Estimation and Analysis of Multifactor Productivity in Truck 

Transportation: 1987-2003

ANTHONY D. APOSTOLIDES, Ph.D.

February 2009

U.S. Department of Transportation

Research and Innovative Technology Administration

Bureau of Transportation Statistics

Offi ce of Advanced Studies

anthony.apostolides@dot.gov

ABSTRACT

The analysis has three objectives: 1) to estimate multifactor productivity (MFP) in truck transportation during 

1987-2003; 2) to examine changes in multifactor productivity in U.S. truck transportation, over time, and 

to compare these changes to MFP of the U.S. business 

sector and other transportation subsectors; and 3) to 

assess the factors that affected changes of MFP in truck 

transportation over the period of analysis. 

 With respect to the calculation framework, the analysis estimates annual MFP in truck transportation in the 

United States over the 1987-2003 period. The data used 

for the estimations are based on the North American Industrial Classifi cation System (NAICS). The basic data 

series were obtained from the Bureau of Economic Analysis. The labor data under NAICS were extrapolated from 

1998 back to 1987. Data on the land input were estimated 

using the method of the Bureau of Labor Statistics, with 

some modifi cations. In future work, other methods will 

be used to estimate the land input.

 With respect to methodology, use is made of the basic 

growth-accounting methodology and the methodology 

using the Tornqvist index number approach. MFP was 

estimated in three different scenarios. In the fi rst one, the 

basic growth-accounting methodology was used, without 

a measurement for land. In the second one, MFP was calculated with the Tornqvist index and without a measurement for land. In the third scenario, MFP was calculated 

with the Tornqvist method and with a measurement for 

land.
2 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

With respect to MFP results, the calculations indicate a mixed record of multifactor productivity in 

truck transportation over the period of analysis. 

Truck MFP increased during the fi rst subperiod—

from 1987 to 1995—and decreased during the 

second subperiod—1995 to 2001. In the last three 

years of analysis—2001 to 2003—truck MFP 

again increased.

The outcomes of the calculations indicate that 

both methodologies (the basic growth-accounting 

methodology and the one using the Tornqvist index) provide very similar results on multifactor 

productivity in truck transportation. That implies 

that either method can be used to provide appropriate estimates of MFP.

 A comparison of truck MFP with other transportation industries and the U.S. economy is possible for years in which MPF data are available 

for the various transportation industries and the 

economy. These data show that truck MFP over 

the 1987-2000 period increased faster, at 1.1% 

per annum, than that of the U.S. private business 

sector, which grew at 0.9% per annum. Thus, the 

trucking subsector contributed positively to the 

growth of multifactor productivity in the U.S. 

economy over this time period. During 1987-

2001, MPF growth in trucking was the same as 

in the U.S. economy, at 0.8% per annum. After 

2001, MFP in trucking grew at a lower rate than 

in the U.S. private economy. In addition, during 

1987-1995, MFP in trucking increased at a faster 

rate, of 2.0% per annum, than MFP in air transportation, which grew at 1.2% annually.

 With respect to factors that affected changes in 

truck multifactor productivity over the period of 

analysis, the assessment considers three subperiods of MFP outcomes: 1987-1995, 1995-2001, 

and 2001-2003. Factors that affected the increases 

in MFP during the fi rst subperiod (1987-1995) 

include: 1) the improvement in the capital input—

indicated by increases in capital per worker, and 

the rapid increases in the use of computer hardware and software (information technologies); 2) 

an improvement in the fuel effi ciency of trucks; 

3) an increase in the average length of haul; 4) 

an increasing use of containers; and 5) positive effects on industry effi ciency from interstate deregulation taking place over time. Factors that affected 

decreases in MFP during the second subperiod 

(1995-2001) include: 1) a declining effi ciency in 

utilizing intermediate inputs; 2) a lower growth 

rate of capital per worker; 3) a lower growth rate 

of utilizing containers; 4) the decrease in industry output in 2001, as a result of the economic 

recession that year and the catastrophic events 

of 9/11/2001; and 5) intrastate deregulation of 

trucking in 1995; this was followed by a period 

of adjustments (entry and exit of fi rms) and uncertainty which appear to have had a negative 

impact on truck MFP. Factors that affected MFP 

increases during the third subperiod (2001-2003) 

include: 1) increasing effi ciency in the utilization 

of intermediate inputs; 2) increases in the use of 

computers; 3) increases in the use of containers; 

and 4) the adjustment of the industry after intrastate deregulation in 1995, which completed and 

made comprehensive the deregulation of truck 

transportation. 

SECTION I—INTRODUCTION

In this study, there is an estimation of multifactor productivity and an assessment of changes in 

multifactor productivity (MFP) in the U.S. truck 

transportation sub-sector during the 1987-2003 

period. The analysis is composed of four sections 

besides the Introduction (Section I). Section II contains a description of the structure and evolution 

of the trucking industry, in the U.S., over the period of analysis. In Section III, there is estimation 

of multifactor productivity in truck transportation 

during 1987-2003. This estimation is carried out 

by two approaches: the basic growth-accounting 

method and the Tornqvist index. In Section IV, 

there is a comparison of MFP in trucking with 

MFP in other transportation subsectors and the 

U.S. business sector. Section V presents an analysis of the factors that affected changes of MFP in 

truck transportation over the period of analysis. 

Finally, the Conclusions (Section VI) present the 

salient points of the estimation and analysis of 

MFP in truck transportation.
APOSTOLIDES 3

SECTION II—TRUCKING INDUSTRY BACKGROUND AND STRUCTURE 

Industry Description and Characteristics

Firms in the truck transportation industry provide 

a link between manufacturers and consumers. 

Businesses, and occasionally individuals, contract 

with trucking companies to pick up and deliver a 

variety of goods. The trucking industry has two 

parts: the “for-hire” segment and the “in-house 

trucking” segment. Within the “for-hire” segment, freight movement is characterized by broad 

service markets of truckload (TL), less-thantruckload (LTL), and small package delivery. The 

trucking industry can also be classifi ed into two 

segments, according to the nature of the freight 

being transported: general freight trucking and 

specialized freight trucking. These parts of the 

trucking industry are described below.

Truckload (TL) carriers specialize in hauling 

large shipments for long distances. TL shipments 

are usually defi ned as those weighing 10,000 

pounds or more. In this segment, a driver employed by a TL fi rm, or a truck owner-operator, 

will pick up a load from a shipper and carry the 

load directly to the consignee, without transferring the freight from one trailer to another. Thus, 

TL carriers do not need a network of terminals. 

This segment of the industry involves substantial 

competition and labor is typically not unionized.

Less than truckload (LTL) carriers consolidate, 

in one truck, several shipments that are going to the 

same general geographic area. LTL shipments are 

usually defi ned as those shipped in amounts that 

weigh less than 10,000 pounds. The consolidation 

of freight requires a network of freight terminals. 

Consequently, LTL carriers are characterized by 

networks of consolidation centers and satellite terminals. In this framework, a pickup-and-delivery 

truck typically transports an LTL shipment from 

the shipper’s dock to the trucking fi rm’s local terminal. There, dock workers unload and recombine 

the shipments with other shipments that are going 

to similar destinations, typically a destination terminal in another city. This transportation may be 

accomplished by large trucks or by another transportation mode—e.g., rail or ship—depending on 

price and service considerations. When the shipment arrives at its destination terminal, the load is 

processed, moved to a pickup-and-delivery truck, 

and then transported to the consignee. There are 

national LTL fi rms and regional LTL fi rms. 

 Besides the TL and LTL service markets, the 

trucking industry can also be classifi ed into two 

segments, according to the nature of the freight 

being transported: general freight trucking and

specialized freight trucking. These segments are 

described below. General freight trucking provides 

transportation of general commodities; this freight 

is not specialized. This segment of the industry is 

further subdivided into local trucking and longdistance trucking. Local trucking establishments 

carry goods within a single metropolitan area and 

its adjacent non-urban areas. Local trucking transports a range of items such as produce to different grocery stores, lumber from the lumber yard 

to construction sites, and debris. Long-distance 

trucking establishments transport goods between 

distant areas—e.g., from city to city—and sometimes between the United States and Canada or 

Mexico. The fi rms in this segment handle a wide 

variety of commodities. 

Specialized freight trucking provides transportation of freight, which requires specialized 

equipment because of freight characteristics relating to size, weight, shape, etc. Specialized freight 

includes petroleum products, refrigerated goods, 

forest products, and dangerous/hazardous materials. The specialized equipment includes fl atbeds, 

tankers, or refrigerated trailers. This segment also 

includes the furniture-moving industry, which 

transports used household, institutional, and 

commercial furniture. Like general freight trucking, specialized freight trucking is subdivided into 

local and long-distance.

Deregulation of Trucking and Evolution of the 

Industry

Legislation affected the trucking industry in the 

1980s and in the 1990s. The Motor Carrier Act 
4 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

(MCA) of 1980 deregulated the interstate portion of the “for-hire” trucking industry. The Act 

initiated signifi cant changes at the interstate level 

by allowing easier entry of trucking fi rms, providing greater pricing fl exibility to fi rms, eliminating 

restrictions on how many customers a contract 

carrier could serve, and reducing restrictions on 

private fl eets.

 A number of years after the deregulation of interstate operations, intrastate operations were also 

deregulated in 1995. Until that year, most States 

controlled the routes, rates, and services of motor 

carriers within their borders. One of the outcomes 

of regulation was empty truck trailers on return 

trips; this contributed to industry ineffi ciency. In 

1995, the Trucking Industry Regulatory Reform 

Act (TIRRA) prohibited all states from regulating 

carriers’ routes, rates, or services. However, states 

were still allowed to regulate such areas as safety, 

hazardous material movement, and vehicle size 

and weight.

Thus, the primary deregulation of the trucking 

industry took place at the interstate level in 1980, 

while the intrastate deregulation of the industry, 

in 1995, completed the deregulation of the trucking industry and made it comprehensive. This 

deregulation resulted in an increase in the entry of 

new fi rms and more fl exible carrier rates. Consequently, the industry experienced more competition and a signifi cant restructuring. 

 Following deregulation, adjustments to the 

industry took place and they included mergers, 

acquisitions, and bankruptcies. Acquisitions in 

the industry involved horizontal or vertical combinations of fi rms. Horizontal mergers involve the 

combination of two fi rms that are engaged in the 

same industry. These mergers decrease the number 

of competitors in the industry and increase their 

size. Vertical mergers involve mergers of transportation fi rms that provide complementary services. 

 Another signifi cant development took place in 

truck transportation from 1980, with interstate 

deregulation, and which apparently continued 

after the intrastate deregulation in 1995. This involved a change in the structure of the industry, 

resulting in a decrease in the relative importance 

of less-than-truckload (LTL) trucking and a corresponding increase in the relative importance of 

truckload trucking (TL).

 There are important implications of this change 

for the structure and performance of the trucking industry. LTL operations were more capitalintensive and labor-intensive than TL operations. 

They were also more heavily unionized. On the 

other hand, the TL segment has been characterized by a higher degree of competition and by 

non-unionized labor. Consequently, the cost of 

transport (per unit of freight) by LTL operations 

was higher than for TL operations. The change 

toward more TL operations indicates a decrease 

in the capital-intensity and labor-intensity of the 

trucking industry over the period of analysis. 

Moreover, this change would lead to a lowering in 

the cost of truck transport. 

SECTION III—ESTIMATING MULTIFACTOR 

PRODUCTIVITY IN TRUCK TRANSPORTATION, 1987-2003 

Introduction 

This analysis utilizes two versions of the growthaccounting methodology to calculate multifactor 

productivity (MFP) in the trucking industry in the 

United States. The initial methodology used is the 

basic growth-accounting of sources of economic 

growth—which includes weighted growth rates 

of production inputs, with the weights being the 

share of the input in total industry costs/output. 

This methodology was initially used in macroeconomic analyses of sources of growth, by analysts 

such as E. Denison1

 and J.W. Kendrick2 who also 

used it to analyze productivity at the sectoral 

and industry levels. The more recent version of 

the methodology has been used—in a somewhat 

different and, what might be called, an enhanced 

form—by government agencies, such as the Bureau of Labor Statistics (BLS), to estimate multifactor productivity at the sectoral and industry 

1

 Denison, 1974 and 1967.

2

 Kendrick, 1973. 
APOSTOLIDES 5

levels.3 This version utilizes the Tornqvist formula 

in the calculations. The basic growth-accounting 

methodology is presented in Appendix A, while 

the enhanced methodology using the Tornqvist 

index is presented in Appendix B.

The section which follows defi nes productivity (labor and MFP) and describes its benefi ts 

(increases). The subsequent section describes the 

data used and their characteristics, the calculations, and the results of the calculations by using 

the two methodological approaches—the basic 

growth-accounting methodology and the enhanced methodology. 

Defi ning Productivity 

Labor productivity is defi ned as output per unit 

of labor, and is calculated by dividing output by a 

measure of the labor input (number of employees 

or labor hours). Increases in labor productivity 

refl ect the effect of two basic factors: 1) increased 

use of capital in production—which increases the 

amount of capital per worker, and 2) technological 

progress, which can include a number of factors, 

and is discussed at a later point under multifactor 

productivity. 

Multifactor productivity (MFP) refers to the productivity of all the inputs used in the production 

process. Multifactor productivity is a more comprehensive measure of productivity than labor 

productivity, or other single-factor productivity 

measures. It indicates the overall production effi ciency of an industry; it relates to increases in 

industry output that are not accounted for by increases in the inputs. 

 For estimating MFP at the industry level, the 

output measure used is total output (rather than 

value added). The inputs used for analysis are: 

labor, capital, and intermediate inputs. The labor 

input is measured in terms of numbers of workers 

or labor hours; while the capital input includes 

structures, equipment, inventories, and land (in a 

broad defi nition of capital). Intermediate inputs 

include purchased electricity, fuels, materials, and 

3

 Bureau of Labor Statistics, 1983; Duke et al., 1992.

services. The weights used to estimate the contribution of each input to output are their shares in 

the total costs of the industry.

With regard to the capital input, one notes that 

reproducible tangible capital and land are two 

distinct factors of production. There are differences between reproducible capital (structures 

and equipment) and land. For one, structures and 

equipment are man-made. They are an output of 

a production process. Land, on the other hand, is 

not man-made; it is a natural resource. Moreover, 

structures and equipment depreciate over time as 

they are used in production; land does not depreciate over time (at least for practical purposes). 

Benefi ts of Productivity Increases

Productivity, or productivity changes, can affect a 

company in an industry and a number of companies in the same industry. Thusly, a change in the 

productivity of the truck transportation industry 

would affect the productivity of the transportation sector. A change in the productivity of a 

sector—such as transportation—would, in turn, 

affect productivity of the U.S. economy.

 The initial and basic result of a productivity increase, at the fi rm or industry level, is a reduction 

in costs and an increase in profi ts (total revenues 

minus total costs). Thus, the productivity increase 

benefi ts directly the affected industry. Subsequently, the increase in profi ts can be followed by lower 

prices of the industry—particularly when there is 

competition among the producers of the industry. 

Competition is affected by the number of producers in the industry, among other things. The higher 

the number of producers in the industry, the more 

the expected competition in the industry. Another 

impact of productivity increases (increase in profits) could be an increase in the labor compensation 

(wages and fringe benefi ts) of the workers working for the affected fi rm/industry, if the company/

industry shares part of the productivity gain with 

the workers.

 All three of these impacts of a productivity increase result in higher incomes in the economy. In 

the case of the business enterprise, there is a direct 
6 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

increase in its profi t/income. If part of that profi t 

goes to the stockholders of the fi rm, in the form 

of higher dividends, their incomes would increase. 

Moreover, a portion of the higher profi t can be kept 

by the company in the form of retained earnings—

with which to fi nance future investment that can 

lead to higher levels of productivity. In the case of 

labor, there could be an increase in the income of 

workers (labor compensation). In the case of the 

consumers/users of the services of the industry, if 

prices of that service decrease, there is an increase 

in the real incomes of the consumers. These are 

the basic benefi ts of productivity increases, and 

the reasons why productivity increases are desirable from the perspective of the company, industry, 

and the economy. A recent study has assessed the 

impact of productivity increases in air transportation.4

 There can also be second-round effects as when 

labor uses its higher income to increase its consumption of various goods and services in the 

economy. This increased consumption stimulates 

sales of various products/services and subsequent 

production of other industries, with possible increases in employment and incomes there. Thus, 

the benefi ts of an initial productivity increase can 

have a ripple effect in the industry and affect positively other industries and the economy.

Data

The data used for the analysis were obtained 

primarily from the Bureau of Economic Analysis (BEA), U.S. Department of Commerce. This 

source provides most data series needed for the 

estimation of trucking MFP. The industry analyzed is the Truck Transportation industry, represented by NAICS 484 (North American Industry 

Classifi cation System). This industry consists of: 

NAICS 4841—General Freight Trucking and NAICS 4842—Specialized Freight Trucking. In turn, 

NAICS 4841 includes: 48411 (Local), and 48412 

(Long Distance). NAICS 4842 includes: 48421 

(Used Household and Offi ce Goods Moving); 

48422 (Local); and 48423 (Long-Distance). 

4

 Apostolides, 2006.

 The data used for the trucking industry refer 

to “for-hire” trucking, whereby businesses, or 

households, hire trucking fi rms to provide transportation of goods. These data do not include 

“in-house” trucking, whereby a business, such as 

a grocery chain, engages its own trucks and truck 

drivers to transport its goods. Presently, suffi cient 

data for in-house trucking are not available to include this segment in the estimation of MFP.5

 The analysis is initially carried out for the period 1998-2003. The choice of the initial period 

was affected by data availability. The primary 

data series, obtained from BEA, include data on 

gross output, labor, capital, and intermediate inputs. Data on output, capital, and intermediate 

inputs are available, under NAICS, from 1987. 

However, labor data under NAICS are available 

only from 1998. Consequently, estimates of MFP 

are initially calculated for the 1998-2003 period. 

Subsequently, labor data under NAICS are extrapolated back to 1987, allowing for calculations on 

trucking MFP to be carried out for the 1987-2003 

period.

In the fi rst phase of calculations, estimates 

are developed for MFP in truck transportation 

without land. The second phase of calculations 

includes a measurement of the land input and its 

incorporated in the estimation of MFP.

Output

Gross output in trucking is measured in terms of 

receipts of the industry. Output includes shorthaul and long-haul trucking. Data on gross output 

are available in current prices and in chain-type 

quantity indexes.

Labor Input

The main data for the labor input are in terms 

of full-time-equivalent workers (FTE). Part-time 

workers are converted (by BEA) into full-time 

equivalents. The labor data do not make a distinction for different types of labor. In this regard, it 

5

 BTS has been doing work in estimating the output of in-house 

trucking. However, other data needed for the estimation of MFP 

are not available.
APOSTOLIDES 7

is noted that BLS, in its work on productivity (labor and MFP), also considers labor to be homogeneous and additive, with no distinction made 

between hours of different groups of employees.6

There are also data available on labor compensation of FTE employees in truck transportation. 

Thus, the MFP estimates are based on labor data 

of full-time equivalent employees. In truck transportation, there are also self-employed truckers; 

these are not included in full-time equivalent employees. The services of self-employed truckers 

would be included in intermediate inputs, which 

include purchased services. The services of selfemployed truckers are obtained by trucking fi rms 

through contractual arrangements.

Capital 

Capital stock data refer to structures and equipment (including software). They are available in 

current prices and in Chain-Type Quantity Indexes for Net Stock. Net capital stock excludes the 

depreciation of capital from gross capital stock. 

Capital stock data of BEA do not include land (or 

inventories of unsold goods).

Intermediate Inputs

Intermediate inputs include purchases of electricity and other energy inputs, purchases of materials, 

and purchases of services. The latter would include the services of self-employed truckers. Data 

for intermediate inputs are available, from BEA, 

in the “GDP-by-Industry” accounts and in the 

Input-Output accounts. In the “GDP by Industry 

accounts,” intermediate inputs are obtained as the 

difference between independent estimates of gross 

output and value added (value of sales minus 

value of purchases of inputs). In the Input-Output 

accounts, intermediate inputs are obtained from 

a combination of source data for industry purchases and indirect techniques, and value added is 

the residual.7

 The value added of a fi rm/industry 

is the value of its sales revenue minus the value of 

its purchases of intermediate inputs. This analysis uses data from the GDP-by-Industry accounts 

6

 Bureau of Labor Statistics, 1983.

7

 Yuskavage, 2001, p. 7.

since that database presents a comprehensive and 

consistent set of data for variables used in the calculations. A tabulation in Appendix Table C indicates that data on intermediate inputs and gross 

output, from the two sources, since 1998 are the 

same for truck transportation. This is consistent 

with the objective of BEA to create integrated annual I-O and GDP-by-Industry accounts. These 

integrated accounts are only available starting in 

1998. Prior to 1998, there were substantial differences in the measure for intermediate inputs from 

the two BEA sources (Yuskavage, 2001).

Weights of Inputs

The labor weight was obtained by relating labor 

compensation (wages and fringe benefi ts) to industry gross output, in current prices (labor compensation/output). The weight for intermediate inputs 

was obtained in a similar manner: by relating the 

cost of these inputs to industry gross output. The 

weight of the capital input was obtained as a residual, for the fi rst phase of calculations, by subtracting the combined percentage shares of labor 

and intermediates inputs from one (representing 

total industry costs).

 The annual weights for the inputs used for the 

calculations with the basic growth-accounting approach are presented in Appendix D.

Land 

Land is one of the primary inputs of industry 

output. Land is non-reproducible capital while 

structures and equipment are reproducible capital. 

Data on land are not available from BEA. BEA’s 

estimates of structures (values) are based on data 

collected by the U.S. Census Bureau. These data 

pertain to new structures and include the cost of 

construction and of site preparation for construction projects. “Construction” data for Census 

exclude land acquisition. Consequently, BEA data 

on fi xed assets include the cost of new structures 

with site preparation, but do not include the cost 

of the land on which the structures are built.

The initial sets of estimates of trucking MFP are 

calculated without a measurement for land. The 

land input is estimated and incorporated in the 
8 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

MFP calculations in the second set of estimates. 

Its magnitude is estimated by the approach used 

by BLS in their estimation of industry MFP (Duke, 

et. al., 1992). The methodology and data for measuring the land input are discussed in a later section (“MFP Calculations with Land”).

Calculations: Basic Growth-Accounting 

Methodology

Calculations: 1998-2003

Estimates of MFP in trucking, from 1998 to 2003, 

are shown in Table 1. These estimates are based 

on the basic growth-accounting methodology, 

using annual growth rates of inputs, weighted 

by their share in total industry cost/output. The 

inputs are labor, capital, and intermediate inputs. 

Land is not included.8

The estimates indicate that for the fi rst three 

years of the period of analysis, multifactor productivity in truck transportation declined (negative rates), while it grew at positive rates during 

the last two years.

8

 The weight of land would be included in the weight of capital 

since the weight of capital is derived as a residual (from 1.00) after 

accounting for the weight of labor and intermediate inputs. 

 With regard to changes in output and factor 

inputs, the data show that over the 1998-2003 

period, output in trucking grew at positive rates 

for the fi rst two years; however, those rates became negative in the last three years of analysis. 

Changes in employment in trucking were similar 

to changes in output, with initially positive rates 

of growth followed by negative ones. Similar patterns can also be observed for capital and intermediate inputs. Changes in the factor inputs over 

time resulted in a positive combined weighted 

growth rate during the fi rst two years of the period of analysis; the growth rate turned negative 

during the last three years of analysis. 

 Inputs in the trucking industry decreased over 

the period of analysis and this was accompanied 

by decreasing output. However, in the last two 

years of analysis, MFP increased while trucking 

output continued to decline. This increase of MFP, 

which accompanied declining output, indicates 

increasing effi ciency in the utilization of industry 

resources. 

Calculations: 1987-2003

It was mentioned previously that data under NAICS are available for the 1987-2003 period for 

gross output, and for the inputs of capital and 

TABLE 1 Growth of Output, Inputs, and Multifactor Productivity in Trucking

 Percentage rates of change

Basic growth accounting methodology, without land input

Year

Growth of gross 

output - quantity 

index 

Growth of 

labor

Growth of 

capital

Growth of 

intermediate 

inputs

Growth of 

combined 

weighted inputs

Growth of 

multifactor 

productivity

‘(1) ‘(2) ‘(3) ‘(4) ‘(5) ‘(6)

1998 7.3 5.0 10.7

1999 5.3 3.9 3.3 9.0 6.6 -1.3

2000 2.5 2.1 2.4 3.8 3.1 -0.6

2001 -6.2 1.4 -2.2 -6.9 -3.7 -2.5

2002 -1.7 -3.3 -2.7 -3.2 -3.1 1.4

2003 -6.0 -1.1 -1.6 -11.3 -6.4 0.4

Sources: Bureau of Economic Analysis internet site. For data on gross output, intermediate inputs, and labor: Annual Industry Accounts: 

http://www.bea.doc.gov/bea/dn2/gdpbyind_data.htm 

For data on fi xed assets: National\Fixed Assets\All Fixed Asset Tables\Section 3 - Private Fixed Assets by Industry: table 3.2ES and table 

3.1ES. http://www.bea.doc/bea/dn/FA2004/SelectTable.asp

Note: Growth rates are computed for variable values between two years. Thus, the growth rate of output shown for 1999 is between the 

output numbers for 1998 and 1999.
APOSTOLIDES 9

intermediate purchases. However, labor data for 

trucking, under NAICS, are available only for 

1998-2003. This factor defi ned the time frame for 

calculations presented in the previous section.

 This section uses extrapolated labor data for 

trucking to expand the analysis to the 1987-2003 

period. Labor data are available for trucking under NAICS for 1998-2003, whereas labor data 

(employment and labor compensation) are available for trucking and warehousing, under SIC 42, 

for the period 1987-2000.

9 There are three years 

of data overlap between NAICS and SIC labor 

data. Consequently, the ratio of labor under NAICS to labor under SIC, in 2000 (the most recent 

9

 Information on this issue was provided by BEA staff.

year), was used to extrapolate the NAICS trucking employment and labor compensation back to 

1987. These calculations are shown in Appendix 

E. These labor data were then used to calculate 

MFP in trucking over 1987-1998. 

The results of the calculations are presented 

in Table 2. They indicate that MFP in trucking 

experienced a mixed record of performance over 

the period of analysis. The years in which trucking MFP experienced positive growth rates are 

observed mostly in the fi rst part of the period of 

analysis—in the late 1980s and early 1990s. The 

last two years of analysis (2002, 2003) also show 

positive growth rates. Negative MFP growth rates 

are observed during the late 1990s, and 2000 and 

2001.

TABLE 2 Growth of Output, Inputs, and Multifactor Productivity in Trucking

 Percent rates of change

Basic growth accounting methodology, without land input

Year

Growth of gross 

output - 

quantity Index

Growth of 

labor

Growth of 

capital

Growth of 

intermediate 

inputs

Growth of 

combined 

weighted 

inputs

Multifactor 

productivity

‘(1) ‘(2) ‘(3) ‘(4) ‘(5) ‘(6)

1988 12.4 (8.3) 3.1 14.7 4.7 7.7 

1989 4.8 2.1 0.7 6.0 3.8 1.0 

1990 5.7 (1.8) (3.8) 12.0 5.2 0.6 

1991 2.0 (1.1) (3.8) (2.1) (2.1) 4.1 

1992 8.3 0.2 (4.5) 9.7 4.5 3.8 

1993 4.0 5.6 2.8 2.1 3.3 0.7 

1994 9.3 5.6 13.3 9.4 8.8 0.6 

1995 2.7 3.9 13.0 3.2 5.0 (2.3)

1996 5.4 2.5 4.7 7.0 5.3 0.2 

1997 4.6 2.9 9.3 4.5 4.8 (0.2)

1998 7.3 4.1 5.0 10.7 7.7 (0.4)

1999 5.3 3.9 3.3 9.0 6.6 (1.3)

2000 2.5 2.1 2.4 3.8 3.1 (0.6)

2001 (6.2) 1.4 (2.2) (6.9) (3.7) (2.5)

2002 (1.7) (3.3) (2.7) (3.2) (3.1) 1.4 

2003 (6.0) (1.1) (1.6) (11.3) (6.4) 0.4 

Sources: Bureau of Economic Analysis internet site. For data on gross output, intermediate inputs, and labor: Annual Industry Accounts: http://www.bea.doc.gov/bea/dn2/gdpbyind_data.htm. 

For labor data for 1987 - 1997, BEA internet site; see Appendix A. For data on fi xed assets: National\Fixed Assets\All Fixed Asset 

Tables\Section 3 - Private Fixed Assets by Industry: table 3.2ES and table 3.1ES. http://www.bea.doc/bea/dn/FA2004/SelectTable.asp

Note:The growth rate for 1988 is calculated with variable values for 1987 and 1988.

Consequently, there are not growth rates for 1987.
10 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

 With regard to individual components of the 

trucking MFP framework, one observes (Table 2) 

that gross output grew at positive rates during the 

period of analysis—with the exception of the last 

three years (2001-2003). Labor also increased at 

positive rates for most years over time, while during the last two years (2002, 2003), it experienced 

negative growth rates.

 Capital data do not indicate a consistent trend 

over time: years of positive growth rates are followed by negative growth rates. Years in which 

capital in the industry had negative growth rates 

include the last three years of analysis. The intermediate inputs also do not show a consistent trend 

over time. In most of the years, these purchases 

experienced a positive growth rate, while in the 

last three years, they had negative growth rates.

 In summary, the data and calculations indicate 

that the trucking industry was increasing in activity/output and inputs in the fi rst half of the period of analysis—the late 1980s and early 1990s. 

Multifactor productivity also increased over this 

period. This situation changed signifi cantly during 

the late 1990s and in 2000 and 2001. During this 

period, trucking experienced decreases in output, 

factor inputs, and multifactor productivity. However, during the last two years of analysis—2002 

and 2003—MFP in trucking increased. During the 

same period, output and factor inputs decreased. 

This implies increasing effi ciency in the utilization 

of the available inputs in the industry.

Calculations with the Tornqvist Index 

Calculations: 1987-2003

Calculations are also carried out by the use of the 

Tornqvist index methodological framework. In this 

case, the inputs of labor, capital, and intermediates 

purchases are aggregated into a chained Tornqvist 

index (See Appendix B). Data on gross output are 

available in terms of a chain-type quantity index. 

Estimates of trucking MFP levels are obtained by 

relating the combined input index to the quantity 

output index. Growth rates of MFP are calculated 

starting with 1989. The results of the calculations 

are presented in Table 3.

 The index numbers in column 3 of the table 

indicate increases and decreases of trucking MFP 

levels over time. One does not observe a persistent trend. The growth rates (column 4) provide 

a picture that is clearer to interpret. These growth 

rates again indicate that MFP in trucking grew at 

positive rates during the late 1980s and the fi rst 

half of the 1990s. This changed in the second half 

of the 1990s and the fi rst two years of the 2000s, 

when one observes negative growth rates of MFP. 

In the last two years of analysis, 2002 and 2003, 

trucking MFP is again observed to grow at positive rates.

 One also observes that these growth rates of 

trucking MFP are quite similar to those obtained 

by using the annually-weighted growth rates 

of inputs (basic growth-accounting methodology), presented in Table 2. The two sets of MFP 

growth rates are compared in Appendix Table 

F. For some years, the two sets of growth rates 

are the same; while for other years, the growth 

rates differ somewhat. Therefore, the calculations 

indicate only small differences in the results from 

the two versions of the estimating methodology. 

Consequently, it appears that these two methods 

are relatively good substitutes for each other.

MFP Calculations with Land

This section presents estimates of the quantity and 

cost share for land used in truck transportation, 

and includes that factor input in calculating MFP 

for the industry. The data for output, capital, and 

intermediate inputs have been described previously. The data for the labor input used refer to 

FTE employees; and the data for labor compensation were obtained from the BEA’s Input-Output 

accounts. The data on FTEs are compatible with 

the Input-Output data on labor compensation (for 

the labor cost share). 

 The land used by the trucking industry for this 

study relates to privately owned land; this includes 

land used for terminals, maintenance facilities, offi ce buildings, parking lots, etc. It does not include 

land used for public capital, such as highways. 

This is similar to the measurement of land by BLS 
APOSTOLIDES 11

for industry studies of multifactor productivity. 

The land used for public capital, such as highways, 

in trucking MFP will be assessed in an upcoming 

study.

 This study estimates a land stocks index by using an approach similar to that of BLS, with some 

modifi cation. In estimating the land input for MFP 

calculations, that agency uses a result from a study 

by Manvel (1968). According to that study, the 

value of industrial land in 1966 accounted for 24% 

of the total value of industrial land and structures 

in 1966. Consequently, in BLS industry studies of 

MFP, an industry’s wealth stock of structures in 

1966 is multiplied by the ratio 0.24/0.76 (land/

structures) to estimate the value of land for the industry in 1966. This estimate is then extrapolated 

backward and forward, in correspondence with 

changes in the gross value of structures stocks in 

constant dollars (of the industry). The gross structures stocks are the capital stocks without deductions for depreciation. In this regard, the position 

is taken that land does not depreciate, since its 

service life is (for practical purposes) infi nite and 

its ability to provide services over time does not 

decline. The resulting land estimate is in constant 

dollars since the calculation uses the constant dollar value of structures as the extrapolator.10 One 

notes that a measurement in constant dollars implies a measurement in quantity terms, since the 

effect of price changes is taken out.

 This study uses the quantity index of the net 

structures stocks of the trucking industry for extrapolation, instead of the gross structures stocks. 

10 Communications with BLS staff, Offi ce of Productivity and 

Technology.

TABLE 3 Multifactor Productivity in Trucking

 (Tornqvist methodology, without land input)

Indexes (2000=100) Growth

Year

Gross output - 

chain-type quantity index Combined inputs 

index 

Multifactor 

productivity 

index 

MFP growth 

(percentage rate of 

change) 

‘(1) ‘(2) ‘(3) ‘(4)

1987 48.704 56.623 86.01 

1988 54.737 58.610 93.39 8.6

1989 57.379 60.812 94.35 1.0

1990 60.676 63.663 95.31 1.0

1991 61.887 62.338 99.28 4.2

1992 67.012 64.977 103.13 3.9

1993 69.712 67.113 103.87 0.7

1994 76.230 73.040 104.37 0.5

1995 78.289 76.714 102.05 -2.2

1996 82.536 80.714 102.26 0.2

1997 86.318 84.544 102.10 -0.2

1998 92.626 91.045 101.74 -0.4

1999 97.515 97.002 100.53 -1.2

2000 100.000 100.000 100.00 -0.5

2001 93.829 96.172 97.56 -2.4

2002 92.202 93.159 98.97 1.4

2003 86.711 86.993 99.68 0.7

Sources: Bureau of Economic Analysis internet site. For data on gross output, intermediate inputs, and labor: 

Annual Industry Accounts: http://www.bea.doc.gov/bea/dn2/gdpbyind_data.htm 

For labor data for 1987 - 1997, BEA internet site; see Appendix A.

For data on fi xed assets: National\Fixed Assets\All Fixed Asset Tables\Section 3- Private Fixed Assets by Industry: 

table 3.2ES and table 3.1ES. http://www.bea.doc/bea/dn/FA2004/SelectTable.asp
12 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

This has been affected by two considerations. First, 

BEA has stopped producing estimates of gross 

capital stocks; consequently, a NAICS-based gross 

structure stock index for the trucking industry is 

not available from that source. In addition, the 

Manvel estimates of the 1966 values for land and 

structure were based on data of locally-assessed 

taxable real estate. Since property assessments 

are expected to refl ect the physical and economic 

conditions of the properties assessed, the land-tostructures ratio can be interpreted as the relationship between the values of land and depreciated 

structures. Therefore, the net stock of structures 

would seem to be appropriate for the estimation 

of land stock.

 A complication in measuring land stocks is that 

the BLS procedure requires the structures (wealth 

stock) of the trucking industry, in constant prices, 

to be available for 1966; however, the BEA structure series (quantity index), under NAICS, is 

available only from 1987 to 2003. SIC data (value 

and quantity), however, are available from BEA 

that go back to 1966.11 Consequently, this study 

extrapolates NAICS data for structures by using 

SIC data for structures for the SIC industry Trucking and Warehousing. Moreover, there are data on 

structures available for overlapping years between 

the SIC and NAICS series. Consequently, the ratio 

between the two series (in current prices) for 1987 

and 1988 (the earliest overlapping years) was used 

to extrapolate the NAIC series backward to 1966. 

This results in an estimate of the land value, in 

current prices, used in 1966 by truck transportation. This value is the same as the value of land in 

1966 dollars (i.e., constant prices). This value in 

constant dollars is subsequently extrapolated forward by the movement of the NAICS Structures 

stock (quantity) for truck transportation.

 The estimated land input is then combined 

with the structures and equipment stock index, by 

Tornqvist aggregation, and this results in a capital 

input index of reproducible and non-reproducible 

capital. The capital input index is approximated 

by the capital stock index. The results of the cal11 The data were kindly provided to BTS by BEA staff, Fixed Asset 

Accounts.

culations on the land input (land index) are shown 

in Appendix G.

Weights of Inputs

The weights of the inputs used in the estimation 

of industry MFP are the cost of each input (labor, 

capital, land, and intermediate inputs) in the total 

costs of the industry. The total costs of the industry are the combined cost of each factor input. 

 Data on costs for labor and intermediate inputs are available in the BEA “GDP-by-Industry” 

accounts. Labor compensation is the labor cost, 

including wages and fringe benefi ts. The value of 

total intermediate inputs is the total intermediate 

input cost. Total industry costs are measured as 

gross industry output, in terms of revenues, minus 

indirect business taxes (sales taxes).

 The weights for structures, equipment, and 

land are estimated in this study. These three types 

of capital assets comprise the capital input of the 

industry. This study measure total capital costs 

(of the capital assets) in the trucking industry by 

the industry’s gross operating surplus. The gross 

operating surplus consists of pre-tax income and 

depreciation of fi xed capital assets.

 To provide a simple description of the concepts:

1. Profi t or Income before (corporate income) taxes = 

Total revenues—Total costs; 

2. Depreciation = amount deducted from income for 

“wear” of fi xed capital assets

3. Gross operating surplus = Income (before tax) + Depreciation.

 This “gross operating surplus” is taken as the 

cost of industry capital. This would be the cost of 

capital for the use of structures, equipment, and 

land—that is, the total costs of the industry and 

it would be the overall weight for the industry 

capital input. 

 The calculations take structures and equipment 

as one segment of industry capital (reproducible 

capital) and land as another segment (non-reproducible capital). Structures-equipment and land 

are eventually combined into a capital index; consequently, one needs the cost of these asset classes, 

to be used as weights in the aggregation. In this re-
APOSTOLIDES 13

gard, total capital costs (gross operating surplus) 

of truck transportation are allocated between 

structures-equipment (costs) and land (costs). 

 This allocation is based on two assumptions, 

needed for the calculations of land costs: The 

source data (BEA) provide data on values; however, one needs data on costs to calculate land 

costs. The two assumptions are: 

1. The share of structures value in the total value 

of structures and equipment (available BEA 

data) is the same as the share of structures cost 

to total costs of structures and equipment.

2. The ratio of the land cost to the cost of structures (net of depreciation) is the same as the 

ratio of the land value to the structures value.

With the above assumptions, the estimation of 

the land cost is estimated by obtaining values for 

the relevant variables in the relationships shown 

above. Initially, the cost of structures is separated 

from total capital costs (gross operating surplus). 

The cost of structures is net of depreciation; the 

structures cost is used to estimate the land cost, 

and land does not depreciate. The land cost is assumed to be equal to 0.24/0.76 times the cost of 

structures (net of depreciation). The steps in the 

estimation of land cost are described in Appendix 

H. The weights used for the inputs in the calculations that include land are shown in Appendix I. 

Results of MFP in Truck Transportation

The estimated levels and growth rates of MFP 

for truck transportation, with a measurement for 

land, are calculated for the period of analysis and 

various subperiods. The results are presented in 

Table 4. The annual growth rates show that MFP 

in trucking grew at positive rates during 1988 to 

1994, and in 1996. It grew at negative rates in 

1995, and during 1997 to 2000. In the last 3 years 

of analysis, 2001 to 2003, truck MFP again grew 

at positive rates.

 The growth rates for longer periods summarize 

changes in truck MFP over time. Over the entire 

period of analysis, truck MFP increased at an 

annual rate of 0.8%. The period of analysis can 

be subdivided into three subperiods: 1987-1995, 

1995-2001, and 2001-2003. The calculation 

results indicate that during the fi rst subperiod 

(1987-1995), truck MFP increased at an average 

rate of 2.0% per annum. In contrast, during the 

second subperiod, of 1995-2001, MFP decreased 

at an annual rate of -0.8%. During the last subperiod (2001–2003), truck MFP again increased, at 

an annual rate of 1.1%.

In addition, it is possible to compare the MFP 

results shown in Table 4 with those of a recent 

study by Triplett and Bosworth (2004). They estimated MFP for the SIC industry Trucking and 

Warehousing, for a shorter period than of our 

analysis. Growth rates of those calculations are 

presented in Table 5 along with BTS-estimated 

growth rates of the NAICS Truck Transportation 

industry—for the two periods shown. In comparing the two sets of MFP results, one notes a general 

consistency between the BTS results and those of 

Triplett-Bosworth even though there is, at least, a 

difference in industry coverage. According to both 

sets of results, the trucking industry shows positive growth rates of MFP during 1987 to 1995; 

they become negative growth rates during 1995 to 

2001. 

 From another perspective of assessing the MFP 

results, one also notes that the MFP estimates in 

Table 3 are quite similar to the results shown in 

Table 4. The estimates in the former table do not 

a) Structures value (BEA)

 Structures & Equipment value (BEA) 

b) Structures cost

 Structures & Equipment cost

=

c) Land value (Manvel)

 Structures value (Manvel) 

d) Land cost

 Structures cost (net)

=
14 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

TABLE 4 Multifactor Productivity in Trucking

 (Tornqvist methodology with land input)

Indexes (2000=100) Growth

Year Output index

Combined input 

index

Multifactor 

productivity

MFP growth 

(percentage) Time period

Growth of MFP 

(annual 

percentage)

1987 48.70 55.8 87.29

1988 54.74 58.2 94.00 7.69

1989 57.38 60.4 94.96 1.02

1990 60.68 63.3 95.83 0.92

1991 61.89 62.0 99.81 4.15

1992 67.01 64.7 103.63 3.83

1993 69.71 66.8 104.41 0.76

1994 76.23 72.7 104.86 0.43

1995 78.29 76.4 102.48 -2.27 1987-1995 2.0

1996 82.54 80.4 102.62 0.13

1997 86.32 84.3 102.37 -0.24

1998 92.63 90.9 101.91 -0.45

1999 97.52 96.9 100.60 -1.29

2000 100.00 100.0 100.00 -0.59

2001 93.83 96.2 97.50 -2.50 1995-2001 -0.8

2002 92.20 93.3 98.86 1.39 1995-2003 -0.4

2003 86.71 87.1 99.57 0.72 2001-2003 1.1

1987-2003 0.8

Sources: Data for output, labor, and intermediate inputs were obtained from BEA Industry Accounts at:

http://www.bea.gov/bea/dn2/gdpbyind_data.htm. Data for fi xed assets, from Fixed Assets

Accounts at: http://www.bea.gov/bea/dn/FA2004/Details/Index.html The BTS calculations are described in the text.

TABLE 5 Comparison of Annual Growth Rates of Truck MFP

BTS Triplett and Bosworth

Periods 1987 to 1995 1995 to 2001 1987 to 1995 1995 to 2001

MFP 2.03 -0.83 0.5 to 1.3 -0.2 to -0.5

Industry NAICS Trucking SIC Trucking and Warehousing

Data BEA BEA, BLS, Census

Source: BTS calculations; and Triplett and Bosworth (2004).
APOSTOLIDES 15

include a measure for the land input while the results of the latter table do. Thus, it would appear 

that the inclusion of the land input does not make 

a noteworthy difference to the MFP results. This, 

however, would seem to be related to the methodology used in this study for the measurement of 

land. The approach used essentially tied the land 

measurement to the magnitude, and change, in the 

stock of structures. That is, changes in land followed changes in the structures. This eliminated 

the effect of actual changes in the land input that 

might have been substantially different—in some 

years—from changes in the structures. In future 

work, it is planned for the measurement of land to 

be carried out by a different approach. 

Two Points

There are two points to note with respect to the 

estimated MFP for truck transportation. First, 

as pointed out, the offi cial statistics of trucking 

output include the output of fi rms whose primary 

output is trucking. They do not include data for 

in-house trucking. Therefore, such data are not 

available for this analysis. 

 Second, there is the matter of contracted services. Trucking services are sometimes contracted 

out by truck carriers to single owner-operators 

of trucks. That activity would be an intermediate 

purchase by the trucking fi rm. Consequently, the 

activity would be counted in the gross output of 

truck transportation. On the input side, the activity would be counted as an intermediate input. 

This measurement would not affect the estimation 

of MFP, since the activity is measured in both the 

output and input sides.

SECTION IV—COMPARISONS OF 

MULTIFACTOR PRODUCTIVITY 

Data are available that make it possible to carry 

out comparisons between MFP in trucking and 

MFP of other transportation industries as well as 

of the U.S. private business sector. Such a comparison provides a broader perspective into the truck 

MFP estimates. Relevant data are shown in Table 

6 on levels and growth of MFP. MFP estimates 

for truck transportation are obtained from Table 

4 of this study, and they relate to NAICS data. 

The other MFP estimates were obtained from BLS 

calculations. The air transportation MFP relates 

to NAICS data while rail MFP relates to SIC data. 

Rail MFP data are available up to 1999 while the 

other three MFP series go beyond that year.

 Estimates of MFP for the three transportation 

industries and the U.S. business sector end at 

different years; so, it is not possible to compare 

trucking with the other three series for the entire 

1987-2003 period. However, all series do go up to 

1999; so, MFP growth rates can be compared for 

the 1987-1999 period. Over that period, truck and 

air MFP increased at similar annual rates, of 1.2% 

and 1.3% respectively; while rail MFP increased 

at the highest annual rate of 3.3%. All three transportation industries experienced growth rates of 

MFP that were higher than that of the U.S. business sector of 0.9% per annum.

 In addition, during 1987-1995, truck MFP increased at a faster rate, of 2.0% per annum, than 

MFP in air transportation, which grew at 1.2% 

annually. During this time period, also, the three 

transportation industries experienced annual MFP 

growth which was at substantially higher rates 

than that of the U.S. economy (of 0.6%). 

 When one compares truck MFP with the MFP 

of the U.S. business sector beyond the 1987-1999 

period, the data in Table 6 indicate that over 1987-

2000, truck MFP increased faster per year (1.1%) 

than the business sector (0.9%). Consequently, 

during this time period, truck MFP contributed 

positively to increases in MFP in the U.S. economy. During 1987-2001, the growth rates of truck 

MFP and U.S. economy MFP were the same (at 

8.0%). During the 1987-2003 period, truck MFP 

grew at 0.8% per annum, compared to the U.S. 

private economy of 1.0% per year. After 2001, 

MFP in trucking grew at a lower rate than in the 

U.S. private economy.

The decrease in truck MFP during 1995-2001 

was affected by a decrease in truck output—observed in Table 4 for the year 2001. This decrease 

in output was affected by two events in that year: 
16 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

TABLE 6 Multifactor Productivity of Rail, Air, Trucking, and the U.S. Private Business Sector 

Indexes (1987=100) Growth rates (percentage)

Year

Rail 

MFP

Air 

MFP

Truck 

MFP

U.S. 

private 

business 

sector

Time 

period

Rail 

MFP

Air 

MFP

Truck 

MFP

U.S. 

private 

business 

sector

‘(1) ‘(2) ‘(3) ‘(4) ‘(5) ‘(6) ‘(7) ‘(8) ‘(9)

1987 100.0 100.0 100.0 100.0

1988 105.8 100.2 107.7 100.8

1989 109.8 98.2 108.8 101.1

1990 113.7 99.0 109.8 101.7 1987-1990 4.4 -0.3 3.2 0.7

1991 117.5 98.8 114.3 101.0

1992 125.0 102.4 118.7 103.6

1993 129.0 99.3 119.6 103.9

1994 131.8 105.8 120.1 104.8

1995 139.6 110.0 117.4 104.5 1987-1995 4.3 1.2 2.0 0.6

1996 144.8 114.2 117.6 106.3

1997 144.9 115.5 117.3 107.3

1998 143.4 114.3 116.7 108.9

1999 147.9 116.4 115.2 110.3 1995-1999 1.5 1.4 -0.5 1.3

2000 119.9 114.6 111.8

2001 114.9 111.7 111.9 1995-2001 0.7 -0.8 1.1

2002 113.3 113.8

2003 114.1 117.0 1987-1999 3.3 1.3 1.2 0.9

1987-2000 1.1 0.9

1987-2001 0.8 0.8

1987-2002 0.8 0.9

1987-2003 0.8 1.0

2001-2003 1.1 2.3

Sources: Truck MFP from Table 4. MFP for rail, air, and the private business sector, from the BLS internet site:

Productivity\Multifactor Productivity. Rail: ftp://ftp.bls.gov/pub/special,request/opt/dipts/indmfp.txt.

Air: http://www.bls.gov/mfp/mprnaics.htm Private business sector: http://www.bls.gov/news.release/prod3.t01.htm

The MFP numbers for truck, air, and the private business sector were converted to 1987=100.

FIGURE 1 MFP in Transportation Industries and U.S. 

 Private Business Sector (1987=100)

1987

1988198919901991199219931994

199519961997199819992000200120022003

90

100

110

120

130

140

150

160

Multifactor Productivity

Rail

Truck Air

U.S. business

SOURCE: The data on which this chart is based were obtained from 

Table 6.
APOSTOLIDES 17

1) an economic recession, and 2) the catastrophic 

events of 9/11/01. Subsequently, industry output 

dropped in 2001 while the inputs decreased but 

by less. 

The MFP levels of Table 6 were then converted 

into a graphical presentation, as shown in Figure 

1. There, it can be observed that truck MFP was at 

a higher level than that of the U.S. business sector 

for most of the period of analysis. However, in 

2001 to 2003, it fell below that of the U.S. business.

 One also observes that truck MFP reached 

higher levels than air MFP, for most years of the 

period of analysis. In 1999, however, this situation was reversed and maintained until 2001. It is 

not possible to make comparisons for 2002 and 

2003, during which years truck MFP increased, 

because of unavailability of air MFP data. Finally, 

while truck MFP briefl y exceeded the level of rail 

MFP in 1988, the latter increased at faster rates 

during the rest of the period of analysis.

SECTION V—FACTORS AFFECTING MULTIFACTOR PRODUCTIVITY IN TRUCKING 

Introduction

Technology and Advances in Technology

Technology is the recipe, the “know-how,” that 

is used by producers in different industries in 

order to produce a product or deliver a service. 

The technology utilized should be the best available technology, in order to produce a product or 

service at the greatest possible level (and quality), 

given the available inputs (resources). The production of a product or service at the maximum level 

(given resources) also implies that it is produced at 

the lowest possible cost (cost per unit). 

The technology of production refers to the mixture, or factor proportions, of the inputs used in 

production, and the ways (or techniques) by which 

the inputs are combined—in order to maximize 

output. For services (as well as for products), the 

main inputs in production are: labor, capital, land, 

and intermediate inputs. In practice, there are various types of these main inputs. For example, the 

capital input includes various type of equipment 

and structures. The intermediate inputs include 

purchased materials, services, and energy inputs 

such as petroleum and electricity. 

At a point in time, a fi rm, or an industry or 

economy, can maximize its output of a service (or 

product) by meeting two conditions: 1) full utilization of available resources (labor, capital, land, 

and intermediate inputs); and 2) by using the best 

technology that is available for the delivery of a 

service (or the production of a product). In the 

case of truck transportation, full utilization of resources means that trucks are full with freight at 

all times, on originating and return trips. It also 

means that trucks use roads that minimize any 

loss of time due to road congestion, construction, 

or accidents. Full utilization of trucks also implies 

the minimizing of the out-of-service time of trucks 

due to maintenance problems. With respect to 

the second requirement, the use of the best available technology includes the utilization of capital 

goods (e.g., equipment, machines) that incorporate in them the latest technological advances. 

This would lead to the highest possible level of 

output and, consequently, productivity. Capital 

goods can include equipment such as computers, 

and software.

 When, and if, the level of maximum output is 

attained, it can only increase further with additional increases in resources (labor, capital, land, 

and intermediate inputs) and improvements in 

technology. Either of these two factors require 

the passage of time. Over time, labor can increase 

through population growth, which can lead to 

higher numbers of labor force in the economy. 

Moreover, man-made capital, such as machines 

and structures, requires time to be created. In addition, improvements to the technology used in 

production can entail improvements in the quality of the inputs or by the discovery of new ways 

of combining the inputs used in the production 

process. Improvements such as these are typically 

the result of research and development activity, 

which requires time as well as expenditures. That 

activity may take place outside the industry that 

may eventually be affected. For example, im-
18 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

provements in computers and software can take 

place in the computer industry; and, subsequently, 

these improved capital inputs can be used in truck 

transportation and lead to production increases. 

The above effects can be illustrated with a production possibilities frontier, shown in Figure 2. 

The discussion will use an economy for illustrative purposes; one could substitute an industry or 

a fi rm, and the outcomes shown would still apply. 

Let us assume that an economy uses its resources 

and makes two outputs: bread and shirts (i.e., food 

and clothes). The potential levels of these two outputs are shown on the two axes of the diagram.

In that case, the production relationship can be 

stated as:

Output = depends on (Labor, Capital, Land, Intermediate Inputs), Technology. The meaning of this 

relationship is that the level of output depends on 

the amounts of the inputs used in production—

i.e., labor, etc.—and the technology used. “Technology” is outside the parenthesis; it is not a 

physical input like labor and capital, and it can 

infl uence the productivity of the physical inputs. 

Its effects are generally incorporated in the MFP 

or residual.

 The production possibilities frontier (Figure 2) 

represents the various combinations of the two 

outputs that would result in the maximum level 

of (total) output. Point B on the curve shows the 

maximum output, which is possible when the 

economy is using its resources fully and utilizing 

the best available technology. Point A, which is 

below B, indicates an output level lower than the 

maximum. That level would be attained if the 

economy’s resources were not fully utilized. That 

could be the result if there was unemployed labor 

or capital in the economy due to an economic 

recession. That point would also be reached if 

fi rms in the economy did not use the best available technology and thus did not maximize their 

output. That level would also be attained if there 

were monopolies in the economy that restricted 

output in maximizing their profi ts. 

In order for the economy to move to a higher 

production possibilities frontier—i.e., at a higher 

level of output, indicated by point C—there would 

be need for time to pass. During this passage of 

time, it would be possible for resources of the 

economy to increase. This would include population growth and hence growth in labor. Over time, 

there could also be an increase in capital—buildings and equipment—and land. Technology could 

also improve over time through the discovery of 

new ways of producing raw materials, intermediate inputs or fi nal products/services. 

 The above discussion can be applied to the 

trucking industry. In that case, the trucking industry could be thought of as making two types of 

output—e.g., the delivery of bread and shirts. The 

analysis would follow the same lines as for the 

economy. The main point is that for the trucking 

industry to deliver the greatest level of transportation services, there is need to: 1) employ fully the 

needed inputs, and 2) use the best available technology. Also, for the level of output of the trucking industry to increase, over time, there would be 

need to employ more resources and/or improved 

technology (including a more effi cient industry 

structure).

Factors Affecting Changes in MFP of Truck 

Transportation

A number of factors can affect changes in multifactor productivity at the industry level. In the 

FIGURE 2 Production Possibilities Frontier

Shirts

A

C

B

Bread

SOURCE: The production possibilities frontier is a widely known and used 

tool in economic courses. For example, see Dolan G. Edwin, 1983.
APOSTOLIDES 19

case of truck transportation, there were increases 

and decreases in MFP over the period of analysis, and these changes can be divided into three 

subperiods for assessment: 1) the subperiod of 

1987-1995, during which truck MFP increased by 

an average annual rate of 2.0%; 2) the subperiod 

of 1995-2001, during which truck MFP declined 

at an average annual rate of –0.8%; and 3) the 

most recent subperiod of 2001-2003, during 

which truck MFP increased at an annual rate of 

1.1%. Thus, the analysis has the challenging task 

of evaluating the factors that resulted in such a 

changing pattern of truck MFP.

 The factors which have affected changes in 

truck MFP—in a positive or negative manner—

include: 1) Improvements in the quality of capital: 

computers, software, trucks (information technologies); 2) The effi ciency of utilizing intermediate 

inputs; this includes the fuel effi ciency/ineffi ciency 

of trucks; 3) Average length of haul; 4) Containerization; and 5) Changes in the structure of the 

industry—particularly following truck deregulation at the interstate and intrastate levels. The text 

below examines the effect of these factors over the 

period of analysis.12

1) Improvements in the Quality of Capital

There were improvements, over time, in the quality of capital used in truck transportation. Capital 

includes buildings, equipment—such as trucks and 

computers—and software. In truck transportation, 

there were increases in the capital input over time; 

and newer capital is typically more effi cient than 

older capital, as it incorporates in it improvements 

in technology (embodied technical progress). 

 Table 7 present data on two measurements of 

the capital input in truck transportation: capital 

and capital per worker. These factors are assessed 

for the entire period of analysis (1987-2003) and 

for the three subperiods—1987-1995, 1995-2001, 

12 Improvements in the labor force could also affect multifactor 

productivity in the industry. These improvements include the 

effects of additional training and education of labor. Lack of 

appropriate data prevent the direct quantifi cation of this factor. 

Consequently, its impact would be included in the multifactor 

productivity.

and 2001-2003. According to these calculations, 

the capital input increased by 43.4% over the entire 1987-2003 period (column 2). This translates 

into an average annual growth rate of 2.3%. With 

respect to subperiods, over 1987-1995, capital 

used in truck transportation increased by 20.6% 

or an annual rate of 2.4%. Over the following 

1995-2001 subperiod, capital increased by a higher annual rate of 3.7%. Then, during the most 

recent 2001-2003 subperiod, capital decreased by 

-2.2% per annum. 

With regard to capital per worker, this ratio 

increased by 20.4% over the period of analysis 

(column 3), or at an average annual rate of 1.2%. 

With respect to subperiods, capital per worker 

increased by 1.7% during the 1987-1995 subperiod; this growth rate declined to 0.9% over the 

next 1995-2001 period. During the most recent 

2001-2003 subperiod, capital per worker did not 

grow. 

 The increase in capital per worker during 1987-

1995 would have affected increases in truck MFP 

through the availability of technological advances 

incorporated in new capital goods. As new investment takes place in an industry, capital investment 

of more recent vintage incorporate newer and 

more effi cient technology as compared to capital 

investment of an older vintage. These technological advances typically contribute positively to 

multifactor productivity. 

 During 1995-2001, there was an increase in 

capital per worker while truck MFP decreased 

during this period. The decrease can be attributed 

to the impact of other factors, which are discussed 

at a later point and are listed in Table 14. In the 

last subperiod, 2001-2003, capital per worker did 

not increase while truck MFP increased. In this 

case, MFP increases were affected by other factors besides technological advances incorporated 

in capital. 

 In order to assess more closely the possible 

sources of technological advances through changes in the capital stock, an assessment is carried 

out of more detailed types of capital assets. A 

channel through which technological advances 
20 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

can affect the productivity of truck transportation 

is through information technologies. This refers 

to the use of computers and computer software 

that results in improved delivery of freight. Later 

text in this section describes the various types of 

information technology used in trucking over the 

period of analysis. Consequently, in carrying out 

the assessment, data were obtained for these two 

variables in truck transportation, as well as data 

on capital stock in the form of trucks. These data 

are presented in Table 8; they are in the form of 

quantity indexes.

 These data show the very rapid increases in 

the stock of both computers and software used in 

truck transportation, over the period of analysis. 

Over time, computers grew more than software. 

For computers and peripheral equipment, the index increased signifi cantly from 100 in 1987 to 

76023 in 2003. For software, the index also increased signifi cantly from 100 in 1987 to 44,232 

in 2003. In terms of growth rates, the stock of 

computers grew at an annual rate of 51.4% over 

the period of analysis. They increased at a higher 

annual rate of 82.5% during the fi rst subperiod 

of 1987-1995. This rate declined to an impressive 

30.5% per annum during the second subperiod of 

1995-2001. During the most recent 2001-2003 

subperiod, computers grew at a still slower rate of 

11.8% per annum. 

TABLE 7 Increases in Capital and Labor in Truck Transportation

Indexes (1987=100) Percentage rates of change Annual percentage rates

Year

Labor - 

index

Capital - 

index

Capital per 

worker - 

index

Growth 

of labor

Growth of 

capital

Growth of 

capital per 

worker Period

Growth of 

capital 

Growth of 

capital per 

worker

(1) (2) (3) (4) (5) (6) (7) (8) (9)

1987 100.0 100.0 100.0

1988 91.7 103.1 112.4 -8.3 3.1 12.4

1989 93.6 103.8 110.9 2.1 0.7 -1.4

1990 91.9 99.8 108.6 -1.8 -3.8 -2.1

1991 90.9 96.0 105.6 -1.1 -3.8 -2.7

1992 91.0 91.6 100.7 0.2 -4.5 -4.7

1993 96.1 94.2 98.0 5.6 2.8 -2.7

1994 101.5 106.7 105.0 5.6 13.3 7.2

1995 105.5 120.6 114.3 3.9 13.0 8.8 1987-1995 2.4 1.7

1996 108.1 126.2 116.7 2.5 4.7 2.2

1997 111.2 138.0 124.1 2.9 9.3 6.3

1998 115.7 144.8 125.1 4.1 5.0 0.9

1999 120.3 149.6 124.3 3.9 3.3 -0.6

2000 122.8 153.1 124.7 2.1 2.4 0.3

2001 124.5 149.8 120.4 1.4 -2.2 -3.5 1995-2001 3.7 0.9

2002 120.4 145.7 121.1 -3.3 -2.7 0.6

2003 119.1 143.4 120.4 -1.1 -1.6 -0.6 2001-2003 -2.2 0.0

1995-2003 2.2 0.7

1987-2003 2.3 1.2

Sources: The data on which these indexes and growth rates are based were obtained from the BEA internet site. For data on labor: Annual Industry 

Accounts; http://www.bea.doc/bea/dn2/gdpbyind_data.htm

For data on fi xed assets: National\Fixed Assets\All Fixed Asset Tables\Section 3 Private fi xed assets by Industry: Table 3.2ES and Table 3.1ES: 

http://www.bea.doc/bea/dn/FA2004/Select/Table.asp. 
APOSTOLIDES 21

TABLE 8 Chain-Type Quantity Indexes for Net Capital Stock of Selected Private Nonresidential Fixed Assets

Index numbers, 1987=100 Growth rates (annual percentage rates) Growth rates for periods (annual)

Year

Computers 

and 

peripheral 

equipment Software

Light 

trucks 

(including 

utility 

vehicles)

Other 

trucks, 

buses 

and 

truck 

trailers

Computers 

and 

peripheral 

equipment Software

Light 

trucks 

(including 

utility 

vehicles)

Other 

trucks, 

buses 

and 

truck 

trailers

Time 

period

Computers 

and 

peripheral 

equipment Software

Light 

trucks 

(including 

utility 

vehicles)

Other 

trucks, 

buses 

and 

truck 

trailers

‘(1) ‘(2) ‘(3) ‘(4) ‘(5) ‘(6) ‘(7) ‘(8) ‘(9) ‘(10) ‘(11) ‘(12) ‘(13)

1987 100.0 100.0 100.0 100.01988 209.6 305.6 104.4 101.6 109.6 205.6 4.4 1.61989 395.5 676.2 101.9 101.2 88.7 121.3 -2.5 -0.41990 552.5 1104.7 94.7 94.9 39.7 63.4 -7.1 -6.31991 724.3 1621.0 87.5 88.7 31.1 46.7 -7.6 -6.61992 1322.0 2866.4 76.0 82.7 82.5 76.8 -13.2 -6.71993 3047.5 6484.6 71.1 84.0 130.5 126.2 -6.5 1.61994 6987.6 14271.0 79.3 92.9 129.3 120.1 11.6 10.71995 12326.0 19711.2 91.5 103.0 76.4 38.1 15.4 10.8 1987-1995 82.5 93.6 -1.1 0.41996 17530.5 22080.8 101.4 103.1 42.2 12.0 10.8 0.11997 30719.2 30209.3 123.8 105.1 75.2 36.8 22.1 2.01998 38498.3 35930.4 130.8 105.9 25.3 18.9 5.6 0.81999 45451.4 40222.4 133.5 105.3 18.1 11.9 2.1 -0.62000 56497.2 46729.0 133.1 102.5 24.3 16.2 -0.3 -2.62001 60875.1 46207.5 126.5 94.2 7.7 -1.1 -5.0 -8.1 1995-2001 30.5 15.3 5.5 -1.52002 65339.5 44700.5 120.4 87.7 7.3 -3.3 -4.8 -6.92003 76022.6 44232.7 117.8 82.7 16.4 -1.0 -2.2 -5.8 2001-2003 11.8 -2.2 -3.5 -6.3

1987-2003 51.4 46.3 1.0 -1.2

Sources: For data on fi xed assets: National\Fixed Assets\All Fixed Asset Tables\Section 3 -Private fi xed assets by Industry: Table 3.2ES and Table 3.1ES: http://www.bea.doc/bea/dn/FA2004/Select/Table.asp. 
22 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

 With regard to software, their stock increased 

steadily and signifi cantly over time, up to 2000; 

it subsequently declined, but was still maintained 

at high levels. The pattern for software stock over 

time is similar to that of computers. During the 

fi rst subperiod, of 1987-1995, the software stock 

increased at an annual rate of 93.6%. This was 

even higher than the rate of increase for computers. However, during the second subperiod, 1995-

2001, software grew at a substantially slower, 

although still impressive, rate of 15.3%. This rate 

declined further to –2.2% during 2001-2003.

 A very different picture is obtained for the stock 

of trucks. Light trucks (column 3) increased much 

slower over the period of analysis than computers or software; they increased by 17.8% over 

the entire 1987-2003 period. In fact, during the 

fi rst subperiod (1987-1995), they experienced a 

decrease—from 100.0 to 91.5, or about -8.5%. In 

terms of growth rates, light trucks increased at an 

annual rate of 1.0% over 1987-2003. During the 

fi rst subperiod (1987-1995), they actually declined 

by –1.1% per annum; during the next 1995-2003 

subperiod, they increased at 5.5% per year, while 

during the most recent 2001-2003 subperiod, they 

decreased at –3.5% per year. 

 The capital stock of “Other trucks, buses, and 

truck trailers” experienced a decline over the entire 1987-2003 period (from 100.0 to 82.7). In 

terms of growth rates, during the entire period of 

analysis, the capital stock of “Other trucks, etc.” 

decreased at an annual rate of -1.2%. During the 

fi rst subperiod (1987-1995), their stock increased 

by 0.4% per annum. However, this changed to an 

annual decline of –1.5% during the 1995-2001 

subperiod; the decline continued at the higher rate 

of –6.3% during the most recent 2001-2003 subperiod.

In summary, these data indicate the rapid 

growth, over the period of analysis, of the two ITrelated capital assets—computers and software. 

By contrast, the capital stock of trucks either increased very little or declined over the same period. 

Consequently, changes in technology in computers 

and software would have been instrumental in affecting increases in truck MFP during 1987-1995. 

Increases in computers during the most recent 

2001-2003 subperiod are also consistent with an 

increasing truck MFP during that subperiod. Since 

computers and software were increasing during 

1995-2001 while MFP declined, it would appear 

that other factors contributed to the decreases in 

truck MFP during the 1995-2001 subperiod. Such 

factors are examined in other parts of this study.

2) Information Technologies: Hardware, 

Software, and Communications 

Technological advances used in truck transportation include information technologies. These 

technologies include the use of computers and 

software as well as various channels of communication such as satellite communications and 

the internet. These technologies have affected all 

aspects of truck transportation services, including 

the operation of the truck, the selection of routes, 

truck maintenance, and the marketing of truck 

services. These technologies can be used by themselves or in combination with other IT technologies; the latter framework seems more typical. 

 The various information technologies that 

affected motor carrier operations include the 

following: a) On-board computers (OBC); b) 

Electronic data interchange (EDI); c) Automatic

vehicle location (AVL); d). Satellite communications (SATCOM): e) Computer-aided dispatching 

(CAD), and Computer aided routing (CAR); f) 

Truck maintenance; and g) Transactions of truck 

services (marketing, operations). These technologies and their impact on trucking productivity are 

discussed below. 

On-Board Computers (OBCs)

 On-board computers are truck-based or handheld computers, used to obtain information on 

truck performance. These computers collect and 

process data received from sensors, and other 

devices, located on trucks. They keep records 

of readings and provide the fl eet operator with 

performance information on the trucks and drivers. OBCs can be used as trip recorders and to 

monitor drivers’ hours of service and vehicle 
APOSTOLIDES 23

performance measures, such as speed and fuel 

consumption. OBCs are also used in conjunction 

with computer-aided routing and dispatching systems and with maintenance-scheduling software. 

On-board computers also become involved in the 

Automatic Vehicle Location system, described 

below. 

On-board computers can contribute to increased 

productivity in the following ways:

Business Transactions. The computer on the 

truck registers delivery times of freight and customer signatures for proof-of-delivery. This has 

reduced paperwork and thus labor time to do 

such paperwork. 

Driver Log. With OBC, drivers can input records of hours of service and fuel consumption. 

Such data make possible an assessment of fuel 

utilization, leading to truck speeds that minimize 

the use of fuel. Increased effi ciency in the use of 

fuel, an intermediate input, would increase MFP. 

A reduction of total intermediate inputs, in relation to output, is not observed in trucking over 

the period of analysis—except for the last 2 to 3 

years. It will be shown that the fuel effi ciency in 

trucking decreased over the period of analysis. 

Data Collection on Vehicle Performance. Onboard-computers provide information on various 

parts of truck performance. These include: engine 

idling, braking, and patterns of shifting and acceleration. The computer also provides data, from 

diagnostic systems, on ancillary equipment on the 

truck such as refrigeration units. Consequently, 

OBCs allow for remote diagnostics prior to a 

malfunction of the truck; this can be followed by 

preventive maintenance. Prompt preventive maintenance and repair improve the performance of 

trucks and reduce their out-of-service time. This 

results in higher levels of output and MFP.

Electronic Data Interchange (EDI)

Electronic Data Interchange (EDI) systems include 

computers and software that are used to send and 

receive electronic messages and data transmission 

between computers of two parties. The transmission can occur between trucking companies and 

shippers (or between any two trading partners). 

This technology enables the transmission of information, including electronic transactions, between 

companies in an easier, more accurate, and timely 

manner. EDI allows for effi cient billing and receipt 

of freight-delivery acknowledgement. 

The use of computers for fi nancial transactions 

reduces paperwork and related labor costs, and 

thus reduces costs of business transactions. This 

increase multifactor productivity. 

Automatic Vehicle Location (AVL) and Satellite 

Communications (SATCOM) 

Automatic vehicle location (AVL) refers to a broad 

category of ground-based or satellite technologies, 

with which it is possible to track the location of 

trucks. Dispatchers, drivers, shippers, and receivers can track a truck from pickup to delivery of 

freight; coordinate inter-modal shipments; and 

perform just-in-time deliveries. In addition to 

vehicle tracking, SATCOM technologies provide 

communication between the dispatchers and the 

truck drivers; this allows for real time coordination of fl eet routing and dispatching activities. 

With an on-board computer, two-way text or 

voice communications can allow for routing and 

dispatching of trucks in current time/real time, as 

well as the (real-time) monitoring of vehicle operating parameters such as speed, etc. With this 

system, the motor carrier can also locate a truck 

in case of a breakdown. This results in less outof-service time, and thus higher levels of output 

(freight delivered) and MFP. 

Computer-Aided Routing (CAR) and 

Dispatching (CAD)

These technologies involve computer hardware 

and specifi c software that are used for dispatching, 

routing, and decision support for route selection 

of trucks. Good route selection can contribute to 

minimizing the time and cost of moving freight. 

These systems are used to schedule drivers and 

trucks subject to parameters, such as allowable 

driving hours, size of load, and origin and destination. The basic systems allow for the planning and 

scheduling of truck activities prior to the dispatch 
24 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

of a truck. In addition, more sophisticated systems 

allow for routing and dispatch decisions based on 

real-time truck locations; estimate delivery times 

and distances; help improve cost estimates; and 

generate route maps. 

 The technologies of computer-aided dispatching (CAD) and computer-aided routing (CAR) 

lead to improved fl eet routing and dispatching. 

This results in an increased utilization of trucks. 

This includes a reduction in the number, and extent, of empty trucks, particularly on back hauls. 

This increases trucking output and, consequently, 

raises truck productivity and MFP.

Computer-aided dispatching and routing provide for improved dispatcher productivity. This 

technology results in less time needed for truck 

carriers’ staff to complete routing procedures as 

compared to previous manual systems. These 

technologies also improve communication effi -

ciency. With a computerized system, information 

to drivers can be relayed instantaneously. Consequently, information on a pick-up of freight can 

be transacted by the truck carrier and the information relayed quickly to an appropriate truck 

driver, who is close to the freight. This results in 

increased output (load) for that particular truck, 

and greater output for the trucking fi rm—and for 

the trucking industry.

Truck Maintenance

Technological advances have affected positively 

truck maintenance through the increased use of 

maintenance-tracking software (MTS). These software improve the maintenance of trucks by tracking and reordering parts for the repair department 

of a truck fl eet. These software also carry out 

real-time diagnostics of trucks. As information 

becomes promptly available on the performance 

of trucks, maintenance tracking software is used 

to schedule preventive and emergency repairs, as 

needed, in the most cost-effective manner. Preventive maintenance reduces maintenance costs as potential problems are repaired before they become 

bigger and more expensive jobs. This also reduces 

the out-of-service time of trucks.

Marketing of Truck Services

There has been an increase in the use of computerized systems for the buying and selling of truck 

transportation services. These include hardware, 

software, the internet, and satellite communications. The result is higher delivered freight output 

for the quantity of labor and capital used; this 

increases production effi ciency/MFP. 

In summary, information technology contributed to productivity in truck transportation in a 

number of ways: 

On the operations side, computers have been 

used for communications between the truck carrier and the truck drivers. These communications 

helped carriers increase vehicle utilization through 

increased monitoring and reducing unnecessary 

out-of-route miles by drivers. Information availability on road work or the closing of roads (as 

a result of accidents) enables the driver to avoid 

the affected roads and choose other routes. These 

computers have also been used to schedule trips 

by trucks, including which freight to deliver and 

which roads to take. Information technologies 

would also contribute to lower fuel costs through 

improved routing. Improved routing would entail 

the choice of the quickest (and lower cost) route 

between two points. 

On the maintenance side, computers have been 

used to schedule regular maintenance checks for 

trucks. Computers have also been used to check 

for problems developing in trucks. This can prevent a breakdown of a truck on the road with the 

accompanying negative effects of the truck being 

out-of-service.

 On the administrative side, the use of computers would include personnel transactions and records. Personnel information would relate to the 

keeping of records for full-time truck drivers and 

those on a contractual basis. Since the trucking industry has had substantial turnover of drivers, the 

keeping of correct and updated personnel records 

would be of particular importance. On the sales 

side, computers have been used to obtain receipts 

when the freight is delivered. This entailed elec-
APOSTOLIDES 25

tronic transactions and the electronic dissemination of such information. Administrative costs fell 

as new technologies were adopted that involved 

paperless transactions. 

 Finally, it is noted that the data on computers and software would not include information 

technology equipment utilized on the trucks themselves. The latter would be part of the truck and 

they would have been included in the measurement of the capital stock for trucks.

 3) Intermediate Inputs

An industry’s MFP can also be affected by its 

effi ciency in utilizing intermediate inputs. In examining this point, the ratio was calculated of 

intermediate inputs to gross output of the trucking industry, and the results are shown in Table 

9. These results indicate that in terms of current 

prices, intermediate inputs accounted for about 

50% of gross output over the period of analysis 

(column 3). Moreover, over time, there was an increase in the ratio of intermediate inputs to gross 

output. Intermediate inputs were 47% of gross industry output in 1987; in subsequent years, the ratio increased and reached a high of 56% in 2000. 

However, from 2001 to 2003, the ratio declined 

(from 55% to 51%). 

 Since the ratio in current prices could be affected by increases in the relative price of intermediate 

inputs (including fuel), tabulations were also carried out in quantity terms. These tabulations are 

in terms of growth rates, and are shown in Table 

9, particularly columns 7 and 8. They support the 

results of calculations in current price.

 The growth rates in quantity terms indicate 

that over the period of analysis, the quantity of 

intermediate inputs increased faster than output 

of the trucking industry. This is also observed 

for the two subperiods of 1987-1995 and 1995-

2001. However, this trend was reversed in 2001, 

and over the most recent 2001-2003 period, both 

the quantity of output and intermediate inputs 

decreases. During that period, intermediate inputs 

decreased at a substantially faster rate annual rate 

(-7.3%) than output (-3.9%).

 Thus, these numbers, in current dollars and in 

quantity terms, indicate that there was a decline 

in the effi ciency with which intermediate inputs 

were utilized in trucking, over 1987-1995 and 

1995-2001. However, there was an increase in the 

effi ciency of utilizing intermediate inputs over the 

most recent period 2001-2003. The decrease in 

the effi ciency of utilizing intermediate inputs, during 1995-2001, was a contributory factor to the 

declining truck MFP during that period. Also, the 

effi ciency of utilizing intermediate inputs in truck 

transportation was increasing over the last three 

years of the period of analysis. This would have 

contributed to the increasing MFP during those 

years. 

In attempting to explain the decrease in effi -

ciency of utilizing intermediate inputs over most 

years of the period of analysis, one notes that a 

major intermediate input in truck transportation 

is fuel. Therefore, an examination is carried out of 

fuel effi ciency in trucking. 

One would expect that improvements in the 

capital input of truck transportation would include the use of newer trucks that incorporate in 

them the results of new technologies. These new 

technologies would include truck engines that are 

more fuel-effi cient than older engines. Improvements in fuel effi ciency are expected to result in 

reduced use of fuels and consequently of intermediate inputs. This would contribute to increased 

effi ciency of the industry in using intermediate 

inputs, which would have contributed positively 

to truck MFP. 

In evaluating such a possibility, data on fuel effi ciency are presented in Tables 10 and 11. Data 

in Table 10 are for heavy single-unit trucks; they 

indicate that there was a rather steady increase in 

the fuel effi ciency of these trucks over the 1987-

2002 period. Their fuel effi ciency increased over 

time from 6.4 miles per gallon (mpg) in 1987 to 

7.5 in 2001; it declined slightly to 7.4 mpg in 

2002. Calculations with growth rates (in the same 

table) show a similar development. Fuel effi ciency 

of these trucks increased at an annual rate of 0.8% 

during 1987-1995; it increased rather substan-
26 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

tially at 1.6% per annum during the 1995-2001 

subperiod. 

 Table 11 presents data on the fuel effi ciency of 

combination trucks. These trucks use one or more 

trailers. Consequently, they would carry greater 

and heavier freight than single unit trucks. The 

data presented indicate, for one, that these trucks 

had lower fuel effi ciency than single unit trucks. In 

1987, the combination trucks obtained 5.7 miles 

per gallon compared to 6.4 miles per gallon for 

the single unit trucks. Moreover, the fuel effi ciency 

of the combination trucks decreased over the period of analysis, from 5.7 mpg in 1987 to 5.2 mpg 

in 2002. That implies a decline of –0.6% per year. 

Consequently in 2002, these trucks were even less 

fuel-effi cient (at 5.2 mpg) than in 1987 (5.7 mpg); 

they were also considerably less fuel-effi cient than 

the single-unit trucks which obtained 7.4 mpg in 

2002.

 With respect to subperiods, the fuel effi ciency 

of combination trucks increased by 0.2% annually, during 1987-1995. However, during the 

subsequent subperiod of 1995-2001, their fueleffi ciency declined signifi cantly at an annual rate 

of -1.2%. This decline in fuel effi ciency would 

have contributed to the decline in the effi ciency 

of utilizing intermediate inputs during 1995-2001 

(shown in Table 9); it would also have been a contributory factor in the declining truck MFP during 

1995-2001. 

 Moreover, the number of miles traveled by the 

less fuel-effi cient combination trucks have been 

greater than those traveled by the single-unit 

trucks, by rather substantial magnitudes (column 

2 of Tables 10 and 11). Consequently, the fuel 

effi ciency of the truck transportation industry, in 

TABLE 9 Intermediate Inputs to Output in Truck Transportation

Current prices Quantity indexes Growth rates (percentage)

Year

Gross 

output 

(current $ 

billions)

Intermediate 

inputs 

(current $ 

billions)

Interm. 

inputs 

to gross 

output 

[(2)/(1)]

Output 

index 

(2000=100)

Interm. 

input index 

(2000=100)

Time 

period Output

Intermediate 

inputs

‘(1) ‘(2) ‘(3) ‘(4) ‘(5) ‘(6) ‘(7) ‘(8)

1987 $82.5 $38.6 0.47 48.70 42.398

1988 $92.9 $45.0 0.48 54.74 48.613

1989 $101.0 $49.9 0.49 57.38 51.553

1990 $111.6 $59.0 0.53 60.68 57.752 1987-1990 7.6 10.9

1991 $111.9 $57.8 0.52 61.89 56.016

1992 $121.1 $64.0 0.53 67.01 62.016

1993 $127.9 $66.2 0.52 69.71 63.298

1994 $141.8 $73.4 0.52 76.23 69.218

1995 $148.0 $77.9 0.53 78.29 71.421 1987-1995 6.1 6.7

1996 $157.0 $85.0 0.54 82.54 76.409

1997 $168.2 $89.8 0.53 86.32 79.823

1998 $184.1 $97.9 0.53 92.63 88.337

1999 $198.7 $108.8 0.55 97.52 96.326

2000 $213.2 $120.4 0.56 100.00 100.000

2001 $205.7 $112.4 0.55 93.83 93.128 1995-2001 3.1 4.5

2002 $204.1 $108.7 0.53 92.20 90.166 1987-2001 4.8 5.8

2003 $196.8 $99.6 0.51 86.71 79.979 2001-2003 -3.9 -7.3

1987-2003 3.7 4.0

Sources: BEA internet site. Annual Industry Accounts: http://www.bea.doc.gov/bea/dn2/gdpbyind_data.htm.
APOSTOLIDES 27

TABLE 10 Fuel Effi ciency of Heavy Single-Unit Trucks, 1980–2002

Year

Registrations 

(thousands)

Vehicle travel 

(million miles)

Fuel use 

(million gallons)

Fuel effi ciency 

(miles per gallon) 

[(2)/(3)]

‘(1) ‘(2) ‘(3) (4)

1980 4,374 39,813 6,923 5.8

1981 4,455 39,568 6,867 5.8

1982 4,325 40,658 6,803 6.0

1983 4,204 42,546 6,965 6.1

1984 4,061 44,419 7,240 6.1

1985 4,593 45,441 7,399 6.1

1986 4,313 45,637 7,386 6.2

1987 4,188 48,022 7,523 6.4

1988 4,470 49,434 7,701 6.4

1989 4,519 50,870 7,779 6.5

1990 4,487 51,901 8,357 6.2

1991 4,481 52,898 8,172 6.5

1992 4,370 53,874 8,237 6.5

1993 4,408 56,772 8,488 6.7

1994 4,906 61,284 9,032 6.8

1995 5,024 62,705 9,216 6.8

1996 5,266 64,072 9,409 6.8

1997 5,293 66,893 9,576 7.0

1998 5,414 67,894 9,741 7.0

1999 5,763 70,304 9,372 7.5

2000 5,926 70,500 9,563 7.4

2001 5,704 72,448 9,667 7.5

2002 5,651 75,887 10,305 7.4

Growth rates—average annual percentage rates

1987–2002 2 3.1 2.1 1

1987-1995 2.3 3.4 2.6 0.8

1995-2001 2.1 2.4 0.8 1.6

2001-2002 -0.9 4.7 6.6 -1.3

Source: U. S. Department of Transportation, Federal Highway Administration, Highway Statistics 2002, 

Washington, DC, 2003, Table VM1 and annual. (Additional resources: www.fhwa.dot.gov).

Note: Heavy single-unit trucks include all single-unit trucks which have more than two axles or more than 

four tires. Most of these trucks would be used for business or for individuals with heavy hauling or towing 

needs.

total, declined over the period of analysis—and 

particularly over the last several years of the period. A declining fuel effi ciency is consistent with, 

and contributes to, the decrease in the industry’s 

effi ciency in the utilization of intermediate inputs 

observed previously. This, in turn, is consistent 

with a declining MFP observed over the 1995-

2001 subperiod.

 4) Average Length of Haul

Changes in the average length of haul (ALOH) 

can affect multifactor productivity in trucking. An 

increase in the average length of haul—affected 

by longer truck trips (distance from origin to 

destination)—can contribute to better fuel effi ciency and an improved utilization of other 

intermediate inputs such as engine oil, etc. This 

would affect positively the effi ciency of utilizing 

intermediate inputs which, in turn, affects MFP.

It has already been observed that truck transportation experienced a decline in the effi ciency of 

utilizing intermediate inputs, with the exception 

of the more recent 2001-2003 period. An objective of analyzing the average length of haul will 

be to assess whether this factor contributed to the 

decline in fuel effi ciency of the industry or whether 

it served as an offsetting factor to that decline. 

Table 12 presents data on the average length 

of haul (ALOH) of trucks. These numbers in-
28 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

dicate that the average length of haul increased 

over the 1985 to 2001 period. This increase took 

place steadily over time—so that while in 1985, 

the ALOH was 589 kilometers, by 1995, it had 

risen to 669 kilometers. By 2001, it had increased 

still further to 781 kilometers. In terms of rates 

of increase, the average length of haul increased 

faster during the more recent 1995-2001 period 

(2.6% per year) as compared to the 1985-95 period (1.3% per year).

 The data indicate that increases in the average 

length of haul would have contributed positively 

to the overall effi ciency/MFP of the trucking industry. With regard to subperiods, the increase 

in the ALOH over 1985-1995 would have contributed to the increase in truck MFP during that 

time. During the second subperiod, 1995-2001, 

the ALOH of trucks is shown to have increased 

while truck MFP declined. During this time, the 

ALOH acted to offset the negative impact of other 

factors on the declining truck MFP. 

5) Containerization 

Containerization refers to the movement of 

commodities in (large) containers rather than in 

smaller units. The use of containers in transportation includes rail-truck and truck-water transTABLE 11 Fuel Effi ciency of Combination Trucks, 1980–2002

Year

Registrations 

(thousands)

Vehicle travel 

(million miles)

Fuel use 

(million gallons)

Fuel effi ciency 

(miles per gallon) 

[(2)/(3)]

‘(1) ‘(2) ‘(3) ‘(4)

1980 1,417 68,678 13,037 5.3

1981 1,261 69,134 13,509 5.1

1982 1,265 70,765 13,583 5.2

1983 1,304 73,586 13,796 5.3

1984 1,340 77,377 14,188 5.5

1985 1,403 78,063 14,005 5.6

1986 1,408 81,038 14,475 5.6

1987 1,530 85,495 14,990 5.7

1988 1,667 88,551 15,224 5.8

1989 1,707 91,879 15,733 5.8

1990 1,709 94,341 16,133 5.8

1991 1,691 96,645 16,809 5.7

1992 1,675 99,510 17,216 5.8

1993 1,680 103,116 17,748 5.8

1994 1,681 108,932 18,653 5.8

1995 1,696 115,451 19,777 5.8

1996 1,747 118,899 20,192 5.9

1997 1,790 124,584 20,302 6.1

1998 1,831 128,159 21,100 6.1

1999 2,029 132,384 24,537 5.4

2000 2,097 135,020 25,666 5.3

2001 2,154 136,584 25,512 5.4

2002 2,277 138,643 26,451 5.2

Growth rates—average annual percentage rates

1987-2002 2.7 3.3 3.9 -0.6

1987-1995 1.3 3.8 3.5 0.2

1995-2001 4.1 2.8 4.3 -1.2

2001-2002 5.7 1.5 3.7 -3.7

Source: U. S. Department of Transportation, Federal Highway Administration, Highway Statistics 2002, Washington, DC, 

2003, Table VM1 and annual. (Additional resources: www.fhwa.dot.gov).

Note: The Federal Highway Administration changed the combination truck travel methodology in 1993. Combination trucks 

include all trucks designed to be used in combination with one or more trailers. The average vehicle travel of these trucks 

(on a per truck basis) far surpasses the travel of other trucks due to long-haul freight movement.
APOSTOLIDES 29

port, and has become more widespread over time. 

Within the continental United States, containers 

are used to transport cargo by truck from a point 

of origin to a particular destination. They are also 

used in the intermodal market, which includes the 

transportation of freight by truck to, and from, 

a train or a ship. Intermodal fi rms link different 

forms of transportation for ultimate delivery to 

the customer. Containers have become an integral 

component of intermodal transportation, which 

has been expanding over time. 

Containers are part of the capital input of the 

truck transportation industry. They represent a 

technological improvement over previous ways 

of transporting freight (use of smaller boxes, etc.) 

and are thus an improvement in the quality of 

the capital input. The technological advances are 

incorporated into the capital input. Thus, the impact of the use of containers would be measured 

in the MFP of the industry. 

The use of containers resulted in an increased 

use of automation in the loading and unloading 

of trucks. Because commodities are in containers, 

cargo is moved by crane or forklift; this procedure 

requires less manual labor than the handling of 

smaller packages. Consequently, the utilization of 

this mode of handling freight reduces the time required to transfer cargo; this increases productivity 

and reduces handling costs. The use of containers 

also tends to reduce the cost of damage or theft of 

freight. The benefi ts of using containers include: 

reduced employee injuries; reduced damage to the 

truck; and improvement in loading effi ciencies. 

Thus, containerization contributes to increased 

productivity/MFP. 

 In order to examine the impact of this factor, 

data on containers were collected and tabulated. 

It is diffi cult to fi nd a central source of such data 

with a comprehensive data base, for the years that 

cover the period of analysis. Consequently, the 

basic tabulation uses data on containers from the 

railroads, and these data are supplemented with 

data from other sources. 

Data on containers are presented in Table 13, 

for the period 1990-2004. They refer to containers used in truck-rail intermodal transportation. 

TABLE 12 Average Length of Haul of Trucks, 1985–2001

Year

Average 

length of haul 

(kilometers) Time period

Growth rates 

(annual 

percentage 

rate)

1985 589

1990 629

1991 641

1992 660

1993 655 1985-1995 1.3

1994 631

1995 669 1990-1995 1.2

1996 686

1997 700

1998 711

1999 737

2000 761

2001 781 1995--2001 2.6

1985-2001 1.8

Source: Bureau of Transportation Statistics; website: http://www.bts.gov/

cgi-bin/breadcrumbs/PrintVersion.cgi?date=20102416.

TABLE 13 Use of Containters in U.S. Railroads

Year Containers Time period

Growth rates 

(annual 

percentage)

1990 2,754,829

1991 3,044,574

1992 3,363,244

1993 3,692,502

1994 4,375,726

1995 4,443,709 1990-1995 10.0

1996 4,841,130

1997 5,244,401

1998 5,419,631

1999 5,700,219

2000 6,288,260

2001 6,332,021 1995-2001 6.1

2002 6,781,022

2003 7,329,768 2001-2003 7.6

2004 8,065,539 2001-2004 8.4

1990-2003 7.8

Source: AAR “Railroad Facts,” 2005 edition, p. 26.

Note: Data for 2004 are preliminary.
30 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

These data indicate that the number of containers used increased by 7.8% per annum over the 

1990-2003 period. Moreover, the fi rst subperiod, 

1990-1995, has the highest annual growth rate, at 

10.0%. This subperiod is similar to the initial subperiod for truck MFP (1987-1995). The following subperiod, of 1995-2001, has a substantially 

lower growth rate for containers, at 6.1% per annum. The most recent subperiod, of 2001-2003, 

has a growth rate that is higher that the previous 

subperiod, at 7.6%. 

 The rates of increase in the number of containers 

used correspond well to the changes in truck MFP. 

Truck MFP was increasing during 1987-1995, 

while the number of containers used increased at 

the highest rate (over 1990-2003) during 1990-

1995. Truck MFP decreased during the following subperiod, 1995-2001, and the numbers of 

container used increased at the lowest rate during 

that subperiod. Finally, truck MFP was increasing 

again during 2001-2003, and the use of containers was also increasing during that subperiod.

 Additional data on containers are presented in 

two appendix tables. These data are consistent 

with, and reinforce, the fi ndings based on data in 

Table 13. First, Appendix J presents data on containers used in waterborne trade of the U.S. That 

is another segment of the container market and relates to truck-ship (or ship-truck) transportation. 

Although these data cover fewer years than the 

rail data, they show similar trends. They indicate 

that the increase of shipping containers during the 

most recent subperiod, of 2001-2003, was greater 

than during the previous subperiod of 1998-2001. 

One notes that truck MFP increased during 2001-

2003, while it decreased during 1995-2001.

 Finally, another set of data are presented in Appendix K. These data refer to containers used in 

trucks that crossed the border of the United States 

for Canada or Mexico. These data indicate a rather steady increase in the use of containers over the 

1996-2002 period (with a decline in 2003). They 

also show a pattern similar to that which has been 

observed. During the most recent 2001-2003 subperiod, the use of containers increased substantially more (at 16.4% annually) than during the 

previous 1996-2001 subperiod (0.6%). And truck 

MFP also decreased during 1995-2001, while it 

increased during 2001-2003.

 The data on containers indicate that the use of 

containers was a factor that affected effi ciency in 

truck delivery and truck MFP. The data indicate 

high growth of containers use during the 1990-

1995 subperiod (or parts of that period) and 

during 2001-2003. By contrast, low increases of 

containers use are observed during the 1995-2001 

subperiod. Changes in truck MFP corresponds 

quite well to changes in containers use: During the 

1990 (1987) to 1995 period, and the 2001-2003 

period, truck MFP increased; while during 1995-

2001, truck MFP declined. 

6) Changes in Industry Structure—

Deregulation

The structure of an industry can change over 

time as a result of deregulation, mergers/acquisitions, and bankruptcies. Such changes can affect 

effi ciency (productivity) in an industry. With respect to mergers, the acquisition of one fi rm by 

another implies that the more effi cient fi rm acquires a less effi cient fi rm. In that case, the more 

effi cient fi rm has typically grown faster (sales), has 

gained signifi cant amounts of revenues and profits, and is able to secure fi nancial resources. All of 

these characteristics enable it to acquire another, 

less effi cient fi rm. Two types of mergers are relevant to the analysis: horizontal and vertical. A 

horizontal merger combines two fi rms in the same 

industry into one fi rm. Consequently, in the new 

post-merger fi rm, there is expected to be merging 

of certain functions of the two pre-merger fi rms; 

these would include fi nance, payroll, and advertising. These developments result in the same output 

being produced but with fewer inputs such as 

labor, equipment, building space, and materials/

services. This results in a reduction in inputs, 

and thus costs, and an increase in multifactor 

productivity. Vertical mergers involve mergers of 

transportation fi rms that provide complementary 

services. The provision of complementary services 

within the same trucking company can increase 

effi ciency. 
APOSTOLIDES 31

 The structure of the trucking industry changed 

considerably over the period of analysis—following deregulation at the interstate level in 1980, 

and at the intrastate level in 1995. The latter completed deregulation in the trucking industry and 

made it comprehensive. The Motor Carrier Act of 

1980 did not affect restrictions on intrastate commerce; and as time passed, the cost of shipping 

across state borders widened signifi cantly from 

the cost of shipping within state borders. In 1994, 

41 states still maintained some type of economic 

regulation over intrastate trucking, and intrastate 

rates were, on the average, 40 percent higher than 

rates for interstate freight delivery of the same 

distance.13 In 1995 the Interstate Commerce Commission Termination Act was passed and it lifted 

economic regulation from intrastate trucking.

 Deregulation—interstate and intrastate—of the 

trucking industry resulted in signifi cant changes. 

There was a notable amount of entry in, as well 

as exit from, trucking. The entry side included 

the appearance of new truckload (TL) fi rms, the 

expansion of less-than-truckload (LTL) fi rms into 

new markets, and the emergence of third parties 

such as brokers. Truckload carriers were no longer 

restricted to predetermined routes and commodities; some of them merged and consolidated with 

others to provide national coverage.

 The change in truck transportation from interstate deregulation, and which apparently continued after the intrastate deregulation in 1995, 

resulted in a decrease in the relative importance of 

less-than-truckload trucking and a corresponding 

increase in the relative importance of truckload 

trucking. Data on shipments, in Appendix L, show 

that in 1989 and early 1990s, the LTL segment of 

the trucking industry accounted for 39% of total 

shipments (LTL and TL). In 1998 and subsequent 

years up to 2003, the relative importance of the 

LTL segment decreased to 29% of total industry 

shipments.

 While few carriers specializing solely in LTL 

trucking were formed since 1980, there was signif13 Federal Highway Administration. “Regulation: From Economic 

Deregulation to Safety Reregulation,” p. 5.

icant geographic expansion by existing LTL fi rms 

into each other’s territories, and entry by other 

carriers, including carriers from other modes (e.g. 

rail). These new entrants included newly formed 

subsidiaries of existing LTL fi rms, and the expanded operations of truckload, small package, 

package express, and air cargo carriers.14

 A comparison of the status of the 100 largest 

motor carriers (of property) between 1979 and 

1991 shows that15 : 1) Forty-nine carriers were 

operating, 37 of which were still among the 100 

largest; and 2) Fifty carriers had ceased operations 

since 1979. At least 35 of these carriers were identifi ed as having fi led for bankruptcy.

 Structural changes in the trucking industry included trucking companies diversifying out of the 

traditional LTL market. For example, Roadway 

Services, Inc. was an LTL fi rm, and it created a 

subsidiary (Roberts Transportation Services) that 

performed almost no standard LTL business. The 

subsidiary was in the business of handling rush 

shipments, of rather high value. Much of its revenues came from shipments that were smaller than 

10,000 lbs. (i.e., technically LTL), but these shipments were not routed through traditional LTL 

sort terminals. Rather, most of these shipments 

were picked up within 90 minutes of a customer 

request and were dispatched directly to their destination.16

 During the period of analysis, the LTL segment 

experienced signifi cant decreases in the number of 

fi rms, accompanied by an increase in the size of 

the average fi rm. In 1975, this segment consisted 

of about 528 fi rms (generating $10.6 billion in 

revenues). By 1989, the segment had shrunk to 

159 fi rms which had $13.4 billion in revenues; 

and by the end of 1993, there were only 108 fi rms, 

generating $16.7 billion in revenues.17 These data 

indicate that over the period 1976-1993, there 

was a substantial change in the structure of the 

LTL segment of the industry and, thus, in the entire trucking industry. 

14 Interstate Commerce Commission, 1992, p. 38.

15 Ibid., p. 52.

16 Ibid., pp. 89-91.

17 Feitler, Corsi, and Grimm, 1998, p. 5.
32 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

 After interstate deregulation, the LTL fi rms experienced mergers and bankruptcies. At the same 

time, a number of LTL carriers, particularly, smaller ones, were able to succeed. From the largest 50 

LTL carriers in 1979, twelve companies survived 

as of 1994 (controlled by 10 corporate parents). 

Of the top 50 fi rms, a number of fi rms merged 

with others that later closed, while a number of 

fi rms shut down operations. Moreover, more closures occurred to fi rms that were relatively smaller 

in the group of the top 50 fi rms. Conversely, more 

of the relatively larger fi rms in the top 50 fi rms 

were able to survive in the post-regulatory environment (of interstate deregulation).18

 With respect to bankruptcies, a number of 

bankruptcies took place in the truck transportation sub-sector. Since effi cient companies are 

expected to survive and grow over time, and 

ineffi cient companies are less likely to survive, 

bankruptcies in truck transportation would tend 

to result in increased effi ciency (productivity) in 

the industry. It would appear that bankruptcies related, for one, to increased competition from new 

industry entrants, typically with lower costs, that 

followed deregulation in 1980 and 1995. 

 It takes several years for the impacts of deregulation to show in the industry structure and 

performance. The positive impacts of deregulation would include the expansion of effi cient 

fi rms in the industry, the entry of new fi rms that 

would need to be competitive—i.e., effi cient—and 

the exit of ineffi cient fi rms from the industry. It 

appears that the effi ciency of the industry was 

affected positively by the comprehensive deregulation completed in 1995. It would have taken 

several years for industry adjustments to take 

place—through mergers/acquisitions, etc.—that 

would result in increased effi ciency in the trucking 

industry. It would seem that the impact on higher 

effi ciency began to be shown during 2001-2003, 

during which period truck MFP was increasing 

again.

18 Rakowski, 1994.

There were adjustments in the industry after the 

interstate and intrastate deregulation of trucking. 

These two periods of deregulation were probably a shock to the industry, with existing fi rms 

attempting to expand while new fi rms were attempting to enter the industry. One outcome of 

the new entrants in the industry was more competition, which eventually resulted in a number of 

(less effi cient) fi rms leaving the industry. In such 

circumstances, there is typically need for a period 

of time to pass, in order for adjustments to take 

place, before the industry reaches some equilibrium between supply of truck services and demand 

for truck services (the former being affected by 

the number and type of fi rms in the industry). It 

would seem that industry adjustments had taken 

place to a suffi cient degree by 2001, and production effi ciency in the industry subsequently began 

to increase—as shown by an increase in trucking 

MFP.

In conclusion, it appears that changes in the 

structure of the (for-hire) trucking industry, as a 

result of mergers/acquisitions and bankruptcies, 

over 1987-2003, resulted, in general, in increases 

of industry effi ciency. This would have affected 

truck MFP increases, during 1987-1995—after 

interstate deregulation—and truck MFP increases 

during 2001-2003, after intrastate deregulation. 

SECTION VI—CONCLUSIONS 

This study estimates MFP in truck transportation, 

over 1987-2003; compares truck MFP to that of 

other transportation industries and the U.S. private business sector; and assesses the factors that 

affected changes in truck MFP over time.

With regard to estimation, the calculations are 

based on two methodological approaches to estimate MFP: 1) the methodology using the basic 

growth-accounting approach; and 2) the more 

complex Tornqvist index approach. With respect 

to the results, the MFP calculations based on the 

basic growth accounting methodology, and without land, can be divided into three time subperiods: 1987-1995, 1995-2001, and 2001-2003. 
APOSTOLIDES 33

During the fi rst subperiod (1987-1995), truck 

MFP increased; during the second subperiod, truck 

MFP decreased; and during the third subperiod, 

(2001-2003), truck MFP again increased. With 

respect to MFP calculations using the Tornqvist 

methodology, and without land, the MFP growth 

numbers are very similar to, and the trend is the 

same as, those of the basic growth accounting 

methodology. That implies that either method can 

be used to provide appropriate estimates of MFP.

When MFP calculations use the Tornqvist index and include a measurement for the land input (similar to the method of BLS), the results of 

truck MFP are very similar to those noted above 

(obtained by either the basic growth-accounting 

methodology or the Tornqvist index). However, 

the measurement of land was not direct; it was 

related to changes of the stock of structures. In future work of BTS, land will be measured by other 

methods. 

According to the MFP results, truck MFP increased at an annual rate of 0.8% over the whole 

period of analysis. With regard to the subperiods, 

truck MFP increased at an annual rate of 2.0% 

during 1987-1995; it decreased at –0.8% annually 

during 1995-2001; and it increased again during 

the most recent 2001-2003 period, at 1.1% per 

annum.

With regard to MFP comparisons, MFP in 

trucking is compared with that of the U.S. private 

business sector and the air transportation and 

train transportation subsectors. All series cover 

the period 1987 to 1999; so, MFP growth rates 

can be compared over that period. During that period, truck MFP and air MFP increased at similar 

annual rates each, of 1.2% and 1.3% respectively, 

while rail MFP increased at the highest annual 

rate of 3.3%. All three transportation industries 

experienced growth rates of MFP that were higher 

than those of the U.S. business sector of 0.9% per 

annum.

 In addition, during 1987-1995, truck MFP increased at a faster rate, of 2.0% per annum, than 

air MFP, which grew at 1.2% annually. During 

this time period, truck MFP also grew at a substantially higher rate than that of the U.S. economy (of 0.6%). Also, truck MFP reached higher 

levels than air MFP for most years of the period 

of analysis. In 1999, however, this situation was 

reversed and maintained until 2001. 

 Truck MFP was at a higher level than that of 

the U.S. business sector for virtually every year 

over the period of 1987-2002. Consequently, over 

this time period, truck MFP contributed positively 

to increases in multifactor productivity of the U.S. 

economy. Only during 2001 and 2002 did the 

level of truck MFP fall slightly below that of the 

U.S. business sector. 

 With regard to factors affecting truck MFP, 

the analysis of truck MFP was also segmented 

into three periods: 1987-1995, 1995-2001, and 

2001-2003. During the fi rst period—1987 to 

1995—truck MFP increased; during the second 

period—1995 to 2001—truck MFP decreased; 

and during the third period—2002 to 2003—truck 

MFP increased again.

 Table 14 presents the various factors which 

affected truck MFP and indicates the directional 

impact of these factors on truck MFP, by a plus (+) 

or minus (-) sign. For example, a plus sign would 

mean that the factor impacts MFP in a positive 

way during that period, whereas, a minus sign 

impacts the MFP in a negative way. 

 The increase in truck MFP during 1987-1995 

would seem to have been affected by the following factors: 1) The relatively high increase in 

capital per worker, including a rapid increase in 

the use of computers and computer software; this 

would have contributed to improvements in the 

quality of capital; 2) An improvement in the fuel 

effi ciency of truck engines that was a result of improvement in the quality of capital; 3) An increase 

in the average length of haul; 4) An increasing use 

of containers in truck transportation; and 5) The 

positive impacts on industry effi ciency as a result 

of interstate deregulation in 1980, including the 

increase in the TL segment of the industry.
34 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

 In the second subperiod, 1995-2001, there was 

a decrease in truck MFP. This would seem to have 

been affected by the following factors: 1) The declining effi ciency of utilizing intermediate inputs; 

2) A lower growth rate of capital per worker; 3) A 

lower growth rate of utilizing containers; 4) The 

decrease in industry output in 2001, as a result 

of the economic recession that year, and the catastrophic events of 9/11/2001; 5) State deregulation 

of trucking in 1995; this was followed by a period 

of adjustment and uncertainty, which appear to 

have had a negative impact on truck MFP. 

 The increasing MFP during the last subperiod 

(2001-2003) seems to have been affected by the 

following factors: 1) Increased use of computers; 

2) Increases in the effi ciency of using intermediate 

inputs; 3) Increases in the use of containers; and 

4) The adjustment of the industry following intrastate deregulation in 1995, which completed and 

made comprehensive the deregulation of truck 

transportation. 

With regard to the contribution of trucking to 

the economy’s multifactor productivity, data show 

that over 1978-2000, truck MFP increased at a 

higher annual rate than the U.S. business sector. 

Consequently, during this period of time, trucking MFP contributed positively and signifi cantly 

to economy MFP increases. Productivity increases 

in the U.S. economy over time have contributed 

signifi cantly to economic growth and to improvements in the standard of living in the country. 

TABLE 14 Factors Affecting Truck MFP in Three Time Periods

 (Directional Impacts)

Time period

1987-1995 1995-2001 2001-2003

MFP movement + _ +

Factors affecting MFP

1. Increase in capital per worker; improved quality of capital. + _ 0

2. Increased use of: 

 a. computers + + +

 b. software + + _

3. Effi ciency of using intermediate inputs. _ _ +

4. Improved fuel effi ciency.

 a. Single-unit trucks + + _

 b. Combinaton trucks + _ _

5. Average length of haul. + + NA

6. Containerization + _ +

7. Interstate deregulation + + +

8. Intrastate deregulation _ +

9. Mergers/acquisitions +

10. Recesssion\9-11-2001 _

SOURCE: The information is obtained from the analysis presented in the paper
APOSTOLIDES 35

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in the United States. New Jersey: Princeton University Press, 

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APOSTOLIDES 37

APPENDIX A. BASIC GROWTH-ACCOUNTING METHODOLOGY

The empirical relationship used to estimate growth of multifactor productivity by the basic growthaccounting methodology is shown below: 

ΔT = ΔQ – [(α∗ ΔLabor) + (β∗ ΔCapital) + ( γ ∗ ΔIntermediate Inputs )]

 T Q Labor Capital Intermediate Inputs 

Where:

ΔT = Growth of MFP

 T

ΔQ = Growth of gross output

 Q

ΔLabor = Growth of labor

 Labor

ΔCapital = Growth of capital

 Capital

ΔIntermediate Inputs = Growth of intermediate inputs

 Intermediate Inputs

α = Share of labor cost in output

β = Share of capital cost in output

γ = Share of intermediate inputs cost in output.
38 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

APPENDIX B. METHODOLOGY WITH THE TORNQVIST INDEX

Multifactor productivity is the ratio of the output index to a weighted average of input indexes. A 

Tornqvist formula expresses the change in multifactor productivity as the difference between the rate of 

change in output and the weighted average of the rates of change in various inputs. 

Let

Ln = the natural logarithm of a variable

A = multifactor productivity

Q = output

I = combined input

K = capital input

L = labor input

M = intermediate input

Wk = the average share of capital cost in total cost in two adjacent periods

Wl = the average share of labor cost in total cost in two adjacent periods

 Wm = the average share of intermediate input cost in total cost in two adjacent periods,

The change in the multifactor productivity is then:

 (1) 

Or

 (2) 

A multifactor productivity index can be further developed by calculating the antilog of ΔLnA, chaining up the resulting annual rates of change, and expressing the resulting series as a percentage of a 

selected base year. Equivalently, the change in the multifactor productivity can be directly expressed as

At/At-1 = (Qt/Qt-1) / (It/It-1). Again, At/At-1 can be chained over time and converted into an 

index number.

 All variables, except for cost shares, are in the form of a constant dollar quantity index. The output 

quantity index is usually derived by defl ating the industry output in current dollars by an appropriate 

price index when the industry output is a single measure. When an industry produces multiple products 

and the output measure of each individual product is available, such individual outputs may be defl ated 

separately by more detailed price indexes. In that case, the total output quantity index can be derived 

through a Tornqvist aggregation such as: 

 (3) , 

where Qi is the output of the ith product, and

 wi is the average share of the ith product in the total output.

∆LnA= Ln( )= Ln( )- [Wk(Ln )+Wl(Ln )+Wm(Ln )] At

At-1 Qt-1

Qt Kt

Kt-1

Lt

Lt-1

Mt

Mt-1

∆LnA= Ln( )= Ln( )- Ln( ) At

At-1 Qt-1

Qt It

It-1

∑ Δ n

LnQw ii 1
APOSTOLIDES 39

APPENDIX C DATA ON GROSS OUPUT AND INTERMEDIATE INPUTS FOR TRUCK 

TRANSPORTATION, FROM THE GDP BY-INDUSTRY ACCOUNTS AND THE INPUT - OUTPUT 

ACCOUNTS.

GDP BY INDUSTRY ACCOUNTS

Year Gross output ($ millions) Intermediate inputs ($ millions)

1998 $184,085 $97,889 

1999 $198,687 $108,849 

2000 $213,173 $120,352 

2001 $205,674 $112,359 

2002 $205,587 $109,936 

2003 $204,278 $105,917 

INPUT-OUTPUT ACCOUNTS

Year

Gross output ($ millions) - 

producers’ prices

Intermediate inputs ($ millions) - 

producers’ prices

1998 $184,085 $97,889 

1999 $198,687 $108,849 

2000 $213,173 $120,352 

2001 $205,674 $112,359 

2002 $205,587 $109,936 

2003 $204,278 $105,917 

NOTE: The number for I-O gross output, for 2002, came from the annual I-O data. 

SOURCES: BEA websites. For GDP-by-Industry data: http://www.bea.gov/industry/gdpbyind_data.htm. 

For Input-Output data: http://www.bea.gov/industry/io_annual.htm.
40 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

APPENDIX D COST SHARES OF THE FACTOR INPUTS IN TRUCKING

(basic growth accounting, without land)

Year

Labor weight: share of labor 

costs in industry output

‘(1)

Capital weight (residual)

‘(2)

Weight of 

intermediate inputs

‘(3)

Total 

[(1)+(2)+(3)]

‘(4)

1987 0.414 0.118 0.468 1.00

1988 0.353 0.163 0.484 1.00

1989 0.337 0.169 0.494 1.00

1990 0.317 0.154 0.529 1.00

1991 0.322 0.164 0.517 1.00

1992 0.313 0.158 0.528 1.00

1993 0.314 0.164 0.518 1.00

1994 0.307 0.175 0.518 1.00

1995 0.309 0.162 0.526 1.00

1996 0.302 0.157 0.541 1.00

1997 0.299 0.164 0.534 1.00

1998 0.292 0.177 0.532 1.00

1999 0.286 0.167 0.548 1.00

2000 0.283 0.152 0.565 1.00

2001 0.296 0.158 0.546 1.00

2002 0.293 0.174 0.533 1.00

2003 0.309 0.185 0.506 1.00

SOURCES: Data on labor and intermediate purchases: BEA, Annual Industry Accounts: http://www.bea.doc.gov/bea/dn2/gdpbyind_data.htm. 

NOTE: The share of capital was calculated as a residual.
APOSTOLIDES 41

Appendix E BEA LABOR DATA FOR TRUCK TRANSPORTATION—NAICS AND SIC (including self-employed truckers)

Extrapolating NAICS data from SIC data

NAICS (BEA internet site) SIC (87) (BEA internet site)

Year

Persons 

engaged in 

production 

(thousands)

‘(1)

Full-time 

equivalent 

employees 

(thousands)

‘(2)

Compensation 

of FTE 

employees 

($ millions)

‘(3)

Compensation

 of FTE 

employees 

($ billions)

‘(4)

Persons 

engaged in 

production

(thousands)

‘(5)

Full-time 

equivalents 

(thousands)

‘(6)

Labor 

compenation, FTEs 

($ millions)

‘(7)

Persons 

engaged in 

production 

(thousands) 

(NAICS)

‘(8)

Full-time 

equivalent 

employees 

(thousands) 

(NAICS)

‘(9)

Compensation 

of FTE 

employees 

($ millions) 

(NAICS)

‘(10)

Compensation 

of FTE 

employees 

($ billions) 

(NAICS)

‘(11)

Compensation 

of FTE 

employees 

($ thousands) 

(NAICS)

‘(12)

Compensaton

 per FTE 

employee 

(NAICS)

‘(13)

1987 1,671 1,426 $42,806 1,282 1,077 $34,146 $34.1 $34,145,674 $31,691 1988 1,544 1,308 $41,138 1,184 988 $32,815 $32.8 $32,815,137 $33,204 1989 1,546 1,335 $42,699 1,186 1,009 $34,060 $34.1 $34,060,322 $33,767 1990 1,498 1,311 $44,302 1,149 991 $35,339 $35.3 $35,339,010 $35,676 1991 1,503 1,296 $45,203 1,153 979 $36,058 $36.1 $36,057,723 $36,823 1992 1,523 1,298 $47,572 1,168 981 $37,947 $37.9 $37,947,438 $38,693 1993 1,600 1,371 $50,368 1,227 1,036 $40,178 $40.2 $40,177,763 $38,786 1994 1,705 1,448 $54,608 1,308 1,094 $43,560 $43.6 $43,559,944 $39,815 1995 1,766 1,505 $57,376 1,355 1,137 $45,768 $45.8 $45,767,934 $40,249 1996 1,844 1,542 $59,348 1,415 1,165 $47,341 $47.3 $47,340,968 $40,633 1997 1,869 1,586 $62,963 1,434 1,198 $50,225 $50.2 $50,224,597 $41,912 1998 1,491 1,247 $53,663 $53.7 1,944 1,650 $67,221 1,491 1,247 $53,621 $53,621,136 $43,011 1999 1,536 1,296 $56,780 $56.8 2,009 1,715 $71,687 1,541 1,296 $57,184 $57,183,594 $44,130 2000 1,565 1,323 $60,281 $60.3 2,040 1,751 $75,570 1,565 1,323 $60,281 $60,281,003 $45,564 2001 1,572 1,341 $60,867 $60.9 2002 1,546 1,297 $59,866 $59.9 2003 1,517 1,283 $60,789 $60.8 

SOURCES: Labor data under NAICS were obtained from table 6.5D at the BEA internet site: http://www.bea.gov/bea/dn/nipaweb/TableView.asp?SelectedTable=186&FirstYear=2002&last year=2003&Freq=Year Labor data under SIC (1987) were obtained from table 6.5C (full-time equivalent employees) at the BEA internet site: http://www.bea.gov/bea/dn/nipaweb/TableViewasp?SelectedTable=185&FirstYear=1999&last year=2000&Freq=YearNOTE: Data refl ect the reclassifi cation of air couriers, from trucking and warehousing to transportation by air.
42 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

APPENDIX F GROWTH OF TRUCKING MFP, ESTIMATED BY THE TWO METHODS 

 (without land)

Percentage Rate of Change

Year

Basic growth accounting methodology

‘(1)

Tornqvist methodology

‘(2)

Difference [(1)-(2)]

‘(3)

1988 7.7 8.6 (0.9)

1989 1.0 1.0 (0.0)

1990 0.6 1.0 (0.4)

1991 4.1 4.2 (0.1)

1992 3.8 3.9 (0.1)

1993 0.7 0.7 0.0 

1994 0.6 0.5 0.1 

1995 (2.3) -2.2 (0.1)

1996 0.2 0.2 (0.0)

1997 (0.2) -0.2 (0.0)

1998 (0.4) -0.4 (0.1)

1999 (1.3) -1.2 (0.2)

2000 (0.6) -0.5 (0.0)

2001 (2.5) -2.4 (0.0)

2002 1.4 1.4 (0.0)

2003 0.4 0.7 (0.3)

NOTES AND SOURCES: The MFP numbers in column 1 are from Table 2, and are based on the basic growth-accounting methodology, 

without land. The MFP estimates in column 2 are from Table 3, and are based on the Tornqvist methodology, without land.
APOSTOLIDES 43

APPENDIX G CAPITAL INPUT INDEX

Indexes (2000=100) Cost shares (%)

Year

Structures and 

equipment index

‘(1)

Land index

‘(2)

Structures and 

equipment cost share

‘(3)

Land cost 

share

‘(4)

Combined capital 

input index

‘(5)

1987 65.31 33.36 0.974 0.026 63.64

1988 67.34 34.10 0.975 0.025 65.61

1989 67.80 35.28 0.973 0.027 66.09

1990 65.19 37.10 0.971 0.029 63.72

1991 62.69 39.57 0.968 0.032 61.46

1992 59.84 41.84 0.965 0.035 58.87

1993 61.51 43.60 0.965 0.035 60.54

1994 69.66 47.33 0.966 0.034 68.46

1995 78.75 54.78 0.966 0.034 77.46

1996 82.45 60.22 0.963 0.037 81.24

1997 90.11 70.02 0.958 0.042 89.00

1998 94.58 80.72 0.953 0.047 93.80

1999 97.67 90.68 0.948 0.052 97.28

2000 100.00 100.00 0.942 0.058 100.00

2001 97.84 106.69 0.934 0.066 98.36

2002 95.18 109.54 0.930 0.070 96.04

2003 93.63 111.68 0.927 0.073 94.72

SOURCES: Calculations are by consultant. For data on structures and equipment: BEA Fixed Asets Accounts at: http://www.bea.gov/bea/

dn/FA2004/Detail/Index.html. Other data were calculated by BTS (consultants); the calcuations are described in the text - section “Calculations with the Tornqvist Index” and “MFP Calculations with Land”.

NOTE: Column 5 is the result of combining the indexes for Structures/Equipment and Land (columns 1 and 2) by using their cost shares 

(columns 3 and 4), through a Tornqvist aggregation.
44 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

APPENDIX H – ESTIMATION OF LAND COST 

With the assumptions provided in the text, the estimation of the land cost is as follows:

 1) Calculate ratio (a) by using data for Structures value, divided by Structures & Equipment 

value (BEA data).

 2) Calculate ratio (b) (Structures cost/Structures + Equipment cost) by assuming that it is equivalent to ratio (a). Structures & Equipment cost is the gross operating surplus of the industry.

 3) Calculate ratio (c) by using the ratio of Land value/Structures value (Manvel study, 1966) and 

data for Structures value (BEA). Non-availability of structures (value) data (from BEA) for the 

earlier years of analysis led to the extrapolation of NAICS structures stock (value) data by using 

SIC structures stock (value) data.

This calculation provides Land value for a specifi c year.

 4) For ratio (d), use the cost of structures net of depreciation. Land is not depreciated. By using the estimate for structures cost and ratio (d), calculate Land cost. This number is used for the 

weight of land in the calculations.
APOSTOLIDES 45

APPENDIX I COST SHARES OF THE FACTOR INPUTS IN

 TRUCKING (percentage)

Year

Intermediate inputs

‘(1)

Capital

‘(2)

Labor

‘(3)

Total 

[(1)+(2)+(3)]

‘(4)

1987 0.474 0.188 0.339 1.000

1988 0.490 0.181 0.329 1.000

1989 0.499 0.183 0.317 1.000

1990 0.534 0.165 0.301 1.000

1991 0.522 0.174 0.304 1.000

1992 0.534 0.169 0.296 1.000

1993 0.524 0.179 0.297 1.000

1994 0.524 0.180 0.296 1.000

1995 0.532 0.170 0.298 1.000

1996 0.547 0.164 0.289 1.000

1997 0.540 0.172 0.288 1.000

1998 0.537 0.168 0.294 1.000

1999 0.553 0.158 0.289 1.000

2000 0.570 0.145 0.285 1.000

2001 0.552 0.149 0.299 1.000

2002 0.538 0.165 0.297 1.000

2003 0.512 0.176 0.312 1.000

SOURCES: Calculations are by consultants. The calculations are explained in the text, in 

section “Weights of Inputs”. Data for output, labor, and intermediate inputs were obtained 

from BEA Industry Accounts at: http://www.bea.gov/bea/dn2/gdpbyind_data.htm.

Data for fi xed assets, from Fixed Asset Accounts at: http://www.bea.gov/bea/dn/FA2004/

Details/Index.html.

NOTE: The weights of the three inputs indicate their relative share in total industry costs. 

They add up to 1.00 (total industry costs).
46 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

APPENDIX J U.S. WATERBORNE FOREIGN TRADE—CONTAINERIZED CARGO

Year Containers - TEUs Metric tons Time period

Growth of containers - 

TEU’s (%)

Growth of metric tons 

(%)

1998 15,556,255 128,363,847

1999 16,563,789 135,903,002

2000 17,937,670 144,825,512

2001 18,116,582 144,573,085 1998-2001 5.2 4.0

2002 19,729,422 144,573,085

2003 21,288,545 154,266,192 2001-2003 8.4 3.3

1998-2003 6.5 3.7

SOURCE: U.S. Department of Transportation, Maritime Administration, Containerized Statistics, on the web: http://www.marad.dot.gov/

MARAD_statistics/index.html.

NOTE: Container capacity is measured in twenty-foot equvalent units (TEU or teu). That is equal to one standard 20 ft. (length) x 8 ft. 

(width) x 8’6’’ ft.(height) container.
APOSTOLIDES 47

APPENDIX K USE OF CONTAINERS IN TRUCK TRANSPORTATION 

(border crossing data)

Year

Loaded truck containers 

(thousands) Time period Growth - percentage 

1996 5,935

1997 3,709

1998 6,223

1999 7,641

2000 7,685

2001 6,110 1996-2001 0.6

2002 8,341

2003 8,273 2001-2003 16.4

1996-2003 4.9

SOURCE: Bureau of Transportation Statistics website: www.transtats.bts.gov/Fields/asp?Table_ID=1358.
48 ESTIMATION AND ANALYSIS OF MULTIFACTOR PRODUCTIVITY IN TRUCK TRANSPORTATION: 1987-2003

APPENDIX L SHIPMENTS BY TRUCKLOAD AND LESSTHAN-TRUCKLOAD FIRMS

(millions of dollars)

Year Less-than-truckload Truckload Total

LTL/total 

(percent)

1989 41,740 65,905 107,645 0.39

1990 45,710 71,412 117,122 0.39

1991 46,626 71,106 117,732 0.40

1992 49,119 77,930 127,049 0.39

1993 51,604 83,396 135,000 0.38

1998 40,298 99,424 139,722 0.29

1999 41,468 104,780 146,248 0.28

2000 44,318 110,086 154,404 0.29

2001 44,207 108,411 152,618 0.29

2002 45,117 108,757 153,874 0.29

2003 45,613 112,724 158,337 0.29

SOURCE: Data for 1989-1993, from U.S. Census Bureau, Transportation Annual Survey, 

1998, Table 4, pl 11. Data for 1998-2003, from U.S. Census Bureau, website: http://www.

census.gov/svsd/www/ services/sas/sas_data/48/sas48_rpt_2004/pdf