OVERHAULING INDUSTRIAL PRODUCTION: 

THE 2002 HISTORICAL AND ANNUAL REVISION 

John J. Stevens 

Board of Governors of the Federal Reserve System, Washington, D.C. 20551 

John.J.Stevens@frb.gov 

Presented August 7, 2003 

KEY WORDS: Industrial production; capacity 

utilization; NAICS; SIC; industry classification

ABSTRACT. The Federal Reserve Board’s monthly 

indexes of industrial production, capacity, and 

capacity utilization, are principal indicators of 

economic activity in the United States’ industrial 

sector. In December 2002, the Federal Reserve 

Board issued a historical revision of these indexes 

going back to 1972. This revision, unprecedented 

among statistical agencies, is the first significant 

historical restatement of economic time series under 

the new North American Industrial Classification 

System (NAICS). This paper reviews the history of 

the industrial production indexes, including the 

NAICS revision; the organizing principles underlying 

NAICS; and the implementation of NAICS at other 

statistical agencies. 

1. Introduction 

Each year in the late fall, the Federal Reserve 

Board (FRB) publishes a comprehensive revision of 

its monthly statistical release on industrial production 

and capacity utilization.1

 These annual revisions are 

an opportunity to incorporate new and revised source 

data, improve underlying statistical methodologies, 

and expand the set of covered industries. The 2002 

annual revision was unprecedented in its scope: 

30 years of published data were retabulated from the 

bottom up using the new North American Industry 

Classification System (NAICS).2

 Previously, the 

 

1

 The “G.17 Statistical Release of Industrial Production and 

Capacity Utilization” is available at 

www.federalreserve.gov/releases/G17. This release reports 

monthly measures of output (IP), capacity, and capacity utilization 

for the industrial sector, which the FRB defines as the 

manufacturing, mining, and electric and gas utilities industries. 

The production indexes and utilization rates are widely reported in 

the media and are used by analysts in government agencies, 

businesses, and universities to measure current developments and 

trends in real output and operating rates in the industrial sector. 

The production indexes are also used by the Bureau of Labor 

Statistics to estimate manufacturing productivity. 

2

 NAICS was jointly developed by Mexico’s Instituto Nacional de 

Estadistica, Geografia e Informatica (INEGI), Statistics Canada, 

and the U.S. Economic Classification Policy Committee. It is a 

production-oriented approach to classifying establishments into 

industries based on the establishment’s primary activity. Particular 

Standard Industrial Classification (SIC) system was 

used. Industrial production and capacity utilization 

(IP/CU) were the first major economic indicators 

issued with substantial history on a NAICS basis. 

The issuance of these revised indicators marked a 

major advance within the statistical community. The 

historical continuity afforded by consistent time 

series data is particularly important to economists 

concerned about low frequency events, like business 

cycles, where each observation is quite important. 

Corrado (2003) provides additional information about 

the revision and its implications for users of the data; 

for example, despite the scope of the revisions, the 

dates of business cycle peaks and troughs were 

essentially unchanged. 

This paper first provides some background on 

the IP/CU indexes, what they measure, and their 

primary applications; readers familiar with IP may 

wish to skip ahead. Sections 3 and 4 provide an 

introduction to the major steps involved in the 2002 

annual revision; further details are available in 

Bayard and Klimek (2003), Corrado (2003), and 

Morin (2003). For illustrative purposes, the “motor 

vehicles and parts” industry (NAICS 3361-3) is 

frequently used to help fix ideas. The economic 

significance of this industry is well known; it is a 

highly cyclical industry with strong upstream and 

downstream linkages. What makes this industry 

useful as an illustrative tool, is that the IP/CU indexes 

for its sub-industries span the set of data types and 

methods that are used more generally to construct the 

nearly 300 individual IP series (plus another 150 or 

so aggregate series) and the 88 capacity series that 

comprise the IP/CU system.3

 The paper concludes by 

 

attention is paid to new and emerging industries, service industries, 

and industries producing advanced technologies. Among North 

American countries, the system facilitates cross-country 

comparisons of detailed industry-level statistics; at higher levels of 

aggregation it strives to maintain comparability with the United 

Nations’ International Standard Industrial Classification System of 

All Economic Activities. See Executive Office of the President of 

the U.S. (2002) for more information on NAICS. 

3

 IP is constructed at a much finer degree of detail than capacity, 

hence the difference in the number of series. However, the 

coverage is the same for both IP and capacity—each capacity 

series has a corresponding IP individual or aggregate series from 

which capacity utilization (the ratio of IP to capacity) is calculated. 
placing the FRB efforts in the context of the broader 

statistical community. 

2. What does IP/CU measure? 

The IP index is a measure of real output as 

opposed to a nominal (i.e., dollar value) output 

measure. In a simple world with a single product of 

constant quality, the IP index would equal the 

number of units produced. Of course, the real world 

is not so simple. For a fairly homogeneous good like 

iron, the corresponding IP index roughly tracks the 

actual tonnage that is mined. For industries with 

heterogeneous goods, the IP index also reflects shifts 

over time in the mix of goods produced: Producing 

one more Jeep Grand Cherokee and one less Ford 

Escort, will, everything else equal, raise the IP index 

for motor vehicles due to the higher value of the 

Cherokee and its greater parts content. For goods 

that improve over time, the IP index also reflects 

embodied technological improvements: Today’s 

Pentium IV microprocessor unit handles far more 

computations per second than yester-year’s 386 

microprocessor and thus represents an increase in real 

output. 

The FRB also constructs indexes of industrial 

capacity to measure the productive capability of the 

country. Specifically, 

The FRB’s capacity indexes attempt to 

capture the concept of sustainable maximum 

output—the greatest level of output a plant 

can maintain within the framework of a 

realistic work schedule, after factoring in 

normal downtime and assuming sufficient 

availability of inputs to operate the capital in 

place. 

In economics jargon, this concept of capacity 

generally conforms to that of a full-input point on a 

production function, i.e., a point at which all 

resources are fully utilized, with the qualification that 

capacity represents a sustainable maximum, rather 

than some higher unsustainable short-term maximum. 

This economic concept of capacity encompasses both 

engineering considerations (How deep is the 

mineshaft? How big is the blast furnace? How wide 

is the pipe?) as well as operating practices (Is it a 2 or 

3 shift plant? What are the prevailing work rules and 

how is the workplace organized?). In practice, 

engineering considerations and operating practices 

are jointly determined and may depend upon the type 

of production process a firm employs. For example, 

a continuous processor (e.g., a refinery) will face a 

slightly different menu of options than an assembly 

line (e.g., an auto plant). 

Depending upon the question being asked, the IP 

and capacity indexes may be of independent interest. 

Part of this stems from the fact that the two indexes 

are measuring very different, yet related, concepts. 

They are different in that capacity is often a less 

tangible notion than IP. Nonetheless, the indexes 

must be consistent with one another, as we are also 

interested in their ratio—capacity utilization—that 

attempts to measure the proportion of the available 

productive capacity that was actually used in a given 

time period. The rate of aggregate capacity 

utilization can be a useful predictor of inflationary 

pressures in the economy. High rates of utilization 

may reflect strong demand (and hence pressure on 

prices in output markets). High rates of utilization 

may also put pressure on input prices, such as wages, 

due to the use of overtime. Rates of utilization are 

also of interest at the industry level as they help 

identify potential bottlenecks in production. For 

example, before the enormous fall in output by the 

producers of semiconductors, computers, and 

communications equipment in 2001, utilization rates 

among chip producers reached as high as 108 

percent, which reflected enormous demand by 

computer and communications equipment makers 

and was consistent with reported shortages of certain 

chip types. Similarly, a sharp increase in utilization 

rates among steel producers might reflect steel 

shortages, which would disrupt the supply chains of 

steel consumers like automakers. As a result, 

automakers would pay higher prices for steel, and 

these prices would likely be passed on, at least in 

part, to auto buyers. 

3. In search of an IP roadmap 

Given the significance of the economic issues 

addressed using the IP/CU data, it was critical that 

the restatement of the production and capacity 

indexes on a NAICS-basis be completed accurately. 

The construction of historical NAICS-based IP/CU 

indexes would have been straightforward if each 

detailed SIC industry corresponded with only one 

NAICS industry; we could have easily renamed and 

reorganized the existing structure to match the new 

system. Unfortunately, the mapping between SIC 

and NAICS is often not one-for-one. Table 1 

illustrates the mapping between NAICS 3361-3, 

motor vehicle and parts, and SIC 371, motor vehicles 

and motor vehicle equipment; this mapping is based 

on the 1997 Census of Manufactures. 

Note first that the coverage for the two 

classifications is not identical. For example, military 

armored vehicles (a part of SIC 3711) is not included 

in the NAICS version of the motor vehicle and parts 

industry. Conversely, travel trailers and campers 
(formerly contained in “Miscellaneous transportation 

equipment industry, SIC 379”) and automotive

stampings (formerly in “Metal forgings and

stampings, SIC 346” a fabricated metal products 

industry) are now included in the motor vehicle and

parts industry. The most complicated aspect of this

mapping is the splitting up of SIC 3711 into multiple

NAICS industries. 

The key to developing historical NAICS indexes,

therefore, was to determine the share of each SIC

industry that should be assigned to each of its

corresponding NAICS industries. The Census 

Bureau took care of this problem for 1997 by

publishing industry-level statistics, such as shipments 

and value-added, on both an SIC and a NAICS basis.

The underlying establishment level data for 1997

were dual-coded. For example, Census reported 

statistics for motor vehicles and passenger car bodies

(SIC 3711) as a single aggregate. From 1997 on, 

under NAICS, the data are split into the five

industries. The 1997 Census data gave us the share

of total output in SIC 3711 that should be attributed

to its NAICS counterparts for automobiles, for light

trucks, and so on. We could have simply applied

those 1997 shares to our historical motor vehicle IP 

aggregate to produce estimates of the value going 

into the five NAICS industries. However, the

assumption that the shares derived from the 1997

data also reflected the distribution of economic

activity in earlier years seemed too strong. 

Instead, a joint project between the FRB and the

Census Bureau’s Center for Economic Studies 

calculated, for earlier years, the actual shares for 

individual NAICS industries from the plant-level

records available at the Census Bureau (see Bayard

and Klimek, 2003). Bayard and Klimek converted

the industry assignment from SIC to NAICS for the 

hundreds of thousands of plant records covered in

each of the Census of Manufactures, back to 1963,

and then aggregated the plant level data to generate

the NAICS shares in each census year. The

conversion techniques were a combination of “exact

matching” and “statistical matching”. Specifically, in 

roughly 75 percent of the cases, NAICS codes were

assigned directly using product code data. For the

remaining cases they employed multinomial logit

models to assign plants to NAICS industries using 

plant-specific probabilities derived from the available 

plant-level data. 

Table 1. A NAICS-based decomposition of the motor vehicle and parts industry.

NAICS NAICS Industry Description SIC SIC Industry Description 

3361 Motor vehicle

 336111 Automobile mfg. 3711 pt. Motor vehicles & car bodies

 336112 Light truck & utility vehicle mfg. 3711 pt. Motor vehicles & car bodies

 336120 Heavy duty truck mfg. 3711 pt. Motor vehicles & car bodies

3362 Motor vehicle body & trailer mfg.

 336211 Motor vehicle body mfg. 3711 pt. + 2 series Motor vehicles & car bodies

 336212-4 Truck trailer, motor home, travel

 trailer & camper mfg.

4 series

3363 Motor vehicle parts mfg.

 33631-6,9 Motor vehicle parts mfg. excluding

 motor vehicle metal stamping 

16 series

 33637 Motor vehicle metal stamping 3465 Automotive stampings

Note. SIC 3711, “Motor vehicle and car bodies,” also goes into NAICS 336992, “Military armored vehicle, tank, & tank component mfg”. SIC 

3465, “Automotive stampings” previously was not in the motor vehicle and motor vehicle equipment industry, SIC 371. 

The advantage of their method is that it allows,

for example, the share of motor vehicles attributed to 

autos, light trucks, and other industries, to vary in

each Census year. This variable-share approach

captures fundamental changes in the structure of 

economic activity at very basic levels. In Figure 1 

(see also Corrado (1993), p. 157) we illustrate the

benefit of the variable-shares approach, which shows

the value of light truck production from 1972 to 2001

under the two approaches to constructing shares. The

lower line shows the value of shipments of light

trucks on a NAICS basis using the variable-shares 

method and the upper line traces the output we would

have obtained if we only had access to the shares

from the 1997 Census. As you can see, if we had 

simply applied the 1997 structure back in time, we

would have significantly overestimated light truck

production for earlier years, particularly prior to the

early 1990s. 

4. Under the hood of the IP/CU system

The mappings provided by Bayard and Klimek

(2003) formed the basis for rebuilding the inner
workings of the IP/CU system. Six primary

databases were reconstructed with these mappings:

Figure 1. Light trucks (NAICS 336112) 

Note. The shaded areas are periods of business recession as

defined by the National Bureau of Economic Research (NBER).

1. Hours. Production worker hours by industry

(Bureau of Labor Statistics). 4.1 Constructing IP

Figure 2. Database structure of IP/CU system

Notes. Hours is the Bureau of Labor Statistics data on production

worker hours by industry. Electric Power is kilowatt usage by

industry. COM/ASM is the Census of Manufactures/Annual

Survey of Manufactures; includes value-added, shipments, and 

investment by industry. SPC is the Census Bureau’s annual

Survey of Plant Capacity by industry. Capital Flows Table is the

Bureau of Economic Analysis’ data on capital expenditures by

industry in various asset categories. NIPA is the Bureau of

Economic Analysis’s National Income and Product Accounts.

2. Electric Power. Power usage by industry

(Federal Reserve System). Each series in the IP/CU system is either an

individual industry or an aggregate of individuals.

The methods and data used to construct individual IP

series vary by industry and by the point in time.

Monthly estimates are typically based on physical

product data, hours data, or electric power use. Some

series are based on physical data, such as motor 

vehicle assemblies. Others, like motor vehicle metal

stamping, are estimated using data on electric power

usage. Still other series, such as motor vehicle parts, 

are estimated using production worker hours 

(although physical data on motor vehicle assemblies

also guide the estimates). Together, the high

frequency physical data (weekly, monthly, quarterly),

electric power data, and production worker hours 

data, provide the monthly patterns to the various IP 

series. The monthly patterns are benchmarked to

annual data from the COM/ASM, Current Industrial 

Reports from the Census Bureau, and other 

comprehensive data sources from other government

agencies and trade associations. 

3. COM/ASM. Census of Manufactures and 

Annual Survey of Manufactures (Census

Bureau). 

4. SPC. Survey of Plant Capacity (Census Bureau). 

5. CFT. Capital Flows Tables (Bureau of

Economic Analysis). 

6. Invest./Capital. Investment/capital database by

industry (Federal Reserve Board). 

Other data, especially physical product/capacity data,

could essentially be mapped one-for-one between

SIC and NAICS. 

Figure 2 displays the primary data sources that

are used to construct industrial production and

capacity utilization. Solid lines denote the links

between the various data. For example, the line

between “Hours” and “IP” indicates that the

production worker hours data are used to construct

IP. Similarly, the Census of Manufactures and 

Annual Survey of Manufactures, are used both for 

industrial production and for capacity. The mappings produced by Bayard and Klimek

(2003) provided the bridge needed to translate each 

database. Some data, like the production worker

hours, were translated directly using the shares. The

electric power data was partially translated, and some

incoming data continues to be translated; generators 

may report on either an SIC or a NAICS basis to ease

reporting burdens. For the mining industries, many 

of which have physical data, the translations were

The balance of this section provides a cursory

look at the changes to data and methods that were

required to complete the revision. These changes are 

discussed in greater detail in Corrado (2003) and

Morin (2003). 
one-for-one, as NAICS did not significantly 

reorganize the classification structure of mining.4

In addition to translating databases, this revision 

was an opportunity to apply current statistical 

methods to previously unrevised years. For example, 

the published indexes now reflect current methods of 

seasonal adjustment5

 for all years, and each aggregate 

series is now calculated as a chain-type index from 

1972-on.6

4.2 Constructing Capacity Utilization 

Our estimates of industrial capacity for a year are 

developed from (1) capacity data reported in physical 

units from government and trade sources and (2) 

models that smooth the implied capacity measures 

from the Survey of Plant Capacity by using estimates 

of capital input by industry. The models in (2) 

primarily use measures of industry capital input to 

determine the annual changes in capacity from one 

year to the next. The capital input figures are 

estimates of the flow of services from an industry’s 

net stocks of physical assets; the net stocks are 

developed principally from investment data, which 

lag a couple of years. Manufacturing investment for 

recent years is forecast using a variety of capital 

spending indicators. 

Most of the capacity data reported in physical 

units was for industries that mapped one-for-one 

from SIC to NAICS (as was the case for IP). For 

industries based on the SPC and capital input data, 

we first translated the appropriate databases (see 

figure 2) and then recomputed our estimates of 

capacity using the restated data. Our translation of 

the SPC involved a non-trivial use of the shares from 

Bayard and Klimek (2003).7

 To derive the new 

measures of capital input by industry, we first 

translated the BEA’s capital flows tables (which 

provide investment by asset category); we already 

had investment by industry from the restated versions 

of the COM/ASM. The rest of the work was standard 

in that we applied the same techniques as before. 

Specifically, we used bi-proportional matrix 

balancing to calculate industry-level investment in 

each asset category (for years without a capital flows 

table); capital stocks were calculated using a 

 

4

 An exception was the newly created “support activities for 

mining (NAICS 213)” industry that consists largely of the former 

“oil and gas field services” industry but that also includes pieces 

from many of the other SIC mining industries. 

5

 Individual IP series are seasonally adjusted using the Census 

X-12 ARIMA; all seasonally adjusted aggregate indexes are 

calculated by aggregating the seasonally adjusted indexes of the 

individual series. 

6

 Documentation of current methods is available at 

www.federalreserve.gov/releases/g17/ip_notes.htm. 7

 Morin (2003) reviews the method used to translate the SPC. 

perpetual inventory model8 and measures of capital 

services were derived from these stocks; finally, the 

implied capacity estimates from the SPC were 

smoothed using the capital input measures and 

utilization rates were calculated. 

4.3. Constructing Analytical Aggregates 

The IP system is intended to serve as an 

analytical tool for understanding the behavior of 

prices and output both for individual industries and 

for aggregates of industries. Over time, the FRB has 

developed useful analytical aggregates that 1) group 

industries by major market group, and 2) group 

industries by their stage of processing. Prior to the 

recent revision, these aggregates were based 

primarily upon a judgmental allocation of each IP 

industry to a market group and a stage of process. 

While all industries were assigned to a single stage of 

process, some were assigned to multiple market 

groups. For example, autos were split between the 

consumer and business market groups. 

The 2002 revision significantly revised the 

market groupings by using the 1992 input-output 

(I-O) tables published by the Bureau of Economic 

Analysis to divide the IP indexes into multiple 

market groups where appropriate.9

 The resulting 

revisions are discussed in Corrado (2003). Similarly, 

the I-O tables were used to assign each industry to a 

single stage of process—crude, primary, 

semifinished, and finished. (Previously there were 

two stages—primary and advanced—that lacked a 

formal I-O explanation for their composition.) The 

stages are designed so that the output of each stage 

serves as the primary input for the subsequent stage; 

the output of finished processing goes to final 

demand.10 

The market groups and stages of process focus 

attention on particular segments of the economy. 

Their role is similar to the earlier discussion about 

bottlenecks in production at the industry level. If IP 

drops in a given month, we can use the market group 

indexes to separate the effects of production for 

consumers from production for businesses. The 

interpretation of the decline is then colored by our 

 

8

 See Gilbert and Mohr (1996) for a detailed discussion of the 

FRB’s capital stock estimates. 

9

 The I-O tables express total production as the sum of its use as an 

intermediate input in each industry and its absorption by the 

various components of final demand (consumption, investment, 

government expenditures, and exports). The 2003 annual revision 

will use the newly published 1997 I-O tables. 

10 More specifically, the industry composition of the four stages 

was primarily intended to minimize “backflows” from one stage to 

previous stages. A secondary objective was to minimize the 

number of cases where an industry’s output “skips” a subsequent 

stage. The approach was modeled on Gaddie and Zoller (1988). 
understanding of the various market forces that are 

impacting these two groups. Similarly, a jump in the 

market group for business equipment may suggest an 

increase, or an anticipated increase, in economic 

activity on the business side. Likewise, if utilization 

rates in the crude processing stage rise noticeably, the 

increase may have implications for commodity prices 

(now or later) and hence impact production in later 

stages. Thus, the industry, market group, and 

stage-of-process indexes all help identify potential 

choke-points in the industrial sector.

5. Restated, Revised, Rebased 

The “3-R” mantra that lends a title to this section 

is also a useful summary of the 2002 annual revision 

of industrial production and capacity utilization. IP 

was restated on a NAICS basis; revised to reflect 

current methods and updated source data for 

production, capacity, price deflators, annual 

benchmarks; and rebased so that all IP indexes equal 

100 in 1997 (previously 1992 was the base year). 

Without a doubt, it is the restatement of 30 years of 

data on a NAICS basis that has drawn the most 

attention from the statistical community. The 

absence of breaks in the time series for IP and CU 

simplifies seasonal adjustment, forecasting, and 

research. 

Other statistical agencies throughout North 

America have made similar shifts toward 

implementing NAICS as a data organizing principle. 

However, none of these agencies have restated such a 

long time series; many have simply drawn a line at a 

particular date, forever separating the SIC and 

NAICS worlds with little or no overlap. Table 2 at 

the end of this paper summarizes the status of these 

efforts for a number of statistical programs. For 

example, as discussed earlier, the Census of 

Manufactures is available on both a NAICS and SIC 

basis for just one year (1997); this short NAICS 

history was the impetus for the joint effort between 

the FRB and the Census Bureau’s Center for 

Economic Studies. The data source with the longest 

history on a NAICS, other than IP/CU, is the Census 

Bureau’s M3 data on manufacturing shipments, 

inventories, and orders, which was restated back to 

1992. Statistics Canada and Mexico’s INEGI have 

not been any more successful than U.S. programs in 

restating historical data. Nonetheless, while this has 

hampered time series analysis, cross-country analysis 

within North America has benefited from the 

harmonized system.11

 

11 For example, Holmes and Stevens (2003) compare the 

geographic concentration of industries in Canada and the United 

States using NAICS-based census data. 

Going forward, economists and other data users 

will continue to reap significant benefits from the 

cross-country data organized on a consistent basis 

and they will begin to reap the benefits of time series 

data accumulated on a NAICS basis. In the 

meantime, the revised IP/CU data provides 

researchers with the only timely data with a lengthy 

NAICS-based history. 

 

6. References 

Bayard, Kimberly and Shawn Klimek, “Reclassifying 

the Census of Manufactures from the Standard 

Industrial Classification System to the North 

American Industry Classification System, 1963 

to 1992,” 2003 Proceedings of the American 

Statistical Association, Business & Economic 

Statistics Section [CD-ROM], Alexandria, VA: 

American Statistical Association. 

Corrado, Carol, “Industrial Production and Capacity 

Utilization: The 2002 Historical and Annual 

Revision,” Federal Reserve Bulletin (April 

2003), pp. 151-176. 

Executive Office of the President of the United 

States, North American Industry Classification 

System, United States, 2002, Office of 

Management and Budget (2002). 

Gaddie, Robert and Maureen Zoller, “New Stage of 

Process Price System Developed for the 

Producer Price Index,” Monthly Labor Review

111 (April 1988), pp. 3-16. 

Gilbert, Charlie E. and Michael F. Mohr, “Capital 

Stock Estimates for Manufacturing Industries: 

Methods and Data,” Board of Governors of the 

Federal Reserve System, 

www.federalreserve.gov/releases/g17/capital_sto

ck_doc-latest.pdf (March 1996). 

Holmes, Thomas J. and John J. Stevens, “Spatial 

Distribution of Economic Activities in North 

America,” in Handbook of Regional and Urban 

Economics, Volume 4, edited by Vernon 

Henderson and Jacques-François Thisse 

(forthcoming 2003). 

Morin, Norman, “NAICS and the 2002 Historical 

Revision of Industrial Production, Capacity, and 

Capacity Utilization,” 2003 Proceedings of the 

American Statistical Association, Business & 

Economic Statistics Section [CD-ROM], 

Alexandria, VA: American Statistical 

Association.
Table 2. NAICS Status of Select Data Releases by Government Agency 

Data year(s) 

released 

Release date 

Federal Reserve Board 

Indexes of Industrial Production, Capacity, and Capacity Utilization 1972-2002 December 2002 

Census Bureau

Quinquennial Economic Census 1997 2000 

Annual Survey of Manufactures 1998 2000 

Annual Wholesale and Retail Trade Surveys 1992-1999 2001 

Monthly Manufactures Shipments, Inventories, and Orders (M3) 1992-2001 2001 

Monthly Wholesale and Retail Trade Surveys 1992-2001 2001 

Internal Revenue Service

Statistics of Income 1998 2000-2001 

Bureau of Labor Statistics

Unemployment Insurance—Covered Employment and Wages (ES-202) 2001 Fall 2002 

Current Employment Statistics (BLS-790) May 2003 June 2003 

Current Population Survey February 2003 January 2003 

Producer Price Indexes January 2004 February 2004 

Bureau of Economic Analysis

Benchmark Input-Output Accounts 1997 2002 

NIPAs 2000-2002 2003 

Capital Flow Input-Output Table 1997 2003 

Annual Input-Output Accounts 1998-2002 2004 

GDP by Industry 1998-2002 2004 

USDIA balance of payments 1999-2003 2004