Converting Historical Industry Time Series Data from SIC to NAICS 

Robert E. Yuskavage 

Bureau of Economic Analysis 

U.S. Department of Commerce 

1441 L Street NW BE-40 

Washington, DC 20230 

robert.yuskavage@bea.gov

I. Introduction 

The introduction of the North American Industry Classification System (NAICS) in the late 1990’s 

offered the promise of more relevant U.S. industry time series data for the 21st century. NAICS 

improved on the long-standing Standard Industrial Classification (SIC) system because it provided 

greater detail for the fast-growing services and high-tech manufacturing sectors and it more consistently 

classified establishments into industries based on similar production processes. U.S. statistical agencies 

faced major challenges, however, implementing NAICS for their on-going statistical programs. Perhaps 

an even greater challenge, given the lack of NAICS source data before 1997, was the need to convert 

historical industry time series data from SIC to NAICS. 

Shortly before the introduction of NAICS, the U.S. experienced an acceleration of economic growth that 

was driven partly by increased production of information and communications technology (ICT). This 

faster growth helped to revive sluggish productivity growth and spurred talk among economists about a 

"new economy." Because the growth surge appeared to originate in a few key economic sectors, 

research interest started to focus more on specific industries as parts of the broader aggregate economy, 

especially the ICT-producing and consuming industries. While the SIC-based industry data were 

initially used for this research, the way these data were organized obscured some of the more important 

contributors to the growth acceleration. NAICS was designed partly to more clearly identify these 

industries in economic statistics. 

While researchers were generally pleased that the new industry data that were classified on a NAICS 

basis better reflected the changing structure of the dynamic U.S. economy at the turn of the century, they 

were also disappointed that consistent historical industry data were no longer available. Consistent 

industry time series data are critical for studying industry contributions to economic growth, structural 

change, and productivity. One of the key datasets for studying these issues are the annual GDP-byindustry accounts prepared by the U.S. Bureau of Economic Analysis (BEA), which provide time series 

data on output and employment for industries going back to 1947. These data have been widely used by 

researchers studying the sources of output and productivity growth at the industry level in the postwar 

U.S. economy and are now available on a NAICS basis back to 1947. 1

 

1

 Output estimates on a NAICS basis for 1987-97 were released in November 2004 (Yuskavage and Pho) and output 

estimates for 1947-76 were released in December 2005 (Yuskavage and Fahim-Nader). Employment estimates on a NAICS 

basis were released in October 2006. To obtain these data, go to http://bea.gov/industry/gdpbyind_data.htm and look under 

Historic Data. 
In describing the data sources and the methodology that were used to convert the published SIC industry 

estimates for 1947 through 1997 to NAICS, this paper focuses on how BEA developed dynamic (timevarying) concordances that capture changes over time in the relative importance of new and emerging 

industries. As will be shown, this feature of the conversion process was quite important to the overall 

reliability of the NAICS estimates. The remainder of the paper is presented in three sections. Section II 

provides background on NAICS, including the motivation for its development, how it differs from the 

SIC system, and issues raised by its implementation. Section III provides background on the GDP-byindustry accounts, how they are used for historical time series analysis, and why it was important to 

recast these accounts on a consistent industry classification basis. Section IV discusses the methodology 

that was used for the conversion, how it compares to the methods used by other statistical agencies, and 

how the resulting estimates were evaluated. Section V is a summary and conclusion. 

 

 

II. Industry Classification and NAICS 

Properly designed industry classification systems serve valuable purposes but they also suffer from 

limited useful lives in a dynamic, changing economy. With such systems, a trade-off exists between 

data that are relevant for recent periods and data that are consistent over long periods. Industry 

classification systems allow statistical data for an economy’s producing units (establishments or 

enterprises) to be aggregated into meaningful categories (sectors), such as manufacturing, trade, and 

services. Aggregate data are used by industry analysts and economic researchers to determine the 

relative sizes of sectors, changes over time in their importance in the economy, their contributions to 

economic growth, and their performance compared with similar sectors in other countries. 

Industry classification systems can become increasingly irrelevant, however, as the economy grows and 

as the relative importance of sectors changes. New and emerging products and processes may not be 

recognized either because they did not exist or were very small when the classification system was first 

developed. The high degree of aggregation often used by statistical agencies for reporting the data, due 

to resource or reliability constraints, is another limitation. Even if new or emerging products have a 

clearly defined place in the classification system, their growth may be obscured if they are a relatively 

small part of an aggregate that includes other types of products. 

Limitations of the SIC System

For more than 50 years, the SIC system was the framework used by the U.S. to classify establishments 

into industries for the purpose of producing economic statistics. The SIC coding system, which grouped 

establishments by their primary activity, was periodically revised to better reflect the U.S. economy’s 

changing industrial organization and structure. The most recent revision was in 1987. Despite ongoing 

efforts to maintain its relevance, the SIC system was subject to criticism about the length of time 

between revisions, inadequate representation of the fast-growing services and high-tech sectors, and the 

lack of a clear conceptual rationale. These concerns were addressed at the 1991 International 

Conference on the Classification of Economic Activities in Williamsburg, Virginia. A direct outcome of 

the conference was the creation the following year of the Economic Classification Policy Committee 

(ECPC) by the Office of Management and Budget. The ECPC was charged with taking a fresh look at 

how to design an industrial classification system for a rapidly-changing economy. 

The Bureau of Economic Analysis (BEA) chaired the ECPC, which included representatives from the 

Census Bureau and the Bureau of Labor Statistics (BLS). Ultimately, the ECPC decided to participate 
in a joint effort with the statistical agencies of Canada and Mexico to develop the North American 

Industrial Classification System (NAICS). The passage of the North American Free Trade Agreement 

in 1994 was also a motivating force for developing a more up-to-date, uniform classification system 

because it would allow more relevant comparisons of economic and financial statistics across countries. 

NAICS was formally adopted as the new U.S. industrial classification system in 1997. Subsequent 

revisions resulted in the 2002 version of NAICS and a revised 2007 version was released earlier this 

year. 

 

Advantages of NAICS 

In general, NAICS improves on the SIC as an industry classification system because it more consistently 

classifies establishments into industries on the basis of similar production processes, it recognizes new 

and emerging industries, and it provides greater detail for the services sector. Some of its more valuable 

features are the establishment of an “information” sector that includes software publishing and other 

new types of communications services, the classification of auxiliaries according to the services they 

provide rather than the industry they serve, and a clearer separation of different types of high-tech goods 

and services such as computers and electronic products and information services. The ECPC decided on 

a production-oriented classification structure for two reasons. First, as a matter of principle, an industry 

classification system should be based on producing units rather than products or services. Second, a 

supply-based conceptual framework enables more accurate comparisons among industries because data 

are consistently classified according to factors related to the production process, such as outputs, inputs, 

and employment. 

Implementation of NAICS posed many considerable statistical and logistical obstacles because the scope 

of the changes was much greater than previous changes to the SIC system. The 1997 version of NAICS 

was first implemented in the 1997 economic censuses conducted by the Bureau of the Census, and these 

were released starting in 1999. Afterwards, the samples for the Census Bureau’s annual, quarterly, and 

monthly surveys were re-drawn and these surveys were then conducted on a NAICS basis. The Internal 

Revenue Service (IRS) Statistics of Income program incorporated NAICS for tax year 1998 data that 

were released in 2000. BLS followed shortly afterwards in early 2003 with the conversion of its 

monthly employment and earnings surveys to the 2002 version of NAICS. BLS producer price indexes 

were converted in 2004. These primary data sources then started filtering into downstream industry 

programs such as BEA’s input-output (I-O) accounts, the BLS productivity programs, and the Federal 

Reserve Board’s index of industrial production. Most industry economic programs were fully converted 

to NAICS by 2004, seven years after the NAICS reference year. 

III. GDP by Industry and the Annual Industry Accounts 

BEA’s annual industry accounts (AIAs) include the integrated GDP-by-industry and annual input-output 

(I-O) accounts. In these accounts, industries are defined on an establishment basis according to NAICS. 

Estimates are published for 61 private industries and four government classifications. The GDP-byindustry accounts feature estimates of nominal and real value added by industry. Value added is defined 

as an industry’s gross output (sales or receipts and other operating income) minus its intermediate inputs 

(energy, materials, and purchased services). Value added summed over all industries equals GDP. 

Intermediate inputs are goods and services acquired from either domestic or foreign sources (imports). 

Price and quantity indexes of gross output, intermediate inputs, and value added are published for 
industries, industry groups, and broad sectors in the GDP-by-industry accounts. Several estimates of 

employment by industry from the national income and product accounts (NIPAs) are also provided. 

Uses of BEA’s Industry Accounts 

The GDP-by-industry accounts are used to study structural change and sources of growth in the U.S. 

economy, to compare U.S. industrial performance with other countries, and to assess the contributions of 

industries and sectors to aggregate productivity growth. Because these accounts are conceptually and 

statistically consistent with the estimates of gross domestic product (GDP) from the NIPAs, they can be 

used to determine the contributions of industries and sectors to aggregate economic growth and 

inflation. These accounts have been widely used by academic researchers studying the contribution of 

industries to the U.S. productivity acceleration of the late 1990’s and to the recent acceleration since 

2001. (See Corrado et. al., Nordhaus, Stiroh, and Triplett and Bosworth for examples.) By providing 

annual estimates of nominal and real gross output, intermediate inputs, and value added for all 

industries, these accounts allow researchers to understand changes over time in the relative importance 

of industries. The nominal (current-dollar) value added estimates provide measures of industry size 

relative to GDP, and the real value added estimates provide measures of industry contributions to real 

GDP growth. 

The annual I-O accounts, which are integrated with the GDP-by-industry accounts, provide a time series 

of detailed, consistent information on the flows of goods and services that are inputs into industry 

production processes and that are included in final expenditures. These accounts are presented in 

standard make and use tables and several supplementary tables, and they provide more detail than the 

GDP-by-industry accounts on the commodities included in gross output and intermediate inputs. The 

make table shows the commodities (goods and services) that are produced by each industry. The use 

table shows the commodity inputs to industry production and the commodities that are consumed by 

final users. 

BEA released the NAICS-based integrated GDP-by-industry and annual I-O accounts for the years 

1998-2003 in June 2004 (Moyer, Planting, Kern, and Kish). The new methodology featured innovative 

procedures that were designed to increase consistency among the estimates in BEA’s economic 

accounts. The quality of the estimates was also improved by the use of annual commodity (product) 

data in a balanced input-output framework. One of the key data sources for implementing the new 

methodology was the 1997 benchmark I-O use table that was available for the first time on NAICS 

basis. This I-O use table was adjusted to incorporate the 2003 comprehensive revision of the NIPAs.2

Challenges Raised by NAICS 

Preparing the estimates on a NAICS basis for the first time, however, also posed challenges. The GDPby-industry accounts use source data from a wide variety of federal economic statistics programs and 

other sources. The estimates are prepared using source data collected by other agencies and the data are 

adjusted by BEA to meet NIPA and industry accounts definitions and conventions. For example, the 

initial estimates of the gross operating surplus portion of nominal value added by industry are based 

largely on tax return data from the IRS Statistics of Income program, compiled on a company basis 

rather than an establishment basis. Estimates of wages and salaries by industry are largely based on data 

from BLS, as are the consumer price indexes and producer price indexes used for deflation. Estimates 

 

2

 See Lawson, Bersani, Fahim-Nader, and Guo for information about the 1997 benchmark I-O accounts. 
of nominal gross output by industry are estimated using data from Census Bureau annual surveys, but 

also use data from regulatory agencies and trade associations. While some of the required source data 

were available on a NAICS basis, BEA needed to convert other source data from SIC to NAICS for the 

initial set of integrated estimates. 

 

At the time the integrated estimates were released, GDP-by-industry estimates for years before 1998 

were available only on the SIC basis and were not based on the new integrated methodology. As a 

result, it was not possible for researchers to conduct industry-level research and analysis for long time 

periods using consistent methodology and classifications. This was especially troublesome given the 

degree of structural change that took place during the 1980’s and 1990’s. Members of BEA’s Advisory 

Committee and academic and non-academic researchers strongly urged BEA to develop a consistent 

historical time-series of industry data on a NAICS basis. This was considered important not only for the 

sake of reliable economic time series data, but also to take advantage of the special features of NAICS 

that more clearly show the sources of growth in the services sector and in high-tech goods-producing 

industries. 

IV. Conversion of the GDP-by-Industry Accounts 

Conversion of the GDP-by-industry accounts from SIC to NAICS for years before 1998 posed several 

major challenges for BEA. As described above, BEA uses a wide variety of data sources, mostly from 

other agencies, to compile the industry estimates of nominal and real value added, and it was not 

feasible to request that all of these data sources be converted from SIC to NAICS on a historical basis. 

As a result, because the methodology that was used for 1998 forward could not be used for years before 

1998, BEA conducted research into using indirect “backcasting” techniques that extrapolate recent 

estimates into the past based on historical relationships. 

Backcasting Issues 

In designing a strategy to overcome the source data limitations, BEA faced a variety of issues, such as 

the time span covered, the number of data items to be provided, and the level of industry detail. Tradeoffs abounded among these issues, and their resolution partly depended on the desired degree of 

accuracy in the converted series. In making key decisions, BEA relied on both its own research and 

suggestions from academic and business users with a strong interest in industry time series. These 

suggestions included providing data for as many years as possible, making maximum use of available 

historical SIC-based data, focusing on the most important data items, and considering aggregation as an 

acceptable means of dealing with both source data limitations and reliability concerns for distant years. 

The backward extrapolation (i.e., backcasting) methodology was ultimately designed to provide 

historical annual estimates that are consistent over time, that preserve the broad patterns observed in the 

previously published SIC-based estimates, and that incorporate the latest results from BEA's inputoutput accounts and national income and product accounts. 

In general, when historical source data classified on a new basis are not available, statistical conversion 

procedures tend to rely heavily on concordances developed for a single year that show the relationship 

between data items classified on both the old basis and the new basis. Such single-year static 

concordances are reliable for a limited number of years before the reference year, but they become 

increasingly unreliable over time as relationships change among the industries. Concordances that 

capture changes over time in the relative importance of new industries yield more reliable results. The 
rest of this section describes the procedures that were used to develop dynamic time-varying 

concordances and how these concordances were used to develop the historical NAICS-based estimates. 

It also describes the special challenges that were involved in converting both current-dollar and real 

(inflation-adjusted) estimates. 

Other Agency Conversions 

In converting the indices of industrial production for manufacturing from SIC to NAICS back to 1972, 

the Federal Reserve Board (FRB) largely avoided the source data problem described above by assigning 

NAICS industry codes to manufacturing establishments in the quinquennial economic censuses and then 

calculating SIC to NAICS conversion factors that varied over time for detailed manufacturing industries 

(Bayard and Klimek, 2003). Separate conversion factors were calculated for shipments, value added, 

inventories, capital expenditures, employment, and other key variables, and these factors were used in 

conjunction with annual survey data for manufacturing industries to develop NAICS-based industry time 

series (Corrado, 2003). This procedure was feasible due to the availability of longitudinal plant-level 

product data for manufacturing and the large number of one-to-one matches at detailed levels within the 

manufacturing sector. Exact matching techniques were used for more than 90 percent of manufacturing 

shipments in each of the economic census years. 

Unfortunately, similar data for developing SIC to NAICS conversion factors were not readily available 

for non-manufacturing industries, which accounted for about 80 percent of private-sector GDP in 1997. 

Consequently, agencies that converted SIC data to NAICS for these industries tended to rely on fixed 

conversion factors from 1997 or other recent years. The Bureau of the Census converted its monthly 

and annual series for wholesale trade and retail trade sales and inventories to NAICS starting with 1992 

partly by assigning NAICS industry codes to employer establishments in the 1992 economic census 

(Shimberg, Detlefsen, and Davie, 2002). In addition, BLS reconstructed its monthly payroll, 

employment, and related series from SIC to NAICS back to 1990 for all detailed NAICS industries. For 

certain higher-level industry aggregates, the conversion went back to 1939. These conversions were 

primarily based on employment ratios computed by assigning both NAICS and SIC codes to 

establishment microdata for March 2001 in the BLS Longitudinal Database (Morisi, 2003).3

BEA Procedures 

For BEA, the conversion of historical SIC data to NAICS relied heavily, by necessity, on concordances 

that were developed from tabulations of aggregate data classified according to both SIC and NAICS. 

The methodology for converting the nominal (current-dollar) SIC industry estimates to NAICS was 

basically the same for the entire period 1947-97, but there were some important differences for the subperiods 1987-1997 and 1947-1986. The conversion methodology was more extensive and included 

more variables for 1987-1997 for two reasons. First, the SIC-based series available for conversion were 

more complete and second, BEA decided that more detailed results could be provided for this period 

without a significant loss of accuracy. This section describes the conversion methodology for currentdollar estimates and for employment for 1987-97 in detail, followed by briefer descriptions of first the 

current-dollar and employment estimates for 1947-86 and then the real (constant-price) estimates for all 

periods. 

 

3

 In October 2004 BLS completed a release of employment and earnings on a NAICS 2002 basis back to 1990 from its 

Quarterly Census of Employment and Wages (QCEW, formerly ES-202) program. These data were later used by BEA for its 

estimates of employment by NAICS industry. 
Current-dollar and Employment Estimates for 1987-97 

The conversion methodology for 1987-97 for each of the industry current-dollar and employment 

estimates can be summarized by the following six-step procedure: 

 

 

Step 1: Develop a 1997 benchmark concordance between SIC and NAICS 

Step 2: Extrapolate the benchmark concordance annually back to 1987 

Step 3: Develop an annual time series of SIC to NAICS conversion matrices 

Step 4: Convert the published SIC estimates to NAICS 

Step 5: Extrapolate the 1997 benchmark NAICS levels back to 1987 

Step 6: Adjust the extrapolated estimates to NIPA control totals. 

Each step is described in more detail below. 

Step 1: Develop a 1997 benchmark concordance between SIC and NAICS. A 1997 benchmark 

concordance between NAICS 97 and SIC 87 was developed from the detailed 1997 benchmark I-O 

accounts, which include data for about 850 private industries at approximately the six-digit NAICS 

level. Each detailed six-digit NAICS industry code was mapped to both a detailed SIC code and a 

higher-level (aggregated) SIC code that corresponds to the GDP-by-industry publication level on the 

SIC basis (approximately 2-digit SIC). For each detailed NAICS industry, the benchmark I-O data set 

included estimates of gross output, intermediate inputs, compensation of employees, taxes less subsidies, 

and gross operating surplus. Summing the last three components yields nominal value added. At a later 

stage, estimates of full-time and part-time employment were added to the file. This concordance was 

based on an unpublished version of the 1997 benchmark I-O use table that was adjusted to incorporate 

the results of the 2003 NIPA comprehensive revision. 

The benchmark concordance included data for 12 different types of auxiliaries that are recognized by 

NAICS, although most auxiliary activity is accounted for by one type: corporate, subsidiary, and 

regional managing offices (NAICS 55114). These auxiliaries were defined as central administrative 

offices in the SIC system. Data for each of the 12 types of auxiliaries were distributed to publicationlevel SIC industries in the benchmark concordance. These allocations were based on special tabulations 

from the 1997 economic census that showed the distribution of auxiliary expenses, payroll, and 

employment according to the SIC industry served. 

Table 1 is an extract from the detailed 1997 benchmark concordance simplified for illustrative purposes. 

The first two columns show the detailed and aggregated NAICS industry codes and the third and fourth 

columns show the detailed and aggregated SIC industry codes. The next four columns indicate that, for 

each detailed NAICS industry identified in the first column, 1997 dollar values were available for gross 

output, compensation of employees, taxes less subsidies, and gross operating surplus. The aggregated 

NAICS and SIC industry codes correspond to the publication level for private industries in BEA’s 

Annual Industry Accounts for 1998 forward and in the GDP-by-industry accounts for 1947-97. Tables 

A, B, and C in the appendix present the publication-level industry descriptions for private industries and 

the related industry codes for the 1997 NAICS, the 1987 SIC, and the 1972 SIC. 
Table 1.-- Extract of 1997 Benchmark Concordance 

Detailed 

NAICS 

Industry 

Code 

Aggregated 

NAICS 

Industry 

Code 

Detailed 

SIC 

Industry 

Code 

Aggregated 

SIC 

Industry Code 

1997 

Gross 

Output 

1997 

Compensation of 

Employees 

1997 

Taxes 

less 

Subsidies 

1997 

Gross 

Operating 

Surplus 

... ... 

... ... ............

111910 111, 112 0132 01-02 $ $ $ $ ... ... ... ... 

............

113310 113-115 2411 24 $ $ $ $ ... ... ... ... 

............

211111 211 1311 13 $ $ $ $ ... ... ... ... 

............

... ... ... ... ............

... ... ... ... ............

713930 713 4493 44 $ $ $ $ 

721310 721 7021 70 $ $ $ $ 

722000 722 5812 52-59 $ $ $ $ ... ... ... ... 

............

812930 81 7521 75 $ $ $ $ ... ... ... ... 

............

Table 1 indicates that the logging industry (NAICS 113310, SIC 2411) is included in the lumber and 

wood products industry (SIC 24) in the SIC system but in the forestry, fishing, and related activities 

industry (NAICS 113-115) under NAICS. Similarly, the eating places industry (NAICS 722000, SIC 

5812) is included in the retail trade industry (SIC 52-59) in the SIC system but in the food services and 

drinking places industry (NAICS 722) under NAICS. 

Step 2: Extrapolate the benchmark concordance annually back to 1987. Detailed concordances 

were developed for each year 1987-1997 by extrapolating back in time the detailed 1997 NAICS 

industry estimates, including employment. For each detailed NAICS industry, an SIC-based value or 

employment series for the period 1987-1997 was matched using the detailed SIC code. Shipments, 

sales, or receipts were used to extrapolate the 1997 benchmark levels of gross output and the value 

added components. These SIC-based series were obtained from the underlying detail used for the GDPby-industry program, and are generally based on Census Bureau annual surveys. The employment series 
for most of the industries were obtained from the BLS ES-202 program, which were also available at the 

four-digit SIC level. 4

Table 2 is an extract of the extrapolated benchmark concordance for the variable gross output, simplified 

for illustrative purposes, for the period 1987-1997. Because of the detailed nature of the data, most of 

the matches between the six-digit NAICS level and the detailed SIC level were exact (one-to-one). 

Sometimes the SIC data had to be combined, and occasionally the SIC data had to be split because the 

NAICS industry was more detailed. These splits were based on data from the 1997 Census NAICS-SIC 

concordance, which has more industries and is more detailed than the 1997 I-O benchmark concordance. 

Employment for NAICS 55114 was extrapolated back to 1988 using data for central administrative 

offices collected as part of the 1987, 1992, and 1997 economic censuses. 

Table 2.-- Extract of Extrapolated Benchmark Concordance for Gross Output 

Detailed 

NAICS 

Industry 

Code 

Aggregated 

NAICS 

Industry 

Code 

Detailed 

SIC 

Industry 

Code 

Aggregated 

SIC 

Industry Code 

1987 

Gross 

Output 

... 

1997 

Gross 

Output 

1987 

Industry 

Shipments 

... 

1997 

Industry 

Shipments 

... ... ... ... ..................

111910 111, 112 0132 01-02 $ $ $ $ $ $ ... ... ... ... 

..................

113310 113-115 2411 24 $ $ $ $ $ $ ... ... ... ... 

..................

211111 211 1311 13 $ $ $ $ $ $ ... ... ... ... 

..................

... ... ... ... ..................

... ... ... ... ..................

713930 713 4493 44 $ $ $ $ $ $ 

721310 721 7021 70 $ $ $ $ $ $ 

722000 722 5812 52 $ $ $ $ $ $ ... ... ... ... 

..................

812930 81 7521 75 $ $ $ $ $ $ ... ... ... ... 

..................

The first four columns are the detailed and aggregated NAICS and SIC industry codes from table 1. The 

next three columns indicate that nominal gross output values were estimated for each year 1987-97. The 

 

4

 Employment data were extrapolated back to 1988 (rather than 1987) due to difficulties with matching data at the four-digit 

SIC level for 1987, which BLS classified on the 1972 SIC basis. 
last three columns indicate that industry shipments data were included in the concordance for each 

detailed NAICS industry. These shipments data were matched based on the SIC code and were used for 

the extrapolation of gross output for years before 1997. For example, for detailed NAICS industry 

113310 (logging), gross output for 1997 from the benchmark concordance was extrapolated back to 

1996 and each year back to 1987 using the annual series of shipments for SIC 2411. This series was 

based on the Census Bureau’s Annual Survey of Manufactures (ASM). Shipments and gross output are 

not exactly the same but are very similar. 

Step 3: Develop an annual time series of SIC to NAICS conversion matrices. The detailed annual 

concordances developed in step 2 were cross-tabulated by the publication-level NAICS industry codes 

and the publication-level SIC industry codes. The resulting annual “conversion matrices” consist of 61 

private SIC-based industries in the columns and 61 private NAICS industries along the rows. Using the 

cross-tabulated dollar and employment amounts, column coefficients (which sum to 1.0) were calculated 

that show the percentage of a publication-level SIC industry’s estimate that should be allocated to a 

specific publication-level NAICS industry. 

Table 3 is a sample hypothetical conversion matrix for any year and any variable. Publication-level SIC 

industry codes (s=61) are shown at the top of the columns and publication-level NAICS industry codes 

(n=61) are shown at the beginning of the rows. Column coefficients sum to 1.0 and some of the 

hypothetical cell coefficients are shown for illustrative purposes. When a one-to-one match exists 

Table 3.-- Sample Conversion Matrix for Publication-level Industry Estimates 

s = 61 

n = 61

SIC s

01,

02 ... 13 ... 24 ... 44 ... 52 ... 70 ... 75 ... 88 

NAICS t 

111,112 1.0 

 

 

113-115 .25 ... 

211 ... 

 

 

... 

44,45 .65 ... 

 

... 

 

... 

 

621 

 

 

... ... 

713 

 

 

 

 

... 

721 .85 ... 

722 .35 .15 ... 

 
... 

 

... ... ... 

81 

Total 1.0 ... 1.0 ... 1.0 ... 1.0 ... 1.0 ... 1.0 ... 1.0 ... 1.0 

 

 

between an SIC industry and a NAICS industry, the coefficient in the cell where the industries intersect 

equals 1.0. The farming industry (s=01,02 and n=111,112) is an example of such a one-to-one match. 

In this case, estimates for the SIC industry are allocated entirely to the NAICS industry, and the NAICS 

industry does not include estimates from any other SIC industry in whole or in part. 

Conversion matrices are compiled for each variable in each year by cross-tabulating the data for that 

variable in the extrapolated benchmark concordance. Conversion matrices with annual weights--as 

opposed to fixed reference-year weights--capture changes over time in the shares used to convert from 

SIC to NAICS. Below is an example that shows the percentages that were used to distribute 

employment for the SIC retail trade industry to the corresponding NAICS industries for 1988 and for 

1997. 

NAICS Industry 1988 1997

 Retail trade 62.9 60.9 

 Food services & drinking places 34.7 36.7 

 Management of companies 1.7 1.7 

 Other industries 0.7 0.7 

If the 1997 conversion matrix shares had been held constant and used for 1988, then employment in the 

NAICS food services and drinking places industry would have been overstated in 1988 and would have 

shown slower growth over the period 1988-97. In contrast, employment in the NAICS retail trade 

industry, which excludes eating and drinking places, would have been understated and would have 

shown faster growth over this period. 

Another way to highlight the impact of using variable versus fixed shares in the conversion matrix is to 

compare the growth rates of gross output for selected industries using the two sets of shares as weights. 

Table 4 presents the annual average growth rate of nominal gross output for 1987-97 for fast-growing 

industries connected with computer equipment, software, and business and professional services. 

Column (1) shows the growth rate using fixed 1997 shares and column (2) shows the growth rate using 

variable annual shares. The third column shows the effect of using variable shares rather than fixed 

shares. Average annual growth rates are significantly higher for the 10-year period using variable 

shares, especially for the computer systems design and related services industry. 
Table 4.-- Nominal Gross Output for Selected Industries 

Fixed vs. Variable Shares, 1987-97 

(Average annual growth rate) 

Industry 

Fixed 

(1997) 

Shares 

(1) 

Variable 

(Annual) 

Shares 

(2) 

Effect of 

Variable 

Shares 

(2) - (1) 

Computer and electronic products 6.3 7.1 0.8 

Publishing industries (includes software) 6.4 7.8 1.4 

Computer systems design & related services 12.2 15.6 3.4 

Administrative and support services 5.4 7.6 2.2 

Step 4: Convert the published SIC estimates to NAICS. Estimates for each of the 61 published 

private SIC industries for 1987-1997 were distributed to the 61 publication-level NAICS industries by 

multiplying the published SIC industry estimates by the column coefficients in the annual conversion 

matrices and summing the allocations along the NAICS rows.5 The published SIC industry estimates 

for gross output, compensation of employees, taxes less subsidies, gross operating surplus, and full-time 

and part-time (FTPT) employment incorporated the results of the 2003 comprehensive NIPA revision. 

The NIPA statistical discrepancy was first distributed among private nonfarm non-housing industries in 

proportion to each industry’s gross operating surplus. Adjustments were also made to impute gross 

output for auxiliaries because such output was not recognized in the SIC system. 

Equation (1) summarizes the conversion of the published SIC-based dollar and employment levels to 

NAICS-based levels (converted estimates) for each variable (data item) in each year. Ct

k represents an n 

x s conversion matrix for variable k in year t, where n is the number of NAICS industries (61) and s is 

the number of SIC industries (61). Matrix elements cns represent column coefficients that sum to 1.0. S 

is an s x 1 column vector of SIC values for variable k in year t. Multiplying C by S yields N, an n x 1 

column vector of NAICS industry values for variable k in year t. 

(1) Ct

k · Stk = Ntk

 (n x s) (s x 1) (n x 1) 

Step 5: Extrapolate the 1997 and 1998 NAICS levels back to 1987. The NAICS industry series 

derived in step 4 were used to extrapolate the 1997 benchmark gross output and value added component 

levels for 1997 back to 1987. This step adjusts for differences in the estimate levels for 1997 between 

the converted estimates from step 4 and the benchmark estimates. The actual backcasting procedure is 

summarized by equation (2), which indicates that the dollar value (V) of a value-added component k 

(k=1,...,3) for NAICS industry i in year t-p equals the value in the following year (t-p+1) multiplied by 

the ratio of the converted values for industry i from vector Nk for both years. For example, 

(2) Vk

i, t-p = Vki, t-p+1 · (nki, t-p / nki, t-p+1) where i = 1,...,n 

 t = 1997

 p = 1,...,10

and where Vk

i, t = benchmark values. 

 

5

 SIC employment for 1987 was distributed to NAICS industries using the conversion matrix for 1988. 
 

For example, the value of compensation of employees for the NAICS computer and electronic products 

industry (NAICS 334) in 1997 is obtained from the 1997 benchmark file. The value in 1996 equals the 

1997 value multiplied by the ratio of compensation for NAICS 334 from the 1996 conversion matrix to 

compensation for NAICS 334 from the 1997 conversion matrix. The converted value from the 1997 

conversion matrix can differ from the benchmark value for various reasons, but these differences are 

usually small. 

In addition to the value-added components, gross output and employment were also converted from SIC 

to NAICS using similar procedures. These variables are not indexed by superscript k because they are 

not part of the value-added summation. The estimation of their backcast values is described by 

equations (2a) and (2b). Superscript go refers to gross output and superscript emp refers to full-time and 

part-time employment: 

(2a) Vgo

i, t-p = Vgo

i, t-p+1 · (ngoi, t-p / ngoi, t-p+1) and 

(2b) Vemp

i, t-p = Vemp

i, t-p+1 · (nemp

i, t-p / nempi, t-p+1). 

For employment, t = 1998 and Vemp

i, t is obtained from published NIPA estimates. At a later stage, the 

employment extrapolators obtained from the conversion matrices for 1990-1998 were replaced by the 

actual NAICS employment estimates from the BLS conversion of the Quarterly Census of Employment 

and Wages data. These converted BLS data were used directly for consistency with the estimates of 

employment by industry from BEA’s regional economic accounts. 

Step 6: Adjust the extrapolated estimates to NIPA control totals. For each year 1987-1997, for 

each extrapolated industry value added component and for employment, the sum over private industries 

was adjusted so that it equals the independent NIPA total for the private sector. The revised SIC 

estimates were used directly for government enterprises and for general government. The aggregate 

NIPA estimates were used as control totals for private industries because NAICS did not affect the 

definition of the private sector. Adjustments to match the controls were made for compensation of 

employees, taxes less subsidies, gross operating surplus, and employment. Value added by industry was 

then obtained as the sum of the three adjusted value-added components for each industry. Intermediate 

inputs by industry were obtained as the difference between gross output and value added. These 

adjustments to controls insure that, in each year, each component separately equals the aggregate NIPA 

amount and that the aggregate value added components sum to GDP. Research has demonstrated that 

conversion of the three components separately yields better overall results than converting nominal 

value added directly. 

Equations (3) through (6) describe these adjustments. In any give year, Vk

T represents the aggregate 

NIPA total for a value-added component, such as compensation of employees. An adjusted value for 

each NAICS industry (Vk′

i ) is obtained by multiplying the unadjusted estimate from the backcast 

procedure in step 5 by a scaling factor. The scaling factor equals the ratio of the NIPA total to the sum 

of the value-added component over private industries: 

 

(3) Vk′

i = Vki

 · (Vk

T / ∑i Vki ). 
Summing the adjusted values of each value added component over all industries yields aggregate values 

that equal the published NIPA totals: 

(4) ∑i Vk′

i = VkT. 

For a given NAICS industry, summing the three adjusted value-added components yields nominal value 

added for the industry. Summing value added over all industries equals GDP. 

 3

(5) ∑ Vk′

i = VAi and 

 k=1 

 n

(6) ∑ VAi = GDP. 

 i=1

Nominal and Employment Estimates Before 1987 

As noted above, the conversion for the years before 1987 was more limited than that for the period 

1987-97. This difference in treatment was partly due to the more limited detail available in the SIC data 

before 1987 and partly due to BEA’s concerns about the reliability of more detailed industry estimates 

for the earlier years. Annual conversion matrices with variable shares for value added were developed 

for 1977-86 on the 1972 SIC basis. The 1977 conversion matrix for value added was held constant for 

1947-76 because of the limited availability of SIC-based source data for extrapolation, especially in the 

non-manufacturing sector. For the years 1947-86, only estimates of value added and employment were 

prepared. Components of value added, gross output, and intermediate inputs were not estimated. 

Estimates for 65 industries were provided for the period 1977-86, but for the period 1947-76 estimates 

were provided for only 22 broad industry groups. 

Real Value-Added by Industry Estimates 

One of the most important uses of industry output measures, both gross output and value added, is for 

time series analysis of economic growth and productivity change at the industry level. These types of 

analyses require inflation-adjusted (real) estimates of outputs and inputs by industry in order to identify 

the impact of changes in quantities or real magnitudes. Price indexes are needed for deflation of both 

industry outputs and inputs. The preferred method for calculating real value added by industry is the 

double-deflation method, in which real value added is estimated as the difference between real (deflated) 

gross output and real (deflated) intermediate inputs. BEA uses a Fisher index number formula for this 

calculation. 

Developing historical real value added by industry estimates on a NAICS basis was very challenging 

because of the need to develop both industry and commodity price indexes on a NAICS basis, and to 

determine the commodity (product) composition of intermediate inputs for deflation. The latter required 

input-output use tables on a NAICS basis before 1997, which were not available at the time. As a result 

of these source data limitations, BEA took different approaches for different time periods. In all 

periods, the price and quantity indexes for farms, government enterprises, and general government were 

obtained directly from the revised SIC-based estimates, since NAICS did not affect the definitions of 

these industries. The different approaches for the different time periods are described below. 
Real Estimates for 1987-97. Real estimates (chain-type quantity indexes) of gross output, intermediate 

inputs, and value added were prepared for each of the 65 detailed industries and for related industry 

groups and aggregates, including private industries and “all industries.” Real value-added estimates 

were computed using the double-deflation method after first computing Fisher price indexes for industry 

gross output and for intermediate input commodities. These aggregate price indexes were calculated 

using the detailed SIC-based price index series that were matched to the benchmark concordance. The 

price indexes were generally available at the same level of detail as the shipments, sales, and receipts 

data used to extrapolate the benchmark concordance (see table 2.) The unpublished NAICS-based 

chain-type quantity indexes for 1997 from the June 2004 release were extrapolated back to 1987 using 

the Fisher quantity relatives computed from the current-dollar values and price indexes. 

The double-deflation procedure used for these estimates is a close approximation of the procedure used 

for the revised SIC-based estimates, and it is similar to the procedure currently used for the annual 

integrated estimates. However, the level of commodity detail for the deflation of intermediate inputs is 

less than in either of those other methodologies. For the deflation of intermediate inputs for 1987-97, IO use tables were prepared that show the commodity composition of intermediate inputs--based on 

about 130 commodities--for each detailed published NAICS industry. The use table for 1997 was based 

on the published 1997 benchmark I-O accounts. Use tables were developed for 1992 and for 1987 by 

converting the published I-O benchmark use tables for those years from SIC to NAICS at the summary 

level of detail (about 130 industries and commodities). Use tables for the other years were developed by 

linear interpolation between benchmark years. Commodity price indexes were compiled for about 130 

commodities from the price index detail in the benchmark concordance. 

Real Estimates for 1947-86. Because of the limited availability of price indexes and input-output 

tables on a NAICS basis before 1987, real value added estimates for 1977-86 were computed using a 

single-deflation method as opposed to the more data intensive double-deflation method. Singledeflation is an alternative deflation method recommended by international statistical organizations when 

the data needed for the preferred double-deflation method are not available. Real estimates (chain-type 

quantity indexes) of value added were prepared for each of the industries and for related industry groups 

and aggregates, including private industries and “all industries.” 

Real value-added estimates were computed using a single-deflation method after first converting SICbased value-added price indexes to NAICS-based price indexes using the same set of annual conversion 

matrices that were used to convert the current-dollar value added estimates. This procedure computes 

the value-added price index relative for each NAICS industry as a weighted average of the value-added 

price index relatives for each of the SIC industries that contribute to the NAICS industry. The weights, 

which were obtained from the annual conversion matrices described above, represent the share of a 

NAICS industry’s current-dollar value added accounted for by a specific SIC industry (row 

coefficients). The following equation describes how the weighted-average value-added price index 

relative for period t-1 to t was computed for each NAICS private nonfarm industry: 

s

s

P

t

P

t

ns

n

P

t

P

t

VA

VA

w

VA

VA ∑=

− −

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

=

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ 60

1

1 1

 

where / for each n 1,...60 and

60

1

= ∑ =

s=

PQ

ns

PQ wns VAns VA
VAP

t represents an industry’s value added price index for period t , 

VAPQ represents an industry’s nominal value added, 

n represents a NAICS industry and s represents an SIC industry and 

ns represents a cell in the nominal value added conversion matrix. 

The SIC-based value-added price indexes for 1977-87 are the revised indexes that were released in June 

2004 as part of the comprehensive revision of the annual industry accounts. The SIC-based value-added 

price indexes for 1947-76 were calculated from previously published SIC industry estimates that were 

last updated in July 1988, before the introduction in 1991 of changes in methodology for real valueadded estimates. The published NAICS-based chain-type quantity indexes for 1987 were extrapolated 

(chained) back to 1947 using the value-added quantity relatives computed from the current-dollar values 

and price indexes. 

Evaluating the results

The converted NAICS estimates were evaluated for reasonableness and consistency primarily by 

comparison with other related estimates. Comparisons were made with the revised SIC-based estimates 

at aggregate levels and with more detailed industry groups whose definitions were not significantly 

affected by the conversion to NAICS. For 1987-97, when the converted results are expected to be the 

most reliable, the average growth rates of real value added and the shares of current-dollar GDP were 

about the same before and after the conversion to NAICS (table 5). Manufacturing’s real growth rate 

was slightly larger under NAICS, but this difference is partly due to the shift of publishing industries 

from nondurable-goods manufacturing to the information sector. As expected, the GDP share of goodsproducing industries and of manufacturing is lower under NAICS than under the SIC. The NAICSbased estimates also show the decline in goods-producing industries’ share of GDP that was seen in the 

SIC-based estimates. Also as expected, the share of GDP for goods-producing industries is similar to 

but slightly lower under NAICS than under the SIC over the longer 1947-87 period (chart 1). 

Chart 1. Private Goods-Producing I

Share of GDP: 1947-87

20

30

40

50

47

50

53

56

59

62

65

68

71

74

77

ndustries 

19

19

19

19

19

19

19

19

19

19

19

1980

1983

1986

NAICS

SIC

Percent of 

GDP
Table 5.--Comparison of NAICS and SIC Nominal Shares and Real Growth Rates, 1987-97 

 

 Average Real Growth Rate Share of Nominal GDP 

 1987 - 1997 1987 1997 

 SIC SIC SIC 

Description NAICS Revised Previous NAICS Revised Previous NAICS Revised Previous 

 

Gross domestic product 3.0 3.0 2.9 100.0 100.0 100.0 100.0 100.0 100.0 

 

All industries 3.0 3.0 2.9 100.0 100.0 100.0 100.0 100.0 100.0 

 

 Private industries 3.3 3.3 3.2 86.1 86.1 86.1 87.3 87.3 87.2 

 Private goods-producing industries 2.8 2.9 N/A 24.9 27.0 27.2 21.9 24.1 23.6 

 Manufacturing 3.4 3.2 2.9 17.1 18.6 18.7 15.4 16.9 16.6 

 Durable goods 4.4 4.6 4.2 10.2 10.8 10.9 9.1 9.8 9.5 

 Nondurable goods 1.9 1.4 1.2 6.9 7.8 7.8 6.3 7.1 7.1 

 

 Private services-producing industries 3.5 3.4 N/A 61.2 58.6 58.8 65.3 62.3 63.2 

 

 Government 1.0 1.0 1.0 13.9 13.9 13.9 12.7 12.7 12.8 
The converse is true for private services-producing industries (chart 2). The NAICS-based estimates 

also show the long-term decline in goods-producing industries’ share of GDP that was seen in the SICbased estimates. The conversion matrix shares that were used to allocate SIC-based industry estimates 

to NAICS industries were held constant for years before 1977. However, because allocations to more 

than one detailed NAICS industry from a single SIC industry usually fell within the same higher-level 

NAICS industry group, errors in the allocation matrix tended to cancel one another at the published 

industry group level. 

Chart 2. Private Services-Producing Industries 

Share of GDP: 1947-87

20

30

40

50

60

70

1947

1950

1953

1956

1959

1962

1965

1968

1971

1974

1977

1980

1983

1986

Percent of GDP

SIC

NAICS

Real estimates before 1977. Because the previously published real estimates for years before 1977 

were based on fixed 1982 relative price weights, they are subject to substitution bias for earlier years 

that are far from 1982. However, the Fisher aggregation procedures that were used to prepare the 

quantity indexes for NAICS industry groups for 1947-76 reduced the impact of the substitution bias. 

For example, real value added estimates for the manufacturing industry group for 1947-76 are not 

affected by substitution bias to the same degree as the estimates for specific manufacturing industries. 

In addition, the aggregation of the NAICS-based estimates over “all industries” yields an estimate that 

very closely matches BEA’s Fisher-index measure of real GDP growth (chart 3). The correlation is 

much closer than it was using the previously published constant 1982 dollar SIC-based estimates. This 

closer correspondence indicates greater consistency of the industry real value added estimates with real 

GDP. 
Chart 3. Value Added Quantity Indexes: 1947-87

20

40

60

80

100

194719491951195319551957195919611963196519671969197119731975197719791981198319851987

1987=100

GDP

All Industries 

V. Summary and Conclusion 

The introduction of NAICS in the late 1990’s offered the promise of more relevant U.S. industry time 

series data for the 21st century, but the transition from the SIC system was difficult for both data 

producers and data users, and conversion was not completed for current economic programs until 2004. 

For data producers such as BEA, part of the difficulty was that statistical agencies that provide source 

data used different approaches and were on different time schedules for the conversion to NAICS. Even 

after current programs were fully converted to NAICS, however, the research community was left 

without consistent historical industry time series data for many of the most important economic 

programs. 

For most programs, NAICS could not be implemented on a historical basis due to the absence of 

NAICS-based source data before 1997. This problem was especially difficult for BEA’s GDP-byindustry accounts program because it uses data from a wide variety of sources along with complex 

estimation procedures. BEA met the need for historical industry time series data by developing 

backcasting procedures that took full advantage of the existing SIC-based data, used time-varying 

concordances with variable shares as far back as possible, and used aggregation as a means of dealing 

with limitations in the methodology, especially for early years. Other U.S. statistical agencies faced 

similar problems and addressed them in various ways. 

Conversion to NAICS has raised new challenges for developing industry time series data, especially for 

complex programs such as the industrial production index, GDP-by-industry, and the BLS industry 

production accounts that are used for productivity research, but it has also presented some opportunities 

for improving methodologies and changing procedures to incorporate better source data. With the 

release of the historical employment estimates in October 2006, BEA completed the conversion of its 
industry series from the SIC system to NAICS.6

 BLS has since released manufacturing sector data for 

production accounts back to 1987. Extending production accounts to the non-manufacturing sector and 

to years before 1987 requires joint work between BEA and BLS. BEA has initiated work to develop 

gross output estimates back to 1972 that could be used to prepare integrated input-output use tables. 

BLS plans to develop output measures for non-manufacturing industries on a NAICS basis as part of the 

effort to extend production accounts, and will work closely with BEA to insure consistency among the 

historical industry output measures. 

 

6

 BEA released estimates of net capital stock, depreciation, and investment by NAICS industry in April 2006. For more 

information, see http://bea.gov/national/FA2004/index.asp. 
References 

Bayard, Kimberly and Shawn Klimek, 2003, “Creating a Historical Bridge for Manufacturing between 

the Standard Industrial Classification System and the North American Industrial Classification 

System,” The Proceedings of the Annual Meeting of the American Statistical Association, August. 

Corrado, Carol, 2003, “Industrial Production and Capacity Utilization: The 2002 

Historical and Annual Revision.” Federal Reserve Bulletin. April. 

Corrado, Carol, Paul Lengermann, Eric J. Bartelsman, and J. Joseph Beaulieu, 2006, “Modeling 

Aggregate Productivity at a Disaggregate Level: New results for U.S. sectors and industries,” Paper 

presented at the Conference on Research on Income and Wealth NBER Summer Institute, July 19. 

Klimek, Shawn and David Merrell, 2000, “On Reclassifying Industries from Standard 

 Industrial Classification System to the North American Industrial Classification 

 System,” The Second International Conference on Establishment Surveys Proceedings, June. 

Kort, John R., 2001, “The North American Industry Classification System in BEA’s Economic 

Accounts,” Survey of Current Business, May, pp.7-13. 

Lawson, Ann M., Brian C. Moyer, Sumiye Okubo, and Mark A. Planting, 2006, “Integrating Industry 

and National Economic Accounts: First Steps and Future Improvements.” A New Architecture for 

the U.S. National Accounts, NBER Studies in Income and Wealth, edited by Dale W. Jorgenson, J. 

Steven Landefeld and William D. Nordhaus. University of Chicago Press. 

Lawson, Ann M., Kurt S. Bersani, Mahnaz Fahim-Nader, and Jiemin Guo, 2002, “Benchmark InputOutput Accounts of the United States, 1997,” Survey of Current Business, December, pp. 19-108. 

Morisi, Teresa. 2003. “Recent Changes in the National Current Employment Statistics Survey.” 

Monthly Labor Review. June. 

Moulton, Brent R., Yvon Pho, and Robert E. Yuskavage, 2003, “Major Changes in the Presentation of 

the U.S. National Accounts,” Paper prepared for the OECD National Accounts Experts Meeting 

Session on National Accounts and Economic Statistics, September. 

Moyer, Brian C., Mark A. Planting, Paul V. Kern, and Abigail M. Kish, 2004, “Improved Annual 

Industry Accounts for 1998-2003: Integrated Annual Input-Output Accounts and Gross-DomesticProduct-by-Industry Accounts,” Survey of Current Business, January, pp. 21-57. 

Nordhaus, William D., 2002, Productivity Growth and the New Economy," Brookings Papers on 

Economic Activity 2002(2): 211-265. 

Shimberg, Michael, Ruth E. Detlefsen, and William C. Davie, Jr., 2002, “Methods Used 

to Develop Retail and Wholesale Time Series Under the North American Industry Classification 

System.” Paper presented at the 2002 annual meeting of the American Statistical Association. 
Smith, George M., Matthew J. Gruenberg, Tameka R. L. Harris, and Erich H. Strassner, “Annual 

Industry Accounts: Revised Estimates. 2001-2003,” Survey of Current Business, January, pp 9-43. 

Stiroh, Kevin J., 2002, “Information Technology and the U.S. Productivity Revival: What Do the 

Industry Data Say?,” American Economic Review, 92(5), December 2002b, 1559-1576. 

Stiroh, Kevin J., 2006, “The Industry Origins of the Second Surge of U.S. Productivity Growth,” Federal 

Reserve Bank of New York, Mimeo, July 2006b. 

Triplett, Jack E. and Bosworth, Barry P. 2004. Productivity in the U.S Services Sector: 

New Sources of Economic Growth. Brookings Institution Press: Washington, DC. 

Yuskavage, Robert E. and Yvon H. Pho, 2004, “Gross Domestic Product by Industry for 1987-2000,” 

Survey of Current Business, November, pp. 33-53. 

Yuskavage, Robert E. and Mahnaz Fahim-Nader, 2005, “Gross Domestic Product by Industry for 1947-

86,” Survey of Current Business, December, pp. 70-84. 
Table A.--1997 NAICS Codes Corresponding to 

Published Industry Descriptions 

1997 NAICS 

Code(s) Annual Industry Accounts industry description

111,112 Farms 

113-115 Forestry, fishing, and related activities 

211 Oil and gas extraction 

212 Mining, except oil and gas 

213 Support activities for mining 

22 Utilities 

23 Construction 

321 Wood products 

327 Nonmetallic mineral products 

331 Primary metals 

332 Fabricated metal products 

333 Machinery 

334 Computer and electronic products 

335 Electrical equipment, appliances, and components 

3361-3363 Motor vehicles, bodies and trailers, and parts 

3364-3366,3369 Other transportation equipment 

337 Furniture and related products 

339 Miscellaneous manufacturing 

311, 312 Food and beverage and tobacco products 

313, 314 Textile mills and textile product mills 

315, 316 Apparel and leather and allied products 

322 Paper products 

323 Printing and related support activities 

324 Petroleum and coal products 

325 Chemical products 

326 Plastics and rubber products 

42 Wholesale trade 

44,45 Retail trade 

481 Air transportation 

482 Rail transportation 

483 Water transportation 

484 Truck transportation 

485 Transit and ground passenger transportation 

486 Pipeline transportation 

487, 488, 492 Other transportation and support activities 

493 Warehousing and storage 

511 Publishing industries (includes software) 

512 Motion picture and sound recording industries 

513 Broadcasting and telecommunications 

514 Information and data processing services 

521, 522 Federal Reserve banks, credit intermediation, and related activities 
Table A.--1997 NAICS Codes Corresponding to 

Published Industry Descriptions 

1997 NAICS 

Code(s) Annual Industry Accounts industry description

523 Securities, commodity contracts, and investments 

524 Insurance carriers and related activities 

525 Funds, trusts, and other financial vehicles 

531 Real estate 

532, 533 Rental and leasing services and lessors of intangible assets 

5411 Legal services 

5415 Computer systems design and related services 

5412-5414,5416-5419 Miscellaneous professional, scientific, and technical services 

55 Management of companies and enterprises 

561 Administrative and support services 

562 Waste management and remediation services 

61 Educational services 

621 Ambulatory health care services 

622, 623 Hospitals and nursing and residential care facilities 

624 Social assistance 

711, 712 Performing arts, spectator sports, museums, and related activities 

713 Amusements, gambling, and recreation industries 

721 Accommodation 

722 Food services and drinking places 

81 Other services, except government 
Table B.--1987 SIC Codes Corresponding to 

Published Industry Descriptions 

1987 SIC 

Code(s) GDP by industry description

01-02 Farms 

07-09 Agricultural services, forestry, and fishing 

10 Metal mining 

12 Coal mining 

13 Oil and gas extraction 

14 Nonmetallic minerals, except fuels 

15-17 Construction 

24 Lumber and wood products 

25 Furniture and fixtures 

32 Stone, clay, and glass products 

33 Primary metal industries 

34 Fabricated metal products 

35 Industrial machinery and equipment 

36 Electronic and other electric equipment 

371 Motor vehicles and equipment 

372-379 Other transportation equipment 

38 Instruments and related products 

39 Miscellaneous manufacturing industries 

20 Food and kindred products 

21 Tobacco products 

22 Textile mill products 

23 Apparel and other textile products 

26 Paper and allied products 

27 Printing and publishing 

28 Chemicals and allied products 

29 Petroleum and coal products 

30 Rubber and miscellaneous plastics products 

31 Leather and leather products 

40 Railroad transportation 

41 Local and interurban passenger transit 

42 Trucking and warehousing 

44 Water transportation 

45 Transportation by air 

46 Pipelines, except natural gas 

47 Transportation services 

481,482,489 Telephone and telegraph 

483-484 Radio and television 

49 Electric, gas, and sanitary services 

50-51 Wholesale trade 

52-59 Retail trade 

60 Depository institutions 
Table B.--1987 SIC Codes Corresponding to 

Published Industry Descriptions 

1987 SIC 

Code(s) GDP by industry description

61 Nondepository institutions 

62 Security and commodity brokers 

63 Insurance carriers 

64 Insurance agents, brokers, and service 

65 Real estate 

67 Holding and other investment offices 

70 Hotels and other lodging places 

72 Personal services 

73 Business services 

75 Auto repair, services, and parking 

76 Miscellaneous repair services 

78 Motion pictures 

79 Amusement and recreation services 

80 Health services 

81 Legal services 

82 Educational services 

83 Social services 

86 Membership organizations 

84,87,89 Other services 

88 Private households 
Table C.--1972 SIC Codes Corresponding to 

Published Industry Descriptions 

1972 SIC 

Code(s) GDP by industry description

01-02 Farms 

07-09 Agricultural services, forestry, and fishing 

10 Metal mining 

12 Coal mining 

13 Oil and gas extraction 

14 Nonmetallic minerals, except fuels 

15-17 Construction 

24 Lumber and wood products 

25 Furniture and fixtures 

32 Stone, clay, and glass products 

33 Primary metal industries 

34 Fabricated metal products 

35 Machinery, except electrical 

36 Electric and electronic equipment 

371 Motor vehicles and equipment 

372-379 Other transportation equipment 

38 Instruments and related products 

39 Miscellaneous manufacturing industries 

20 Food and kindred products 

21 Tobacco products 

22 Textile mill products 

23 Apparel and other textile products 

26 Paper and allied products 

27 Printing and publishing 

28 Chemicals and allied products 

29 Petroleum and coal products 

30 Rubber and miscellaneous plastics products 

31 Leather and leather products 

40 Railroad transportation 

41 Local and interurban passenger transit 

42 Trucking and warehousing 

44 Water transportation 

45 Transportation by air 

46 Pipelines, except natural gas 

47 Transportation services 

481,482,489 Telephone and telegraph 

483-484 Radio and television 

49 Electric, gas, and sanitary services 

50-51 Wholesale trade 

52-59 Retail trade 

60 Banking 
Table C.--1972 SIC Codes Corresponding to 

Published Industry Descriptions 

1972 SIC 

Code(s) GDP by industry description

61 Credit agencies other than banks 

62 Security and commodity brokers 

63 Insurance carriers 

64 Insurance agents, brokers, and service 

65 Real estate 

67 Holding and other investment offices 

70 Hotels and other lodging places 

72 Personal services 

73 Business services 

75 Auto repair, services, and parking 

76 Miscellaneous repair services 

78 Motion pictures 

79 Amusement and recreation services 

80 Health services 

81 Legal services 

82 Educational services 

83 Social services 

86 Membership organizations 

84,89 Miscellaneous professional services 

88 Private households