• TABLE OF CONTENTS
HIDE
 Front Cover
 Table of Contents
 List of Figures
 List of Tables
 Introduction
 Backhaul decision model
 Scenario analysis
 Moral implications and empirical...
 Summary
 Footnotes














Group Title: Economics report - University of Florida Agricultural Experiment Station ; 106
Title: The economic efficiency of operating transportation vehicles empty or full
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Permanent Link: http://ufdc.ufl.edu/UF00027408/00001
 Material Information
Title: The economic efficiency of operating transportation vehicles empty or full
Series Title: Economics report
Physical Description: iii, 25 p. : ill. ; 28 cm.
Language: English
Creator: Beilock, Richard P
Publisher: Food and Resource Economics Dept., Agricultural Experiment Station, Institute of Food and Agricultural Sciences, University of Florida
Place of Publication: Gainesville Fla
Publication Date: 1982
 Subjects
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
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Bibliography: Includes bibliographical references.
Statement of Responsibility: Richard P. Beilock, Richard L. Kilmer.
General Note: Cover title.
General Note: "October 1982."
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Bibliographic ID: UF00027408
Volume ID: VID00001
Source Institution: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: aleph - 002199024
oclc - 09448480
notis - ALD8904

Table of Contents
    Front Cover
        Front Cover
    Table of Contents
        Page i
    List of Figures
        Page ii
    List of Tables
        Page iii
    Introduction
        Page 1
    Backhaul decision model
        Page 2
        Page 3
        Page 4
        Page 5
        Page 6
        Page 7
    Scenario analysis
        Page 8
        Page 9
    Moral implications and empirical evidence of behavior in U.S. trucking industry
        Page 10
        Page 11
        Page 12
        Page 13
        Page 14
        Page 15
        Page 16
        Page 17
        Page 18
        Page 19
        Page 20
        Page 21
    Summary
        Page 22
    Footnotes
        Page 23
        Page 24
        Page 25
Full Text
october 1982
"tober 1982
G.


Economics Report 106


The Economic Efficiency
of Operating Transportation
Vehicles Empty or Full


Food and Resource Economics Department
Agricultural Experiment Stations
Institute of Food and Agricultural Sciences
University of Florida, Gainesville 32611


Richard P. Beilock
Richard L. Kilmer











TABLE OF CONTENTS


1. TABLE OF CONTENTS . . . .


2. LIST OF FIGURES . . . .

3. LIST OF TABLES . . . .

4. INTRODUCTION . . . .


5. BACKHAUL DECISION MODEL . . .

IMPACT OF TIME WHEN COMPARING SCENARIOS

MODEL SPECIFICATION . . .

6. SCENARIO ANALYSIS . . . .

DIRECT RETURN . . .. .


. . . i

. . . ii

. . . .iii


INDIRECT RETURN . . . . .

7. MODEL IMPLICATIONS AND EMPIRICAL EVIDENCE OF BEHAVIOR IN
U.S. TRUCKING INDUSTRY . . . .

EFFECT OF DISTANCE ON FULL OR EMPTY MOVEMENT DECISIONS .

ECONOMIC REGULATION OF TRANSPORTATION . . .


DIRECT SEARCH COSTS (DSC), EMPTY MILEAGE, AND REGULATIONS

RATE INFLEXIBILITY AND REGULATIONS . . .

8. SUMMARY . . . . . . .

9. FOOTNOTES . . . . . . .


. 1

. 2



. 3
. 6

. 8

8
. 8

. .. 9



S. 10

S. 10


. .15

. 17

. 21

. 22










LIST OF FIGURES


Figure 1. Scenarios for going from B to D via A or via C and A. 4

Figure 2. Future net return comparison across scenarios .... . 5

Figure 3a. Tariff rates and additional costs of securing and carrying
a load as implied by a positive full load distance
relationship .............. ......14

Figure 3b. The relationship between distance and the probability
of obtaining a loaded backhaul . . .... 16










LIST OF TABLES


Table 1. Percent empty vehicles by length of previous haul . .11


Table 2a.


Comparison of percent empty truck miles by type of
equipment and authority ........... ........19


Table 2b. Comparison of percent empty truck miles by type of
equipment, authority, and type of highway (Intrastate
or Interstate). ........................ .21









The Economic Efficiency of
Operating Transportation Vehicles Empty or Full

By Richard Beilock and Richard L. Kilmer*

INTRODUCTION

While it is generally recognized that virtually no transportation

system can operate without empty mileage, it is often assumed that a

system or any part of a system is operating more efficiently the smaller

the empty mileage incurred. This belief is often grounded in energy con-

cerns and in the common sense notion that if a vehicle could be loaded,

then it is a waste if it is not. This belief may be false. Minimizing

empty mileage will not generally maximize the economic benefit or social

welfare of the system. In this paper, a conceptual framework is

developed to determine the costs and benefits to carriers of running

empty or full. The implications of individual carrier behavior are

related to the performance of the transportation system. Backhaul

movements of over-the-road motor vehicles serve as the focus of the

analysis. Comparisons are made between what is theoretically

anticipated and empirical evidence from the U.S. trucking industry. It

is shown that this evidence generally conforms to the behavioral

expectations of the model.

Minimizing empty mileage is a technical, rather than an economic

goal. For any transportation system reducing empty mileage may be a

sound strategy to follow up to a point. Beyond that point, the costs

incurred in realizing further reductions would tend to outweigh any

benefits to be gained from the reductions. Under certain conditions it

becomes a rational strategy to opt for empty mileage even if a load
*Richard Beilock and Richard L. Kilmer are Assistant Professors,
Institute of Food and Agricultural Sciences, Food and Resource Economics
Department, University of Florida, Gainesville, Florida 32611.









could be obtained. Thus a system with more empty miles could be more

desirable than one with fewer empty miles.

BACKHAUL DECISION MODEL

Backhauls for over-the-road motor vehicles offer an excellent

framework in which to examine the empty mileage question. Most motor

vehicles operate from a home base (A) to which they must periodically

return for maintenance and/or the personal needs of the operator.

Selection of the outbound or fronthaul movement away from A is based

upon the carrier's perceptions or expectations regarding the

profitability of the entire trip. No doubt, when a carrier accepts a

load from A to B there is some expectation of the routing and loads

which will be taken to facilitate the return. Once at B, unless the

carrier is contractually obligated to follow some course of action, the

optimum strategy for the return trip should be reconsidered. It is at

this point that we will begin.

To facilitate the analysis of the operator's decision making

procedure the following simplifying assumptions are made: (1) the

carrier's objective is to maximize net returns, (2) the carrier is a

price taker, (3) loads may be taken to and/or hauled from only one

intermediate point (C) prior to returning to A from B, (4) regardless

of how the operator elects to return to A, he anticipates that the next

load outbound from A will be to D, (5) regardless of when D is reached,

the anticipated future earnings rate is the same, and (6) costs and

revenues are independent of one another. Any of these assumptions may

be dropped without altering the validity of the model but at the cost

of greatly increased complexity.

With only one possible intervening point (C), the operator's









choice of return strategies reduces to five scenarios (Figure 1): (1)

return empty from B directly to A, (2) return full from B directly to A,

(3) travel full to C and return empty to A, (4) travel empty to C and

return full to A, and (5) travel full to C and return full to A.

Being a maximizer of net returns, the optimum strategy is dependent'

upon: (1) the value or urgency of having the vehicle return to A, (2)

the revenues and costs associated with securing, transporting, and

handling freight at the various points (A, B, C, and D), and (3) costs

associated with running empty. Time is important because each scenario

takes different amounts of time to complete. In the next section, the

importance of time will be discussed.

Impacts of Time When Comparing Scenarios

If the net returns (wk) of two scenarios are being compared, and if

one scenario can be completed x days sooner, an amount must be added to

account for the anticipated interest and earnings (AIE) which would be

realized by the operator during those x days. AIE depends upon the size

of the time differential, the anticipated rate at which interest can be

made on money earned and the net returns from future trucking activities.

The natural log future net returns (FNRk) from each scenario are

plotted against time (Figure 2). The beginning time at B is to. The

time it will take the operator to return to A, secure and deliver a load

to D is tk units of time. The natural log of the net return associated

with tk is Inrk. At any point in time, the slope of the dashed lines

indicates the rate at which it is anticipated that interest and earnings

will be made.2 Therefore, line FNRK represents the future or t5 value

of returning to A, delivering a load to D, and being free to engage in

other productive activities t5 tk (t k) units of time sooner than
jk










Figure 1. Scenarios for going from B to D via A or via C and A.


KEY
Full =
Empty = -- --
Scenarios = 1, 2, 3, 4, 5









Figure 2. Future net return comparison across scenarios.


Lnr5
O m -


Lnir2


Lnr, -
LT I /_ ,


to tl


-o -M 4


Lnir3


-d


w g- M


o-
400 SOM


Ln 4
, M


- U -I s I


t3 t4


LnFNR2




LnFNR3


LnFNR1

LnFNR4 LnAIE51


Time


*Ln = natural logarithm


*LnTk,
LnFNRk
LnAIE5k,


__


m


a









would be the case if loads had been acquired at both B and C (Scenario 5).

The vertical distance between rk and FNRk represents the total net earnings

to be realized in t5k, denoted AIE5k. The most attractive scenario is

3
that for which the FNRk (equal to r5 if Scenario 5) is the highest.

Model Specification

Employing the above methodology for comparing all scenarios at t5,

the amount of time necessary to complete scenario 5, the decision process

may be stated as a net return maximization problem as follows:

(1) MRNR = Maximum {FNR1, FNR2, FNR3, FNR4, FNR5}

where:

(2) FNR1 = -ERCBA + (CTCAD)(CCFI) DSCAD -PLUAD FRCAD


RC1 RISK1 + AIE51


CTCBA DSCBA LUBA -

DSCAD PLUAD FRCAD -

CTCBC DSCBC PLUBC -

SCAD PLUAD FRCAD

-ERCBC + (CTCCA)(CCF4) -

(CTCAD)(CCF4) DSCA -

AIE54

CTCBC DSCBC PLUCA -

DSC PLUCA FRCCA +
CA CA CA


FRCBA

RC2 -

FRCBC

RC3. -

DSCCA

PLUAD
AD


+ (CTCAD)(CCF2) -

RISK2 + AIE52

- ERCCA + (CTCAD) (CCF3)-

RISK3 + AIE53

- PLUCA FRCCA +

- FRCA RC4 RISK+
AD


FRCBC + (CTCCA)(CCFt) -

(CTC D)(CCF5) DSCAD -


PLUAD FRCAD RC5 RISK5


(3) FNR2 =




(4) FNR3 =




(5) FNR4 =






(6) FNR5 =









where: MRNR is the maximum future net returns among all scenarios measured

at time t5; t5 is the length of time to complete the longest scenario (Scen-

ario 5); FNRk is the future net returns from scenario k at time t5; and

ERC.. is the total empty running cost between i and j which includes fuel,

labor costs, on-the-road meals and lodging, scale fees, tie down checks,

etc. CTCij is the current (t ) total compensation received for hauling a

load from i to j, and CCFk is the expected compensation change factor asso-

ciated with scenario k (assumed to be 1 for CTCBA and CTCBC). For example,

CCFk equals .8 if a 20 percent decrease is expected in CTC. DSC.i is the

direct search costs associated with locating a load to haul from i to j

including telephone costs, brokerage fees, costs of lodging and meals, and

labor costs during the search; PLUij represents the positioning, loading,

and unloading costs associated with hauling from i to j; FRCij is the total

full running cost between i and j (includes same costs as ERC); RCk is

the cost of being away from home base A for the amount of time associated

with scenario k and includes delayed vehicle maintenance, overtime charges,

and personal costs of being away from home, etc.; RISKk is the risk asso-

ciated with scenario k to include weather and road conditions, crime and

uncertainties related to price expectations and search success; AIESk

is the anticipated interest in nk and future trucker earnings after

delivery at D for an amount of time equal to the difference between the

time it took to go from B to D in scenario k as opposed to scenario 5;

,k is the net return from scenario k up to tk; CCFk is the expected com-

pensation change factor associated with scenarios 4 and 5 between B and C.5









SCENARIO ANALYSIS

Scenarios 1 and 2 (direct empty and direct full return to A) will be

discussed first. Next, the indirect return scenarios (3, 4 and 5) will be

discussed. The operator would, however, compare all five possibilities

and select the most attractive one, i.e., the one with the highest FNR.

Direct Return

If the vehicle were to return directly to A, deciding whether or not

to carry a load would depend upon the relative net returns. Subtracting

FNR1 from FNR2 yields:

(7) FNR2 FNR1 = AFNR21 CTCBA + CTCAD (CCF2 CCFI) +

(AIE52 AIE51) (FRCBA ERCBA) DSCBA

PLUBA RISK2 RISK) (RC2 RC1)

If AFNR21 is positive, then a full return (scenario 2) would be preferable


to. an empty return (scenario 1).

AFNR21 will be algebraically greater (smaller) if it is anticipated

that during the added time necessary to search for, secure and handle a

full return to A, the freight rates for hauling a load from A to D will

increase (decrease) (CCF2 increases (decreases) relative to CCF1). An

operator naturally prefers to arrive at A when rates are higher. If

rates from A to D are expected to rise, the search for a full backhaul

from B to A becomes attractive as a delaying tactic.6

If the costs of remaining away from home base for the additional

time necessary to attain a full load (AIE2 minus AIE1 and RC2 minus

RCi) or the added risks (RISK2 minus RISK1) are large, then the incentive

to return full is smaller (i.e., AFNR21 is smaller). For motor vehicles










a more rapid return may be desirable because of the personal needs of the

driver or vehicle maintenance requirements. In addition, if the risks

associated with search are greater away from home base due to lack of

familiarity with potential shippers, this will encourage foregoing the

search and returning empty (assuming risk averseness). Finally, AFNR21

is greater the smaller the added linehaul cost associated with running

loaded rather than empty (FRCBA ERCBA).

When a vehicle and load are matched at a given location an exter-

nality is created in that search costs (DSCBA) generally rise for remain-

ing vehicles. This is true because the number of places to search remains

the same, but the number of potential loads is decreased (by one). At a

certain point, all carriers will balk and go elsewhere rather than search

at length for a load in a well picked over market. This is not to say

that remaining loads will never be picked up, but that they must wait

until the density of available loads increases back to a point where

searches once more become economical at the locale. Therefore, in a world

of nonzero search costs some empty mileage is efficient even if matching

fronthaul and backhaul loads exist. This implies that economically

efficient transportation systems generally must be technically

overcapacity. Reductions in this "necessary" empty mileage may be

realized if the costs of search related information could be lowered.

Indirect Return

If the alternative of returning from B to A via C is considered, the

selection of Scenario 1, 2, 3, 4, or 5 depends on which is expected to

yield the highest return. An examination of Equations 4, 5, and 6 reveals

that, as with direct returns, the decision to seek a load or not on each










leg of the journey is dependent upon the differences between full and

empty running costs, the costs of locating and handling loads, the magni-

tude and direction of anticipated rate movements at points down-the-line

during the time necessary to acquire and handle loads, and the relative

risks and other (road) costs associated with loading or not loading. The

operator will choose the scenario or course of action which is most

attractive. This corresponds to the scenario with the highest FNR given

the assumption of net return maximization.

In the next section of the paper some implicationsof themodel and

empirical evidence regarding observed behavior will be presented. The

impacts of economic regulations will be of particular concern. Observed

empty movement patterns and rate structure-cost relationships, and impli-

cations of vehicle speculation will also be addressed. Next, as a brief

aside, the implications of positive levels of empty mileage to a society

where energy is highly valued will be discussed. It will be shown that

a carrier's decision to run empty may not be at variance with society's

desire to reduce energy consumption. Finally, the results will be sum-

marized and conclusions drawn.

MODEL IMPLICATIONS AND
EMPIRICAL EVIDENCE OF BEHAVIOR
IN U.S. TRUCKING INDUSTRY

Effect of Distance on Full or Empty Movement Decisions

In an analysis of the National Motor Transport Data Base, Paxson

(1979)7 found that the percentage of empty trucks was negatively related

to the length of the previous loaded haul. This relationship held

regardless of the type of vehicle or the authority class, i.e., under

ICC regulations or exempt, (see Table 1). The reason for the observed

positive distance-full truck relationship may be discovered by taking the










Table 1. Percent empty vehicles by length of previous haul.


Previous
500 1000- 1500-
<500 1000 1500 2000


Carrier Type
Regular Route
Common Carriage

Irregular Route
Common Carriage

Private Carriage

Contract Carriage

Exempt* Carriage


10 7 3


33 18 14


2 N/A


10 8


All Types 31 20 15 11 8 6 19


Trailer Type

Regular Van 24 16 12 9 7 4 17

Reefer Van 34 17 12 9 7 7 13

Flatbed Trailer 33 22 17 15 11 6 29

Tanker Trailer 44 28 31 35 24 N/A 39

Moving Van 27 20 16 5 7 7 12

Special Trailers 38 22 20 15 14 N/A 20


All Trailers 32 19 14 11 8 7 19


An "N/A" denotes that the category has no observations or only a
small number (less than 20) of observations.

*Includes Agricultural Cooperative Hauls.

Source: Paxson [1979].


Length
2000-
2500


of haul

<2500


(miles)
all
mileage


N/A


5 18









partial derivative with respect to distance (DBA) of the difference between

the future net returns from scenarios two and one (AFNR21: Equation (7)).


8AFNR21
(8)
( DBA













As it does not seem

a load would change

between scenarios 2


8CTCBA CTCAD(CCF2 CCF1) t21
+ +
3DBA t21 _DBA

8(AIE52 AIE51) 3t21 (FRCBA ERCBA)
at21 DBA aDBA

DSCBA PLUBA RISK2 RISKj)
3DBA DBA aDBA

a(RC2 RC1) 3t21
3t21 3DBA

likely that the time required to search for and handle

systematically with distance, the time difference

and 1 (t21) is not assumed to be a function of distance.


Therefore, Bt21/SDBA would be approximately zero, which eliminates the

second, third, and eighth right-hand side elements in Equation (8).

Likewise, there is no obvious reason to hypothesize that direct search cost.

(DSC) or positioning, loading and unloading costs (PLU) would be system-

atically related to distance. This eliminates the fifth and sixth right-

hand side terms. Finally, it seems reasonable to assert that risks would

be only weakly related to distance, which allows the seventh right hand

side element to be dropped.8 This reduces Equation (8) to:
8AFNR21 3CTCBA B(FRCBA ERCBA
(9) =D
DBA aBA DBA

The first right-hand side term is the rate of compensation per mile (or

per unit distance). This would normally be expected to be positive,

ceteris paribus. The second term is the per mile (or per unit distance)








additional costs associated with running full as opposed to empty (made

up primarily of added fuel costs).9 If the per mile freight rate were

larger on average than the costs of running full rather than empty,

then the incentive to return full would increase with distance (see

Figure 3a).

As the vehicle is already at B and must return to A, the cost of an

empty return (ERC) is a sunk cost. Therefore, regardless of distance,

this cost should not affect full versus empty backhaul decisions.

Rather, in the short-run, the decision to return full depends upon

whether the compensation (CTC) is at least as great as the tntil

variable cost (TVC) associated with securing, handling, and transporting

a load. TVC is made up of two components: terminal or distance-

unrelated costs and running or distance-related costs. Distance-related

costs are composed primarily of added fuel and labor costs from carrying

a load rather than traveling empty (FRC ERC). This component gives

total variable cost (TVC) its upward slope across distance (see Figure

3a). The zero distance TVC intercept (O1) is the terminal cost and is

made up of direct search cost (DSC), positioning, loading, and unloading

costs (PLU),and added risk cost (RISK) and road costs (RC) due to

handling the load. If the current total compensation (CTC) over

distance is as shown in Figure 3a, beyond distance OM the carrier will

seek a full return, while at a shorter distance (from A) the carrier

will elect to return empty.

If all carriers face the same costs and revenues, then at distances

larger (smaller) than OM all carriers will return full (empty). If,

as would seem to be more realistic, carriers face different costs, then

there would be differences in the critical (OM) distance. Within one










Figure 3a. Tariff rates and additional costs of securing and carrying
a load as implied by a positive full load distance
relationship


FRC + other costs


P CTC (Rate)

ERC + other
costs


Backhaul Distance


ITotal variable costs equal full running costs (FRC) minus empty
running costs (ERC) plus direct search costs (DSC) plus positioning,
loading and unloading costs (PLU) plus risk cost (RISK) plus road costs
(RC) of attaining a load running full rather than empty.

01 = DSC + PLU + RISK + RC.


Tariff
Rate,
Costs









mode, carrier-to-carrier variations in total variable running costs

(FRC ERC) would not likely be great. That is, the added fuel and labor

costs associated with carrying a load are probably similar from truck to

truck. Therefore, the slope or increase in total variable cost (TVC)

across distance is similar between trucks.

Current total compensation (CTC) also is likely to be similar among

truckers at any given distance (at least within equipment and regulatory

classes). On the other hand, terminal or distance-unrelated variable

costs probably vary significantly among carriers. Some trucks would

perform their own brokerage services or sleep in their truck sleepers

or be generally adept at positioning and loading in unfamiliar

surroundings. Others would require brokerage services or motel, or have

difficulties with positioning and loading.

If a survey of truckers were taken, it would reveal a frequency

distribution of total variable costs (TVC's) with variations in terminal

costs accounting for virtually all of the variation. In Figure 3b a

hypothetical frequency distribution has been assumed. In this situation

the proportion of operators opting for a full return would increase

with distance from home as a logistic function (or a logistic-like

function if the frequency distribution were unimodel and symmetric about

the mean-see Figure 3b). In a recent study of haulers of perishable

goods from Florida, evidence was found which suggested that the incidence

of the full backhauls is positively related to distance by a logistical

function (Kilmer, Ramirez and Stegelin, 1982, p. 16-23).10

Economic Regulation of Transportation

The model facilitates examination of the impacts of economic

regulation on transportation. Regulations may affect rate levels and











Figure 3b.


CTC,
TVC






I









Probability
of a
Backhaul





.5


1.0


The relationship between distance and the probability
of obtaining a loaded backhaul.


i


g









restrict rate variations, control entry, and determine permissible

routes and load types.

Direct Search Costs (DSC), Empty Mileage, and Regulations

Higher search costs (DSC) discourage full movements (see Equation 7).

Direct search cost (DSC) depends upon the volume and types of goods

available in a region to be hauled, the types of goods which vehicles

may legally and physically carry, and factors influencing the ability of

carriers and shippers to make themselves known to one another.

Regulations may lower direct search cost (DSC) by providing mechanisms,

such as published tariffs and routes, and licensed and bonded brokers

which reduce information costs. In a recent study of agricultural

truck brokers in Florida, (Beilock, Freeman, and Stegelin, 1982)11 there

was some evidence that direct search cost (DSC) for carriage of

agricultural goods may have been increased by the dropping of broker

bonding and licensing requirements. Without these standards, a broker's

value as an information source and logistics coordinator is eroded.

In another study (Beilock and Freeman, 1982)12 a similar result was

suggested with regard to household goods carriers in Florida. Since

the removal of uniform rate control and entry restrictions for intrastate

traffic on July 1, 1980, the majority of the already established

household goods carriers surveyed reported being able to raise rates,

despite the fact that rates in the State have generally been stable or

falling. These rate increases are thought to stem from the ability of

established household goods carriers to charge for a service charac-

teristic which was previously taken for granted reliability.

Regulations may affect the degree of vehicle specialization. If

carriers are restricted with regard to commodities which may be carried,









they will tend to operate fleets with equipment specialized for carrying

those goods (Miller, 1973, p. 9).13 This in turn, further restricts the

types of goods which an operator may carry. In general, the more

specialized a vehicle, given the amount of authority held, the higher

are direct search costs (DSC), and the more empty mileage which would be

expected (Curve D, Figure 3b). The converse should also be true. Since

holding an authority does not exclude one from carrying exempt cargoes;

within equipment classes, vehicles with authority would be expected to

have lower direct search cost (DSC) and less empty mileage than those

without. The findings of the 1976 Interstate Commerce Commission (ICC)
14
survey of trucks on interstate highways (ICC, 1977),14 generally support

these expectations. In Table 2a, it can be seen that for every category

of equipment, save for tank trucks, vehicles under ICC authority had the

lowest percent of empty miles. Moreover, as one moves from the most

flexible vehicle (refrigerated vans) to the least flexible (flats and

tank trucks), the percentage of empty vehicles tends to increase.

ICC authorities are licenses or permits to haul interstate cargo.

Those holding such authorities have an advantage (lower search costs -

DSC) in competing for interstate commerce which they do not possess with

respect to intrastate commerce. For operators without ICC authority,

the reverse would be the case, with a competitive advantage over or on

a roughly equal basis for intrastate hauls and at a disadvantage to ICC

carriers for interstate hauls. Therefore, within equipment types, it

would be expected that ICC carriers would be more apt to run empty when

traveling within a state than between states relative to those without

ICC authority (Curve D, Figure 3b). Evidence of this relationship may

be seen by examining the empty miles, recorded in the ICC study, for









Table 2a. Comparison of percent empty truck miles by type of equipment
and authority.



Lower Upper
No. of Limit Percent Limit
Equipment Authority Trucks (Percent) Empty (Percent)


Refrigerated ICC 1,008 9.1 11.0 13.0
Van
Exempt 586 10.9 13.7 16.5

Private 560 18.5 24.2 29.9


Van ICC 3,909 10.4 12.2 13.9

Exempt 443 23.2 27.4 31.6

Private 2,266 23.9 26.7 29.6


Flat or Lowboy ICC 1,322 12.8 16.6 20.4

Exempt 124 11.4 18.5 25.6

Private 847 18.8 23.2 27.6


Tank ICC 529 38.4 41.0 43.7

Exempt 85 12.1 20.1 28.1

Private 456 28.6 37.5 46.3



1
Ninety percent confidence interval
Source: ICC "Empty/Loaded Truck Miles on Interstate Highways
During 1976," April, 1977, pp. 8,9.









van and refrigerated vans of the authority groups by interstate and

intrastate roadways (see Table 2b). Refrigerated vans with ICC

Authority were almost six times more likely to be running empty if they

were on intrastate as opposed to interstate roads versus twice as likely

for refrigerated vans of exempt carriers and only half again more likely

for those of private carriers. Similarly, for unrefrigerated vans those

with ICC Authority were two and a half times more likely to be running

empty if on intrastate roads versus nine percent less likely for exempt

carriers and twenty-eight percent more likely for private carriers.

Rate Inflexibility and Regulations

It was argued that anticipated rate changes at a point to which one

will or may go influences the urgency of the journey. In particular,

if rates for loads secured at such a point are expected to rise (fall),

then there is an incentive to delay (expedite) the trip to that point so

as to be able to secure a load there with a higher remuneration (see

Equation 7). Therefore, there are different levels of urgency for

vehicles to go to a point as demands for freight rise and fall. This

translates into differing economically optimum amounts of empty (and

direct) returns as-conditions change.

This points up an interesting implication for regulation. As rate

regulation has traditionally implied price rigidity, regulations may lead

to greater or smaller amounts of empty mileage than would be economically

optimal. Without rate flexibility it becomes difficult for shippers to

signal carriers of unusual demand conditions. Indeed, carriers would

have to be made aware of changing direct search cost (DSC) due to changing
15
probabilities of securing loads at the going, regulated rate. It

seems doubtful that such information could be as readily communicated as










Table 2b.


Comparison of percent empty truck miles by type of equipment,
authority, and type of highway (Intrastate or Interstate).


Lower1 Upper
No. of Limit Percent Limit
Equipment Authority Trucks (Percent) Empty (Percent)


Refrigerated
Van
(Interstate)


Refrigerated
Van
(Intrastate)


Van
(Interstate)


Van
(Intrastate)


ICC

Exempt

Private


ICC


Exempt

Private


ICC

Exempt

Private


ICC

Exempt

Private


942

547

421


6.8

9.2

15.6


64 40.1

36 17.5


137


3,322

348

1,769


23.8


8.1

21.8

21.8


20.5


94 19.9


485


27.9


Ninety percent confidence interval
Source: ICC "Empty/Loaded Truck Miles on Interstate Highways
During 1976," April, 1977, pp. 8,9.


9.1

12.5

20.5


54.5

24.4

34.7


10.0

26.7

24.7


26.0

30.1

34.9


11.4

15.7

25.4


68.9

31.2

45.7


11.9

31.6

27.7


31.6

40.4

41.9









changing rate levels. Therefore, with regulated rates more vehicles than

would be economically optimal would be expected to be returning empty to

a region whose demand for transport is rising, and the reverse when

demands are falling. Thus, the same regulations can generate too much or

too little empty mileage in the same region for the same commodities as

demands peak and ebb.


SUMMARY

In this paper a theoretical framework has been developed to analyze

the determinants of decisions to move vehicles empty or to seek out and

carry a load. Backhauling of over-the-road motor vehicles was employed

as an example to facilitate the discussion. Whenever possible, compari-

sons were made between theoretical expectations and empirical evidence.

In general, the evidence corresponds to these expectations. It was argued

that minimizing empty mileage may not be economical or even energy effi-

cient. Decisions to operate empty or full were found to be dependent upon

several factors including rate levels and expected rate levels, search

costs, risk, and the relative costs of operating empty or full. Regula-

tions may alter search costs by commodity, route or other restrictions or

raise rates by granting monopoly rights, etc. Regulations may promote

specialization of operations and equipment which, in turn, reduce the

opportunities for full roundtrip hauls. Rate rigidity commonly associated

with regulations also may reduce the ability of shippers to signal car-

riers of swings in their demand for carriage, resulting in uneconomically

large amounts of empty movements to points where demands are rising and

the reverse when demands are falling.









FOOTNOTES

1. The underlying justification for assumptions 5 and 6 is that an oper-

ator's expectations regarding the costs and returns from future acti-

vities become less specific the more remotely into the future those

activities become. Therefore, the carrier will be assumed to have

fairly specific short term expectations regarding rate levels at B,

C, and A. He (she) expects, however, to go to D regardless of how

the return trip to A from B is accomplished. The time and costs

associated with the haul from A to D are anticipated to be the same

regardless of when the trip is taken. Finally, since the arrival at

D, regardless of routing and loads handled, will be fairly distant

in time, the rate of earnings after D has been reached is not assumed

to depend upon the timing of the arrival.

2. The nonlinear curves in Figure 2 are based on the assumptions that

the rate of interest is equal across scenarios, and that the net

returns from future trucking operations (after arriving at D) are

linear with respect to time and equal across the scenarios.

3. The approach described in this paragraph is conceptually equivalent

to that first employed by Fisher (I. Fisher, The Theory of Interest,

(MacMillan Press, New York), 1930), to compare alternative projects

or the same project with an adjustable duration.

4. As CCF describes the anticipated proportional change in the current

(to) rate CTC, CTC times CCF is equal to the anticipated future rate.

5. It might be well to note that any or all of the above cost and reve-

nues elements could be specified in more detail. For example, a

wealth of literature exists concerning queuing theory, risk, price

expectations formation, etc. However, the purpose of this paper is








to present the basic model and its implications.

6. This type of behavior is similar to that of a grain speculator who

attempts to withhold stocks from the market until prices are at their

highest. In so doing both the operator and the grain speculator serve

society by rationing the product until the need is the greatest.

7. D. Paxson, "Motor Carrier Deregulation and Opportunities for Reducing

Empty Truck Mileage," Association of American Railroads Staff Paper

79-2, 1979.

8. The argument can be made that the time necessary for DSC, PLU, and

RISK may be positively related to distance due to less familiarity

with more remote regions. If this were the case, there would be an

added per mile cost element associated with Equation 9.

9. The extra weight of a cargo normally reduces the gallons per mile.

For example, Boles has estimated the reduction in miles per gallon

associated with running full rather than empty to be .7 (5.2 MPG full,

4.5 MPG empty: Boles, P. Owner-Operator Costs of Hauling Fresh

Fruits and Vegetables in Refrigerated Trucks, Washington, D.C.: USDA

Report No. 82, 1980). Labor costs may also be incurred if the

vehicle must go somewhat slower or if the driver required additional

compensation for monitoring the load and the increased difficulty

in vehicle unloading.

10. R. Kilmer, H. Ramirez and F. Stegelin, Economic Impact of Empty

Backhauls in Florida Fresh Fruit and Vegetable Truck Transportation,

Florida Agricultural Experiment Station Bulletin No. (to be assigned), ,

Gainesville, (in press).

11. R. Beilock, J. Freeman and F. Stegelin, "Agricultural Truck Brokers









and Deregulation: The Case of Florida," (Gainesville, Unpublished

manuscript), 1982.

12. R. Beilock and J. Freeman, "An Analysis of the Effects of Florida

Motor Carrier Deregulation," (Gainesville, Unpublished manuscript),

1982.

13. E. Miller, "Effects of Regulation on Truck Utilization, "

Transportation Journal, (Fall 1972): 5-14.

14. U.S. Interstate Commerce Commission, Empty/Loaded Truck Miles on

Interstate Highways During 1976, ICC Bureau of Economics, Washington,

D.C., 1977.

15. The probability of obtaining a load may not change if the unusual

demand condition is not volume related. For example, suppose a

shipper customarily sends a certain quantity of a good to a receiver

each week. One week the receiver may be willing to pay a premium

to carriers in order to expedite their services.




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