O’Rourke, Laurence
1
The Impact of Differential Pricing on Barge Freight Transportation
Prepared for the Transportation Research Board
Submitted July 31, 2002
Word Count 5,649 + 1 Table
Laurence O’Rourke
Senior Associate
ICF Consulting
9300 Lee Highway
Fairfax, VA 22031
lorourke@icfconsulting.com
Phone: 703-934-3186
Fax: 703-934-3740
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ABSTRACT
Through the Staggers Act (1980) and the Railroad Revitalization and Regulatory Reform Act
(1976), Congress deregulated railroad pricing to improve the financial health of the industry.
Deregulation legalized differential pricing; the policy of charging customers different prices
according to their willingness to pay.
While the railroads have returned to profitability,
shippers have been angered by railroad pricing strategies that are seen as abusive. Railroads
have refused to quote rates to competing transportation facilities or have set prices to divert
traffic onto the rail network. This study seeks to measure the impact of differential pricing of
rail services on barge transportation in the Ohio River Basin. It provides an economic
measurement of this effect by constructing a model to predict freight traffic volumes at barge
terminals in the Ohio River Basin.
Keywords: Differential Pricing, Intermodal, Modal Diversion, Barge Freight, Railroad Market
Power
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INTRODUCTION
In 1980, Congress passed the Staggers Rail Act. The act mandated the end to rigid railroad rate
regulation by the Interstate Commerce Commission (ICC). Differential pricing policies legalized
by the Staggers Act have been a source of significant political debate. In freight markets where
competition exists from other transportation modes, rail rates are set close to variable costs. For
captive shippers who are served by only one railroad, rates are often 300% of variable cost.
(Kahn, 1999) Shippers have alleged that railroad rates to competing modes of transportation
have been increased to divert traffic onto the rail network. For instance, in the Ohio River Basin,
railroads may be increasing rates on rail movements to intermodal barge terminals in order to
divert freight traffic to rail lines running parallel to the river. Industry proponents have argued
that such diversion of traffic benefits all shippers by increasing the traffic on the rail network and
allowing railroads to spread fixed infrastructure costs over more freight shipments, thus lowering
costs and prices for everyone (Cunningham and Jenkins, 1997). This research conducts a
measurement of the impact of differential pricing on the transportation of freight by barge in the
Ohio River Basin.
The pricing and service policies of railroads are currently issues of great contention. The
railroads have argued that shippers have access to regulatory remedies to abusive pricing
practices through the rate reasonableness and competitive access provisions of the Staggers Act.
Shippers have insisted that abuse of railroad market power is having a substantial economic
impact. Public policy makers have also been concerned about the size of the freight traffic
diversion because of the potential environmental impact. Barge freight movements are less
energy intensive and cleaner per ton-kilometer for some air pollutants.
This research tests the hypothesis that differential pricing has diverted traffic from barge
to rail. The paper is divided into six sections. Section 1 describes the theoretical and historical
background of the differential pricing issue. Section 2 describes the data and methodology
employed. Section 3 presents the results of the analysis. Section 4 discusses these results and
places them in the context of other research. Section 5 provides some closing comments and
conclusions from this research.
HISTORY AND THEORY OF DIFFERENTIAL PRICING
The regulation of railroad market power is a policy issue with much history. The Interstate
Commerce Commission (ICC) was created in 1887 to restrain railroad market power and pricing
practices. On local lines where the railroads held monopoly positions, they frequently charged
small shippers higher prices for moving freight on short hauls than where charged for
competitive long haul movements. The Commerce Act, which created the ICC, specifically
forbade such long-haul/short-haul pricing practices. The law also stated that rates should be "just
and reasonable". The law established the ICC as the agency that would hear shipper complaints
and required the railroads to file public rates.
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Over time, the extent of railroad regulation grew. In the Transportation Act of 1940, the
use of umbrella rate making was encouraged. Under this policy, the ICC considered the impact
that railroad rates would have on competing modes of transportation. Umbrella rate making
tended to favor other modes of transportation. ICC regulation was slow to accommodate
changes in the economy following World War II. Less regulated transportation modes were able
to capture freight from the railroads. Between 1947 and 1977, while the freight market grew by
91%, the volume of tonnage hauled by railroads decreased. (Congressional and Administrative
News, 1980) Truck and barge operators captured this freight traffic. By the mid-1970's, several
major railroads where on the verge of bankruptcy and the industry as a whole was experiencing
poor financial performance which impeded its ability to attract new capital. Several pieces of
legislation where enacted to improve the profitability of the industry.
The Railroad Revitalization and Regulatory Reform Act of 1976 limited the scope of ICC
ratemaking authority to those situations where a railroad had "market dominance". The use of
umbrella rate making was abolished. The profitability of railroad capital became an explicit goal
pursued through the regulatory process. The law also made provision for expedited merger
proceedings. In 1980 the Staggers Act deregulated the industry further. The purpose of the act
was to rely on market forces and the shipper demand for transportation services to set prices.
The act formally addressed the issue of differential pricing by establishing a range of differential
pricing which was considered reasonable on its face. Additionally, the law placed the burden of
proof for challenging the reasonableness of a rate on the shipper. Private contracts where
legalized, allowing railroads to negotiate separate and unpublished rates with shippers. During
the 1980's, the railroads underwent fundamental restructuring. Through downsizing,
consolidation and the use of differential pricing, the railroads increased their profitability and
productivity.
The result of deregulation was the concentration of market power in the railroad industry.
In 1976 there were 63 Class I railroads in the U.S, by 2002 there were only 8. Four megacarriers currently control 95 percent of the gross ton-kilometers moved on the system. With the
ICC focused on returning the industry to profitability, railroads were given free rein to
consolidate their market positions. Many shippers believe that the lack of competition has
removed the incentives for railroads to provide quality service. A GAO report found that a
majority of shippers believed that their service has deteriorated since 1990. (GAO, April 1999)
The ICC Termination Act of 1995 carried deregulation in a new direction by abolishing
the ICC and transferring some of its functions to the Surface Transportation Board (STB). The
STB is located in the Department of Transportation. One of the motivations behind the law was
to obtain a greater balance between shipper and railroad interests. Many lawmakers felt that the
ICC was beholden to the interests of the railroad industry. By placing the STB in the
Department of Transportation, and requiring that it be re-authorized every two years, lawmakers
sought to make the agency more accountable. The act also required that a simplified procedure
for rate complaints be established which would expedite shipper complaints.
In 1999 a number of legislative proposals were introduced to address shipper concerns.
The "Railroad Competition and Service Improvement Act of 1999" (S.621) proposed to expand
the power of the STB to consider competitive access solutions in a range of cases where the
market dominance of an existing railroad had not been established. The law called for the
development of measures of railroad service quality and proposed to allow the STB to mandate
competitive access solutions where rail service falls below a certain threshold.
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The legislation endorsed several different competitive access remedies. It proposed to
allow the STB to mandate trackage rights in a more expansive set of circumstances. Trackage
rights are legal agreements that allow the trains from one railroad to run over the lines of another.
The law proposed to overturn the STB's "bottleneck decision". Railroads are currently
not required to quote rates from every origin and destination point on their system. Railroads
often refuse to quote rates at certain choke points on their network to restrict access of shippers
to competing transportation facilities. S.621 sought to overturn these practices and promote
competition by requiring railroads to quote rates to all switching points on their network. S.621
also sought to cap freight rates for small agricultural shippers at 180% of variable cost. In short,
the law was a direct attack on differential pricing and the philosophy of the Staggers Act. These
congressional proposals proved to be too politically contentious and did not become law.
The recently introduced “Railroad Competition, Arbitration and Service Acts of 2002”
has proposed a more limited set of reforms. The act seeks to offer more service choices to
shippers by promoting the use of trackage rights for unit trains and haulage agreements for
carload traffic. Additionally the law seeks to promote competition by eliminating paper barriers
to smaller carriers competing in new markets. Lastly, the law seeks to create a clearly defined
final offer arbitration system that will create a more efficient and streamlined dispute resolution
process for rate cases.
The proponents of competitive access solutions for rail service argue that it is the analog
of the de-regulation that has occurred in the gas pipeline, electric utility and the
telecommunications industries. In these cases, monopolists were required to make their
networks available to competitors at a rate that allowed them recover their costs. (Faucett, 1999)
The competition that ensued spurred innovation and resulted in cost savings for the consumer.
Opponents of a more competitive regime for railroads argue that differential pricing is
economically efficient and that most shippers would be worse off if railroads where unable to
price differentially. Railroads have high fixed costs to maintain their network. These costs are
not assignable to any particular shipment, but are the overhead of running a railroad. The
marginal cost of moving a given train over this network, the "variable cost", is often very low.
Railroads face the dilemma of how to allocate their fixed costs to specific shippers. Their
solution is to price differentially. Those shippers who are most price sensitive, such as those
who can switch to truck or barge, are charged lower prices. These prices recover the variable
costs of the movement and allow the railroad to spread a small fraction of its fixed costs onto
these shippers as well. For those "captive shippers", who do not have any other transportation
options, the railroad charges higher prices, thus allocating the majority of the fixed costs of the
system to them.
Some economists argue that differential pricing benefits even captive shippers. If
competition or regulation forced prices to be equalized, those shippers who are the most price
sensitive would exit the system. The remaining captive shippers would then pay the full fixed
cost of the rail network. (Erickson, 1999)
Differential pricing is thus a rather slippery issue. Those who favor competition are
really making an argument for a more extensive regulatory regime to guarantee competition.
The history of industry regulation at the ICC is not one that should instill one with confidence.
The current regulatory regime, managed by the STB, is expensive, cumbersome and little utilized
by shippers. They find the agency to be responsive primarily to the interests of the railroads.
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Those who support the policies embodied in the Staggers Act and the reliance on demand
based pricing are making the case for the free exercise of market power by a small number of
quasi-monopolists. This can also be a rather economically costly policy, as recent service and
system failures have shown.
This paper contributes to this debate by conducting an economic measurement of the
impact of railroad market power on barge transportation. Defining and measuring the magnitude
of impacts across different modes of transportation is an important first step in addressing the
issue of railroad pricing. Rail pricing strategies that shift freight away from barge have a wide
variety of impacts. While some academics have argued that the pricing practices of railroads are
not inherently inefficient (Kleit, 1990), the impacts of the exercise of rail market power may
have environmental externalities associated with them. (USACE, Rock Island, 1996)
Additionally, the regulations governing rail service pricing provide a mechanism to transfer
wealth from one set of private agents to another. Even if this transfer does not reduce efficiency,
it is a substantial concern of policy makers.
DATA AND METHODOLOGY
Several data sources were used for this research. These include the U.S. Army Corps of
Engineers (USACE) Waterborne Commerce Statistics, the Navigation Data Center Publications
and Waterway Data CD, the Inland River Guide, the Association of American Railroads revenue
data and the Bureau of Economic Analysis (BEA) industry earnings data. Each of these data
sources is described in more detail below.
Waterborne Commerce Statistics
The U.S. Army Corps of Engineers collects waterborne commerce statistics on all barge freight
movements on the inland waterway system. This information includes dock of origin, dock of
destination, four-digit commodity code description and tons moved. The data are gathered from
freight tariffs submitted by barge operators. The data contain fields describing other levels of
geographic detail, including port equivalent origin and destination, county origin/destination,
state origin/destination, and BEA origin/destination. Every barge freight movement on the
inland waterway system is represented in the data.
A subset of this data consisting of all commodity movements on the Ohio River System
was used for this analysis. The Ohio River System includes the Ohio River and all of its
tributaries. The river system encompasses the Tennessee River, the Cumberland River, the
Green River, the Kentucky River, the Big Sandy River, the Kanawha River, the Monongahela
River, and the Allegheny River, among others. Every freight movement with an origin or a
destination on the river system was represented in the data for the years 1970-1996. Each record
in the data file represents the total annual movements, in tons, of a volume of one commodity
from a specific dock of origin to a specific dock of destination. The database contains 182,804
records.
To facilitate the analysis, the four digit commodity codes were aggregated into seven
commodity groups. These were agricultural chemicals, coal, coke, grain, industrial chemicals,
petroleum products and all other commodities. These categories were chosen to focus on major
waterway commodities. Analysis of grain, coal and industrial chemicals was particularly
important since these commodities are the ones for which shippers have been the most active in
seeking rate relief.
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Navigation Data Center Publications and Waterway Data CD
The Army Corps of Engineers conducts periodic surveys of barge terminal operators and their
facilities. An electronic database with information on all of the terminals on the inland waterway
system is available to the public from the Navigation Data Center. Information compiled
includes port and dock code, a description of the terminal facility, types of equipment on site and
a field describing the rail access available at the terminal. About 1,200 terminals on the Ohio
River System were reviewed and coded for rail access. Those terminals served by a rail line not
connected to the rail network where coded as not having rail access.
Inland River Guide
The Inland River Guide, published by the Waterways Journal, contains information on the
terminals, barge operators, boat stores and shipyards on the inland waterway system. Of
particular relevance to this project was the designation of terminals as "private" or "public".
Public terminals are those which serve numerous shippers. Private terminals are docks that have
been constructed by a factory or electric utility to meet its own needs. It was felt that differential
pricing would likely have a greater impact on public facilities. Commodities shipped to private
terminals are most likely to be used at the site, and not require any further transportation.
Commodities shipped to public terminals are likely to require additional transportation (truck or
rail). Public terminals are thus much more likely to be effected by railroad pricing policies. Of
the approximately 1,200 barge terminals in the study region, 85 were coded as public terminals.
Association of American Railroads Revenue Data
Data on average annual revenue per ton-kilometer from 1983 to 1996 was obtained from the
Research Department at the Association of American Railroads. Revenue per ton-kilometer
reflects the total cost charged to shippers divided by the total ton kilometers moved by the
railroads. This data was employed as a proxy for railroad productivity improvements. Over
time, revenue per ton-kilometer has declined.
BEA Region Earnings Data
This data consists of earnings data by industry for each Bureau Economic Analysis region.
Three components comprise earnings - wage and salary disbursements, other labor income, and
proprietors’ income. A negative value is rare but can occur when a negative proprietor’s income
outweighs wage and salary disbursements. In some cases, earnings may also be set to zero to
prevent the disclosure of information that could be attributed to specific firms. The BEA level
economic proxies used in the analysis were manufacturing earnings (400) and agricultural
service earnings (110). Agricultural service earnings were used to predict destinations of grain
and agricultural chemicals. Manufacturing earnings were used to predict terminating commodity
flows for all other commodity groups.
Economic proxies were chosen in the belief that industrial activity in certain sectors was
associated with the demand for commodity movements. In some cases, it was necessary to use
aggregated industry categories to avoid missing data in the more detailed industry categories.
For example, it would be desirable to use electric generation services earnings as a proxy for coal
movements. This would have been logical since the majority of coal moved on the river is used
in utilities. For a number of BEA's, electric generation services earnings were not disclosed to
protect confidential industry information. Thus the aggregate manufacturing earnings category
was used as an economic proxy for all non-agricultural commodities.
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DATABASE CONSTRUCTION
The original intent of this research was to run the model on the disaggregated dock level
database. It was thought that since the database contained over 182,000 records, the large N
would facilitate obtaining statistically significant results. At the dock level there is a lot of
"noise" in the data. The flow of commodities shifts between docks and varies greatly by year.
For instance, if a particular dock was being repaired, the volume of commodity moved might be
zero for one year. When the facility was reopened, thousands of tons might be delivered there.
Data was aggregated by BEA region, commodity group and year to smooth out these
fluctuations. The percent of tonnage flowing through docks with rail access and the percent of
tonnage flowing through public terminals was calculated. These percentages were used as the
proxies for rail access and destination at public ports.
Choosing the relevant time period to model was another important consideration. The
data set contained commodity movements for the years 1970 to 1996. Again, it was initially
thought that using a greater number of years would increase the amount of data available to fit
the model, and thus increase the precision of the estimates. Initial regression results using the
full time series were not significant. A number of features of the pre-1980's data made it
unlikely to reveal linear relationships between the independent variables and the dependent
variable (terminating tonnage). Oil price shocks, the decline of the integrated steel mills and the
bubble in the farm economy all had rather drastic effects on the volume of commodities being
shipped on the Ohio River. The decline of the integrated steel mills (which use coke as an input
to the steel making process) shows up in the data as a rapid drop in coke shipments during the
1970's. Coal movements also display a similar pattern because a substantial portion of coal is
converted into coke. Demand for these commodities recovers during the 1980's and is less
variable thereafter.
Shipments of petroleum products were also radically effected by the oil shocks. The
volumes of these commodities moved by barge show highly variable behavior, sometimes
changing by a factor of five within a few years. Changes in the price of oil radically shifted the
patterns of supply and demand.
It is interesting to note that the highly unstable nature of the economy during the 1970s
and the difficulty which government planners had in predicting future economic activity based
on current trends was one of the arguments for rate de-regulation. Data from 1983 onward was
used to estimate the model since a visual inspection of the data suggested that the model
variables where more likely to have a linear form in these years.
FORM OF THE MODEL
The model estimated used average revenue per ton kilometer, industry earnings, percent of
tonnage moved through public terminals and percent of tonnage moved through terminals with
rail access to predict the volume of barge freight terminating in a particular BEA region. A
separate model was estimated for each of the seven different commodity groups. The form of
the model, including error term, is as follows.
Yi = C + b1X1 + b2X2+ b3X3 + b4X4 + µi
Yi = Terminating Tonnage
The dependent variable in the model is total tons of a particular commodity, transported by barge
and delivered to a BEA region.
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X1 = Revenue per ton-kilometer
This variable controls for the impact of productivity increases in the railroad industry. One
would expect that there would be a positive relationship between rail revenue per ton and barge
shipments. As the cost of rail transportation falls, shippers will transport fewer goods by barge.
X2 = Industry Earnings
This variable controls for the impact of the economy on the demand for freight transportation
services. One would expect that industry earnings would be positively associated with barge
freight shipments. As industry earnings increase in a particular BEA region, barge shipments of
freight also increase because they are an input to this economic activity.
X3 = Percent of Tonnage Delivered to Public Terminals
One would expect this variable to have a negative relationship to the tonnage of barge shipments.
Public terminals are the places where freight will most likely be transferred to another mode.
Thus, public terminals are the places at which railroads may employ differential pricing to
capture freight. If a higher percentage of tonnage flows through public ports, we expect this to
reduce the overall volume of barge tonnage terminating in a BEA region.
X4 = Percent of Tonnage Delivered to Terminals With Access to Rail
One might expect that rail access would have an inverse relationship to the volume of barge
shipments over time. If a terminal has rail access, railroads may differentially price their
services to capture freight traffic volume.
MODEL ESTIMATION
Tests where run on the data to check for multicollinearity and heteroskedasticity. To test for
multicollinearity, auxiliary regression where used to test for linear relationships between the
dependent variables. Multicollinearity was not found, indeed as the results will show, the
amount of noise in the data made it difficult to identify strong relationships between any of the
variables.
Heteroskedasticity was identified as a problem. By graphing the predicted values of the
model against the residual error terms, it was clear that the variance of the error term was not
constant. As the predicted volume of tonnage increased, the size of the error terms also
increased. White-Huber heteroskedastic consistent standard errors where used to correct for
heteroskedasticity.
PRESENTATION OF RESULTS
The model results are shown in Table 1. While the F statistic on all of the models was
significant, not all the coefficients were significant. The sign of the public terminal coefficient
was the one hypothesized for every model. It was significant on all but one of the models.
Each of the models are discussed separately below:
Agricultural Chemicals
The model for agricultural chemicals was the best among the models estimated. The R2 was .21, the
highest among all of the commodity models. All of the coefficients are significant.
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Coal
The model for coal has significant coefficients for average revenue per ton-kilometer and public
terminal percentage. The percentage of variation in the data explained by the model (R2) is only 8%.
The sign on the coefficient for average revenue per ton kilometer is not the one hypothesized.
Coke
The model for coke shows significant coefficients for only the public terminal variable. The
earnings coefficient is close to significance at the 95% level, but its sign is not the one hypothesized.
This may be due to the fact that the decline of the integrated steel producers, the main users of
coking coal, reduced demand for coke while total manufacturing earnings were still growing.
Grain
The sign on the earnings variable is not the one hypothesized. Variability in the market for grain
may explain this. Both the public terminal and rail access coefficients are significant.
Industrial Chemicals
Both earnings and the rail access coefficients are close to significance, although their signs are not
the ones hypothesized. The public terminal variable is significant and shows the hypothesized
relationship.
Petroleum Products
With an R2 of .03, the results are not very meaningful for this model.
All Other Commodities
All of the coefficients except earnings are significant. This makes some sense as the commodity
group contains a number of different commodities, some of which may not be directly related to
manufacturing earnings.
DISCUSSION OF RESULTS
The public terminal variable is significant and has the right sign for all of the models. This is
consistent with the hypothesis that railroads are engaging in pricing practices to divert intermodal freight onto the rail network. The relatively low R2 on all the models shows that other
explanatory variables or better data should be considered for use in future research.
Previous research suggests that agricultural chemicals are the commodity for which
differential pricing has the greatest impact. (USACE, Rock Island District) This is consistent
with the results of this research, which show the agricultural chemical model to be the strongest.
The impact of rail rates on coal and grain shipments have also previously been found to be
substantial. The coefficients for the grain and coal models are significant, although the R2 on
these models are not particularly strong.
The significance of the public terminal variable is notable in the context of the findings of
a number of other studies. In "The National and Regional Economic Benefits of Commercial
Navigation on the Snake River" Daeger and Burton argue that a major benefit of investment in
inland waterways is to compel lower rates from the railroads. The benefits of water-compelled
rates are often used to justify investment in inland waterway facilities. Freight traffic diversion
at public barge terminals may not be a sign of a malfunction of the competitive market place, but
rather, an outcome that occurs by design.
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Other researchers have argued that barge transportation has social benefits. Rail pricing
policies, which use market power to divert freight from the inland waterway system, may be
imposing external environmental costs on society. A study by the Tennessee Valley Authority
found that barge transportation is in general environmentally cleaner per ton-kilometer, having a
marginal pollution abatement benefit to the nation of approximately .003 cents per ton-kilometer
move. (Bray, Burton, Dager, Henry, Koroa; 1998) The research findings of this study provide
one measurement of how substantial the economic and environmental impact of differential
pricing may be.
CONCLUSIONS
The measurement of the impact of rail pricing policies on barge transportation is important for a
number of reasons. Differential pricing may impose external costs on society that need to be
factored into the current debate concerning open access and competition in the rail industry. A
number of studies have examined the impact of the inland waterways on rail rates, but little
research has been conducted to examine this issue from the other side, calculating the economic
impacts of differential pricing on inland waterway traffic. The results of this research suggest
that differential pricing has had a measurable impact on barge freight commodity movements.
FUTURE RESEARCH
There are a number of ways that future research might improve on the research design employed
in this study. Regional rail pricing data from the Carload Waybill Sample could be employed to
measure rail prices directly. Rail tonnage data from the waybill sample could be used to
calculate a modal split for freight traffic by BEA region. Modeling mode share as the dependent
variable instead of the volume of tonnage would most likely improve the model.
A measure of the concentration of the rail industry in the study region might also be
employed. Since differential pricing is likely to exert more force in the presence of industry
concentration, it might be instructive to model this using an industry concentration index. The
dates of major mergers might also be used as dummy variables. Alternatively, the impact of
specific railroads could be modeled on freight traffic. The specific railroad serving each terminal
is available from the Navigation Data Center CD-ROM.
An index of service quality might also be a useful addition to the model. Shippers choose
transportation based on both the price and quality of service. Unfortunately, there currently is no
widely accepted index of rail service quality. (GAO, April 1999). Similarly, barge rates might
also be an important addition to the model. Transportation decisions are not driven by the cost of
a particular mode, but by the relative cost of the available modes. Unfortunately, it is difficult to
obtain barge rate data. Approximately 90% of barge freight moves under private, long-term
contracts. The rates set competitively on the barge spot market are highly variable and may not
be reflective of the rates paid by the industry as a whole.
In summary, alternative models could be constructed. The results of such an analysis
would be relevant to the ongoing debate about the proper role of competition in the railroad
industry.
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ACKNOWLEDGEMENTS
The author would like to thank the Army Corps of Engineers for funding a number of projects
related to forecasting barge commodity movements at Jack Faucett Associates. Additionally, the
author would like to thank his colleagues at Jack Faucett Associates and ICF Consulting for their
encouragement and support of this research.
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REFERENCES
1. Bray, Larry; Burton, Mark; Dager, Chris; Henry, Ron; Koroa, M. The undervalued Benefits of
Water Transportation. Tennessee Valley Authority, September 1998
2. Cunningham, Paul; Jenkins, Robert. "Railing at Open Access". Regulation. Spring, 1997
3. Dager, Chris; Burton, Mark. "The National and Regional Economic Benefits of Commercial
Navigation on the Snake River". Institute for Water Resources, June 30, 1998
4. Erickson, Thomas. "The over simplification Pendulum." Traffic World. May 10, 1999
5. Faucett, Jack. "Open Access Could Work." Traffic World. April 12, 1999
6. Haulk, Jake. Inland Waterways as a Vital National Infrastructure: Refuting Corporate Welfare
Attacks. Allegheny Institute for Public Policy, 1998
7. Kahn, Fritz. "Myths About Competitive Access". Traffic World, March 15, 1999
8. Kleit, Andrew. "The Unclogged Bottleneck: Why Competitive Access Should Not Be an
Antitrust Concern". Transportation Review. Vol. 26, No. 3
9. Rail Rates and the Availability of Water Transportation: The Upper Mississippi Basin,
United States Army Corps of Engineers, Rock Island District, June 1996
10. Rail Road Regulation: Changes in Railroad Rates and Service Quality since 1990. General
Accounting Office, GAO/RCED-99-93, April 1999
11. Rail Road Regulation: Current Issues Associated with the Rate Relief Process.
General Accounting Office, GAO/RCED-99-46, February 1999
12. U.S. Code Congressional and Administrative News, 96th Congress, Second Session
13. S.621 -- Heavy Regulation and Bad Economics. Association of American Railroads
Web Page
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LIST OF TABLES
TABLE 1: Regression Results
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TABLE 1: Regression Results
Coefficients
Robust
Standard
Error
t
P Value
0.01
115,579
-199,705
124,232
-132,015
0.002
90,244
31,973
35,738
161,496
5.63
1.28
-6.25
3.48
-0.82
0.000
0.202
0.000
0.001
0.415
0.05
-8278656
-6703023
828357
21800000
0.120
3,559,150
1,795,373
116,725
637,933
0.41
-2.33
-3.73
0.71
3.42
0.679
0.021
0.000
0.479
0.001
-0.01
314,917
-737,432
292,392
-87,706
0.007
253,837
116,335
162,513
392,642
-1.89
1.24
-6.34
1.80
-0.22
0.060
0.216
0.000
0.074
0.824
R = .14
Earnings (Agricultural Services)
Average Revenue Per Ton Kilometer
Public Terminal Percentage
Rail Access Percentage
Constant
-0.94
-403,702
-208,875
313,599
798,882
0.529
368,036
66,030
86,013
635,430
-1.78
-1.10
-3.16
3.65
1.26
0.079
0.275
0.002
0.000
0.212
R2 = .17
Earnings (Manufacturing)
Average Revenue Per Ton Kilometer
Public Terminal Percentage
Rail Access Percentage
Constant
-0.01
314,998
-737,432
292,392
-87,706
0.007
253,838
116,336
162,514
392,642
-1.89
1.24
-6.34
1.80
-0.22
0.060
0.216
0.000
0.074
0.824
-0.03
-491,437
-542,498
-39,728
1,810,598
0.015
442,853
770,573
185,636
753,235
-1.73
-1.11
-0.70
-0.21
2.40
0.086
0.268
0.482
0.831
0.017
0.01
-2,696,391
-2,643,059
1,683,064
5,988,693
0.022
773,424
541,342
451,960
1,429,606
0.36
-3.49
-4.88
3.72
4.19
0.721
0.001
0.000
0.000
0.000
Commodity
Agricultural Chemicals (N=198)
2
R = .2147
Earnings (Agricultural Services)
Average Revenue Per Ton Kilometer
Public Terminal Percentage
Rail Access Percentage
Constant
Coal (N=184)
2
R = .0892
Earnings (Manufacturing)
Average Revenue Per Ton Kilometer
Public Terminal Percentage
Rail Access Percentage
Constant
Coke (N=177)
2
R = .1703
Earnings (Manufacturing)
Average Revenue Per Ton Kilometer
Public Terminal Percentage
Rail Access Percentage
Constant
Grain (N=99)
2
Industrial Chemicals (N=177)
Petroleum Products (N=213)
2
R = .03
Earnings (Manufacturing)
Average Revenue Per Ton Kilometer
Public Terminal Percentage
Rail Access Percentage
Constant
All Other Commodities (N=228)
2
R = .1213
Earnings (Manufacturing)
Average Revenue Per Ton Kilometer
Public Terminal Percentage
Rail Access Percentage
Constant
TRB 2003 Annual Meeting CD-ROM
Paper revised from original submittal.