1. Overview

1.1 Overview

The Freight Benefit/Cost Study was a multi-year effort originating in the Federal Highway Administration, Office of Freight Management and Operations, supported by HLB Decision Economics (Subsequently HDR|HLB Decision Economics) and ICF Consulting. The project started as an assessment of previously unaccounted for benefits of infrastructural investment, and it has become a permanent tool for future benefit assessments.

1.1.1 Background

The Freight Benefit/Cost Study project went through three phases of development. The first phase focused on developing the theory and logic. The second phase determined the sensitivity of a firm to infrastructural investment on a national level. The third phase determined sensitivities to infrastructural investment on a regional level and constructed a tool for state and local entities to estimate additional benefits derived though logistics rearrangements from highway performance improvements.

1.1.2 Phase I – Methodological Overview

Phase I constructed the basic economic concepts. In previous attempts by other research papers to capture the benefits of infrastructure investment, long-term benefits had not been calculated primarily because the logistics-exclusive firm had yet to appear as a commonly used method of reducing costs. However, since their adoption as a major service, businesses with efficient economies have seen their costs drop down to 9.5% from the previous 30%. In the new logistics framework, logistics firms are the principle recipients of the benefits from infrastructure work, but their benefits had not been measured. This drop directly relates to the cost of logistics. The lower price for logistics then causes more logistics to be purchased. Hence, this new gap from the old price and consumption is the overall amount of benefit that is netted when highways investments are made.

1.1.3 Phase II – National Estimation of Benefits

Phase II developed an estimate of the relationship between investment and benefits. Phase II was meant to establish the long-term benefits of highway-freight improvements by examining the interaction between freight transportation demand, transportation costs, and the condition of the nation's highway system. These interactions were assessed beyond the traditional travel-time savings model. The method adopted in Phase II allowed the quantification of the effects of transportation system reorganization in relation to the:

• immediate cost reduction to carriers and shippers
• the impact of improved logistics while keeping output fixed
• increased demand
• new products
• improved products

1.1.4 Phase III – Regional Estimation of Benefits

Phase III determined the relationship among various sectors and added an interface for practitioners to use. While Phase II provided acceptable results for the national level, Phase III determined the inputs needed for custom use at the regional level. Once the factors for the inputs were determined, the final half of Phase III constructed the Highway Freight Logistics Reorganization Benefits Estimation Tool.

1.2 Economic Logic

1.2.1 Benefits

In economics, every action has a series of consequences that fall into the category of either cost or benefit. Traditionally, an action is undertaken by an individual or firm because there is some form of benefit. The benefit can be as simple as personal fulfillment or as complex as fourth-tier reactions, for example. There are relatively consistent rules regarding what is included in the scope of a Benefit Cost Analysis (BCA).

1.2.2 Traditional Methodology

Traditionally, only first-order reactions to infrastructural investment have been taken into account. For example, when infrastructure is improved, conventional CBAs largely considers the benefits in travel time savings, reduced fuel costs, decreased accident rates, lower environmental impacts, and reduced cost of shipping. This reduced cost of shipping is a first step in a multi-phased series of benefits that occur as firms shift expenditures away from maintaining stock on hand and toward other more productive uses. The Highway Freight Logistics Reorganization Benefits Estimation Tool seeks to measure these second order benefits.

Effects of Improved Freight Transportation

First-order benefits	Immediate cost reductions to carriers and shippers, including gains to shippers from reduced transit times[1] and increased reliability.
Second-order benefits	Reorganization-effect gains from improvements in logistics.[2] Quantity and quality of firms' outputs do not change.
Third-order benefits	Gains from additional reorganization effects due to changes in product quality or changes in demand for products and output from broader access to suppliers and markets.
Other effects	Effects that are not considered as benefits according to the strict rules of benefit-cost analysis, but may still be of considerable interest to policy makers. These could include increases in regional employment, or growth rate of regional income.

1.2.3 Conceptualizing Logistics Reorganization Benefits

Logistics has become one of the largest businesses in the world with firms in every sector doing at least some business with logistics providers. Since the inception of these firms, and the growing need for immediacy to deliver lower inventory costs, the benefit from the reduction in logistics costs is more readily capitalized upon by logistics firms. Typically, these savings are passed on to producers who pass them on to consumers and reinvest in their own logistics needs. Logistics includes warehouses, trucks, fuel for trucks, teamsters, drivers, and other things that require a large amount of capital. When it becomes easier to ship via highway across country, fewer warehouses need to be built or maintained. Furthermore, the carrying cost of maintaining production input stock on hand is reduced, as firms can better rely on the shipping system to deliver goods when needed. This is a benefit that the Highway Freight Logistics Reorganization Benefits Estimation Tool seeks to measure.

Figure 1 gives a visual representation of the effects of infrastructure investment. In the very short run, shippers and carriers have a few degrees of freedom in responding to transportation network changes; delivery schedules and routings can be changed, but origins and destinations are fixed. In the longer run, truck-fleet characteristics can be changed while, over the long term, the number, sizes, and locations of factories and warehouses can all be changed.

Figure 1. Direct Very Short Run Benefits of Road Improvements with Marginal-cost Tolls

Suppose that an additional lane in each direction materializes on the freeway connecting Here and There. On this day, the relevant demand schedule is the vertical line in Figure 1. Surprised users discover that congestion has diminished sharply and that the full prices of trips—congestion tolls plus travel times—have fallen fromOP₁ toOP₂. However, too little time has elapsed for them to adjust their travel behavior to take advantage of this change.

As news of the expressway improvement spreads, price and output levels change in a number of related markets. Increased speeds and reduced prices on the expressway induce additional use that results in increased congestion and tolls. The increased accessibility that the improvement affords may increase the values of neighboring residential, commercial, and industrial sites. The improvement's lower transportation costs apply to goods shipments and person trips. As a result, the delivered price of goods produced "Here" and sold "There" fall. Faster and more reliable travel may induce cost-saving changes in the production, distribution, and inventory practices of Here and There business firms.

1.3 Phase Objectives, Methodology, and Conclusions

1.3.1 Phase I

Phase I developed the theory upon which other phases were constructed. At its conclusion, a White Paper[3] addressed various methodologies that had been previously employed and contrasted these with a proposed new method. The primary objective of Phase I was to ensure that the BCA framework recognized the gains in economic welfare (efficiency) that follow from transportation infrastructure improvements.

The framework employed looks at performance improvements as a sort of subsidy on logistics. The infrastructure input functioned as a reduction to the logistics cost that would later return to the firm. Price sensitivity was a determinant of how much returned to the firm, with less price sensitivity allowing more to return to the firm since there would not be a great benefit to lowering the price. Phase II applied the framework developed under Phase I to determine the data required and construct the benefit estimates.

The Phase I study arrived at the following conclusions:

• First, in a world where marginal-cost prices prevail in all markets including those for transportation facilities, only information on the use made of improved transport facilities is necessary to completely measure the benefits it yields to its users and provider.
• Second, in the absence of marginal-cost pricing on roads and other governmentally provided facilities, benefit measurement becomes much more difficult.
• Third, determining the full benefit of an improvement requires analyzing its use and the use of other facilities that the improvement affects.

Lastly, if elements of a monopoly exist in the markets that the road users participate, sellers' transportation demand schedules understate total benefits. In the simple model used to illustrate the phenomenon, a monopoly hid about one-third of the benefits.

1.3.2 Phase II

The purpose of Phase II was to determine the elasticities (price sensitivities) that exist for highway freight on a national level. The same sensitivities were determined on a more specific, regional basis under Phase IIIA.

Freight demand can be expressed as a function of the demand for goods and services, level of economic activity, general trends, freight charges, and congestion levels. Freight charges, in turn, are a function of freight demand, distance, congestion levels, and factor costs, including fuel and labor costs.

Two types of statistical analyses were undertaken to investigate the effects of highway performance on the demand for trucking and on trucking rates. One was a cross-sectional analysis based on data across 30 corridors for 1998, and the second was a cross-sectional and time-series (panel data) analysis that builds on the cross-sectional database by including historic data on all relevant variables.

The results of the cross-sectional analysis were not usable for the trucking-demand and the trucking-rate equations as they demonstrated that highway performance variables, such as delay and V/C ratio, are positively correlated with freight demand. Numerous alternatives for structuring the analysis were tested, but none provided results that could be used. Three sets of analyses were then conducted based on different approaches to defining corridors. Nevertheless, in every case, the statistical relationships between highway performance and the demand for trucking/trucking rates that the study sought to define were counter-intuitive or insignificant.

However, in the case of the trucking-demand equation, the panel data yielded useful results. The estimated coefficients showed that lowering the volume/capacity ratio would lead to an increase in truck miles; it showed the same result when the volume/capacity ratio was converted to a measure of delay (i.e., as delay goes down, demand for highway freight goes up).

Phase II concluded that the formula's determinations could be replicated at a smaller scope for use at the project level and be incorporated into a tool.

1.3.3 Phase III

Given the results for the national analysis of the reorganization impacts of highway performance improvements, a similar methodology was employed to develop regionalized sensitivities (elasticities). The corridors included in the national analysis were tested to indicate the robustness of results when segregated into regions of various sizes and constitutions. This analysis indicated that the most reliable results could be obtained using a three-region approach consisting of East, Central, and West.

The overall goal of the analysis was to develop regional data points required to estimate additive freight benefits reflecting the added value of specific highway performance improvement efforts at a regional level. In order to develop estimates of the additional reorganization benefit, the methodology required that two types of elasticities be estimated for each region: 1) performance elasticity and 2) price elasticity.

A panel of data on corridor performance, demand for freight movement, freight prices, and regional economic activity was then constructed for these regions. Regression analysis was applied to this panel to develop estimates of performance elasticity of demand. The study successfully estimated the elasticities of demand with respect to performance for each of the three regions using data on performance, volume, and other data for 59 corridors in total. See Table 1.

Table 1. Corridors Included in the Regional Analysis

East Region – 18 corridors		Central Region – 18 corridors		West Region – 23 corridors
Atlanta-Jacksonville	ATL-JAX	Amarillo-Oklahoma City	AMA-OKL	Barstow-Amarillo	BAR-AMA
Atlanta-Knoxville	ATL-KNX	Billings-Sioux Falls	BIL-SIO	Barstow-Bakersfield	BAR-BAK
Atlanta-Mobile	ATL-MOB	Chicago-Cleveland	CHI-CLE	Barstow-Salt Lake City	BAR-SAL
Birmingham-Nashville	BGH-NSH	Cleveland-Columbus	CLE-COL	Dallas-El Paso	DAL-ELP
Birmingham-Chattanooga	BIR-CHA	Dayton-Detroit	DAY-DET	Dallas-Houston	DAL-HOU
Detroit-Pittsburgh	DET-PIT	Indianapolis-Chicago	IND-CHI	Denver-Kansas City	DEN-KAN
Harrisburg-Philadelphia	HAR-PHI	Indianapolis-Columbus OH	IND-COL	Denver-Salt Lake City	DEN-SAL
Knoxville-Harrisburg	KNX-HAR	Kansas City-St Louis	KNC-STL	Galveston-Dallas	GAL-DAL
Miami-Atlanta	MIA-ATL	Knoxville-Dayton	KNX-DAY	Laredo-San Antonio	LAR-SAN
Miami-Richmond	MIA-RIC	Louisville-Columbus	COL-LOU	Los Angeles-Tucson	LAX-TUC
Mobile-New Orleans	MOB-NOR	Louisville-Indianapolis	IND-LOU	Nogales-Tucson	NOG-TUC
New Orleans-Birmingham	NOR-BIR	Memphis-Dallas	MEM-DAL	Portland-Salt Lake City	POR-SAL
Boston-New York City	NYC-BOS	Memphis-Oklahoma City	MEM-OKL	Portland-Seattle	POR-SEA
New York City-Cleveland	NYC-CLE	Nashville-Louisville	NSH-LOU	San Antonio-Dallas	SAN-DAL
Harrisburg-New York City	NYC-HAR	Nashville-St Louis	NSH-STL	San Diego-Los Angeles	SDG-LAX
Philadelphia-New York City	PHI-NYC	Omaha-Chicago	OMA-CHI	San Francisco-Los Angeles	SFO-LAX
Columbus-Pittsburgh	PIT-COL	St Louis-Oklahoma City	STL-OKL	San Francisco-Portland	SFO-POR
Richmond-Philadelphia	RIC-PHI	St Louis-Indianapolis	STL-IND	San Francisco-Salt Lake City	SFO-SAL
				San Antonio-Houston	SAN-HOU
				Seattle-Billings	SEA-BIL
				Seattle-Blaine	SEA-BLA
				Seattle-Sioux Falls	SEA-SIO
				Tucson-San Antonio	TUC-SAN

The panel data analysis on these corridors indicated that demand for shipping services varied with the expected speed of delivery and that differing levels of variance can be expected in different geographic areas. As highway performance improves, demand for freight movement increases in each region. The impact of improvement is strongest in the Central region. This is possibly due to the greater likelihood of highway freight being chosen over other alternatives (such as the air or water modes) to transport goods between coasts as the cost of shipping declines.

From the regional estimations, the study developed the underlying network of assessment for smaller project use. Using the smaller regional data, the models were custom fitted with relevant data to construct new assessments of future infrastructural improvements. In addition, data on commodities was included as an influencing variable.

Phase III resulted in the construction of a Microsoft Excel©-based tool that can be used to estimate additive freight benefits resulting from highway performance improving investments. These benefits can be summed with the conventional benefits expressed through CBA to present a more complete picture of the return on highway investment.

The next section describes the Highway Freight Logistics Reorganization Benefits Estimation Tool, its use, and data requirements.

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