Transportation Services Index Frequently Asked Questions
For complete methodology, see Documentation for the Transportation Services Index.
- What is the Transportation Services Index (TSI)?
- What does the index tell us?
- What areas of transportation are covered by the index?
- Why was the index developed?
- What does the index provide that other transportation data do not?
- How was the index put together?
- What are some limitations to the index?
- What are some areas that the index does not cover?
- Why aren't passenger automobiles, private or in-house trucking, and so forth. covered in the TSI?
What is the Transportation Services Index (TSI)?
The Transportation Services Index (TSI) is a monthly measure of the volume of services performed by the for-hire transportation sector. The index covers the activities of for-hire freight carriers, for-hire passenger carriers, and a combination of the two.
What does the index tell us?
The TSI shows the change in the output of transportation services from one month to the another month. The index can be examined with other economic indicators to provide a better understanding of the current and future course of the economy. The movement of the index over time can be compared with other economic measures to understand the relationship of transportation to long-term changes in the economy.
What areas of transportation are covered by the index?
The freight transportation index consists of for-hire:
- trucking (parcel services are not included),
- freight railroad services (carloads and intermodal units),
- inland waterway traffic,
- pipeline movements (petroleum and natural gas), and
- air freight.
The index does not include international or coastal steamship movements, private trucking, courier services, or the United States Postal Service.
The passenger transportation index consists of for-hire:
- local mass transit,
- intercity passenger rail, and
- passenger air transportation.
The index does not include intercity bus, sight seeing services, taxi service, private automobile usage, or bicycling and other nonmotorized means of transportation.
The components have been selected to give the best coverage possible of the for-hire transportation industry, subject to current limitations on the availability of monthly data. They are grouped together to match the classifications used in the National Income and Product Accounts.
Economists, forecasters, and others use monthly economic measures to understand the performance of the economy, to understand the short-term relationships among different sectors of the economy, and to forecast the performance of the economy, particularly business cycles. To do this they use measures called "indicators," such as employment, manufacturing production, sales, business inventories, purchasing managers' plans, and consumer confidence, among other things. In addition to giving information that is valuable in its own right, the indicators often have a relationship with the growth of the economy, measured by Gross Domestic Product (GDP).
There are several types of indicators:
- Coincident indicators tend to move along with GDP—that is, when GDP increases, they increase, and vice versa.
- Leading indicators portend changes in GDP—that is, when they go up, GDP tends to go up some time later; when they go down, GDP tends to eventually drop.
- Lagging indicators tend to follow fluctuations in GDP—when GDP goes up, these measures ultimately go up, and when GDP goes down, these indicators tend to later decline.
All of these indicators help economists, forecasters, investors, and business decision-makers better understand the course of the economy. Leading indicators are especially useful in forecasting turning points in the economy, which are of particular interest to economic decision-makers. Coincident indicators also are useful in determining the current state of the economy.
In fiscal year 2002, researchers from the State University of New York at Albany (Kajal Lahiri and Vincent Yao) and George Washington University (Herman Stekler) studied the relationships between transportation data and the economy. At the time, there was no multimodal monthly indicator measuring the production of transportation services. BTS learned that many in the forecasting and academic communities desired such a measure that could be used in conjunction with other indicators, such as inventory change and consumer confidence, to forecast future economic performance (leading indicator) or at minimum, confirm evidence of current economic performance (coincident indicator).
Support for the research was provided through a BTS research grant on "Leading Economic Indicators for the Transportation Industry." One of the outcomes of the research was the creation of a set of indexes that reflected passenger, freight, and total transportation services output. These indexes, which originally were designed to serve as coincident measures of the transportation sector of the economy, were recognized as valuable measures that BTS should produce and provide to the public and since have been found to be leading economic indicators (see TSI and the Economy Revisited).
What does the index provide that other transportation data do not?
The TSI is a multimodal, seasonally adjusted economic measure of transportation measured on a monthly basis. See What the Transportation Services Index, Dow Transportation Index, and Cass Freight Index Tell Us for a comparison of the TSI to other transportation economic indicators that reflect the responses of transportation providers to the economy’s demands for moving freight and/or passengers.
How was the index put together?
BTS staff gather monthly data for each mode of transportation from a range of government and private sources. Below is a summary. For complete documentation, see Documentation for the Transportation Services Index.
Some data series lag other data series. BTS, therefore, forecasts the one or two missing months, using a statistical technique known as AutoRegressive Integrated Moving Average (ARIMA).
The input data are highly seasonal, reflecting trends such as stores increasing inventory for the holiday season and households taking summer vacations. Seasonal trends make it difficult to observe underlying long-term changes in the data, as well as monthly shifts and short-term trends, which are best viewed using seasonally adjusted data. To control for seasonal influences, BTS seasonally adjusts the input data before indexing and weighting to create the indexes. BTS uses X-12-ARIMA in SAS to deseasonalize the data. BTS uses concurrent seasonal adjustment. The models used in the X-12 procedure can be found in this Excel workbook.
Index numbers characterize the magnitude of change over time. They describe trends of these changes with respect to a base period. The base year for the TSI is 2000; the average of the 12 months in 2000 for each of the deseasonalized series). Using 12 months means any change in the index is with respect to the 2000 annual average
Weighting and Chaining
After deseasonalizing the data, BTS indexes each series individually using the 12-month average of data from the year 2000 as the base. BTS then weights each series by its contribution to the economy, as measured by Gross Domestic Product (GDP). Value-added is the contribution of an industry to GDP. The U.S. Department of Commerce’s Bureau of Economic Analysis provides an annual measure of the value each for-hire transportation industry adds to GDP. The annual value-added estimates undergo several stages of conversion prior to being used as weights. First, BTS splits the rail and air value-added into passenger and freight values using passenger and freight revenue data. Next, BTS divides the modal value-added GDP values by the indexed input series (annualized by taking an average of the monthly index values). BTS performs this calculation to avoid double counting changes in output in creating the TSI. Prior to adjustment, the value-added values capture changes in quantity and price of the transportation services produced in the economy. Finally, BTS uses linear interpolation to estimate monthly value-added numbers from the adjusted annual value-added numbers. BTS then uses the Fischer Ideal methodology (see Lent 2004) to combine the modal indexes and chains them to generate period-to-period changes that are independent of the base year.
What are some limitations to the index?
- By its nature, the TSI takes a macro-level view of transportation and cannot substitute for detailed data in examining local and mode-specific transportation issues.
- The TSI does not cover 100% of the for-hire transportation industry.
- In some cases "tons" are used as a proxy for ton-miles and "passengers" as a proxy for passenger-miles. While there is justification for these substitutions, given data limitations, it would be ideal to have actual ton-miles and passenger-miles for all modes. Similarly there are some modes for which BTS must rely on privately collected, proprietary data.
What are some areas that the index does not cover?
Does the TSI show the benefits of each transportation mode?
No, the index is not an estimate of the benefits of each mode of transportation. Transportation exists to serve the rest of the economy, by providing mobility and accessibility for passengers and freight, and the TSI measures only monthly physical output. It does not address such topics as the impact of freight modes on business logistics or the impact of passenger modes on communities. The "value added" measure used the weighting of the index measures the contribution of the mode to gross domestic product; it is not an evaluation of the external benefits of the transportation mode.
Why aren't passenger automobiles, private or in-house trucking, and transportation-related inputs covered in the TSI?
The TSI does not include passenger movement by private automobiles or in-house trucking operations run by retailers, manufacturers, and other nontransportation firms for their own purposes. The index also does not cover transportation equipment, fuel, and other related goods and services required to produce transportation services. BTS does not include these components due to the lack of monthly data for them.