CREATING A HISTORICAL BRIDGE FOR MANUFACTURING BETWEEN THE STANDARD 

INDUSTRIAL CLASSIFICATION SYSTEM AND THE NORTH AMERICAN INDUSTRY 

CLASSIFICATION SYSTEM

Kimberly N. Bayard and Shawn D. Klimek

Board of Governors of the Federal Reserve and the U.S. Census Bureau

I. Introduction

Since the 1930s, the U.S. government has 

classified industries according to the Standard 

Industrial Classification (SIC) system. The SIC 

system has been periodically updated and revised to

mirror changes in the underlying structure of the

economy; the most recent change was in 1987. In

the early 1990s, a movement began to construct a

more sweeping revision to industry classification.

In 1997, representatives from the statistical

communities of the U.S., Canada, and Mexico

finalized the new North American Industry

Classification System (NAICS). NAICS was 

designed to reflect the significant changes in the

economy in the 1990s and to standardize industry

definitions across North America.1

The Census Bureau was one of the first

U.S. government entities to collect and publish

NAICS-based data for the manufacturing sector

with the release of the 1997 Census of Manufactures

(COM). In addition, the Census Bureau published

SIC-based statistics for the 1997 COM, and also

issued a correspondence between the two

classification systems. However, the Census Bureau

did not release NAICS-based manufacturing 

statistics for any year prior to 1997. 

This paper documents a joint Federal

Reserve Board-Census Bureau project to convert the

plant-level information in the Censuses of

Manufactures from the SIC system to NAICS, and

describes the subsequent historical SIC-NAICS 

concordance that arises. Economists at the Federal

Reserve Board used this concordance to obtain,

back to 1972, NAICS-based output benchmarks.

These benchmarks were used in the construction of

the recently published NAICS-based historical

revision of the index of Industrial Production (IP)

and the related measures of capacity and capacity

utilization. 

The issuance of a NAICS-based industry

time series for IP fills a gap for users of industry

statistics. The statistical community’s transition to

NAICS created a break in industry-level data that

has made it difficult at best to perform basic

manipulations such as trend analysis, forecasting,

and seasonal adjustment. The Federal Reserve

Board’s issuance of historical NAICS-based time

series for IP provides data users with many of the

tools necessary to perform standard analyses. 

II. Motivation and Data Source 

One of the most useful data products 

provided by the Census Bureau following the

issuance of the 1997 COM industry statistics (and,

indeed all Economic Censuses) is the official bridge

table between SIC and NAICS. This bridge

provides information for each four-digit SIC

industry that allows one to calculate the share of 

establishments, shipments, employment, and payroll

associated with its corresponding six-digit NAICS 

industries. Table 1 shows an example of the type of

information conveyed in the bridge table for SIC 

3578, (Calculating & accounting machines, except

electronic computers), and how this industry

corresponds to its two associated NAICS industries

(Office machinery manufacturing and other

computer peripheral equipment manufacturing).

2

Table 1: Sample bridge between SIC and

NAICS for SIC 3578 (1997) 

NAICS

Value of 

Shipmts

($1,000)

Shr 

of 

Ship

. 

Paid

Empl

Shr of 

Empl. 

Calc. & acc.

machines, ex.

comp. 

2,014,806 -- 7,683 --

333313 Ofc mach.

mfg 144,380 0.07 966 0.13

334119

Other comp.

peripheral

equip. mfg

1,870,426 0.93 6,717 0.87

From the table above, it is clear that, in 

1997, most of SIC 3578 corresponded to “other

computer peripheral equipment manufacturing”

(NAICS 334119), which includes products such as 

computer monitors, keyboards, printers, and

automatic teller machines. A much smaller share of

SIC 3578 corresponded to “office machinery

manufacturing” (NAICS 333313), which includes 

 1

 A history and description of the various industry

classification systems can be found in the manuals

(Executive Office of the President, 1998, 1987, 

1972). 

2

 Some industry concordances are omitted for failure 

to meet disclosure requirements. An electronic copy

of the complete bridge table can be found at

http://www.census.gov/epcd/ec97brdg. 

 1
the production of mailhandling machinery and 

equipment, calculators, and typewriters. 

The table also illustrates why it is 

somewhat limiting that the only publicly available 

SIC-NAICS bridge that exists is for 1997. One 

might expect that the value of shipments of 

typewriter and calculator manufacturing relative to 

computer monitors and printers was much greater 

in, say, 1972 than it was in 1997. However, because 

there is no available information for prior years, data 

users can only speculate as to how to construct the 

relevant breakdown. 

The absence of historical industry data that 

allowed for the construction of a bridge between 

SIC system and NAICS led us to construct such a 

bridge directly from plant-level data. The Center for 

Economic Studies at the Census Bureau maintains 

the Longitudinal Research Database (LRD), a 

database of plant-level records for all establishments 

that appeared in either a COM or an ASM since 

1963. We used these microdata files to convert the 

industry code for each establishment in each COM 

from the SIC basis to NAICS. 

Specifically, we rely on four information 

sources to convert the censuses to a NAICS basis: 

the Census Bureau’s product level concordance 

table that shows a translation of 1987 SIC product 

codes to 1992 SIC product codes to 1997 NAICS 

product codes; the 1997 census that contains both 

SIC and NAICS codes for each establishment; plantspecific identifiers that do not change over time, and 

the existence of both current-year and the 1987 SIC 

equivalent industry codes for all establishments on 

census files prior to 1997. These last two pieces of 

information result from CES-specific work to 

standardize over time all files in the LRD. As for 

the industry information, because all LRD 

establishments have 1987 SIC information, the 

translation of SIC to NAICS is possible even when a 

single NAICS code corresponds to multiple SIC 

codes. 

III. Methodology 

Our general approach to assign 

establishments to NAICS industries takes place in 

reverse chronological order. The 1997 COM 

contains both SIC and NAICS industry codes and 

provides our initial link between SIC and NAICS 

classifications. As described in more detail below, 

we use the 1997 classifications as a base to help 

recode the 1992 COM to NAICS. Once all 

establishments in the 1992 COM are reclassified to 

NAICS, these industry codes are then used to help 

in the reclassification of the 1987 COM, and this 

process is repeated back to the 1963 COM.3

Within each COM year, we assign 

establishments to NAICS industries in a series of 

successive steps. Because the LRD contains a lot of 

information for each establishment, there are often 

many options for how we wish to go about the 

industry assignment process. We believe, however, 

that some methods are likely to be more reliable 

than others. We first attempt to assign each 

establishment to a NAICS industry by the most 

reliable method; any establishment that can’t be 

matched is considered to be a residual and we 

attempt to match it in a subsequent step. 

Briefly, we assign establishments to 

industries in one of two general ways: exact 

matching or statistical matching. The exact 

matching procedures include a “product method” 

where we classify the types of products made by the 

plant, an “industry method” where we classify a 

plant to a NAICS industry based on its SIC industry, 

an “establishment method” where we identify the 

SIC industry of an establishment at a certain point in 

time and use the plant’s presence and industry code 

in earlier censuses to assign it to a NAICS industry. 

The statistical assignment procedure looks at a set of 

characteristics of each plant to help determine 

whether they are more consistent with assignment of 

that plant to one particular NAICS industry or 

another. There are a few “special cases,” that is, 

plants that we cannot assign by one of the methods 

listed above, and we resort to either shipmentsweighted or manual assignment. 

 Step 1: Product Method 

 The first step in reclassifying an 

establishment to a NAICS industry uses product 

level information collected by the Census Bureau 

for some establishments in the COM. The product 

code is essentially an extension of the SIC-based 

industry code where the first few digits are identical 

to the industry code and the last several digits 

specify a particular product within that industry. 

The plant is assigned to the industry that 

corresponds to the products that represent greatest 

value of the establishment’s shipments. A plant 

may make several products, some of which may fall 

outside of the establishment’s assigned industry (see 

below for an illustration). 

We use a concordance that maps these SIC 

product codes to NAICS product codes. Sometimes, 

we can assign NAICS product codes to all of an 

 

3

 COMs are generally conducted every five years. 

The LRD contains information from the COMs 

conducted in 1997, 1992, 1987, 1982, 1977, 1972, 

1967, and 1963. 
establishment’s products; other times we can only

assign a subset of them. When we assign NAICS

product codes to the majority of the establishment’s 

overall shipments, we then use these product codes

as a guide to determine the NAICS industry

associated with the greatest value of shipments for

each establishment. 

Step 3: Establishment Method

The next method of assigning NAICS 

industries to manufacturing establishments uses

unchanging establishment identifiers to link plants

across censuses. Recall that our assignment

approach takes place in reverse chronological order. 

Therefore, once we have assigned a NAICS code to 

an establishment in, say, 1992, we can use this

information to help inform the assignment of the

plant in 1987. We use the plant identifiers to match 

establishments from the census in year t to the 

census in year t-5, and if the plant operates in the

same four digit SIC in both censuses, then we assign

the NAICS industry for the establishment in year t

to the establishment in year t-5. Implicitly, this

method assumes that there is no change within the

establishment that would cause a change in

classification between censuses. 

Consider the following hypothetical plant

that produces three products and whose primary

industry is 2282 (represented by the first four digits

of the product code):

Product 1: SIC92 =2282231, shipments=4000  

NAICS97 =313112xxxx

Product 2: SIC92 =2282241, shipments=3000 

NAICS97 =313312xxxx

Product 3: SIC92 =2269021, shipments=3000 

NAICS97 =313312xxxx

Step 4: Multiple Characteristics Method

In this case, 70 percent of the plant’s shipments in (“Statistical Matching”) 

1992 are in SIC 2282. After matching in NAICS 

product data, we see that the primary industry for

the establishment derived from the product codes is

NAICS 313312, which accounts for 60 percent of

the establishment output. If we had assigned a

NAICS industry to the establishment solely based

on the primary four-digit SIC industry, then the 

plant would be classified into industry 313112.4

 

The next method of assigning 

establishments to NAICS industries uses a statistical

model that examines characteristics of each plant to

help determine whether the plant “looks like” other

plants in one particular NAICS industry or another.

The assignment techniques described in the first

three steps work well for some establishments and

industries, but are less useful for classifying those

plants that are small so that Census does not collect

data on their products, or are in SIC industries that

map to multiple NAICS industries, or do not survive

from census to census. As we illustrate below, the

“statistical assignment” group accounts for a 

relatively small fraction of establishments and

employment. 

We can also assign a NAICS industry when

only a subset of SIC products match to a unique

NAICS product code. For example, for the

hypothetical plant above, even if we had been able

to match only product 2 and product 3 to NAICS 

products codes, we would have been able to assign 

the establishment to the proper industry. Other

combinations of products matched to NAICS 

product codes are insufficient to assign the plant by

this method. For all cases where we cannot assign 

more than half of the plant’s shipments to a NAICS

industry, we turn to another method of industry

assignment.

Recalling that our assignment procedure 

starts with the most recent census and works

backwards, the basic idea behind our statistical

assignment method is to look at the characteristics

of the plants in the same SIC industry in year t, and,

based on the distribution in the subsequent (t+5)

census, determine the probability that each plant

gets assigned to each of its corresponding NAICS

industries. 

Step 2: Industry Method

The next method of assigning NAICS 

industries uses an industry-level concordance file.

Many four-digit SIC industries correspond to only

one six-digit NAICS industry; we assign plants in

these SIC industries to the relevant NAICS industry.

Only establishments in those industries with a oneto-one correspondence can be assigned by this

method. More than 80 percent of all SIC industries

in manufacturing are represented by this one-to-one

scenario. The remaining SIC industries are split into

as many as seven NAICS industries.

Specifically, we run a multinomial logit

regression for each SIC industry associated with

more than one NAICS industry using the data for all 

observations in the industry in the census of year

t+5. We run the following model: 

i d swte inmsa

NAICS d te d tvste





  

 

( _ ) ( )

( _ ) ( _ )

4 5

1 2 3

The variable te is the total employment at the plant,

tvste is the total value of shipments per worker, swte

is the average wage, and inmsa is a dummy for 

whether or not the establishment is in an

 

4

 The NAICS manual shows that SIC 2282 matches

only to NAICS 313112. 
metropolitan statistical area.5

 The d_ prefix implies

that all variables are measured as deviations from

the SIC industry medians. We use the vector of

coefficients to construct establishment level

probabilities for all plants in these SIC industries. 

IV. Results 

We then draw from the resulting

distribution for each establishment and assign the

establishment to a NAICS industry. We perform

this exercise 1,001 times and construct industry

level aggregates for a small set of variables typically

used in Census Bureau publications: number of 

establishments, total employment, total value of

shipments, production worker hours, and salaries

and wages. We then construct deviations of these

totals from the industry median and aggregate across

all the 474 NAICS manufacturing industries. To

determine the “best” draw, j, we pick the one draw

that satisfies the following:

( ) ( ) ( )

min ( ) ( )

3 4 5

1 2

j j j

j j j j J

mdtvs mdph mdsw

mdestabs mdte

  

 

 

  

In this section, we present some statistics

about the industry assignment process described in

the previous section. Table 2 shows the share of 

establishments assigned NAICS industries by each 

of the five methods for census years 1963 to 1992. 

Roughly 80 percent of all establishments are

assigned NAICS industries by our exact methods.

Although the share of establishments assigned using 

product data falls significantly over time, the

percentage of establishments assigned by the

industry and establishment methods rises over the

same period. For 1992, we assign 46.2 percent of 

all establishments by the product method, but by

1967 that number falls to just below 3 percent. One

reason is that seven-digit product codes are specific

to the Census Bureau and need not be time

consistent in the same manner as the SIC system.

Also, over time (retrospectively), establishment

assignment grows in importance from 24.6 percent

in 1992 to 50.4 percent in 1963. Similarly, the share

of establishments assigned by the industry method

grows from 11.1 percent in 1992 to 21.1 percent in

1963. It is important to note that although the share

of matches based on product assignment falls over

time, these matches are replaced by one of our other

exact matching techniques. The proportion of 

establishments assigned NAICS industries by

statistical matching techniques remains relatively

constant over time at roughly 20 percent. The share 

of special cases is negligible. 

where  and .





5

1

1

k

k J  {1,..,1001} 6

 

Step 5: Special Cases

Even after completing the exact and

statistical matching, a few establishments escape

classification. Some of these “special cases” are

those in sparsely populated industries where there

are too few observations to run a multinomial logit

regression. In these cases, we use a shipments

weighted probability that is constant for all

establishments in the industry. Other special cases

include the small set of establishments with industry

codes on the data files that were never updated when

there were revisions to the SIC system in 1972 and

1987. In these instances, we used information from

the concordances in the SIC and NAICS manuals to

manually assign NAICS industry codes to plant

records. 

Table 2. Shares of Establishments by Method of

Assignment

Type of

assignment 1992 1987 1982 1977 1972 1967 1963

All “Exact” 

Matching

.820 .810 .794 .776 .809 .799 .813

product 

method .462 .262 .164 .108 .111 .029 .098

estab method .247 .379 .404 .388 .461 .519 .504

Industry

method .111 .169 .226 .280 .237 .251 .211

statistical 

assignment

.179 .190 .205 .219 .188 .201 .183

Other .000 .000 .001 .005 .003 .001 .005

 

5

 These variables contain few missing values in the 

censuses. Some of the multinomial regressions

contain very few observations, so we wanted to

avoid having to exclude plants whenever possible. 6

 All variables are measured as deviations from

industry medians; “mdestabs” refers to the number 

of establishments, “mdte” refers to total 

employment, “mdtvs” refers to total value of

shipments, “mdph” refers to production worker 

hours, and “mdsw” refers to salaries and wages. In

this example we set the weights (1 to 5) equal, but 

the method allows for differential weighting.

Table 3 shows the share of total

manufacturing shipments that are assigned NAICS
industries based on each method.7

 This table shows 

that nearly all shipments are classified by one of the 

exact matching techniques. Exact methods account 

for more than 90 percent of all manufacturing 

shipments in every census year back to 1963. So 

although roughly 20 percent of establishments are 

assigned to NAICS industries by statistical methods, 

these establishments account for a much smaller 

share of overall shipments. 

 One concern is that although there are 

relatively few establishments that are statistically 

assigned, these establishments may represent a 

proportionately larger share of overall shipments. 

However, the data do not support this concern. For 

example, in 1992, 17.9 percent of all manufacturing 

establishments were assigned by the statistical 

method, but this group accounts for only 3.4 percent 

of total shipments in the sector. Although the share 

of establishments assigned by statistical techniques 

grows by just a few percentage points as we look 

back over earlier censuses, the share of shipments 

doubles, but is never more than 7 percent. The 

shares of shipments assigned by the product method 

(and establishment method) show even more 

dramatic patterns of decline (and increase) as 

compared to the shares of establishments. Also, the 

industry matching method, while relatively 

important in terms of establishments, is much less 

important from a shipments weighted perspective. 

Table 3. Share of Shipments by Method of 

Assignment 

Type of

assignment

1992 1987 1982 1977 1972 1967 1963

All “exact” 

matching 

.966 .965 .941 .938 .939 .925 .933 

product 

data 

.798 .611 .363 .305 .271 .029 .109 

Estab. 

links 

.149 .294 .441 .539 .563 .769 .718 

Ind. data .019 .060 .137 .094 .105 .127 .106 

Stat. 

assignment 

.034 .035 .058 .058 .056 .073 .064 

other .000 .000 .001 .005 .006 .002 .003 

V. Evaluating data quality 

 Because there is no existing historical SICNAICS bridge table to check our data against, it is 

difficult to evaluate both the matching process and 

the quality of the data. One possibility, however, is 

to match all establishments by all possible methods 

to see how often each method yields the same result. 

Obviously, we cannot match all establishments by 

all methods (or we would have just assigned 

 

7

 Constructing this table on an employmentweighted basis does not qualitatively change the 

results. 

establishments to NAICS industries using the most 

reliable method). However, we can often match 

plants assigned to NAICS industries by one of the 

“exact” techniques by statistical assignment. To the 

extent that, for example, statistical assignment 

produces the same industry assignment as one of the 

exact techniques, we will gain confidence in the 

methodology. 

 Table 4 shows the percentage of matches, 

where calculable, that are assigned to the same 

industry by different techniques for 1992. The value 

in the upper left-hand corner of the table shows that 

more than 96 percent of the time that we assigned a 

plant by the (most reliable) product method, the 

industry method would have yielded the same result. 

The establishment method appears even more 

reliable – the “similarity rate” is more than 99 

percent for establishments matched by both the 

product method and the industry method. As 

expected, the statistical matching method is less 

likely than other methods to yield a match identical 

to the industry identified by one of the “exact” 

methods. Although this “similarity rate” is lower 

for the statistical matching cases, it is still fairly 

high, which gives us reasonable confidence in the 

matches made by this method.8

 Table 4: Percentage Matched to Same 

industry by different techniques 

Product 

method 

Industry 

method 

Estab. 

method 

Industry 

method 

96.6 X X 

Establishment 

method 

99.1 99.6 X 

Statistical 

method 

70.8 N/A 74.8 

 Another way to evaluate the assignment 

methodology is to look at new industries, or 

industries we are familiar with, to see if they behave 

in predictable ways. For example, the SIC industry 

shown in Table 1, calculating & accounting 

machines, except electronic computers, corresponds 

to two different NAICS industries -- office 

machinery manufacturing and other computer 

peripheral equipment manufacturing. As noted 

above, we might expect the share of computer 

peripherals to be higher in recent years and much 

lower in earlier censuses. We compare the shares 

generated from the matching procedure, which we 

call “variable shares” because they are allowed to 

 

8

 We are currently exploring comparisons of these 

similarity rates with those constructed by random 

and shipments-weighted assignment. 
change over time, with the shares implied by the 

1997 Census data (the only publicly available

data on shares), which we call “fixed shares.” 

This comparison gives us a glimpse into whether

the constructed data behave “reasonably.” 

Figure 1 shows the comparison of

variable and fixed shares in four industries. The

top left panel, panel A, shows the total value of

shipments of SIC 3578 attributable to the NAICS 

industry for computer peripherals calculated

using variable shares and fixed shares. As

expected, if we only had access to the 1997 

public data and just applied those shares back in

time, we would have likely overstated shipments

by a substantial amount. The use of variable

shares seems to provide a much more reasonable 

historical picture. 

The other three panels in figure 1 

present a similar story. Panel B, in the upper 

right corner, shows total shipments of motor 

vehicles (SIC 3711) attributable to light trucks

under both the fixed and variable shares

calculations. Panel C, in the lower left corner, 

shows for special industry machinery (SIC

3559), the shipments associated with

semiconductor machinery equipment; and panel

D, in the lower right corner, shows how much of

the shipments of drawing and insulating of

nonferrous wire (SIC 3357) should be attributed

to fiber optic cable manufacturing under the two

share methods. Each of these cases presents a

similar scenario: if we had just taken the 1997

share and applied it backwards in time, we would

likely have overestimated shipments for these

new or growing industries.

9

Panel D illustrates how the variable

shares pick up more accurately real activity in an

industry. Although first listed as an industry in

the 1997 NAICS manual, fiber optic cable

appeared as a product in the 1987 SIC manual,

and there were no references in the 1972 manual.

This coincides with the initial development of

fiber optic cable as we know it in the early 1970s

and more widespread production by the early

1980s (Hecht, 1999). The variable shares 

method seems to reflect this history more

accurately than the fixed shares method. 

Figure 1: Comparing Fixed and Variable Shares

1975 1980 1985 1990 1995 0.0

0.5

1.0

1.5

2.0

0.0

0.5

1.0

1.5

2.0

Variable shares

Fixed shares

Panel A Shipments of SIC 3578 attributable to other computer peripheral equipment (N334119) (billions of dollars)

1975 1980 1985 1990 1995 0

20

40

60

80

100

120

0

20

40

60

80

100

120

Variable shares

Fixed shares

Panel B Shipments of SIC 3711 attributable to light trucks (N336112) (billions of dollars)

1975 1980 1985 1990 1995 0

2

4

6

8

10

12

0

2

4

6

8

10

12

Variable shares

Fixed shares

Panel C Shipments of SIC 3559 attributable to

semiconductor machinery equipment (N333295) (billions of dollars)

1975 1980 1985 1990 1995 0.0

0.5

1.0

1.5

2.0

2.5

3.0

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Variable shares

Fixed shares

Panel D Shipments of SIC 3357 attributable to

fiber optic cable manufacturing (N335921) (billions of dollars)

Although these cases do not present a

formal test or evaluation of our methodology, the

consistency of these cases is reassuring to us that

the data appear reasonable and provide a sense

that our methodology generates the type of 

results one might expect. 

V. Conclusion

This paper highlights the need for a 

historical bridge between the SIC and NAICS

systems and briefly describes the method we use

to generate a bridge for the manufacturing sector. 

We assigned each establishment in all COMs

from 1963 to 1992 to a NAICS industry and 

examined the distribution of the different

assignment methods. Although it is difficult to

evaluate the quality of these assignments, we

present two ways of looking at these matches.

Once we were confident in the resulting data, we

used the new historical concordances as an input

into the reclassification to NAICS of the Federal 

Reserve’s Index of Industrial Production. Our

hope is that this work can eventually be more

widely employed by the data using community. 

 

9

 Graphs of employment shares present a similar 

picture. 
References

Hecht, Jeff (1999). City of Light: The Story of 

Fiber Optics. Oxford University Press. 

Executive Office of the President, Office of 

Management and Budget. “North American 

Industry Classification System, United States 

1997”. Lanham, MD: Bernan Press, 1998. 

Executive Office of the President, Office of 

Management and Budget. “Standard Industrial 

Classification System, 1987”. 

Executive Office of the President, Office of 

Management and Budget. “Standard Industrial 

Classification System, 1972”.