MPC 03-144

THE DIFFERENTIAL EFFECTS OF 

RAIL RATE DEREGULATION

U.S. Corn, Wheat, and Soybean Markets

Prepared by

John Bitzan

Kimberly Vachal

Tamara VanWechel

Dan Vinje

Upper Great Plains Transportation Institute

North Dakota State University

Prepared for

Transportation and Marketing Program

Agricultural Marketing Service, United States Department of Agriculture

June 2003
Acknowledgment

The researchers want to acknowledge the valuable contribution of the Transportation and Marketing

Program at the United States Department of Agriculture in their support for and discussions of this

research.

Disclaimer

The contents of this report reflect the views of the authors, who are responsible for the facts and accuracy

ofthe information presented herein. This document is disseminated underthe sponsorship ofthe Department

of Transportation, University Transportation Centers Program, in the interest of information exchange. The

U.S. government assumes no liability for the contents or use thereof.
i

EXECUTIVE SUMMARY

It isimportant to understand the distribution and incidence of influences associated with deregulation of rail

rates. The objective of this research was to provide insight into inter- and intra-commodity rail rate

differentials observed since rates were deregulated in 1981. A cross-sectional/time-series analysis of U.S.

corn, wheat, and soybean shipments was considered in the assessment of rail grain rate differentials. County

level railshipment characteristicsfortwo decades were considered in the analysis. The time period selected,

1981 through 2000, coverstwo decades of pricing by railroadsin the deregulated environment. As expected,

results suggest that market-based pricing has become more prevalent in later years. The tendency for

railroads to implement more market-based pricing in recent years implies that rail demand elasticity is

becoming an increasingly important factor in the relative competitiveness of U.S. grain producers.

The overall benefit of rail deregulation, measured in terms of rail productivity and decreasing in rail rates

for shippers, is well established in previous research and consistent with the findings in this research.

Important findings in research go beyond the broad discussion to show that these benefits are not distributed

uniformly across or within commodities. Furthermore, as market-based pricing has become more prevalent

the variance in distribution of benefitsisshown to increasingly favorthose grain producerslocated in regions

with higherlevels ofintermodal competition. In a competitive market environment, trends in relative, as well

as overall, rates should be considered in assessing the impacts of policy and investment initiatives. This

research will help us to better understand the ultimate consequences of future policy and investment

decisions, in terms of overall and relative competitiveness of grain commodities and U.S. grain producers.
ii
iii

TABLE OF CONTENTS

1. INTRODUCTION ............................................................ 1

1.1 Market Overview ....................................................... 2

1.2 Toward Rail Industry Deregulation ........................................ 10

1.3 Objectives and Organization ............................................. 10

2. RESEARCH REVIEW ........................................................ 13

3. METHODOLOGY AND DATA ................................................ 15

3.1 Empirical Model ...................................................... 15

3.2 Data ................................................................ 19

4. EMPIRICAL RESULTS ....................................................... 25

4.1 Wheat ............................................................... 33

4.2 Soybean ............................................................. 37

4.3 Corn ................................................................ 41

5. CONCLUSION .............................................................. 47

6. REFERENCES .............................................................. 49
iv

LIST OF FIGURES

Figure 1. Composition of U.S. Grain Production, 1996 to 2000 .......................... 2

Figure 2. U.S. Corn, Wheat, and Soybean Production Patterns, Average Production Density

1996 to 2000 .......................................................... 3

Figure 3. Rail-to-Production Ratio for Corn, Wheat, and Soybeans, 3-year Running Average

1981 to 2000 .......................................................... 4

Figure 4. Regional Rail Shipment, Production Activity, and Ratio for Corn, Wheat, and

Soybeans, Average 1996 to 2000 ........................................... 6

Figure 5. Rate Differentials for Wheat and Soybean Revenue-per-Ton-Mile Compared

to Corn Baseline Revenue-per-Ton-Mile, 1981 to 2000 ......................... 7

Figure 6. Unit/Shuttle Train Volumes as Percent of Total Rail Shipments of Corn, Wheat,

and Soybeans, 1981 to 2000 .............................................. 7

Figure 7. Trends in Regional Corn Rail Rate per Ton-Mile, 1981 to 2000 .................. 8

Figure 8. Trends in Regional Wheat Rail Rate per Ton-Mile, 1981 to 2000 ................. 9

Figure 9. Trends in Regional Soybean Rail Rate per Ton-Mile, 1981 to 2000 ............... 9

Figure 10. USDA Production Region Map ........................................... 20

Figure 11. Time Effect Simulation of Rates per Ton-Mile for Corn, Soybeans, and Wheat,

1981 and 2000 ........................................................ 30

Figure 12. Cumulative Percentage Decrease in Rates between 1981 and 2000, Relative

to 1981 .............................................................. 31

Figure 13. Simulated Intermodal Barge Competition Effects on Rail Rates Between 1981

and 2000, Cumulative at Various Distance from the Nearest Barge

Loading Facility ....................................................... 32

Figure 14. Average Distance of Shipment Origin from the Nearest Barge Loading Facility

for Wheat Shipments, 1981 to 2000 ....................................... 33

Figure 15. Average Cars per Shipment by Originating Region, Wheat 1981 and 2000 ......... 34

Figure 16. Average Short-Line Distance of Wheat Shipments by Region, 1981 and 2000 ...... 35

Figure 17. Average Herfindahl Index of Origin Railroad Concentration by Region, Wheat

Shipments 1981 and 2000 ............................................... 35

Figure 18. Simulated Wheat Rates by Region, 1981 and 2000 (All variables are placed at

their 1981 and 2000 mean levels for the region) .............................. 36

Figure 19. Simulated Wheat Rate Savings Due to Time Trend and Due to Changes in Time

Controlling for Shipment Characteristics, 1981 to 2000 ........................ 37

Figure 20. Average Distance of Shipment Origins from the Nearest Barge Loading Facility,

Soybeans 1981 and 2000 ................................................ 38

Figure 21. Average Short-Line Distance by Originating Region, Soybeans, 1981 and 2000 ..... 39

Figure 22. Average Number of Cars per Shipment by Originating Region, Soybeans 1981

and 2000 ............................................................. 39

Figure 23. Average Herfindahl Index of Origin Railroad Concentration by Region,

Soybeans 1981 and 2000 ................................................ 40

Figure 24. Simulated Soybean Rates by Region, 1981 and 2000 .......................... 40

Figure 25 Simulated Soybean Rate Savings Due to Time Trend and Changes in Time

and Shipment Characteristics, 1981 to 2000 ................................. 41

Figure 26. Average Distance of Shipment Origins from Nearest Barge Loading Facility for

Corn,1981 and 2000 .................................................... 42

Figure 27. Average Number of Cars by Originating Region for Corn, 1981 and 2000 ......... 42
v

Figure 28. Average Short-Line Distance by Originating Region for Corn,1981 and 2000 ...... 43

Figure 29. Average Herfindahl Index of Origin Railroad Concentration by Region, Corn,

1981 and 2000. ........................................................ 44

Figure 30. Simulated Corn Rates by Region - 1981 and 2000 (all variables placed at their

1981 and 2000 mean levels for the region) .................................. 44

Figure 31. Simulated Corn Rate Savings Due to Time Trend and Due to Changes in Time

and Shipment Characteristics, 1981 to 2000 ................................. 45
vi

LIST OF TABLES

Table 1. Rail Shipment-to-Production Ratios for Corn, Wheat, and Soybeans, by Region

and Time Period ............................................................ 5

Table 2. Corn Shipment Characteristics, Waybill Averages 1981 to 2000 ..................... 21

Table 3. Wheat Shipment Characteristics, Waybill Averages 1981 to 2000 .................... 22

Table 4. Soybean Shipment Characteristics, Waybill Averages 1981 to 2000 .................. 23

Table 5. Estimation of Revenue per Ton-Mile .......................................... 26
1To the extent that abandonment has ensured continued rail viability, all shippers have benefitted.

1

1. INTRODUCTION

It is well documented that railroad deregulation in the United States has been successful in a broad overall

context. Studies have shown that increased productivity, decreased rates, and increased profitability in the

rail industry can be attributed to deregulation. While evidence suggests that benefits have been shared by

shippers, in terms such as continued rail viability, rate savings, and improved service, the degree to which

these benefits have accrued across shippers and industries has been given sparing consideration. Given the

increasing level of competition associated with globalized markets, it may be especially important to

understand these differentials in projecting the effects of future investments and policies. 

In some cases, overall improvements in consumer welfare may have been achieved at the expense of a

particular segment of shippers. An example of this type of improvement is the efficiency gains associated

with reduced systemcostsrealized as a result of an accelerated abandonment oflight-density rail lines. These

efficiency gains have resulted in lowerrates and betterservice formany shippers, but have resulted in service

decline or elimination for others.1 Other improvements in consumer welfare have been realized by most, if

not all, shippers, but accruement of improvement gains has varied widely among shippers. For example,

although most railshippers have realized decreased rail rates as a result of deregulation, rate reductions have

been larger for some shippers than others. This was an expected consequence of deregulation. In a

deregulated rate setting environment where the elasticity of demand for service varies widely among

shippers, private industry is compelled to use market-based pricing in its rational decisions to achieve profit

maximization goals.

Certainly, the increased flexibility in pricing and in maintaining infrastructure that have resulted from

deregulation have been necessary and beneficial. Increased flexibility for the industry has encouraged

market-based infrastructure investments, customerservice, and technological adaptations. While the overall

results are not disputed, several industry participants perceive inequities in the current system. Moreover,

several legislative proposals focused on these perceived inequities have suspected fundamental changes in

the philosophy of rail industry regulation.

Thisresearch focuses on the long-standing relationship between the rail industry and production agriculture.

The grain industry is heavily dependent on an effective and efficient rail system to move its large, bulkpackaged shipments. A substantial portion of the delivered cost of grain is often attributed to transportation,

in terms of the actual cost, reliability, and market access. The grain industry, unlike other bulk commodity

markets such as coal and fertilizer, covers a multitude of origins and commodities. Understanding the

incidence of gain associated with deregulation for shippers within the grain industry is complex but critical

as rail industry oversight is considered in future policy and investment discussions

For the most part, the effects of deregulation on shippers and carriers across industries have been well

documented. Little attention, however, has been given to level and source of rate changes associated with

rail industry deregulation within industries. The objective of this research is to assess intra-industry rail rate

changes for corn, wheat, and soybeans across time and geography, specifically considering the aspects of

intra- and intermodal transportation competition. The incidence of gain has varied with differences in factors

such as competitive environments, technology and investment decisions. This research will investigate the
2Other commodities included in the total for U.S. grain production are barley, cottonseed, flaxseed, oats,

rice, rye, sorghum, and sunflower.

2

Wheat

15%

Soybean

17% Other

8%

Corn

60%

Figure 1. Composition of U.S. Grain Production,

1996 to 2000

differential effects of policy change,

considering factors that have influenced in

pricing service for corn, wheat, and soybeansin

a deregulated rail industry. 

1.1 Market Overview

Corn, wheat, and soybeans are included in this

analysis of rail industry deregulation on rate

differentials in production agriculture. Figure

1 shows that these three commodities accounted for approximately 92 percent of U.S. grain

and oilseed production between 1996 and 2000,

considering production of eleven primary

grains and oilseeds2(National Agricultural

Statistics Service (NASS), 2003). The

production and marketing characteristics for

corn, wheat, and soybeans vary, resulting in

unique transportation demand functions among and within commodities. For instance, the primary origin

states for corn and soybeans are somewhat similar, with Iowa and Illinois leaders in production (Figure 2).

Wheat, in contrast, is concentrated west of the Mississippi in Kansas and North Dakota. In addition to the

more western production region, wheat has subclasses including durum, spring, and winter. The qualities

associated with these classes create distinct, yet interrelated, wheat markets with varying transport system

demands and abilities. In comparison, the corn and soybean products are each generally treated as

homogeneous commodity markets with a high degree of substitutability within products, considering the

range of quality characteristics.

Another distinction is in the markets attributed to each commodity. Approximately 80 percent of corn

production is used domestically. In comparison, only about 60 and 65 percent of the production of wheat

and soybeans, respectively, are consumed by domestic markets. These differences lend to the important

differences in transport service demand for corn, wheat, and soybeans across time and geography.

Considering the 20-yearspan of this research, approximately 35 percent of the grain produced in the United

States was marketed via rail. However, the share for rail has declined in more recent years. The average

share for railroads, considering the rail-to-production ratio, has declined 9 percent, to 32 percent, during the

most recent five-year period from 1996 to 2000 compared to the share for over the rest of the two-decade

period. To better understand the relative sensitivity of different types of grain traffic to rail industry changes,

such as deregulation and the consequential phenomena such as network and industry rationalization, it is

important to understand the landscape of rail customers among various commodities.
3

Wheat

Soybean

Corn

Figure 2. U.S. Corn, Wheat, and Soybean Production Patterns, Average

Production Density 1996 to 2000
3For all of these rail traffic estimates, it should be noted that the Waybill data may include two shipments to

describe activity for a single bushel of grain. For example, the Eastern Corn Belt includes Chicago - a major

gateway between the eastern and western railroads. Examination of Waybill data suggests that some “doublecounting” of bushels may occur when the bill of lading for a shipments is terminated in Chicago and a new bill of

lading created as a shipment resumed from its Chicago origin to shipment to final destination. This double-counting

suggests that rail volumes may be somewhat inflated for statistics such as regional grain originations.

4

-

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

198319851987198919911993199519971999

Ratio, 3-yr Running Average

Corn Soybean Wheat

Figure 3. Rail-to-Production Ratio for Corn, Wheat, and

Soybeans, 3-year Running Average 1981 to

2000

One example of a difference in the rail customer landscape among commodities is the relative dependence

on rail in marketing. Corn and soybeans are relatively less dependent, as commodities, than wheat on rail

in repositioning their product at domestic and export facilities (Figure 3).3 During the 20-year marketing

period, 29 percent of the corn and 24 percent of the soybean production, or approximately 1 in 4 bushels, was

marketed via rail. In more recent years, 1996 to 2000, the share has been stable for soybeans and declining

slightly for corn, with 24 and 27, respectively, percent of the average production marketed via rail.

Approximately 2 of every 3 bushels of wheat produced is marketed via rail. As with corn, wheat shows a

decreasing tendency to use rail in the most recent five-year period (Figure 2). The variation in use of rail for

commodity marketing may be attributed to factors such as distance to market, access to modal alternatives,

and size of shipments.

The level of rail use also has distinct patterns among major producing regions within specific commodities

(Table 1). The ratios illustrated in Figure 4 differentiate the use of rail among regions and commodities

considering the ratio of rail shipments to production between 1996 and 2000. The prevalence of rail in

movement of wheat is evident across regions, with the exception of regions adjacent to export facilities

including the Southeast, Delta, Pacific Northwest, and Western regions. The Central and Northern Plains

account for 49 percent of the nation’s wheat production. The Central Plains is characterized by a higher railto-production ratio of 0.74, compared

to 0.58 for the Northern Plains. The

Corn Belt shows the greatest

propensity for use of rail in the

shipment of its regional wheat

production (rail-to-production ration of

.78). The largest producing regions for

corn and soybeans are the Eastern and

Western Corn Belts. These regions

accounted for 69 and 70 percent of the

corn and soybean production,

respectively, between 1996 and 2000.

Rail utilization is similar across these

regions and commodities with a

slightly greater propensity to use rail in

the corn market. The Northern Plains

region has the highest rail-toproduction ratio for corn, and the

Central Plains has the highest ratio of

rail to production for soybeans. 
5

Table 1. Rail Shipment-to-Production Ratios for Corn, Wheat, and Soybeans, by Region

and Time Period

Region

Time

Period

Corn Wheat Soybean

Rail/Prod

Ratio

Production

(1,000 bu) 

Rail/Prod

Ratio

Production

(1,000 bu) 

Rail/Prod

Ratio

Production

(1,000 bu) 

Central Plains 8185 0.32 974,224 1.00 607,109 0.29 109,045 

8690 0.47 1,141,306 1.09 491,695 0.45 132,872 

9195 0.36 1,326,025 0.81 512,051 0.43 156,984 

9600 0.31 1,678,616 0.74 557,141 0.37 229,415 

Delta 8185 0.07 18,010 0.04 89,998 0.04 233,658 

8690 0.60 39,485 0.03 68,359 0.05 164,065 

9195 0.64 56,212 0.04 47,245 0.03 176,892 

9600 0.12 109,071 0.03 65,618 0.02 173,054 

Eastern Corn Belt 8185 0.25 2,866,232 0.34 216,986 0.18 650,975 

8690 0.31 2,677,659 0.61 204,377 0.28 658,545 

9195 0.29 3,083,407 0.85 187,804 0.29 815,753 

9600 0.28 3,258,360 1.03 194,672 0.25 953,079 

Northeast 8185 0.05 288,871 0.20 25,792 0.01 25,592 

8690 0.08 223,314 0.43 27,536 0.03 31,421 

9195 0.14 232,754 0.42 31,859 0.12 38,074 

9600 0.09 246,068 0.53 35,647 0.13 42,143 

Northern Plains 8185 0.23 227,872 0.47 530,473 0.22 40,753 

8690 0.41 245,785 0.60 485,483 0.56 60,344 

9195 0.30 293,038 0.76 602,642 0.50 81,698 

9600 0.32 468,680 0.58 591,395 0.50 169,612 

Pacific Northwest 8185 0.33 34,308 0.35 305,235 n.a. n.a.

8690 0.15 25,163 0.36 264,917 n.a. n.a.

9195 0.13 24,034 0.32 290,508 n.a. n.a.

9600 0.02 29,216 0.28 317,323 n.a. n.a.

Southeast 8185 0.17 418,792 0.41 110,466 0.23 221,187 

8690 0.21 312,652 0.39 88,155 0.25 148,022 

9195 0.16 348,532 0.30 89,839 0.16 139,733 

9600 0.16 327,765 0.32 96,846 0.11 133,564 

Southern Plains 8185 0.36 73,118 1.12 273,942 0.31 10,475 

8690 0.32 78,642 1.37 215,148 0.48 8,550 

9195 0.14 121,370 0.90 195,625 0.82 9,390 

9600 0.18 142,578 0.68 209,201 0.49 10,490 

West 8185 0.01 46,035 0.22 95,781 n.a. n.a.

8690 0.02 35,412 0.28 66,329 n.a. n.a.

9195 0.01 29,847 0.35 56,049 n.a. n.a.

9600 0.02 47,118 0.33 61,441 n.a. n.a.

Western Corn Belt 8185 0.23 2,255,300 0.78 193,782 0.22 583,373 

8690 0.39 2,186,155 2.01 152,598 0.32 609,829 

9195 0.34 2,394,370 1.40 131,375 0.23 681,396 

9600 0.31 2,941,135 0.78 131,887 0.24 882,944 

n.a.: not available due to limited number of observations

Time period #### =19## to 19##/20##
6

- 500 1,000 1,500 2,000 2,500 3,000 3,500

Central Plains

Delta

Eastern Corn Belt

Northeast

Northern Plains

Pacific Northwest

Southeast

Southern Plains

West

Western Corn Belt

Million Bushels

Rail

Production

- 100 200 300 400 500 600 700

Central Plains

Delta

Eastern Corn Belt

Northeast

Northern Plains

Pacific Northwest

Southeast

Southern Plains

West

Western Corn Belt

Million Bushels

Rail

Production

- 200 400 600 800 1,000 1,200

Central Plains

Delta

Eastern Corn Belt

Northeast

Northern Plains

Pacific Northwest

Southeast

Southern Plains

West

Western Corn Belt

Million Bushels

Rail

Production

Corn

Wheat

Soybean

.31

.12

.18

.16

.02

.32

.28

.31

.78

.33

.68

.32

.28

.58

.53

1.00

.03

.74

.24

.49

.11

.02

.32

.25

.37

.13

Figure 4. Regional Rail Shipment, Production Activity, and Ratio for Corn, Wheat, and

Soybeans, Average 1996 to 2000
7

-20%

-10%

0%

10%

20%

30%

40%

1981198319851987198919911993199519971999

percent of corn rate per ton mile

Wheat Soybean

Figure 5. Rate Differentials for Wheat and Soybean Revenueper-Ton-Mile Compared to Corn Baseline Revenueper-Ton-Mile, 1981 to 2000

0%

10%

20%

30%

40%

50%

60%

70%

1981198319851987198919911993199519971999

Corn Wheat Soybean

Figure 6. Unit/Shuttle Train Volumes as Percent of

Total Rail Shipments of Corn, Wheat, and

Soybeans, 1981 to 2000

In addition to overall rail utilization, there are also important differences in rail shipment characteristics

among commodities and regions. Trends in average revenue per ton-mile, shipment distance, density, and

train size are important indicators of rail pricing strategies and productivity gains in the grain market. As

previously discussed, the real revenue

per ton-mile hastrended downward for

all three commodities since

deregulation of the rail industry in the

early 1980s (Tables 2, 3, and 4).

The convergence of these rates may

suggest similar market environments

for rail rate setting decisions. Soybean

and corn per-mile revenues have

converged over the most recent five

years (Figure 5). Corn revenue per

ton-mile is used as the baseline index

(Corn=0 percent) since it is the largest

volume commodity among the three.

The rate differential between corn and

soybeans has converged and shifted

over the past decade. Soybean rates

were an average 6 percent higher than

corn during the 1980s and an average 3

percent lower than corn during the

more recent decade, considering the

revenue per ton mile measure. 

Wheat revenue per ton-mile has been

consistently higher than corn across both

decades. Wheat and corn rates did tend toward

convergence during the 1980s, as a rate

differential of 25 percent had been reduced to

10 percent in 1993. The trend was reversed in

1993, as the revenue per ton-mile for the two

commodities widened to 17 percent in 1994.

Overall, rates have converged during the last

decade compared to the early decade of rate

deregulation from 1981 to 1990. Differences

in rates of convergence among commodities is

an important aspect of grain marketing, as the

relative transportation rates are a critical cost

factor in the competition to reposition grains

from producing region to domestic and export

consumption points.
4

 Those regions with more than 1 percent of U.S. production of the specified commodity are

included in the graph.

8

-100%

-50%

0%

50%

100%

150%

1981198419871990199319961999

Percent of Eastern Corn Belt RPTM

Eastern Corn Belt

Central Plains

Northeast

Northern Plains

Southeast

Southern Plains

Western Corn Belt

Figure 7. Trends in Regional Corn Rail Rate per Ton-Mile, 1981

to 2000

The rate relationship among and within commodities is affected by many market factors, including railroad

pricing strategies, shipper investment decisions, domestic/export market consumption, and government

policy. For example, industry investment in rail infrastructure such as unit and shuttle train facilities may

affect average rate relationships among commodities as investment may allow greater access to lower rates

associated with larger volume shipments (Figure 6). The empirical results provided in a subsequent section

of this research provide greater insight into the factors influencing the rail revenues per ton mile for each of

these commodities.

Regional variations in the rate relationship may also provide insight for understanding the rate differential

among commodities. In addition, the regional variation may indicate the price elasticity of rail demand as

regions with less elastic demand are likely to experience relatively higherrates as deregulatorymarket-based

pricing practices become more mature. As with the discussion of commodity-based rate convergence, the

rates for the 1981 to 2000

time period are indexed to a

region to illustrate the relative

rate trends. The base for the

index in this discussion is

defined as the Eastern Corn

Belt. Figure 7 illustrates the

variation in average revenue

per ton-mile for regions

compared to the Eastern Corn

Belt.4 Trend lines show a

convergence in revenue per

ton-mile for regions in the

western United States,

including the Central Plains,

Northern Plains, and Western

Corn Belt. With the declining

trend for the Western Corn

Belt, these regions appear to

have converged at about 23

percent of the Eastern Corn

Belt rate. The Southern Plains

rate also exhibits tendencies

to converge with these

regions. Convergence of these rates indicates that the markets served by these regions create similar

competitive forces for aspects of transportation demand in marketing a product that is highly substitutable

among regions. These may include shipper rail investment, geographic competition, and transport market

alternatives. In contrast, it seems that the Northeast and Southeast have and continue to pay relatively high

ratesfor railservice. This may be attributed to factors such less shipper investment in rail infrastructure and

more inelastic demand associated with fewer available transport options. This eastern market is largely
9

-40%

-20%

0%

20%

40%

60%

80%

100%

1981198319851987198919911993199519971999

Deviation from Eastern Corn Belt RPTM

Eastern Corn Belt

Central Plains

Northern Plains

Pacific Northwest

Southern Plains

Figure 8. Trends in Regional Wheat Rail Rate

per Ton-Mile, 1981 to 2000

-50%

0%

50%

100%

150%

200%

1981198419871990199319961999

Deviation from Eastern Corn Belt RPTM

Eastern Corn Belt

Central Plains

Delta

Northeast

Northern Plains

Southeast

Western Corn Belt

Figure 9. Trends in Regional Soybean Rail

Rate per Ton-Mile, 1981 to 2000

viewed as a less-than-trainload destination, serving feed and dairy operations. Thus, the opportunity to

achieve advantages associated with investment in larger-scale railshipment operationsmay be ratherlimited.

Regional rate differentials for wheat and soybean rail shipments are illustrated in Figures 8 and 9,

respectively. The five regions depicted in the wheat rate graphic account for 83 percent of U.S. wheat

production. The reduction of other regions’ rates relative to the Eastern Corn Belt is evident in the wheat

rate trends. However, large regional rate differences still exist. There does seem to be convergence in the

Southern Plains and Central Plains around the baseline Eastern Corn Belt Rates. The Northern Plains and

Pacific Northwest show convergence tendencies in the middle of the 20-year time period, but have again

diverged. In addition to the divergence, the Northern Plains is characterized by the highest rate per ton-mile

over more recent years as it was in the early stages of deregulation. Considering regional rate trends, the

convergence of Southern and Central Plains rates is likely related to similarities in transport market

conditions, including demand elasticity influences such as transport alternatives and other market factors

such as shipper investments.

Regional differencesin soybean rate perton-mile somewhatmimic those ofthe larger but closely related corn

market. The declining trend and convergence of rates in the western regions, including the Northern Plains,

Central Plains, and Western Corn Belt,strongly resemblesthe corn market as both show strong convergence

among these regions and a shift of these regions from rate above the baseline Eastern Corn Belt rate in the

initial years of deregulation to rates slightly under the baseline in more recent years. As with corn, the

Northeast region is associated with relatively high rates among the regions, but there is more variability in

the year-to-year trend for this rate. This variability may be related to production patterns, market shifts, and

other underlying market phenomenon. 

The brief overview of commodity and regional rate differential included in this market discussion is based

on the Waybill Sample of rail rates. The information identifies several convergence/divergence tendencies

among and within commodities. Research results presented in a later section will provide additional insight

into the sources of rate differentials. 
10

1.2 Toward Rail Industry Deregulation

In years leading to passage of the Staggers Act in 1980, railroads were subject to government dictate in their

rate setting practices. For many decades, rate bureaus had set and coordinated rail rates. The bureaus

equalized rates, so the market provided little incentive for investment/innovation in infrastructure, technology

or service. In 1970, the bankruptcy of the nation’s largest railroad, Penn Central, along with six other

northeastern railroads provided rather blatant evidence of the pending financial demise of the railroad

industry. Amidst the national movement toward deregulation of industries, including energy and

communications, policymakerssought to avert the very real possibility for other failures by reducing profitmaking restrictions imposed on the railroad industry by regulation (Winston, 1993). Congress began

regulatory reform of the railroad industry by offering more operating freedoms under the Railroad

Revitalization and Regulatory Reform (4R) Act of 1976. The 4R Act gave the railroads greater pricing

flexibility and eased restrictions on railroad track rationalization and mergers. Significant regulatory changes

did not occur, however, until the introduction of confidential contract rates, regulatory exemption on certain

commodities and the encouragement of rail rationalization by the ICC in 1979 (Bitzan, 1994).

The introduction of the Staggers Rail Act of 1980 represented one of the most dramatic changes in federal

policy toward railroads since the Interstate Commerce Act of 1887. The 1887 act had instilled the longstanding rules of railroad regulation that had been adopted in response to concerns of monopoly practices

in the railroad industry. The Staggers Act sought to disengage government from the market, accepting that

competitive forces would constrain any potential abuses by railroads(Fuller et al., 1987; MacDonald, 1989).

Under the Staggers Act railroad rate setting would be deregulated with the exception of markets in which

the railroads were deemed to be dominant. 

1.3 Objectives and Organization

Although the Staggers Rail Act was enacted over twenty years ago, its impacts continue to influence rail

pricing across regions and commodities. The purpose of this research is to investigate how the structure of

rail rates for shipping grain products has evolved in a deregulated market environment. The differential

impacts of deregulation on rates across commodities, market competition levels, regions, and time will be

considered. Specific objectives of the study are to formulate and estimate statistical rail rate models to

examine the effects of deregulation on rates,focusing on differences among shipperswith varying elasticities

of demand for rail service, and make an assessment of rail deregulation’s impacts on rates and how the

impacts have varied among major grains and across producing regions.

Although much can be learned by considering the generalrate structure for commodities, a disaggregate level

of analysis will be established forthisresearch to elucidate intra and intercommodity rate differentials within

the grain industry. A rail rate model is defined to assess how differences in regional characteristics affect

rail rates, and how the effects of deregulation have varied among regions. For example, the model will show

how the effects of deregulation on rates have varied with differences in railroad competition and the

proximity to waterborne alternatives. The assessment will include an application of rate function parameter

estimates to hypothetical post deregulation shipments to illustrate the effects of factors influencing the

elasticity of demand for railservice on rate changesresulting from deregulation. In addition, the assessment

will examine the time effects of deregulation and how they have differed among grain commodities and

regions.
11

The findings presented in this research will be valuable in discussions regarding the implications of policy

changes in railroad regulation for distinct grain producer groups, defined by location and commodity. It is

imperative that policymakers understand the impacts of past regulatory change when formulating future rail

transportation policy. This research will enhance the base of knowledge available to decision makers, such

as Congress, the Surface Transportation Board, and the United States Department of Agriculture, by

documenting important impacts of past regulatory change within the grain industry.

The next section of this report includes a brief review of previous research on rail industry deregulation.

This research was referenced in developing the model presented in section three. Empirical results for a

corn, wheat, and soybean model are presented in the following sections. A summary of findings completes

the research document.
12
13

2. RESEARCH REVIEW

Numerous studies have been conducted to examine the effects of deregulation on rail rates. These studies

have varied in their methods, model formulation and level of data aggregation. As indicated previously,

results have generally found decreases in rail rates as a result of deregulation.

One of the first aggregate studies conducted after the passage of the Staggers Act arrived at a rather unique

conclusion (Boyer, 1987). Boyer concluded that the most likely effect of deregulation was an increase in

rates. There are two possibilities that may explain the contestable results of Boyer’s study. In his

examination of average revenues per ton-mile, individual rate changes may have been unobservable due to

the lack of explanatory variables necessary to explain variances when using aggregate data. The other

possibility, as pointed out in Bitzan’s 1994 study, is the limited number of observations.

Evidence of benefits associated with rail industry deregulation are found in a study of coal and grain rates.

In their analysis, Barnekov and Kleit (1990) conclude that efficiency gains from deregulation are between

$11.5 and $18.5 billion per year. A reduced form is used to measure the gradual implementation of the

Staggers Act. Estimated annual welfare gainsin the United States from rail deregulation were between $5.3

and $7.2 billion in lower ratesto shippers, about $5 to $10 billion in reduced inventory-related logistic costs,

roughly $500 million in higher profits to railroads, and slightly more than $700 million in savings to

taxpayers. 

Wilson’s (1992) analysis of deregulation on 34 different commodity classifications over a 17-year period

suggested dramatic differences across commodity classifications. These differences are evident in terms of

magnitude and direction of effects. Initially, rates for commodities rose under deregulation, implying greater

market power and modest costs savings. By 1988, however, deregulation induced lower rates for most

commodity classifications. This suggests that advances in productivity had dominated any adverse market

power effects. Variations in the effects of deregulation are partially explained by differences in the

characteristics of commodities. 

Burton (1993) finds that railroads have increased their responsiveness to both intramodal and intermodal

competition since the passage of the Staggers Act. He also concludes that railroads have shared productivity

benefits of deregulation with railshippers, in terms oflowerrates. The responsiveness of the railroads, along

with the changes made by shippers following the Staggers Act, has resulted in lower rates. This study

indicates that railroad deregulation has led to decreased rates for shippers of almost all commodities. 

In a more recent study into the impacts of deregulation on rail rates, Dennis (2000) examines trends in

railroad revenue per ton-mile for ten commodities. These top ten commodities, in terms of revenue, account

for 90 percent of rail revenue. In his study, Dennis uses a theoretical representation of market fundamentals,

in terms of a reduced form equation, to define the appropriate regression model. The analysis estimates the

level and source of benefits associated with deregulation of the rail industry, considering rates between 1982

and 1996. He estimates shipper benefits of deregulation to be $28 billion (in 1996 dollars) between 1982

and1996, considering rate reductions. Ninety percent of the rate reduction is attributed to increased rail

productivity. 
14

While many studies have generalized that shippers have benefitted from the effects of deregulation, other

studies have suggested that these benefits have not been shared equally among shippers(McFarland’s 1989;

Atkinson and Kerkvliet 1986; MacDonald, 1989). A relatively captive segment of shippers can be

characterized as experiencing increased relative shipping rates due to a lack of intermodal and intramodal

competition brought about through geographical conditions or rail-line abandonments. The incidence of

differential effects for grain shippers are given specific consideration in this research.

In a study of the effects of rail industry deregulation on the grain industry, MacDonald (1987) finds that

intermodal and intramodal competition strongly influence railratesfor export bound grain. MacDonald uses

the 1983 Waybill Sample to study rail rates for shipments of corn, wheat, and soybeans from inland points

to export positions. He finds that the distance of origins from water loading facilities and railroad

competition are important in influencing rate levels.

In a second study, MacDonald (1989) uses regression analysis to examine the temporal effects of railroad

deregulation on grain transportation considering a wider scope of regions and commodities. He finds that

wheat rates drop an average of 21.7 percent during 1981-85 and corn rates decline by 12.4 percent over the

same period. Approximately one-third of the rate decline is attributed to increased rail productivity as

measured by increased train size. Competition among railroads is most beneficial for shippers in close

proximity to barge facilities. 

Fuller et al.(1987) measured the impact of deregulation on export-grain rates. The study focused on the price

spread between port and a hinterland region consisting of the states of Kansas, Iowa, Indiana, and portions

of surround states. Using price spreads rather than published rates isolates the impact of deregulation on

transportation charges. Export elasticities are found to be relatively insignificant, implying that post-Staggers

decline in rates had little or no correlation to the decline of export demand.

In an impact study of the Staggers Act on the Kansas wheat market (Babcock et al.,1985), the authors

conclude that rail rate reductions appeared to be responses to market conditions created by many events,

including reduced export flow of wheat, surpluses of transport equipment, changes in transport technology,

cost relationships and others. 
15

ln ln ln ln ln

ln

ln ln

RPTM CARS SHRT LOAD HERF BDIST

GPROD TRANS TIME TIMESQ

TBDIST THERF NE SE DE

NP CP SP WCB PNW WEST TNE

TSE TDE TNP TCP TSP TWCB

TPNW TWEST MULTI UNIT SHUTTLE

CORN

= + + + + + +

+ + +

+ + + + +

+ + + + + + +

+ + + + + +

+ + + + +

b b b b b b

b b b b

b b b b b

b b b b b b b

b b b b b b

b b b b b

b

0 1 2 3 4 5

6 7 8 9

10 11 12 13 14

15 16 17 18 19 20 21

22 23 24 25 26 27

28 29 30 31 32

33 + + + +

+ + + + +

b b b

b b b b b e

34 35 36

37 38 39 2 40 3 41 4

SYBN TCORN TSYBN

TSQCORN TSQSYBN Q Q Q

3. METHODOLOGY AND DATA

Multi-variate regression analysis, in the form of ordinary least squares (OLS), will be used to assess trends

in rate differentials among U.S. regions over the past two decades. The model presented in this section

includes variables to distinguish the differential effects of deregulation on rail rates for corn, wheat, and

soybean shipments across the United States, considering rail shipments from 1981 to 2000. Variables

identify three commodities and ten origin regions to allow for a discussion of the relative incidence of

deregulation impacts on rail rates over the past two decades. As each commodity has distinct characteristics

across regions, in its production, markets, and logistics, this level of disaggregation provides valuable

insights. This section includes a definition of the working rail rate model, and describes data used in

estimating the rate function, including scope, sources, and limitations. 

3.1 Empirical Model

The following mathematical representation of rail rates defines the model used in the regression analysis

performed for this research. The single dependent variable included in all analysis is real revenue per tonmile. Revenue per ton-mile (also referred to as “rate” per ton-mile) is a function of operating and supply

characteristics, demand factors, and control variables, such as location and time. 

The independent variables are transformed into natural log form, as indicated by the ln denotation, to better

represent the relationship between the dependent and independent variables overtime and space.In addition,

transforming the continuous variables into natural logarithms allows for the coefficients to be interpreted as

elasticities. The base model is:

RPTM = real revenue per ton-mile (in 2002 prices)

CARS = number of railcars in the shipment

SHRT = length of haul, in short-line miles

LOAD = load weight per railcar

HERF = rail market concentration index

BDIS = distance from nearest barge loading facility 
16

e

GPROD = total U.S. grain production

TRANS = transit shipment, identifier for length of haul under 50 miles

TBDIST = time and barge distance interaction term

THERF = time and rail market concentration interaction term

TIME = time trend, year of shipment 

TIMESQ = squared time trend

NE = Northeast Region (Connecticut, Delaware, District of Columbia, Maine,

Maryland, Massachusetts, New Hampshire, New Jersey, New York,

Pennsylvania, Rhode Island, Vermont, West Virginia)

SE = Southeast Region (Alabama, Florida, Georgia, Kentucky, North Carolina,

South Carolina, Tennessee)

DE = Delta Region (Arkansas, Louisiana, Mississippi)

NP = Northern PlainsRegion (Montana,NorthDakota, South Dakota, Wyoming)

CP = Central Plains Region (Colorado, Kansas, and Nebraska)

SP = Southern Plain Region (New Mexico, Oklahoma, and Texas)

WCB = Western Corn Belt Region (Iowa, Minnesota, Missouri)

PNW = Pacific Northwest Region (Idaho, Oregon, Washington)

W = West Region (California, Nevada, Utah)

MULTI = multi-car train shipment, includes 24 to 49 cars

UNIT = unit train shipment, includes 50 to 109 cars

SHUTTLE = shuttle train shipment, includes 110 cars or more

CORN = commodity identifier for corn shipment

SYBN = commodity identifier for soybean shipment

Q2 = cyclical indicator, 2nd quarter shipment period (April through June)

Q3 = cyclical indicator, 3rd quarter shipment period (July through September)

Q4 = cyclical indicator, 4

th quartershipment period (Octoberthrough December)

TSQCORN = time-squared interaction term for corn shipment

TSQSYBN = time-squared interaction term for soybean shipment

T##### = time interaction term for associated variable “######”, including region

and commodity

= Normal Effects Error Term

The operating and supply characteristics included in the model are shipment size, distance, and load factor.

Train size is an important aspect of railroad operations, as it is a substantial determinant in the degree of

labor and administration economiesthat can be achieved with large-volume shipments. Several components

of rail costs are fixed with respect to distance such as cost of switching, classifying and loading cars. These

costsremain the same irrespective ofthe distance shipped, and therefore do not increase proportionatelywith

mileage. Costs and rates per mile should decline with distance. The measure of train size is the number of

cars per shipment (CARS). 
17

Average train size has increased over the past decade as the railroads have shared the benefits of larger train

economies with shippers in their pricing structures (Tables 1, 2, and 3). The distance is indicated by length

of haul fromorigin to destination for a shipment and is measured in short line rail miles(SHRT). As distance

increases the railroad is able to attain economies of size associated with administration and labor costs.

Thus, an inverse relationship is expected for the distance and revenue per ton-mile. Load factor is measured

by the average per car weight for the shipment (LOAD). Technological advancements and investment in

rolling stock and power units have lent to an increase in average load factor for rail cars.

The elasticity of demand for rail service varies across time, space, and commodity. Measures of intra- and

intermodal competition are considered as the influential factors in the relative elasticity of grain rail service

demand. Representation of modal competition includes distance from the nearest barge loading facility

(BDIST) and local rail competition (HERF) as inter- and intramodal measures, respectively. Despite their

limited flexibility to serve origins/destination and handle small shipments, barges are very active the grain

industry asthey offer the low-cost alternative for long distance, bulk-productshipments. Because barges are

a major competitor for long- haul, large-quantity bulk commodity shipments, it would be expected that as

distance increases from barge loading facilities the influence of this competition will wane and the rail rate

per ton-mile will tend to increase. BDIST is the distance from the centroid in the origin county to the closest

barge facility. It is a measure of the strength of intermodal competition. As the distance from a barge

loading facility increases, the movement from inland truck origin to barge facility movement becomes more

costly and less competitive. 

The level of local rail competition is represented by considering the relative size of market shares for

railroadsin the local market. The measure of rail competition is estimated as a Herfindahl-Hirschman index.

The index is calculated as the sum of the squared market shares for each railroad with traffic in the local

market, with the county borders defining the local market. The index ranges from 0 to 1, with 0 and 1

representing perfectly competitive and monopoly rail markets, respectively, as rail rates are expected to be

higher in areas with lower degrees of intermodal and intramodal competition. Thus, as distance to barge

facility increases and as rail power becomes more concentrated, the revenue per ton-mile is expected to be

higher.

The spatial and time influences on demand elasticity are considered in several terms in the models.

Underlying spatial variability in demand elasticity (possibly due to geographic and product competition) is

accounted for by including regional groupings of rail origins. The regional definition used by USDA

Transportation and Marketing defines nine regions, including Central Plains, Delta, Eastern Corn Belt,

Northeast, Northern Plains, Pacific Northwest, Southeast, Southern Plains, West, and Western Corn Belt.

These regions are grouped based on similarities in agricultural production characteristics. A map of the

regions is presented in Figure 10. The regions have been used in previous analyses and are useful in

commodity- and geographic-based discussions of market phenomena.

The underlying time trend is established by TIME, in the year-to-year trend for rail revenue per ton-mile.

To allow a changing time trend over time, the squared time variable (TSQUARE) is included in the model.

TIME is expected to be inversely related to revenue per ton-mile, as the rail industry has had greater pricing

flexibility and cost savings in the deregulated environment. However, the savings may have changed over

time.
18

Interaction of time and selected variables allows delineation of differences in change or rates of change

across competitive factors, space, and time. Several interaction terms were included to allow an assessment

ofthe differing impacts of deregulation as a result of differing levels of transportation competition overtime.

The first two interaction terms, THERF and TBDIST, are indicators of a change in the effects of intramodal

and intermodal competitive influences on rail rates over time, respectively. Both are expected to have a

positive relationship with rail rates. 

It is posited that the industry has shifted from the former cost-based regulated structures to market-based

differential pricing in a deregulated environment. With market-based pricing, captivity of shippers becomes

more important. Therefore, over time regions with lesser degrees of rail competition (more captive) may

accrue relatively less of the benefits associated with deregulation.

Similarly, the TBDIST variable is expected to show that as market-based pricing has become more prevalent

over time there has been an increasing differential between shippers with more competition and less

competition in their transportation markets. Captive shippers may have received relatively less benefit from

deregulation than those shippers located in closer proximity to barge facilities.

Regional- and commodity-based time interaction terms are also included to allow for variations in change

of rates over time across regions. Nine region/time interactions terms are included as T####, with “####”

referring to regional definition established with the USDA production region variables. TCORN, TSYBN,

TSQCORN, and TSQSYBN, are commodity/time interaction terms with the first two variables measuring

the effects of commodity differences over time, relative to wheat, and the latter two allowing for a change

in the effects of time, relative to wheat, during the two decade time span of the study. The time and timesquared interaction terms may be influenced by factors such as the initial rate, competition levels, and

production geography. These interaction terms are not discussed in terms of expected signs but do provide

important insight in discussing implications for producers of a specific commodity and producers located

in a specific region.

An industry demand variable is also included to account for year-to-year variability in the market demand

for rail grain transportation. The demand control variable is a measure of U.S. grain production (GPROD).

It is defined asthe total annual production ofseven major agricultural commodities, including wheat, barley,

corn, oats, sorghum, rye, and soybeans (National Agricultural Statistics Service, 2003). It is expected that

the relationship between revenue per ton-mile and total grain production will be positive, signifying that as

the demand for rail shipment increases, rail rates will increase.

To measure the seasonal affects ofrail ratessince deregulation, three quarterly dummy variablesindicate the

shipment time period. Q2, Q3, and Q4 refer to the second, third, and fourth monthly quarters of the year.

The variation is for seasons measured in comparison to the first quarter of the year, as it is the quarter not

included in the model.

Three additional dummy variables are included to delineate rail rate categories. Rail rates are published by

the railroads as a single price per car from a train that ranges between a minimum and maximum number of

cars. These tariff rang7es are generally defined as single car, multiple car (MULTI), unit train (UNIT), and

shuttle (SHUTTLE) train shipments. The strict definition of these ranges varies by railroad and commodity.
5Formally known as the Interstate Commerce Commission (ICC).

6

Initial attempts were made to extend the scope of the study by including the Annual Rail Waybill Sample,

Master File from 1972 through 2000. After considering cautionary remarks from the Surface Transportation Board

and conducting a review of this data, it was determined that the reliability was not satisfactory in years prior to 1981.

7Dennis (2000), addressed Wolfe’s cautions by comparing Rate Per Ton Mile (RPTM) data derived from

the Waybill Sample to that derived from the Association of American Railroads’ (AAR) Freight Commodity

Statistics (FCS) database (AAR, 1982-1996). 

Dennis found an overall adjustment factor of 3 percent was needed for both revenue and tonnages in the Waybill

sample database to match FCS values. 

19

For the purposes of this research, single car rates apply to rail shipments including 1 to 24 cars, multiple car

shipments include 25 to 49 cars. These shipments are generally bound for domestic origins, including

processors and feedlots. The unit (50 to 109 cars) and shuttle (110+cars) shipments, which provide the

greatest potentialforrail and shipper economies ofshipmentsize are generally bound for export destinations.

The MULTI, UNIT, and SHUTTLE control variables are included to adjust the intercept for shifts between

rate rangesin the rail tariff. The variables may also be used in a general discussion of the rates for shipments

destined for the domestic and export markets.

3.2 Data

The primary source of data forthe analysisisthe Surface Transportation Board Annual Rail Waybill Sample,

Master File for the period 1981-2000.5,6 This data is stratified to provide representative analysis for both

cross-sectional and in time-series evaluation. Although the Waybill data does have some limitations, it is

the best source for historical railshipment data (Dennis, 20007; MacDonald, 1987; Wolfe, 1997). The issue

of outliers, such as the shipments with unrealistic car load densities and trip distances of 0 miles, was

addressed by removing the extreme observations below the 1

st percentile and above 99th percentile for

shipment characteristics that included revenue per ton mile, load density, train size, and trip distance, by

commodity. Initially, there were approximately 244,000 observations in the data set. After transformation

to eliminate outliers and missing data is considered, about 240,000 observations are included in the model

estimation. Although the number of observations is reduced slightly by these limitations, the overall model

fit was improved by eliminating the influence of these extreme observations.

With regard to missing data, the short line miles variable was often not reported prior to 1984. To correct

forthis, an average distance between origin and destination county, retrieved fromdata forsubsequent years,

is included as a proxy for the actual short line miles. In filling in the prior omitted distances with the

estimation, it was assumed that the distance from county A to county B wasthe same regardless of what year

the shipment occurred. 

Several variables are defined and appended to the base Waybill data set of shipments and characteristics;

these include measures of intramodal and intermodal competition and several control variables. As noted

previously, the level of intramodal competition is represented by a measure of market concentration. The

Herfindahl-Hirschman index is used to estimate rail industry concentration at the county level. The index
20

West

Pacific

Northwest Northern

Plains

Central

Plains

Southern

Plains

Western

Corn

Belt

Eastern

Corn

Belt

Delta

Southeast

Northeast

Figure 10. USDA Production Region Map

is equal to a summation of the squared values ofindividualrailroadsshare, including all railroads originating

shipments from the county. 

Herf = (Si )

i

n

2

=1

∑

where = Share of railroad i in all railroad movements of grain originating in a county. 

The measure used to represent the level of intermodal competition is distance from origin county to the

nearest competing barge service provider. Highway miles were used to measure the distance from a centroid

in each county to the nearest barge loading facility. 

Descriptive statistics for corn, wheat, and soybeans and their respective weighting factors are presented in

Tables 2, 3, and 4. Select statistics from these tables were discussed previously, in the Market Overview.

As discussed previously, the regional delineations for this study are based on USDA production region

definitions. A map of the regions is included in Figure 10.
21

Table 2. Corn Shipment Characteristics, Waybill Averages 1981 to 2000

Year Tons RPT1 RPTM2 LOAD3 CARS3 BDIST3 HERF3

1981 43,014,231 29.7 0.0373 93.8 2.3 90.8 0.789

1982 37,956,564 23.6 0.0322 96.2 4.3 85.5 0.802

1983 49,146,182 23.4 0.0313 96.3 5.4 86.6 0.753

1984 54,338,736 24.3 0.0287 96.6 6.5 90.9 0.780

1985 46,046,915 18.9 0.0257 96.6 6.6 82.5 0.791

1986 45,813,662 17.4 0.0261 97.0 6.1 76.9 0.786

1987 63,096,608 16.7 0.0243 97.1 7.7 78.5 0.802

1988 66,633,040 17.6 0.0240 97.0 8.9 89.9 0.809

1989 77,599,150 17.7 0.0236 96.0 10.2 87.2 0.813

1990 69,481,358 18.0 0.0244 95.2 8.9 88.4 0.832

1991 58,744,538 18.1 0.0250 95.2 9.2 93.9 0.834

1992 61,888,288 17.5 0.0253 96.1 9.4 91.2 0.838

1993 62,421,873 17.1 0.0248 95.7 9.5 87.0 0.826

1994 56,437,473 16.5 0.0257 96.3 8.6 90.8 0.769

1995 80,801,498 20.1 0.0228 97.6 9.7 99.1 0.745

1996 69,112,979 19.0 0.0237 98.2 8.2 104.6 0.751

1997 64,138,368 19.5 0.0232 97.5 9.9 107.9 0.671

1998 65,653,837 18.2 0.0233 99.3 11.0 104.2 0.790

1999 73,981,465 18.0 0.0217 99.8 10.7 105.4 0.789

2000 68,943,535 18.2 0.0206 99.5 12.7 93.3 0.784

NOTE: RPT=Rate per ton; RTPM=Rate per ton-mile; LOAD=Rail Car Weight; CARS=Train Size;

BDIST = Distance to barge loading facility; HERF =intramodal competition index for rail

shipments 

1Averages weighted by ton; 2Averages weighted by ton-mile; 3Averages weighted by expansion factor
22

Table 3. Wheat Shipment Characteristics, Waybill Averages 1981 to 2000

Year Tons RPT1 RPTM2 LOAD3 CARS3 BDIST3 HERF3

1981 40,998,060 30.4 0.0632 96.7 1.4 221.5 0.710

1982 44,486,141 27.2 0.0499 97.4 2.2 247.6 0.788

1983 44,567,287 25.4 0.0436 97.9 2.7 256.9 0.789

1984 53,303,937 22.2 0.0390 96.7 3.7 251.4 0.671

1985 40,552,061 20.6 0.0389 95.9 3.6 265.7 0.661

1986 40,674,442 19.3 0.0332 94.0 4.2 258.4 0.683

1987 45,973,218 18.1 0.0304 93.0 5.0 260.0 0.697

1988 58,137,847 18.6 0.0302 93.0 6.4 246.1 0.726

1989 58,777,314 16.5 0.0306 94.7 8.8 202.9 0.736

1990 40,072,133 19.4 0.0307 96.6 7.4 240.8 0.728

1991 41,986,187 19.5 0.0284 96.2 8.4 243.8 0.754

1992 45,772,222 19.8 0.0281 96.6 8.4 251.5 0.737

1993 49,001,960 21.5 0.0292 96.6 8.8 258.1 0.728

1994 43,260,687 23.3 0.0310 96.3 6.6 243.0 0.666

1995 44,687,234 22.7 0.0295 98.4 5.7 238.8 0.661

1996 43,324,276 22.5 0.0279 98.4 5.9 229.7 0.665

1997 40,150,580 22.2 0.0289 98.2 5.3 231.0 0.611

1998 43,549,120 21.0 0.0287 99.0 5.9 225.1 0.705

1999 43,139,259 20.6 0.0276 99.1 5.9 216.4 0.676

2000 40,360,639 19.7 0.0259 99.6 7.7 211.7 0.670

NOTE: RPT=Rate per ton; RTPM=Rate per ton-mile; LOAD=Rail Car Weight; CARS=Train Size;

BDIST = Distance to barge loading facility; HERF =intramodal competition index for rail

shipments 

1Averages weighted by ton; 2Averages weighted by ton-mile; 3Averages weighted by expansion factor
23

Table 4. Soybean Shipment Characteristics, Waybill Averages 1981 to 2000

Year Tons RPT1 RPTM2 LOAD3 CARS3 BDIST3 HERF3

1981 8,338,889 20.2 0.0450 93.4 1.6 64.8 0.836

1982 11,027,022 18.9 0.0331 96.5 3.1 78.3 0.884

1983 12,165,133 15.8 0.0337 95.8 4.1 80.7 0.898

1984 11,620,430 15.7 0.0325 95.5 5.1 79.6 0.867

1985 9,124,700 13.7 0.0303 95.0 4.4 87.7 0.883

1986 16,703,031 12.8 0.0245 95.0 5.0 81.6 0.818

1987 16,841,978 11.7 0.0233 94.9 6.4 77.6 0.857

1988 16,679,549 13.6 0.0261 93.9 7.2 81.8 0.855

1989 12,365,356 13.9 0.0271 92.3 6.3 84.2 0.874

1990 14,308,603 13.4 0.0269 94.4 6.9 86.5 0.878

1991 15,342,786 13.3 0.0258 93.9 8.4 84.7 0.901

1992 16,824,297 13.7 0.0245 93.9 9.5 78.7 0.870

1993 15,888,676 13.6 0.0235 95.5 10.2 83.8 0.900

1994 14,884,494 12.9 0.0231 96.4 8.4 93.2 0.870

1995 18,558,977 15.6 0.0224 96.5 7.9 103.4 0.824

1996 20,945,977 15.5 0.0231 97.4 7.9 110.9 0.814

1997 18,386,921 16.1 0.0223 97.9 9.0 119.5 0.814

1998 18,129,898 15.6 0.0222 97.8 8.4 123.3 0.856

1999 18,791,880 16.3 0.0213 99.3 9.5 136.7 0.858

2000 18,492,194 16.5 0.0199 93.5 15.5 112.5 0.849

NOTE: RPT=Rate per ton; RTPM=Rate per ton-mile; LOAD=Rail Car Weight; CARS=Train Size;

BDIST = Distance to barge loading facility; HERF =intramodal competition index for rail

shipments 

1Averages weighted by ton; 2Averages weighted by ton-mile; 3Averages weighted by expansion factor
24
8The time effects will be discussed subsequently.

25

4. EMPIRICAL RESULTS

The results of the log-linear estimation of rail revenue per ton-mile between 1981 and 2000 for corn,

soybeans, and wheat is presented in Table 5. Variables included in the model explain approximately 73

percent of the variation in revenue per ton-mile. Further, most explanatory variables have their expected

signs and most are significant at conventional levels.

In examining the parameter estimates for variables expected to influence movement costs, all have their

expected signs and are significant at the 1 percent level. To the extent that demand side variables are

accounted forin the estimation, parameter estimates onmovement characteristicsshould reflect the influence

of such characteristics on costs. The number of rail cars in a shipment and the commodity weight per car

have a negative influence on rate per ton-mile,since unit costs per ton decrease with increased train weight.

Similarly, multi-car, unit-train, and shuttle-train dummy variables all have negative influences on rate per

ton-mile due to declines in unit costs with increased weight and due to increases in loading and switching

efficiency with these larger train sizes. Short-line miles have a negative influence on rate per ton-mile due

to the spreading of fixed terminal costs over longer distances.

Variables influencing the elasticity of demand for a particular rail shipment include the HerfindahlHirschman Index of railroad competition, the distance of the shipment origin from the nearest water loading

facility, and commodity/regional dummy variables. As expected, the Herfindahl-Hirschman Index and the

distance of the shipment origin from the nearest water loading facility both have a positive influence on rate

per ton-mile, suggesting a decrease in the elasticity of demand for a particular railroad shipment with less

intramodal and intermodal competitive alternatives. Commodity dummy variables for corn and soybeans

both have negative and statistically significant parameter estimates,suggesting lowerratesforthemovement

of such products in comparison to wheat. These commodity dummy variables largely reflect differences in

geographic and product competition among different commodities. Commodities with more substitutes and

that are produced in many regions are likely to realize lower railroad rates. Since corn and soybeans have

many substitutes, with wide spread U.S. production, their negative signs relative to wheat are expected.

Similarly, regional dummy variables reflect differences in geographic and product competition among

regions. Regions whose primary grains are also produced in adequate supply elsewhere are more likely to

receive favorable rates for their shipments. In this model, all regional dummies are interpreted in relation

to the Eastern Corn Belt Region (the region left for the estimation). After controlling for shipment

characteristics and other competitive conditions,severalregions,such asthe Northern Plains,Central Plains,

Southern Plains, Western Corn Belt, and West, experienced higher rates than the Eastern Corn Belt (at least

initially).8 Other regions, such as the North East and the South East, experienced lower rates.
26

Table 5. Estimation of Revenue per Ton-Mile

Variable Parameter Estimate

Intercept 2.3301*

(0.0843)

Number of Rail Cars -0.0258*

(0.0012)

Short-Line Miles -0.5120*

(0.0011)

Commodity Weight per Car -0.5173*

(0.0035)

Herfindahl-Hirschman Index 0.0788*

(0.0035)

Distance from Barge Facil. 0.0212*

(0.0015)

Annual Grain Production 0.0124**

(0.0051)

Time -0.0956*

(0.0011)

Time * Time 0.0035*

(0.00005)

Time * Dist from Barge Facil. 0.0018*

(0.0002)

Time * Herfindahl-Hirshman Index -0.0031*

(0.0004)

Multi-Car Dummy (26-51 cars) -0.0944*

(0.0050)

Unit-Train Dummy (52-109 cars) -0.1046*

(0.0061)

Shuttle-Train Dummy (110+ cars) -0.5552*

(0.0234)

Corn Dummy -0.1547*

(0.0045)
Table 5. Estimation of Revenue per Ton-Mile

Variable Parameter Estimate

27

Soybean Dummy -0.2561*

(0.0064)

Time * Corn Dummy 0.0362*

(0.0011)

Time * Soybean Dummy 0.0153*

(0.0017)

Time * Time * Corn Dummy -0.0017*

(0.00006)

Time * Time * Soybean Dummy -0.0007*

(0.00009)

North East Dummy -0.0338**

(0.0140)

South East Dummy -0.1019*

(0.0061)

Delta Dummy 0.0696*

(0.0137)

Northern Plains Dummy 0.2871*

(0.0064)

Central Plains Dummy 0.2569*

(0.0049)

Southern Plains Dummy 0.2540*

(0.0059)

Western Corn Belt Dummy 0.1600*

(0.0044)

Pacific Northwest Dummy 0.0137***

(0.0080)

West Dummy 0.1147*

(0.0116)

Time * North East Dummy -0.0164*

(0.0012)
Table 5. Estimation of Revenue per Ton-Mile

Variable Parameter Estimate

28

Time * South East Dummy 0.0130*

(0.0007)

Time * Delta Dummy -0.0184*

(0.0014)

Time * Northern Plains Dummy -0.0019*

(0.0006)

Time * Central Plains Dummy -0.0134*

(0.0005)

Time * Southern Plains Dummy -0.0132*

(0.0007)

Time * Western Corn Belt Dummy -0.0151*

(0.0004)

Time * Pacific Northwest Dummy 0.0080*

(0.0009)

Time * West Dummy -0.0009

(0.0013)

Quarter 2 Dummy 0.0371*

(0.0024)

Quarter 3 Dummy 0.0215*

(0.0023)

Quarter 4 Dummy 0.0191*

(0.0024)

Short Distance Movement Dummy 0.3124*

(0.0049)
Table 5. Estimation of Revenue per Ton-Mile

Variable Parameter Estimate

29

Adjusted R2 = 0.7368

F = 16,378

N = 239,854

standard errors in parentheses

*significant at the 1 percent level

**significant at the 5 percent level

***significant at the 10 percent level

All continuous variables (except time) in natural logarithms
9

Indirect effects of deregulation on rates may also have occurred to the extent that shipment size and

distance changes were the result of deregulation.

30

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

$ Per Ton-Mile (Current Prices)

Corn

Soybeans

Wheat

1981 1986 1991 1996 2000

Figure 11. Time Effect Simulation of Rates per Ton-Mile for Corn, Soybeans, and

Wheat, 1981 and 2000

Ofspecial interest in this estimation are the changesin rates over time. Since railroad deregulation occurred

in 1980, changes in rates since 1981 provide insight into the effects that deregulation has had on rates for

various shipment types. Because many variables are interacted with time in the estimation, the total effect

of time on rate per ton-mile depends on the commodity shipped, the distance from the nearest water loading

facility, the railroad concentration at the origin, and the region where the shipment originated. 

Figure 11 shows simulated corn, soybean, and wheat rates per ton-mile when placing all variables at their

mean levels for the entire period, except for time. The simulation shows the changes in rates that have

occurred solely due to changes in the parameters over time. Thus, it may be thought of as simulating the

direct effect of deregulation.9 As the figure shows, while the rates per ton-mile have come down on all three

commodities, their rates have converged somewhat relative to one another.
31

-50%

-45%

-40%

-35%

-30%

-25%

-20%

-15%

-10%

-5%

0%

19811982198319841985198619871988198919901991199219931994199519961997199819992000

Percentage Savings Relative to 1981

Wheat

Corn

Soybeans

Figure 12. Cumulative Percentage Decrease in Rates between 1981 and 2000, Relative to

1981

Figure 12 shows the simulated percentage changes in these same rates over the 1981 through 2000 time

period. As the figure shows, all three commodities show large percentage decreases in rates since 1981. The

trend also shows a reversal in rates over the rate trend since the mid to late 1990s. A larger percentage

decrease isillustrated for wheat rates until the mid-1990s, but a similar decrease in ratesfor all commodities

by the year 2000. As wheat is attributed with the highest rates at the onset of deregulation, this is not

unexpected. 
32

-60.0%

-50.0%

-40.0%

-30.0%

-20.0%

-10.0%

0.0%

1 50 100 150 200 250 300 350 400

Distance from the Nearest Barge Facility

Percent Difference in Rates (1981-2000)

Wheat

Corn

Soybeans

Figure 13. Simulated Intermodal Barge Competition Effects on Rail Rates Between 1981

and 2000, Cumulative at Various Distance from the Nearest Barge Loading

Facility

As mentioned previously, the change in rates over time depends on a variety of competitive characteristics.

The move to deregulation brought about an entirely new philosophy in rate determination. Rates became

moremarket-based, as opposed to the heavy reliance on costsin rate-setting that existed priorto deregulation.

Consequently, factors affecting the elasticity of demand are expected to have a more pronounced effect after

deregulation than before deregulation. Because all data in our estimation was post-deregulation, an increased

importance of demand elasticity variables over time is expected.

Regarding intermodal elasticity between rail and barge, the distance from the nearest barge loading facility

shows an increasing importance in our estimation over time. Longer distances to barge loading facilities

mean less intermodal competition considering potential truck-barge combinations. Figure 13 simulates the

rate savings since 1981 at various distances from the nearest water loading facility for wheat, corn, and

soybeans. As the figure shows, rate savings were larger in areas with more intermodal competitive options.

The parameter estimate of 0.0212 suggests that as distance to the nearest water loading facility is increased

by 1 percent, the rate per ton-mile increases by 0.02 percent in the initial rate period of 1981. The parameter

estimate of .0018 on the time/barge distance interaction term suggests that a 1 percent increase in distance

from the nearest water loading facility leads to a .055 percent increase in rate per ton-mile in 2000.

Therefore, as expected, the influence of intermodal competition has strengthened during the deregulated

environment.
33

0

50

100

150

200

250

300

350

400

Northern

Plains

West Central Plains Southern

Plains

Pacific NW North East Western

Corn Belt

South East Delta Eastern Corn

Belt

Miles

Figure 14. Average Distance of Shipment Origin from the Nearest Barge

Loading Facility for Wheat Shipments, 1981 to 2000

Conversely, the parameter estimate for the time interaction with the Herfindahl Index of origin railroad

concentration showed decreasing importance over time. This was not expected, since an increased reliance

on market factors in rate setting should also lead to increasing importance of intramodal competition.

However, one possible explanation for the decreasing importance of this variable over time is the large

increase in truck sizes and the resulting increase in the ability of trucks to compete over longer distances.

Longer truck competition has expanded the size of markets over which railroads compete. If railroads

compete over large geographic areas because of the ability of trucksto haul at low costsfor longer distances,

the concentration of railroads in a particular county may be irrelevant. In addition, the role of rail

competition may also be diminished by increasing consumption by the local market, including processing,

feeding and dairy industries, which are frequently served by trucks.

The changes in rates over time also varied among regions. Each region has a unique story with regard to the

commodities produced, markets served, and transport utilized, and consequently realize different levels of

geographic and product competition. Regions are characterized by differences in the availability of terminal

markets, the volume and scope of agricultural processing, and movement characteristics. The following

sections explore some of the characteristics of the various regions and highlight differences in rate changes

that have occurred since 1981.

4.1 Wheat

Figure 14 shows the average distance of origin rail points for wheat from the nearest barge loading facility

over the 1981-2000 period. As the figure shows, shipments originating in the Northern Plains, West,

Southern Plains, andCentral Plains experienced limited intermodal competition, while those in EasternCorn

Belt, Delta, and Southeast experienced heavy intermodal competition.
34

0

2

4

6

8

10

12

14

16

18

20

Eastern

Corn Belt

North East Northern

Plains

South East Central

Plains

Western

Corn Belt

Southern

Plains

Pacific NW West Delta

Average Cars per Shipment

1981

2000

Figure 15. Average Cars per Shipment by Originating Region, Wheat 1981

and 2000

Figure 15 shows the average cars per shipment for wheat shipments in 1981 and 2000, by region. The

average number of cars per shipment may be an indicator of rail investment by the origin regions, to the

degree it can be realized with access to markets capable of receiving larger rail shipments. As the figure

shows, all regions averaged less than four cars per shipment in 1981, while only three regions average less

than four carsin 2000. These regions, the Northeast, Southeast, and West, are primarily feed grain and small

mill destinations so the incentive for local shippers to invest in rail capacity is weak. Several regions had

very large increases in shipment size over the period, including the Southern Plains, the Central Plains, the

Western Corn Belt, and the Northern Plains. Given the more aggressive trainload pricing practices of

western rail carriers that are seeking efficiencies in large-quantity export markets as well as the domestic

markets, the difference in average cars per shipment seems highly reflective of railroad pricing incentives.

Figure 16 shows the average short-line distance of wheat shipments for 1981 and 2000, by region. The

shipment distance reflects incentives for inland shipment consolidation, including those related to trucking

costs associated with production agriculture and those related to efficiency gains available to elevators,

railroads, and terminal markets. As the figure shows, most regions have experienced large increases in

shipment distance since 1981. Further, the ranking of regions in terms of shipment distances has remained

relatively stable with the Northern Plains shipping the longest distances in 1981 and 2000.
35

0

100

200

300

400

500

600

700

800

900

1000

Northern

Plains

West Central

Plains

Western

Corn Belt

Southern

Plains

Pacific NW Eastern

Corn Belt

Delta North East South East

Average Distance

1981

2000

Figure 16. Average Short-Line Distance of Wheat Shipments by Region,

1981 and 2000

0

0.2

0.4

0.6

0.8

1

North East Pacific NW South East Eastern

Corn Belt

West Northern

Plains

Delta Southern

Plains

Central

Plains

Western

Corn Belt

Herfindahl Index

1981

2000

Figure 17. Average Herfindahl Index of Origin Railroad Concentration by

Region, Wheat Shipments 1981 and 2000

Figure 17 shows the average Herfindahl Index of origin railroad concentration for wheat shipments in 1981

and 2000, by region. As with Figure 16, six regions exhibit an increase in origin railroad concentration over

thistime period, while the otherfourshowed a decrease. Decreases in levels of rail competition, as measured

by concentration in rail shipments, characterize the eastern regions of the United States. This is expected,

with greater track density and with the larger number of small railroads that operated in the east compared

to the west in 1981. Rail industry rationalization has been more pronounced, in terms of rail track abandoned

and railroad consolidation, in the western region.
36

$0.0837

$0.0787

$0.0732 $0.0717 $0.0716 $0.0709 $0.0688 $0.0672 $0.0656 $0.0653

$0.0275 $0.0293

$0.0395

$0.0269

$0.0324

$0.0527

$0.0296

$0.0366

$0.0333

$0.0291

$0.0000

$0.0100

$0.0200

$0.0300

$0.0400

$0.0500

$0.0600

$0.0700

$0.0800

$0.0900

Southern

Plains

Central

Plains

North East Western

Corn Belt

Northern

Plains

South East Delta Pacific NW West Eastern

Corn Belt

Rate per Ton-Mile (Current Prices)

1981

2000

Figure 18. Simulated Wheat Rates by Region, 1981 and 2000 (All variables

are placed at their 1981 and 2000 mean levels for the region)

Figure 18 showssimulated wheat rates by region in 1981 and 2000, obtained by placing all variables at mean

levels by region and year. As the figure shows, there are large differences in rates among the regions in

1981, the initial year in the study that was largely reflective of rates effective under deregulation, and the

simulation for the competition environment in 2000. Moreover, all regions experience large decreases in

rates over this time period.

To gain more insight into the rate savings on wheat shipments, we simulated rate savings due to the time

trend and due to changesin time and independent variables. Changes due to the time trend reflect differences

in intermodal, intramodal, geographic, and product competition among regions, while changes due to

changing independent variables reflect changes in shipment efficiency due to larger and longer shipments

and changesin demand elasticity variables. Figure 19 shows the simulated wheat rate savings. As the figure

shows, several regions realized large savings in rates since 1981 due to time trend changes alone. Regions

realizing 40 percent savings or more from the time trend include two regions with a great deal of

transportation competition (theWesternCornBelt and theDeltaRegion), and three that aremajor destination

points for feed grain (the Southern Plains, the Central Plains, and the North East). This is not surprising,

since areas with waterway competition and nearby access to export facilities and areas with major feed

markets where truck is a viable option for transporting grain are likely have a higher elasticity of demand for

rail transportation. With a move to competitively determined rates, such as that occurring as a result of

deregulation, areas with high demand elasticity for rail transport are the areas where rate savings should be

the largest.
37

-80%

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%

Southern

Plains

Central

Plains

Western

Corn Belt

Delta Eastern

Corn Belt

Northern

Plains

West North East Pacific

NW

South East

Percent Rate Savings

Time Trend Rate Savings

Total Rate Savings

Figure 19. Simulated Wheat Rate Savings Due to Time Trend and Due to

Changes in Time Controlling for Shipment Characteristics, 1981

to 2000

When adding the rate savings attributable to changesin shipment characteristics, areas with the largest gains

relative to the time trend alone include the Pacific Northwest, the Northern Plains, the Eastern Corn Belt,

the Southern Plains, theWest, theWesternCornBelt, and theCentral Plains. Not coincidentally, these areas

are those that realized the largest increasesin average shipment distance between 1981 and 2000, and all but

the West and Eastern Corn Belt realized increases in shipment size by 500 percent or more.

4.2 Soybean

Just as rate savings for wheat shippers have varied among regions, they also have for soybean shippers.

Figures 20 through 23 showcharacteristics of areaswhere railsoybean shipments originated during the 1981-

2000 period.

As Figure 20 shows, regions where average distance of origin rail points from the nearest barge loading

facility were longest for wheat are the same regions where average distance of origin rail points from the

nearest barge loading facility are longest forsoybeans. However, the average distances of origin points from

the nearest barge loading facilities are shorterforsoybeansthan they were for wheat, overall. This is because

areas most wellsuited to soybean production are closerto navigable waterways. The closer average distance

ofsoybeansto water loading facilitiesin comparison to wheat is one reason why soybean rail rates are lower

than wheat rail rates.
10The Northern and Southern Plains regions had too few shipments in 1981 to present reliable shipment

mean characteristics.

38

0

50

100

150

200

250

300

Southern Plains Northern Plains Central Plains Western Corn Belt South East Eastern Corn Belt Delta

Miles

Figure 20. Average Distance of Shipment Origins from the Nearest Barge

Loading Facility, Soybeans 1981 and 2000

Figure 21 shows the average rail cars per shipment for soybeans in 1981 and 2000, by region.10 Just as for

wheat, all regions averaged less than four cars per shipment in 1981, while only the Southeast and Delta

regions averaged less than four cars per shipment in 2000. However, the increase in shipment size for

soybean rail shipments has been much larger than that for wheat. In 2000, the Eastern Corn Belt, the

Northern Plains, the Western Corn Belt, the Southern Plains, and the Central Plains regions all averaged

fifteen or more rail cars per shipment. Overall, grain production densities, large domestic processor

initiatives, and the smaller geographic distribution of soybean production, compared to wheat, may explain

the large increase in train size for soybeans compared to wheat.
39

0

5

10

15

20

25

30

35

Eastern Corn Belt Central Plains South East Western Corn

Belt

Delta Northern Plains Southern Plains

Average Cars per Shipment

1981

2000

Figure 21. Average Number of Cars per Shipment by Originating Region,

Soybeans 1981 and 2000

0

0.2

0.4

0.6

0.8

1

South East Delta Western Corn

Belt

Central Plains Eastern Corn Belt Northern Plains Southern Plains

Herfindahl Index

1981

2000

Figure 22. Average Short-Line Distance by Originating Region, Soybeans,

1981 and 2000

Average short-line distance, orthe shortestrail distance between two points,forsoybean shipments by region

is shown in Figure 22. As the figure shows, large increases in shipment distance occurred in the Central

Plains, Southeast, and Western Corn Belt regions between 1981 and 2000.
40

0

0.2

0.4

0.6

0.8

1

South East Delta Western Corn

Belt

Central Plains Eastern Corn Belt Northern Plains Southern Plains

Herfindahl Index

1981

2000

Figure 23. Average Herfindahl Index of Origin Railroad Concentration by

Region, Soybeans 1981 and 2000

$0.0708

$0.0631 $0.0622

$0.0591

$0.0413

$0.0187

$0.0256

$0.0201

$0.0294

$0.0266 $0.0287 $0.0264

$0.0000

$0.0100

$0.0200

$0.0300

$0.0400

$0.0500

$0.0600

$0.0700

$0.0800

Central Plains Delta Western Corn

Belt

South East Eastern Corn

Belt

Northern Plains Southern Plains

Average Rate Per Ton-Mile (Current Prices)

1981

2000

Figure 24. Simulated Soybean Rates by Region, 1981 and 2000

These changes may be related to increased production, exports, and rail rationalization factors. Figure 23

showsthe average HerfindahlIndex of origin railroad concentration forsoybean shipmentsin 1981 and 2000,

by region. Three of the five regions for which soybean shipment averages are available in 1981 and 2000

show an increase in origin railroad concentration.

Simulated soybean rates by region are shown for 1981 and 2000 in Figure 24. Just as for wheat, there are

large differences in rates among regions in 1981 and 2000, and regions generally have experienced large

decreases in rates. 
41

-80%

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%

Central Plains Western Corn

Belt

Delta South East Eastern Corn Belt

Percent Rate Savings

Time Trend Rate Savings

Total Rate Savings

Figure 25. Simulated Soybean Rate Savings Due to Time Trend and

Changes in Time and Shipment Characteristics, 1981 to 2000

Figure 25 showsthe simulated soybean rail rate savings between 1981 and 2000 forthe five regionsthat have

shipments in both time periods. The largest rate savings from the time trend alone are for the Central Plains,

Western Corn Belt, and Delta regions. These regions have a great deal of transportation competition and

nearby access to export markets or feed markets. As a result, the elasticity of demand for rail transportation

in such regions is likely to be high. When examining the rate savings from changes in shipment

characteristics, it is apparent that the greatest savings occurred in the Southeast, Central Plains, and the

Western Corn Belt. These three areas experienced more than a 100 percent increase in shipment distance,

while those experiencing smaller rate savings from changes in shipment characteristics realized less than a

15 percent increase in shipment distance. Moreover, the Central Plains and Western Corn Belt regions also

realized large increases in shipment sizes.

4.3 Corn

Figures 26 through 29 illustrate average shipment and competitive characteristics for corn rail shipments

between 1981 and 2000, across the ten regions. As Figure 26 shows, the average distance from the nearest

barge loading facility for rail corn origins is higher for the Southern Plains, West, and Northern Plains

regions just as it was for wheat and soybeans. Areas with origins close to water loading facilities include

the Delta, Eastern Corn Belt, and Southeast regions. Moreover, just as with soybeans, there are many corn

areas where origin shipment regions are in close proximity to water loading facilities. This is a partial

explanation for lower corn rates relative to wheat rates. However, another explanation is the widespread

production of corn and the substitutability of corn and soybeans as feed products.
42

0

50

100

150

200

250

300

350

Southern

Plains

West Northern

Plains

Central

Plains

North East Western

Corn Belt

Eastern

Corn Belt

South East Delta Pacific NW

Miles

Figure 26. Average Distance of Shipment Origins from Nearest Barge

Loading Facility for Corn,1981 and 2000

0

5

10

15

20

25

30

Central

Plains

Eastern

Corn Belt

Western

Corn Belt

Northern

Plains

South East Pacific NW Southern

Plains

North East Delta

Average Number of Cars

1981

2000

Figure 27. Average Number of Cars by Originating Region for Corn, 1981

and 2000

Figure 27 shows the average number of cars per shipment for corn rail shipments by originating region in

1981 and 2000. As the figure shows, all regions averaged less than four cars per shipment in 1981, while

all but two averaged more than five cars per shipment in 2000. Particularly impressive is the increase of

approximately two cars per shipment to nearly twenty-seven cars per shipment in the Western Corn Belt

between 1981 and 2000. The train size may be an indicator of shipper investment in rail origination

equipment. In addition, the extent to which these investments may be realized is directly related to domestic

and export market investment in rail receiving equipment.
43

0

200

400

600

800

1000

1200

1400

1600

Northern

Plains

Central

Plains

Southern

Plains

Eastern

Corn Belt

Western

Corn Belt

South East Pacific NW North East Delta

Average Distance (Miles)

1981

2000

Figure 28. Average Short-Line Distance by Originating Region for

Corn,1981 and 2000

Figure 28 showsshort-line distance by region in 1981 and 2000 for rail corn shipments. Surprisingly, unlike

wheat and soybeansthere have not been large increasesin shipment distance in most regions. One exception

is the Western Corn Belt where short-line distance increased from 540 miles to nearly 800 miles between

1981 and 2000. This shift in distance may be attributed to factors such as greater consolidation at origin

pointsin response to railroad marketing incentives, and technological advancesin grain production that have

affected the production geography and density.

Figure 29 shows the average Herfindahl Index of origin railroad concentration by region in 1981 and 2000.

As the figure shows, four of six regions that had shipment averages for both 1981 and 2000 showed an

increase in concentration overthe time period. The increase in concentration may be reflective of rail system

rationalization, in terms of rail line abandonment, and the consolidation of rail carriers operating in the

industry.
44

0

0.2

0.4

0.6

0.8

1

Pacific NW South East Northern

Plains

Southern

Plains

Central

Plains

Eastern

Corn Belt

Western

Corn Belt

North East Delta

Herfindahl Index

1981

2000

Figure 29. Average Herfindahl Index of Origin Railroad Concentration by

Region, Corn - 1981 and 2000.

$0.0678

$0.0537

$0.0504

$0.0455 $0.0434 $0.0419

$0.0348

$0.0411

$0.0196

$0.0285 $0.0282

$0.0214 $0.0236

$0.0444 $0.0424

$0.0000

$0.0100

$0.0200

$0.0300

$0.0400

$0.0500

$0.0600

$0.0700

$0.0800

Pacific NW South East Western

Corn Belt

Southern

Plains

Eastern

Corn Belt

Central

Plains

Northern

Plains

Delta North East

Rate Per Ton-Mile (Current Prices)

Figure 30. Simulated Corn Rates by Region - 1981 and 2000 (all variables

placed at their 1981 and 2000 mean levels for the region)

Figure 30 shows the simulated average corn rail rates by region in 1981 and 2000. As was the case for other

commodities, considering the 1981 to 2000 time period, there were large decreases in rates that averaged 40

percent across regions for which the data were available. Rate decreases ranged from 23 percent in the

Southeast to a high of 61 percent in the Western Corn Belt.
45

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%

Western Corn

Belt

Central Plains Southern

Plains

Eastern Corn

Belt

Northern

Plains

South East

Percent Rate Savings

Time Trend Rate Savings

Total Rate Savings

Figure 31. Simulated Corn Rate Savings Due to Time Trend and Due to

Changes in Time and Shipment Characteristics, 1981 to 2000

Figure 31 shows the changes in these rates as simulated by the time trend and by changes in shipment

characteristics added to the time trend. As the figure shows, the Western Corn Belt, the Central Plains, and

the Southern Plains regions - all regions with substantial feed markets - experienced the largest decreases

in rates attributable to the time trend. Not surprisingly, the only region experiencing substantial rate savings

resulting from changes in shipment characteristics was the Western Corn Belt Region - the only region with

large increases in shipment distance and shipment size during this period.
46
47

5. CONCLUSION

It isimportant to understand the distribution and incidence of influences associated with deregulation of rail

rates. The objective of this research was to provide insight into inter- and intra-commodity rail rate

differentials observed since rates were deregulated in 1981. A cross-sectional/time-series analysis of U.S.

corn, wheat, and soybean shipments was considered in the assessment of rail grain rate differentials. County

levelrailshipment characteristicsfor two decades were considered in the analysis. The time period selected,

1981 through 2000, coverstwo decades of pricing by railroadsin the deregulated environment. As expected,

results suggest that market-based pricing has become more prevalent in later years. The tendency for

railroads to implement more market-based pricing in recent years implies that rail demand elasticity is

becoming an increasingly important factor in the relative competitiveness of U.S. grain producers.

The overall benefit of rail deregulation, measured in terms of rail productivity and decreasing in rail rates

for shippers, is well established in previous research and consistent with the findings in this research.

Important findings in research go beyond the broad discussion to show that these benefits are not distributed

uniformly across or within commodities. Furthermore, as market-based pricing has become more prevalent

the variance in distribution of benefitsisshown to increasingly favorthose grain producerslocated in regions

with higherlevels ofintermodal competition. In a competitive market environment, trends in relative, as well

as overall, rates should be considered in assessing the impacts of policy and investment initiatives. This

research will help us to better understand the ultimate consequences of future policy and investment

decisions, in terms of overall and relative competitiveness of grain commodities and U.S. grain producers.
48
49

6. REFERENCES

Atkinson, S., Kerkvliete, J., Measuring the multilateral allocation of rents: Wyoming low-sulfur coal. 

Rand Journal of Economics. Vol. 17, No. 3, Autumn 1986. 

Babcock, M., Sorenson, L., Chow, M., Klindworth, K., 1985. Impact of the Staggers Rail Act on

agriculture: a Kansas case study. Proceedings of the Transportation Research Forum 26, 364-

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