base model is taken as the best model.
Goodness of Fit
Table 5.5 contains the Akaike Information Criterion (AIC), the Schwarz Criterion (SC),
and the negative of twice the log likelihood (-2 LogL) for the intercept–only model and the
fitted model. AIC and SC can be used to compare different models, and the ones with
smaller values are preferred.
From Table 5.6, and Figures 5.1 and 5.2, when variables in the base model approach
10, the model does not show any further improvement to the goodness of fit and stays at
almost the same level. On the other hand, after that point, several variables show
insignificances as discussed previously.
If 10 variables are chosen in the model and use Chi-square as the selection criteria, the
base model used by this study would have the highest score among all these models with
10 variables (Table 5.7). From Table 5.8, the analysis result of maximum likelihood
90
Table 5.5. Model Fit Statistics
Criterion Intercept Only Intercept and Covariates AIC 1266.169 1089.158 SC 1271.914 1152.348
-2Log L 1264.169 1067.158 Tale 5.6. Goodness of Fit Test Results
Models 1 2 3 4 5 6 7 8 Intercept only 1264 1264 1264 1264 1264 1264 1264 1264
Intercept and Covariates 1192 1118 1105 1088 1082 1077 1076 1074 ρ = 1 – (LL(β)/ LL(0)) 0.06 0.12 0.13 0.14 0.14 0.148 0.15 0.15
-2Log L = -2(LL(H)-LL(.) ) 144 292 319 351 365 374 376 381
Models 9 10 11 12 13 14 15 Intercept only 1264 1264 1264 1264 1264 1264 1264
Intercept and Covariates 1071 1067 1066 1066 1065 1064 1064 ρ = 1 – (LL(β)/ LL(0)) 0.15 0.16 0.16 0.16 0.16 0.158 0.16
-2Log L = -2(LL(H)-LL(.) ) 387 394 397 397 399 400 400
estimates for the first five models in Table 5.7 shows that the base model has better Chi-
square values than others. In the base model, only two variables are beyond
the significance level, and all other models have three or four insignificant variables.
Another value that measures the goodness of fit to the model is the prediction result.
The frequencies of actual outcomes versus predicted outcomes are 7.8% to 5.5%. They are
close. From all the comparisons above, it can be said that the base model used by this
study is a good model.
Marginal Effects
The marginal effects of each variable in the model and the partial derivative with
respect to each variable are shown in Table 5.9. From the calculation results, as expected,
91
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0 5 10 15 20Number of variables in Modle
ρ V
alue
s
Figure 5.1. Goodness of Fit-1.
0
50
100
150
200
250
300
350
400
450
0 2 4 6 8 10 12 14 16Number of variables in Model
-2 L
og L
Val
ues
Figure 5.1. Goodness of Fit-2.
92
Table 5.7. Regression Models Selected by Score Criterion
Chi-square 10 Best variables in the models 280.87: RrCP RrUP Cap1 Shuttle2 Cap2 Yield Herfinda Nearele Noinfip SD
280.19: RrCP RrUP Cap1 Shuttle2 Cap2 Yield Herfinda Nearele SD Croppro
280.06: RrCP RrUP Cap1 Shuttle2 Cap2 Yield Herfinda Noinfip SD Eledenf
279.19: RrCP RrUP Cap1 Shuttle2 Cap2 Yield Herfinda Nearele SD Area
279.14: RrCP RrUP Cap1 Shuttle2 Cap2 Yield Herfinda Noinfip SD Proele
279.06: RrCP RrUP Cap1 Shuttle2 Cap2 Yield Herfinda Nearele Noinfip SD Eledenf
279.04: RrCP RrUP Cap1 Shuttle2 Cap2 Yield Herfinda Nearele SD Eledenf
279.00: RrCP RrUP Cap1 Shuttle2 Cap2 Yield Herfinda Nearele SD Proele
278.98: RrCP RrUP Cap1 Shuttle2 Cap2 Yield Herfinda Nearele SD Portdis
278.97: RrCP RrUP Cap1 Shuttle2 Cap2 Yield Herfinda SD Eledenfi Croppro
278.76: RrCP RrUP Cap1 Shuttle2 Cap2 Yield Herfinda Noinfip SD Croppro
278.58: RrUP Cap1 Shuttle2 Cap2 Yield Herfinda Nearele Noinfip SD Proele
278.54: RrUP Cap1 Shuttle2 Cap2 Yield Herfinda Nearele Noinfip SD Croppro
278.41: RrUP Cap1 Shuttle2 Cap2 Yield Herfinda Nearele Noinfip SD Area
278.33: RrUP Cap1 Shuttle2 Cap2 Yield Herfinda Nearele Noinfip SD Portdis
Table 5.8. Comparison of Analysis of Maximum Likelihood Estimates with 10 Variables in the Models
Base Model 10-1 Model 10-2 Model 10-3 Model 10-4 Model Pr > Pr > Pr > Pr > Pr > Variables Chi-square Variables Chi-square Variables Chi-square Variables Chi-square Variables Chi-square Interc <.0001 Interc <.0001 nterc <.0001 Interc <.0001 Interc <.0001 Capacity1 .0001 RrUP 0.025 RrUP 0.042 RrCP 0.089 RrCP 0.062 Shuttle2 <.0001 RrCP 0.068 RrCP 0.081 RrUP 0.021 RrUP 0.035 Capacity2 0.028 Capacity1 <.0001 Capacity1 <.0001 Capacity1 <.0001 Capacity1 <.0001 Nearele 0.041 Shuttle2 <.0001 Shuttle2 <.0001 Shuttle2 <.0001 Shuttle2 <.0001 Noinfip 0.072 Capacity2 0.005 Capacity2 0.031 Capacity2 0.006 Capacity2 0.030 Herfindale 0.042 Yield 0.001 Yield 0.004 Yield 0.002 Yield 0.001 Yield 0.001 Herfindale 0.032 Herfindale 0.036 Herfindale 0.023 Herfindale 0.042 SD 0.214 Nearel 0.098 Noinfi 0.049 Nearel 0.103 Noinfi 0.121 RrUP 0.028 SD 0.263 SD 0.154 SD 0.231 SD 0.309 RrCP 0.065 Croppro 0.361 Eleden 0.099 Area 0.806 Proele 0.351
93
all signs have the same signs as in the base model. This result suggests that all variables
have the changes as presented in the model.
From Table 5.9, it can be seen that Yieldi, Nearelei, and Noinfipi have lower marginal
effects on shuttle adoption: 0.000707, -0.00167, and –0.00162. If the mean values of every
variable are changed and put into the marginal effect formulas, the trends of changes that
are to occur are shown in Figures 5.3 to 5.9. From these figures, most variables present an
almost linear change in direction along with the changes of the mean value of that variable,
except two variables: Capacity1i, the elevator’s own storage capacity, and Nearelei, the
distance to the nearest elevator, j.
Table 5.9. Marginal Effects of Each Variable
∂P( Li = 1) / ∂(Capacity1i) = Exp - (β0 + βi Xi ) * β3 / [1+ Exp - (β0 + βi Xi ) ]2 = 0.00984
∂P( Li = 1) /∂(Capacity2j) = Exp - (β0+ βi Xi ) * β4) / [1+ Exp - (β0 + βi Xi ) ]2 = -0.00261
∂P( Li = 1) / ∂(Nearelei) = Exp - (β0 + βi Xi ) * ∂6 / [1+ Exp - (β0 + βi Xi ) ]2 = -0.00167
∂P( Li = 1) / ∂(Noinfipi) = Exp - (β0 + βi Xi ) * β7 / [1+ Exp - (β0 + βi Xi ) ]2 = -0.00162
∂P( Li = 1) /∂(Yieldi) = Exp - (β0 + βi Xi ) * β8 / [1+ Exp - (β0 + βi Xi ) ]2 = 0.00071
∂P( Li = 1) / ∂(SDi) = Exp - (β0 + βi Xi ) * β9 / [1+ Exp - (β0 + βi Xi ) ]2 = -0.00348
∂P( Li = 1) / ∂(Herfindahli) = Exp - (β0 + βi Xi ) * β10 / [1+ Exp - (β0 + βi Xi ) ]2 = 0.01940
From Figure 5.3, when an elevator’s own capacity is lower than 17 million bushels,
every million bushel increase can lead to a larger probability of adopting shuttle trains.
The trend in this part is roughly a linear function. After this point, any greater unit increase
in capacity will cause a smaller increase in the probability of adopting shuttles, a linear and
94
diminishing effect. This interesting result can be explained by the elevator’s competition
power. If an elevator’s capacity is large, it has a larger market share in the local area.
Other elevators would be small or weak opponents. In this situation, this elevator does not
have pressure like a small elevator to change the present situation. The key point here is
that, although storage capacity is an important factor, its effect on shuttle adoption decision
becomes less sensitive when it becomes high.
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0.05
0 5 10 15 20 25 30 35Own Storage Capacity of Elevator i
( 1,000,000 bushels)
Mar
gian
l Effe
ct
Figure 5.3. Marginal Effect of Own Storage Capacity of Elevator i.
From Figure 5.4, the marginal effects of Capacity2i, the storage capacity of the nearest
elevator, shows a basically linear effect. Every million-bushel increase in neighbor’s
capacity discourages an elevator’s intention to adopt shuttle trains. However, the rate in
this effect decreases along with this variable increase, which reflects an attitude that the
98
elevator is more sensitive in shuttle adoption when considering a neighbor is big or small
than when considering the neighbor is big or very big.
From Figure 5.5, the marginal effects of the variable Nearelei, the distance to the
nearest elevator, are not linear either. When the distance is shorter than 30 miles, the
marginal effects that increase are a roughly linear line. After the distance reaches 30 miles,
it becomes nonlinear, which shows a concave shape, implying that the marginal effect
diminishes.
Based on the data and information on hand, it seems hard to find the real reason behind
the result why these two show a difference with others. However, from the statistics, these
two factors have one thing in common: their mean values of the observations are very
small, 1.34 for Capacity1i and 4.2 for Nearelei, and close to their minimum values. At the
same time, their observation values spread a wide range: from 0 to 32 for Capacity1i and
from 0 to 129 for Nearelei. If the marginal effects are calculated around their mean values,
like other variables, these two also show a roughly straight-line effect.
Impacts of Alternative Spatial Representation of Variables
Motivation
As a more comprehensive way to capture spatial variables, alternative variables were
derived and included in the shuttle adoption model. This procedure was done after a
specific function of Arcview 3.2 became available. These variables were analyzed to
determine if the 10 variables in the original base model could be replaced with better
variables. One reason for doing this additional analysis is to test the hypothesis that an
elevator competes with all its surrounding neighbors, not just the nearest ones. Under this
assumption, a far away shuttle adopter would have a similar influence on an elevator’s
99
shuttle adoption decision as a nearer elevator that has not adopted shuttle. Theoretically,
and also as in other industries, this consideration is reasonable.
Data and Data Manipulation
Four alternative variables were derived: total number of elevators that adopted shuttle
trains in a 50-mile radius, Totshuti; total number of elevators in a 50-mile radius,
Noin50milei; total storage capacity in a 50-mile radius, Capa1sumi; and average distance to
the surrounding elevators in a 50-mile radius, Disi. These variables were derived for each
observation. To do so, a new command in Arcview 3.2, “Identify Features Within a
Distance,” was used to derive these values. In the derivation of the above four alternative
variables, every elevator i is selected as the center of location. These values of the
elevators surrounding elevator i are calculated.
The selection of the 50-mile radius is based on the study by Berwick et al. (2001) that
the draw area for the shuttle facilities is estimated to be near a 60-mile radius in North
Dakota. In other states, there are many terminal elevators located at river ports or seaports,
so a 50-mile radius was selected.
Table 5.10 contains the summary of these four alternative variables. The average
storage capacity in a 50-mile radius is 60.6 million bushels, ranging from 0 to 303.9 million
bushels. The average number of elevators that adopted a shuttle is 3.2, ranging from 0 to
19 in different places. The average number of elevators in the above area is 42.7, ranging
from 0 to 108. The average distance to the surrounding elevators in a 50-mile radius is
32.4, ranging from 0 mile to 48 miles.
100
Table 5.10. Summary of Four Alternative Variables Variable Unit Mean Min. Max.
Capa1sumi 1,000,000 Bushels 60.6 0 303.9 Totshuti 0 or 1 3.2 0 19
Noin50milei # 42.7 0 108 Disi Miles 32.4 0 48
Econometric Specification
The above four variables were used to replace some variables in the earlier analysis.
This procedure was done using three different methods: these four alternative variables
were added in the model besides the original variables; similar variables in the base model
were replaced with these alternative variables; or selected changes in one or two variables
were made.
The “Selection” function of SAS System 8 was used to choose the best variables in a
ranked order based on the Chi-square scores and significant levels. More than 300 models
are presented by the SAS System 8. According to these ranks and based on assumptions for
the base model, different combinations of variables were selected in three methods as
described in the previous paragraph. Those variables that always showed insignificance
and were not proper factors based on the Chi-square values in the model were excluded in
the analysis. Those variables that cause several other variables of different categories to be
insignificant are also taken as an improper factor, such as average storage capacity per
elevator in a 50-mile radius makes more than two agronomic variables insignificant.
Results
The results are summarized in Table 5.11. Total storage capacity in a 50-mile radius,
Cap1sumi, which should be a better measurement for the competitive capacities of
surrounding elevators, showed insignificance in any model. When it was changed to
102
average capacity in the 50-mile radius, the result changed little. This result reconfirmed the
initial assumption for the shuttle adoption model, compared with a 50-mile radius, that an
elevator is more concerned about its nearest neighbor, big or small, than other elevators
even though this assumption has some deviation in the real world. If average elevator size
in the 50-mile radius is high, a smaller nearest neighbor would make an elevator feel much
less pressured in terms of competition. Therefore, total or average storage capacity in the
surrounding area does not seem to be a good indicator in the model.
The total number of elevators in a 50-mile radius, NoIn50milei, showed an outcome
different from the other three alternative variables. Replacing the total number of
elevators in the county with this variable, the other variables have roughly the same
effects. The changes were among these agronomic factors: SDi and Herfindahi became
weaker, and Yieldi became stronger. However, one should not draw the conclusion that
NoIn50milei is better variable than the variable Noincountyi, the total number of elevators
in a county, because all the agronomic data are used on the county basis and NoIn50milei is
based on distance; they are somewhat unmatched.
The total number of elevators that adopted shuttle trains in a 50-mile radius, Totshuti,
was used to replace the nearest elevator’s shuttle adoption situation, Shuttle2i. This
variable caused agronomic factors to become insignificant, especially Herfindahli, which
was very stable in the base model before.
Average distance to the surrounding elevator in a 50-mile radius, Disi, was not a good
variable compared with the distance to the nearest elevator variable, Nearelei. When it was
included in the model, four or five variables become insignificant. Based on this result,
Disi should be excluded in the model.
103
The conclusion that can be drawn from these results is that an elevator mainly competes
with its nearest neighbors. (Other elevators do have competitive effects on it.) This
conclusion fits the real-world situations.
Since only a 50-mile radius was buffered to calculate data and the impacts of other
radius sizes were not estimated, it must be noticed that the result obtained from estimation
can only show that including competitors within a 50-mile radius is not a good predictor as
the nearest neighbor. Other radius sizes, such as 25 or 75 miles, might lead to different
results.
Summary and Results
Variables RrUPi, RrCPi (and RrBNSFi), Capacity1i, Capacity2j, Shuttle2j, Nearelei,
Noinfipi, Yieldi, SDi, and Herfindahli show significant effects in the model. Other
variables, Portdisi, Areai, Cropproi, Eledenfipi, and Proelei, do not show this effect, or they
are correlated with others. Therefore, they are not important factors affecting the shuttle
adoption decision. Among the variables in the model, RrCPi, RrUPi, Capacity1i, Yieldi,
Herfindahli, and Shuttle2j have positive effects on the shuttle adoption decision while
Capacity2j, Nearelei, Noinfipi, and SDi have negative effects. Shuttle2j, the nearest
elevator’s shuttle adoption decision, is the most important and biggest factor that affects
shuttle adoption.
The marginal effects of each variable have the same signs as they are in the model, and
all show the rough linear trends, except their own storage capacity, Capacity1i. This
nonlinear marginal effect is due to the reason that, if an elevator has a storage capacity big
enough, it will also have a large market share and big competition power. After this
104
capacity reaches some point, the elevator will be less likely to adopt shuttle trains beyond
that point.
The individual variables, Shuttle2j, RrUPi, RrCpi, and Capacity1i, have the higher
parameter values, so they show stronger impacts on the shuttle adoption decision; Yieldi,
Nearelei, and Noinfipi have smaller impacts on shuttle adoption. Other variables thought to
be important are included but are found to be insignificant at the 10 percent level and/or are
excluded from the final model for other econometric reasons. These variables include
Proelei, Cropproi, Areai, Portdisi, and Eledenfipi.
As a more comprehensive way to capture spatial variables, four alternative variables
were estimated in the shuttle adoption model: Totshuti, Noin50milei, Capa1sumi, and Disi.
A-50 mile radius size was selected. Cap1sumi shows insignificance in any model.
Therefore, it does not seem to be a good indicator in the model. NoIn50milei shows a
similar effect as the total number of elevators in the county, Noinfipi, on the model and
other variables. Totshuti causes agronomic factors to become insignificant, especially
Herfindahli. Disi is not a good variable compared with Nearelei, and it changes four or five
variables from significant to insignificant. Based on this result, the four alternative
variables are excluded in the model. Since only a 50-mile radius was buffered to calculate
data and the impacts of other radius sizes were not estimated, the result can only show that
including competitors within a 50-mile radius is not a good predictor as the nearest
neighbor. Other radius sizes, such as 25 or 75 miles, might lead to different results.
The results of this study do not match some study results (Lieberman, 1987) that
incumbent firms in concentrated industries do not respond positively to expansion by other
incumbents and that incumbents in relatively unconcentrated industries do not increase
105
their investment activity in response to new entry. No matter if the elevator industry is
concentrated or unconcentrated, the result here does not fit some previous conclusions
because any elevator’s shuttle adoption decision will always have a strong reaction from its
competitor and will lead to a similar movement, also adopting shuttle trains. On the other
hand, the findings of this study give a very good example for some early studies on exit
(Besanko et al., 2000) that, when exit barriers are high, such as the relationship-specific
productive assets of elevators, an elevator will stay in the market and compete with rivals
even if the outcomes of the competitions are not optimal.
The findings support the multiple theories of divestment in declining industries. As
predicted by the “shakeout” theory, small-share firms exhibited high rates of exit, and
small-scale plants are most likely to close. Comparing results of this study with several
earlier studies, the elevator industry shows a strong trend similar to other industries that, in
the process of consolidation, larger elevators are more likely to take investment and stay
longer in the market, which economy of scale gives them competitive advantages.
106
CHAPTER 6
CONCLUSIONS The elevator industry has undergone structural changes over the last few decades.
Shuttle train technology adoptions by the elevators present the new trend. The competitive
structure could be changing as adoptions continue and more elevators exit the industry.
The elevator industry will remain changing.
The purpose of this study is to determine the factors that affect the likelihood of an
elevator adopting shuttle trains. Many elevators are induced to adopting shuttle train
technology by railroads. The number of elevators that adopt shuttle trains is growing, and
many others are under construction to update facilities in order to adopt shuttle trains.
Over the last several years, there were 180 elevators that adopted shuttle trains in the 2,309
elevators studied in this study, which is about 7.8 percent. It is important for elevators and
railroads to understand the key factors affecting the likelihood of the probability of an
elevator’s actually acting.
Summary of Results
Logit analysis is used to estimate the shuttle adoption models. Agronomic data in the
nine states, North Dakota, South Dakota, Montana, Minnesota, Colorado, Kansas,
Nebraska, Oklahoma, and Texas, for five different years, 1996, 1997, 1998, 1999, and
2,000, are used in this study. The elevators’ characteristics and competitive factor data of
nearly 2,500 elevators that are on the rail lines of the railroads, BNSF, UP, and CP, are
collected in the nine states and the observations of 2,309 elevators are used in the analysis.
107
The results identify the factors that influence the likelihood of an elevator adopting
shuttle trains. Shuttle train adoption is specified as a function of three categories of
variables: own-elevator characteristics, agronomic characteristics, and competitive factors
that are influential to adopting shuttle train technology. The results indicate the probability
of future adoption by the elevators in this industry.
There are three categories of effects that determine shuttle adoption. They include
elevators’ own characteristics, competitive, and agronomic conditions. Elevator’s own
characteristics include the railroad line it is on and storage capacity. These results indicate
that a large elevator is more likely to move first to adopting shuttle trains than a smaller
elevator. Competitive conditions include capacity, shuttle adoption situation, distance, and
the number of elevators in the county. These results indicate that intense competition and
weak competitors stimulate adoptions. Agronomic conditions include crop yield,
fluctuation of crop production, and crop production diversity. These results indicate that
stable and high crop yield helps elevators in making the decision to invest in shuttle
adoption. More specifically, the elevator’s own storage capacity; railroad companies UP,
CP, and BNSF; nearest competitor elevator’s shuttle adoption; the average crop yield for
the main crops; and the Herfindahl index of crop diversities have positive effects on an
elevator’s shuttle adoption decision. On the other hand, the size or storage capacity of the
nearest competitive elevator, standard deviation of crop yield in a county, distance between
an elevator and its nearest competitor elevator, and the number of elevators in a county
have negative effects on an elevator’s elevation. In the shuttle adoption model, almost all
of these variables are significant.
108
The nearest competitor elevator’s shuttle adoption has a positive and the strongest
effect on shuttle adoption, indicating that adoption is more likely to occur at an elevator
that has a neighbor that has adopted shuttle trains. This result shows that oligopolic
competition in the elevator market forces non-adopters to choose to fight back and adopt
shuttle trains, and not to exit the market. Other competitive characteristics, such as
competitors’ size, number of elevators in a county, and the distance to an elevator are
negative influential factors to shuttle adoption decision, which implies that, when facing a
strong rival far away from it, or if there are many elevators in one market, an elevator’s
desire for adoption will be reduced. An elevator’s own size is also an important positive
determinant of adoption; a big competitor is more likely to make changes.
The railroads, UP and CP, also have positive and strong influences on shuttle adoption;
this conclusion also means that BNSF has a negative effect on an elevator’s shuttle
adoption. It shows that different railroads have different promotion results even if all
railroads are engaged in this new mechanism adoption.
A high Herfindahl Index of crop diversities, high yield of crops, and low production
fluctuation all have positive effects on adoption. The implication from these agronomic
factors is that a stable agricultural environment provides a stable market, or demand, for
elevators, which results in more confidence for elevators to make investment decisions to
adopt new technology.
Several implications are drawn from the study. First, in a declining elevator industry, a
bigger elevator has higher probabilities than smaller ones to take strategic movement first.
This conclusion matches the studies in other industries that bigger firms can survive longer
and exit later with lower costs because of the economies of scale. Second, when
109
competition is intense, elevators have more pressure from the outside to make changes, like
adopting new technology, such as shuttle trains. When competition is weak, elevators have
less pressure to move. Even when competition is intense, if the competitor is strong and
big, it discourages elevators to invest strategically in new mechanisms. A smaller firm
seems to have some disadvantages in the competition with larger firms in the race of new
technology adoption.
Limitations of the Study
One improvement that can be made for this study is that it would be more accurate to
take all direct competitive effects from all surrounding elevators into analysis. The
simplifications in this study, which takes only the nearest elevator’s shuttle adoption
decision as an influential factor and neglects all other elevators’ adoption decisions even if
they are in the same area in this study, is a little bit oversimplified. If the second or even
the third nearest elevators’ shuttle adoption decisions had been included in the study, that
scenario would be much more appropriate.
Another factor that may have explanatory power in estimating shuttle train technology
adoption that is not included in the model is the grain diversity handled by an elevator.
With the same storage capacity or size, an elevator that handles only one kind of grain is
much different than an elevator that handles several different kinds of grains because their
economies of scales are different. It would be easier for the elevator that just handles one
crop to accept a shuttle train. Therefore, this factor would have influence on the shuttle
adoption decision. The reason why this variable is not included in the model is the data
availability.
110
As discussed in the previous chapters, the locations of all elevators in this study are
taken as the center of the zip code areas. In some crop production areas, one postal zip
code area is quite large. In those areas, elevators are actually not geographically located
together. If the result of this study is applied to the terminal elevators, especially in the
port areas where there are several elevators located together, it should have different results
from that of the inner crop production areas. If a study focuses on a small area, such as in
one state, it should take the actual location as the basis to calculate the distances among
elevators.
In this study, only those elevators on the three railroads are studied. In the same nine
states studied, there are several other railroads. There are a large number of elevators on
those rail lines, and some of them are not too far away from the elevators analyzed in this
study. From this aspect, those elevators should also have effects on shuttle train adoption.
It would be ideal if all elevators are included in the study. Although it is impossible for a
researcher to get all information about those elevators, it is an imperfection of this study.
The amount of grain handled annually by an elevator, or turnover, is a better
measurement for an elevator’s production volume than an elevator’s storage capacity since
some elevators have higher turnovers than others. Amount handled by an elevator is more
directly related to the shuttle adoption decision. When the adoption model was estimated,
this point was realized already. However, the lack of data needed to the study blocks any
further efforts on this trial. A similar study should include this variable in the model if the
data are there.
Some earlier studies take elevator’s ownership as an explanatory factor for elevator
study. From the analysis of this study, it is important also. However, the original data of
111
this study do not contain this information. Otherwise, the phenomenon of shuttle adoption
could be illustrated better.
Need for Further Study
This study estimates factors affecting the probability of an elevator adopting shuttle
train. It looks at the whole picture instead of a part of it from a “map” standpoint of view.
It does not estimate factors determining which elevators make the adoptions. Further study
would determine the factors that influence whether an elevator makes shuttle adoption.
This study result finds that large-sized elevators that are close to the elevator that has
adopted shuttle trains are more likely to adopt shuttle trains. This neighboring factor could
be different from just one near adopter or from several near adopters. A more complete
study could determine the influences on shuttle adoption from not only the nearest elevator,
but also the second, third, or fourth elevator in the surrounding area if proper software is
available for the geographical data generation. This calculation might be complicated; it is
actuate and closer to the real-world situation.
A more complete study of this type of analysis may be conducted on this topic if data
are available to measure other factors such as elevator business type: one crop or multiple
crops, terminal or rural elevator, crop production area, or transportation center, and the
directions of grain flows, such as export or domestic market.
Further study can also be better done by segregating the study area according to the
production types, such as wheat production areas, corn production areas, or others.
Different agricultural areas, or crop production areas, differ too much in characteristics.
Comparing two elevators with the same size but located in different crop production areas
seems to not be so reliable.
112
BIBLIOGRAPHY
“2001 Grain Elevator Directory,” The Burlington Northern Santa Fe Railway Company,
Forth Worth, TX,
http://www.bnsf.com/business/agcom/elevator/elevmenu.html, 2001.
Agresti, A. “Analysis of Ordinal Categorical Data,” John Wiley & Sons, Inc., New York,
1984.
Bain, J. “Barriers to New Competition: Their Character and Consequences in
Manufacturing Industries,” Cambridge, MA, Harvard University Press, 1956.
Berwick, M., J. Bitzan, B. Lantz, D. Tolliver, and K. Vachal. “North Dakota Strategic
Freight Analysis: Agricultural Sector — Summary Report,” MPC 01-127.5, Upper
Great Plains Transportation Institute, North Dakota State University, Fargo, October
2001.
Bertrand, J. “Book Review of Research sur Les Principles Mathematiques de la
Theorie des Richesses,” Journal des Savants, 67, pp. 499-508, 1883.
Besanko, D., D. Dranove, and M. Shanley. “ Economics of Strategy,” John Wiley & Sons,
Inc., New York, 2000.
Booth, L., V. Kanetkar, I. Vertinsky, and D. Whistler. “An Empirical Model of Capacity
Expansion and Pricing in an Oligopoly with Barometric Price Leadership: A Case
Study of the Newsprint Industry in North America,” The Journal of Industrial
Economics, XXXIX, pp. 255-276, March 1991.
Bresnahan, T. F. and P. C. Reiss. “Do Entry Conditions Vary Across Markets?” Brookings
Papers on Economic Activity: Special Issue on Microeconomics, No. 3, Martin Baily
and Clifford Winston, eds., pp. 833-871, 1987.
Bresnahan, T. F. and P. C. Reiss. “Entry and Competition in Concentrated Markets,”
Journal of Political Economy, 99, No. 5, pp. 977-1009, 1991.
Bulow, J., J. Geanakopolos, and P. Klemperer, “Multimarket Oligopoly: Strategic
Substitutes and Complements,” Journal of Political Economy, 93, pp. 488-511, 1985.
Case, A. “Neighborhood Influence and Technological Change,” Regional Science and
Urban Economics, 22, pp. 491-508, 1992.
113
Collett, D. “Modeling Binary Data,” Chapman & Hall, New York, 1991.
Cournot, A. “Mémoire sur les Applications du Calcul des Chances à la statistique
Judiciaire,” Journal des Mathématiques Pures et Appliquées, 12, pp. 257-334, 1838.
Cox, D. R. and E. J. Snell. “Analysis of Binary Data,” Second Edition, Chapman and Hall,
New York, 1989.
“Crop Acreage and Yield, Crop Year 1996-2000,” United States Department of
Agriculture, National Agricultural Statistics Service, 1996, 1997, 1998, 1999, 2000,
http://www.usda.gov/nass/aggraphs/cropmap.htm.
Dahl, B. L. and W. W. Wilson. “Factors Affecting the Supply of Quality Characteristics
Wheats: Comparisons Between the United States and Canada,” Agricultural
Economics Report, No. 374, Agricultural Experiment Station, North Dakota State
University, Fargo, April 1997.
Deily, M. E. “Exit Strategies and Plant-closing Decisions: The Case of Steel,” RAND
Journal of Economics, 22, pp. 250-263, 1991.
Dubin, R. A. Estimating Logit Models with Spatial Dependence, in “New Direction in
Spatial Econometrics,” L. Anselin and Raymond Florax, eds., Springer-Verlag, Berlin,
1995.
Duetsch, L. L. “Structure, Performance, and the Net Tate of Entry into Manufacturing
Industries,” Southern Economic Journal. 41, No. 3, pp. 450-456, January 1975.
Elzinga, K. and T. Hogarty, “The Problem of Geographic Market Definition Revisited:
The Case of Coal,” Antitrust Bulletin, 23, pp. 1-18, 1978.
Fisher, F. “Industrial Organization, Economics, and the Law,” The MIT Press, Cambridge,
MA, 1991.
Florax, R. J. G. M. and S. Rey. The Impacts of Misspecified Spatial Interaction in Linear
Regression Models, In “New Directions in Spatial Econometrics,” L. Anselin and R.
Florax, eds., Springer-Verlag, Berlin, pp. 111-135, January 1995.
Friedman, J. W. “Oligopoly and the Theory of Games,” North-Holland Publishing
Company, New York, 1977.
Furnival, G. M. and R. W. Wilson. “Regression by Leaps and Bounds,” Technometrics,
16, pp. 499-511, 1974.
114
Ghemawat, P. and B. Nalebuff. “Exit,” Rand Journal of Economics, 16, No. 2, pp. 184-
194, Summer 1985.
Gilbert, R. J. “The Role of Potential Competition in Industrial Organization,” Journal of
Economic Perspectives, 3, No. 3, pp. 107-127, 1989.
Gilbert, R. J., Preemptive Competition, In “New Developments in the Analysis of Market
Structure,” Joseph E. Stiglitz and F. Mathewson, eds., The MIT Press, Cambridge,
MA, pp. 90-125, 1986.
Gilbert, R. J. and M. Lieberman, “Investment and Coordination in Oligopolistic
Industries,” RAND Journal of Economics, 18, pp. 17-33, 1987.
Greene, W. “Econometric Analysis,” Fourth Edition, Prentice Hall, Englewood Cliffs, NJ,
2000.
Greenhut, M. L., G. Norman, and C. S. Hung. “The Economics of Imperfect Competition:
A Spatial Approach,” Cambridge University Press, New York, 1987.
Hosmer, D. W., Jr. and S. Lemeshow. “Applied Logistic Regression,” John Wiley & Sons,
Inc., New York, 1989.
Johnson, R. “Johnson Urges Railroad to Drop Proposed Shipping fee Plan,” North Dakota
Department of Agriculture Press Releases, Bismarck, ND, August 13, 2001.
Judd, K. L. “Credible Spatial Preemption,” Rand Journal of Economics, 16, pp. 153-166,
1985.
Karakaya, F. and M. J. Stahl. “Barriers to Entry and Market Entry Decisions in Consumer
and Industrial Goods Markets,” Journal of Marketing, 53, pp. 80-91, April 1989.
Kennedy, P. “A Guide to Econometrics,” Fourth Edition, The MIT Press, Cambridge, MA,
1998.
Klindworth, K. “Comments at the National Grain and Feed Association Rail
Transportation Symposium, Ponte Vendra Beach,” May 15-17, 2000.
Lieberman, M. B. “Post-entry Investment and Market Structure in the Chemical Processing
Industries,” RAND Journal of Economics, 18, pp. 533-549, 1987.
Lieberman, M. B. “Empirical Evidence, Exit from Declining Industries ‘Shakeout’ or
‘Stakeout’?” RAND Journal of Economics, 21, 1990.
Luce, R. D. and H. Raiffa. “Games and Decisions,” John Wiley & Sons, Inc., New York,
1957.
115
Mattson, J. W. “Estimating Structural Changes in the United States Baking Industry,”
Thesis, Department of Agricultural Economics, North Dakota State University, Fargo,
Feburary 2000.
McCullagh, P. and J. A. Nelder. “Generalized Linear Models,” Chapman and Hall, New
York, 1989.
McMillan, D. P. Spatial Effects in Probit Models: A Monte Carlo Investigation, In “New
Directions in Spatial Econometrics,” L. Anselin and R. Florax, eds., Springer-Verlag,
Berlin, January 1995.
“Membership Directory 1999-2000,” Montana Grain Elevator Association, Helena, MT,
2000.
“Membership Directory 1999-2000,” Nebraska Grain and Feed Association, Lincoln,
Nebraska, 2001.
“Membership Directory 2000,” Kansas Grain and Feed Association, Topeka, KS, 2000.
“Membership Directory 2000-2001,” Texas Grain & Feed Association, Fort Worth, TX,
2001.
“Membership Directory 2001,” Colorado Grain and Feed Association, Denver, CO, 2001.
“Membership Directory 2001,” Minnesota Grain and Feed Association, Minneapolis, MN,
2001.
“Membership Directory 2001,” South Dakota Grain & Feed Association, Aberdeen, SD,
2001.
Moorthy, K. S. “Using Game Theory to Model Competition,” Journal of Marketing
Research, XXII, pp. 262-282, August 1985.
Murphy, W. J. “G420-Tables for Weights and Measurements: Crops,” Department of
Agronomy, University of Missouri, Columbia, October 1, 1993.
Nash, J. “Equilibrium Points in n-person Games,” Proceedings of the National Academy
of Sciences, 36, pp. 48-49, 1950.
Nelder, J. A. and R. W. M. Wedderburn. “Generalized Linear Models,” Journal of the
Royal Statistical Society, A, 135, pp. 370-384, 1972.
Novak, P. and S. Schlecht. “Strategic Adoption of 100 Car Loading Elevators in North
Dakota,” Work Report, Department of Agricultural Economics, North Dakota State
University, Fargo, March 2000.
116
Orr, D. “The Determinants of Entry: A Study of the Canadian Manufacturing Industries,”
Review of Economics and Statistics, 56, pp. 58-66, February 1974.
“SAS/STAT User’s Guide,” Version 8, SAS Institute, Inc., Cary, NC, 1999,
http://www.zi.unizh.ch/software/unix/statmath/sas/sasdoc/stat/chap39/sect23.htm
Schnake, L. D. and C. A. Stevens, Jr. “Inland Grain Elevator Operating Costs and Capital
Requirements, 1982,” Bulletin 644, Agricultural Experiment Station, Kansas State
University, Manhattan, October 1983.
Spence, M. “Entry, Capacity, Investment and Oligopolistic Pricing,” Bell Journal of
Economics, 8, pp. 534-544, 1977.
Train, K. “Qualitative Choice Analysis: Theory, Econometrics, and an Application,” The
MIT Press, Cambridge, MA, 1986.
Vachal, K. “Shuttle Trains,” North Dakota Strategic Freight Analysis, MPC Report No.
01-127.3, Upper Great Plains Transportation Institute, North Dakota State University,
Fargo, October 2001.
Vachal, K., J. Bitzan, and B. Baldwin. “Implications of a North American Grain Marketing
System for Prairie Transportation & Elevators,” MPC Report No. 97-84, Upper Great
Plains Transportation Institute, North Dakota State University, Fargo, September
1997.
Weiss, L. W. “The Geographic Size of Markets in Manufacturing,” The Review of
Economics and Statistics, 54, pp. 245-257, August 1972.
Wilson, W., S. Priewe, and B. Dahl. “Forward Shipping Options for Rail: A Strategic
Risk,” Journal of Agricultural and Resource Economics, 23, pp. 1-19, December
1998.
Wilson, W. W. and W. W. Wilson, “Mergers and Acquisitions in the North American Flour
Milling Industry,” NC-186 Meeting, Minneapolis, MN, October 29, 1992.
Yip, G. S. “Barriers to Entry: A Corporate Strategy Perspective,” Lexington Books,
Lexington, MA, 1982.
