Disentangling Regulatory Policy:

The Effects of State Regulations

on Trucking Rates

Timothy P. Daniel

Andrew N. Kleit

FEDERAL TRADE COMMISSION

November 1995
2

 Disentangling Regulatory Policy:

The Effects of State Regulations

on Trucking Rates

Timothy P. Daniel

Andrew N. Kleit

______________________________

Bureau of Economics Staff Report

Federal Trade Commission

Washington, D.C. 20580
3

November 1995
FEDERAL TRADE COMMISSION

ROBERT PITOFSKY Chairman

MARY L. AZCUENAGA Commissioner

JANET D. STEIGER Commissioner

ROSCOE B. STAREK, III Commissioner

CHRISTINE A. VARNEY Commissioner

BUREAU OF ECONOMICS

JONATHAN B. BAKER Director

RONALD S. BOND Associate Director for Operations and 

Policy

PAUL A. PAUTLER Associate Director for Consumer 

Protection and Research

GARY L. ROBERTS Associate Director for Antitrust

DENIS A. BREEN Assistant Director for Economic Policy

Analysis

ROBERT D. BROGAN Assistant Director for Antitrust

GERARD R. BUTTERS Assistant Director for Consumer

 Protection

TIMOTHY P. DANIEL Assistant Director for Antitrust

TIMOTHY A. DEYAK Assistant Director for Antitrust

The authors are from the Federal Trade Commission and

Louisiana State University, respectively. This project was begun when

both authors were in the Bureau of Economics of the Federal Trade

Commission. We are grateful to Ed Rastatter of the Department of

Transportation for granting us access to the data analyzed in this

report, and to Bruce Allen of Wharton who provided them to us in

machine readable form. We thank Paul Pautler, Ed Rastatter and Jim

McDonald for comments on an earlier draft. We also are grateful to

Lynn Carpenter, Karen Condor, Effie Georges, Dolly Howarth,

Andrew Kim, George Pascoe, and Carolyn Samuels for their research

and programming assistance. 

The views expressed are those of the authors and do not

necessarily reflect the views of the Commission or any individual

Commissioner.
 With respect to airline deregulation, a 1990 Department of 1

Transportation study concluded that "air travelers have benefited from

the changes brought about under deregulation by receiving more

service at a lower cost." (U. S. Department of Transportation (1990),

Executive Summary, p. 1.) For railroads, which were deregulated by

the 1980 Staggers Act, a 1990 GAO Study concluded that "shippers

have benefited from reduced railroad regulation. Since 1980, rail

rates, adjusted for inflation, have declined an average of about 22

percent. In addition, service has improved: train reliability has

increased and freight car shortages have declined." (U.S. General

Accounting Office (1990), p. 4. See also Barnekov and Kleit (1990)

and Burton (1993). Winston et al. (1990) examined both railroads and

interstate trucking, the latter having been deregulated by the 1980

Motor Carrier Act (MCA) and concluded that federal deregulation of

these two industries provides net benefits to consumers of over $16

billion (1988 dollars) each and every year.

 The focus of this study is economic regulation, e.g., rate 2

regulation and entry controls. We do not examine safety regulation. 

Unless otherwise noted, we will use the word "regulation" to refer

only to economic, and not safety, regulation. For a discussion of

safety regulation, see Alexander (1992).

I. Introduction

Numerous studies have documented the sizable gains to

consumers that followed federal deregulation of various transportation

sectors. Each of these studies estimated the effects of federal 1

deregulation on price by comparing post-deregulation prices with

estimates of what prices would have been had economic regulation

continued. While indisputably informative, such analyses nonetheless 2

assume that regulation is an all-or-nothing phenomenon; it either exists

or it does not. In fact, transportation regulations take a variety of

forms including rate regulation, entry restrictions, and the provision of

antitrust immunity for decisions made jointly by competitors. To date,

no study has attempted to estimate the independent effect on

transportation prices from these different types of economic

regulations. 
6

This study fills that gap by estimating the relationship between

intrastate trucking rates and three different types of state-level

regulations: (1) the strictness with which rates are regulated; (2) the

requirements placed on motor carriers seeking to enter the market; and

(3) whether the state provides antitrust immunity for decisions made by

motor carrier rate bureaus. By combining a cross-section of intrastate

trucking rates with information on the motor carrier laws and

regulations in place at the time, we can estimate the relationships

between particular types of regulations and trucking rates.

Our basic conclusions are consistent with previous studies

which found that motor carrier regulations tend to raise rates. In the

less-than-truckload ("LTL") sector (shipments of less than 10,000

pounds), there is a positive relationship between each of the three

regulations studied and intrastate trucking rates. That is, motor carrier

freight rates tend to be significantly higher in states that strictly

regulate rates, in states that impose significant restrictions on new

entrants, and in states that provide antitrust immunity for rate bureau

decisions. Entry restrictions have the largest rate-increasing effect: in

the LTL sector, significant entry restrictions raise trucking rates over

20 percent. With regard to the other two types of regulations

analyzed, strict rate regulation raises LTL trucking rates over 5

percent, and antitrust immunity raises LTL trucking rates over 12

percent.

In the truckload ("TL") sector (shipments of more than 10,000

pounds), the results are somewhat different. A strong positive

relationship emerges between trucking rates and the degree to which

the state regulates those rates. This positive relationship is stronger

than that found in the LTL sector. Specifically, states that regulate

rates strictly have TL rates over 32 percent higher than states that do

not regulate rates strictly. Unlike the results for the LTL sector,

however, there is no significant relationship between TL trucking rates

and either entry requirements or the provision of antitrust immunity.
 Keeler (1989) used the survivorship technique to obtain results 3

consistent with the view that trucking firms experience modest

economies of scale. Yet, Keeler stated that using this finding to

support reregulation of the trucking industry would be "seriously

wrong for several reasons." He then elaborated by citing to (1) the

mobility of trucks, which implies that trucking markets would in many

instances be national in scope; (2) the generally low level of sunk costs

in the trucking industry; and (3) empirical studies documenting the

gains to consumers and shippers from trucking deregulation.

7

II. The Regulation of Motor Carriers

Motor freight transportation would appear to approximate the

textbook definition for atomistic competition: modest (or nonexistent)

economies of scale and ease of entry into and exit from particular

routes. (Wilson and Dooley (1993)) Yet, despite these characteristics, 3

the trucking industry was highly regulated at the federal level from

1935 to 1980 and in most states for even longer. Regulations typically

took the form of entry restrictions (e.g., carriers were provided

authority to transport a particular commodity on a particular route only

after showing that such service met a compelling public need) and rate

regulations (e.g., tariffs were approved by the relevant regulatory

agency and could only be changed up or down after a demonstration

that the proposed changes were justified.) 

At the national level, the regulation of motor carriers was the

focus of considerable economic and political debate in the 1970s,

culminating in the passage of the Motor Carrier Act ("MCA") in 1980. 

The MCA, in conjunction with its liberal interpretation by the

Interstate Commerce Commission ("ICC"), eliminated large parts of

the federal regulatory regime. In particular, motor carriers could enter

interstate markets much more easily and interstate motor carriers could

change their prices with significantly less regulatory oversight. As

noted in the introduction, economic studies have shown that these

changes yielded significant benefits for consumers.
 The MCA grants antitrust immunity for some but not all of a rate 4

bureau's activities. Rate bureaus cannot, for example, collectively

establish single-line rates, that is, rates on routes that can be handled

by a single carrier. By contrast, the MCA does provide antitrust

immunity for joint-line rates (routes involving more than one carrier)

and for general rate increases (across the board increases on an entire

menu of rates.)

 Petition of the United States Department of Justice for an Order 5

Requiring the Members of the Rocky Mountain Motor Tariff Bureau to

Show Cause Why Their Antitrust Immunity to Discuss and Agree on

General Rate Increases Should Not Be Withdrawn, filed with the

Interstate Commerce Commission, December 19, 1989.

8

At the federal level, an important remaining vestige of the preMCA regulatory regime is the provision of limited antitrust immunity

for rate bureaus. The effect on rates from providing immunity to rate 4

bureaus is not clear. On the one hand, as the Department of Justice

has argued before the ICC, immunity from the antitrust laws could

raise rates by facilitating tacit or explicit collusion among the rate

bureau members. Supporters of antitrust immunity for rate bureaus 5

(Hausman (1983) and Tye (1987)) counter by arguing that immunity is

necessary to foster the efficient exchange of information among the

bureaus' members. Under the assumption that the industry is

competitive, any benefits stemming from the efficient exchange of

information would be passed on to shippers as lower rates.

At the state level, the extent of motor carrier regulation varies

significantly. Some states have completely deregulated the motor

carrier industry. Other states strictly limit entry by providing

operating certificates to prospective entrants only after a showing that

the entry fills a compelling public need that cannot be met by existing

carriers. Some states continue to regulate rates strictly; others provide

for little or no regulatory review of rate changes. While every state

permits motor carriers to belong to rate bureaus, only about half of
 See, for instance, the discussion of state laws and regulatory 6

initiatives in Consumer Cost of Continued State Motor Carrier

Regulation, Twenty-first Report by the Committee of Government

Operations, House Report 101-813 (October 5, 1990). In 1992, the

Michigan Public Service Commission reconsidered its state's trucking

regulations. See, In the Matter of the Proposed Revisions to the Motor

Carrier Rules, Order Publishing Proposed Revisions to the Motor

Carrier Rules and Providing Notice of Hearing, Case No. T-1210,

Michigan Public Service Commission (released August 14, 1992).

9

them grant motor carriers antitrust immunity for the bureaus' joint

activities, such as rate making and scheduling.

Decisions regarding the regulation of the motor carrier industry

continue to arise at both the federal and state level. Notwithstanding

the significant reforms contained in the 1980 MCA, economic

regulations, such as tariff-filing requirements and continued antitrust

immunity for some rate bureau decisions, remain at the federal level. 

In addition, forty-two states continue to regulate intrastate truckers,

and states frequently consider proposals to relax or to expand the

extent to which truckers are regulated in their states. Such regulation 6

has an important economic impact because approximately two-thirds of

all shipments are intrastate. (Allen et al. (1990), p. 9) Proponents of

continued or expanded economic motor carrier regulation typically

claim that certain regulations provide stability to the industry, prevent

"destructive competition", and do not contribute to higher prices. Our

empirical analysis provides a direct test of this last claim.

III. Data

A. Rate Data

Our data contain point-to-point trucking rates, both interstate

and intrastate, announced by motor carrier rate bureaus and on file

with the relevant regulatory agencies in the continental U.S. during the
 The eleven excluded states fall into three categories. First, the 7

rate data do not contain intrastate rates for the eight states that had

deregulated their motor carrier industries by 1987: Alaska, Arizona,

Delaware, Florida, Maine, New Jersey, Vermont, and Wisconsin. 

Second, Hawaii was excluded because interstate trucking rates cannot

exist there. Third, Montana and Wyoming were excluded because the

dataset did not contain any intrastate rate information for these states.

 At the interstate level, the rates were filed by one of the ten 8

dominant interstate rate bureaus, whose operating areas (generally) do

not overlap. At the intrastate level, various motor carrier rate bureaus

typically operate; the data contain the rates filed by one of the larger

(or the largest) rate bureaus operating in the state. 

 We note that there appears no way other than relying on filed 9

rates to obtain data on intrastate rates from enough states to conduct an

analysis as comprehensive as this. Actual transaction prices are not

(continued...)

10

spring of 1987. Each intrastate route has a companion interstate route

emanating for the same origin city and terminating in a city of similar

size after travelling roughly the same distance. The data contain 708

of these "triads" with origins in thirty-nine states. 

7

For each route the data contain the rates, denominated in cents

per hundred pounds shipped, for a variety of commodity classes

(typically twelve) and for a number of weight categories (typically less

than 500 pounds through 20000 pounds), as well as the mileage

between the origin and destination cities.

Three points about the rate data deserve mention. First, the

rates are those filed with a regulatory agency by a motor carrier rate

bureau. While filed rates are available to shippers, they often are not 8

the rates actually charged. Nonetheless, we believe that the filed rates

should be representative of those actually charged, especially when

discounting is accounted for (see below).9
 (...continued) 9

publicly available, and data on particular carriers (such as those

contained in the American Trucking Association's Annual Report) are

not state-specific. (See, e.g., Ying and Keeler (1991) and Winston et

al. (1990)).

 We obtained information on interstate discounts from the ICC. 10

The applicable discounts were culled by ICC staff from filings made by

the interstate rate bureaus. Because the variation around the average

was relatively small, and because it is difficult to determine which

discount to apply to routes that traverse the territory of two rate

bureaus, we chose to use the simple average for all interstate rates.

 Of the thirty-nine states included in this study, thirteen do not 11

permit discounting. For those states that do permit discounting, Allen

et al. (1990) attempted to obtain two estimates for the discount

typically available -- one from a shipper, and another from a state

regulatory official. For states in which they obtained two estimates for

the typical discount, we conducted our analysis using the higher

discount, the lower discount, and the average of the two. Our

empirical results do not change depending on which of these intrastate

discounts are used; the results reported below use the higher discount

(where more than one was available).

11

Second, carriers typically offer shippers discounts from filed

rates. We account for discounting by reducing the filed rate by the

discount generally available. We discount all interstate rates by 27.81

percent, which is the average of the discounts provided by rate bureaus

during the spring of 1987. For intrastate rates, we use the discounts 10

typically offered during this period as reported in Allen et al. (1990).11

Third, the rates analyzed in this study are class rates. Motor

carriers typically offer two types of common carrier rates: class rates

and commodity rates. Commodity rates, as the name implies, pertain

to a particular commodity (such as lumber) and are typically offered to

larger-volume shippers who can provide truckload quantities. Class
 The data indicated that shipments under class rates tended to be 12

shorter in distance and smaller in size than those under commodity

rates. Of the total amount of interstate traffic carried under common

carrier rates, shipments under class rates accounted for approximately

73% of the revenues collected and 49% of the tonnage shipped. 

12

rates, by contrast, are offers to ship goods in particular classes at

specified rates. Each product is assigned to a numbered class, and

rates are higher for higher-numbered classes. An alternative to

common carrier shipments is contract carriage, which occurs when

relatively large shippers contract directly with carriers for a series of

shipments of merchandise over a period of time. Such shipments are

moved under "contract" rates, but information from these contracts is

not publicly available.

Recent data do not exist on the proportion of shipments

handled under class -- as opposed to commodity or contract -- rates. 

Detailed data on interstate common carriage shipments were collected

by the ICC in the late 1970s and early 1980s. According to the Motor

Carrier Ratemaking Study Commission ("MCRSC") (1983, p. 182)

approximately 87.9% of common carriage interstate shipments in 1980

were made under class rates, 5.1% under commodity rates, 1.9%

under "commodity column" rates, and 2.6% under exception rates. 

Thus, in 1980 the overwhelming percentage of common carrier

shipments were moved under class rates. The percentage of common 12

carrier shipments handled under class rates was high for less-thantruckload (LTL) shipments (90.0%) but significantly lower for

truckload (TL) shipments (27.1%). These figures imply that our data

may reflect more accurately the rate structure for LTL shipments than

for TL ones.

As noted above, the data contain rates for a number of different

classes for each route. After examining the data, we discovered that

the rates for various classes, holding route and weight fixed, were very

highly correlated. In many cases, the rates were perfectly correlated,

that is, the rate for class 100 was exactly twice that for class 50, and
 In order for a triad to be included, the computerized rate base 13

had to have an interstate rate and an intrastate rate for the weight class

of interest. 

13

the rate for class 150 exactly three times that for class 50. Given the

high correlation across classes, we arbitrarily chose to analyze class

100 rates.

We focus on three different weights: less-than-500 pounds,

2000 pounds, and 20000 pounds. The first two are less-than-truckload

(LTL) categories; the last is a truckload (TL) weight. We have two

reasons for focusing on these weight levels. First, our empirical

analysis (described below) uses rate data from both interstate and

intrastate routes. These three weights had the largest number of

"matches" in the data. Second, it has been argued that any benefits of

antitrust immunity and collective decision making are more likely to

arise in the LTL, rather than the TL, sector of the industry. In the

LTL sector, coordination among carriers in scheduling routes and

transferring shipments at warehouse facilities could reduce the costs of

providing shipping services, and possibly help carriers avert

"destructive competition." While the need for and benefits from such

inter-firm coordination seem less likely for TL shipments, we will test

for such benefits in both sectors.

B. Summary Statistics

The data contain routes, both intrastate and interstate, with

origins in thirty-nine states. We have added to the rate and mileage

figures the populations of the origin and destination cities, taken from

the 1980 Census. Table 1 contains some basic summary statistics for

the routes for each of the three weight categories included in the

analysis.13
14

Table 1

SUMMARY STATISTICS

(various weight categories)

 

WEIGHT CATEGORY

 

<500 lbs. 2000 lbs. 20000 lbs.

 

Number of routes 673 644 290

Number of states with 38 36 17

origin cities

Average mileage

(std. deviation)

 intrastate route 198 200 237

(115) (117) (125)

 interstate route 202 204 242

(113) (114) (123)

Average population

(std. deviation) 

 origin city 214,015 197,291 307,730

(601,663) (546,637) (842,485)

 destination, intrastate 67,119 57,110 85,819

(156,450) (89,113) (218,027)

 destination, interstate 62,290 59,076 70,928

(111,889) (104,289) (129,116)

Table 1 reveals that the intrastate and interstate routes are, on

average, approximately the same length and terminate in cities of

approximately the same size. Origin cities are, on average,

approximately three to four times larger than destination cities. 

Figures M1-M3 are maps showing, for each weight class, the states

included in the analysis.
 In the actual survey, respondents were offered more choices than 14

are contained in the brackets. The responses in the brackets distill the

responses into two categories for use in the empirical analysis. A copy

of the 1987 Baker survey is contained in Appendix A of this report.

15

C. State-level regulations

Our information on the motor carrier regulations in place in the

various states in 1987 is taken from a survey of state motor carrier

regulations compiled by Daniel Baker, an attorney affiliated with the

Transportation Lawyers' Association (TLA). On behalf of the TLA,

Baker annually surveys individuals familiar with the motor carrier laws

and regulations that exist in each state.

The Baker survey contains a number of questions concerning

motor carrier regulation. We use the answers to the following four

questions to characterize a state's regulatory regime. In brackets

following the questions are the possible responses. 

14

(1) To what extent does the state regulate motor common

carrier rates? [strict regulation; not strict regulation]

(2) What is required to obtain motor common carrier

authority from the state? [strict requirements; not

strict requirements]

(3) How effective are protests to motor common carrier

applications? [very effective; somewhat effective; not

effective]

(4) Does antitrust immunity exist for tariff bureaus that

publish motor carrier rates? [YES, bureaus are

immune; NO, bureaus are not immune]
16

Question 1 is a measure of the degree to which state regulators

are involved in establishing and maintaining a particular tariff

structure. Note that protests by incumbent carriers (question (3)) can

only be effective in states where significant entry barriers already exist

(question (2)). Finally, question (4) pertains to whether rate bureau

actions are shielded from antitrust scrutiny at the state level. 

Based on the responses to these questions, we characterize

state-level trucking regulation with four 0-1 dummy variables.

RATEREG = dummy variable equal to one if the

state strictly regulates motor carrier

rates; zero otherwise.

ENTRY1 = dummy variable equal to one if the

state has strict entry requirements and

if protests by incumbent carriers

against applications for new entry are

very effective; zero otherwise.

ENTRY2 = dummy variable equal to one if the

state has strict entry requirements and

if protests by incumbent carriers

against applications for new entry are

somewhat effective; zero otherwise.

IMMUNE = dummy variable equal to one if the

state grants rate bureaus antitrust

immunity; zero otherwise.

Table 2 contains the number of states in which these four

variables assumes the value one for each of the three weight classes

analyzed. 
17

Table 2

Number of states with various motor carrier regulations in 1987

(percent of total in parentheses)

WEIGHT CATEGORY

(n = number of states included in analysis)

 

<500 lbs. 2000 lbs. 20000 lbs.

 (n=38) (n=36) (n=17)

 

Variable

RATEREG 19 (50%) 18 (50%) 11 (65%)

ENTRY1 8 (21%) 9 (25%) 4 (24%)

ENTRY2 23 (61%) 23 (64%) 9 (53%)

IMMUNE 22 (58%) 21 (58%) 12 (71%)

 

Figures M4-M6 are maps showing the status of each regulation

(rate regulation, entry restrictions, and antitrust immunity) in each state

in the continental U.S. in the spring of 1987.
18

STATE LISTING FOR FIGURE M4

RATE REGULATION BY STATE, 1987

States that have strict rate regulation:

AL, CA, GA, IL, LA, MI, MS, MO, NE, NV,

NM, NC, OK, OR, PA, RI, SC, TX, WA, WV

States that do not have strict rate regulation:

AR, CO, CT, ID, IN, IA, KS, KY, MD, MA,

MN, NH, NY, ND, OH, SD, TN, UT, VA

States not included in analysis:

AZ, DE, FL, ME, MT, NJ, VT, WI, WY
19

STATE LISTING FOR FIGURE M5

ENTRY REGULATION BY STATE, 1987

States that impose very strict entry requirements:

AL, LA, NC, OH, OK, OR, TN, TX, WA

States that impose somewhat strict entry requirements:

AR, CO, CT, GA, IL, IN, IA, KY, MA, MI,

MN, MS, MO, NE, NV, NH, NM, ND, PA, RI,

SD, VA, WV

States with relatively low entry requirements:

CA, ID, KS, MD, NY, SC, UT

States not included in analysis:

AZ, DE, FL, ME, MT, NJ, VT, WI, WY
20

STATE LISTING FOR FIGURE M6

ANTITRUST IMMUNITY BY STATE, 1987

States that provide antitrust immunity to motor carriers:

CA, GA, ID, IL, KS, KY, MA, MI, MN, MO,

NE, NV, NM, NY, NC, OK, OR, PA, RI, SC,

TX, VA, WV

States that do not provide antitrust immunity:

AL, AR, CO, CT, IN, IA, LA, MD, MS, NH,

ND, OH, SD, TN, UT, WA

States not included in analysis:

AZ, DE, FL, ME, MT, NJ, VT, WI, WY
 We note, however, that we do not have to qualify similarly the 15

(continued...)

21

Our aim is to estimate the independent effect on intrastate

trucking rates from each of these four regulatory variables. In other

words, we seek to estimate how trucking rates change when a

particular regulation changes, holding constant the status of the other

regulations included in the analysis. In reviewing our regulation

variables, we discovered that some regulations tended to occur

together. That is, states with one regulation (say, strict rate regulation)

tended also to have another regulation (say, strict entry controls.) This

empirical fact requires that we interpret our results carefully. Suppose,

for example, that we wish to estimate the relationship between

regulation A and trucking rates, but it turns out that regulation A only

exists in states that also impose regulation B. In such circumstances,

the coefficient on regulation A provides an estimate of the likely effect

on trucking rates from imposing regulation A provided that regulation

B already exists. In this example, we cannot obtain an estimate of the

likely effect on trucking rates from imposing regulation A in states

where regulation B does not exist currently. We conclude this section,

therefore, with a brief discussion of the degree to which these state

regulatory variables tend to occur together and how this influences the

interpretation of the empirical results.

Most states with strict rate regulation also have strict entry

requirements. Specifically, in the less than 500 pound analysis, of the

19 states with strict rate regulation fully 17 also have somewhat or

very strict entry requirements. In the 2000 pound analysis, every state

with strict rate regulation also has somewhat or very strict entry

requirements. In the 20000 pound analysis, of the 11 states with strict

rate regulation 10 also have somewhat or very strict entry

requirements. This pattern indicates that the coefficient on the variable

RATEREG should be interpreted for the most part (in the case of the

2000 pound analysis, in its entirety) as estimating the relationship

between strict rate regulation and trucking rates provided that

somewhat or very strict entry requirements already exist.15
 (...continued) 15

estimated relationship between trucking rates and entry controls

because a number of states that impose entry requirements (either

somewhat or very strict) do not also impose strict rate regulation.

22

The other regulatory variables are not as highly correlated, and

we conclude that the variation is sufficient that we need not qualify the

empirical results in the manner described in the previous paragraph. 

With respect to strict rate regulation (RATEREG) and antitrust

immunity (IMMUNE), there is some tendency for states to have both

regulations but there also is some variation. Specifically, in both the

less than 500 pound and the 2000 pound analyses, four states have

RATEREG=1 and IMMUNE=0 and seven states have the opposite. 

In the 20000 pound analysis the comparable numbers are two states

and three states.

Finally, while states that provide antitrust immunity for rate

bureaus do tend also to impose strict entry requirements, there appears

to be enough variation to identify the relationships between these

regulations and trucking rates. In the less than 500 pound analysis, of

the 22 states with antitrust immunity, five had low entry requirements. 

Comparable amounts of variation exist in the 2000 (20000) pound

analyses; of the 21 (12) states with antitrust immunity, 3 (3) have low

entry requirements.

IV. Empirical Estimation of the Effects of Regulation

A. Hypotheses

As discussed in the previous section, we examine three types of

state-level motor carrier regulations: rate setting regulations; entry

regulations; and regulations providing antitrust immunity for the joint

decisions made by intrastate rate bureaus. Based on economic theory

and past research, we have a number of hypotheses concerning the

relationships between these regulatory variables and trucking rates. 
23

1. Direct Hypotheses

We expect a positive relationship between trucking rates and

the extent to which state regulators are involved in establishing and

maintaining a particular tariff structure. In principle, active rate

regulation by a state could contribute to lower trucking rates. This

could arise if the trucking industry possessed characteristics conducive

to noncompetitive pricing, e.g., significant economies of scale and

significant sunk costs. The general academic consensus on this issue,

however, is that this is not the case. (See, for example, Keeler

(1989).) We therefore hypothesize that rates will be higher in states

that actively regulate trucking rates.

We also predict a positive relationship between trucking rates

and the severity of entry regulations. At the federal level, the passage

of the MCA in 1980 and subsequent interpretations by the Interstate

Commerce Commission made entry into new routes by existing

carriers and by new carriers much easier, leading to significantly lower

trucking rates. (Winston et al. (1990) and Ying and Keeler (1991).) 

Moreover, given experiences at the federal level, we expect this

relationship to be especially strong in states where incumbent carriers

can effectively deter or delay new entry by protesting prospective

entrants' applications for operating authority. (U.S. Department of

Transportation (1979), pp. 5-6)

Finally, as mentioned earlier, it is not possible to predict the

relationship between trucking rates and the provision of antitrust

immunity for rate bureaus. On the one hand, immunity from the

antitrust laws could raise rates by facilitating coordination among the

rate bureau members. On the other hand, immunity might be

necessary to foster the efficient exchange of information among the

bureaus' members, and to promote the efficient use of existing

capacity. 
 One might hypothesize further that the positive effect on rates 16

from combining strict entry requirements and antitrust immunity would

be weaker in states with strict rate regulation. This might arise

because strict rate regulation already exerts a positive effect on rates,

thereby limiting the additional impact from including strict entry

requirements and antitrust immunity. Testing for this effect would

require segmenting the data even further. We are prohibited from

doing so, however, because of the degree of collinearity among our

regulatory variables.

24

2. Interactive Hypotheses

We also examine somewhat more complex hypotheses to allow

for interactions among the three types of regulations. First, even if on

average there is a positive relationship between trucking rates and

antitrust immunity for rate bureaus, it could be diminished, perhaps

eliminated, in states where entry is largely unobstructed. The

argument here is straightforward: attempts by rate bureaus to raise

rates behind the shield of antitrust immunity would attract entry (absent

regulatory barriers), thereby defeating the attempted rate increase. 

Consequently, we hypothesize that the relationship between trucking

rates and antitrust immunity will be stronger (weaker) in states with

strict (low) entry requirements. Similarly, we hypothesize that the

relationship between trucking rates and entry restrictions will be

stronger (weaker) in states that grant (do not grant) antitrust immunity

to rate bureaus. We test these hypotheses by analyzing only those

observations from states with strict entry requirements (or from states

that grant antitrust immunity). We expect the combination of antitrust

immunity and strict entry requirements to contribute to significantly

higher trucking rates.16

Second, we consider whether the relationship between antitrust

immunity and trucking rates is different in states with strict rate

regulation than in those without strict rate regulation. We hypothesize

that the combination of antitrust immunity and strict rate regulation

could facilitate collusion among the motor carriers operating in a state. 
 Douglas and Miller (1974) discuss this effect in the context of the 17

regulated airline industry.

25

Incentives to reduce rates could be dampened considerably in states

where truckers can legally meet to discuss rates and other matters, and

where rates cannot be reduced without regulatory approval. Under this

hypothesis, the relationship between antitrust immunity and trucking

rates would be stronger in states that also strictly regulated trucking

rates.

Finally, we consider whether the relationship between entry

regulations and trucking rates is different in states that regulate rates

strictly. Two competing hypotheses exist here. First, it might be the

case that states with both strict rate regulation and strict entry controls

(as opposed to one or the other) are ones in which the state legislature

and regulators are particularly sensitive to incumbent truckers'

concerns that deregulation of rates and entry would diminish their

ability to serve shippers profitably. If this view of regulation holds,

the combination of these two regulations would facilitate collusion

among the incumbent carriers and lead to higher trucking rates. Under

this view, we would expect the positive relationship between entry

restrictions and trucking rates to be even stronger in states that also

regulate rates strictly. On the other hand, free entry might not reduce

rates in states with strict rate regulation. Instead, it could be the case

that firms enter until industry profits are zero, resulting in an

inefficient use of capacity as any supra-competitive profits are

competed away on other margins. Under this alternative line of 17

reasoning, the positive relationship between trucking rates and strict

rate regulation would not be stronger in states that also strictly regulate

entry. Similarly, this view would imply that the positive relationship

between entry restrictions and rates would not be stronger in states that

also regulate rates strictly.
26

B. Estimation Approach 

Estimating the relationship between state motor carrier

regulations and intrastate trucking rates is conceptually

straightforward: regress intrastate rates from a number of states (the

dependent variable) on a series of regulatory variables and other

variables likely to affect trucking rates (collectively, the independent

variables). Prior to combining into one analysis the rate and regulation

variables from a number of states, we reviewed carefully the pattern of

rates on file in each of the states. This review indicated that within a

particular state for a particular weight category there is a strong

statistical relationship between the filed rate and the distance of the

route. In other words, the rates for a particular weight category within

a particular state can be largely explained by the following simple

formula:

(1) Rijk = jk + jk * Mijk + uijk

where,

R = the log of the rate for route i, weight j, ijk

state k

M = the log of mileage for route i, weight j, ijk

state k

u = error term with mean 0 and variance ijk

2

jk, jk = state-specific parameters subject to

estimation for weight j and state k.

We ran equation (1) separately for each of the states and each

of the weights included in our dataset. This involved 88 separate

regressions: 37 for the less than 500 pound category, 35 for the 2000

pound category, and 16 for the 20000 pound category. Of these 88

regressions, fully 50 (57%) had R s greater than 0.95 and only 12 2

(14%) had R s less than 0.80. 2
 In a separate set of analyses, we included as independent 18

variables the populations of the origin and the destination cities, and

the population densities of the counties lying between these cities. 

Including these variables did not increase significantly the explanatory

power of the state-specific regressions, and had no meaningful affect

on the estimated relationships between intrastate trucking rates and

state trucking regulations.

 States with a relatively large constant term ( ) tend to have a 19

relatively small elasticity with respect to mileage ( ) and vice versa.

 While we have some a priori expectations concerning the 20

(continued...)

27

These findings indicated that the rates within a state for a

particular weight class tended to follow closely the simple formula

depicted in equation (1). We also noted that the formulas across 18

states varied significantly. We concluded that a properly specified 19

empirical model would account for the observed within-state

regularities. Thus, we chose not to combine the data from the various

states and regress trucking rates on a constant, the distance of the

route, and a series of regulatory variables. Doing so would force the

constant term and the elasticity of trucking rates with respect to

distance to be the same across states, restrictions that do not appear

appropriate.

We proceeded as follows. As shown in equation (1), each state

"formula" relating rates to mileage contains two parameters: a

constant term ( ) and the elasticity of rates with respect to mileage ( ). 

We hypothesized that each state's "formula" depended on the cost

conditions particular to that state, and on the state's motor carrier

regulations. Thus, we modify equation (1) by interacting and 

with variables that control for state-specific cost conditions and

regulations. Note that this specification is as general as possible: We

permit regulations to affect both a state's constant term ( ) and its

elasticity of rates with respect to mileage ( ).20
 (...continued) 20

direction of the relationships between trucking rates and various

regulations, we have none concerning whether these relationships are

sensitive to the distance of the route.

28

Based on the preceding discussion, the equation we estimate is:

(2) Rijk = 0 + 1 * INDEXk + 2 * REGk + 0 * Mijk

+ 1

 * Mijk * INDEXk +

2

 * Mijk * REGk + uijk

where,

R = the log of the rate for route i, weight j, ijk

and state k

INDEX = variable measuring state k's cost k

conditions

REG = vector of 0-1 dummy variables k

describing motor carrier regulations in

state k

M = the log of the mileage for route i, ijk

weight j, and state k

u = error term with mean 0 and variance ijk

2

's, 's = parameters to be estimated

and bold-faced items represent vectors.

As mentioned above, in this specification the relationship between a

particular regulation and rates is a function of mileage.

The variable INDEX is intended to capture the cost conditions k

affecting the trucking industry in state k. In constructing this variable

we exploited the fact that our data contain interstate trucking rates as
29

well as intrastate ones. All interstate rates are subject to a common

regulatory structure -- the 1980 MCA. Thus, holding distance

constant, variations in interstate rates cannot be due to regulatory

differences; they must be due instead to differences in local cost

conditions, such as labor and fuel costs, congestion costs, and

topographical features. We therefore used the interstate rate

information to construct a set of state-specific indices reflecting each

state's cost conditions.

To compute a particular state's cost index we proceeded as

follows. We identified all of the interstate routes in our data that either

originated or terminated in the state. Then, for each state, we ran a

simple regression akin to equation (1) above: the dependent variable

was the log of the interstate rate, and the independent variables were a

constant and the log of mileage. The two coefficients generated by

such a regression provide a measure of the cost conditions particular to

that state; states with higher costs would have larger coefficients. 

Creating a state specific cost index involved two additional steps. 

First, we used these coefficients to predict the log of an interstate rate

for a route of a distance equal to the average intrastate route in the

state. Naturally, states with shorter routes would have lower predicted

rates than states with longer ones. To create an index that could be

compared across states, we divided each state's predicted rate by the

length of the state's average intrastate rate. This normalizes the

predicted interstate rate to a "per mile" equivalent (expressed in logs),

permitting one to compare meaningfully the index from a small state to

one from a larger one.

Prior to presenting the empirical results, we note that our

empirical specification in equation (2) treats state-level regulations as

exogenous variables. Such an approach would not be proper if

regulations and prices are determined simultaneously. It could be the

case that the level of prices affects the regulations that exist as well as
 Several recent studies of state-level regulations have attempted to 21

correct for the possible simultaneity between prices and regulation. 

See, for instance, Mathios and Rogers (1989) and Lanning, Morrisey,

and Ohsfeldt (1991). For a discussion of recent empirical studies of

regulation, see Joskow and Rose (1989) and Klevorick (1991).

 The cross-section nature of our data differs from the time-series 22

data utilized by Lanning et al. (1991) in their study of hospital

regulations. There, the regulations of interest were imposed in the

midst of the time period covered by the data, making endogeneity a

(continued...)

30

vice versa. If this were the case, then equation 2 would suffer from

specification error and the estimated coefficients would be biased.21

We believe treating state-level regulations as exogenous does

not introduce simultaneity bias for two reasons. First, the troublesome

bias discussed above would arise in the following circumstances: 

suppose that states without regulation contain only a few trucking firms

who could effectively collude to raise prices ten percent above

competitive levels. Now assume that state regulation in the public

interest would only be partially successful, lowering prices to five

percent above competitive levels. A naive regression model of this

situation could show that regulation raised prices five percent, when in

fact it had the opposite effect.

We do not believe that the situations we are examining fit the

above circumstances. In that scenario, trucking firms that operate

intrastate trucking routes could be expected to oppose state regulation. 

For the most part, however, intrastate trucking firms support economic

regulation, claiming various efficiency grounds. This fact, combined

with the generally unconcentrated structure of the industry, lead us to

conclude that this type of bias is not a problem here.

Second, our data stem from a cross-section of rates for the

spring of 1987. Simultaneity bias would not exist if the regulations 22
 (...continued) 22

serious concern.

 States first implemented motor carrier regulations in the first 23

three decades of this century, and most states amended their

regulations to make them consistent with the 1935 Motor Carrier Act. 

It is well accepted that the 1935 Act was implemented, in large

measure, to insulate railroads from trucking competition. Between

1940 and 1980, changes in both state-level and federal motor carrier

regulations were rare. Since the passage of the 1980 Motor Carrier

Act, which deregulated many aspects of interstate trucking, a number

of states have reconsidered their motor carrier regulations. Several

states amended their regulations in ways similar to the 1980 MCA; a

few others deregulated their intrastate motor carrier industries

completely. This latter group is not included in our data, because the

absence of regulation implies the absence of filed tariffs, which we rely

on for our rate data.

 According to the Baker survey, two of these states (ND and SD) 24

enacted partial deregulation in 1985. We assume that by the spring of

1987 the effects of these changes would be fully realized in trucking

rates.

31

at issue were pre-determined, that is, if regulations were affected by

prior years' rates but not by 1987 rates. Of the thirty-nine states 23

included in the data, the Baker survey indicates that thirty-five did not

pass economic motor carrier legislation in 1986 or 1987. We believe

that for these states it is reasonable to assume that the state-level

regulations in effect during the spring of 1987 were pre-determined.24

The four states that did enact meaningful economic legislation or

amend meaningful economic regulations in 1986 or 1987 were

California, Colorado, Georgia, and Utah.

In California, the state Public Utility Commission reimposed

significant rate regulations in April 1986, including requirements that

some rates be raised ten percent on the grounds that overly low rates
32

and profit margins induced truckers to compromise on safety. 

Throughout 1986 and 1987, motor carrier regulation continued to be

debated in California. Still, the April 1986 decisions by the CAPUC

were implemented in May 1986, and should have been reflected in the

1987 rates. Thus, we believe it is reasonable to interpret the California

regulations as exogenous.

The Baker survey does not provide details on the regulatory

and legislative changes that reportedly occurred in Colorado in 1986. 

Our review of the trade press failed to uncover any mention of any

changes. In addition, the description of Colorado's state motor carrier

laws and regulations compiled annually by the National Association of

Regulatory Utility Commissioners was identical for the period 1985-

1987. Thus, we assume that any changes in Colorado in 1986 were

not substantial.

Georgia enacted legislation in 1985 intended to relax its entry

requirements for intrastate truckers; this law went into effect on

January 1, 1986. The new, more "relaxed", law, however, still

required prospective entrants to establish that their entry would fulfill

an important public need. While this change could facilitate entry (it is

potentially less burdensome than having to show that existing carriers

cannot provide adequate service), in our parlance it still qualifies as

"somewhat strict" entry requirements. Thus, the change in Georgia

does not appear to be a significant one. Further, given its enactment

date of January 1, 1986, we believe that the effects of the changes in

Georgia's regulatory regime would be incorporated into rates by the

spring of 1987.

Finally, the 1987 Baker survey indicates that in 1986 Utah

passed a law similar to the 1980 MCA, and that in 1987 the Utah

legislature did not consider further trucking deregulation. As in the

other states, we assume that these facts support our contention that the

effects of the 1986 changes would have been reflected in 1987 rates.
33

We conclude, therefore, that the cross-section nature of our

study and the relative fixity of state-level regulations concerning the

motor carrier industry, permits us to treat state-level motor carrier

regulations as exogenous variables.
 That is, we ran the regression only on those observations where 25

ENTRY1 or ENTRY2 equals one, and dropped ENTRY1 and

ENTRY2 as independent variables.

 An alternative approach to assessing the magnitude of such 26

interaction effects among the regulatory variables would be to add to

equation (2) a series of additional independent variables that interact

the various regulatory variables with each other. This approach yields

results qualitatively and quantitatively very similar to those reported

below.

34

C. Estimation Results

We estimated equation 2 separately for each of the three weight

classes in our data: less than 500 pounds; 2000 pounds; and 20000

pounds. Initially, all four of the regulatory variables were included as

independent variables. To examine whether the effect of a particular

regulation depends on the status of the other regulations, we also

estimated equation 2 for subsets of the data, determined by the status

of a particular regulatory variable. For example, we limited the

analysis to those observations where entry regulations are strict.25

From this analysis we can learn whether the relationship between

trucking rates and a particular regulation depends on the status of other

regulations.26

Table 3 lists the dependent and independent variables included

in the analysis; the dependent variable is the log of the intrastate rate. 

Table 4 contains the means and standard deviations for these variables

for each of the three weight categories analyzed.
35

Table 3

Variables included in the regression analysis

Variable Variable description

______________________________________________________________________

LRATE Log of the intrastate rate, which is expressed in cents per hundred

weight for a shipment of a particular weight on a particular route

LMILES Log of the route's distance, in miles

LINDEX Local Cost index -- estimate of the log of the per mile cost of a

typical interstate shipment that either originates or terminates in

the state 

RATEREG dummy variable equal to one if the state strictly regulates motor

carrier rates; zero otherwise;

ENTRY1 dummy variable equal to one if the state has strict entry

requirements and if protests by incumbent carriers against

applications for new entry are very effective; zero otherwise;

ENTRY2 dummy variable equal to one if the state has strict entry

requirements and if protests by incumbent carriers against

applications for new entry are somewhat effective; zero otherwise;

IMMUNE dummy variable equal to one if the state grants rate bureaus

antitrust immunity; zero otherwise;

M_LINDEX LMILES times LINDEX

M_RATEREG LMILES times RATEREG

M_ENTRY1 LMILES times ENTRY1

M_ENTRY2 LMILES times ENTRY2

M_IMMUNE LMILES times IMMUNE

______________________________________________________________________
36

Table 4

Means and Standard Deviations of Variables 

Included in the Regression Analysis

(various weight categories)

______________________________________________________________________

WEIGHT CATEGORY

< 500 pounds 2000 pounds 20000 pounds

 (n=673) (n=644) (n=290)

 

Variable Mean Std. Mean Std. Mean Std.

Dev. Dev. Dev.

 

LRATE 7.132 0.348 6.637 0.343 5.655 0.513

LMILES 5.095 0.675 5.101 0.684 5.246 0.662

LINDEX 2.176 0.317 1.606 0.302 0.665 0.276

RATEREG 0.489 0.500 0.475 0.500 0.634 0.482

ENTRY1 0.220 0.414 0.258 0.438 0.248 0.433

ENTRY2 0.608 0.489 0.636 0.482 0.538 0.499

IMMUNE 0.574 0.495 0.564 0.496 0.662 0.474

M_LINDEX 10.949 1.288 8.063 1.167 3.389 1.089

M_RATEREG 2.549 2.650 2.487 2.659 3.448 2.648

M_ENTRY1 1.161 2.201 1.383 2.365 1.332 2.338

M_ENTRY2 3.034 2.498 3.162 2.468 2.784 2.622

M_IMMUNE 2.970 2.614 2.928 2.630 3.611 2.612

______________________________________________________________________
37

Tables R1 - R3 contain the regression results for the less than

500 pound, 2000 pound, and 20000 pound regressions. The first

column of each table includes all four of the regulatory variables as

independent variables, and therefore includes all of the observations in

the analysis. The other three columns in each table contain the results

from running the regression on subsets of the data, where the subsets

are determined by the status of a particular regulation. The second

column limits the analysis to states where rates are regulated strictly

(RATEREG=1); the third column limits the analysis to states where

entry is very or somewhat restricted (ENTRY1=1 or ENTRY2=1);

and the fourth column is limited to states where rate bureaus are

granted antitrust immunity (IMMUNE=1). We use the results from

columns (2) through (4) to examine whether the relationship between a

particular regulation and trucking rates depends on the status of the

other regulations.
Table R1:Regression Coefficients

Dependent Variable: Log of intrastate rate

WEIGHT: less than 500 pounds

(1) (2) (3) (4)

ALL ENTRY1=1 or

OBS. RATEREG=1 ENTRY2=1 IMMUNE=1

 

LMILES 0.270*** 0.513*** 0.040 0.495***

(0.104) (0.111) (0.110) (0.102)

LINDEX 0.414** 0.609*** -0.314 0.467**

(0.206) (0.213) (0.216) (0.232)

RATEREG -0.301 --- -0.630** -0.240

(0.237) (0.255) (0.240)

ENTRY1 -0.222 0.382 --- 0.211

(0.323) (0.375) (0.396)

ENTRY2 -0.342 0.811** --- 0.928***

(0.213) (0.332) (0.298)

IMMUNE 0.300 -0.259 1.029*** ---

(0.235) (0.375) (0.253)

M_LINDEX -0.013 -0.087* 0.148*** -0.050

(0.044) (0.048) (0.048) (0.051)

M_RATEREG 0.068 --- 0.147*** 0.037

(0.045) (0.050) (0.047)

M_ENTRY1 0.085 0.017 --- 0.019

(0.061) (0.070) (0.074)

M_ENTRY2 0.119*** -0.047 --- -0.095*

(0.042) (0.062) (0.057)

M_IMMUNE -0.026 0.064 -0.167*** ---

(0.046) (0.072) (0.050)

CONSTANT 4.684*** 3.670*** 5.869*** 3.905***

(0.518) (0.555) (0.508) (0.484)

Mean Dep Var 7.132 7.210 7.167 7.206

Adj. R 0.418 0.611 0.436 0.528 2

No. of obs. 673 329 557 386

Standard errors in parentheses.

*** (**) (*) denotes significance at the 1 (5) (10) percent level.
Table R2: Regression Coefficients

Dependent Variable: Log of intrastate rate

WEIGHT: 2000 pounds

(1) (2) (3) (4)

ALL ENTRY1=1 or

OBS. RATEREG=1 ENTRY2=1 IMMUNE=1

 

LMILES 0.252*** 0.481*** 0.444*** 0.502***

(0.089) (0.111) (0.064) (0.091)

LINDEX 0.273 0.744*** 0.332** 0.607***

(0.204) (0.208) (0.166) (0.214)

RATEREG -0.102 --- -0.502** -0.772***

(0.227) (0.219) (0.279)

ENTRY1 -0.940*** -0.346 --- 0.448

(0.325) (0.269) (0.502)

ENTRY2 -0.545** DD --- 1.163***

(0.223) (0.419)

IMMUNE 0.435** -0.323 0.907*** ---

(0.207) (0.367) (0.217)

M_LINDEX 0.002 -0.109** -0.011 -0.068

(0.042) (0.045) (0.035) (0.045)

M_RATEREG 0.040 --- 0.118*** 0.162***

(0.044) (0.043) (0.056)

M_ENTRY1 0.218*** 0.071 --- -0.037

(0.063) (0.050) (0.095)

M_ENTRY2 0.143*** DD --- -0.169**

(0.044) (0.080)

M_IMMUNE -0.062 0.074 -0.153*** ---

(0.041) (0.070) (0.043)

CONSTANT 4.613*** 3.906*** 3.826*** 3.399***

(0.445) (0.549) (0.316) (0.468)

Mean Dep Var 6.636 6.768 6.661 6.719

Adj. R 0.565 0.558 0.565 0.573 2

No. of obs. 644 306 574 363

DD : variable dropped due to perfect collinearity.

Standard errors in parentheses.

*** (**) (*) denotes significance at the 1 (5) (10) percent level.
Table R3:Regression Coefficients

Dependent Variable: Log of intrastate rate

WEIGHT: 20000 pounds

(1) (2) (3) (4)

ALL ENTRY1=1 or

OBS. RATEREG=1 ENTRY2=1 IMMUNE=1

 

LMILES -0.221 1.569*** 0.166 1.832***

(0.137) (0.344) (0.124) (0.368)

LINDEX -2.260*** 13.677*** -2.714*** 12.774***

(0.640) (2.920) (0.630) (2.992)

RATEREG 0.151 --- -0.517 1.278**

(0.471) (0.628) (0.628)

ENTRY1 -2.463*** -1.474 --- -2.749***

(0.482) (0.925) (0.872)

ENTRY2 -1.847*** 0.630 --- -0.246

(0.328) (0.842) (0.680)

IMMUNE -1.246** -0.544 0.075 ---

(0.520) (0.585) (0.613)

M_LINDEX 0.449*** -1.976*** 0.588*** -2.097***

(0.126) (0.539) (0.135) (0.544)

M_RATEREG 0.024 --- 0.159 -0.190

(0.089) (0.121) (0.116)

M_ENTRY1 0.474*** 0.301* --- 0.521***

(0.090) (0.161) (0.156)

M_ENTRY2 0.340*** -0.074 --- 0.069

(0.062) (0.143) (0.119)

M_IMMUNE 0.230 0.098 -0.035 ---

(0.099) (0.112) (0.117)

CONSTANT 6.637 -4.561** 4.441*** -5.248**

(0.726) (1.896) (0.653) (2.031)

Mean Dep Var 5.655 5.851 5.664 5.777

Adj. R 0.676 0.742 0.680 0.585 2

No. of obs. 290 184 228 192

Standard errors in parentheses.

*** (**) (*) denotes significance at the 1 (5) (10) percent level.
 Suppose one were interested in the percentage difference in 27

trucking rates in states that strictly regulate trucking rates compared to

rates in states that do not strictly regulate trucking rates, for a shipment

of less than 500 pounds on a 200 mile route. Looking at column (1) of

Table R1, one would first add the coefficient on the variable

RATEREG to the product of the log of 200 times the coefficient on the

variable M_RATEREG. The value of this expression is: -0.301 +

log(200) * 0.068 = 0.0593. The percentage increase in trucking rates

associated with the presence of strict rate regulation for a less than 500

pound shipment on a 200 mile route would then be e - 1 = .0611, .0593

or 6.11 percent.

41

Two issues arise in using the results in Tables R1 - R3 to

generate estimates of the relationships between trucking regulations and

trucking rates. First, the dependent variable is the log of the intrastate

rate, and the regulatory variables are 0-1 dummy variables. In such

circumstances, the expression for the percentage change in the

dependent variable associated with the dummy variable is e - 1, where

 is the estimated coefficient on the dummy variable. Second, in our

empirical specification, the relationship between trucking regulations

and trucking rates is a function of mileage, which means that there is

no single value for the relationship between a regulation and trucking

rates. Rather, the relationship will depend on the length of the route.27

We use the results from the regressions to examine the

relationships between various trucking regulations and trucking rates. 

The results from the two LTL regressions are very similar, but differ

somewhat from those from the TL regression. We therefore first

discuss the LTL results and then move on to the TL results.

1. LTL Regressions (weight = <500 and 2000 pounds)

As explained above, in our specification the relationship

between trucking rates and a particular regulation is a function of the

distance of the route. Two coefficients from the regression results

must be combined to generate estimates of the relationships between

trucking rates and trucking regulations. Tables E1 and E2 contain the

estimated percentage changes in trucking rates associated with various
 Tables E1 and E2 rely on column (1) from the regression results. 28

Thus, they represent the average relationship between trucking rates

and the various trucking regulations, holding the other regulations

constant. Later, we will present and discuss results based on columns

(2) through (4) of the regression results, which permit the relationship

between a regulation and rates to depend on which other regulations

pertain in the state.

42

state-level motor carrier regulations for the two LTL weights. Each

of these tables provides the estimated relationship between regulations

and rates for three different mileages - the 25th percentile mileage of

the routes included in the analysis, the median mileage, and the 75th

percentile mileage.28
43

Table E1

Average Percentage Change in Intrastate Trucking Rates

Associated with Various Motor Carrier Regulations

 

WEIGHT = less than 500 pounds

25th 75th

Percentile Median Percentile

Mileage Mileage Mileage

(110) (178) (271)

 

Regulation

Strict Rate

Regulation 1.72% 5.09%* 8.12%**

(RATEREG=1) (3.23) (2.66) (3.47)

Very Strict

Entry Regul. 19.54%*** 24.55%*** 29.10%***

(ENTRY1=1) (5.14) (3.93) (4.49)

Somewhat Strict

Entry Regul. 24.43%*** 31.78%*** 38.55%***

(ENTRY2=1) (3.44) (3.13) (3.81)

Antitrust Immunity

for Rate Bureaus 19.58%*** 18.10%*** 16.83%***

(IMMUNE=1) (3.05) (2.65) (3.63)

 

Standard errors in parentheses.

*** (**) (*) denotes significance at the 1 (5) (10) percent level.

Source: Calculated from results of regression (1), Table R1
44

Table E2

Average Percentage Change in Intrastate Trucking Rates

Associated with Various Motor Carrier Regulations

 

WEIGHT = 2000 pounds

25th 75th

Percentile Median Percentile

Mileage Mileage Mileage

(110) (181) (277)

 

Regulation

Strict Rate

Regulation 9.10%*** 11.32%*** 13.24%***

(RATEREG=1) (2.94) (2.53) (3.50)

Very Strict

Entry Regul. 8.58% 21.00%*** 32.69%***

(ENTRY1=1) (5.05) (4.27) (5.24)

Somewhat Strict

Entry Regul. 13.77%*** 22.18%*** 29.84%***

(ENTRY2=1) (3.57) (3.53) (4.47)

Antitrust Immunity

for Rate Bureaus 15.55%*** 12.05%*** 9.15%***

(IMMUNE=1) (2.59) (2.38) (3.36)

 

Standard errors in parentheses.

*** (**) (*) denotes significance at the 1 (5) (10) percent level.

Source: Calculated from results of regression (1), Table R2
 We had expected ENTRY1 to increase trucking rates more than 29

ENTRY2 because ENTRY1 represents more stringent entry

requirements. We found, however, the opposite pattern for the median

length route: ENTRY2 increased trucking rates more than ENTRY1. 

At the median mileages, however, the differences are not statistically

significant in the 2000 pound analysis, though they are significant at

the 10% level in the less than 500 pound analysis.

 Although all of the estimated relationships are positive, 2 of the 30

24 coefficients presented in Tables E1 and E2 are not significant at

standard levels. 

45

Tables E1 and E2 indicate that the four regulatory variables are

positively and statistically significantly related to LTL trucking rates,

with entry restrictions having the largest effect. These findings are

consistent with our a priori expectations. 

Looking at the median length route, very strict entry

requirements raise LTL trucking rates between 21 and 25 percent, and

somewhat strict entry requirements raise LTL rates between 22 and 32

percent. Antitrust immunity for rate bureaus increases LTL trucking 29

rates between 12 and 18 percent, and strict rate regulation increases

LTL rates by between 5 and 11 percent.

These general conclusions hold up for the other two mileages

analyzed, the 25th and 75th percentile mileages. In these analyses, all

four of the regulations are positively related to trucking rates, and the

largest effect stems from entry regulations.30

As described earlier, our statistical formulation permits the

effect of a regulation to be a function of mileage. In the LTL analysis,

the only type of regulation that displayed a significant mileage effect

was entry regulation. The first column of tables R1 and R2 indicates

that the positive relationship between entry restrictions and trucking
 Note the positive significant coefficient on M_ENTRY2 in the 31

less than 500 pound regression, and the positive significant coefficients

on M_ENTRY1 and M_ENTRY2 in the 2000 pound regression. 

46

rates increases with mileage. Two explanations can be offered to 31

explain this result. First, in a given state, obtaining authority to serve

a longer route may be more difficult than obtaining authority to serve a

shorter one. This might arise because relatively longer routes typically

include a number of shorter routes along the way. Thus, a motor

carrier requesting authority to serve a relatively longer route may need

to engage in more complex discussions with the regulatory agency, and

may be more likely to confront protests by incumbent carriers seeking

to delay or prevent the granting of the authority. Second, the shorter

routes in our sample tend to arise in relatively smaller states, i.e., ones

that can be served relatively easily with interstate shipments from

neighboring states. Consequently, restricting entry into relatively short

intrastate routes may tend to have a relatively smaller impact on rates

than restricting entry into relatively long ones, where interstate

shipments do not provide as viable a competitive threat.

Next, we examine whether the relationship between a particular

regulation and trucking rates depends on the status of the other

regulations. For this, we use the results from columns (2) - (4) of

Tables R1 and R2. As before, the relationship between a particular

regulation and trucking rates is a function of mileage, so the

coefficients in the regression tables by themselves do not provide

sufficient information to determine these relationships.

We use the coefficients from the regression tables to compute

the percentage changes in trucking rates associated with various

regulations depending on the status of the other regulations; the results

are contained in Tables S1 and S2.
47

Table S1

Percentage Change in Intrastate Trucking Rates

Associated with Various Motor Carrier Regulations

Depending on the Status of Other Regulations

 

WEIGHT = less than 500 pounds

(mileage = median mileage of 178 miles)

ENTRY1=1

RATEREG=1 or IMMUNE=1

ENTRY2=1

(n=329) (n=557) (n=386)

 

Regulation

Strict Rate

Regulation ---- 14.26%*** -4.84%

(RATEREG=1) (2.99) (3.13)

Very Strict

Entry Regul. 60.24%*** ---- 36.41%***

(ENTRY1=1) (4.27) (4.46)

Somewhat Strict

Entry Regul. 76.49%*** ---- 54.83%***

(ENTRY2=1) (3.96) (3.58)

Antitrust Immunity

for Rate Bureaus 7.62%** 17.50%*** ----

(IMMUNE=1) (3.11) (2.71)

 

Standard errors in parentheses.

*** (**) (*) denotes significance at the 1 (5) (10) percent level.

Source: Calculated from regressions (2), (3), and (4) in Table R1
48

Table S2

Percentage Change in Intrastate Trucking Rates

Associated with Various Motor Carrier Regulations

Depending on the Status of Other Regulations

 

WEIGHT = 2000 pounds

(mileage = median mileage of 181 miles)

ENTRY1=1

RATEREG=1 or IMMUNE=1

ENTRY2=1

(n=306) (n=574) (n=363)

 

Regulation

Strict Rate

Regulation ---- 11.59%*** 7.30%**

(RATEREG=1) (2.51) (3.51)

Very Strict

Entry Regul. 2.22% ---- 29.46%***

(ENTRY1=1) (2.78) (5.55)

Somewhat Strict

Entry Regul. n/a ---- 32.92%***

(ENTRY2=1) (4.64)

Antitrust Immunity

for Rate Bureaus 6.52%** 11.84%*** ----

(IMMUNE=1) (3.11) (2.29)

Standard errors in parentheses.

*** (**) (*) denotes significance at the 1 (5) (10) percent level.

Source: Calculated from regressions (2), (3), and (4) in Table R2
49

The basic message from Tables S1 and S2 is that interaction

effects are important in determining the magnitude of a regulation's

relationship with trucking rates, but they do not alter the basic finding

that each of these regulations tends to be associated with higher

trucking rates. Particularly noteworthy is the increase in the effect on

rates from entry regulations when the analysis is limited to states

where rate bureaus are provided antitrust immunity (IMMUNE=1). 

In the LTL analyses, the percentage increase in rates when strict entry

controls exist increases to the neighborhood of 30 to 55 percent when

the analysis is limited to such states. This result squares with

expectations: the positive effect of entry regulation on rates is even

more pronounced when rate bureaus provide immunity for jointly

coordinated activities.

To examine whether the generally positive relationship between

strict entry regulations and trucking rates also held in states that did

not offer antitrust immunity to rate bureaus, we conducted (but do not

report the results here) a separate analysis for states that did not offer

antitrust immunity for rate bureaus (i.e., states in which

IMMUNE=0). In this analysis, the relationship between trucking

rates and entry controls remained positive and statistically different

from zero at the median mileage distances. From these results we

conclude that strict entry restrictions, by themselves, contribute to

higher trucking rates, and that this positive effect is strengthened when

entry controls are combined with the provision of antitrust immunity

for rate bureaus. 

In a similar vein, Tables S1 and S2 indicate that the positive

relationship between antitrust immunity and trucking rates is greater in

those states where entry regulations are very or somewhat strict

(ENTRY1=1 or ENTRY2=1). This confirms that the generally

positive relationship between antitrust immunity and trucking rates is

driven by states that combine antitrust immunity with strict entry

requirements.
 We find it interesting that practically every state with strict rate 32

regulation also had strict entry controls. Of the 20 states with strict

rate regulation, 18 also had somewhat or very strict entry restrictions. 

In fact, in the 2000 pound analysis, every state with strict rate

regulation (RATEREG=1) also had either very or somewhat strict

entry restrictions (ENTRY1=1 or ENTRY2=1).

 Recall that we discounted the intrastate rates according to the 33

discounts typically offered by carriers in the state. While we believe it

is important to account for such discounting given the prevalence of the

practice, we nonetheless reran the LTL regressions using the

undiscounted rate data. In these analyses, the positive relationships

between trucking rates and rate regulation and entry regulation

diminished in magnitude relative to those reported in the text, and in

some instances the relationships even became negative. These results

confirm the importance of accounting for discounting, and demonstrate

that failing to do so (i.e., relying solely on filed tariffs) could lead to

incorrect inferences concerning the relationships between trucking rates

and regulations.

50

Finally, note that in the less than 500 pound analysis entry

restrictions tend to increase rates in the neighborhood of 60 percent to

75 percent in states that also impose strict rate regulation. This finding

indicates that some rates are particularly high where states not only

regulate rates, but also restrict entry, thereby deterring firms from

serving potentially profitable routes. This entry barrier permits the

firms already operating to retain the rents created by the regulated

rates.32

In sum, three basic findings emerge from the LTL regressions.

First, the three types of state trucking regulations examined in this

study clearly affect LTL trucking rates. On average, each is positively

and significantly related to LTL trucking rates.33

Second, the positive relationship between strict entry

requirements and trucking rates is particularly strong in states that also
 In the 2000 pound analysis, collinearity among the regulatory 34

variables prevented determining how entry regulations affected rates in

states that also strictly regulate their rates.

 As a check on these findings given the possible endogeneity 35

between trucking rates and trucking regulations, we conducted the

statistical analysis after deleting observations from the four states

where trucking regulations changed in 1986 (CA, CO, GA, UT). In

the LTL analysis, the basic findings discussed above were not altered

when these four states were deleted.

51

grant antitrust immunity to rate bureaus; in such states, strict entry

requirements drive up intrastate trucking rates in the neighborhood of

30 percent to 55 percent. In the less than 500 pound analysis, we also

found that the positive relationship between strict entry restrictions and

rates increases markedly in states that regulate rates strictly. These 34

findings provide strong support for relaxing entry restrictions

generally, especially in states that also grant antitrust immunity to rate

bureaus or regulate rate levels. 

 Third, the generally positive and significant coefficient on

RATEREG indicates that strict regulation of rates by state authorities

does tends to raise trucking rates. On the other hand, the combination

of strict rate regulation and rate bureau antitrust immunity has, on

balance, a negative effect on LTL trucking rates.35

2. TL Regression (weight = 20000)

Before discussing the TL results, we should note that we place

relatively less weight on the results from the TL analysis compared to

those from the LTL analysis. Recall that our rate data contain

common carrier class rates. Unlike LTL shipments, TL common

carrier shipments are typically transported under commodity, not class

rates. Thus, class rates may be relatively poorer measures of TL

shipment rates than they are of LTL rates.
52

Table E3 contains the average percentage change in 20000

pound trucking rates associated with particular regulations for three

mileage levels; these figures rely on the coefficients from the first

column of Table R3. The results from the TL analysis (20000 pounds)

differ somewhat from those discussed above for the LTL regressions. 

Perhaps the most surprising result from Table E3 is that the

positive relationship between entry restrictions and trucking rates that

was so evident in the LTL analysis is not present in this TL analysis. 

At the median mileage distance, neither of the two entry regulation

variables have a statistically significant relationship with trucking rates. 

In fact, the relationship between trucking rates and entry restrictions is

negative at short distances, and becomes positive only for relatively

longer routes. Note, however, that the (unexpected) negative

relationship between entry restrictions and rates arises on short, TL

routes -- ones that would appear to be the exception rather than the rule

in most trucking markets. Thus, we do not believe that these findings

weaken significantly the general finding from the LTL regressions, and

from the results from relatively long TL routes, that entry restrictions

tend to increase trucking rates.

The first row of Table E3 indicates that TL trucking rates are

significantly higher -- over 30 percent -- in states that strictly regulate

trucking rates, and that this relationship is insensitive to the distance of

the route. This latter finding is consistent with that found in the LTL

analysis that strict rate regulation tends to contribute to higher trucking

rates.
53

Table E3

Average Percentage Change in Intrastate Trucking Rates

Associated with Various Motor Carrier Regulations

 

WEIGHT = 20000 pounds

25th 75th

Percentile Median Percentile

Mileage Mileage Mileage

(137) (214) (304)

 

Regulation

Strict Rate

Regulation 31.15%*** 32.58%*** 33.72%***

(RATEREG=1) (5.70) (4.64) (5.90)

Very Strict

Entry Regul. -12.22%* 8.44% 28.07%***

(ENTRY1=1) (7.03) (6.05) (6.70)

Somewhat Strict

Entry Regul. -15.94%*** -2.18% 10.22

(ENTRY2=1) (5.37) (5.11) (5.89)

Antitrust Immunity

for Rate Bureaus -10.68%** -1.02% 7.31%

(IMMUNE=1) (5.57) (5.01) (6.93)

 

Standard errors in parentheses.

*** (**) (*) denotes significance at the 1 (5) (10) percent level.

Source: Calculated from results of regression (1), Table R3
 As in the LTL analysis, we reran the TL regressions using 36

undiscounted rate data. Two significant differences emerged from

those reported in the text: the positive relationship between rate

regulation and TL rates diminished in magnitude (though it remained

statistically significant) and the relationship between TL rates and entry

restrictions became negative and significant. We conclude from these

results, as we did from the LTL ones, that relying on undiscounted rate

data could lead to incorrect inferences concerning the relationships

between trucking rates and regulations.

54

Table E3 also indicates that, at the median distance in the TL

analysis, there is no significant relationship between antitrust immunity

and motor carrier rates. While at shorter distances the relationship is

negative, we noted above that such routes -- short, TL ones -- would

appear to be rare. Thus, as with the entry results discussed above,

these results seem too weak to alter the basic conclusion reached in the

LTL analysis that the provision of antitrust immunity tends to increase

trucking rates.36

Table S3 contains the results from running the TL regression

on subsets of the data depending on the status of other regulatory

variables. In these regressions, the general finding that entry

restrictions, when combined with other regulations, increase rates tends

to reemerge. In particular, note the positive and significant coefficients

on the ENTRY2 variable. This suggests that in the TL sector, as in

the LTL sector, the combination of entry restrictions and either

antitrust immunity or strict rate regulation contributes to higher

trucking rates. 

The first row of Table S3 indicates that the strong positive

relationship between strict rate regulation and trucking rates arises in

states that either restrict entry or that provide antitrust immunity to rate

bureaus. Finally, the last row of Table S3 provides some support for

the proposition that the provision of antitrust immunity helps to reduce

TL trucking rates in states where entry requirements are somewhat or

very strict. We do not place considerable weight on this finding
 As in the LTL analysis, we reran the TL regressions after 37

deleting observations from the states where were trucking regulations

were changed in 1986. After deleting these observations, the

relationship between entry restrictions and trucking rates was either

insignificantly different from zero or negative, the positive relationship

between strict rate regulation and rates increased in magnitude, and the

relationship between trucking rates and the provision of antitrust

immunity remained insignificantly different from zero. The only

puzzling aspect of these findings is the sometimes negative relationship

between entry restrictions and shipping rates. As noted in the text, we

place less weight in the findings from the TL analysis than in those

from the LTL analyses.

55

because any efficiencies should be more likely to arise in LTL

shipments, not TL ones, and the LTL analysis consistently concluded

that antitrust immunity was associated with increased trucking rates.37
56

Table S3

Percentage Change in Intrastate Trucking Rates

Associated with Various Motor Carrier Regulations

Depending on the Status of Other Regulations

 

WEIGHT = 20000 pounds

(mileage = median mileage of 214 miles)

ENTRY1=1

RATEREG=1 or IMMUNE=1

ENTRY2=1

(n=184) (n=228) (n=192)

 

Regulation

Strict Rate

Regulation ---- 40.07%*** 29.26%***

(RATEREG=1) (6.19) (6.01)

Very Strict

Entry Regul. 15.27% ---- 5.04%

(ENTRY1=1) (9.96) (8.98)

Somewhat Strict

Entry Regul. 26.20%*** ---- 13.01%*

(ENTRY2=1) (9.51) (7.32)

Antitrust Immunity

for Rate Bureaus -2.07% -10.82%** ----

(IMMUNE=1) (4.78) (5.49)

Standard errors in parentheses.

*** (**) (*) denotes significance at the 1 (5) (10) percent level.

Source: Calculated from regressions (2), (3), and (4) in Table R3
57

V. Conclusion

In this study, we have sought to disentangle the effects on

trucking rates from various types of trucking regulation imposed at the

state-level. To do so, we have characterized state-level regulations by

a series of attributes, rather than merely a single 0-1 variable as in

many regulation studies. While the empirical results vary to some

extent depending on the particular weight category examined, some

important regularities emerged from this analysis.

The LTL results strongly indicate that each of the regulations

considered in this study -- rate regulation, entry regulation, and the

provision of antitrust immunity for decisions made jointly -- are

positively related to trucking rates. In other words, the familiar

finding that trucking regulation increases trucking rates can apparently

be extended to each of these three regulatory components. Given the

prevalence of LTL shipments in intrastate trucking, deregulating even a

portion of a state's regulatory apparatus would appear likely to benefit

that state's consumers and shippers by lowering LTL shipping rates. 

In the TL sector, our analysis indicates that rate regulation increases

rates considerably, but that the relationships between trucking rates and

the other two types of regulations are less significant. 

Both the LTL and TL results indicate that combining entry

regulations with either strict rate regulation or state-level antitrust

immunity contributes to significantly higher trucking rates. Based on

this finding, we believe that significant reductions in trucking rates

could occur if states with multiple forms of economic regulation started

by loosening their restrictions on entry.

With respect to the regulation of trucking rates, our analysis

reveals a positive relationship between trucking rates and regulations

that strictly regulate them, and this positive effect tends to be enhanced

in states that also restrict entry. These findings suggest that state

legislators and regulators should give serious consideration to the

argument that an unfettered market serves as an effective regulator of
 In the TL analysis, the positive relationship between trucking 38

rates and the provision of antitrust immunity does not arise.

58

trucking rates. Studies at the federal level have identified significant

rate declines following the removal of federal rate setting and entry

restricting powers, and our results provide support for the same

prospects at the state level.

We were particularly interested in examining the relationship

between the provision of antitrust immunity and trucking rates. Such

immunity is granted in a high percentage of states as well as at the

federal level for some rate bureau activities and calls for the removal

or extension of this immunity periodically surface. For LTL

shipments, we conclude that, on average, there is a significant positive

relationship between the provision of antitrust immunity and trucking

rates. This finding casts doubt on the proposition that common 38

carrier rate bureaus should be provided antitrust immunity to permit

them to coordinate their rates and schedules, and that any cost savings

would likely be passed on to shippers in the form of lower rates. To

the contrary, the finding suggests that antitrust immunity facilitates rate

increases, not cost reductions, among motor carriers. 
 Mr. Baker describes the survey procedure as follows: 39

"Generally, it consisted of distributing questionnaires to and seeking

information from persons, agencies and organizations which are

eminently qualified and have direct knowledge of the governing motor

carrier laws, regulations and policies of the states. In addition, the

committee conducts a constant review and study of the activities and

policies of the legislatures and regulatory agencies of the respective

states. Information from these sources was utilized to prepare a

preliminary annual summary which was sent to the participants in this

study for verification and any recommended changes."

 These conflicts were reported in the final survey table. This 40

study uses the responses to four of the questions included in the Baker

survey for each of thirty-nine states. Out of a total of 156 responses,

the final 1987 survey reports 12 (7.7%) conflicts.

59

APPENDIX A

The Baker Survey

The 1987 Baker survey covered a number of topics, including

entry requirements, the extent to which the state regulates rates,

whether the state permits motor carrier rate bureaus to operate in the

state and, if so, whether the bureaus enjoy antitrust immunity. The

survey is sent to several individuals in each state familiar with the

relevant state laws and regulations. Once the initial responses are

compiled, a preliminary table is distributed to the respondents for

verification. Any comments on the preliminary table are incorporated

into the final survey table.39

Because the Baker survey was sent to multiple individuals in

each state, conflicting responses did occasionally arise. To resolve 40

these discrepancies, we reviewed the responses from the 1986 Baker

survey. If only one response were listed in the final 1986 Baker

survey, and if this response matched one of the responses listed in the

final 1987 survey, and if the state did not alter its motor carrier
 See California Public Utilities Commission (1988), page 11. 41

60

regulations between 1986 and 1987, then we assumed that the 1986

response also applied to 1987. 

This approach resolved all of the discrepancies save one: the

responses to the question regarding entry conditions in California. 

One respondent to the 1987 survey indicated that entry conditions in

California in 1987 were relatively easy; the other respondent indicated

that entry conditions were difficult. (The same conflicting responses

arose in the 1986 survey.) We decided to characterize California's

entry conditions as easy based on a 1988 Report on the California

trucking industry submitted to the California Public Utilities

Commission by its Strategic Planning Division. According to this

report, "entry requirements have never been strict."41
61

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