2.0 FREIGHT MOVEMENT BY HIGHWAY

2.1 Introduction

This chapter presents the data sources and methodology used for preparing the 2008 FAF provisional freight tonnage and value estimates for the highway mode of transportation. It covers both domestic and transborder (international) highway freight transportation. The estimation methods are formulated based on the FAF² 2002 benchmark estimates, and the latest publicly available and reliable information from different data sources. Improved approaches are applied by incorporating any new publicly available information since the last provisional estimates.

2.2 Principal Data Sources

The following are the main data sources used in developing the estimates for freight movement by highways.

Monthly Trucking Tonnage Report – Published by the American Trucking Associations (ATA) and provides up-to-date information on the trends of for-hire trucking activities. This monthly trucking tonnage index is based on an ongoing ATA survey of monthly tonnage by Class I and II general freight carriers. It includes both large and small truckload carriers, along with less-than-truckload carriers. The data are released with five weeks of time lag.

County Business Pattern (CBP) Database – Published by the U.S. Census Bureau on an annual basis and provides national, state, and county level data on payroll, employment, and number of establishments by detailed North American Industry Classification System (NAICS) industries. The series provides subnational economic data by industry and excludes data on self-employed individuals, employees of private households, railroad employees, agricultural production employees, and most government employees. The report is released with a two-year time lag. It can be accessed at http://www.census.gov/econ/cbp/index.html.

Gross State Product (GSP) – Prepared by the Bureau of Economic Analysis (BEA) of the U.S. Department of Commerce and provides data on GSP including components of GSP such as compensation of employees, operating surplus, taxes, etc. Gross domestic product (GDP) by state is the state counterpart of the nation's GDP and is derived as the sum of the GDP originating in all the industries in the state. The data are published with a one-year time lag. It can be accessed at http://www.bea.gov/regional/.

State Personal Income – Published by BEA of the U.S. Department of Commerce on a quarterly basis. Data on state personal income, employment, and compensation for NAICS industries are available from this source. Personal income is the income received by all persons from all sources. It is measured before the deduction of personal income taxes and other personal taxes and is reported in current dollars (no adjustment is made for price changes). Data are published with a three-month time lag. It can be accessed at http://www.bea.gov/regional/.

Monthly Manufacturers' Shipments, Inventories, and Orders (M3) Survey – Conducted by the Census Bureau, it provides broad-based monthly statistical data on the economic conditions in the domestic manufacturing sector. It measures current industrial activity and provides an indication of future production commitments. The value of shipments measures the value of goods delivered during the month by domestic manufacturers. The data are released with a two-month time lag. The survey results can be accessed at http://www.census.gov/indicator/www/m3/.

Monthly Wholesale Trade Survey – The Census Bureau provides monthly estimates of sales and inventories of wholesale trade industries. This provides statistics on sales and inventory/sales ratios along with standard errors. Data are both seasonally adjusted and unadjusted. The data are released six weeks after the close of the reference month. It can be accessed at http://www.census.gov/mwts/www/mwts.html.

Surface Transborder Freight Database (TFD) – Published by the Bureau of Transportation Statistics (BTS) and contains data on North American merchandise trade by commodity, surface mode (rail, truck, pipeline, mail, and other), and by port of entry and geographic detail for the U.S. trade to and from Canada and Mexico. This source provides the dollar value of both imports and exports, and tonnage of imports. The data are published with a three-month time lag. It can be accessed at http://www.bts.gov/programs/international/transborder/.

Producer Price Index (PPI) – Measures the average changes over time in the prices received by domestic producers of goods and services. This measures price changes from the point of view of the producer. The data are reported by detailed industry and detailed type of commodities. The Bureau of Labor Statistics (BLS) publishes these data on a monthly basis with a time lag of one month. The data can be obtained from http://www.bls.gov/ppi/home.htm.

Import/Export Price Indexes – The International Price Program (IPP) of the BLS produces import and export price indexes, which measure the change over time in the prices of goods or services purchased from abroad by U.S. residents (imports) or sold to foreign buyers by U.S. residents (exports). BLS publishes the Import/Export Price Indexes monthly with a time lag of two weeks. The data are available at http://www.bls.gov/mxp/home.htm.

Commodity Flow Survey (CFS) – The CFS is the primary source of national and state level data on domestic freight shipments by American businesses. It is a shipper-based survey that collects information on how U.S. establishments ship raw materials and finished goods; the types of commodities shipped by mode of transportation; the value, weight, origin, and destinations of shipments; and the distance shipped. The survey is conducted every five years. The latest survey covers 2007 and was released in December 2008.

2.3 Methodology for Domestic Highway Freight

The method used for preparing the annual provisional O-D freight flow estimates for domestic highway freight shipment involves the following steps:

1. Determine the annual growth of highway freight tonnage and value at the national level.
2. Estimate the growth factors for each O-D pair at the FAF region level.
3. Estimate the annual growths of each O-D pair by applying the respective O-D regional growth factors to the national annual growth.
4. Determine the provisional freight level (in terms of tonnage and value) of each O-D pair for 2008 by adding the growths to the freight level of the corresponding O-D pair in the FAF² benchmark year or the provisional estimate of the previous year.

This approach can be characterized as an updating approach. In comparison to producing provisional commodity O-D estimates entirely from updated input data, this approach, which produces provisional estimates by adding estimated growths (or changes) to the corresponding estimates in the benchmark year, has the following advantages:

• It fully utilizes all relevant new information including the most recent data, which become available after the benchmark year, to allow the provisional estimates to capture any changes that occurred after the benchmark year.
• It takes full advantage of the knowledge and detailed information embodied in the estimates of the benchmark year, but not available for the provisional estimates.

2.3.1 Determine Annual Growth of Highway Freight at the National Level

The following four definitions are important for the discussions presented in this document.

Estimates of highway freight – indicates the level of or volume of highway freight in a year with units of short ton and dollar. “Estimates of highway freight” and “highway freight” are used interchangeably in this discussion.

Growth – is defined as the change in highway freight either in terms of tons or dollar value between two years. Unless otherwise specified, it is calculated as the difference between highway freight for the current year and highway freight for the previous year. The units are tons and dollars.

Rate of growth – indicates the relative magnitude of growth when growth is compared to the level of the base year. Rate of growth is expressed in percent. “Rate of growth” and “growth rate” are used interchangeably in this report.

Current year – refers to the year for which provisional estimates are prepared.

2.3.1.1 Freight Tonnage

The monthly trucking tonnage index published by the ATA in the Monthly Truck Tonnage Report, the 2007 CFS preliminary results, and the truck tonnage reported in the FAF benchmark year are used in preparing the provisional domestic freight tonnage carried by trucks on the highway.

First, the growth rates of for-hire and private trucking freight shipments are calculated between 2002 and 2007 using data from CFS. This growth rate is applied to the 2002 FAF benchmark estimate to derive the total tons of freight for 2007. The result of this effort provides a more reasonable and robust estimate for 2007, based on which the 2008 provisional estimates are prepared. The formula for deriving the 2007 estimate is given by the following equation.

Where
T_t = Total tons by truck for year t (t = 2007)
T₀ = Total tons by truck for FAF benchmark year, i.e., 2002
T_c2007 = CFS truck (private and for-hire trucking) tonnage for 2007
T_c2002 = CFS truck (private and for-hire trucking) tonnage for 2002.

This estimate provides the 2007 tons of freight shipment by truck. Then, the 2007 total tons of freight is disaggregated at two-digit Standard Classification of Transported Goods (SCTG) commodity level of detail by implementing the following procedure.

1. First, the 2007 value of output at the two-digit SCTG commodity level is multiplied by the corresponding ton-per-value of output ratios for the FAF benchmark year to derive the tons of freight by type of commodity for 2007. The information on the value of output by type of commodity is obtained from the Census Bureau's Monthly M3 Survey. The type of commodity from this data source is established based on each industry's primary product.
2. Second, commodity shares are calculated based on the commodity distribution of the above tons of freight estimates for 2007, and these shares are used to break down the growth in national highway tons of freight, estimated with CFS data, into growth in tons of freight by two-digit SCTG commodity.
3. Finally, the growth in tons by type of commodity is added to the 2002 estimates to obtain the national freight tonnage by type of commodity for 2007.

In this method, both highway freight weight/value ratio by commodity and highway shipment tonnage to output-value ratio by commodity in 2007 year are assumed to remain the same as in the FAF benchmark year. The advantage of this method is that it utilizes the latest and most reliable indicator on the growth of highway freight from the 2007 CFS.

The 2008 provisional freight flow estimates are prepared on the basis of the 2007 estimates and the monthly trucking tonnage index from ATA. The trucking tonnage index of 2008 divided by the trucking tonnage index of 2007 and multiplied by the 2007 tons of freight would provide the total tons of freight shipment by truck mode for 2008. The formula is given as follows:

Where
T_t = Total tons of freight by truck for year t (t = 2008)
T₀ = Total tons of freight by truck for year 0 (year 0 = 2007)
I_t = Trucking tonnage index for year t (t = 2008)
I₀ = Trucking tonnage index for year 0 (year 0 = 2007)

The total tons of freight shipment for the provisional year are disaggregated at the two-digit SCTG commodity level of detail using the following procedure:

1. First, the 2008 value of output by commodity is multiplied by the corresponding ton-per-value of output ratios for 2002 to derive tons by type of commodity for the provisional year.
2. Second, commodity shares are calculated based on the commodity distribution of the above tonnage estimates, and these shares are used to break down the current year growth in national highway freight tons into commodity details.
3. Finally, the current year growth in tons by type of commodity is added to the 2007 estimates to provide the national tons of freight by type of commodity for the provisional year.

In this method, both highway freight weight/value ratio by commodity and highway shipment tonnage to output-value ratio by commodity in the current year are assumed to remain the same as in the FAF benchmark year.

2.3.1.2 Highway Freight Value

Freight value is determined not only by its weight but also by its weight-value ratio. Weight-value ratio, in turn, changes over time due to changes in the commodity composition of freight and changes in their prices. The value of freight for the provisional year has been estimated based on data from the 2002 FAF benchmark database, the 2007 CFS, ATA's trucking tonnage index, and the value of output by industry.

The growth rate of the value of freight shipment in current dollars for the highway (truck) mode is calculated using the combined values of freight for private and for-hire trucking from the 2002 and 2007 CFS. This growth rate has been applied to the 2002 FAF benchmark estimate to obtain the national total value of freight in current dollars for 2007. Then, the growth in the 2007 total value of freight is disaggregated at two-digit SCTG commodity detail by implementing the following steps.

1. First, the 2007 value of output by type of commodity is multiplied by the corresponding ton-per-value of output ratios for 2002 to obtain freight tonnage by type of commodity.
2. Second, commodity shares are calculated based on the above tons of freight estimates, and these shares are used to disaggregate the growth in total tons of highway by commodity.
3. Third, multiplying the growth in tons at the two-digit SCTG commodity level with their corresponding value-per-ton ratios of the FAF benchmark year yields the growth of freight values (in 2007 dollars) by type of commodity for 2007. Note that the values utilized in calculating the 2002 value-per-ton ratios are adjusted for prices to make them in 2007 current dollars by applying changes in the PPI of commodities. The PPIs are obtained from the BLS.
4. Fourth, based on the value estimates above, value shares by type of commodity are calculated, and these shares are applied to distribute the growth in the total value of freight among two-digit SCTG commodities in 2007.
5. Fifth the 2002 FAF freight values at the two-digit commodity level of detail for the highway mode are valued at 2007 prices by applying the PPIs.
6. Sixth, the results from step four and five are combined and value shares by commodity are calculated, and these shares are applied to disaggregate the total current value of freight for 2007.
7. Finally, the growth in the current dollar value of freight are adjusted for inflation by applying changes in PPIs at two-digit SCTG commodity levels to obtain values in 2002 dollars. These are added to the FAF benchmark values to arrive at the 2007 values in 2002 dollars.

The 2008 freight values are prepared using the 2007 estimates, the value of output by industry, and the FAF benchmark value-per-ton ratios.

1. First, the current year output of commodities is multiplied by the corresponding 2008 ton-per-value of output ratios to arrive at the tons of freight by type of commodity for 2008.
2. Second, commodity shares are calculated based on the above tons, and these shares are used to disaggregate the growth in tons (i.e., total tons for 2008 minus total tons for 2007) into two-digit SCTG commodity details.
3. Third, multiplying the growth in tons by commodity with the corresponding value-per-ton ratios of 2002 provides the 2008 growth of freight values in 2002 dollars. These are added to the 2007 values (in 2002 dollars) to provide the provisional year value of freight in 2002 dollars.
4. Finally, the 2007 current dollar value of freight estimates, and the 2008 freight value growth (in 2002 dollars) are adjusted for inflation, and these two are added to obtain the current dollar value of freight for 2008.

This approach takes advantage of the available up-to-date information on the growth of highway freight tonnage and value, as well as the changes in composition of commodities and prices over time. In this method, the 2008 tons-to-value of output ratios by commodity is assumed to remain the same as that in 2002.

2.3.2 Estimate Growth Factors for Each O-D Pair by FAF Regions

The purpose of preparing growth factors is to enable the annual provisional commodity O-D estimates to capture the impacts of differences in regional growths on freight shipments. A State-County-FAF Region approach was used in estimating the regional growth factors. There are three reasons for using this approach. 1) All the necessary economic data for estimating regional growth factors are currently available at the state level, not at the FAF regional level. 2) Most of the available county-level economic data that can be used for estimating regional growth factors are not readily available in a timely fashion for preparing provisional estimates. Such data are usually released with a time lag of more than one year, and hence could not be used as primary inputs for our purpose. 3) Counties are sub-regions to both states and FAF regions, and hence they provide a bridge for the crosswalk between states and FAF regions.

The approach for estimating growth factors for each O-D pair involves the following steps:

1. Determine annual state growth rates
2. Estimate county share of state growth
3. Estimate annual FAF regional growth
4. Estimate annual growth factors for each O-D pair at the FAF region level.

2.3.2.1 Determine Annual State Growth

The best indicator of the size and growth of a state's economy is its GSP. Similar to GDP at the national level, GSP measures the annual net output of a state's economy. Given the positive link between freight and output, freight grows as the economy grows, and hence GSP can serve as a reasonable indicator of freight growth.

GSP estimates are published by the BEA and can be used to directly calculate the annual growth rates of states. However, the GSP data are only available with a lag of one year. Currently, 2006 is the latest year for which GSP data are readily available. This creates a timeliness problem for FAF annual provisional commodity O-D estimates, whose annual updates for a year are scheduled to be completed at the end of the same year. In order to overcome this problem, the State Quarterly Personal Income statistics from BEA are used to calculate state annual growth rates for the current year.¹ Using the quarterly personal income statistics, the current year growth of GSP by state is estimated by the following relationship:

ΔGSPs = SG_s * GSP_s,t-1

Where:
ΔGSPs = Current year growth of GSP for state s ($)
SG_s = Current year GSP growth rate (approximated by the growth of personal income) for states (%)
GSP_s,t-1= GSP for previous year for state s ($).

2.3.2.2 Estimate County Share of State Growth

In order to calculate the growth in a FAF region, state growth factors are allocated among counties of that state. Then the county's share of the state growth is estimated. Then county growths are summed up to yield FAF regional growth. ² Current year growths of counties are estimated using the following formula:

ΔCG_k,s = ΔGSP_s*CS_k,s

Where:
ΔCG_k,s = Current year growth of county k in state s ($)
ΔGSP_s = Current year growth of GSP for state s ($)
CS_k,s= Share of county k in the GSP of state s ($).

The county share of state GSP is estimated with the most recent data on total payroll of a county, which is obtained from the Census Bureau's CBP. These data are released with a lag of two years.

2.3.2.3 Estimate FAF Regional Growth

Current year FAF regional growth is calculated by summing up current year growths of counties within a given FAF region.

ΔRG_j = ∑ΔCG_k,j

Where:
ΔRG_j = Growth for region j
CG_k,j = Current year growth of county k in region j.

2.3.2.4 Estimate Annual Growth Factors for Each O-D Pair of FAF Regions

Estimates of current year growths for all FAF regions provide the basic input information necessary for estimating annual growth factors of FAF O-D pairs. Instead of attempting to estimate the economic-spatial relationship between each pair of FAF regions using geo-spatial interaction models, such as various gravity models, the approach uses an interregional flow modifier method ³ for deriving growth factors of FAF O-D pairs. The method involves the following basic steps.

A. Converting economic growth into pseudo-growth in highway freight

The conversion of economic growth into pseudo-growth in highway freight tonnage is given by the following relationship:

Where:
ΔPGT_i,j = Pseudo-growth in highway freight tonnage between two regions (region i and region j)
ΔCEG_i,j = Combined economic growth of the two regions for the current year , (i.e., region i and region j)
CGSP_i,jt-1 = Combined economic size of the two regions (i.e., region i and region j) for previous year or t-1
T_i,j,t-1 = Highway freight tonnage between region i and region j for previous year.

The combined economic growth and the combined economic size of region i and j in the above formula are established based on the real dollar GSP of region i and j.

Similarly, the conversion of economic growth into pseudo-growth in highway freight value is accomplished using the following formulation:

Where:
ΔPGV_i,j = Pseudo-growth in highway freight value between two regions (region i and region j)
ΔCEG_i,j = Combined economic growth of the two regions for the current year , (i.e., region i and region j)
ΔCGSP_i,j,t-1 = Combined economic size of the two regions (i.e., region i and region j) for previous year or t-1
V_i,j,t-1 = Highway freight value between region i and region j for previous year.

The freight value is estimated using current dollar values of the combined economic growth and the combined economic size of regions i and j. The combined economic size and growth of the regions are estimated using GSP statistics.

B. Estimating annual growth factor for each FAF O-D pair

Let ΔTPGT be the sum of all pseudo-growths of all FAF O-D pairs in highway freight tonnage (= ∑ΔPGT_i,j), and let ΔPGT_i,j be pseudo-growth in highway freight tonnage of each O-D pair. Then the annual freight tonnage growth factor for each FAF O-D pair, GFT_i,j, is given by:

Let ΔTPGV be the sum of all pseudo-growths of all FAF O-D pairs in highway freight value (= ∑ΔPGV_i,j), and let ΔPGV_i,j be the pseudo-growth in freight value of each O-D pairs. Then the annual freight value growth factor for each FAF O-D pair, GFV_i,j, is given by:

The separation between tonnage growth factors and value growth factors recognizes the differences in commodity components and their prices among FAF O-D pairs. The main advantage of the interregional flow modifier method is that it captures the special economic-spatial relationships developed over time among FAF regions and at the same time recognizes recent changes in these relationships.

2.3.3 Estimate Growth of Highway Freight for Each FAF O-D Pair

Once the annual growth factors are established, the estimation of growth in highway freight for each FAF O-D pair is straightforward and is obtained through the following formula.

1. Let ΔGT be the annual growth of national highway freight tonnage,⁴ let GFT_i,j be the annual freight tonnage growth factor of an FAF O-D pair between region i and region j. The annual growth for the FAF O-D pair in highway freight tonnage, G_i,j, is given by the formula:
   ΔG_i,j = ΔGT*GFT_i,j
   
2. Let ΔGV be the annual growth of national highway freight value, let GFV_i,j be the annual freight value growth factor of FAF O-D pair between region i and region j. The annual growth for the FAF O-D pair in highway freight value, ΔG_i,j, is given by the formula:
   ΔG_i,j = ΔGV*GFV_i,j

2.3.4 Determine the Provisional Freight Flow Estimates for Each FAF O-D Pair

The provisional estimate of highway freight tonnage of a FAF O-D pair for the current year is calculated by adding its estimated annual tonnage growth to its freight tonnage in the 2002 FAF benchmark year (or the provisional estimate of the previous year if the current year is two or more years away from the benchmark year).

FT_i,j,t = FT_i,j,t-1 + ΔGT_i,j,t

Where:
FT_i,j,t = Highway freight tonnage for O-D pair i and j for year t
FT_i,j,t-1 = Highway freight tonnage for O-D pair i and j for year t-1.
ΔGT_i,j,t = Estimated annual tonnage growth for O-D pair i and j for year t.

Similarly, the provisional estimate of highway freight value of a FAF O-D pair is calculated in the updating year by adding its estimated annual growth of freight value to its freight value in the FAF² benchmark year (or the provisional estimate of the previous year if the updating year is two or more years away from the benchmark year).

FV_i,j,t = FV_i,j,t-1 + ΔGV_i,j,t

Where:
FV_i,j,t = Highway freight value for O-D pair i and j for year t
FV_i,j,t-1 = Highway freight value for O-D pair i and j for year t-1
ΔGV_i,j,t = Estimated annual growth of value for O-D pair i and j for year t.

2.4 Methodology for International Highway Freight

U.S. international highway freight shipments include freight flows between the U.S. and Canada, and between the U.S. and Mexico. Statistics on imports to and exports from Canada and Mexico by surface mode (highway, rail, and pipeline) are available from the North American TFD. Three sets of data are reported in the TFD, namely, state imports and exports by type of commodity using the Harmonized Schedule (HS) commodity classification method, state imports and exports by port of exit or entry, and U.S. imports and exports by port and commodity. The data on U.S. imports and exports by port and commodity details is a new addition to the TFD, beginning in 2007. However, no details are available on imports and exports by type of commodity, port of exit or entry, and origin and destination states in this database or from any other data source.

The TFD provides values and tons of imports, and value of exports. Data are not available on the weight of exports from this or any other known data sources. Beginning from 2007, the transborder data are reported for the 50 U.S. states and the District of Columbia, and for an unidentified (unknown) U.S. state.

2.4.1 Methodology for International Highway Freight

Based on import and export statistics from the TFD, and using the newly available information, an enhanced methodology of estimation has been developed. The methodology for preparing the 2008 provisional estimates involves the following steps:

1. Prepare details of state imports and exports by type of commodity, and port of exit or entry.
2. Allocate imports and exports of the unknown state to the 50 states and the District of Columbia.
3. Estimate the weight of exports, which is not provided in the TFD.
4. Convert data from TFD port of exit or entry to FAF international gateways.
5. Disaggregate state imports and exports into FAF Regions.
6. Convert data from HS commodity classification to SCTG commodity classification.

2.4.1.1 Preparing State Imports and Exports by Commodity and Port Detail

As pointed out above, an improved approach has been formulated and applied to estimate state imports and exports at the commodity and port of exit or entry level of detail. This approach keeps the original state imports and exports by type of commodity, and state imports and exports by port of exit or entry unchanged. In other words, when state-level imports and exports by type of commodity, and state-level imports and exports by port of exit or entry are derived from detailed estimates of state imports and exports by FAF region, international gateways, and type of commodity estimates, the results would be consistent with the actual data from the TFD. The methodology for imports and exports is exactly the same. The methodology for import freight flows is presented below.

Let
m_c = U.S. total imports from country c (i.e., Canada or Mexico)
m_ic = U.S. total imports of commodity i from country c
= Total imports of State s from country c
mc^p = U.S. total imports through Port p from country c
mc^sp = Total imports of State s through Port p from country c
= Imports of commodity i by State s from country c
= Imports of commodity i through Port p from country c
= Imports of commodity i by State s through Port p from country c.

Among the above variables, is unknown. The unknown variable can be estimated from available data. Assuming that the imports of a commodity by a state through a port is proportional to the share of that port in the U.S. total imports of that commodity, the imports of a commodity by a state through a port can be derived using the following equation.

The estimates derived from this equation should have the following two important properties:

However, the problem with derived from equation (1) is that . This means that state imports by port when derived from may not be consistent with the actual data from the Transborder Freight Database. In order to address this problem, we introduce a measure of state port propensity for imports , which is given by the following equation:

A state port propensity defined this way has the following important property:

Different sets of port propensity are used for imports from Canada and imports from Mexico. The purpose of port propensity is to adjust the results of equation (1) or in order to make them closer to reality by taking into account the special relationship between a port and a state. The implicit assumption for this adjustment is that all commodities of a state's imports have the same port propensity. The adjustment of state imports by type of commodity through port can be done using the following relationship.

Since the assumption that all commodities of a state's imports have the same port propensity is not true, the adjusted estimates of the imports of a commodity by a state through a port loses the two important properties that has, which means that , and .

In order to bring these two important properties back to the estimates, further adjustments are required. The adjustments can be done through an iterative process. The number of rounds of iteration depends on the accuracy requirement for the estimates. The higher the accuracy, the more rounds of iteration will be needed.

The goal is to make .

From equation 2,. One way to achieve the goal is to adjust the by a factor , where .

Since can be determined from available data, can be also calculated from the data. Once is determined, we can adjust as follows:

, where is adjusted .

Next, we try to achieve by following the same approach.

Since , we can adjust the by a factor , where .

Similar to can be determined from available data. Once is determined, the adjustment of can be done as follows:

, and
.

The adjustment of using will achieve the goal of . However, the adjustment changes the actual commodity composition of state imports and hence may not be equal to . Therefore, we need to continue the adjustment process until the two important properties of detailed estimates hold true. At the 2^nd round of the adjustment process, we will have:

And at the n^th round of the iteration process, we will have:

The iteration process continues until and are approaching 1, and the difference between , and between are approaching zero.

Although the above discussion concentrates on imports, the same approach has been used for estimating details of state exports at commodity and port level of detail.

2.4.1.2 Allocating Imports and Exports of Unknown State to the Fifty States and the District of Columbia

Starting from 2007, the TFD provides imports to and exports from an unknown U.S. state. The imports and exports of the unknown state should be allocated among the fifty states and the District of Columbia. For this purpose, two sets of data (imports and exports by port, and imports and exports by commodity) are obtained for the unknown state. First, similar to other states, details of state-level imports and exports by type of commodity and port of exit or entry are estimated using the methodology discussed above. Second, the share of each state's imports and exports by port and commodity detail in the total U.S. imports and exports by port and commodity detail are calculated. Finally, these shares are applied to the data on imports and exports of the unknown state to allocate the unknown state imports and exports to the 50 states and the District of Columbia. As a result of this adjustment, the final estimates of imports and exports for each state would be greater than the actual figure reported in the TFD. However, the total imports and exports, including the commodity composition or port of exit and entry data, would not be affected by this adjustment.

2.4.1.3 Estimating Export Tonnage

Data on the tonnage of U.S. exports to Canada and Mexico by truck mode are not available from the TFD or any other data source. In order to fill in this data gap, an imports weight-value ratio approach has been used. Two sets of weight-value ratios at the two-digit SCTG commodity level of detail are used for this purpose. One set of weight-value ratios is calculated based on imports statistics from Canada and the other set of ratios is computed using imports from Mexico. These ratios are country-specific and, therefore, recognize differences in the characteristics of U.S. trade with these two countries. The ratios have been smoothed using a simple moving average (SMA) method to reduce the impacts of over time and extreme variations. Multiplying the export values by the weight-value ratios of imports provides the tonnage of exports. This method assumes that the weight-value ratios of U.S. exports to Canada are the same as the weight-value ratios of U.S. imports from Canada, and the weight-value ratios of exports to Mexico are the same as the weight-value ratios of imports from Mexico.

2.4.1.4 Converting Data from Transborder Port to FAF International Gateways

The category of port of exit or entry in the TFD and the international gateways in the FAF database are different. We converted the data from the former to the latter. The conversion involves two steps: (1) establishing a crosswalk between Transborder's port of exit/entry and FAF international gateways; this requires identifying the exact location of the port of exit/entry of the TFD and assigning them into the proper FAF international gateways; and (2) applying the crosswalk to the freight data from the TFD.

2.4.1.5 Disaggregating State Imports and Exports into FAF Regions

The statistics on state-level imports and exports by type of commodity and international gateways are disaggregated at the FAF region O-D level of detail using the level of economic activity of each region during the year, which reflects the changes in the characteristics of freight shipment among regions. The economic activity of each FAF region has been approximated by employees' salaries and benefits received during the period. For this purpose, data on county-level payroll have been obtained from the Census Bureau's CBP. The county-level payroll is aggregated at the FAF region level of detail. The shares of FAF region payroll are calculated and applied to disaggregate state imports and exports by type of commodity and international gateways at the FAF region O-D level of detail.

2.4.1.6 Converting Data from Harmonized Schedule (HS) to Standard Classification of Transportation Goods (SCTG)

The commodity details in the TFD are reported using the HS commodity classification method. The data were converted into SCTG commodity classifications using a BTS working cross-walk between HS and SCTG. The cross-walk used for this purpose is based on the two-digit HS commodity since the data from the TFD are provided only at the two-digit HS commodity level of detail. For a more precise conversion of HS to SCTG, it would be advisable to use a four- or six-digit HS commodity data, and a cross-walk established based on this greater level of detail.

2.4.2 Value of Imports and Exports in Constant Dollars

The current dollar values of imports and exports are deflated by import and export price indexes, respectively, to obtain constant dollar freight values. The BLS publishes price indexes for imports and exports by selected HS commodity using 2000 as the base year. The price indexes are converted to the two-digit SCTG commodity level of detail by employing relative weights from the BLS. These price indexes are adjusted to reflect 2002 as the base year for deriving freight values in constant dollars.

---