Executive Summary
The Freight Benefit/Cost Study is 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 International.[1] The Freight Benefit/Cost Study project has gone through three phases of development. Phase I focused on developing the theory and logic. Phase II determined the sensitivity of a firm to infrastructural investment on a national level. This phase, (Phase III) establishes the approach, sensitivities and data inputs required to calculate long-term benefits of highway-freight infrastructural investment on a regional level and will investigate the construction of a tool for state and local entities to estimate additional benefits derived though logistics rearrangements from highway performance improvements.
This Phase III report assesses impacts of improvements beyond traditional travel time savings within the conventional benefit cost analysis framework. That is, the methodology adopted allows for the quantification of the effects of transportation system improvements in relation to (1) immediate cost reduction to carriers and shippers, (2) the impact of improved logistics while keeping output fixed, and (3) additional gains from reorganization such as increased demand and new or improved products.
Methodology
Given results for a national analysis of the reorganization impacts of highway performance improvements from Phase II of the Freight Benefit/Cost Study, this study applies similar methodology to that utilized previously. The previously examined corridors 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.
A panel of 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 in order to develop estimates of performance elasticity of demand.
The following equation was used to develop separate estimates for each region (East, Central, and West) when demand for daily truck traffic is specified as a function of delay and real per capita income growth:
Log(AADTTc,t) = βc + β1Delayc,t + β2Incomec,t
where:
AADTT = Average annual daily truck traffic
Delay = Average delay per mile
Income = Real per capita income growth
t = 1993, 1994,… 2003
c = Corridor
βc
are corridor-specific constant, or fixed effects, where:
c = 1,…, 16 for East,
c = 1,…, 18 for Central,
c = 1,…, 21 for West
| 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 | ||||
Data
Data on heavy-duty vehicle traffic volumes, freight rates, and commodity flows were collected from several different sources, including performance and volume data from the Highway Performance Monitoring System (HPMS), commodity data from the Freight Analysis Framework (FAF), and regional economic activity data from the Bureau of Labor Statistics and the Bureau of Economic Analysis. Data on 30 corridors collected for the national study formed the core of the database constructed for the regional analysis. To these, data for 29 additional corridors were added. The original dataset was also improved by adding an additional three years of observations. In total, 55 corridors were included in the regional analysis with 381 combined observations ranging from 1992 to 2003.
Summary of Empirical Findings
The overall goal of the analysis is to develop regional data points required to estimate additive freight reorganization benefits reflecting the added value of specific highway performance improvement efforts. In order to develop estimates of the additional reorganization benefit, the methodology requires that two types of elasticities be estimated for each region:
- Elasticity of Demand with respect to performance
- Elasticity of Demand with respect to price
The study successfully estimated elasticities of demand with respect to performance for each of the three regions. The elasticities were developed applying a multiple regression approach to an unbalanced panel of performance, volume, and other data for the 55 corridors.
| Region | Coefficient on Delay | Implied Elasticity | Interpretation |
|---|---|---|---|
| East | -0.005117 | -0.0076 | Other things being equal, a 10% increase in delay per mile reduces freight demand by 0.076%. |
| Central | -0.069076 | -0.0175 | Other things being equal, a 10% increase in delay per mile reduces freight demand by 0.175%. |
| West | -0.015586 | -0.0070 | Other things being equal, a 10% increase in delay per mile reduces freight demand by 0.07%. |
Due to lack of data regarding freight rates, however, significant difficulties estimating the price elasticity of demand were encountered at the national level during the previous study. These inputs were developed using a review of the existing literature. A similar approach was applied to developing regional price elasticities. Table ES-3 shows the price elasticities applied to the regional additive freight reorganization benefit estimation.
| U.S. | Regional Differences When Compared to the National Level (-0.97 = 100%) | Regional Estimate of Elasticity of Demand with Regard to Price | ||
|---|---|---|---|---|
| East | 115.3% | East | -1.12 | |
| -0.97 | Central | 99.6% | Central | -0.97 |
| West | 86.9% | West | -0.84 | |
Additive Freight Benefit Factors
The study estimates total benefits associated with highway investment by establishing a relationship between elasticity of demand with respect to highway performance, elasticity of demand with respect to price, and a set of other region-specific variables. The intent of this work is to establish the approach and basic input data required to develop a tool to establish corridor specific additive reorganization benefit factors in a subsequent task.
The calculation indicates the additional benefit related to reorganizing logistics that may accrue from an estimated performance improvement to be used in benefit-cost analyses (BCAs) that do not independently account for the value of improved freight management.
Table ES-4 provides the implied elasticity of demand with respect to performance by region and the typical additive benefit factors calculated for the corridors represented in the sample used in this study. A subsequent task will involve the development of a calculation tool that can be used to estimate a reorganization benefit specific to the AADTT, performance improvement, and other characteristics of the corridor being assessed.
| East | Central | West | |
|---|---|---|---|
| Implied Elasticity | -0.0076 | -0.0175 | -0.0070 |
| Additive Benefit Factor | 16.7% | 14.8% | 12.7% |
Regional Commodity Characteristics
A regional analysis of FAF commodity flow data was done. Data available included top commodities by volume (weight) and by value. The data describes regional mixes of similar freight movement. Except for the Central region, finished goods were not significant contributors to freight volume. In the Central region, machinery and motorized vehicles were the ninth and tenth largest categories of shipment by volume. This greater than average volume of finished and semi-finished goods in the Central region may explain the higher than average elasticity of demand with respect to highway performance in the region.
Conclusions and Recommendations
This study examines the implications of monetizing the impact of logistical reorganization into the conventional benefit-cost analysis approach at a regional level. As it has been stated previously, by improving the reliability and predictability of transit times, highway capacity investments have a material impact on the business case for firms to invest in advanced production, distribution, and customer service logistics. These logistical technologies and business processes enable firms to operate with greater productivity, thereby enhancing their competitiveness, profitability, and shareholder value. Productivity growth throughout the economy generates improved personal incomes and living standards. Productivity growth is widely regarded to be the single most important means of improving the living standards in the United States. Yet conventional BCA does not account for the value of productivity improvements generated by the adoption of advanced logistics. As a result, the conventional framework understates the economic value of capital investment in highway infrastructure.
This study examines the quantitative significance of this shortcoming in the conventional BCA framework at a regional level. The study finds that the conventional framework underestimates the economic benefits of highway investment by 13–17 percent, depending on the region.
It is recommended that this result and the associated calculations be made available to practitioners in a tool designed to simply elicit project and area specific information and return expected freight logistics benefits achievable through performance improving projects.
- FHWA Freight Benefit/Cost Study Reports are available at http://www.ops.fhwa.dot.gov/freight/
