4.0 FREIGHT MOVEMENT BY RAIL

4.1 Introduction

This chapter describes the data sources and methodology for generating the provisional estimates of volumes and value for freight moved by rail. This includes both domestic and international origins and destinations. Specific commentary on the application of the methodology described below in generating 2008 provisional estimates is provided at the end of this chapter.

The most efficient method for updating the railroad freight flows depicted within the FAF² would employ the confidential version of the Surface Transportation Board's annual Carload Waybill Sample (CWS). However, the scope of the current project explicitly states that data sources used in the updates must be publicly available. The public use CWS is of limited use for flow analysis given that origin and/or destination information is removed in many records to preserve confidentiality. Accordingly, an alternative method was developed that makes heavy use of the public use CWS, but which also relies on the existing FAF² flows, annual rail traffic data from the Association of American Railroads (AAR), the Surface Transborder Freight Dataset (TFD), and data available from the U.S. Census Bureau.

The railroad freight flows depicted within FAF² have two dimensions – commodity and geographic. It is essential to reflect variations across both dimensions. The proposed process begins by using the AAR annual car loading data to obtain traffic growth rates for the 19 AAR commodities. Next, the 19 AAR commodity groups were mapped into the 43 FAF² commodity groups, so that each FAF² commodity flow will have an associated overall commodity-based growth rate. These rates may be used to develop control flow totals for each commodity.

Next, the growth rates for each FAF² flow element were modified to reflect any volume variations that are attributable to O-D location. Adjustments may be based on any combination of three factors – demographic variations, variations in industry-specific economic activity, and variations in general economic conditions.⁸

The final methodological step combines the O-D- specific growth rates with existing FAF² flow data in an application of a traffic growth factor model such as that developed by Fratar.⁹ These models have a number of attributes and limitations. Most notably, however, they do not require the incorporation of impedances, and so are relatively simple to implement.¹⁰

The methodology for estimating international flows employed the annual rail records of the STFD, which are complete for rail movements. These shipments were disaggregated to the FAF² region level. For the purpose of the FAF² update, railroad carload and rail/truck intermodal shipments were treated as separate modes, even though many calculations were integrated across the two.

There are a number of challenges inherent in the processes described above. These include, but are likely not limited to:

• There are difficulties in mapping the 19 AAR commodity groups into the 43 FAF2 commodity groups.
• The AAR data are presented for each Class I carrier. Thus, it is possible for a particular shipment to be duplicated if it is interchanged between two carriers.
• There is no immediately available source for establishing changes in commodity values for domestic shipments.
• AAR data are expressed in carloads; FAF2 flows are expressed in tons. Thus, changes in car loading weights could distort the AAR-based growth rates.
• Time lag between data availability and development of provisional estimates

The proposed response to each of these challenges is provided in the descriptions of the methodologies presented in the following sections.

4.2 Principal Data Sources for Rail Freight

Weekly Railroad Traffic – This weekly publication contains information on carload and intermodal traffic for the U.S. Class I railroads, the two large Canadian railroads, a major Mexican railroad, and selected U.S. non-Class-I railroads. It includes carload information for 19 commodity groups and intermodal traffic, which is reported for trailers and containers.

Carload Waybill Sample (CWS) – A stratified sample of carload waybills for terminated shipments by railroad carriers. Waybill data are used to create a movement-specific Confidential Waybill File and a less detailed Public Use Waybill File. This is published by the Surface Transportation Board (STB) and can be accessed at: http://www.stb.dot.gov/stb/industry/econ_waybill.html

Surface Transborder Freight Database (TFD) – Published by the BTS, this database 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 a: http://www.bts.gov/programs/international/transborder/

County Business Pattern (CBP) Database – Published by the U.S. Census Bureau on an annual basis, this resource provides national, state, and county level data on payroll, employment, and number of establishments by detailed 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

Producer Price Index (PPI) – Measures the average change 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 BLS publishes these data on a monthly basis with a time lag of one month. It can be accessed at: http://www.bls.gov/ppi/

4.3 Domestic Rail Flows

The primary data source for the FAF² domestic rail flow update was the STB Public Use version of the CWS. The waybill sample is a stratified sample of the population of rail movements that originate or terminate within the United States. The actual degree of sampling depends on the variability of shipment characteristics across commodities. In most cases, the sample represents between one and two percent of the overall shipment population.

The initial step involved gleaning tonnage variations between 2002 and 2008. CWS commodity definitions (based on Standard Transportation Commodity Codes) were bridged to the Standard Classification of Transported Goods (SCTG) definitions employed within the FAF². Once this was accomplished, national traffic growth factors were calculated based on variations in tonnage between 2002 and 2008.

Unfortunately, the O-D information in the Public Use CWS is left incomplete in order to protect the confidentiality of both shippers and rail carriers. Consequently, it was impossible to use the CWS to identify geographic variations in traffic volumes that might be relevant to inter-temporal variations in the FAF² flows. To remedy this problem, industry-specific employment values, derived from CBP data, were used to build indexes reflecting FAF region employment, population, and income trends for both origin and destination regions. The hypothesized relationship between employment, demographic values, and rail flows are summarized in Table 4.1.

Table 4-1. Relationship between Employment, Demographic, and Rail Flow

STCG	Origin Index Component	Destination Index Component
2	NAICS 111 Employment	NAICS 311 Employment
3	NAICS 111 Employment	NAICS 311 Employment
4	NAICS 311 Employment	NAICS 311 Employment
5	NAICS 311 Employment	NAICS 311 Employment
6	NAICS 311 Employment	NAICS 311 Employment
7	NAICS 311 Employment	NAICS 311 Employment
8	NAICS 312 Employment	NAICS 445 Employment
9	NAICS 312 Employment	NAICS 447 Employment
10	NAICS 327 Employment	NAICS 234 Employment
11	NAICS 327 Employment	NAICS 234 Employment
12	NAICS 327 Employment	NAICS 234 Employment
13	NAICS 327 Employment	NAICS 327 Employment
14	NAICS 212 Employment	NAICS 331 Employment
15	NAICS 212 Employment	NAICS 221 Employment
16	NAICS 211 Employment	NAICS 324 Employment
17	NAICS 324 Employment	NAICS 447 Employment
18	NAICS 324 Employment	NAICS 221 Employment
19	NAICS 324 Employment	FAF Region Total Employment
20	NAICS 325 Employment	FAF Region Total Employment
21	NAICS 325 Employment	FAF Region Total Employment
22	NAICS 325 Employment	NAICS 111 Employment
23	NAICS 325 Employment	FAF Region Total Employment
24	NAICS 326 Employment	FAF Region Total Employment
25	NAICS 113 Employment	NAICS 321 Employment
26	NAICS 321 Employment	NAICS 321 Employment
27	NAICS 322 Employment	FAF Region Population
28	NAICS 322 Employment	FAF Region Population
29	NAICS 323 Employment	FAF Region Population
30	NAICS 313 Employment	FAF Region Population
31	NAICS 327 Employment	FAF Region Population
32	NAICS 331 Employment	FAF Region Population
33	NAICS 332 Employment	NAICS 332 Employment
34	NAICS 333 Employment	FAF Region Population
35	NAICS 335 Employment	FAF Region Population
36	NAICS 336 Employment	FAF Region Population
37	NAICS 336 Employment	FAF Region Population
38	NAICS 334 Employment	FAF Region Population
39	NAICS 337 Employment	FAF Region Population
40	FAF Region Population	FAF Region Population
41	FAF Region Population	FAF Region Population
43	FAF Region Population	FAF Region Population

The formal derivation of the indexes is explained in the following equation. Once established, the indexes were applied to the national growth factors to simulate geographic variations in flows. Finally, the adjusted growth factors were applied to the 2002 FAF² values to yield 2008 estimates.¹¹

where OVAL and TVAL represent the appropriate O-D employment or population variables.

Revising the FAF² data also required estimating changes in the value of commodity flows. Changes in values are a function of both changed flow volumes and per-unit commodity value variations. Flow changes were based on tonnage volume flows as described above. Per-unit values were based on commodity-specific variations where possible, as captured by changes in the components of the PPI. The bridge between FAF commodity definitions and PPI values is provided in Table 4.2.

Table 4-2. FAF Commodity Definitions and PPI

Producer Price Index Component	2002 – 2005 Percentage Change
Industrial Commodities Less Fuels	0.106993
Farm Products	0.19697
Industrial Chemicals	0.480754
Lumber	0.164127
Pulp Paper And Allied Products	0.089833
Crude Petroleum	1.210604
Chemicals And Allied Products	0.263989
Iron And Steel	0.263989
Steel Mill Products	0.523855
Motor Vehicle Parts	0.001771
Plastics Material And Resins Manuf.	0.534587
Aluminum Plate, Sheet, And Foil Manuf.	0.112846
Automobile And Light Duty Vehicle Manuf.	0.001483

4.3.1 Rail Flows to Deep-Draft Ports

Somewhat inexplicably, the FAF² rail flows over U.S. deep-draft ports entirely neglect import flows and capture export flows only. Consequently, export flows alone were updated from 2002 values to reflect changed economic conditions in 2008. The basis for this update was export data obtained from the U.S. Department of Commerce. As in the case of domestic rail flows, the initial step involved reconciling export data commodity definitions with the commodity definitions used within the FAF².

Next, because the Department of Commerce export volumes are expressed in dollar values only (as opposed to tons), it was necessary to account for intertemporal changes in commodity values between 2002 and 2008. As in the case of domestic flows, the PPI was used for this purpose. Once price variations were accounted for, export data were used to scale 2002 FAF flows to reflect 2008 FAF² deep-draft port flows. Given that no geographic variation is reflected in the Department of Commerce data, the revised FAF² values assume that the distribution of rail export flows across US ports is unchanged between 2002 and 2008.¹²

The final step in the adjustment to the FAF data involved again using price index data in order to inflate the value field in the 2002 data. Where possible, industry or product specific values were used. In the absence of such data, the value corresponding to “Industrial Commodities Less Fuels” was used.

4.4 Methodology for International Rail Freight

4.4.1 Methodology for International Freight

FAF contains international rail freight shipments of two types: (1) all-rail shipments to/from Canada and Mexico and (2) shipments to/from countries outside of North America that use rail for the domestic portion of the movement. Different methodologies are used for addressing the two categories.

4.4.2 Transborder Rail Freight to and from Canada and Mexico by U.S. State and Port of Entry or Exit

The approach for estimating rail freight flows between the U.S. and Canada, and between the U.S. and Mexico, is as follows:

1. Determine state-level transborder rail freight to and from Canada and Mexico for the current year using information from BTS' TFD;
2. Disaggregate state level transborder rail freight flow to FAF region-level based on FAF patterns from the base year; and
3. Allocate FAF region level flows to and from Canada/Mexico to ports of entry/exit (actually border crossing points) based upon FAF² patterns from the base year or data on port use from the current year TFD.

4.4.3 Determine State-Level Transborder Rail Freight to and from Canada and Mexico

BTS's TFD provides freight data on tons and value of exports and imports from Canada and Mexico to the United States by rail. The data are reported by O-D state, country, and type of commodity. A separate group of files provide data on total weight and value through ports of entry/exit by O-D state and county, without regard to specific commodities. The rail records are extracted from all of the annual TFD datasets for the target year.

The TFD uses the HS commodity classification, rather than the SCTG employed by FAF. Using a cross-walkmatching HS and SCTG codes, the TFD records are processed to add the appropriate SCTG. The port of entry/port of departure (POE/POD) in the TFD is described using Customs Port Codes. A translation table maps these codes to FAF regions and international gateways.

The following sections describe processing steps to handle import and export data.

4.4.3.1 Imports

The following TFD files contain import data at the commodity level:

• 09yyyy–imports from Mexico with state of destination and 2-digit commodity detail, where yyyy is the year of release, e.g. 2008; and
• 10yyyy–imports from Canada with state of destination and 2-digit commodity detail.

The import records are processed to tally the total weight and value by HS commodity. Separate totals are kept for imports from Mexico and Canada. This information is used in processing the export records as described in the next section.

For compatibility with FAF, the weights and values in each record are converted from kilograms to thousand short tons expressed as kilotons and from dollars to millions of dollars, respectively. The origin field is set to the appropriate FAF code for Mexico or Canada.

4.4.3.2 Exports

Export data at the commodity level is contained in the following TFD files:

• 3ayyyy–exports to Mexico with state of origin and 2-digit commodity detail; and
• 4ayyyy–exports to Canada with state of origin and 2-digit commodity detail.

Unlike the import records, the export records lack weight. Accordingly, the weight for each flow must be imputed. To estimate weight, value is multiplied by a weight/value ratio for the commodity with different ratios used for Canada and Mexico. These ratios are derived using the commodity tallies collected from the import records. To minimize variance, the tally is based at the HS level, a lower level of aggregation than the SCTG.

As with the import records, the weights and values in each record are converted from kilograms to kilotons and from dollars to millions of dollars, respectively. The destination field is set to the appropriate FAF code for Mexico or Canada.

4.4.3.3 Initial Aggregation

Following initial processing, the import records for Mexico and Canada are combined into a single file representing all import rail traffic for the target year. The separate export record files are also combined. As a result of the conversion from HS to SCTG, each combined file may contain several records for a given origin, destination, and SCTG. The files are processed to combine these records into a single record containing the totals for the origin, destination, and SCTG.

4.4.4 Disaggregate Estimates of Transborder Rail Freight by State to FAF-Level Estimates

The state-level transborder rail freight tonnage and value are disaggregated to FAF-level estimates using the existing patterns from the original FAF base year.

4.4.4.1 Imports

The target year estimate of rail freight import tonnage W_i,c,r,t of commodity i from country c to FAF region r in state s for year t, is calculated as:

W_i,c,r,t = W_i,c,s,t*P_i,c,s,r,t-1

where:
W = Rail freight tonnage of imports,
P = share variable,
i = commodity,
c = country of origin (Canada or Mexico),
s = destination state,
r = destination FAF region (in state s),
t = target year, and
t-1 = base year.

The share variable P is based upon the weight of i destined from c to r in the base year as a portion of the total weight destined from c to s. Domestic FAF regions lie entirely within state boundaries; a crosswalk table allows state totals to be derived from FAF totals. The share variable is formally calculated as:

The value of the import tonnage for the commodity is calculated in a similar fashion using the same share variable, P:

V_i,c,r,t = V_i,c,s,t*P_i,c,s,r,t-1

where V is the value and all subscripts have the same meaning as previously.

Where commodity flows did not exist in the base year, the flow is allocated in equal portions to each FAF region in the state. Future refinements may use county level indicators from the Census CBP Database to disaggregate the flows.

4.4.4.2 Exports

Disaggregation of exports from the state to the FAF region level follows a similar approach to that of imports. The target year estimate of rail freight export tonnage of commodity i from FAF region r in state s to country c for year t, is calculated as:

W_i,r,c,t = W_i,s,c,t*P_i,s,r,c,t-1

where all variables and subscripts remain as previously defined. The share portion for r is calculated as follows:

This share is also used to apportion value for the flow.

4.4.5 Allocate FAF-Level Flows to Ports

The final processing step is to allocate FAF region level imports and exports across ports. FAF defines a number of international gateways, and these correspond well to the limited number of international rail border crossings in North America. The term “port” is used here to generally include rail border crossings. However, a significant portion of the FAF rail transborder records do not presently use the designated gateways, instead having ports identified as regular FAF regions. No effort was made to improve allocation of shipments to the defined rail border crossings.

4.4.5.1 Imports

For flows occurring in both the base year and the target year, import tonnage of a given commodity from a foreign source to a FAF region is allocated among ports of entry using the following formula:

W_i,c,p,r,t = W_i,c,r,t*P_i,c,p,r,t-1

where:
W = Rail freight tonnage of imports,
P = share variable,
i = commodity,
c = country of origin (Canada or Mexico),
p = port,
r = destination FAF region (in state s),
t = target year, and
t-1 = base year.

The value of the import tonnage for the commodity is calculated in a similar fashion using the same share variable, P:

V_i,c,p,r,t = V_i,c,r,t*P_i,c,p,r,t-1

where V is the value and all subscripts have the same meaning as previously.
The port share is again based upon the weight of i destined from c to r via p in the base year as a portion of the total weight of i destined from c to r via all ports involved in the trade. Mathematically, this share is expressed as:

The set X contains all ports handling commodity i between country s and region r during the base year.

In the case where a commodity was not handled by the FAF region in the base year, the allocation is based on the each port's share of total trade, by value, from the origin country to the FAF region's state during the target year. This information is found in the port level TFD, which provides total weight and value for all freight moved between a country and state via a port.

4.4.5.2 Exports

Allocation of export flows between a FAF region and foreign country via ports follows a similar approach to that of imports. The target year estimate of rail freight export tonnage of commodity i from FAF region r in state s to country c for year t, is calculated as:

W_i,r,p,c,t = W_i,r,c,t*P_i,r,p,c,t-1

with all variables and subscripts as previously defined. The share portion for r becomes:

with X being the set of ports handling commodity i between region r and country s during the base year. Again, the share is also used to apportion value for the flow. Flows not existing during the base year are apportioned to ports based upon the target year TFD port level export data for the state of origin.

4.4.6 Combine Import and Export Data

The previous steps result in two output files for transborder rail freight:

• Import flows (weight and value) by commodity, country, port, and destination domestic FAF region; and
• Export flows (weight and value) by commodity, domestic origin FAF region, port, and country.

The final processing step is to combine the two files and eliminate any records for which the weight is less than 0.01 kilotons and the value less than 0.01 million dollars. The current FAF limits these values to two decimal points, so values less than these will appear as zeros. In practice, a single rail carload carries 0.07 to 0.10 kilotons, so this step will have minimal impact on overall flows.

4.5 2008 Provisional Rail Estimates – Commentary on Methodology

The methodology described in the previous sections was used in updating the 2007 rail files so that they reflect 2008's economic changes. AAR data on commodity-specific car loadings were used to estimate traffic changes. Elements from the PPI were used to calculate associated changes in commodity values. The resulting adjustment factors were applied uniformly to the 2007 data. A summary of adjustment factors is provided in Table 4.3. The repetition in some parameter values reflects the difficulty in bridging across three very different sets of commodity definitions.

While this methodology yields a reasonable approximation of the changes to actual rail freight flows, it has two notable limitations. First, under such a methodology, existing traffic can never fully disappear over a network lane and, similarly, new flows cannot emerge. Unfortunately, this limitation cannot be mitigated without the use of primary data, which are unavailable for update purposes. The second limitation is linked to the geographic implication of the FAF regions over which rail freight flows are defined. Given the current methodology, the pair-wise flow volumes observed in the updated data are proportional to the pair-wise volumes evident in the preceding year's data. Thus, what appears to be dynamic is actually static.

Prior to developing the 2008 estimates, the study team investigated potential methodological changes that might, to some degree, improve the geographic distribution of rail freight flow adjustments, so that the resulting estimates more accurately reflect regional shifts in economic activity. This consideration, however, was undertaken with understanding that both temporal and fiscal constraints could not be substantially relaxed. The findings of this investigation are summarized below.

Table 4.4 summarizes estimated domestic rail tonnages by commodity for 2008. Clearly, both the value of and ability to improve the geographic precision of annual updates is confined to a few commodity groups – specifically, coal and related products, agricultural inputs and outputs, and chemicals.

While intermodal rail shipments briefly eclipsed coal as a source of rail revenue, coal continues to dominate rail tonnage. Therefore, any attempt to adapt current methodologies to better reflect economic trends should center on the production and consumption locations of this commodity.

Coal is mined where it is found. Nonetheless, coal characteristics (Btu, sulfur, ash content, etc.), resulting mine-mouth prices, and environmental constraints can easily affect coal sourcing decisions. Similarly, these same elements, combined with demand conditions, can affect electric utility dispatching practices. Finally, international market conditions routinely alter coal export volumes. Any consideration of alternative FAF update methods must be mindful of these three aspects.

Table 4-3. 2008 Adjustment Factors

STCG	Commodity Definition	AAR Adjustment Factor	PPI Adjustment Factor
2	Cereal grains	0.000	0.126
3	Other agricultural products	-0.129	0.126
4	Animal feed and products of animal origin, not elsewhere classified (n.e.c.)	-0.004	0.126
5	Meat, fish, seafood, and their preparations	-0.004	0.126
6	Milled grain products and preparations, and bakery products	-0.027	0.126
7	Other prepared foodstuffs and fats and oils	-0.004	0.126
8	Alcoholic beverages	-0.129	0.126
9	Tobacco products	-0.129	0.126
10	Monumental or building stone	-0.083	0.048
11	Natural sands	-0.083	0.048
12	Gravel and crushed stone	-0.083	0.048
13	Nonmetallic minerals n.e.c.	-0.083	0.048
14	Metallic ores and concentrates	-0.057	0.048
15	Coal	-0.009	0.048
16	Crude Petroleum	0.044	0.306
17	Gasoline and aviation turbine fuel	0.044	0.306
18	Fuel oils	0.044	0.306
19	Coal and petroleum products, n.e.c.	0.044	0.048
20	Basic chemicals	0.033	0.044
21	Pharmaceutical products	-0.02	0.044
22	Fertilizers	0.033	0.044
23	Chemical products and preparations, n.e.c.	-0.02	0.048
24	Plastics and rubber	-0.02	0.048
25	Logs and other wood in the rough	-0.119	-0.011
26	Wood products	-0.119	-0.011
27	Pulp, newsprint, paper, and paperboard	-0.078	0.019
28	Paper or paperboard articles	-0.078	0.019
29	Printed products	-0.02	0.019
30	Textiles, leather, and articles of textiles or leather	-0.02	0.048
31	Nonmetallic mineral products	-0.015	0.048
32	Base metal in primary or semi-finished forms and in finished basic shapes	-0.078	0.038
33	Articles of base metal	-0.078	0.038
34	Machinery	-0.02	-0.008
35	Electronic and other electrical equipment components and office equipment	-0.02	0.048
36	Motorized and other vehicles (including parts)	-0.053	0.000
37	Transportation equipment, n.e.c.	-0.053	0.000
38	Precision instruments and apparatus	-0.02	0.048
39	Furniture, mattresses and mattress supports, lamps, lighting fittings	-0.02	0.048
40	Miscellaneous manufactured products	-0.02	0.048
41	Waste and scrap	0.000	0.048
43	Mixed freight	-0.02	0.048

Table 4-4. 2008 Tonnage Estimates by Commodity – Domestic File

STCG	Estimated 2008 Tons (X 1K)	Percentage of Total
15	893,656	47.1%
2	185,126	9.8%
19	115,009	6.1%
22	90,285	4.8%
41	77,193	4.1%
20	72,468	3.8%
12	70,174	3.7%
7	47,194	2.5%
32	37,725	2.0%
26	32,806	1.7%
14	32,617	1.7%
31	32,375	1.7%
24	32,051	1.7%
13	25,243	1.3%
4	24,873	1.3%
27	24,390	1.3%
3	22,629	1.2%
11	16,512	0.9%
6	11,807	0.6%
36	11,095	0.6%
17	8,367	0.4%
18	6,579	0.3%
8	5,657	0.3%
33	5,420	0.3%
37	4,896	0.3%
34	2,143	0.1%
43	1,489	0.1%
28	1,269	0.1%
25	894	0.0%
35	786	0.0%
30	705	0.0%
23	610	0.0%
5	555	0.0%
29	544	0.0%
40	508	0.0%
39	362	0.0%

Table 4.5 provides state-specific coal production totals for 2006 and 2007. Again, there are three important points. First, while volumes changed to a small degree, the relative shares of the leading coal producing states did not change in any sort of meaningful way. Second, the production activity, as identified for FAF regions within producing states almost certainly did not change given that the FAF regions are largely defined on a metro / non-metro basis. Finally, these data do not include values for 2008, the period for current FAF data estimates.

A similar accounting for annual changes in facility dispatching and corresponding coal consumption leads to a similar conclusion – if the base FAF estimates adequately capture the regional distribution of domestic coal flows, then increased precision in annual updates is both impossible and unnecessary.

Table 4-5. Coal Production by State ('000 tons)

State	2007 Production	2006 Production	Percent Change
Wyoming	453,568	446,742	-1.5%
West Virginia	153,480	152,374	-0.7%
Kentucky	115,280	120,848	4.6%
Pennsylvania	65,048	66,029	1.5%
Montana	43,390	41,823	-3.7%
Texas	41,948	45,548	7.9%
Colorado	36,384	36,322	-0.2%
Indiana	35,003	35,119	0.3%
Illinois	32,445	32,729	0.9%
North Dakota	29,606	30,411	2.6%
Virginia	25,346	29,740	14.8%
New Mexico	24,451	25,913	5.6%
Utah	24,307	26,018	6.6%
Ohio	22,575	22,722	0.6%
Alabama	19,327	18,830	-2.6%
Arizona	7,983	8,216	2.8%
Mississippi	3,545	3,797	6.6%
Louisiana	3,127	4,114	24.0%
Tennessee	2,654	2,804	5.3%
Maryland	2,301	5,054	54.5%
Oklahoma	1,648	1,998	17.5%
Alaska	1,324	1,425	7.1%
Kansas	420	426	1.4%
Missouri	236	394	40.1%
Arkansas	83	23	-260.9%
Washington	0	2,580	100.0%
TOTAL	1,145,479	1,161,999	1.4%

The volume of coal moved domestically for import and export is considerably more variable. Table 4.6 provides total import and export volumes for the period between 2002 and 2008. These volumes are even more erratic when the data are disaggregated to distinguish between trading partner and steam versus metallurgical coal.

Table 4-6. U.S. Coal Imports and Exports
(Values in thousands of tons)

Year	Exports	Imports
2002	39,601	16,875
2003	43,014	25,044
2004	47,998	27,280
2005	49,942	30,460
2006	49,647	36,246
2007	59,163	36,347
2008	59,191	25,107

Unlike domestic coal volumes, 2008 data are available, so that the available information is consistent with the FAF update period. Hence improved update methods might be possible. However, the rewards attributable to additional work are likely to be imperceptible. First, overall import and export volumes represent less than 10 percent of total U.S. production. And second production and port locations are highly invariant, so that even if volumes do change significantly, changes in relative regional activity are unlikely.

Because of coal's tonnage dominance, it is important to consider the possible avenues to and rewards of more advanced methods for annual FAF updates in some detail. However, a less rigorous consideration of other prominent railed commodities leads to similar conclusions. Agricultural inputs and outputs are the second most dominant commodity groupings – grains and fertilizers. Fertilizers are produced where inputs are available and shipped to where land is farmed. Farmland is where it is. Thus, while flow volumes may change noticeably based on both domestic and international market conditions, relative volumes in flow lanes are unlikely to change from one year to the next in a way that can be captured. The same is true for chemicals, aggregates, lumber, or other heavily railed commodities.

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