Directory UMM :Data Elmu:jurnal:I:International Journal of Production Economics:Vol68.Issue3.Dec2000:

Transportation Research Part E 35 (1999) 269±290
www.elsevier.com/locate/tre

The impact of railroad mergers on grain transportation
markets: a Kansas case study
Joon Je Park a,1, Michael W. Babcock b,*, Kenneth Lemke c,2
a

c

Department of Agricultural Economics, North Dakota State University, Fargo, ND 58105, USA
b
Department of Economics, Kansas State University, Manhattan, KS 66506, USA
Data Transmission Network Corporation, 9110 West Dodge Road, Suite 200, Omaha, NE 68114, USA
Received 14 July 1998; received in revised form 28 March 1999; accepted 8 June 1999

Abstract
While there have been many studies of the impact of railroad deregulation on agricultural transportation
markets there have been very few that address the impact of railroad mergers on rail grain prices and the
distribution of eciency gains. The purpose of this paper is to add to the sparse literature regarding
the e€ect of railroad mergers on agricultural transportation markets. Given the ever declining number of

Class I railroads, this research is very timely.
The speci®c objectives of the research are as follows: (1) Analyze the impact of the Burlington Northern
(BN)±Santa Fe (SF) merger on the ability of the BNSF to increase prices on movements of Kansas wheat to
Houston, Texas. (2) Analyze the impact of the Union Paci®c (UP)±Southern Paci®c (SP) merger on the
ability of the UPSP to increase prices on movements of Kansas wheat to Houston, Texas. (3) Analyze
changes in Kansas wheat logistics system costs as a result of the BN±SF and UP±SP mergers.
Two models are developed to achieve the objectives of the study. A network model of the wheat logistics
system is used to identify the least cost transportation routes from the Kansas study area to the market at
Houston, Texas. A pro®t improvement algorithm is developed to measure the amount by which railroads
can raise their prices above variable cost.
The BNSF and UPSP achieve only minor increases in market power (measured by the ratio of revenue to
variable cost) because the merged railroads have only slight advantages in cost relative to other railroads
that serve the same areas as the merged railroads. Wheat shippers bene®t from merger-induced reductions
in transportation and handling costs. Shippers are likely to capture a signi®cant share of these cost reductions since intrarailroad competition is present after the mergers. Transport cost reductions accompany

*

Corresponding author. Tel.: +785-532-4571; fax: +785-532-6919.
E-mail address: mwb@ksu.edu (Michael W. Babcock)
1

Tel.: +701-231-7498; fax: +701-231-7400
2
Tel.: +402-390-2328; fax: +402-255-3667

1366-5545/99/$ - see front matter Ó 1999 Elsevier Science Ltd. All rights reserved.
PII: S 1 3 6 6 - 5 5 4 5 ( 9 9 ) 0 0 0 1 4 - 9

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mergers due to more direct routing of wheat shipments and the assumption that the merged railroad operates at the costs of the lower cost partner. Ó 1999 Elsevier Science Ltd. All rights reserved.

1. Introduction
The Staggers Rail Act of 1980 greatly reduced regulation of the railroad industry. The act
recognized the high degree of intermodal competition in most transportation markets. Also in
1980 there were 39 Class I railroads, indicating potential intramodal competition. Thus competition rather than regulation would determine rail prices.
The Staggers Rail Act also contained provisions designed to expedite railroad mergers. For
example, merger decisions by the ICC had to be made within 30 months of the date of application;
this was later reduced to 15 months by the 1995 ICC Termination Act. Although the impact of

deregulation is dicult to measure, the number of Class I railroads has declined from 39 in 1980
to nine in 1998.
Proponents of mergers cite several potential advantages. Some of these would bene®t shippers
while others would assist merging carriers. In end-to-end mergers, shippers have direct access to
more markets on the merged system. Reduced need for interlining also results in faster delivery
time and less administrative costs for shippers. In parallel mergers the elimination of duplicate
facilities reduces the costs of the combined system. Parallel mergers will reduce rail costs and if
there is strong competition, this will turn rail cost reductions into reduced prices.
Market power was the major reason for regulating rail prices. The recent rapid consolidation of
the rail industry concerns many observers including many who initially supported the Staggers
Act. Critics of rail mergers argue that the potential cost savings are overstated and that the
primary e€ect will be an increase in rail prices. They contend that mergers will not produce resource savings but they merely transfer income from shippers to railroads.
There have been many studies of the impact of railroad deregulation on agricultural transportation markets. These include Adam and Anderson (1985), Babcock (1981, 1984), Babcock
et al. (1985), Casavant (1985), Chow (1984), Fuller et al. (1983), German and Babcock (1982),
Klindworth et al. (1985); MacDonald (1987, 1989), US Department of Agriculture (1982, 1984),
Sarwar and Anderson (1986), Fuller et al. (1987), Sorenson (1984), Wilson (1985), Hauser (1986),
Thompson et al. (1990), Kwon et al. (1994) and Schmitz and Fuller (1995). However there have
been comparatively few studies of the e€ects of rail mergers on agricultural transportation
markets. The few that exist have examined railroad eciency gains, market share changes, and the
e€ect of merger con®guration on eciency gains (i.e. end-to-end vs. parallel mergers). Only Lemke and Babcock (1987) directly address the impact of railroad mergers on rail grain prices and

the distribution of eciency gains. The purpose of this paper is to add to the sparse literature
regarding the e€ect of railroad mergers on agricultural transportation markets. Given the ever
declining number of Class I railroads, this research is very timely.
The objectives of the paper are as follows:
1. Analyze the impact of the Burlington Northern (BN)±Santa Fe (SF) merger on the ability of
the BNSF to increase prices on movements of Kansas wheat to Houston, Texas.
2. Analyze the impact of the Union Paci®c (UP)±Southern Paci®c (SP) merger on the ability of
the UPSP to increase prices on movements of Kansas wheat to Houston, Texas.

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271

3. Analyze changes in Kansas wheat logistics system costs as a result of the BN±SF and UP±SP
mergers.

2. The study area
The grain origin area of this study includes all of 57 counties and parts of 15 other counties in
central and western Kansas that comprises about two-thirds of the Kansas land area (Fig. 1). This
area is divided into 342 production origins (12 ´ 12 square mile areas).

In 1996 the area produced about 217 million bushels of wheat which was 85% of the stateÕs
production. 3 The area also produced about 77% of total Kansas sorghum production and 81% of
the stateÕs corn production. 4
The area is served by four Class I railroads including Burlington Northern (126 miles), Union
Paci®c (852 miles), Santa Fe (658 miles), and Southern Paci®c (259 miles). In addition there are
®ve short lines serving the area (not shown in Fig. 1) including Kyle Railroad (632 miles), Kansas
Southwestern (302 miles), Central Kansas Railroad (882 miles), Cimmaron Valley (182 miles),
and the Garden City Western (45 miles). Thus the total miles of rail line in the study area is 3938.
These railroads serve study area elevators with a total storage capacity of 670 million bushels.
The study focuses on wheat because rail transportation is the dominant transportation mode
for Kansas export wheat. This is not the case for corn or sorghum. For the 1992±1993 crop, small
country elevators moved 52% of their wheat shipments by rail. The corresponding percentages for
large country elevators and terminal elevators were 69% and 99%, respectively. 5
Most of the wheat grown on Kansas farms travels though a system of country elevators and
inland terminal elevators to domestic ¯our mills or export terminals at coastal ports. Port terminals on the Gulf of Mexico have been the major destination for Kansas export wheat. During
the 1992±1993 crop year, 62% of all Kansas wheat shipments were to Gulf of Mexico ports.
The wheat logistics system in Kansas is summarized in Fig. 2. Trucks are used to ship wheat
from farms to country elevators, inland terminal elevators, or subterminal elevators. Country
elevators ship wheat to inland terminals or domestic ¯our mills by truck or rail. Inland terminal
elevators and subterminal elevators ship wheat by rail and by truck to other inland terminals or to

®nal markets. Inland terminal elevators have functioned as intermediate collection, storage, and
routing points. Subterminals are designed for rapid loading of unit trains of wheat.
In the study area, inland terminal elevators are located at Salina, Wichita, and Hutchinson,
Kansas. Subterminals are located at Colby, Dodge City, Wakeeney, Liberal, and Ogallah, Kansas
and Enid, Oklahoma.
Houston, Texas was selected as the only market in order to make the results of the study more
realistic. The study uses a transportation cost minimizing algorithm to obtain least cost wheat
movements. The model minimizes total transportation and handling costs for the movement of
wheat from production origins to Houston, Texas. If other destinations had been included as

3
4
5

Computed from data in Kansas Department of Agriculture (1997).
Computed from data in Kansas Department of Agriculture (1997).
Kansas Department of Agriculture (1993).

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Fig. 1. Production origins and subregions in the study area.

markets, the least cost solution would indicate that some production origins do not ship wheat to
Houston. This is undesirable for three reasons. First, an objective of the study is to determine the
ability of railroads to increase prices on wheat shipments to Houston. To do this it is necessary to
®nd the least cost route from each study area production origin to Houston. Second, rail service to
Houston is available from every elevator site in the study area, so railroads have established prices
for these movements. Third, in the 1992±1993 crop year over 60% of Kansas wheat shipments

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273

Fig. 2. Wheat logistic system in Kansas.

moved to Gulf of Mexico ports for export, and signi®cant amounts moved from each production
origin.
Exclusion of non-Gulf port areas will not prevent achieving the objectives of the study. This is

because those other ports are served by other wheat supply areas which have a locational advantage. Thus the Paci®c Northwest ports are served by Washington, Montana, and the Dakotas.
California ports are served by Utah, California, and Arizona. The western Great Lakes ports are
served by Montana, the Dakotas, and Minnesota.

3. Models and procedures
Two models are required to achieve the objectives of the study. A network model is required to
identify the least cost transportation routes from the study area to Houston. The network model is
a variation of the linear programming methodology. A pro®t improvement algorithm is needed to
measure the amount by which railroads can raise their prices above variable cost in the face of
changing market conditions.
3.1. The network model
The network model includes 342 production origins (12 miles by 12 miles in area), 280 country
elevators, 6 subterminals, 3 inland terminals, and one destination (Houston). The initial
movement of wheat is from a production origin to a country elevator, terminal elevator or a
subterminal. A production origin may ship wheat to any country elevator that is within a 50 mile

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Fig. 3. Types of shipment patterns of the study from country elevators to Houston, Texas.

radius; it may ship to a subterminal if it is within a 70 mile radius. A production origin can ship
to any terminal. Because one of the objectives of this study is to examine the ability of railroads
to increase their market power and pro®ts, production origins must be allowed to deliver Kansas
wheat farther than historical average mileages in order to identify transportation costs of alternative routes. 6
In the network model, wheat moves through the logistics system to the ®nal demand point.
After the wheat is assembled at country elevators or subterminals, the following types of shipment
patterns may occur (Fig. 3):
1. Multicar (15 car) rail shipments from country elevators to Houston, Texas.
2. Multicar (15 car) rail shipments from country elevators to inland terminals or subterminals.
This is followed by a 100 car unit train movement from inland terminals or subterminals to
Houston, Texas.
3. Commercial truck movement from country elevators to inland terminals or subterminals. The
subsequent movement is a unit train shipment of 100 cars from inland terminals or subterminals to Houston, Texas.
4. Commercial truck movement from country elevators served by a given railroad to another
country elevator served by a competing railroad (s). This is followed by multicar (15 car) shipments to inland terminals or subterminals. The next movement is 100 car unit train movement
from inland terminals or subterminals to Houston, Texas.

6

In 1992 the average distance travelled to deliver wheat from study area farms to country elevators was 11 miles.

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275

5. Commercial truck movements from country elevators served by a given railroad to an inland
terminal or a subterminal served by a competing railroad (s). The subsequent shipment is unit
train shipment of 100 cars from inland terminals or subterminals to Houston, Texas.
Intramodal competition between Class I railroads is simulated by not allowing Class I railroads
with routes to Houston, Texas to transfer wheat trac to another Class I railroad. Class III
railroads that do not have direct routes to Gulf ports are allowed to transfer wheat shipments to
railroads that are not competitors for the same production origins. For example, the Kyle interlines with the Santa Fe before the BN±SF merger and with the BNSF after the merger but not
with the Union Paci®c. This is because the Kyle and the Union Paci®c both serve the northern
portion of the study area while Santa Fe does not.
The objective function of the network model is to minimize total logistics system costs, which
include grain transportation and handling costs, subject to the following constraints: 7
(1) The ¯ow of wheat into each transshipment point exactly equals the ¯ow out of the transshipment point.
(2) The total amount of wheat supplied exactly equals the total amount of wheat demanded.

(3) All grain transportation and handling cost coecients are greater than or equal to zero; all
endogenous variables are greater than or equal to zero.
A transportation cost minimizing algorithm is used to solve the network model for the least
cost movements of wheat. This algorithm requires that the following capacity constraints be
added to the model:
(4) Storage at each facility must be equal to the storage capacity (total storage capacity of all
the elevators at the location is used).
(5) The quantity of wheat shipped by each mode between each location must be less than or
equal to the total storage capacity at the origin location.
(6) No grain stocks will remain at the farm or at transshipment points at the end of the crop
year.
The cost minimizing algorithm is used to solve the network model. 8
3.2. The pro®t improvement algorithm
The pro®t improvement algorithm is used to evaluate each railroadÕs ability to raise prices
above variable costs. The algorithm simulates a range of prices from variable cost to a level that
diverts all trac to rival railroads or other transport modes. The algorithm then identi®es the set
of constrained Nash Equilibrium prices: a set of prices that maximizes the railroadÕs net revenues
subject to the constraint that the prices of all rival railroads are set equal to their respective
variable costs.

7
8

For a detailed discussion of the mathematics of the network model, see Park (1998), pp. 80±83.
For a more detailed discussion of the cost minimizing algorithm, see Chow (1984).

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The pro®t improvement algorithm starts with all railroad rates set equal to variable costs and
systematically generates changes in rates for a given railroad that will increase its excess of revenues over variable costs. The algorithm uses the solution from the network model to calculate
shadow prices for alternative routes from production origins to ®nal destination. Shadow prices
represent the increase in total transportation costs if one unit of grain is shipped over a route that
is not part of the least cost solution. Shadow prices indicate a railroadÕs cost advantage over its
competitors on a speci®c route. In the pro®t improvement algorithm, shadow prices are used to
determine the amount a railroadÕs prices may be increased without loss of trac on each of its
routes. The algorithm also evaluates the impact of raising prices to levels where some trac is
diverted to other carriers.
In this study, railroad rates are initially set equal to variable costs. The rates of competing
railroads are also held at variable cost. In Phase I, the algorithm increases the prices of the
railroad being analyzed by an amount equal to the cost disadvantage of the lowest cost alternative
route. In Phase II, the algorithm increases prices above those in Phase I to levels that divert some
wheat shipments to competitor carriers. Hence, in Phase II, the total volume of wheat shipped by
the railroad being analyzed is decreased relative to the railroadÕs volume in Phase I. The price
increases are terminated when the algorithm reports no additional contribution to net revenues.
3.3. Procedures
In this study, a procedure for evaluating the potential e€ects of Class I railroad mergers is
developed. The ®rst step in this procedure is to identify geographic areas where each railroad has a
cost advantage in the movement of Kansas wheat. This is done by ®rst ®nding the least cost
solution to the network model and then determining the geographic area served by individual
railroads.
The second step is to determine the cost disadvantage of the least cost carrierÕs best competitor
for each wheat origin and the cost disadvantage of other routes the least cost carrier could use to
provide service to each wheat origin. The least cost solution to the network model is used to
determine these values.
The third step is to derive estimates of the least cost carrierÕs ability to raise prices when
competitor prices are held constant at variable cost. By assuming that rival railroads set price at
variable cost, the simulation imposes the maximum constraint on the ability of study area railroads
to increase their Kansas export wheat transportation prices.
The fourth step is to simulate changes in competition. Mergers are simulated by removing the
restriction that carriers cannot interchange trac and by eliminating interchange charges between
merging carriers.
The ®fth step is to estimate the carrierÕs ability to raise prices after the mergers. The procedure
used to derive these estimates is the same as in steps one through three outlined above for the premerger railroads.
Finally, the estimates of each railroadÕs market power (ability to raise prices) after the mergers
is compared with its market power before the mergers. In this study, market power is de®ned as
the ratio of rail prices to rail variable costs. Market power is reported for each railroad a€ected by
mergers. The market power value reported for a railroad is calculated by dividing the estimated
total revenue of all the railroadÕs shipments by the total rail variable cost of these shipments. This

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277

measure of market power was selected because it was the ratio used by the former Interstate
Commerce Commission to test for rail market dominance.
Two estimates of market power and net revenue (railroad revenue minus variable cost) were
calculated. The ®rst estimates are the solutions to the ®rst iteration of Phase I of the pro®t
improvement algorithm. These are the market power and net revenue estimates of the railroads
if they restrict themselves to the movements of the least cost solution. The second set of estimates are the solutions to Phase II of the pro®t improvement algorithm which allows railroads
to deviate from least cost routes if net revenue can be increased by doing so. Since the pattern of
results is the same for both phases, in the interest of brevity only the Phase II results are reported.

4. The data
The network model requires the speci®cation of several predetermined structural variables
including the following:
(a) the number of storage periods within the crop year;
(b) the size and number of production origins in the study area;
(c) sites and storage capacities of country elevators in the study area;
(d) sites of inland terminals; and,
(e) sites of export terminals.
Multiple crop year time periods are not appropriate for this study. The objectives of the paper
require identi®cation of geographic areas where individual railroads have a cost advantage. Effective storage capacity constraints at production origins and multiple time periods would force
the network model to select other than least cost routes for some wheat shipments. Therefore, a
model with multiple time periods would not provide accurate information about the potential
market areas of individual railroads or the ability of a railroad to restrict a competitor railroadÕs
prices.
Each production origin is represented by a 12 by 12 square mile area resulting in 342 farm
production origins in the study area.
A total of 280 locations (including 13 country elevators in Nebraska) were selected for country
elevator sites based on the elevator size and the presence of rail service. A total of 46 locations
were not included in the network model because they have no rail service. The storage capacities
for country elevators are obtained from Kansas Grain and Feed Association (1994).
Five elevator locations (Colby, Dodge City, Liberal, Ogallah, and Wakeeney, Kansas) in the
study area and one elevator location (Enid, Oklahoma) out-of-state were selected from country
elevator locations in the study area to be subterminal sites. A country elevator is considered a
subterminal site if an elevator facility at that location has railroad service and has sucient
storage and loading capacity for 100 car shipments.
Three inland terminals (Hutchinson, Salina, and Wichita, Kansas) were selected as intermediate
trans-shipment terminals. It is assumed that the storage capacity at inland terminals is sucient
and no restriction for inadequate storage facilities is necessary.

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Houston, Texas is chosen as the export terminal and is used to measure the shipping distances
from the study area.
The network model requires exogenous estimation of the quantity of wheat supplied for export
from each of the 342 production origins. The average Kansas wheat production of 1994, 1995 and
1996 is assumed to be the total wheat supply. During the assumed time frame, the study area
produced an average 283.93 million bushels of wheat which was 87.4% of the total Kansas wheat
production. The three year average production of each study area county is divided by the area of
the county to obtain the average production for each square mile. The wheat supply for export of
each production origin is then estimated by multiplying the average production of each square
mile by the number of square miles in the production origin. The wheat production data is from
Kansas Farm Facts (Kansas Department of Agriculture, 1995, 1996). By assumption, the quantity
of wheat demanded at Houston is equal to the total three year average production (284 million
bushels) of the study area.
The network model requires transportation cost estimates for farm trucks, commercial trucks,
and railroads. Costs are used rather than prices because the objectives of the paper require estimates of the least cost carrierÕs cost advantage relative to its competitors. Also cost estimates are
needed to calculate revenue/variable cost ratios, our measure of market power.
In this study, it is assumed that movements from production origins are by custom wheat
harvester trucks. The prices charged for these wheat shipments are from Kansas Department of
Agriculture (1994). These trucks have a high percentage of variable costs so there is little di€erence between prices and costs.
Commercial truck costs are assumed to have a cost advantage relative to railroads for short
hauls. This is because truck costs increase in direct proportion to distance whereas rail costs do
not due to the high percent of ®xed costs in railroad cost structures. Thus after the short line
railroad cost function is estimated, the commercial truck cost function is assumed to have a cost
advantage relative to the short line for 0±50 miles, but higher costs than the short line for distances that are greater than 50 miles. The estimated commercial trucking cost function used in the
model is based on Berwick (1997) and is as follows: 9
Dollars/1000 Bushels ˆ 9.8 + 0.7376 (one way mileage).
The mileage between country elevators, subterminals, and terminals in the study area is estimated by aggregating the highway map distances between origins and destinations.
Estimates of Class I railroad variable costs are from the ICC Railroad Costing Program (ICC,
1991). The 1994 costs of BN, SF, UP, and SP are used for wheat shipments from the study area to
Houston. When these railroad merge, the combined railroad is assumed to operate at the costs of
the lowest cost partner. This assumption has a parallel in the merger simulation literature (Werden and Froeb, 1996, p. 72 and Werden and Froeb, 1994, p. 415). For example, since BN has
lower costs than SF, the BNSF is assumed to operate with the BN cost structure. This assumption

9
Berwick (1997) calculated a trucking cost function in terms of cost per mile for a ®ve axle, 42 foot hopper bottom
truck trailer. This is the type of commercial truck commonly used to transport grain and thus the type of truck assumed
in this study. Trucking costs included in BerwickÕs study are labor, waiting time, fuel, interest, and maintenance and
repair. The commercial truck cost function used in this paper was obtained by regressing BerwickÕs trucking costs
estimates on one way miles for distances from 25 to 300 miles.

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279

Table 1
E€ect of post-merger cost assumptions on post-merger railroad revenue/variable cost ratios
Railroad

Revenue/variable cost ± railroads retain their
pre-merger costs

Revenue/variable cost ± merged railroad
assumes the costs of the low cost partner

Subregion 1
Kyle
BN
UP

1.050
1.042
1.039

1.048
1.057
1.038

Subregion 2
CKR
Kyle
BN
UP

1.041
1.048
1.045
1.030

1.040
1.042
1.058
1.027

Subregion 3
CV
BNSF
UPSP

1.050
1.035
1.054

1.041
1.050
1.061

Subregion 4
CKR
KSW
BNSF
UPSP

1.020
1.022
1.041
1.038

1.020
1.018
1.043
1.040

seems reasonable since there is reduced economic incentive for a merger if the merging ®rms are
unable to reduce their costs.
The results of the analysis are not sensitive to the costs that are assumed for merged railroads.
According to the data in Table 1, the revenue/variable cost ratios, corresponding to the assumption that the merged railroad experiences no cost reduction, are virtually identical to the
ratios obtained by assuming the merged railroad operates at the costs of the lowest cost partner.
This indicates that most of the post-merger cost reductions observed in the empirical section of the
paper are due to the more direct routing enabled by the mergers. Cost reductions due to economies of scale may also occur in merged railroads. Unfortunately the only empirical rail costing
data (ICC, 1991) available is unable to measure economies of scale. Thus the analysis in this paper
is only able to measure post-merger cost reductions due to more direct routing and operation of
the merged railroad at the costs of the most ecient partner.
Short lines have lower costs than Class I railroads. The three major sources of cost savings are
labor, equipment, and maintenance of way. Labor costs are lower due to more ¯exible work rules,
smaller crews, and lower wages and bene®ts. Short lines formed since 1970 operate with an average of 0.54 employees per mile of track compared with 1.8 employees for Class I railroads. 10
Therefore, short line railroads are able to operate rail lines at a lower cost than that of Class I
railroads.
Since the ICC costing program only measures Class I rail costs and there is no other cost
function for short lines, some assumption must be made regarding the measurement of short line

10

See Dooley (1991).

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J.J. Park et al. / Transportation Research Part E 35 (1999) 269±290

railroad costs. Among Class I railroads, Illinois Central (IC) has the lowest cost structure. Thus
the cost function of IC is regarded as the short line railroad cost function.
Grain handling costs were supplied by Mack N. Leath of the Economic Research Service, US
Department of Agriculture.

5. Empirical results
Before discussing the impact of the BN±SF and UP±SP mergers on railroad costs and prices it
is useful to discuss the nature of the results generated by the network model and the pro®t improvement algorithm. While the estimates of rail variable costs derived in this study are estimates
of actual costs, the values derived for rail revenues and prices are not estimates of actual revenues
or prices. These values underestimate actual rail prices and revenues because they are derived
under the stylized assumption that competitor railroads set prices that are equal to variable costs.
While these estimated prices do not provide estimates of actual railroad prices, they do provide an
indication of expected trends in price/variable cost ratios (measures of market power) due to
railroad mergers.
There are alternative methodologies for evaluating the impact of railroad mergers on railroad
grain prices. For example, regression analysis could be performed with actual railroad prices as
the dependent variable. The explanatory variables could include various railroad demand and cost
factors as well as merger e€ects (perhaps proxied by a dummy variable). The diculty with this
approach is that actual rail price data are not available by year and by individual railroad. To
empirically estimate the regression model would require actual railroad price data for many years
prior to and after the railroad merger. In addition the actual railroad price data would have to be
railroad speci®c to examine the e€ects of mergers. Railroad revenue is published in Carload
Waybill Statistics compiled by the Association of American Railroads, however the data are not
available by railroad. Also, Wolfe and Linde (1997) raises serious questions concerning the use of
Waybill data for railroad price studies.
The objective of this study is not to measure actual railroad price changes resulting from
mergers but rather changes in the ability of merged railroads to raise their prices. The methodology used in the study was selected because it systematically measures the ability of merged
railroads to raise their prices. Nevertheless we indirectly measured actual railroad prices both
before and after the mergers to determine if actual railroad price changes are consistent with the
empirical results of the analysis.
The study area is divided into four subregions to facilitate handling of the large computational
requirements of the network model (Fig. 1). Results of these four subregions are aggregated to
evaluate results for the whole study area logistics system.
5.1. Impact of the BN±SF merger in subregions 1 and 2
The UP obtains no bene®t from the merger with SP in subregion 1. This is because UP has
more direct routes to Houston than SP, and UP has lower costs than SP.
As a result of the BN±SF merger, there is an increase in the market power and net revenue of
the BN and SF railroads and a reduction in the market power of the other railroads serving

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Table 2
Estimated net revenue and revenue/variable cost ratios for a€ected railroads before and after BN±SF merger when least
cost routes are recalibrated (subregion 1)
Railroad

KYLE
BN
UP

Pre-merger

Post-merger

Revenues±variable costs
(dollars per 1000 bushels)

Revenue/variable cost

Revenues±variable costs
(dollars per 1000 bushels)

Revenue/variable cost

121.0
101.8
57.2

1.053
1.040
1.042

15.3
112.4
41.5

1.048
1.057
1.038

subregion 1. This occurs because the cost of shipping wheat to the Gulf of Mexico on the BN is
greatly reduced due to more direct routing. Also it is assumed that after the merger the combined
railroad assumes the costs of the lowest cost partner. Thus since BN costs are lower than SF costs,
BNSF is assumed to operate at BN cost. Therefore, most of the increase in market power and net
revenue occurs because the combined BNSF railroad is assumed to be a more ecient carrier.
Table 2 contains estimated net revenue for the railroads serving subregion 1 before and after the
BN±SF merger after least cost routes have been recalibrated by the pro®t improvement algorithm.
As the table indicates, BN net revenue (revenue ÿ variable costs) increased from $101.80 per 1000
bushels before the merger to $112.40 after the merger. In contrast, Kyle net revenue plunged from
$121.00 per 1000 bushels before the merger to only $15.30 after the merger. The corresponding
®gures for UP are $57.20 (pre-merger) and $41.50 (post-merger).
The market power (revenue/variable cost) of BN increases from 1.04 (pre-merger) to 1.057 (postmerger), while the corresponding ®gures for Kyle are 1.053 and 1.048. Likewise the market power
of UP falls from 1.042 to 1.038. These ratios are well below that which is generally accepted as the
minimum price needed to cover total rail costs. 11 These low ratios are attributable to the normalization assumption that both UP and Kyle are setting a price equal to variable cost. Therefore,
the combined BNSF still faces a high degree of competition from the Kyle and UP railroads.
While the subregion 1 ratios of rail revenue to variable costs show changes in market power,
they do not provide a complete picture of the impact of the BNSF merger on the a€ected railroads. The impact on trac volume is also important.
As a result of the BN±SF merger, the revenue/variable cost ratio of the Kyle falls from 1.053 to
1.048. This appears to be a relatively insigni®cant amount. However, this measure of market power
does not re¯ect the fact that the Kyle is no longer the least cost carrier for most of the wheat origins
where it had a pre-merger cost advantage. The total volume of wheat shipped by the Kyle in
subregions 1 and 2 falls from 39 040 thousand bushels to 29 176 thousand bushels, a decline of 25%.
Railroads have a high percentage of ®xed assets, and a signi®cant reduction in trac volume would
force a railroad to either raise prices, due to higher cost per unit shipped, or accept reduced pro®ts
(or increased losses) until the size of the railroad could be adjusted to new trac levels. Thus, the
potential reduction in trac volume is far more important to the management of the Kyle than the

11

Railroads have a high proportion of ®xed and common costs and must therefore set prices substantially in excess of
variable costs in order to cover total costs. Prices exceed variable costs by the largest amounts for those commodities
with price inelastic demand for railroad transport.

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J.J. Park et al. / Transportation Research Part E 35 (1999) 269±290

change in average revenue/variable cost ratio. It is very likely that the Kyle would strenuously resist
any major loss of trac by improving service and/or reducing price.
The results of the model show that after the merger the BNSF should dominate the movement
of wheat from subregion 1. However, the BNSFÕs cost advantage relative to Kyle and UP is
relatively small and the Kyle cannot a€ord to lose a signi®cant volume of its trac. Therefore,
there should be signi®cant cost reductions and a very high level of competition among the carriers
after the merger. The ®nancial position of the Kyle could be seriously threatened.
The BNSF merger should bene®t most shippers. The cost advantage of the combined BNSF
will allow it to compete for more trac. This increased competition should drive down prices for
most wheat shippers in the short run. The long run e€ects are not as clear. The combined BNSF
has a cost advantage for most of the wheat shipments from the north portion of subregion 1. This
advantage may enable it to drive the Kyle from this market. If this occurs, shippers could face
increased prices in the long run.
Table 3 contains total transportation and handling costs plus estimated pro®ts from Phase II of
the pro®t improvement algorithm for a€ected origins in subregion 1. As the data in the table
indicate, these values decline after the merger for all a€ected production origins in subregion 1,
with an average decrease of 6.25%. To the extent that lower costs and limitations on the estimated
ability of railroads to raise prices translate into reductions in actual rail prices, wheat shippers
would bene®t from the BN±SF merger.
Table 4 contains estimated net revenue for the railroads serving subregion 2 before and after the
BN±SF merger after least cost routes have been recalibrated by the pro®t improvement algorithm.
The merger has virtually no impact on the pro®ts of Central Kansas Railway (CKR) and the UP.
Table 3
E€ect of BN±SF merger on transportation and handling costs plus estimated pro®ts when railroads raise prices and
recalibrate least cost routes (subregion 1)
Production origin

Transportation and handling costs plus estimated pro®ts ($/1000 bushels)
Pre-merger

Post-merger

BB
BC
BD
BE
BF
BG
BH
BI
BJ
CB
CC
CD
CE
CF
CG
CH
CI
CJ

1120.8
1040.3
998.6
976.3
975.3
985.4
949.3
937.3
948.3
958.7
964.5
961.7
886.4
952.3
984.3
956.3
974.8
981.2

1051.30
960.2
945.3
912.8
896.1
952.4
889.2
875.3
862.3
884.3
901.5
925.3
825.3
896.3
925.3
914.9
900.8
935.8

ÿ6.2
ÿ7.7
ÿ5.34
ÿ6.5
ÿ8.12
ÿ3.35
ÿ6.33
ÿ6.61
ÿ9.07
ÿ7.76
ÿ6.53
ÿ3.78
ÿ6.89
ÿ5.88
ÿ5.99
ÿ4.33
ÿ7.59
ÿ4.63

975.1

914.10

ÿ6.25

Average

Percent change

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J.J. Park et al. / Transportation Research Part E 35 (1999) 269±290

Table 4
Estimated net revenue and revenue/variable cost ratios for a€ected railroads before and after BN±SF merger when least
cost routes are recalibrated (subregion 2)
Railroad

CKRa
KYLE
BN
UP
a

Pre-merger

Post-merger

Revenues±variable costs
(dollars per 1000 bushels)

Revenue/variable cost

Revenues±variable costs
(dollars per 1000 bushels)

Revenue/variable cost

63.50
101.30
85.30
48.50

1.041
1.05
1.038
1.03

63.20
85.40
92.40
48.00

1.04
1.042
1.058
1.027

Central Kansas Railway.

The net revenue of BN increases somewhat while Kyle net revenue decreases from $101.30 per
1000 bushels (pre-merger) to $85.40 (post-merger). The same pattern occurs in the railroad revenue/variable cost ratios. There is virtually no change in the market power (revenue/variable cost
ratio) of CKR and UP, but market power of Kyle declines somewhat from 1.05 (pre-merger) to
1.042 (post-merger). The market power of BN increases from 1.038 (pre-merger) to 1.058 (postmerger). Thus there is a redistribution of pro®ts and market power in subregion 2 from the Kyle
to BN. This is because the former BN is able to reduce its costs after the merger due to more direct
routing. Also the former SF in subregion 2 assumes the lower costs of the BN after the merger.
However the BNSF cost advantage relative to the other railroads serving subregion 2 is small and
the combined railroad still faces a high degree of intramodal competition.
5.2. Impact of the BN±SF and UP±SP mergers in subregions 3 and 4
Since there are no former BN lines in these subregions the only e€ect of the BN±SF merger is
that the SF assumes the lower cost structure of the BN.
The merger between the UP and the SP allows the SP to use more direct UP routes to the Gulf
of Mexico for the wheat it can obtain on its line from Liberal to Hutchinson, Kansas, signi®cantly
lowering the SPÕs costs. Also it is assumed that the combined railroad assumes the lower costs of
the UP.
Table 5 contains subregion 3 estimated net revenue (revenue±variable costs) and revenue/
variable cost ratios for a€ected railroads before and after the BN±SF and UP±SP mergers when
least cost routes are recalibrated by the pro®t improvement algorithm. As the data in the table
Table 5
Estimated net revenue and revenue/variable cost ratios for a€ected railroads before and after BN±SF and UP±SP
mergers when least cost routes are recalibrated (subregion 3)
Railroad

CVa
BNSF
UPSP
a

Pre-merger

Post-merger

Revenues±variable costs
(dollars per 1000 bushels)

Revenue/variable
cost

Revenues±variable costs
(dollars per 1000 bushels)

Revenue/variable
cost

107.90
45.00
2.80

1.052
1.036
1.013

14.20
49.70
139.00

1.041
1.05
1.061

Cimmaron Valley.

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J.J. Park et al. / Transportation Research Part E 35 (1999) 269±290

indicate, UPSP net revenue jumps from $2.80 per 1000 bushels before the two mergers to $139
after the mergers. Net revenue of BNSF increases somewhat from $45 per 1000 bushels before the
mergers to $49.70 after the mergers. This result is due to the fact that the former SF is assumed to
operate at the lower costs of the BN after the BN±SF merger. The reduced costs allow the former
SF to divert trac from Cimmaron Valley (CV) railroad. The net revenue of CV plunges from
$107.90 (pre-merger) to only $14.20 (post-merger). Thus the mergers could threaten the long term
viability of CV.
Table 5 also reveals that the subregion 3 market power (revenue/variable cost ratio) of UPSP
increases from 1.013 (pre-mergers) to 1.061 (post-mergers). The market power of BNSF rises from
1.036 to 1.050 while that of the CV falls from 1.052 to 1.041. Thus while the market power ratio of
the UPSP increases after the mergers, it is still only slightly greater than one. This is because of the
normalization assumption that the BNSF and CV set their prices at variable cost.
Table 6 contains total transportation and handling costs plus estimated pro®ts from Phase II of
the pro®t improvement algorithm for a€ected origins in subregion 3. As the data in the table
indicate, these values decline after the mergers for all a€ected production origins in subregion 3,
with an average decrease of nearly 25%. To the extent that lower costs and limitations on the
estimated ability of railroads to raise prices translate into reductions in actual prices, wheat
shippers would bene®t from the BN±SF and UP±SP mergers.
The reduction in transportation and handling costs is greater in subregion 3 than subregion 1
because there are more sources of saving in these costs. In subregion 3, the former SF assumes the
lower costs of the former BN in the combined railroad while the former SP assumes the lower
costs of the former UP. Also the SP routes to Houston are very circuitous so its merger with UP
resulted in large cost reductions due to more direct routing.
Table 7 contains estimated net revenue for the railroads serving subregion 4 before and after the
BN±SF and UP±SP mergers after least cost routes have been recalibrated by the pro®t improvement algorithm. The mergers have almost no impact on the net revenue of CKR and Kansas
Table 6
E€ect of UP±SP and BN±SF mergers on transportation and handling costs plus estimated pro®ts when railroads raise
prices and recalibrate least cost routes (subregion 3)
Production origin

Transportation and handling costs plus estimated pro®ts ($/1000 bushels)

Percent change

Pre-merger

Post-merger

QE
PF
QG
PH
OI
PI
OJ
PJ
OK
PK
NL
OL
PL

958.40
1045.30
988.20
1069.40
1080.4
1079.8
1075.3
1158.4
1185.5
1182.3
1083.8
1088.3
1189.2

795.2
764.2
786.5
795.3
796.4
805.7
779.4
859.2
895.3
886.3
784.8
825.4
894.6

ÿ17.00
ÿ26.90
ÿ20.40
ÿ25.60
ÿ26.3
ÿ25.4
ÿ27.5
ÿ25.8
ÿ24.5
ÿ25
ÿ27.6
ÿ24.2
ÿ24.8

Average

1091.1

820.6

ÿ24.8

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J.J. Park et al. / Transportation Research Part E 35 (1999) 269±290

Table 7
Estimated net pro®ts and revenue/variable cost ratios for a€ected railroads before and after BN±SF and UP±SP
mergers when least cost routes are recalibrated (subregion 4)
Railroad

CKRYa
KSWb
BNSF
UPSP
a
b

Pre-merger

Post-merger

Revenues±variable costs
(dollars per 1000 bushels)

Revenue/variable cost

Revenues±variable costs
(dollars per 1000 bushels)

Revenue/variable cost

54.20
48.50
85.70
54.30

1.021
1.022
1.041
1.038

53.60
48.20
95.40
89.20

1.020
1.018
1.043
1.040

Central Kansas Railway.
Kansas Southwestern Railway.

Southwestern (KSW) while the net revenue of BNSF increases somewhat and the net revenue of
UPSP increases substantially from $54.30 per 1000 bushels (pre-mergers) to $89.20 (post-mergers). Net revenue of BNSF increases because it is assumed that the former SF operates at the
lower costs of the BN after the merger. Net revenue of UPSP increases as a result of the SP
obtaining the direct routes of the UP to Houston and by operating at the lower costs of the UP.
The BN±SF and UP±SP mergers produce virtually no change in the market power of the four
railroads serving subregion 4. This is likely due to strong intermodal competition from commercial trucks which have a cost advantage for short hauls to terminal elevators at Wichita and
Hutchinson, Kansas as well as Enid, Oklahoma.

6. Estimating actual railroad prices before and after mergers
The foregoing investigates potential impacts on pricing by merged railroads on the assumption
that competitor railroads price at variable cost. A more direct approach is to examine actual rail
pricing before and after a merger. But reliable actual railroad price data do not exist. However
according to Fuller et al. (1987) grain prices in an export grain corridor re¯ect the port price and
the transportation and marketing charges necessary to market the grain to the respective port.
Thus the di€erence between the Houston wheat price and the Kansas farm level wheat price is
equal to transportation and marketing charges for shipping wheat from Kansas to Houston. This
is called the geographic price spread which is computed as follows:
Price Spread ˆ Houston Wheat Price ÿ Kansas Wheat Price
ˆ Transportation Costs ‡ Marketing Costs:
This assumes that the grain handling industry is suciently competitive so that any change in
transportation price will be re¯ected in the geographic price spread. There is empirical support for
this assumption. Babcock et al. (1985) found that changes in export rail wheat transport prices
from selected Kansas origins to the Gulf of Mexico are nearly identical to changes in geographic
price spreads between Kansas and the Gulf of Mexico. Klindworth et al. (1985) found the
transportation price to be the largest component of the Kansas-Gulf of Mexico price spread; for
14 Kansas grain elevators the export rail wheat price averaged 84±92% of the geographic price
spread. Thus we can be reasonably sure that the Kansas-Houston geographic price spread is

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J.J. Park et al. / Transportation Research Part E 35 (1999) 269±290

approximately the same as the actual rail wheat transportation price from Kansas origins to
Houston for export.
Table 8 contains geographic price spreads for 52 grain elevator locations in the study area. The
BN±SF merger was completed in September 1995 while the UP±SP merger was consummated in
July 1996. The pre-merger price spreads in Table 8 are the average price spreads (measured in
cents per bushel) for the second quarter of 1994 while the post-merger price spreads are the average price spreads for the second quarter of 1998.
An examination of the table indicates that only eight of the 52 price spreads increased after the
mergers and only two of the eight increased by more than 5%. The other 44 price spreads decreased after the merger with 17 of the 44 price spreads decreasing by more than 10%. Thus the
analysis indicates that actual railroad wheat transport prices in the study area declined following
the mergers which is consistent with the conclusion of the network model analysis that mergers
did not increase the ability of merged railroads to raise their prices. Stated another way, declining
post-merger railroad prices are not consistent with the hypothesis that mergers increase railroad
market power.
The results of this study are consistent with those of Lemke and Babcock (1987) which found
that the Union Paci®c±Missouri Paci®c (MP) merger resulted in little or no gain in market power
since at least one other railroad served the same areas as UPMP. In contrast, an evaluation of the
proposed SF±SP merger found large increases in ability to raise prices since no other railroad
served southwest Kansas at that time. Thus this study and Lemke and Babcock (1987) conclude
that mergers do not signi®cantly increase mark