NAICS

Published

December 15, 2022

Overview

Website

The North American Industry Classification System (NAICS) is the standard used by Federal statistical agencies in classifying business establishments for the purpose of collecting, analyzing, and publishing statistical data related to the U.S. business economy. NAICS was developed under the auspices of the Office of Management and Budget (OMB), and adopted in 1997 to replace the Standard Industrial Classification (SIC) system. It was developed jointly by the U.S. Economic Classification Policy Committee (ECPC), Statistics Canada, and Mexico’s Instituto Nacional de Estadistica y Geografia, to allow for a high level of comparability in business statistics among the North American countries.

The structure of NAICS is hierarchical. The first two digits of the structure designate the 20 NAICS sectors that represent general categories of economic activities. NAICS uses a six-digit coding system to identify industries and their placement in this hierarchical structure of the classification system. The first two digits of the code designate the sector, the third digit designates the subsector, the fourth digit designates the industry group, the fifth digit designates the NAICS industry, and the sixth digit designates the national industry. On the highest 2-digit level there are 20 sectors. Classification is comparable between the USA, Canada and Mexico up to a 5-digit level, with some exceptions. A zero as the sixth digit generally indicates that the NAICS industry and the U.S. industry are the same.

Digits Level
2 sector
3 subsector
4 industry group
5 NAICS industry
6 national industry

Classification is revised every five years and is currently available for 1997, 2002, 2007, 2012, 2017 and 2022.

Source files

Main tables

This module provides access to dataframes built from source files.

  • Manual. A PDF with complete information about the classification. All other files are parts of the manual. Available from 2017.

  • Code file. Table of 2-6 digit codes and corresponding sector, subsector, industry group and industry titles. Available for all* years. Subset files with only 6-digit industries are also available.

  • Structure. Essentially the same as Code file with addition of indicators for trilateral aggreement (comparability between US, Canada and Mexico) and change from previous revision. Available from 2017.

  • Structure Summary. Counts of subsectors, industry groups and industries in each sector. Available from 2017.

  • Definitions. A PDF with full descriptions of every industry. Available for all* years.

  • Descriptions. Table that contains everything from the Definitions file, except Cross-References. Available from 2017.

  • Cross-References. Table that contains industry cross-references from the Definitions file. Available from 2017.

  • Index file. Table of 6-digit industry codes and description of index items withing each industry (usually multiple). Available from 2007.

* IMPORTANT. For 1997, only definitions PDFs by sector are available in the NAICS section of the Census website. More useful code file in table form can be found in the CBP program documentation. However, this version is missing industries not covered by CBP. Notably, farming subsectors “111: Crop production” and “112: Animal production” are not included. Data source for 1997 should be updated to a complete one once found.

1997 code | 2002 code | 2007 code | 2012 code | 2017 code | 2022 code | 2007 index | 2012 index | 2017 index | 2022 index | 2017 descriptions | 2022 descriptions | 2017 summary | 2022 summary |

Structure summary

Total counts of classes at every level by sector, same format as analogous source tables.

Sector Name Subsectors (3-digit) Industry Groups (4-digit) NAICS Industries (5-digit) 6-digit Industries (U.S. Detail) 6-digit Industries (Same as 5-digit) 6-digit Industries (Total)
0 11 Forestry, fishing, hunting, and agriculture support 3 8 8 9 6 15
1 21 Mining 3 5 10 28 1 29
2 22 Utilities 1 3 6 6 4 10
3 23 Construction 3 14 28 0 28 28
4 31-33 Manufacturing 21 86 184 406 67 473
5 42 Wholesale trade 2 18 69 0 69 69
6 44-45 Retail trade 12 27 61 19 53 72
7 48-49 Transportation & warehousing * 9 27 40 23 31 54
8 51 Information 4 9 28 12 22 34
9 52 Finance & insurance 5 10 28 15 23 38
10 53 Real estate & rental & leasing 3 8 19 9 15 24
11 54 Professional, scientific & technical services 1 9 34 17 29 46
12 55 Management of companies & enterprises 1 1 1 3 0 3
13 56 Admin, support, waste mgt, remediation services 2 11 29 23 20 43
14 61 Educational services 1 7 12 7 10 17
15 62 Health care and social assistance 4 18 30 16 23 39
16 71 Arts, entertainment & recreation 3 9 23 3 22 25
17 72 Accommodation & food services 2 7 11 7 8 15
18 81 Other services (except public administration) * 3 13 29 30 18 48
19 95 Auxiliaries (exc corporate, subsidiary & regional mgt) 0 0 0 0 0 0
20 Total 83 290 650 633 449 1082
Sector Name Subsectors (3-digit) Industry Groups (4-digit) NAICS Industries (5-digit) 6-digit Industries (U.S. Detail) 6-digit Industries (Same as 5-digit) 6-digit Industries (Total)
0 11 Agriculture, Forestry, Fishing and Hunting 5 19 42 32 32 64
1 21 Mining 3 5 10 28 1 29
2 22 Utilities 1 3 6 6 4 10
3 23 Construction 3 10 28 4 27 31
4 31-33 Manufacturing 21 86 184 406 67 473
5 42 Wholesale Trade 3 19 71 0 71 71
6 44-45 Retail Trade 12 27 61 24 51 75
7 48-49 Transportation and Warehousing 11 29 42 25 32 57
8 51 Information 7 16 30 12 24 36
9 52 Finance and Insurance 5 11 32 15 27 42
10 53 Real Estate and Rental and Leasing 3 8 19 9 15 24
11 54 Professional, Scientific, and Technical Services 1 9 35 17 30 47
12 55 Management of Companies and Enterprises 1 1 1 3 0 3
13 56 Administrative and Support and Waste Management and Remediation Services 2 11 29 23 20 43
14 61 Educational Services 1 7 12 7 10 17
15 62 Health Care and Social Assistance 4 18 30 16 23 39
16 71 Arts, Entertainment, and Recreation 3 9 23 3 22 25
17 72 Accommodation and Food Services 2 7 11 7 8 15
18 81 Other Services (except Public Administration) 4 14 30 30 19 49
19 92 Public Administration 8 8 29 0 29 29
20 Total 100 317 725 667 512 1179
Sector Name Subsectors (3-digit) Industry Groups (4-digit) NAICS Industries (5-digit) 6-digit Industries (U.S. Detail) 6-digit Industries (Same as 5-digit) 6-digit Industries (Total)
0 11 Agriculture, Forestry, Fishing and Hunting 5 19 42 32 32 64
1 21 Mining, Quarrying, and Oil and Gas Extraction 3 5 10 28 1 29
2 22 Utilities 1 3 6 6 4 10
3 23 Construction 3 10 28 4 27 31
4 31-33 Manufacturing 21 86 184 405 67 472
5 42 Wholesale Trade 3 19 71 0 71 71
6 44-45 Retail Trade 12 27 61 24 51 75
7 48-49 Transportation and Warehousing 11 29 42 25 32 57
8 51 Information 6 12 27 10 22 32
9 52 Finance and Insurance 5 11 31 15 26 41
10 53 Real Estate and Rental and Leasing 3 8 19 9 15 24
11 54 Professional, Scientific, and Technical Services 1 9 35 19 29 48
12 55 Management of Companies and Enterprises 1 1 1 3 0 3
13 56 Administrative and Support and Waste Management and Remediation Services 2 11 29 25 19 44
14 61 Educational Services 1 7 12 7 10 17
15 62 Health Care and Social Assistance 4 18 30 16 23 39
16 71 Arts, Entertainment, and Recreation 3 9 23 3 22 25
17 72 Accommodation and Food Services 2 7 11 7 8 15
18 81 Other Services (except Public Administration) 4 14 30 30 19 49
19 92 Public Administration 8 8 29 0 29 29
20 Total 99 313 721 668 507 1175
Sector Name Subsectors (3-digit) Industry Groups (4-digit) NAICS Industries (5-digit) 6-digit Industries (U.S. Detail) 6-digit Industries (Same as 5-digit) 6-digit Industries (Total)
0 11 Agriculture, Forestry, Fishing and Hunting 5 19 42 32 32 64
1 21 Mining, Quarrying, and Oil and Gas Extraction 3 5 10 28 1 29
2 22 Utilities 1 3 6 10 4 14
3 23 Construction 3 10 28 4 27 31
4 31-33 Manufacturing 21 86 180 269 95 364
5 42 Wholesale Trade 3 19 71 0 71 71
6 44-45 Retail Trade 12 27 58 20 49 69
7 48-49 Transportation and Warehousing 11 29 42 25 32 57
8 51 Information 6 12 27 10 22 32
9 52 Finance and Insurance 5 11 31 15 26 41
10 53 Real Estate and Rental and Leasing 3 8 19 9 15 24
11 54 Professional, Scientific, and Technical Services 1 9 35 19 29 48
12 55 Management of Companies and Enterprises 1 1 1 3 0 3
13 56 Administrative and Support and Waste Management and Remediation Services 2 11 29 25 19 44
14 61 Educational Services 1 7 12 7 10 17
15 62 Health Care and Social Assistance 4 18 30 16 23 39
16 71 Arts, Entertainment, and Recreation 3 9 23 3 22 25
17 72 Accommodation and Food Services 2 6 10 8 7 15
18 81 Other Services (except Public Administration) 4 14 30 30 19 49
19 92 Public Administration 8 8 29 0 29 29
20 Total 99 312 713 533 532 1065
Sector Name Subsectors (3-digit) Industry Groups (4-digit) NAICS Industries (5-digit) 6-digit Industries (U.S. Detail) 6-digit Industries (Same as 5-digit) 6-digit Industries (Total)
0 11 Agriculture, Forestry, Fishing and Hunting 5 19 42 32 32 64
1 21 Mining, Quarrying, and Oil and Gas Extraction 3 5 11 24 4 28
2 22 Utilities 1 3 6 10 4 14
3 23 Construction 3 10 28 4 27 31
4 31-33 Manufacturing 21 86 180 264 96 360
5 42 Wholesale Trade 3 19 71 0 71 71
6 44-45 Retail Trade 12 27 57 17 49 66
7 48-49 Transportation and Warehousing 11 29 42 25 32 57
8 51 Information 6 11 25 12 19 31
9 52 Finance and Insurance 5 11 31 15 26 41
10 53 Real Estate and Rental and Leasing 3 8 17 11 13 24
11 54 Professional, Scientific, and Technical Services 1 9 35 20 29 49
12 55 Management of Companies and Enterprises 1 1 1 3 0 3
13 56 Administrative and Support and Waste Management and Remediation Services 2 11 29 25 19 44
14 61 Educational Services 1 7 12 7 10 17
15 62 Health Care and Social Assistance 4 18 30 16 23 39
16 71 Arts, Entertainment, and Recreation 3 9 23 3 22 25
17 72 Accommodation and Food Services 2 6 10 8 7 15
18 81 Other Services (except Public Administration) 4 14 30 30 19 49
19 92 Public Administration 8 8 29 0 29 29
20 Total 99 311 709 526 531 1057
Sector Name Subsectors (3-digit) Industry Groups (4-digit) NAICS Industries (5-digit) 6-digit Industries (U.S. Detail) 6-digit Industries (Same as 5-digit) 6-digit Industries (Total)
0 11 Agriculture, Forestry, Fishing and Hunting 5 19 42 32 32 64
1 21 Mining, Quarrying, and Oil and Gas Extraction 3 5 11 14 7 21
2 22 Utilities 1 3 6 10 4 14
3 23 Construction 3 10 28 4 27 31
4 31-33 Manufacturing 21 86 176 248 98 346
5 42 Wholesale Trade 3 19 69 0 69 69
6 44-45 Retail Trade 9 24 48 16 41 57
7 48-49 Transportation and Warehousing 11 29 42 25 32 57
8 51 Information 6 11 24 10 19 29
9 52 Finance and Insurance 5 11 27 13 22 35
10 53 Real Estate and Rental and Leasing 3 8 17 11 13 24
11 54 Professional, Scientific, and Technical Services 1 9 35 20 29 49
12 55 Management of Companies and Enterprises 1 1 1 3 0 3
13 56 Administrative and Support and Waste Management and Remediation Services 2 11 29 25 19 44
14 61 Educational Services 1 7 12 7 10 17
15 62 Health Care and Social Assistance 4 18 30 16 23 39
16 71 Arts, Entertainment, and Recreation 3 9 23 3 22 25
17 72 Accommodation and Food Services 2 6 10 8 7 15
18 81 Other Services (except Public Administration) 4 14 30 24 20 44
19 92 Public Administration 8 8 29 0 29 29
20 Total 96 308 689 489 523 1012

Examples

Random sample of codes.

Code 
get_df(2022, 'code').sample(5).fillna('')
CODE TITLE DIGITS CODE_2 CODE_3 CODE_4 CODE_5 CODE_6
4 111110 Soybean Farming 6 11 111 1111 11111 111110
742 334112 Computer Storage Device Manufacturing 6 31-33 334 3341 33411 334112
1379 5161 Radio and Television Broadcasting Stations 4 51 516 5161
393 315120 Apparel Knitting Mills 6 31-33 315 3151 31512 315120
1062 441 Motor Vehicle and Parts Dealers 3 44-45 441

Full hierarchy of a randomly selected 6-digit code.

Code 
d = get_df(2022, 'code')
r = d.query('DIGITS == 6').sample()
q = ' or '.join(f'CODE == "{c}"' for c in r.iloc[0, 3:])
d.query(q).fillna('')
CODE TITLE DIGITS CODE_2 CODE_3 CODE_4 CODE_5 CODE_6
270 31-33 Manufacturing 2 31-33
805 336 Transportation Equipment Manufacturing 3 31-33 336
845 3366 Ship and Boat Building 4 31-33 336 3366
846 33661 Ship and Boat Building 5 31-33 336 3366 33661
848 336612 Boat Building 6 31-33 336 3366 33661 336612

6-digit industries in the “115” subsector.

Code 
get_df(2022, 'code').query('CODE_3 == "115" and DIGITS == 6')
CODE TITLE DIGITS CODE_2 CODE_3 CODE_4 CODE_5 CODE_6
119 115111 Cotton Ginning 6 11 115 1151 11511 115111
120 115112 Soil Preparation, Planting, and Cultivating 6 11 115 1151 11511 115112
121 115113 Crop Harvesting, Primarily by Machine 6 11 115 1151 11511 115113
122 115114 Postharvest Crop Activities (except Cotton Ginning) 6 11 115 1151 11511 115114
123 115115 Farm Labor Contractors and Crew Leaders 6 11 115 1151 11511 115115
124 115116 Farm Management Services 6 11 115 1151 11511 115116
127 115210 Support Activities for Animal Production 6 11 115 1152 11521 115210
130 115310 Support Activities for Forestry 6 11 115 1153 11531 115310

Index items of “115115” and “115116” industries. Notice duplication in INDEX_ITEM, e.g. “Farm labor contractors” and “Labor contractors, farm”.

Code 
d0 = get_df(2022, 'code').query('CODE == "115115" or CODE == "115116"')
d1 = get_df(2022, 'index')
d0.merge(d1, 'left')
CODE TITLE DIGITS CODE_2 CODE_3 CODE_4 CODE_5 CODE_6 INDEX_ITEM
0 115115 Farm Labor Contractors and Crew Leaders 6 11 115 1151 11511 115115 Agriculture production or harvesting crews
1 115115 Farm Labor Contractors and Crew Leaders 6 11 115 1151 11511 115115 Crew leaders, farm labor
2 115115 Farm Labor Contractors and Crew Leaders 6 11 115 1151 11511 115115 Farm labor contractors
3 115115 Farm Labor Contractors and Crew Leaders 6 11 115 1151 11511 115115 Labor contractors, farm
4 115116 Farm Management Services 6 11 115 1151 11511 115116 Farm management services
5 115116 Farm Management Services 6 11 115 1151 11511 115116 Management services, farm

Concordances

Concordances, also known as crosswalks, are tables that link industries of a given revision of NAICS to other revisions or other classifications. Census page lists the following concordances.

  • Between consecutive NAICS revisions: 2017-2022, 2012-2017, 2007-2012, 2002-2007, 1997-2002.

  • Between 1997 or 2002 NAICS and 1987 SIC.

  • From NAICS to ISIC (United Nations’ International Standard Industrial Classification of All Economic Activities).

  • From 2002 NAICS to NACE (Statistical Classification of Economic Activities in the European Community).

Concordances are symmetric, i.e. X-to-Y concordance table differs from Y-to-X table only by row ordering.

The main challenge of working with concordances is that links are not always one-to-one. In this module we provide concordance tables with indicators of link types.

('naics', 1997, 'naics', 2002) ('naics', 2002, 'naics', 1997) ('naics', 2002, 'naics', 2007) ('naics', 2007, 'naics', 2002) ('naics', 2007, 'naics', 2012) ('naics', 2012, 'naics', 2007) ('naics', 2012, 'naics', 2017) ('naics', 2017, 'naics', 2012) ('naics', 2017, 'naics', 2022) ('naics', 2022, 'naics', 2017)

Sometimes one needs to identify all industries involved in a split directly or indirectly. The function find_concordance_group() returns all connected industries, starting from a given set of codes.

Function viz_concordance() presents links as a diagram.

Summary

Tables below summarize types of links between consequtive years in NAICS concordances. The first table shows forward concordances, i.e. from year t to t+5. The second table show backward concordances, i.e. from year t to year t-5.

  • 1-to-1 same: industry and code did not change between years, this is the most common case.
  • 1-to-1 diff: industry did not change, but it’s numerical code changes.
  • clean split: industry was split into multiple, but all parts only came for the one source industry.
  • messy split: industry was split, but some parts were joined from other industries.
  • join: industry was joined and became a part of another industry.
1997->2002 2002->2007 2007->2012 2012->2017 2017->2022
total in "from" 1169 1179 1175 1065 1057
1-to-1 same 1043 1140 976 1037 916
1-to-1 diff 20 2 10 8 12
clean split 3 2 1 0 1
messy split 95 11 5 4 8
join 8 24 183 16 120
2002->1997 2007->2002 2012->2007 2017->2012 2022->2017
total in "from" 1179 1175 1065 1057 1012
1-to-1 same 1043 1140 976 1037 916
1-to-1 diff 20 2 10 8 12
clean split 0 2 67 5 26
messy split 22 20 5 4 53
join 94 11 7 3 5

Examples

1-to-1 links and clean splits

Code 
d = get_concordance_df('naics', 2017, 'naics', 2022)
d = pd.concat([
    d.query('NAICS_2017 == "115111"'),
    d.query('NAICS_2017 == "515112"'),
    d.query('NAICS_2017 == "325314"')
])
viz_concordance(d[['NAICS_2017', 'NAICS_2022', 'FLAG_2017_TO_2022']].values, 2017, 2022)

NAICS_2017 TITLE_2017 NAICS_2022 TITLE_2022 DUP_2017 DUP_2022 FLAG_2017_TO_2022
56 115111 Cotton Ginning 115111 Cotton Ginning False False 1-to-1 same
792 515112 Radio Stations 516110 Radio Broadcasting Stations False False 1-to-1 diff
257 325314 Fertilizer (Mixing Only) Manufacturing - except compost manufacturing 325314 Fertilizer (Mixing Only) Manufacturing True False clean split
258 325314 Fertilizer (Mixing Only) Manufacturing - compost manufacturing 325315 Compost Manufacturing True False clean split

Clean joins

From 2012 to 2017 three industries joined into one. However, not every join is clean. The easiest way to identify a clean join from year t0 to t1 is to look for a clean split from year t1 to t0.

Code 
d = get_concordance_df('naics', 2012, 'naics', 2017)
d = find_concordance_group(d, 'NAICS_2012', 'NAICS_2017', ['454111'])
viz_concordance(d[['NAICS_2012', 'NAICS_2017', 'FLAG_2012_TO_2017']].values, 2012, 2017)

NAICS_2012 TITLE_2012 NAICS_2017 TITLE_2017 DUP_2012 DUP_2017 FLAG_2012_TO_2017
638 454111 Electronic Shopping 454110 Electronic Shopping and Mail-Order Houses False True join
639 454112 Electronic Auctions 454110 Electronic Shopping and Mail-Order Houses False True join
640 454113 Mail-Order Houses 454110 Electronic Shopping and Mail-Order Houses False True join
Code 
d = get_concordance_df('naics', 2017, 'naics', 2012)
d = find_concordance_group(d, 'NAICS_2017', 'NAICS_2012', ['454110'])
viz_concordance(d[['NAICS_2017', 'NAICS_2012', 'FLAG_2017_TO_2012']].values, 2017, 2012)

NAICS_2017 TITLE_2017 NAICS_2012 TITLE_2012 DUP_2017 DUP_2012 FLAG_2017_TO_2012
638 454110 Electronic Shopping and Mail-Order Houses 454111 Electronic Shopping True False clean split
639 454110 Electronic Shopping and Mail-Order Houses 454112 Electronic Auctions True False clean split
640 454110 Electronic Shopping and Mail-Order Houses 454113 Mail-Order Houses True False clean split

Messy splits and joins

By construction, messy split links will always come with joins. From 2017 to 2022, three industries (“212111: Bituminous Coal and Lignite Surface Mining”, “212112: Bituminous Coal Underground Mining” and “212113: Anthracite Mining”) were re-classified into two (“212114: Surface Coal Mining” and “212115: Underground Coal Mining”). This creates a problem, for example, when one wants to identify anthracite mining industry under 2022 NAICS, because the two parts of that industry (surface and underground mining) were combined with other types of coal mining.

Code 
d = get_concordance_df('naics', 2017, 'naics', 2022)
d = find_concordance_group(d, 'NAICS_2017', 'NAICS_2022', ['212111'])
viz_concordance(d[['NAICS_2017', 'NAICS_2022', 'FLAG_2017_TO_2022']].values, 2017, 2022)

NAICS_2017 TITLE_2017 NAICS_2022 TITLE_2022 DUP_2017 DUP_2022 FLAG_2017_TO_2022
66 212111 Bituminous Coal and Lignite Surface Mining 212114 Surface Coal Mining False True join
67 212112 Bituminous Coal Underground Mining 212115 Underground Coal Mining False True join
68 212113 Anthracite Mining - anthracite surface mining 212114 Surface Coal Mining True True messy split
69 212113 Anthracite Mining - anthracite underground mining 212115 Underground Coal Mining True True messy split

All splits and joins

Every link from 2012 to 2017 concordance that is not 1-to-1.

Build this module

Cells with “nbd-module” tag are converted to an importable Python module.

Converted notebook "nbs/naics.ipynb" to module "pubdata/naics.py".