1

Agricultural 

Marketing 

Service

March 2016

What is the Issue?

This research investigates the pricing behavior of railroads for wheat shipments by exploring the relationship between 

intra-State railroad competition and railroad rates for wheat in the largest wheat-producing States (Idaho, Kansas, 

Minnesota, Montana, North Dakota, Oklahoma, South Dakota, Texas, and Washington). The objectives of the research 

are to: (1) measure the impact of the intensity of intra-State railroad competition on railroad wheat rates; (2) model 

the impacts of railroad costs, intra-State railroad competition, and intermodal competition on rail wheat rates; (3) 

identify and measure the major cost determinants of railroad wheat rates; and (4) examine the hypothesis that railroad 

intramodal competition varies within a State with implications for intra-State variation in railroad wheat rates. 

How Was the Study Conducted?

The study used ordinary least squares (OLS) regression analysis to estimate the following model in double-log 

specificationi utilizing the 2012 Surface Transportation Board (STB) Confidential Waybill sample and other data:

RATE = b0 + b1 CARWT + b2 TSW + b3 DIST + b4 BARGE + b5 HHI + e1

Where: 

RATE: Rail revenue per ton mile

CARWT: Weight of each loaded hopper car

TSW: Total shipment weight

DIST: Distance in rail miles between origins and destinations

BARGE: Distance from origin to nearest barge loading location

HHI: Herfindahl-Hirshman Indexii

This is a summary of “Impacts of Intramodal Competition on 2012 Railroad Rates for Wheat” by Michael W. 

Babcock, Department of Economics, College of Arts and Sciences, Kansas State University. This paper was 

developed with funding from the Agricultural Marketing Service (AMS) through cooperative agreement number 

14-TMXXX-KS-0026. The full paper is available at http://www.k-state.edu/economics/staff/websites/babcock/

Intramodal%20Railroad%20Competition%20Impacts%20on%20Railroad%20Wheat%20Rates%2010-2015.pdf.

Impacts of Intramodal Competition on 2012 Railroad 

Rates for Wheat (Summary) mmfmm

Peter Caffarelli and Jesse Gastelle
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What Did the Study Find?

In principle, longer distances and larger trains should be lower cost per ton-mile, because fixed costs are spread over 

more ton-miles. As expected, the distance in rail miles from origin to destination (DIST) and the total shipment weight 

(TSW) each had a negative correlation with railroad rates and were significant at the 1-percent level.iii Economic theory 

predicts that as the costs of alternatives to rail transportation rise (or fall), the price of rail should rise (or fall). The 

distance from origin to nearest barge loading location (BARGE) had the expected positive correlation and was significant 

at the 1-percent level. The weight of each individual covered hopper car (CARWT) was not significantly related to RATE. 

This result is somewhat surprising because car weight should be similar to distance and train weight in that higher 

weights should lower per ton-mile rates, as they spread out the fixed costs. The test outcome is possibly the result of a 

data problem, because car weight only takes on two values and may not have given the model enough variation to make 

an accurate estimate. The Herfindahl-Hirschman Index (HHI) was also not significantly related to wheat rates. 

This result is unexpected because, in theory, additional railroads should compete with each other by lowering prices to 

attract customers.

The degree of railroad competition varies by State. Previous research has found that the presence of two railroads of 

roughly equal size in a grain transportation market results in lower rail rates. Based on the number of wheat shipments 

from the Crop Reporting Districts (CRDs) served by one class railroad, Idaho and North Dakota have the most “single 

carrier” shipments, while Kansas, Minnesota, and Texas have the fewest. Idaho has no CRDs served by at least two Class 

I railroads while all seven of the Kansas CRDs are served by at least two Class I railroads. For wheat, 35 CRDs (61 percent 

of the total CRDs) are served by at least 2 Class I railroads. Moreover, substantial variation in HHIs within seven of the 

nine States reflects variation in railroad competition within States. 

When comparing the high and low HHI of CRDs in each State, the study found that Idaho has no variation, and 

Washington’s high and low HHI only differed by 6 percent. Conversely, the other States have very large percentage 

differences in HHI ranging from 88 percent (Oklahoma) to 212 percent (Minnesota). Thus, intramodal competition 

within States appears to be present in most cases. The absence of the expected significant relationship between railroad 

competition and wheat rates is possibly the result of anomalies in the 2012 data. This could be the impetus for further 

research using a multi-year panel dataset.

Overall, the study found that railroad cost factors (shipment distance and total shipment weight) and intermodal 

competition are important determinants of railroad wheat tariff rates.

Preferred citation:

Caffarelli, Peter and Gastelle, Jesse, Impacts of Intramodal Competition on 2012 

Railroad Rates for Wheat (Summary), U.S. Department of Agriculture, Agricultural Marketing Service, March 2016. 

Web <http://dx.doi.org/10.9752/TS215.03-2016>

Photo credit: ©Kevin Cavanaugh

i Ordinary least squares regression analysis is a method of estimating the relationship between two variables, while holding other important 

variables constant. The double-log specification provides a better fit to non-linear relationships, and transforms the interpretation of the coefficients 

into percentages.

iiThe Herfindahl-Hirschman Index (HHI) is a widely used measure of competition. In general, it is defined as the sum of the squares of the market 

shares of firms in an industry. Here it measures the market shares of railroads in a crop reporting district. It can be unintuitive because a large HHI 

implies few firms are in the industry. A small HHI implies many firms are in the industry.

iii In statistical analysis, hypothesis tests are used to test the validity of a claim. The hypothesis in question is called the null hypothesis. In this case, 

the null hypothesis being tested is that the coefficient on DIST is equal to zero (i.e. DIST has no significant impact on RATE). Economists generally 

accept that a test outcome below the 5 percent level indicates strong (significant) evidence against the null hypothesis and suggests that there is a 

relationship between DIST and RATE.